Open Peer-Review: Decision-Making Process of Homecare Professionals Using Telemonitoring of Activities of Daily Living for Risk Assessment in the SAPA Project: An Embedded Mixed-Methods Multiple-Case Study, and other submissions (2024)

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• Open Peer-Review: Decision-Making Process of Homecare Professionals Using Telemonitoring of Activities of Daily Living for Risk Assessment in the SAPA Project: An Embedded Mixed-Methods Multiple-Case Study, and other submissions

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Titles/Abstracts of Articles Currently Open for Review:

• Decision-Making Process of Homecare Professionals Using Telemonitoring of Activities of Daily Living for Risk Assessment in the SAPA Project: An Embedded Mixed-Methods Multiple-Case Study

  Date Submitted: Jul 24, 2024

  Open Peer Review Period: Jul 31, 2024 - Sep 25, 2024

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  Background: Older adults with cognitive deficits face difficulties recalling daily obstacles and lack self-awareness, amplifying the challenges for homecare clinicians to obtain reliable information on functional decline and homecare needs. The result may be suboptimal service delivery. Telemonitoring of ADL has emerged as a tool to optimize ADL homecare needs evaluation. Utilizing ambient sensors, telemonitoring of ADL gathers information about an individual's ADL behaviors within the home, such as preparing meals and sleeping. However, there is a significant gap in the comprehension of how ADL telemonitoring data can be integrated into clinical reasoning to better target homecare services. Objective: The current paper aimed to describe 1) how ADL telemonitoring data is used by clinicians in the process of maintaining care recipients with cognitive deficits at home as well as 2) the impact of ADL telemonitoring on homecare service delivery. Methods: We used an embedded mixed-methods multiple-case study design in which our cases of interest were three health institutions located in the greater Montreal region and offering public homecare services. An ADL telemonitoring system, named NEARS-SAPA, was deployed within those three health institutions for 4 years. Within each case were embedded sub-cases (care recipient, informal caregiver, clinician(s)). For the objectives of the present paper, we used the data collected during 45-60 min interviews with clinicians only. Quantitative metadata were also collected on each service provided to care recipients before and after the implementation of NEARS-SAPA to triangulate the qualitative data. Results: We analyzed 27 sub-cases, comprising 23 clinicians, that completed a total of 57 post-implementation interviews concerning 147 telemonitoring reports. Data analysis showed a 4-step decision-making process used by clinicians 1) Extraction of relevant telemonitoring data, 2) Comparison of telemonitoring data with other sources of information, 3) Risk assessment of the care recipient’s ADL performance and ability to remain at home, and 4) Maintenance or modification of the intervention plan. Quantitative data reporting the number of services received allowed to triangulate qualitative data pertaining to step 4. Overall, the results suggest a stabilization in monthly services following the introduction of the ADL telemonitoring system, particularly in cases where services were increasing prior to its implementation. This is consistent with qualitative data indicating that, in light of the telemonitoring data, most HSCP decided to maintain the current intervention plan rather than increasing or reducing services. Conclusions: Results suggest that ADL telemonitoring contributed to service optimization on a case-to-case basis. ADL telemonitoring may have an important role in reassuring clinicians about their risk management and the appropriateness of services delivery, especially when questions remain as to the relevance of services. Future studies may further explore the benefits of ADL telemonitoring for public healthcare systems with larger-scale implementation studies.

• Preoperative Anxiety Management Practices in Pediatric Anesthesia: A Comparative Analysis of an Online Survey presented to Experts and Social Media Users

  Date Submitted: Jul 23, 2024

  Open Peer Review Period: Jul 30, 2024 - Sep 24, 2024

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  Background: Managing preoperative anxiety in pediatric anesthesia is challenging, as it impacts patient cooperation and postoperative outcomes. Both pharmacological interventions and non-pharmacological interventions are used to reduce children’s anxiety levels. However, the optimal approach remains debated, with evidence-based guidelines still lacking. As a consequence, many different approaches exist. Objective: To increase understanding of the current anxiety management practices, we conducted a public survey via social media platforms, aiming to compare anesthesia providers from an “expert” group and a “social media” group in terms of pediatric anesthesia expertise and to identify differences in preoperative anxiety management between the two groups. Methods: Two surveys were conducted: The first survey targeted attendees of the Scientific Working Group on Pediatric Anesthesia in June 2023 forming the ‘Expert Group’ (EG), and the second survey targeted followers of a pediatric anesthesia platform on social media forming the ‘Social Media Group’ (SG). Both surveys with 24 items were conducted using the same online platform. Questions were grouped into five categories: Pediatric Anesthesia Expertise, Representativity, Structural Conditions, Practices of Pharmacological Management and Practices in Non-Pharmacological Management. The primary objective was to assess the pediatric anesthesia expertise of the SG compared to the EG. Secondary objectives were the differences in the clustered categories with regards to preoperative anxiety management. Results: The study included 198 respondents, with 194 analyzed after excluding 4 due to prior participation or missing data (82 in EG and 112 in SG). The EG cohort exhibited significantly greater professional experience in pediatric anesthesia than the SG cohort (median 19 vs. 10 years, p<0.001), higher specialist status (97.6% vs. 64.6%, p<0.001), and a greater pediatric anesthesia volume (43.9% vs. 12.0% with more than 500 cases per year, p<0.001). Regarding the representativity, two items out of four were statistically significant (level of care of institution, annual case load of institution). Regarding the overall anxiety management practices used, there is a heterogeneous response pattern within both groups, with only five out of 17 items showing statistical significance (feasibility of parental presence during induction, known anxiety measurement tools, induction-based prescription of drugs, minimum age and use of non-pharmacological interventions). Conclusions: Although the respondents do not reflect the level of expertise as a survey of a scientific working group, social media surveys on pediatric anesthesia may be feasible to get an overview of a specific topic when there is great heterogeneity overall. In our case, both cohorts showed little difference in the management of preoperative anxiety in daily practice with very heterogeneous approaches. Evidence-based recommendations could help to standardize preoperative anxiety management and improve anxiety levels in children. Clinical Trial: not necessary

