Open Peer-Review: Interpretable Machine Learning Models for Predicting In-Hospital Mortality in Patients with Chronic Critical Illness and Heart Failure: A Multicenter Study, and other submissions (2024)

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• Open Peer-Review: Interpretable Machine Learning Models for Predicting In-Hospital Mortality in Patients with Chronic Critical Illness and Heart Failure: A Multicenter Study, and other submissions

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• Interpretable Machine Learning Models for Predicting In-Hospital Mortality in Patients with Chronic Critical Illness and Heart Failure: A Multicenter Study

  Date Submitted: Jun 1, 2024

  Open Peer Review Period: Jun 7, 2024 - Aug 2, 2024

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  Background: Heart failure (HF) is a leading cause of morbidity and mortality among patients in intensive care units (ICUs), particularly those with chronic critical illness (CCI). Objective: We aimed to develop and validate a machine learning (ML) model to predict in-hospital mortality for in CCI patients with CCI and HF. Methods: Retrospective data encompassing medical records from over 200 hospitals were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU Collaborative Research Database (eICU-CRD). Patients diagnosed with CCI and HF at their first ICU admission were included. The MIMIC-III and -IV datasets were used as a derivation cohort, while that from eICU-CRD was employed as a validation cohort. Key predictive features were identified utilizing the recursive feature elimination with 10-fold cross-validation method. Subsequently, multiple ML algorithms were evaluated, including Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Extreme Gradient Boosting, Naive Bayes, Light Gradient Boosting Machine, and Adaptive Boosting. The performance of the models was assessed via metrics such as area under the receiver operating characteristic curve (AUROC), decision curve analysis, accuracy, sensitivity, specificity, and F1 score. Furthermore, model interpretability was enhanced by applying the SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods, providing insights into the contribution of individual features to the predictive outcomes. Results: A total of 780 (males: 451 [57.8%]) and 610 (males: 343 [56.2%]) patients with CCI and HF were allocated to the derivation and validation cohorts, respectively. Eleven features were selected to develop the prediction models. Among all models, the SVM algorithm-based model demonstrated high predictive accuracy (derivation cohort: AUROC, 0.781; sensitivity, 0.739; specificity, 0.691; and F1 score, 0.613; validation cohort: AUROC, 0.683; accuracy, 0.645; sensitivity, 0.607; specificity, 0.656; and F1 score, 0.44). The SHAP and LIME analyses evaluated the feature contributions, highlighting Sequential Organ Failure Assessment score, oxyhemoglobin saturation, diastolic blood pressure, and systolic blood pressure as significant predictors of in-hospital mortality. Conclusions: The SVM model developed in this study effectively predicts in-hospital mortality in patients with CCI and HF and can serve as a reliable tool for early intervention and improved patient management. Furthermore, this ML model combines high accuracy with interpretability, thereby substantially contributing to clinical predictive analytics.

• Effects of an 8-week app-based mindfulness intervention on mental health in working women: A randomized controlled trial

  Date Submitted: Jun 1, 2024

  Open Peer Review Period: Jun 7, 2024 - Aug 2, 2024

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  Background: Although working women experience increased work-related stress, preventive interventions to reduce its negative effects on their mental health are insufficient. Objective: This study evaluated the effectiveness of an 8-week mindfulness-based self-help intervention via a smartphone application across four domains (general psychological, work-related, family-related, and work-to-conflict) among working women. Methods: This study recruited women workers via various media sources, such as crowdsourcing sites and social networking services. Participants were randomly assigned to the intervention (n=106) or waitlist control groups (n=107). Participants in the intervention group practiced guided mindfulness meditation every day at their convenience via an app on their cell phones for eight weeks. The app provides an 8-week program with four meditation contents per two weeks. Participants in the waitlist control group lived as usual for eight weeks. We conducted web-based questionnaires to assess participants’ general psychological (life satisfaction, perceived stress, depressive and anxiety symptoms, trait anger, mindfulness), work-related (work performance, job satisfaction, quantitative job overload, job control), family-related (family satisfaction, partner satisfaction), and work-to-family conflict indicators. Results: An analysis of covariance, controlled for pre-intervention scores, revealed that the intervention significantly increased life satisfaction (b=1.47, β=0.11, P=.005) and decreased perceived stress (b=-2.00, β=-0.17, P=.012), depressive and anxiety symptoms (b=-1.24, β=-0.15, P=.02), and trait anger/reaction (b=-0.59, β=-0.11, P=.04). The intervention group demonstrated significantly increased life satisfaction (t93=-3.36, P=.001) and decreased depressive and anxiety symptoms (t93=2.35, P=.02). Conclusions: The app was effective in reducing perceived stress, depressive and anxiety symptoms, and trait anger/reaction, and in improving life satisfaction among working women. However, to improve work- and family-related indicators, higher-intensity interventions may be required, such as modifying the intervention content or extending its duration. Clinical Trial: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) UMIN000051796; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000059110.

• Impact of Skin Pigmentation on Pulse Oximetry SpO2 and Wearable Pulse Rate Accuracy: A Meta-Analysis

  Date Submitted: Jun 4, 2024

  Open Peer Review Period: Jun 6, 2024 - Aug 1, 2024

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  Background: Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities. Objective: This meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation. Methods: We identified 23 pulse oximetry studies (N=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (N=176; 140,771 paired photoplethysmography-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square (Arms) values to the regulatory threshold of 3% and PR 95% limits of agreement (LoA) values to ±5 bpm, according to the standards of the American National Standards Institute, Advancing Safety in Medical Technology, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% confidence intervals (CI). Results: For SpO2, Arms were 3.96%, 4.71%, and 4.15% and pooled mean biases were 0.70% (95% CI: 0.17 to 1.22), 0.27% (95% CI: -0.64 to 1.19), and 1.27% (95% CI: 0.58 to 1.95) for light, medium, and dark pigmentation, respectively. For PR, 95% LoAs were -16.02 to 13.54, -18.62 to 16.84, and -33.69 to 32.54 and pooled mean biases were -1.24 bpm (95% CI: -5.31-2.83), -0.89 bpm (95% CI: -3.70-1.93), and -0.57 bpm (95% CI: -9.44-8.29) for light, medium, and dark pigmentation, respectively. Conclusions: SpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching FDA guidance and industry standards thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation.

• Association between the 5G cost and reliability in healthcare

  Date Submitted: May 29, 2024

  Open Peer Review Period: Jun 5, 2024 - Jul 31, 2024

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  In this article, we look at the hidden side of the prevailed advantages, specifically the reliability and cost issues associated with the implementation of 5G in healthcare. Healthcare is a safe industry and has a tolerance of margin of error to be zero to none. Given that healthcare is essential for mankind, the introduction of new technology has to be reliable. Furthermore, without a proper analysis of cost and funding, any decision of such investment will be bound to fail. In reliability, we have discussed how 5G interferes with the current proven successful healthcare practice, how 5G is susceptible to data theft and hacking and the misappropriate use of medical data for ulterior motives. In cost, the upgrade requirements needed, how costs are derived from one aspect to another and the funding to support the change are also discussed.There are still many loopholes and questions left unanswered in this aspect, though it is believed to bring positive changes to the healthcare industry, through our findings, we conclude that this technology is unready, and the aforementioned problems have to be dealt with prior to introducing it to the society.

• The effect of aerobic virtual‐reality‐based exercise on restless legs syndrome, sleep quality, and quality of life among patients undergoing hemodialysis

  Date Submitted: May 24, 2024

  Open Peer Review Period: May 31, 2024 - Jul 26, 2024

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  Background: Background: Virtual reality (VR) therapy for hemodialysis (HD) patients can improve physical function, maintain patient compliance, and have a positive effect on functional capacity, strength, and health-related quality of life. Nevertheless, the utilization of this treatment for HD patients remains restricted, particularly in the context of Indonesia where its development has yet to be established. Objective: Objectives: the primary objective of this research attempt was to determine the effect of aerobic exercise using virtual reality technology on restless legs syndrome, sleep quality, and quality of life in patients undergoing hemodialysis therapy. Methods: Methods: The present study employs a quasi-experimental design with a two-group pretest-posttest design. The inclusion criteria for this study include of patients with HD who are over the age of 20, have been undergoing hemodialysis for a minimum of 6 months, exhibit cooperative behavior and compositional awareness, and express a willingness to participate as respondents. The KDQOL-SF, IRLSG Scale, and PSQI were used to measure study outcome. Results: Results: There were 95 people that could have participated, but only 75 ended up doing so due to exclusions and dropouts. The intervention significantly decreased RLS score and improved sleep quality, but quality of life did not increase significantly. In the control group, RLS score did not decrease significantly, sleep quality did not improve, and quality of life did not increase significantly. Conclusions: Conclusion: Intradialytic aerobic exercise supported with virtual reality may have additional effects in RLS, sleep quality, and quality of life. Further research is needed to assess the effect of VR-based intervention, both alone and when combined with other therapies.

• AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-enhanced Large Language Models

  Date Submitted: May 22, 2024

  Open Peer Review Period: May 29, 2024 - Jul 24, 2024

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  Background: Background and significanceSeveral studies have employed machine learning methods to support and enhance the medical management and process of rare diseases. Sanjak et al [18] introduced an innovative method for clustering over 3,000 rare diseases using node embeddings within a knowledge graph. This approach facilitates a deeper understanding of the relationships between different diseases and opens possibilities for drug repurposing. Alsentzer et al [19] developed Shepherd, a deep learning model designed for diagnosing rare diseases. This model effectively leverages clinical and genetic patient data along with existing medical knowledge to uncover new disease-gene associations. This work exemplifies the potential of AI in medical diagnostics, even in scenarios with limited labeled data. Rashid et al. [20] explored a unique approach in rare disease research through the National Mesothelioma Virtual Bank. They utilized REDCap and a web portal query tool to integrate and manage clinical data from multiple institutions. This method demonstrates the power of combining data management tools and web technologies to enhance research and collaboration in the field of rare diseases.The advent of LLMs has led to their increasing application in the medical field. Datta et al [21] developed AutoCriteria, an LLM-based information extraction system that has shown high accuracy and generalizability in extracting detailed eligibility criteria from clinical trial documents for various diseases. This represents a scalable solution for clinical trial applications. In the context of rare diseases, there have been specific research efforts utilizing LLMs. Shyr et al [22] explored the performance of ChatGPT in extracting rare disease phenotypes from unstructured text, using zero- and few-shot learning techniques. This study demonstrated potential in certain scenarios, particularly with tailored prompts and minimal data. Oniani et al [23] proposed Models-Vote Prompting (MVP), an approach that improves LLM performance in few-shot learning scenarios by aggregating outputs from multiple LLMs through majority voting.However, these studies on LLM applications for rare diseases are still preliminary. Both focused on evaluating the basic capabilities of LLMs in identifying rare diseases or used simple prompt ensembles to slightly enhance LLM performance. They did not explore the task of extracting relationships between rare diseases and related phenotypes. Additionally, these studies primarily explore basic applications of LLMs and do not extend to a comprehensive LLM-based system. Building on these foundational works, our research continues to delve into the use of LLMs for rare disease applications. Unlike prior efforts, we propose an integrated and useful system aimed at extracting rare disease information from unstructured text. The elaborate methods incorporated into this system significantly enhance extraction accuracy compared to the use of pure LLMs, marking a substantial advancement in this field. Objective: Rare diseases, also known as orphan diseases, are relatively uncommon in isolation and sometimes receive less individual attention in medical research due to their low prevalence. [1] The likelihood of an individual being affected by a rare disease is relatively low. However, when considering the global population, many individuals are impacted. In the United States, rare diseases affect approximately 30 million people; [2] globally, the number rises to between 300 and 400 million. [3] Furthermore, the rare disease patient population, distributed across 5,000 to 10,000 distinct diseases, [4] suffers from a significant lack of medical knowledge due to the rarity of a given illness. Consequently, patients often face prolonged and costly diagnostic processes and intensive treatments, with many of these diseases lacking approved therapies. [5,6] This situation underscores the substantial burden placed on both patients and healthcare systems. [7] Online resources, including open-source databases, offer valuable references for medical professionals, contributing to the development of a comprehensive rare disease knowledge system. Examples of such databases include the Unified Medical Language System (UMLS) [8] and the Orphanet Human Phenotype Ontology (HPO). [9,10] Specifically, Orphanet’s database provides detailed information linking rare diseases, genes, and phenotypes, which greatly aids in the identification and diagnosis of rare diseases, among other related processes. However, these databases require considerable human effort for curation and maintenance. Therefore, there is an urgent need to develop methods that can support the process of establishing and enhancing rare disease medical knowledge systems.Natural Language Processing (NLP) techniques are instrumental in automatically processing unstructured text to extract structured and clinically relevant information. This technique is especially beneficial for information extraction and knowledge discovery in the medical field. Recently, Large Language Models (LLMs) have demonstrated exceptional proficiency in language understanding and generation, garnering significant attention in the open NLP domain. [11,12] Their ease of use allows humans to complete a wide range of complex tasks in everyday life. Moreover, the extensive knowledge stored within their parameters equips them to excel in domain-specific applications, such as medicine and healthcare. [13] Current research is beginning to evaluate the capabilities of the most powerful LLMs, such as ChatGPT and GPT-4, across various medical applications. These applications include licensing examinations, [14] question answering, [15] and medical education. [16] Notably, several studies have demonstrated that LLMs are effective few-shot medical Named Entity Recognition (NER) extractors, exhibiting superior few-shot learning capabilities compared to other NLP methods. [17] In the context of rare diseases, where resources are often limited, LLMs emerge as valuable tools for extracting information about these conditions, showcasing their utility in enhancing medical knowledge systems.In this paper we introduce Automated Rare Disease Mining (AutoRD) as an efficient tool for extracting information about rare diseases and constructing corresponding knowledge graphs. The system processes unstructured medical text as input and outputs extraction results and a knowledge graph. It is comprised of several key stages: data preprocessing, entity extraction, relation extraction, entity alignment, and knowledge graph construction. Among these, entity and relation extraction are the most critical parts. AutoRD is an LLM-based system built upon GPT-4. [12] We employ prompts as instructions to guide the LLMs through the entity and relation extraction processes. The model leverages its strong zero-shot capabilities to identify and extract entities and to analyze relationships between them. Although LLMs are pre-trained with extensive knowledge, they sometimes lack precise medical information. To address this, we enhance the LLM's medical knowledge using rare disease and phenotype ontologies. This is achieved by designing sophisticated prompts that incorporate relevant knowledge. We conducted experiments to evaluate the system and identified the advantages and limitations of AutoRD. In summary, our contributions can be summarized as follows:1.We propose AutoRD, an automated end-to-end system which efficiently extracts rare disease information from text and builds knowledge graphs. This is a useful and practical system which can help medical professionals discover information about rare diseases.2.We use ontology-enhanced LLMs in the module of rare disease entity extraction and relation extraction. This approach harnesses the few-shot learning capabilities of LLMs and integrates medical knowledge from ontologies, resulting in an improved performance beyond what LLMs achieve alone.3.We conduct experiments and provide extensive analysis to demonstrate the effectiveness of AutoRD. Methods: DataTo improve the medical understanding of LLMs, we incorporated three medical ontologies into AutoRD: Orphanet Rare Disease Ontology (ORDO)[10], HPO-ORDO Ontology Module (HOOM) [10] , and Mondo Disease Ontology (Mondo) [24] . For assessing the entity and relation extraction capabilities of AutoRD, the RareDis-v1 dataset [25] was employed. Prior to use, this dataset underwent several reprocessing steps including manual review and revision of annotation errors followed by reshuffling. We have named this new dataset RareDis2023.AutoRD FrameworkWe present AutoRD, an innovative system designed to automatically extract rare disease information from medical texts and create a knowledge graph. The AutoRD framework is illustrated in (Figure 1) and consists of a pipeline structure that includes data preprocessing, entity extraction, relation extraction, entity calibration, and knowledge graph construction. The extraction steps, which include entity and relation extraction, are the core components of the system. In these steps, we utilize large language models, along with medical ontologies, to effectively extract information from the texts.Figure 1. The AutoRD framework processes medical texts as input data and outputs entities related to rare diseases and rare disease triples, which are the results of the extraction process. Subsequently, it constructs a knowledge graph based on these extraction results. During the entity and relation extraction steps, ontology-enhanced large language models are utilized to enhance performance. Task DefinitionGiven a medical text T, AutoRD is designed to first extract entities E = {E1, E2, ..., En} and relations R = {R1, R2, …., R}, and then output a knowledge graph KG based on E and R.The entity types listed in (Table 1) include 'rare_disease', 'disease', 'symptom_and_sign', and 'anaphor'. We group 'symptom' and 'sign' together because they both represent phenotypic abnormalities that may suggest a disease or medical condition. Distinguishing between them is not crucial in the context of rare disease research.Table 1. Entity type in the entity extraction task. Definitions and examples of all entity types: 'rare_disease', 'disease', 'symptom_and_sign', and 'anaphor'. The definitions and examples are based on the original RareDis dataset definitions. [25] Entity TypeDefinitionExamplerare_diseaseDiseases which affect a small number of people compared to the general population. A disease is often considered to be rare when it affects less than 1 in 2000 individuals. [26] acquired aplastic anemia, Fryns syndrome, giant cell myocarditisdiseaseAn abnormal condition of a part, organ, or system of an organism resulting from various causes such as infection, inflammation, environmental factors, or genetic defect, and characterized by a patterned group of signs and/or symptoms.cancer, Alzheimer, cardiovascular diseasesymptom_and_signSigns and symptoms are abnormalities that may suggest a disease. A symptom is a physical or mental problem that a person experiences that may indicate a disease or condition; it is a subjective finding reported by the patient. In contrast, a sign is an observable or otherwise discoverable feature that is considered abnormal. fatigue, dyspnea, pain inflammation, rash, abnormal heart rate, hypothermiaanaphorPronouns, words, or nominal phrases that refer to a rare disease (which is the antecedent of the anaphor)This disease, These diseasesRelation types are displayed in (Table 2), which include 'produces', 'increases_risk_of', 'is_a', 'is_acron', 'is_synon', and 'anaphora.' Each relation type represents a specific kind of relationship between a subject and an object, both of which can be any medical term entity.Table 2. Relation types in the entity extraction task. Definitions of all relation types: 'produces', 'increases_risk_of', 'is_a', 'is_acron', 'is_synon', 'anaphora'. The definitions are based on those in the original RareDis dataset. [25]Relation TypeDefinitionproducesRelation between any disease and a sign or a symptom produced by that disease.increases_risk_ofRelation between a disease and a disorder, in which the presence of the disease increases the likelihood of the presence of the disorder.is_aRelation between a given disease and its classification as a more general disease.is_acronRelation between an acronym and its full or expanded form.is_synonRelation between two different names designating the same disease.anaphoraRelation between an antecedent and an anaphor entity. The antecedent must be a rare disease.Data PreprocessingBefore the system performs entity and relation extraction, we first preprocess the data due to the token limit of LLMs. In our system, we use GPT-4, which has a token limit of 8,000. Our maximum length for prompts is approximately 1,000 tokens, including the length of both input and output in the prompt slot. We divide lengthy input documents into segments containing fewer than 2,000 tokens to adhere to the token limit. To minimize entity relations across segments, we recognize that relations typically occur within a single natural paragraph. Therefore, we segment documents at natural paragraph boundaries, ensuring each segment contains fewer than 2,000 tokens. For paragraphs that have been segmented, we re-extract relations from the segmented middle portion to identify new relations.We process medical knowledge data from ontology files downloaded from official websites. ORDO encompasses rare diseases that have been discovered up to the present day. From this ontology, we extract the names and definitions of all rare diseases. Mondo offers a unified medical terminology covering various medical concepts, from which we extract all disease, symptom, and sign concepts along with their definitions. Additionally, HOOM is an ontology that annotates the relationships between clinical entities and phenotypic abnormalities and reports their frequencies of occurrence. We extract information from HOOM as triples, consisting of (Rare Disease, Frequency, Phenotype). After preprocessing the ontology files, we can easily integrate medical knowledge from these ontologies into LLMs to enhance their knowledge base. Data in the RareDis dataset also requires preprocessing for evaluation. The input texts in RareDis are all shorter than 512 tokens and consist of single paragraphs from medical literature, which contains a total of 1,040 data elements (texts and labels). We have corrected some errors in the annotations of the original dataset. To evaluate performance and compare it with the fine-tuning baseline, we divided the dataset into training, validation, and test sets in a 6:2:2 ratio, resulting in 624, 208, and 208 entries, respectively. The training set is used for training fine-tuning models and selecting some exemplars for LLMs, while the validation set is utilized to select the best fine-tuning models during training. In alignment with our task definition, we have merged 'Symptom' and 'Sign' from the original dataset into one entity type, ‘symptom_and_sign’. We have named the newly processed dataset RareDis2023.Entity ExtractionAfter preprocessing, AutoRD subsequently carries out entity extraction. We drew inspiration from the concept of chain-of-thought (CoT) [27] to structure the entity extraction process. CoT proposes that tackling complex problems step by step can enhance the performance of LLMs. Similarly, we divided entity extraction into three sub-steps. In each step, an LLM completes a specific, smaller task. This division of the task allows us to integrate external medical knowledge more effectively from ontologies into the LLMs during this process. The three steps are: extract medical terms, extract more terms, and extract entities.The first step, extract medical terms, extracts basic medical terms from the text. We only employ a string-match algorithm with negation detection in this process. A dictionary is built from the medical ontology Mondo. We use Mondo here because it encompasses nearly all standard medical terms. For each text, we use a string-match algorithm to search any medical terms in ontologies and save candidate medical terms as temporary results. For negation detection, we initially make a list of negation keywords manually, followed by the creation of a regular expression template. This template is then used to identify these keywords and extract the complete terms together. In this ontology, many terms include a free-text definition useful for model comprehension, so we make use of this information in subsequent steps.The next step, extracting more terms, utilizes LLMs. The prompt template can be found in the left side of (Figure 2). We input the original text and medical terms extracted from the previous step into the LLMs. The LLMs then output additional medical terms. These include terms that are medically relevant but did not directly match an ontology term, including lemmatizations. This process leverages the strong language comprehension capabilities of LLMs for more flexible term extraction. In this step, LLMs also identify anaphors. In the prompt, we first outline the specifics of the current task and provide clear definitions of the entity types. Additionally, we include guidelines for the LLMs on identifying entities which are difficult to recognize. This part is significant and can be continuously improved by medical experts based on the performance of the LLMs and the results they produce. In many cases, LLMs may have misunderstandings in this task, so we need to use prompts to adjust for and correct their interpretations. Finally, we define the output format of the LLMs to be easily parsed, such as in JSON format.Figure 2. Content of all prompt templates in AutoRD. This figure presents the simplified content of all prompts to provide a clear framework of the prompt structure. The black text represents the original text of the instructions. Grey text indicates a summary of each part of the instructions. Blue text highlights the prompt slots, where external information and inputs can be inserted. The final step, entity extraction, also involves the use of LLMs. The instruction prompt template can be found in the central part of (Figure 2). The input for this step includes the medical terms and anaphors extracted during the previous step, while the output is comprised of all extracted entities with their appropriate categorizations. The framework of the prompt is formatted like earlier steps; however, additional external information is incorporated into the prompt slots, including medical terms, exemplars, anaphors, and rare disease knowledge. Here, 'rare disease knowledge' refers to terms that can be definitively classified as rare diseases, achieved by matching candidate entities with terms in ORDO. Furthermore, we utilize the concept of in-context learning (ICL). [28] ICL employs exemplars to enhance the performance of LLMs. Each exemplar is a gold input-output pair, demonstrating the correct method of processing input and generating output for LLMs. This approach is beneficial for guiding the output format of LLMs and providing them with reference material and knowledge to inform their responses. Exemplars can be randomly selected from the training set. After completing these three steps, we can extract entities from text using LLMs.Relation ExtractionIn our methodology, relation extraction is conducted after entity extraction. All identified entities are fed into LLMs, which then output the extracted relations. The prompt template used for instructing the LLMs is depicted in the central part of (Figure 2). The underlying logic of this process is akin to that of entity extraction. In the prompt, we initially provide an overview of the current task and establish clear definitions for both entity and relation types. We also include additional considerations for the LLMs to consider during relation extraction. Finally, we define the output format for the LLMs, which is structured to be easily parsed in JSON format. The prompt also contains examples of relation extraction.For the extraction of rare disease knowledge, we utilize HOOM, an ontology which consists of rare disease-phenotype triples. This ontology provides information on symptoms and signs associated with rare diseases. We employ rare diseases as keys to construct a dictionary, enabling the identification of related triples through string matching. This external medical knowledge aids the LLMs in acquiring information about existing relationships between rare diseases and certain phenotypes.Entity CalibrationOur goal is to construct a knowledge graph based on the extraction results. We consider that many entities might not have defined relationships with other entities. Moreover, after analyzing the extraction results, we observed that entities without any relationships are more likely to be irrelevant or falsely ascribed as medical entities within the context. For instance, the system identifies the term 'disorder' during the entity extraction phase. However, in the relation extraction, the system fails to detect any 'anaphora' or other relations, indicating that it is merely a generic term and can be disregarded in this context. In other instances, some false symptoms and signs are also effectively eliminated. Therefore, we introduce entity calibration as an additional step after relation extraction. The prompt template for this task can be seen on the right side of (Figure 2). In this step, we provide all results obtained from the previous steps and use the LLMs to reanalyze the relationships, filtering out unrelated entities. By combining the results from both entity and relation extraction phases, we obtain the comprehensive outcome of the entire extraction process.Knowledge Graph ConstructionAfter extracting entities and relations, we postprocess the data to prepare for knowledge graph construction. This includes aligning entities, which involves merging identical nodes in the knowledge graph. For each triple, we assess whether the subjects or objects are the same. We begin by converting the names to lowercase and then determining if they match. Additionally, we transform all anaphoric relations to their original names.After postprocessing, we can easily construct the knowledge graph based on these triples. Specifically, we utilize Neo4j [29] for this purpose. Neo4j is a highly flexible and scalable graph database, designed to store and process complex networks of data. It enables efficient querying and management of interconnected information. Using the API of Neo4j, we add the rare disease triples into the graph database one by one. As a result, we can visualize our rare disease knowledge graph within the Neo4j platform.EvaluationFor the entity and relation extraction component, we quantitatively evaluate the performance of AutoRD using the processed RareDis2023 dataset. Regarding our method, AutoRD, we specifically selected 'gpt-4-0613', a version of GPT-4 from OpenAI, for the LLMs. We set the LLM's temperature to 0 to ensure the most stable output. For each prediction with exemplars, we randomly choose 5 exemplars from the training set. The performance of AutoRD is evaluated exclusively on the test set, and detailed prompt templates are available in the source code. To analyze the improvement our method brings compared to using only LLMs, we evaluate the performance of the base LLM. We use the same LLM, 'gpt-4-0613', and maintain all other settings identical to AutoRD. The detailed prompt template can be found in the source code. For our fine-tuning model baseline, we selected BioClinicalBERT [30]. Entity recognition is performed through token classification based on BIO labels, and relationships are identified by concatenating the embeddings of two entities, followed by a linear classification. This model is trained on the training set, optimized according to the validation set, and finally evaluated on the test set. Detailed experimental settings are available in the source code. In terms of evaluation metrics, we use Precision, Recall, and F1 metrics in a named entity recognition setting. For entity and relation extraction, we measure entity F1 and relation F1 respectively. The final overall results are represented by the overall F1 score, calculated as the mean of entity F1 and relation F1. We consider replicated entities in our extraction measurements, which are instances of the same entity occurring at different positions within the text. If the name of an extracted entity is correct, we regard it as true. The evaluation of relation extraction is not limited to correctly identified entities and for all true entities.In the test set, the entity instances include the follow number of each type: 463 ‘disease’, 1054 ‘rare_disease’, 1255 ‘symptoms_and_signs,’ and 334 ‘anaphor’. Numbers of each type of relation include 1261 ‘produces’, 62 ‘increases_risk_of’, 188 ‘is_a’, 55 ‘is_acron’, 22 ‘is_synon’, and 331 ‘anaphora’.For the knowledge graph construction, we conduct qualitative experiments and provide examples of the knowledge graph results. In this case, we only use the extraction results from the test dataset of RareDis2023. Results: Main Experimental ResultsThe primary experimental results are presented in (Table 3), which includes comparisons of entity and relation extraction performance among BioClinicalBERT (a fine-tuning model), Base GPT-4 (a base LLM), and AutoRD (our method). Overall, AutoRD achieves an overall F1 score of 47.3%. The system demonstrates superior performance over these two baselines, with an improvement of 0.8% in overall F1 score compared to the fine-tuning model and a 14.4% improvement compared to the base LLM. Recall is deemed more important than precision in this context because human effort can be used to validate extracted results. The primary goal is to extract all gold entities initially. In terms of recall, our overall recall improved by 18.4% compared to Base GPT-4 and by 6.6% compared to the fine-tuning models. For each extraction objective, AutoRD achieves an overall entity extraction F1 score of 56.1% (‘rare_disease’: 83.5%, disease: 35.8%, ‘symptom_and_sign’: 46.1%, ‘anaphor’: 67.5%) and an overall relation extraction F1 score of 38.6% (‘produces’: 34.7%, ‘increases_risk_of’: 12.4%, ‘is_a’: 37.4%, ‘is_acronym’: 44.1%, ‘is_synonym’: 16.3%, ‘anaphora’: 57.5%).Table 3. The main experimental results of entity and relation extraction. Our methods surpass both the fine-tuning model (BioClinical-BERT) and the base LLM (Base GPT-4) in terms of overall F1 score (%).MethodtypePrecisionRecallF1BioClinicalBERT Entityrare_disease80.587.783.9disease53.246.049.3symptom_and_sign62.362.562.4anaphor89.993.791.7entity_overall70.972.071.4Relationproduces49.713.621.4increases_risk_of0.00.00.0is_a80.04.38.1is_acron0.00.00.0is_synon0.00.00.0anaphora82.923.336.3relation_overall57.013.421.7overall64.042.746.5BaseGPT4 Entityrare_disease94.838.454.7disease22.559.832.7symptom_and_sign48.741.744.9anaphor45.269.554.7entity_overall43.246.344.7Relationproduces26.53.35.8increases_risk_of9.68.18.8is_a33.030.931.9is_acron17.121.819.2is_synon0.00.00.0anaphora41.755.347.6relation_overall32.515.621.1overall37.930.932.9AutoRD(Ours) Entityrare_disease93.175.683.5disease26.654.935.8symptom_and_sign45.846.546.1anaphor59.079.067.5entity_overall51.861.156.1Relationproduces37.232.434.7increases_risk_of11.813.112.4is_a41.434.037.4is_acron49.240.044.1is_synon12.822.716.3anaphora52.463.757.5relation_overall39.837.538.6overall45.849.347.3First, comparing Base GPT-4 with BioClinicalBERT reveals that BioClinicalBERT, with its ample training data, aligns well with the original dataset's distribution and excels in multiple metrics. However, LLMs were not trained to fit this distribution. This discrepancy leads to issues such as misclassifications and ambiguous entity boundaries in LLMs. In relation extraction, however, the fine-tuned model fails to detect relations like ‘increases_risk_of’, ‘is_acron’, and ‘is_synon’, whereas Base GPT-4 detects all but ‘is_synon’. This demonstrates the strong zero-shot capabilities of LLMs, especially for relations sparsely represented in the training set. When comparing AutoRD with BioClinicalBERT, it is apparent that while our method falls somewhat short in entity extraction, it excels in relation extraction. Specifically, the entity F1 score is 15.1% lower than this baseline, but the relation F1 score is 16.9% higher. This results in an overall performance that is 0.8% better. Relation extraction plays a pivotal role in the construction of knowledge graphs, as it is essential towards understanding the underlying relationships between entities. AutoRD leverages the few-shot learning capability of LLMs to better analyze relationships between medical entities. However, we observed a lower precision in AutoRD, primarily because it identifies too many entities as 'diseases' and sometimes misclassifies ‘symptom_and_sign’ according to its extraction results. Furthermore, when comparing AutoRD with Base GPT-4, it is evident that our method significantly improves performance by 18.4%. The most notable improvement is a 37.2% increase in the recall of rare disease entities from Base GPT-4. Base GPT-4, with its poor analysis capability and lack of sufficient medical knowledge, struggles to identify all types of rare diseases. Overall, our approach demonstrates substantial improvements in most metrics.Ablation StudyTable 4. The results of the ablation experiment. It clearly shows that each key component contributes to the improvement of AutoRD in terms of F1 score (%). The symbol (∇) represents the magnitude of the performance drop.MethodEntity F1∇Relation F1∇Overall F1∇AutoRD (Ours)56.1-38.6-47.3-AutoRD w/o Knowledge53.8-2.336.1-2.545.0-2.3AutoRD w/o Exemplars52.9-3.234.9-3.743.9-3.4AutoRD w/o Notice44.7-11.433.7-4.939.2-8.1We conduct an ablation study to analyze the contribution of various components within AutoRD to the overall system. The results are presented in (Table 4), which clearly shows that each key component contributes to the improvement of AutoRD. Note that 'Knowledge' represents the external knowledge sourced from medical ontologies, while 'Notice' refers to the reminders for the LLMs. This study suggests that AutoRD can effectively utilize knowledge from both medical ontologies and exemplars. The 'Notice' component brings a significant improvement of 8.1% in overall F1. The current notices for LLMs in AutoRD have been carefully fine-tuned, demonstrating their effectiveness in adjusting and correcting for the LLMs' interpretations for extraction tasks. Error AnalysisWe perform error analysis for each entity and relation type. To illustrate the distribution of extraction results, we use two confusion matrices: one for entity extraction results and another for relation extraction results. The results are shown in (Figure 3) and (Figure 4), respectively. The term 'Error' in the 'Predicted' axis refers to entities that have been incorrectly extracted, whereas 'Error' in the 'True' axis denotes real entities that were not extracted. We exclude replicated entities to simplify the computation of the confusion matrices.In the entity extraction confusion matrix, there is significant confusion between the categories of 'disease' and 'rare_disease', possibly due to overlapping textual features. The 'symptom_and_sign' category exhibits high classification accuracy but is also prone to being misclassified as 'Error', suggesting the need for more distinctive features or additional contextual information in the dataset. The 'Anaphor' category was accurately classified with fewer errors, indicating that the system effectively captures its linguistic features. However, many predicted entities are incorrectly extracted and are labeled as 'Error', indicating that LLMs tend to extract more information with a low precision.Figure 3. The confusion matrix of the entity extraction task results in RareDis2023. The confusion matrix for the relation extraction indicates varying degrees of performance across different categories. The 'produces' relation is often identified correctly but also often misclassified as ‘Error’, indicating recognition issues. ‘Increases_risk_of’ is more frequently an ‘Error’ than correct, demonstrating the recognition difficulty of this relation. ‘Is_a’ has moderate success but high error rates as well. ‘Is_acron’ and ‘is_synon’ rarely hit true positives and mostly fall into ‘Error’, possibly due to acronym variability and synonym recognition failure. 'Anaphora' resolution is relatively accurate but also misclassified, hinting at context comprehension challenges. The 'Error' category's high rate of both true and false positives is primarily affected by the false results from entity extraction step before it.Figure 4. The confusion matrix of the relation extraction task results in RareDis2023. Qualitative ResultsQualitative results are showcased in (Figure 5), which depicts all the extracted results from the RareDis2023 dataset. Our qualitative results have been validated by medical experts and have shown satisfactory outcomes. This visualization provides a global perspective, highlighting the relationships among various rare diseases and their associated signs and symptoms in a concise knowledge graph. Figure 5. The example of constructed knowledge graph from RareDis2023. The result is a clear and well-structured knowledge graph. Specific extraction results of the knowledge graph are depicted in (Figure 6). This figure offers visualizations from a local perspective, illustrating an ideal structure of the knowledge graph. In this structure, rare diseases are positioned at the center of radial formations, with connections extending to entities like symptoms and signs. For example, in Figure 6, the rare disease 'Turcot syndrome' is associated with 'abdominal pain', 'bleeding', 'fatigue', etc.Figure 6. An example provides a detailed view of a specific local section of the constructed knowledge graph. Additionally, we experimented with training specialized medical LLMs and compared their performance. Specifically, we utilized Camel-Platypus2-70B [31], a healthcare-tailored model that is an extension of Llama-2 [32], through continuous training. Our experiments revealed that, without specific training, this type of model struggles to execute complex tasks such as joint entity and relation extraction. It appears that the inherent medical knowledge is not readily applicable in these scenarios. Conclusions: Principal ResultsOur experimentation demonstrates the effectiveness of our proposed system, AutoRD. It significantly improves upon the base LLM and even outperforms fine-tuning models without requiring any training. Within several designs, the incorporation of medical ontologies has notably enhanced the LLMs by addressing gaps in medical knowledge. Furthermore, the results achieved in knowledge graph construction by our system are commendable. We highlight the advantage of LLMs in low-resource scenarios like rare disease extraction, showcasing their vast potential. Our meticulously designed system, AutoRD, substantiates this claim. The emergence of LLMs is generating unparalleled opportunities in the phenotyping of rare diseases. These models facilitate the automatic identification and extraction of concepts related to these diseases. Our prompts are easily adjustable due to their clear structure, allowing for simple modifications. Additionally, medical knowledge derived from external sources can be updated at any time within the AutoRD system.LimitationsNevertheless, there is considerable potential for further improvement with respect to AutoRD. For instance, integrating advanced text processing tools and specialized medical tools into our system could amplify its capabilities. In the future, we can deploy more powerful medical LLMs as base models to enhance medical understanding. Moreover, medical experts can contribute more tailored prompts to improve LLMs’ performance.Our work has potential limitations and avenues for extension. For example, we have only evaluated AutoRD on a single dataset, so the results may not fully reflect the system's performance across the entire spectrum of rare diseases or in other long-text scenarios. Additionally, the prompts we designed are intuitive, but there is still room for continuous tuning and experimentation of different prompts. We acknowledge that AutoRD may not be the optimal LLM application for this task, yet it significantly improves upon the baseline performance of LLMs. This work aims at demonstrating the potential of LLM applications in the healthcare field. ConclusionsWe developed AutoRD as an automatic and end-to-end system specifically designed for extracting information about rare diseases from text and building knowledge graphs. This system incorporates various innovative designs, including ontology-enhanced LLMs, to augment its medical knowledge base. The effectiveness of AutoRD has been validated through experimental evaluations, highlighting the valuable applications of LLMs in healthcare. In the future, we intend to explore how to broaden the application of LLMs to other healthcare processes in low-resource environments, including the identification of potential rare diseases in patients. Furthermore, ongoing research is focused on improving the performance of LLMs with respect to informing and augmenting medical decision-making strategies.

• Transitioning Perspectives in Digital Health through Phenomenology Integration

  Date Submitted: May 29, 2024

  Open Peer Review Period: May 29, 2024 - Jul 24, 2024

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  The evolution of Digital health, from its early days as eHealth to its current expansive scope, reflects a significant transformation in healthcare delivery and management. This transition underscores the integration of digital technologies across the health continuum, from prevention and diagnosis to treatment and rehabilitation. The emergence of digital health has introduced innovative solutions but also posed challenges, particularly in aligning technological advancements with health needs, human experiences, and ethical considerations.This position paper aims to explore the integration of phenomenology in digital health, advocating for a paradigm that emphasizes the centrality of human experience in the design and implementation of digital health solutions. It specifically seeks to address challenges related to relevance, individuals who "speak" different languages, ensuring long-term use, addressing digital and health literacy, coordinating various sources, and navigating ethical issues in the rapidly evolving digital health landscape.Drawing upon years of research and practical experience in communication technologies and health, this paper employs a reflective approach to examine the intersection of digital health and phenomenology. It reviews the historical development of digital health, identifies the challenges faced during its evolution, and discusses the potential of phenomenological methods to enhance user-centered design and ethical practices in digital health.The integration of phenomenology into digital health facilitates a deeper understanding of user experiences, enabling the development of more responsive and ethical digital health solutions. Participatory design models, informed by phenomenological perspectives, offer a pathway to bridge the gap between technological innovation and human-centric healthcare. The paper highlights successful practices in digital health development, including mobile applications for vaccination decision-making and platforms for managing chronic conditions, illustrating the benefits of a phenomenological approach.Transitioning perspectives in digital health through phenomenology integration represents a critical step towards realizing the full potential of digital technologies in healthcare.

• User engagement with DiabeteWise: Forging a novel, unbiased, person-centered pathway to diabetes access and uptake

  Date Submitted: May 21, 2024

  Open Peer Review Period: May 28, 2024 - Jul 23, 2024

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  Background: Despite shown benefits of diabetes technologies for people living with diabetes, barriers to device education and uptake can prevent the realization of these potential benefits. DiabetesWise is an unbranded, data-driven online resource that tailors device recommendations based on preferences and priorities of people with insulin-requiring diabetes. Objective: To examine engagement with DiabetesWise and its correlates. Methods: A sample of 458 participants (Mage=37.1, SD=9.73; 66% female; 81% type 1 diabetes) with minimal diabetes device use at enrollment were invited to use DiabetesWise. Their website activity was tracked, and they completed online surveys. Chi-square and t-tests evaluated correlates of engagement. Results: Most participants logged into DiabetesWise at least once (69%), which was associated with increased likelihood of starting a new device within 6 months (13.7% vs. 1.5%, p=.005). Logging in was also associated with being female, having public/no insurance, having lower income, and receiving care in a private practice/community health setting. Nearly 38% logged in multiple times, which was more common for participants receiving diabetes care through primary care. Most who logged in used the Check Up tool to assess current device needs and preferences (85%), of whom 74% were using meter and injections. Most Check Up recommendations were for meter and pump (63%), followed by sensor and pump (35%), then sensors with smart pump (2%). Cost was the most frequent priority for device decisions (51%). Nearly half the participants who completed the Check Up reported at least moderate diabetes distress (41%). Conclusions: DiabetesWise is an innovative, unbiased pathway to promote diabetes device education and awareness. Highest initial engagement was observed among people with fewer resources; repeated use was observed among those receiving diabetes care through primary care. DiabetesWise may help offset disparities in diabetes technology access and uptake.

• Machine Learning-based Public Online Prediction Platform for Predicting In-hospital Mortality Risk in Patients with Sepsis

  Date Submitted: May 28, 2024

  Open Peer Review Period: May 28, 2024 - Jul 23, 2024

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  Background: The precise prediction of mortality risk is essential for managing patients with sepsis. Objective: This study aimed to create a machine learning (ML) system that could accurately predict the likelihood of in-hospital death in critically ill patients with sepsis. Methods: This single-center retrospective study included 352 patients with sepsis and septic shock who presented to a tertiary hospital between 2010 and 2015. The Boruta technique was used for feature selection. Several machine learning models, including logistic regression, XGBoost, LightGBM, Random Forest, Support Vector Machine, and Gaussian NB, have been developed to predict quality metrics. The performance of the model was compared with traditionally utilized scores. The Shapley additive explanation (SHAP) was used to evaluate the value of features. Results: The in-hospital mortality rate of patients with sepsis was 48.3%. The model creation included the use of a set of 26 variables. The model with RF had the greatest predictive potential among the six models, with an area under curve (AUC) value of 0.785 (0.633–0.935). The development of a public online prediction platform has enhanced the clinical feasibility of risk-prediction models. Conclusions: TheRF model created in this study is a precise indicator of mortality among patients with sepsis in hospitals.

• Addressing Underestimation and Explanation of Retinal Fundus Photo-based Cardiovascular Disease Risk Score: Model Development and Analysis

  Date Submitted: May 28, 2024

  Open Peer Review Period: May 28, 2024 - Jul 23, 2024

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  See Also

  IAHE Fastlane 1964 - [PDF Document]June 2018, Volume 27, Number 2 [DOI: 10.13164/re.2018-2]

  Background: Image-based artificial intelligence (AI) algorithms have been employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos. At present, these algorithms tend to underestimate CVD risk. In addition, the inner workings of these algorithms are unclear. Objective: To resolve the underestimation problem and investigate the mechanism of the AI model. Methods: An ordinal regression Deep Learning (DL) model was proposed to predict 10-year CVD risk scores. The mechanism of the DL model in understanding CVD risk was explored using methods such as transfer learning and saliency maps. Results: Model development was performed using data from 34,652 participants with good-quality fundus photographs from the UK Biobank and a dataset for external validation collected in Australia comprised of 1376 fundus photos of 401 participants with a desktop retinal camera and a portable retinal camera. The mean [SD] risk-level accuracies across cross-validation folds was 0.772 [0.008], while AUROC for over moderate risk was 0.849 [0.005] and the AUROC for high risk was 0.874 [0.007] on the UK Biobank dataset. The risk-level accuracy for images acquired with the desktop camera data was 0.715, and the accuracy for portable camera data was 0.656 on the external dataset. Conclusions: The DL model described in this study has minimized the underestimation problem. Our analysis confirms that the DL model learned CVD risk score prediction primarily from age- and sex-related image representation. Model performance was only slightly degraded when features such as the retinal vessels and colours were removed from the images. Our analysis identified some image features associated with high CVD risk status, such as the peripheral small vessels and the macula areas.

• Health Care 2024: How Consumer Facing Devices Change Health Management and Delivery

  Date Submitted: May 20, 2024

  Open Peer Review Period: May 27, 2024 - Jul 22, 2024

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  Embarking on a journey into the future of healthcare shaped by technological advances and the impact of the COVID-19 pandemic, we delve into the transformative landscape shaped by the integration of wearable technology, medically regulated devices, and advanced software. The ability to offer consumers unprecedented access to vital signs, advanced biomarkers, and environmental data enables a host of new capabilities to fill gaps in existing knowledge and permit individualized insights and education. Continuous monitoring enables individualized insights, emphasizing the need for a re-definition of health and human performance that is de-centralized, dynamic and personalized. The challenge lies in managing the massive amounts of continuous wearable data, necessitating new definitions of health data and secure practices. The COVID-19 pandemic has accelerated the adoption of digitized consumer-facing diagnostics and software, transforming the traditional patient role. Consumers now have the tools to identify and understand an impending or existing disease state before they encounter traditional healthcare delivery health systems, making self-diagnosis commonplace. This shift empowers consumers to actively participate in their health, contributing to a new era where patients are in control of their well-being, from wellness to disease.Physicians in 2024 will engage with more informed and educated consumers, leveraging advanced analytic tools for diagnostics and streamlined patient management. Wearable devices play a pivotal role in enhancing patient engagement, while virtual reality and tailored software can be utilized by physicians to offer immersive learning experiences about conditions or upcoming procedures. Clinician decision support models and virtual care solutions will contribute to recruiting and maintaining healthcare providers amidst a growing workforce shortage. Healthcare delivery organizations are transforming to improve outcomes at a lower cost, with partnerships with digital technology companies enabling innovative care models. 2024 marks a historic moment where digital health and human performance solutions empower consumers to actively participate in their care. Physicians embrace digital tools, fostering richer patient partnerships, while healthcare organizations seize unprecedented opportunities for multi-location care delivery, addressing cost, workforce, and outcome challenges.

• Buying medicine online: Comparing the convenience, quality, and price of online vs. offline medicine

  Date Submitted: May 20, 2024

  Open Peer Review Period: May 27, 2024 - Jul 22, 2024

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  Background: People buy medicines online for various reasons, such as convenience and cost savings. In Indonesia, the Ministry of Health allows registered platforms to sell medicines online; however, unregistered sellers are still widely circulated. While numerous studies have highlighted the quality of online medicines, comparisons with medicines purchased from licensed physical pharmacies are rare. Objective: To explore the conveniences of buying medicine online on several online platforms in Indonesia, investigate the quality, and compare the price and quality of medicine bought online to medicine purchased in physical pharmacies. Methods: The online sellers were categorised by regulatory status, and the conveniences of each were explored. Between February and May 2022, samples were collected from 732 physical pharmacies and 328 online platforms. Quality and price comparisons were conducted across different types of medicine sellers. Results: Obtaining samples from other online sellers was not always easy. Some platforms have complex registration processes, and we found locating unregulated sellers challenging. Notably, 58.8% of sampled medicines exceeded the median retail price from physical pharmacies for the same dose and form. Branded generic medicines exhibited the widest price variation, ranging from 0.15 to 16.4 times the median offline retail price, while generic medicines ranged from 0.18 to 4.1 times. Regarding quality, the failure rates were 7.9% online and 8.7% offline, showing no significant difference. Among online sellers, unregulated ones had a higher failure rate (11.2%) compared to regulated (3.0%) and semi-regulated (4.5%) sellers. Antibiotics were 2.6 times more likely to fail quality tests than other medicines (13.8% vs. 5.1%). Conclusions: The conveniences of purchasing medicine online vary among sellers, and prices are not consistently lower than offline sellers. In terms of quality, as long as patients buy medicine online from regulated sellers, they don’t have to worry about the quality of medicines.

• Gaming Time and Impulsivity as Independent Yet Complementary Predictors of Gaming Disorder Risk: A Pre-Registered Report

  Date Submitted: May 27, 2024

  Open Peer Review Period: May 27, 2024 - Jul 22, 2024

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  Background: Gaming has become a widely embraced leisure activity, offering potential cognitive benefits while also posing the risk of addiction. Prolonged gaming time (Direct Gaming Involvement, DGI), along with increased impulsivity —a key element of poor self-regulation—has been identified as linked to Gaming Disorder (GD). Despite existing studies in this field, the relationship between impulsivity and DGI remains poorly understood. Objective: The present study explores the connections between impulsivity, measured both by self-report and behavioral assessments, DGI, and GD within a cohort of 82 participants aged 18 to 36 (M = 22,71; SD = 3,23). Methods: The preregistered hypotheses proposed correlations between DGI and GD risk (Hypothesis 1) as well as between impulsiveness and GD risk (Hypothesis 2). Furthermore, it was hypothesized that augmenting a regression model with impulsiveness-related variables alongside DGI would enhance its predictive capacity for GD risk (Hypothesis 3). Lastly, it was anticipated that impulsivity would act as a moderator in the relationship between DGI and GD risk (Hypothesis 4). Results: DGI exhibited a significant correlation with GD (r = 0.24, p < 0.05), confirming Hypothesis 1. Only self-reported measures of impulsivity (BIS-Brief: r = 0.28, p < 0.05; BIS-Brief Self-Control subscale: r = 0.33; p < 0.05; NAS50 Inhibition and Adjournment subscale: r = -0.26; p < 0.05) and one behavioral metric (Stop Signal Reaction Time: r = -0.24, p < 0.05) showed a correlation with GD, partly supporting Hypothesis 2. Self-report measures of impulsivity exclusively predicted GD when included in a regression model with DGI (BIS-Brief: β = 0.25, p < 0.05; BIS-Brief Self-Control subscale: β = 0.28, p < 0.05; NAS50 Inhibition and Adjournment subscale: β = -0.26, p < 0.05), partly supporting Hypothesis 3. The interaction between DGI and impulsivity, aside from one behavioral metric (Go/No-Go Reaction Time: β = -.43; p < 0.05) was deemed insignificant, rejecting Hypothesis 4 almost entirely. Conclusions: These findings suggest that impulsivity and DGI, although associated with GD risk, are independent variables. The intricate links between impulsivity and GD warrant further exploration for a comprehensive understanding. Further research should aim to clarify these relationships and explore potential interventions targeting both DGI and impulsivity to mitigate GD risk. Clinical Trial: NA

• A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study

  Date Submitted: May 27, 2024

  Open Peer Review Period: May 27, 2024 - Jul 22, 2024

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  Background: Long wait times for mental health services could lead to delays in early detection and management of suicidal ideation and behaviors, which are crucial for effective mental health care and suicide prevention. The use of digital technology is a potential solution for efficiently identifying youth with high suicidality. Objective: To evaluate the use of a digital suicidality notification system designed to detect and respond to suicidal needs in youth mental health services; to examine characteristics of young people displaying high levels of suicidal ideation and behaviors. Methods: Young people aged 16-25 years presenting to participating mental health services between November 2018 and October 2023 completed a multidimensional online assessment collecting demographic, clinical, social, and functional data. The initial assessment was mandatory however, reassessment was encouraged to monitor their symptoms throughout care. When their suicidality score exceeded a predetermined threshold informed by clinical expertise and established service policies, the online platform immediately generated a notification, alerting treating clinicians. Subsequent clinical actions and their response times were analyzed. Results: A total of 2021 individuals participated and 11% (N=226) expressed high suicidality. Of the 292 notifications generated, over 75% (222/292) were resolved, with a median response time of 1.9 days (range: 0-50.8 days). Clinical actions taken to address suicidality were conducting safety plans (60%), safety checks (18%), psychological therapy (8%), transfer to another service (3%), and scheduling of new appointments (2%). Young people with high suicidality were more likely to present with multidimensional indicators of complexity, including disengagement from work or education, heterogenous psychopathology, substance misuse, and recurrent illness. Conclusions: The digital suicidality notification system effectively elicited clinical actions when triggered for high suicidal ideation and behaviors. The multidimensional assessment revealed complex symptom presentations of youth that generated a notification. Therefore, this study demonstrates that the digital system can efficiently stratify care for young people with varying levels of suicidality. By expediting the delivery of care to those displaying elevated suicidality, the system can play a pivotal role in preventing its detrimental impacts on mental health.

• Digital Phenotype-driven Prediction for Restless Legs Syndrome: A Machine Learning Study

  Date Submitted: May 27, 2024

  Open Peer Review Period: May 27, 2024 - Jun 13, 2024

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  Background: Restless legs syndrome (RLS) is a relatively common neuropsychiatric disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance on subjective reporting. Objective: This study aimed to propose a predictive machine learning model based on digital phenotypes for RLS diagnosis. Methods: Self-reported lifestyle data were integrated via a smartphone application with objective biometric data from wearable devices to obtain 85 features processed based on circadian rhythms. Prediction models used these features to distinguish between the non-RLS (International Restless Legs Study Group Severity Rating Scale [IRLS] score ≤10) and RLS symptom groups (1020). Results: The XGB model showed the highest performance in predicting both the RLS symptom group and the severe-RLS symptom group. For the RLS symptom group, when using only wearable device data, the AUC, accuracy, precision, recall, and F1 scores were 0.71, 0.70, 0.79, 0.81, and 0.79, respectively, while these scores combining wearable device and application data were 0.75, 0.75, 0.82, 0.82, and 0.82, respectively. For the severe-RLS symptom group, when using only wearable device data, XGB achieved AUC, accuracy, precision, recall, and F1 scores of 0.70, 0.81, 0.88, 0.91, and 0.89, respectively, while these scores combining wearable device and application data were 0.69, 0.82, 0.87, 0.93, and 0.90, respectively Conclusions: Diverse digital phenotypes clinically associated with RLS were processed based on circadian rhythms to demonstrate the potential of digital phenotyping for RLS prediction. Thus, our study establishes early detection and personalized management of RLS. Clinical Trial: Clinical Research Information Service (CRIS) KCT0009175

• Internet-Based Interventions for Preventing Premature Birth in Preconceptional Women of Childbearing Age: A Systematic Review

  Date Submitted: May 18, 2024

  Open Peer Review Period: May 24, 2024 - Jul 19, 2024

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  Background: Preconception health is essential for preventing premature birth, yet engagement in preconception care remains low. Despite the development of several internet-based interventions post-COVID-19, a thorough evaluation of their effectiveness in enhancing preconception care and preventing premature births is necessary. Objective: This systematic review aims to assess the study designs and evaluate the effectiveness of internet-based interventions in preventing premature birth among preconceptional women of childbearing age. Methods: We conducted a comprehensive search of MEDLINE, Embase, CINAHL, and Cochrane Library databases to identify randomized trials and quasi-experimental studies on internet-based interventions for preventing premature birth. The search was global and included studies published up to December 2023, without language or geographic restrictions. Two authors independently assessed the risk of bias using the revised Cochrane RoB tool (RoB 2), adhering to PRISMA guidelines. A meta-analysis was not conducted due to heterogeneity in populations, measurements, and interventions. Results: Eleven articles were included, with varying study approaches. The overall risk of bias was high in most studies. Interventions improved knowledge of reproductive health but had no significant effect on self-efficacy related to preconception health promotion. While some behavioral changes to reduce preconception care risks were effectively promoted, impacts on folic acid use, contraception initiation, and dual method use were inconsistent. Furthermore, there were no significant reductions in sexually transmitted infections or unplanned pregnancies. Conclusions: Internet-based interventions showed mixed effectiveness across different reproductive health outcomes, with general ineffectiveness in improving reproductive health status. The results, derived from a limited number of studies with a high risk of bias, suggest a need for caution in their application. Future research, including robust clinical trials, is vital to develop, evaluate, and disseminate effective and safe internet-based interventions for preconception care. Clinical Trial: PROSPERO CRD42021277024; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277024

• Investigating the usage of an online peer support forum for obsessive-compulsive disorder: Thematic analysis

  Date Submitted: May 24, 2024

  Open Peer Review Period: May 24, 2024 - Jul 19, 2024

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  Background: Obsessive-compulsive disorder (OCD) is a debilitating chronic anxiety disorder with low rates of remission. r/OCD is a peer support forum hosted by the Reddit website with over 180,000 users and 100-200 new posts daily. While peer support is significantly associated with decreased symptom severity and better treatment outcomes, online forums can also be an outlet for performing and accommodating compulsions (e.g., seeking and receiving reassurance), which can ultimately exacerbate OCD. This subreddit provides an insight into the utility of online peer support forums for individuals with OCD. Objective: We analyzed posts from r/OCD, an online peer support forum for individuals with OCD, to characterize the content and interactions that take place in the subreddit. Methods: All new posts added to the subreddit within a 24-hour period from August 4, 2023 to August 5, 2023 (n = 132) were manually downloaded. Through inductive thematic analysis, posts were coded, and codes were organized into overarching themes. Results: Content analysis yielded four main themes: 1) Identifying OCD symptoms, 2) Sharing OCD experiences, 3) Coping with symptoms, and 4) Sharing treatment and recovery experiences. The user base appeared knowledgeable about OCD. Most posts involved users describing their symptoms, questioning if a particular symptom is OCD, and/or asking other users if any had similar experiences. A minority of posts solicited and provided advice on therapy and medication. Users were supportive and encouraging of each other’s recovery journeys. Conclusions: Online peer support forums for OCD appear to have mixed utility for users. Such outlets may be beneficial for receiving social support and exchanging peer experiences related to treatments. However, they may also facilitate compulsions that paradoxically reinforce obsessions; thus, clinicians should critically evaluate potential usage of forums like r/OCD among patients, as this may delay treatment response. Close moderation by healthcare professionals may be important for ensuring a net positive impact of online peer support forums for OCD.

• Artificial intelligence in dental radiology: improving the efficiency of reporting with ChatGPT – a comparative study

  Date Submitted: May 18, 2024

  Open Peer Review Period: May 24, 2024 - Jul 19, 2024

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  Background: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in healthcare. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation. Objective: This study aimed to assess the effectiveness of ChatGPT in generating radiology reports from dental panoramic radiographs (OPG), comparing the performance of AI-generated reports with those manually created by dental students. Methods: One hundred dental students were tasked with analyzing OPGs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist. Results: Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports. Conclusions: The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. Clinical relevance: This underscores the need for further refinement in the AI’s prompt design and the development of robust validation mechanisms to enhance its utility in clinical settings.

• Oura Ring as a Tool for Ovulation Detection

  Date Submitted: May 17, 2024

  Open Peer Review Period: May 24, 2024 - Jul 19, 2024

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  Background: Oura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or non-hormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health. Objective: This study assessed the performance of Oura Ring’s physiology method of estimating ovulation compared to the traditional calendar method, which estimates ovulation dates based on past menstrual cycle lengths. We evaluate both methods' strengths, weaknesses, and limitations across various factors, including cycle length, cycle variability, and participant age. Methods: We applied the physiology and calendar methods to estimate the ovulation dates in 1155 ovulatory menstrual cycles. An ovulation prediction kit (OPK) provided the reference ovulation date, which served as a benchmark to evaluate the accuracy of each method. Performance metrics included the error in days between the estimated and reference ovulation dates and the percent of ovulations detected. Results: The physiology method outperformed the calendar method, with a mean absolute error (MAE) of 1.26 days compared to 3.44 days. Both methods demonstrated stable performance across adults aged 18 - 52. Cycle variability did not impact the physiology method’s performance; however, the calendar method had performance significantly worse in participants with irregular cycles, with an MAE of 6.63 days. The cycle length presented challenges for the physiology method, with a slightly reduced detection rate in cycles shorter than 26 days (from 98% in typical cycles to 93% in short cycles). Specifically, physiology method error was stable in cycle lengths up to 35 days but increased from an MAE of 1.18 to 1.70 days in abnormally long cycles. However, the physiology method still far outperformed the calendar method in abnormally long cycles, which had an MAE of 7.32 days. Conclusions: The Oura Ring’s physiology method estimates ovulation dates with approximately 2.7 times greater accuracy than the calendar method in typical menstrual cycles. For users with abnormally long or irregular cycles, the calendar method’s accuracy significantly diminishes, whereas the physiology method remains relatively stable, experiencing only minor reductions for detection in shorter cycles and slight accuracy declines in abnormally long cycles. These findings highlight the limitations of the calendar method, especially in atypical cycles, and underscore the enhanced reliability of the physiology method.

• Machine Learning in the Management of Patients undergoing Catheter Ablation for Atrial Fibrillation: a Scoping Review

  Date Submitted: May 24, 2024

  Open Peer Review Period: May 24, 2024 - Jul 19, 2024

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  Background: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation (AF), its variable therapeutic effects among individual patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of AFCA patients. Objective: This scoping review aims to evaluate the current scientific evidence on the application of ML for managing AFCA patients, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field. Methods: Adhering to the PRISMA extension for Scoping Reviews guidelines, relevant studies were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. Methodological quality assessment tools (QUADAS-2 and PROBAST) were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies. Results: The analysis of 23 included studies showcased ML's contributions in (1) identifying potential ablation targets, (2) improving ablation strategies, and (3) patient prognosis. The patient data utilized in these studies comprised demographics, clinical characteristics, various types of imaging (n=9, 39%), and electrophysiological signals (n=7, 30%). In terms of model type, deep learning, represented by CNN, was most frequently applied (n=14, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models showed a high risk of bias due to lack of external validation (n=14). Conclusions: Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of AFCA patient management. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation and further exploration of model generalization and interpretability.

• Mapping Interventions to Enhance Digital Health Literacy Across Diverse Populations: Scoping Review

  Date Submitted: May 23, 2024

  Open Peer Review Period: May 23, 2024 - Jul 18, 2024

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  Background: Digital health literacy (DHL) is essential for understanding and using electronic health information effectively. Large inequalities in access, uptake and use of digital health technologies across population groups necessitate targeted interventions to improve DHL to ensure equitable healthcare access. Objective: The scoping review aimed to identify intervention strategies aimed at enhancing DHL in patients and the general population. Methods: The scoping review used the Joanna Briggs Institute (JBI) Scoping Review Methodology and reported in accordance with PRISMA-ScR guidelines. The review protocol was registered on the Open Science Forum. Eligibility criteria included studies published after 2012, and using the PICO criteria, focused on a broad population, proposed interventions aimed at improving DHL, with no control criteria, and no restrictions on the outcomes. The search strategy included electronic databases (MEDLINE, PsycINFO, and Web of Science), and the team used AS Review, an AI-based software, to screen titles and abstracts. Data was extracted using a modified JBI data extraction tool, and no formal study quality assessment was conducted. The data synthesis process included developing a narrative summary that categorizes findings by intervention mode, study design, outcomes, and target audience, with the goal of providing insights into the scope and characteristics of DHL interventions. Results: The scoping review found a total of 5149 articles through the search, 44 of which met the eligibility criteria for data extraction and analysis. The studies focused on a variety of demographic groups and health conditions, including interventions for older adults, children and adolescents, diabetes, HIV, cardiovascular disease, mental health, and others. Sample sizes ranged from 10 to 1642. Most interventions were carried out in North America and Europe, and the study designs ranged from mixed methods to randomized controlled trials. Interventions for older adults aimed to improve DHL and a variety of methodologies and modes of delivery resulting in positive shifts in attitudes and skills. Interventions via mobile apps, online platforms, and in-person sessions aimed at diabetes, children, adolescents, HIV, cardiovascular disease, mental health, and other conditions yielded positive results, including increases in DHL, self-management skills, and health-related behaviors. Some interventions targeted DHL directly, while others pursued a health outcome by using a comprehensive approach that incorporated a DHL improvement component. Conclusions: The scoping review analyzes interventions that improve DHL across a variety of populations and conditions. It emphasizes the importance of DHL in supporting informed decisions and navigating digital health environments. Many interventions had positive outcomes, but methodological limitations such as small sample sizes and a lack of control groups call the findings into question, emphasizing the need for more rigorous study designs. Future research should address gaps in sustainability, scalability, and the impact of DHL on health inequalities and taking cultural sensitivity into account.

• Evaluating the Cognitive Levels of Generative AI via Bloom’s Taxonomy: A Cross-sectional Study

  Date Submitted: May 15, 2024

  Open Peer Review Period: May 22, 2024 - Jul 17, 2024

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  Background: Generative AI has garnered awareness in the medical field, yet its potential is constrained by inherent limitations. By responding to inputs through predicting the next word from its memory-based archive, we aim to explore some of these constraints from a medical education and psychological perspective, utilizing Bloom’s taxonomy. Objective: To assess AI's cognitive functions in the medical sector by examining its performance through medical licensing exams and applying Bloom's taxonomy. Methods: Questions from the Taiwan Medical Licensing Examination (TMLE) (August 2022) and the third step of the United States Medical Licensing Examination (USMLE) (August 2022) were classified based on Bloom's taxonomy levels. The ChatGPT versions were tasked through individual prompts, with questions entered separately into ChatGPT-3.5 and ChatGPT-4 using different accounts. After each response, the chat logs were erased and reset to ensure the independence of each answer. Responses from ChatGPT-3.5 and ChatGPT-4, collected between January and February 2024, were analyzed. The questions from both exams were available online during the study period. Results: Although the overall performance of ChatGPT-4 surpassed that of ChatGPT-3.5, the analysis of responses from both models across various cognitive levels revealed no significant correlation between their performance and the levels of Bloom's taxonomy. This lack of significance persisted even when considering the strength of ChatGPTs in their extensive databases classified under "remember," compared to other cognitive levels labeled as "non-remember." Conclusions: In the medical field, ChatGPT models may utilize their "remember" function to answer all types of questions across all categories defined by Bloom's taxonomy. Further research is required focusing on different versions, medical specialties, and the level of difficulty assessed by individuals from various backgrounds.

• Investigating older adults’ perceptions of artificial intelligence tools for medication decisions: A vignette-based experimental survey in the U.S.

  Date Submitted: May 21, 2024

  Open Peer Review Period: May 21, 2024 - Jul 16, 2024

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  Background: Artificial intelligence (AI) tools may be able to personalize advice about medications. Objective: To identify older adults’ perceptions of using AI tools when deciding to start or stop medications. Methods: We conducted a vignette-based online experiment in which participants aged ≥65 years from the United States were asked to report their likelihood of stopping a medication by source of information using a 6-point Likert scale (scale anchors 1=not at all likely and 6=extremely likely). Three medications were presented in a randomized order: aspirin (risk of bleeding), ranitidine (cancer-causing chemical), or simvastatin (lack of benefit with age). Five sources of information were presented: primary care provider (PCP), pharmacist, AI that connects with the electronic health record (EHR) and provides advice to the PCP for approval before sharing the recommendation (‘EHR-PCP’), AI with EHR access that directly provides advice (‘EHR-Direct’), and AI that asks questions to provide advice (‘Questions-Direct’) directly. We calculated descriptive statistics to identify participants who were extremely likely (score 6) to stop the medication and used logistic regression to identify demographic predictors of being likely (score 4-6) as opposed to unlikely (scores 1-3) to stopping a medication. Results: Older adults (n=1,245) more frequently reported being extremely likely to stop a medication when the recommendation came from a PCP [ranging from 60% (aspirin) to 69% (ranitidine)] compared to a pharmacist [ranging from 18% (simvastatin) to 29% (ranitidine)]. Older adults were extremely likely to stop a medication when recommended by AI [EHR-PCP: 15% (aspirin) to 23% (ranitidine); EHR-Direct: 10% (aspirin and simvastatin) and 17% (ranitidine); Questions-Direct: 10% (aspirin) to 16% (ranitidine). In adjusted analyses, characteristics that increased the likelihood of stopping a medication when recommended by AI included being Black or African American as compared to White (ranging from Questions-Direct: OR 1.28, 95% C.I. 1.06, 1.54 to EHR-PCP: OR 1.42, 95% C.I. 1.17, 1.73), higher self-reported health (ranging from EHR-PCP: OR 1.09, 95% C.I. 1.01, 1.18 to EHR-Direct: OR 1.13 95% C.I. 1.05, 1.23), higher confidence in using an EHR (ranging from Questions-Direct: OR 1.36, 95% C.I. 1.16, 1.58 to EHR-PCP: OR 1.55, 95% C.I. 1.33, 1.80), and higher confidence using applications (ranging from EHR-Direct: OR 1.38, 95% C.I. 1.18, 1.62 to EHR-PCP: OR 1.49, 95% C.I. 1.27, 1.74). Older adults with higher health literacy were less likely to stop a medication when recommended by AI (ranging from EHR-PCP: OR 0.81, 95% C.I. 0.75, 0.88 to EHR-Direct: OR 0.85, 95% C.I. 0.78, 0.92). Conclusions: These findings suggest that older adults have reservations about stopping a medication when it is recommended by AI. However, individuals who are Black or African American, have higher self-reported health, or higher confidence in using an EHR or applications may be receptive to AI-based medication recommendations. Clinical Trial: N/A

• Best practices for engagement in remote participatory design: Mixed method analysis of four remote studies with family caregivers

  Date Submitted: May 14, 2024

  Open Peer Review Period: May 21, 2024 - Jul 16, 2024

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  Background: Digital health interventions are a promising method for delivering timely support to under-resourced family caregivers. The uptake of digital health interventions among caregivers may be improved by engaging caregivers in their design through Participatory design (PD). Recent years have seen a shift towards conducting PD remotely, which may enable participation by previously hard-to-reach groups. However, little is known regarding how best to facilitate engagement in remote PD among family caregivers. Objective: The objective of the present study was to 1) understand the context, quality, and outcomes of family caregivers’ engagement experiences in remote PD, and 2) learn which aspects of the observed PD approach facilitated engagement or need to be improved going forward. Methods: We analyzed qualitative and quantitative data from evaluation and reflection surveys completed by research partners and community partners (family caregivers) across four separate remote PD studies conducted between 2021-2023. Each study focused on building digital health interventions for family caregivers. For each study, community partners met with research partners for 4-5 design sessions over the course of 6 months. After each design session, community partners completed an evaluation survey. Additionally, after one of four studies, research and community partners completed a reflective survey and interview. Descriptive statistics were used to summarize quantitative evaluation and reflection survey data, while thematic analysis was used to understand all qualitative evaluation and reflection survey data. Results: The average effectiveness and satisfaction ratings for each session ranged between 4 and 5 on a five-point scale. Qualitative data relating to the Engagement Context identified that the identities of partners, the technological context of remote PD, and partners' understanding of the project and their role all influenced engagement. Within the domain of Engagement Quality, relationship building and co-learning; satisfaction with pre-work, design activities, time allotted, and the final prototype; and inclusivity and the distribution of influence contributed to partners' experience of engagement. Data pertaining to Partner Outcomes indicated that partners felt ongoing interest in the project after its conclusion, felt gratitude for participation, and gained a sense of meaning and self- esteem from engaging in remote PD. Conclusions: These results point to high satisfaction with remote PD processes and few losses specific to remote PD. Results also demonstrate specific ways in which processes can be changed to improve partner engagement and outcomes. Community partners should be involved from study inception in defining the problem to be solved, the approach to be used, and their roles within the project. Throughout the design process, virtual tools may be used to check partners' satisfaction with design processes and perceptions of inclusivity and power sharing. Emphasis should be placed on increasing psychosocial benefits of engagement (e.g., sense of community, purpose) and increasing opportunities to participate in disseminating findings and future studies.

• Barriers and Enablers in Integrating Patient-Generated Health Data for Shared Decision-Making Between Healthcare Professionals and Patients: A Scoping Review

  Date Submitted: May 13, 2024

  Open Peer Review Period: May 20, 2024 - Jul 15, 2024

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  Background: Advancements in technologies and increased adoption of wearables and smartphones by individuals have led to an abundance of patient-generated healthcare data. These data, when used effectively, could help to further augment the process of shared decision-making to enable patient-centred care. However, the possible utilization of patient-generated (health) data (PGHD) introduces complexities and challenges which warrant considering both health care professional and patient perspectives. Objective: Summarize the relevant works in the past 10 years from the perspectives of the key stakeholders – healthcare professionals (HCPs) and patients (PATs) - on potential barriers and enablers to the integration of PGHD for shared decision-making. By looking at both perspectives, we are able to identify the key challenges and opportunities with PGHD throughout the patient’s journey. Methods: Electronic searches were done 3 databases – PubMed, ACM Digital Library and IEEE. Enablers and barriers mentioned by the stakeholders in included papers were extracted. Thematic analysis was performed using a qualitative analysis software, MaxQDA. The six-stage workflow model initially proposed by West at al was used as a reference for deductive coding. Subsequently, considering barriers and enablers faced by both the HCPs and PATs uncovered various tensions and alignments of perspectives which could be addressed in future work and can inform concepts, designs and development in the area of PGHD for shared decision-making. Results: Fifty-three publications were included in the scoping review. Six main overarching themes for barriers and enablers were identified: 1) Patient-Provider Relationship, 2) Patient Characteristics, 3) Organizational Factors, 4) Medical Ethics and Law, 5) Data-driven workflow and 6) Design and Technology. The six-stage workflow was further expanded based on the new findings.to include four additional stages which include contextual considerations outside of traditional clinical environments. In addition to partially corroborating previously established barriers in the six-stage workflow model, several new barriers and enablers were identified throughout all stages. This model helps to further align needs of HCPs and PATs beyond the clinical setting and could benefit system designers who plan to integrate DHTs involving PGHD for shared decision making. Conclusions: This scoping review demonstrates that there are several factors to consider for effectively integrating PGHD in health-related shared decision-making. Notably, such factors extend outside the boundaries of traditional clinical settings. Although there is agreement between HCPs and PATs on certain factors, there are also tensions to be addressed. Our findings suggest that apart from lifting the barriers to the integration of PGHD, there can be a role for digital health technologies in mediating alignment between HCPs and PATs on effectively using PGHD for SDM.

• Success factors of growth-stage digital health companies: A systematic literature review

  Date Submitted: May 13, 2024

  Open Peer Review Period: May 20, 2024 - Jul 15, 2024

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  Background: Digital health technologies (DHTs) have received increasing attention over the last decade, showcasing novel opportunities for the healthcare industry. This period has attracted substantial investment and revealed the challenges companies face in maintaining growth. However, to our knowledge, a systematic overview of the factors that drive success in growth-stage digital health companies is lacking, which limits our understanding of this important industry. Objective: This work identifies and discusses key factors that make growth-stage DHT companies successful. Specifically, we address three questions: (1) What are the success factors of growth-stage digital companies in general, and (2) digital health companies in particular? and (3) How do these success factors vary across DHTs. Methods: Following established PRISMA guidelines, a systematic literature review was conducted to answer the questions. A comprehensive literature search was conducted using management and medical literature databases: EBSCO, PubMed, ProQuest, Scopus and Web of Science. The review spanned scientific articles published from 2000 to 2023, employing a rigorous screening process and quality assessment using the CASP checklist. Results: Overall, 2,972 studies were screened, and 36 were included in the final analysis. We identified 52 success factors and categorized them into four internal factor categories (Product and Services, Operations, Business Models, and Team Composition), and six external factor categories (Customers, Healthcare Sytem, Government and Regulators, Investors and Shareholders, Suppliers and Partners and Competitors). Our findings reveal 19 success factors specific to growth-stage digital health companies. Also, success factors vary significantly across DHTs, highlighting each segment's unique challenges and opportunities. Conclusions: Essential characteristics contributing to the success of growth-stage digital health companies have been identified. This work, therefore, fills a knowledge gap and provides relevant stakeholders, including investors and entrepreneurs, with a valuable resource that can support informed decision-making in investment decisions and, in turn, enhance the success of fast-growing digital health companies. Additionally, it provides the research community with a direction for future studies, enhancing the understanding, implementation, and growth of DHTs.

• The Paradigm Shift from Patient to Health Consumer:25 Years of Value Assessment in Health

  Date Submitted: May 14, 2024

  Open Peer Review Period: May 17, 2024 - Jul 12, 2024

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  While economic analyses and health technology assessment have come a long way in their multi-faceted assessment of the clinical, economic, ethical, legal, and societal perspectives that may be impacted by a new technologies and procedures, these approaches do not reflect underlying patient preferences that may be important in the assessment of “value” in the current value-based healthcare revolution. Also, the arbitrary nature of the threshold in these studies limit a value-based approach to measuring dollars in terms if an increase in the QALY gained. The major challenges that come with the transformation to a value-based healthcare system lead to questions such as: “how are economic analyses, often the basis for policy and reimbursem*nt decisions, going to switch from a societal to an individual perspective?”; and “how do we assess (economic) value, then, taking into account individual preference heterogeneity as well as varying heuristics and decision rules?”These challenges, both related to including the individual perspective in cost effectiveness analysis, have been widely debated. The societal perspective measures cost-effectiveness of treatment in terms of costs and Quality-Adjusted-Life-Years (QALY), where QALYs assume a health state that is more desirable is more valuable and, therefore, value is equated with preference or desirability. This approach has major empirical and conceptual shortcomings such as inconsistencies among values obtained from the standard-gamble, time-trade-off, and visual-analog-scale elicitation formats and more importantly, the linearity assumptions that violate the assumption of diminishing marginal utility. This paper reviews 25 years of value assessment approaches in health. It first describes the foundation of value assessment in other fields, then in the second part discusses the application of these methods in health economics. In the third part, it explains why value assessment works differently in health and a one-to-one copy from other fields in not always appropriate. It will be challenging to take into account the complexities of individual preferences and behaviors, especially if they are not met at the societal level. The paper does conclude with suggestions and opportunities to further improve value assessment methods in health in the years to come.

• Publication Counts in Context: A Deeper Dive into Research Trends

  Date Submitted: May 16, 2024

  Open Peer Review Period: May 16, 2024 - Jul 11, 2024

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  This article discusses the extensive use of publication counts as indicators of trends in the scientific activities of individual researchers, research groups, and entire disciplines. However, with the growing number of articles in general, these counts might produce false impressions among scientists. We propose a straight-forward yet effective normalization method, which enables further context of publication counts by using a query and a reference term. Additionally, an open access implementation is readily available.

• Mobile and web-based interventions for promoting healthy diets, preventing obesity, and improving health behaviours in children and adolescents: A systematic review of randomized controlled trials.

  Date Submitted: May 16, 2024

  Open Peer Review Period: May 16, 2024 - Jul 11, 2024

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  Background: Global data indicate a notable increase in childhood and adolescent obesity associated with later noncommunicable diseases such as metabolic disorders and cardiovascular disease. Whether digital technology provides effective instruments to mitigate this issue is still unclear. Objective: The aim of this article is to present conclusions from a systematic review of the scientific literature on the effectiveness of mobile and web-based interventions in promoting healthy diets, preventing obesity, promoting physical activity, and improving attitudes and knowledge towards nutrition in children and adolescents compared to traditional interventions and a lack of any intervention. Methods: This review systematically examined randomized controlled trials focusing on mobile and web-based interventions. The databases searched included PubMed, Scopus, and Google Scholar, with the inclusion criteria centred around interventions targeting dietary habits, anthropometric measurements, physical activity, and nutrition-related knowledge and attitudes in children and adolescents. Results: Overall, 31 articles were included. Of these, 19 articles reported an effect on one or more of the outcomes. The studies utilized diverse intervention strategies, most commonly games. Key findings suggest that for more than half of the tools, games improved dietary intake, particularly increased fruit and vegetable intake. The results for the other outcomes were mostly inconclusive. Conclusions: The mixed outcomes of the reviewed studies highlight the complex interplay between intervention design and behavioural change efficacy. While some interventions have shown promising short-term effects, especially in terms of increasing fruit and vegetable intake and improving nutritional knowledge, how to sustain these changes remains unclear. Future research should focus on differentiating between the effective and ineffective features of digital tools, investigating their long-term effectiveness, and investigating the role of integrating parents and caregivers, especially for small children. This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42023423512.

• Advancing Preeclampsia Prediction: A Tailored Machine Learning Pipeline for Handling Imbalanced Medical Data

  Date Submitted: May 9, 2024

  Open Peer Review Period: May 16, 2024 - Jul 11, 2024

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  Background: Preeclampsia represents a significant challenge in obstetrics. Effective early prediction is crucial for timely intervention, yet the development of predictive models is complicated by the class imbalances inherent in clinical data. Objective: This study aims to develop a robust pipeline that enhances the predictive performance of ensemble machine learning models for the early prediction of preeclampsia in an imbalanced dataset. Methods: We evaluated combinations of six ensemble machine learning algorithms and eight resampling techniques across a spectrum of minority-to-majority ratios. Using statistical methods, we systematically identified and optimized these configurations, focusing on key performance metrics such as Geometric Mean. Results: The strategic optimization of variable selection and settings proved crucial. The configuration using the Inverse Weighted Gaussian Mixture Model for resampling, followed by the Gradient Boosting Decision Trees algorithm, with an optimized minority-to-majority ratio of 0.09, was identified as the most effective, achieving a Geometric Mean of 0.6694. This configuration significantly outperformed the baseline across all evaluated metrics, demonstrating substantial improvements in model performance. Conclusions: This study establishes a robust pipeline that significantly enhances the predictive performance of models for preeclampsia within imbalanced datasets. Our findings underscore the importance of a strategic approach to variable optimization in medical diagnostics, offering potential for broad application in various medical contexts where class imbalance is a concern.

• Unpacking Early Digital Addiction and Developmental Challenges in Young Children: A Scoping Review Towards Rethinking Digital Habits

  Date Submitted: May 8, 2024

  Open Peer Review Period: May 15, 2024 - Jul 10, 2024

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  Background: In today's intricate socio-economic landscape, working parents confront challenges in continuously supervising their children's actions, frequently turning to screen devices as a convenient substitute to keep their offspring occupied. Evidence indicates that disproportionate screen time engagement during a very early stage of life (0-3 years) increases substantially with age leading to adverse influence on children's cognitive, linguistic, and academic success over time. In response to this matter, a personalized mHealth solution can appear as a practical proposition to help parents manage potential threats. Objective: The aim of this qualitative systematic analysis is to underscore the existing blind spots in parental ignorance concerning screen time management, explore the recommended effective strategies for redirecting children under 3 years of age from unwarranted screen contact and lastly, establish a realistic as well as a holistic framework that supports cognitive progression amongst younger children within a context of their domestic setting. Methods: A systematic search of academic databases including Google Scholar, PubMed, IEEE Xplore, and Elsevier was conducted. Qualitative studies pertaining to the recognition of parental decision-making factors, their repercussions, shortcomings, and proposed conquering strategies to alleviate screen media contact in infants and toddlers (aged 0-3 years) were included. Finally, this review paper will integrate the advocated perspectives and propose an actionable replacement tailored to permit families in promoting mindful digital engagement. Results: In total, our comprehensive review included 36 articles. Parents’ perceptions were grouped into 9 distinct categories. It was found that parents generally consider digital devices beneficial for numerous reasons. On the contrary, negative effects such as cognitive harm, dependence and social isolation were detected, however, parents are bound to depend on digital devices due to their modern lifestyle demands. Various authorities have identified difficulties and have developed countermeasures such as limitations on usage and co-viewing, but their implementation must be refined accounting for the challenges of modern parents. The proposed solution could leverage four pivotal features: (i) Screen time tracking and monitoring mechanism, (ii) A reservoir for parental training, (iii) An alternative activity advocator, finally (iv) an interactive artificial intelligence assistant. Conclusions: Overall, the majority of parents have a positive perspective towards the recommended intervention strategies and perceive them as an effective solution. However, they also recognize a reasonable gap in these approaches, due to the lack of appropriate tools, guidance, and sufficient time for implementation. The findings of this study could offer future investigators valuable insights into the design of an empathetic and practical mHealth application, aiming to manage their children’s screen time more efficiently, improve adherence to healthy screen habits, and foster a digital eco-system where technology itself serves as a promoter for progress and well-being, rather than a liability. Clinical Trial: N/A

• A Social Network Analysis of Organ Donation Conversations on X: Developing the OrgReach Social Media Marketing Strategy for Organ Donation Awareness

  Date Submitted: May 7, 2024

  Open Peer Review Period: May 14, 2024 - Jul 9, 2024

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  Background: The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes is critical. Objective: The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed the role of education. We investigate the influence of prominent profiles and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions. Methods: Data was retrieved from X between October 2023 and May 2024, covering a seven-month period. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm. Results: Our analysis reveals an "elite tier" shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. Conclusions: The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study identifies strategies that can feed into the marketing strategies for organ donation outreach and awareness.

• Health Information Seeking Behavior and Wearable Use in Adults Visiting the Emergency Department

  Date Submitted: May 14, 2024

  Open Peer Review Period: May 14, 2024 - Jul 9, 2024

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  Background: The use of symptom checkers and chatbots to find health information has significantly increased over the last decade. Individual health data using wearable health trackers, and applications has also become more common. However, little is known about current use after the COVID-19 pandemic. Both providers and patients could utilize wearable devices to monitor their health. Objective: This study's purpose was to assess the attitudes and behaviors of people regarding health information and wearable technology. Methods: The study used a cross-sectional survey with a convenience sample of adults, both patients and accompanying companions, in the Emergency Department (ED). Data was gathered and analyzed using Qualtrics software. Descriptive statistics were done for all variables. Chi square or F tests were done to compare demographic groups. Results: The study included 500 participants, 79.6% were between 18 and 54 years of age, 64.4% female and 64.0% were of White non-Hispanic race. Contacting a physician was the most common source of information about symptoms 43.2%, followed by internet searches 28.4%. More than 80% of participants reported ever using the internet for health information. Most participants had little or no trust in information from the internet 55.8% but would have trust in academic brand names 75.4%. Half the participants 47.1% said they use a smartwatch or fitness tracker, with heart rate being the most common health item participants tracked. Women and those less than 55 years of age were more likely to use a smartwatch or fitness tracker. The reason most participants who did not use a smartwatch or fitness tracker reported was not feeling it was necessary 48.3% followed by cost 31.2%. Most participants 66.3% were interested in a personalized health app or device. Conclusions: Our study showed that most people have used the internet for information about symptoms, although there was little trust in information from websites. Participants said they would trust sites with academic brand names more than others. The use of smartwatches or fitness trackers was higher than previously reported, with heart rate being the most used parameter. The use of personal physiological data in conjunction with symptom checker was appealing to many individuals.

• Effect of a Clinical Decision Support System-based Antibiotic Prescription Audit and Feedback Visit on Antibiotic Prescribing in Primary Care: a Multi-arm Cluster-Randomized Trial.

  Date Submitted: May 14, 2024

  Open Peer Review Period: May 14, 2024 - Jul 9, 2024

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  Background: While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is crucial. Objective: To evaluate multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. Methods: A cluster-randomized, open-label, controlled trial of 2501 general practitioners (GPs) in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention (health insurance representative visit with prescription feedback and delivery of a treatment leaflet for cystitis and tonsillitis) and the second intervention (Clinical Decision Support System [CDSS]-based visit with prescription feedback and CDSS demonstration on antibiotic prescribing). The control group received no intervention. Data on systemic antibiotic dispensing were obtained from the Health National Insurance System (Système National d'Information Inter-Régimes de l'Assurance Maladie, SNIIRAM) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using ANCOVA adjusted for annual antibiotic prescription volume at baseline. Results: Overall, 2501 GPs were randomized (mean age 53.4 years; 1099 women [43.9%]). At 12 months, the mean volume of systemic antibiotics per GP decreased by 209.1 DDD (95% CI -319.8 to -98.3, p<0.001) in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systematic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (114.8 DDD; 95% CI -231.8 to 2.1; p=0.056). Conclusions: A health insurance visit combining feedback and a CDSS demonstration resulted in a 4.2% reduction in the total volume of systematic antibiotic prescriptions at 12 months. Clinical Trial: ClinicalTrials.gov Identifier: NCT04028830.

• Ethics of Conversational Artificial Intelligence in Mental Health: A Scoping Review

  Date Submitted: May 10, 2024

  Open Peer Review Period: May 10, 2024 - Jul 5, 2024

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  Background: Conversational artificial intelligence (CAI) emerges as a promising new digital technology for mental healthcare. CAI applications, like psychotherapeutic chatbots, are already available in app stores. Objective: This scoping review aims to provide a comprehensive overview of the ethical considerations surrounding the use of CAI as a therapist for individuals with mental health disorders. The secondary aim is to delineate future research directions in this evolving field. Methods: We conducted a systematic search in PubMed, Embase, APA PsycINFO, Web of Science, Scopus, The Philosopher’s Index, and ACM Digital Library. Our search comprised three elements concerning embodied AI, ethics, and mental health, separated by AND commands. We defined CAI as a conversational agent that interacts with a person and uses NLP to formulate output. We included articles discussing ethical challenges related to AI-driven conversational agents that are aimed at functioning as a therapist for individuals with mental health issues. We added additional articles through snowball searching. We only included articles in English or Dutch. Additionally, all types of articles were considered except abstracts of symposia . Screening for eligibility was done by two independent researchers (MRM and TS). An initial charting form was made based on the expected considerations and further revised and complemented during the charting process. The ethical challenges were divided into different themes. When a certain concern occurred in more than two articles, we identified it as a distinct theme. Results: We included 73 articles, of which 90% were published in 2018 or later. Most were reviews (27%) followed by articles that used empirical data collection methods such as surveys or other qualitative methods (14%). The following 10 themes were distinguished: (1) Harm (reduction) and safety (discussed in 52% of articles), the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) Explicability, transparency, and trust (25%), including topics such as the effects of “black-box” algorithms on trust; (3) Responsibility and accountability (26%); (4) Empathy and humanness (21%); (5) Justice (33%), including themes such as health inequalities due to differences in digital literacy; (6) Anthropomorphisation and deception (18%); (7) Autonomy (11%); (8) Effectiveness (30%); (9) Privacy and confidentiality (64%); and (10) Concerns for healthcare workers’ jobs (12%). Other themes were discussed in 14% of articles. Conclusions: Our scoping review has comprehensively covered various ethical aspects of CAI in mental healthcare. However, certain themes, including the climate impact of AI, the responsibility gap, and especially the nuanced examination of therapeutic processes, are less explored . Additionally, the scarcity of qualitative studies and underrepresentation of key stakeholders highlight areas for future research to deepen our understanding of the ethical implications of CAI in mental health.

• Benefits of Communication Skills Training and Interactive E-Picture Book App for Pediatric Cancer Truth-Telling: A Nonrandomized Controlled Trial

  Date Submitted: May 10, 2024

  Open Peer Review Period: May 10, 2024 - Jul 5, 2024

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  Background: There is a dearth of communication skills training (CST) studies that specifically focus on guiding healthcare professionals (HCPs) to communicate with pediatric cancer patients. Few studies have developed and tested innovative interventions regarding pediatric cancer truth-telling for sick children and their parents. Objective: This study aimed to develop and evaluate the effectiveness of an online pediatric CST (PedCST) program and an interactive e-picture book application. Methods: This experimental study enrolled 43 HCPs from pediatric cancer wards and 29 sick children and their parents. The study included an online PedCST designed for HCPs and an interactive e-picture book application tailored for children with leukemia and their parents. Repeated measures analysis of variance and paired t-test were used for data analysis. Results: Online PedCST effectively enhanced the HCPs’ self-confidence and communication skills when communicating with sick children and their parents. These positive effects lasted for three months after the intervention (P<0.001, η2=0.668–0.137). Although the interventions had a limited impact on improving parents’ quality-of-life and emotional distress (P>0.05), they showed a medium-to-large effect on enhancing sick children’s quality-of-life (P<0.001, d=1.217) and symptom distress (P<0.001, d=0.577–0.872). Conclusions: The online PedCST offered substantial benefits to HCPs in conducting truth-telling to sick children and their parents. The interactive e-picture book application proved valuable not only in improving sick children’s quality-of-life and symptoms/emotional distress but also enhanced parents’ satisfaction with the communication process. These findings suggest the adoption of both interventions in clinical practice to enhance the processes and experiences of pediatric cancer truth-telling.

• Evaluating GPT-4 as a Clinical Decision Support Tool in Ischemic Stroke Management

  Date Submitted: May 9, 2024

  Open Peer Review Period: May 9, 2024 - Jul 4, 2024

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  Background: Cerebrovascular diseases are the second most common cause of death worldwide and one of the major causes of disability burden. Advancements in artificial intelligence (AI) have the potential to revolutionize healthcare delivery, particularly in critical decision-making scenarios such as ischemic stroke management. Objective: Here, we evaluated the effectiveness of GPT-4 in providing clinical support for emergency room neurologists comparing its recommendations with expert opinions and real-world outcomes. Methods: A cohort of 100 patients with acute stroke symptoms was retrospectively reviewed. Data used for decision-making included patients’ history, clinical evaluation, imaging study results, and other relevant details. Each case was independently presented to GPT-4, which provided a scaled recommendation (1-7) regarding the appropriateness of treatment, the use of tissue plasminogen activator, and the need for endovascular thrombectomy. Additionally, GPT-4 estimated the 90-day mortality probability for each patient and elucidated its reasoning for each recommendation. The recommendations were then compared with a stroke specialist and actual treatment decision. Results: Agreement of GPT-4’s recommendations with the expert opinion yielded an AUC of 0.85 [95% CI: 0.77-0.93], and with real-world treatment decisions, an AUC of 0.80 [0.69-0.91]. Mortality prediction, GPT-4 accurately identified 10 out of 13 within its top 25 high-risk predictions (AUC = 0.89 [95% CI: 0.8077-0.9739]; HR: 6.98 [95% CI: 2.88-16.9]), surpassing supervised machine-learning models. Conclusions: This study demonstrates the potential of GPT-4 as a viable clinical decision-support tool in the management of acute stroke. Its ability to provide explainable recommendations without requiring structured data input aligns well with the routine workflows of treating physicians. Future studies should focus on prospective validations and exploring the integration of such AI tools into clinical practice.

• Digital outpatient services through a mobile app for adults: findings after 6 months of a multicenter non-randomized controlled trial

  Date Submitted: May 8, 2024

  Open Peer Review Period: May 8, 2024 - Jul 3, 2024

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  Background: Patients with long-term needs for health services are often expected to participate actively in specialized outpatient care, regardless of their condition or digital skills. Health literacy and digital literacy are seen as requisites for active participation to exploit the potential of digital outpatient services. However, associations between participation in a digital outpatient care service and health literacy remain unclear. Objective: The objective of the current study was to evaluate whether digital outpatient care for 6 months resulted in improved health literacy, health-related quality of life (HRQL), digital/eHealth literacy and utilization of healthcare services compared with usual care. Methods: We conducted a multicenter nonrandomized trial with one intervention arm and one control arm. Patients were allocated 1:2 in favor of the intervention arm. Eligible patients were aged 18 years or older and receiving outpatient care in the pain, lung, neurology, or cancer departments at two Norwegian university hospitals. Patients in the intervention arm received digital outpatient care utilizing a tailored combination of patient reported outcome (PRO) measures, self-monitoring, and chats for timely contact with the outpatient clinic. Patient responses were assessed by healthcare workers, via a dashboard that included a traffic light system to draw attention to the most urgent patient reports. The control group received care as usual. The data were collected at enrollment/baseline and after 3 and 6 months. The primary outcome was the change in health literacy according to the Health Literacy Questionnaire (HLQ) domain “Understanding health information well enough to know what to do” at 6 months. The secondary outcomes were four additional domains from HLQ, seven domains of digital/eHealth literacy, HRQL, acceptability of the digital intervention, and health service use. The data were analyzed using SPSS, with univariate methods. Results: A total of 162 patients were recruited, with 55 allocated to the control arm and 107 to the intervention arm. After 6 months of follow up, data were available for 135 individuals (attrition rate 17.3%). There was no statistically significant change in the primary outcome, “Understanding health information well enough to know what to do” at 6 months. After 3 months, the health literacy domains “Actively managing my own health”, and “Understanding health information well enough to know what to do,” as well as both physical and mental HRQL, improved in the digital outpatient intervention group compared with the control group. Overall, the participants in digital outpatient care had a high satisfaction rate when evaluating the digital outpatient care platform. Conclusions: The present study explored digital outpatient care comprising PRO measures, asynchronous messaging, and remote monitoring on clinical indications for patients with chronic pain, ILD, epilepsy, or cancer. Although no significant differences were observed in patients’ health literacy regarding their understanding of health information after 6 months, our data indicate an improvement in health literacy domains and HRQL at 3 months. Despite our mixed results, the participants reported high satisfaction with the digital outpatient care intervention, and our findings highlight the potential of digital interventions in outpatient care. Clinical Trial: NCT05068869 https://clinicaltrials.gov/ct2/show/NCT05068869 International Registered Report Identifier (IRRID): DERR1-10.2196/46649

• The Evolution of Health Information Technology for Enhanced Patient-Centric Care in the United States: A comprehensive look at enhanced interoperability, electronic prescribing, public health reporting, and patient access to health information

  Date Submitted: Apr 29, 2024

  Open Peer Review Period: May 7, 2024 - Jul 2, 2024

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  Background: Health information technology has revolutionized health care in the United States. Interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and electronic patient access to health information have improved care and outcomes. Objective: This objective of this analysis is to examine progress and the Office of the National Coordinator for Health IT’s (ONC’s) mission to enhance health care through data access and exchange. Methods: This analysis leverages data on end-users of health IT to capture trends in engagement in interoperable clinical care data exchange (ability to find, send, receive, and integrate information from outside organizations), e-prescribing, electronic public health reporting, and capabilities to enable patient access to electronic health information. Data were primarily sourced from the American Hospital Association Annual Survey Information Technology Supplement (2008 to 2023), Surescripts e-prescribing utilization data (2008 to 2023), the National Cancer Institute's Health Information National Trends Survey (2014 to 2022), and the National Center for Health Statistics' National Electronic Health Records Survey (2009 to 2023). Results: Since 2009, there has been a remarkable 10-fold increase in EHR use among hospitals and 5-fold increase among physicians. This rapid digitization enabled the interoperable exchange of electronic health information, electronic prescribing, electronic public health data exchange, and the means for patients and their caregivers to access crucial personal health information digitally. Now, 70% of hospitals are interoperable, with many providers seamlessly integrated within EHR systems. Notably, nearly all pharmacies and 92% of prescribers possess e-prescribing capabilities, marking an 85-percentage point increase since 2008. In 2013, 40% of hospitals and a third of physicians allowed patients to view their online medical records. Patient empowerment has increased, with 97% of hospitals and 65% of physicians possessing EHRs that enable patients to access their online medical records. As of 2022, three-quarters of individuals report being offered online access to portals, and over half (57%) report actively engaging with their health information through their patient portal. Electronic public health reporting has also had an uptick, with most hospitals and physicians actively engaged in key reporting types. Conclusions: Federal incentives have served as catalysts for the widespread adoption of electronic health records (EHRs) and the rapid digitization in health care. We found tremendous growth in health IT capabilities. Interoperability initiatives have gained considerable momentum and have fostered collaboration across health care entities. However, challenges persist in achieving nationwide interoperability, stemming from technical, organizational, and policy challenges and optimizing the benefits of these technologies. Enhanced data standardization, governance structures, and the establishment of robust health information exchange networks are crucial steps forward. Interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and patient access to health information have grown substantially over the past quarter-century and have been shown to improve health care outcomes. However, interoperability hurdles, usability issues, data security, and equitable patient access persist. Addressing these demands will require collaborative efforts among stakeholders, refining standards, and enhancing policy frameworks.

• From Doubt to Confidence: How We Overcame Fraudulent Survey Submissions from Bots and Other Survey Takers of a Web-based Survey

  Date Submitted: May 3, 2024

  Open Peer Review Period: May 3, 2024 - Jun 28, 2024

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  In 2019, we launched a web-based longitudinal survey of people who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. Many lessons were learned, effective risk mitigation strategies were identified and implemented, and, ultimately, we completed 5 waves of data collection with reasonable confidence in the integrity of the data. This paper aims to share our experiences with challenges and mitigation strategies with researchers building and utilizing their own web-based samples, particularly samples that target lower prevalence populations.

• Use of video consultations in outpatient medical care in Germany and characteristics of their user groups: analysis of claims data

  Date Submitted: May 3, 2024

  Open Peer Review Period: May 3, 2024 - Jun 28, 2024

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  Background: Supplementing outpatient medical care with the use of video consultations could, among other benefits, improve access, especially in structurally disadvantaged areas. Objective: This claims data analysis, carried out as part of the German research project "Preference-based use of video consultation in urban and rural regions", aims to analyze the use of video consultations and the characteristics of its user groups. Methods: Claims data from three Statutory Health Insurance Funds (SHIFs) and four Associations of Statutory Health Insurance Physicians (ASHIPs) from the period April 2017 to the end of 2020 were used. A sample of around six million insured and 33,100 physicians / psychotherapists was analyzed. In addition to data on the use of video consultations, patient data on sociodemographic characteristics, diagnoses and place of residence were included. To analyze the physicians’ perspective, specialty groups, demographic characteristics and the type of practice location were also included. Descriptive analyses were performed according to different subgroups. Results: From 2017 to 2019, video consultations had almost no relevance in outpatient care in the German health care system. Although this changed significantly with the start of the Covid 19 pandemic, there was also a clear decline in the use of video consultations as the number of infections flattened out. Video consultations are mainly used in psychotherapeutic care. Younger age groups and those located in urban areas use video consultations more frequently; this applies to both patients and service providers. Conclusions: The widespread and lasting use of video consultations will only succeed if the potential user groups accept this form of service provision and recognize its advantages. Further analyses should therefore investigate the preferences of user groups for the use of video consultations.

• Stakeholder consensus on an interdisciplinary terminology to enable development and uptake of medication adherence technologies across health systems: an online real-time Delphi study

  Date Submitted: Apr 28, 2024

  Open Peer Review Period: May 2, 2024 - Jun 27, 2024

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  Background: Technology-mediated medication adherence interventions have proven useful, yet implementation in clinical practice is low. The ENABLE COST Action (CA19132) online repository of medication adherence technologies (MATech) aims to provide an open access, searchable knowledge management platform to facilitate innovation and support medication adherence management across health systems. To provide a solid foundation for optimal use and collaboration, the repository requires a shared interdisciplinary terminology. Objective: We consulted stakeholders on their views and level of agreement on the terminology proposed to inform the ENABLE repository structure. Methods: A real-time online Delphi study was conducted with stakeholders from 39 countries, active in research, clinical practice, patient representation, policy making, and technology development. Participants rated terms and definitions of MATech and of 21 attribute clusters on product and provider information, medication adherence descriptors, and evaluation and implementation. Criteria of relevance, clarity and completeness were rated on 9-point scales, and free-text comments provided interactively. Participants had the possibility to reconsider their ratings based on real-time aggregated feedback and revisit the survey throughout the study period. We quantified agreement and process indicators for the complete sample and per stakeholder group, and performed content analysis on comments. Consensus was considered reached for ratings with disagreement index (DI) below 1. Median ratings guided decisions on whether attributes were considered mandatory, optional or not relevant. We used results to improve the terminology and repository structure. Results: Of 250 stakeholders invited, 117 rated the MATech definition, of which 83 rated all attributes. Consensus was reached for all items. The definition was considered appropriate and clear (median ratings 7.02 and 7.26, respectively). Most attributes were considered relevant and mandatory, and sufficiently clear to remain unchanged, except ISO certification (considered optional, median relevance rating 6.34), and medication adherence phase, medication adherence measurement, and medication adherence intervention (candidates for optional changes, median clarity ratings 6.07, 6.37, and 5.67, respectively). Subgroup analyses found several attribute clusters considered moderately clear by some stakeholder groups. Results were consistent across stakeholder groups and across time, yet response variation was found within some stakeholder groups for selected clusters, suggesting targets for further discussion. Comments highlighted issues for further debate and provided suggestions which informed modifications to improve comprehensiveness, relevance, and clarity. Conclusions: By reaching agreement on a comprehensive MATech terminology developed following state-of-the-art methodology, this study represents a key step in the ENABLE initiative to develop an information architecture that has the potential to structure and facilitate the development and implementation of MATech in health systems across Europe. The debates and challenges highlighted in stakeholders’ comments outline a potential roadmap for further development of the terminology and the ENABLE repository.

• Design and deployment of Digital Health Interventions (DHIs) to reduce the risk of the Digital Divide: a systematic scoping review conducted to inform development of the Living with Covid Recovery (LWCR) DHI

  Date Submitted: Apr 27, 2024

  Open Peer Review Period: May 2, 2024 - Jun 27, 2024

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  Background: Digital health interventions (DHIs) aim to support health-related knowledge transfer e.g., through websites or mobile applications (apps). They have the potential to either increase health inequalities due to the digital divide or to reduce health inequalities by making healthcare available to those who might not otherwise be able to access it, such as geographically remote populations. They can also overcome language barriers though translated content and enable people to access support and advocacy from family members or friends. However, public health programmes and patient-level healthcare delivered digitally need to consider ways to mitigate the digital divide through DHI design, deployment, and engagement mechanisms, to reach digitally excluded populations. Objective: The objective of this systematic scoping review was to identify the features of DHI design and deployment conducive to improving access to, and engagement with, DHIs by people from demographic groups likely to be affected by the digital divide. The review was conducted during the evolving Covid-19 pandemic, and its findings informed the rapid design, deployment, and evaluation of a post-Covid-19 rehabilitation DHI called ‘Living With Covid Recovery’ (LWCR). LWCR needed to be engaging and usable for patients with a wide range of demographic characteristics, to avoid exacerbating existing health inequalities as far as possible. LWCR was introduced as a service in 33 participating NHS hospital clinics from August 2020, was used by 7,679 patients, and the study ran until 20th December 2022. Methods: This systematic scoping review followed the methodology recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR) guidance. The following databases were searched for primary research studies published in English from 1 October 2011 to 1 October 2021: Cochrane Library, Epistemonikos, NICE Evidence, PROSPERO, PubMed (with MEDLINE and Europe PMC) and Trip. In addition, we used OpenGrey and Google Scholar to search for grey literature. We selected publications that met the following inclusion criteria: primary research papers that explored and/or evaluated features of DHI design and deployment intended to enable access to and engagement by adults from demographic groups likely to be affected by the digital divide (e.g., older age; minority ethnic groups; lower income/education level). The data from studies that met the review inclusion criteria were extracted, narratively synthesised, and thematically analyzed. Results: A total of 22 papers were included in the review. Inclusion criteria were met for 19 papers of 1245 hits retrieved by the search and three further papers were added from a search of publications included in relevant reviews. DHIs evaluated in the studies included:telehealth, virtual assistants, text message interventions, decision aids and e-health learning programs. The main themes resulting from analysis of extracted data relating to design considerations included: co-development with end-users and user testing for iterative design cycles to produce DHIs that help improve digital skills and digital health literacy through use; tailoring for low literacy levels through animations, pictures, videos and writing for a low reading age; use of virtual assistants to collect information from patients and guide use of a DHI. For deployment, themes revealed included: provide devices and data, if possible, otherwise use text messages or signpost to sources of cheap/free devices and free WiFi; provide ‘human support’ for implementation / onboarding and troubleshooting; provide tailored digital skills education as part of the intervention; and incorporate peer/family support. Conclusions: Taking these “universal precaution”’ can help reduce the digital divide. The results helped guide the iterative design and successful deployment of the LWCR DHI. They also have wider implications for practitioners, policy makers, and researchers, and will inform best practices in the design and delivery of DHIs for equitable health improvement

• Comparative Evaluation of Ecological Momentary Assessment, Global Physical Activity Questionnaire, and Bouchard’s Physical Activity Record for Measuring Physical Activity: A Multilevel Modeling Approach

  Date Submitted: Apr 24, 2024

  Open Peer Review Period: Apr 30, 2024 - Jun 25, 2024

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  Background: There is growing interest in the real-time assessment of physical activity and physiological variables. Acceleration, particularly those collected through wearable sensors, has been increasingly adopted as an objective measure of physical activity. However, sensor-based measures often pose challenges for large-scale studies due to their associated costs, inability to capture contextual information, and restricted user populations. Smartphone-delivered Ecological Momentary Assessment (EMA) offers an unobtrusive and undemanding means to measure physical activity to address these limitations.Objective: To evaluate the usability of EMA by comparing its measurement outcomes with two self-report assessments of physical activity: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard’s Physical Activity Record (BAR).Methods: 235 participants (137 females, 98 males, 94 repeated) participated in one or more 7-day study. Waist-worn sensors provided by Actigraph™ captured accelerometer data while participants completed three self-report measures of physical activity. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with eight sub-domains of physiological activity (overall physical activity; overall excluding occupational; move; moderate and vigorous exercise; moderate and vigorous occupational; sedentary) to model accelerometer data.Results: Among the three measurement outcomes, EMA (β = .185, p = .005) and BAR (β = .270, p < .001) exhibited higher overall performance over GPAQ (β = .140, p = .019). EMA also showed a more balanced performance, compared to other measurement tools, in modeling various physical activity domains, including occupational, leisure, and sedentary behaviour.Conclusions: Multilevel modeling on three self-report assessments of physical activity indicates that smartphone-delivered EMA is a valid and efficient method for assessing physical activity.telemedicine; smartphone; wearable electronic devices; physical activity

• The CeHRes Roadmap 2.0: an update of a holistic framework for development, implementation, and evaluation of eHealth technologies

  Date Submitted: Apr 17, 2024

  Open Peer Review Period: Apr 22, 2024 - Jun 17, 2024

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  Background: To ensure that an eHealth technology fits with its intended users, other stakeholders, and the context within which it will be used, thorough development, implementation, and evaluation processes are necessary. The CeHRes (Centre for eHealth Research & Wellbeing) Roadmap is a framework that can help shape these processes. While it has been successfully used in research and practice, new developments and insights have arisen since the Roadmap’s first publication in 2011 – not just within the domain of eHealth, but also within the different disciplines in which the Roadmap is grounded. Because of these new developments and insights, a revision of the Roadmap was imperative. Objective: The objective of this viewpoint paper is to present the updated pillars and phases of the CeHRes Roadmap 2.0. Methods: The Roadmap was updated based on four types of sources: (1) experiences with its application in research, (2) literature reviews on eHealth development, implementation and evaluation, (3) discussions with eHealth researchers, and (4) new insights and updates from relevant frameworks and theories. Results: The updated pillars state that eHealth development, implementation and evaluation (1) are ongoing and intertwined processes, (2) have a holistic approach in which context, people, and technology are intertwined, (3) consist of continuous evaluation cycles, (4) require active stakeholder involvement from the start, and (5) are based on interdisciplinary collaboration. The CeHres Roadmap 2.0 consists of five interrelated phases, of which the first is the contextual inquiry, in which an overview of the involved stakeholders, the current situation, and points of improvement is created. The findings from the contextual inquiry are specified in the value specification, in which the foundation for the to-be-developed eHealth-technology is created by means of formulating values and requirements, preliminarily selecting behaviour change techniques and persuasive features, and initiating a business model. In de Design phase, the requirements are translated into several lo- and hi-fi prototypes that are iteratively tested with end-users and/or other stakeholders. A version of the technology is rolled out in the operationalization phase, using the business model and an implementation plan. In the summative evaluation phase, the impact, uptake and working mechanisms are evaluated using a multi-method approach. All phases are interrelated by continuous formative evaluation cycles that ensure coherence between outcomes of phases and alignment with stakeholder needs. Conclusions: While the CeHRes Roadmap 2.0 consists of the same phases as the first version, the objectives and pillars have been updated and adapted, reflecting the increased emphasis on behaviour change, implementation, and evaluation as a process. There is a need for more empirical studies that apply and reflect on the CeHRes Roadmap 2.0 to provide points of improvement, because just as any eHealth technology, the Roadmap has to be constantly improved based on input of its users.

• Governing eHealth in the Context of Fragmented Decision Authority and Plural Interests: A Case Study of the Norwegian eHealth Governance Model

  Date Submitted: Apr 26, 2024

  Open Peer Review Period: Apr 20, 2024 - Jun 15, 2024

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  Background: Background: Governments and policymakers struggle to achieve a balance between hierarchical steering and horizontal governance in systems characterized by fragmented decision authority and multiple interests. To realize its “One Citizen – One Journal” eHealth policy vision, the Norwegian government established a special eHealth board of stakeholders to ensure eHealth policy development. The aim was to create an inclusive governance model that aligned stakeholders’ interests with government ambitions through coordination and consensus. Little empirical knowledge exists on how countries realize such governance models.Objective: The objective of this study was to investigate how the Norwegian inclusive eHealth governance model developed as a tool to align the government’s policy ambitions with stakeholders’ concerns from January 2012 to December 2022. Objective: Objective: The objective of this study was to investigate how the Norwegian inclusive eHealth governance model developed as a tool to align the government’s policy ambitions with stakeholders’ concerns from January 2012 to December 2022. Methods: Methods: In a longitudinal case study we analyzed 16 policy documents and 175 consultation documents issued between January 2012 and December 2022 related to the Norwegian “One Citizen – One Journal” policy implementation process. We used a qualitative approach and employed thematic analysis. Results: Results: (1) The national policy implementation process progressed through three phases, with changes in stakeholder inclusion and perceived influence on the decision-making process characterizing transitions from phase to phase. (2) Tension developed between two contrasting views regarding stakeholders’ autonomy and top-down government authority. Regional health trusts, municipalities, healthcare professional organizations, and industry actors became increasingly concerned about the model’s ability to balance stakeholders’ autonomy concerns with top-down government authority. On the other hand, patient organizations wanted a hierarchical model to ensure equal access to care and quality of care through coherent digital solutions. (3) Governmental insensitivity to participation, lack of transparency, and decreasing trust between the government and stakeholder groups challenged the legitimacy of the inclusive horizontal governance model. As a response, the government changed its approach and adjusted the model to an inclusive bottom-up network model that combined horizontal and hierarchical decision-making. Conclusions: Conclusions: We conclude that Norway’s “One citizen – one Journal” policy trajectory was characterized by a process that unfolded across three distinct phases. Furthermore, the process was characterized by two contrasting stakeholder perspectives: one concerning the extent of justifiable top-down governance to realize a national journal and the other regarding the impact of top-down governance on stakeholders’ autonomy and freedom to govern their own electronic health record implementation process. Finally, it was characterized by diminishing trust in the inclusive governance model. The National eHealth Governance Board faced challenges in establishing legitimacy as a top-down defined horizontal inclusive governance model, primarily attributed to its handling of dilemmas related to participation, transparency, and trust. These dilemmas represent significant obstacles to inclusive governance models and necessitate ongoing vigilance and responsiveness from governmental entities.

• Enhancing Lives: How Positive Ageing Perceptions, Quality of Life, and Social Support Drive Technology Acceptance and Readiness in Older Adults through Indoor Assistive Technology Study

  Date Submitted: Apr 18, 2024

  Open Peer Review Period: Apr 18, 2024 - Jun 13, 2024

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  Background: The ageing population is experiencing more mobility limitations and functional impairments, prompting research into assistive technologies as solutions. These innovations aim to support the health, well-being, and independence of older adults and individuals with mobility challenges. Indoor mobility, vital for daily activities and independence, significantly impacts the lives of these individuals. However, restricted indoor mobility can negatively affect their quality of life and increase the risk of falls. Objective: This study aims to explore the influences of positive ageing perception, quality of life enhancement and social support on indoor assistive technology acceptance and readiness among older adults. Methods: This cross-sectional study was conducted at a gerontechnology laboratory. Participants were required to physically visit the laboratory. The session lasted approximately 60 minutes and consisted of participation in a demonstration of various indoor assistive technologies, as well as the completion of a questionnaire. The demonstrated assistive technologies included a wide range of devices. Participants' positive perceptions of ageing, quality of life enhancement, social support, technology acceptance, and technology readiness were assessed using validated scales. Data analysis was conducted using SPSS 26.0, including descriptive statistics, correlation analysis, and linear regression. Results: A total of 104 older adults aged 60 or above participated and completed the evaluations. The participants' mean age was 67.92 years. Regression analysis revealed that positive ageing perception was positively associated with attitudinal beliefs and gerontechnology confidence. Quality of life enhancement was positively associated with behavioural intention. However, social support showed negative associations with gerontechnology confidence and security. Notably, no significant relationships were found between positive ageing perception and control beliefs, behavioural intention, optimism, innovativeness, comfort, and security. Quality of life enhancement had no significant relationships with attitudinal beliefs, control beliefs, gerontechnology confidence, optimism, innovativeness, comfort, and security. Social support also had no significant associations with attitudinal beliefs, control beliefs, behavioural intention, optimism, innovativeness, and comfort. Conclusions: This study sheds light on the factors influencing older adults' acceptance and readiness to adopt assistive technologies in an indoor setting. The findings underscore the significance of cultivating positive ageing perceptions and emphasising quality of life enhancement through these technologies. It is crucial to address concerns related to gerontechnology confidence, security, and social support to foster greater acceptance and readiness for technology use among older adults. Further research is needed to delve into the underlying mechanisms and develop targeted interventions that promote successful technology adoption in this population. These insights provide valuable guidance for researchers and practitioners seeking to enhance older adults' well-being and quality of life in the digital age. Clinical Trial: N/A

• Associations between Low Self-Control, Meaning in Life, Internet Gaming Disorder Symptoms, and Functioning in Chinese Adolescents: A Cross-sectional Structural Equation Model

  Date Submitted: Apr 13, 2024

  Open Peer Review Period: Apr 17, 2024 - Jun 12, 2024

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  Background: Internet gaming disorder (IGD) is a prevalent public health issue among adolescents. Few studies have, however, examined the relationships between IGD symptoms, low self-control, and meaning in life (MIL). Objective: The present study aimed to examine the mediating role of IGD symptoms in the relationships between low self-control and meaning in life and adolescents’ family and school functioning. Methods: A sample of 2,064 adolescents (46.9% females, mean age = 14.6 years) was recruited from five middle schools in Sichuan, China in 2022. Indirect effects of low self-control and MIL on family and school functioning via IGD symptoms were analyzed via structural equation modeling (SEM). Results: All scales showed satisfactory model fit and scalar measurement invariance by gender. Males showed significantly greater IGD symptoms and lower levels of self-control than females. Impulsivity, temper, search for meaning, and lower presence of meaning were significantly associated with greater IGD symptoms. There were significant indirect effects from impulsivity, temper, and presence of meaning to family and school functioning via IGD symptoms. Multigroup SEM across gender found that the positive association between search for meaning and IGD symptoms existed in males but not females. Presence of meaning significantly and negatively moderated the association between impulsivity and IGD symptoms. Conclusions: The findings support a mediating role of IGD symptoms in the relationships between low self-control and MIL and functioning and a buffering role of MIL on the associations between impulsivity and IGD symptoms among the ethnic minority adolescents. The results have implications for targeted interventions to help males with lower self-control and presence of meaning.