• Breaking Down the Lockdown: The Impact of Stay-At-Home Mandates on Uncertainty and Sentiments

  Date Submitted: Jul 23, 2024

  Open Peer Review Period: Jul 30, 2024 - Sep 24, 2024

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  Background: Since the spread of the SARS-CoV-2 virus and the lockdown measures went hand-in-hand, it is difficult to distinguish how public opinion reacted to the lockdown measures from the reactions to COVID-19. Objective: We analyze the causal effect of COVID-19 lockdown policies on sentiment and uncertainty using the Italian lockdown in February 2020 as a quasi-experiment. Communities inside and just outside the lockdown area were equally confronted with COVID-19 at the time of the implementation of the policy, offering a form of random allocation of the lockdown. The two areas had also balanced socioeconomic and demographic characteristics before the lockdown, indicating that the definition of the boundaries of the area under strict lockdown approximates a randomized experiment. This allows to identify the causal impact of lockdowns on public emotions, disentangling the changed due to the policy itself, from the changes induced by the spread of the novel virus. Methods: We employ Twitter data, natural language models (N = 24,261), and a difference-in-differences approach to compare sentiment changes within (n=1,567) and outside (n=22,694) the lockdown areas before and after the beginning of the lockdown. Tweets are classified into four categories—economics, health, politics, and lockdown policy—to analyze the corresponding emotional responses. Results: We find that the lockdown had no significant effect on economic uncertainty (b=0.005, SE=0.007, t(125)=0.70, P =.48) or economic negative sentiment (b=-0.011, SE=0.0089, t(125)=-1.32, P =.19), but increased uncertainty about health (b=0.036, SE=0.0065, t(125)=5.55, P<.001) and the lockdown policy (b=0.026, SE=0.006, t(125)=4.47, P<.001) and negative sentiment towards politics (b=0.025, SE=0.011, t(125)=2.33, P =.02), suggesting that lockdowns have wide externalities beyond health. Conclusions: Our results emphasize the need for authorities to use these findings to improve future policies and communication efforts to mitigate uncertainty and social panic.

• The efficacy of VR in the application of musculoskeletal diseases: An umbrella review

  Date Submitted: Jul 21, 2024

  Open Peer Review Period: Jul 26, 2024 - Sep 20, 2024

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  Background: Musculoskeletal disorders are the leading cause of disability in people, and managing them can be challenging. Virtual reality (VR) technology has been recognized as a promising simulation tool in the field of medicine and rehabilitation, and is an important part of the rehabilitation care of patients in the field of orthopedics. The efficacy of VR interventions for musculoskeletal disorders remains to be determined. Objective: To analyze the impact of the virtual reality on musculoskeletal diseases rehabilitation and assess the consistency of evidence from existing systematic reviews and meta-analyses. Methods: The PubMed/Medline, Embase, and Cochrane Library databases were searched for relevant articles published up to April 2024. Literature screening, quality evaluation, and data extraction were performed based on predefined inclusion and exclusion criteria. The Measurement Tool to Assess Systematic Reviews (AMSTAR) 2 was used to evaluate the methodological quality of the included meta-analyses. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to rate the evidence level for each outcome as high, moderate, low, or very low. Furthermore, the ratings were classified into four categories based on the evidence classification criteria: I (convincing); II (highly suggestive); III (suggestive); IV (weak); and non-significant. Results: Results from 15 meta-analyses were synthesized. Seven meta-analyses had high, eight had moderate, and the remaining had low AMSTAR 2 ratings. Virtual reality (VR) shows promising results in musculoskeletal rehabilitation, significantly reducing knee pain (MD=-1.38, 95%CI: -2.32, -0.44, P=.004, I²=94%) and enhancing balance. In Fibromyalgia Syndrome, VR effectively decreases pain (SMD=-0.45, 95%CI: -0.70, -0.20, P<.01), fatigue (SMD=-0.58, 95%CI: -1.01, -0.14, P=.01), anxiety (SMD=0.50, 95%CI: -0.908, -0.029, P=.04), and depression (SMD=0.02, 95%CI: -0.76, -0.15, P=.003), also improving life quality. For back pain sufferers, VR lessens pain-related fears (MD=-5.46, 95%CI: -9.40, -1.52, P=.007, I²=90%) and pain itself (MD=-1.43, 95%CI: -1.86, -1.00, P<.01, I²=95%). Post-arthroplasty, it positively impacts knee functionality (MD=8.30, 95%CI: 6.92, 9.67, P<.01, I²=24%) and lowers anxiety (MD=-3.95, 95%CI: -7.76, -0.13, P=.04, I²=0%). Conclusions: Virtual reality has shown potential value in rehabilitating various musculoskeletal conditions. It can reduce pain, improve psychological state, and promote patient motor function recovery.

• The Use of Educational Technology in Clerkship Education: A Rapid Review

  Date Submitted: Jul 19, 2024

  Open Peer Review Period: Jul 25, 2024 - Sep 19, 2024

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  Background: Medical education has increasingly adopted EdTech to provide effective instruction. The COVID-19 pandemic accelerated this trend, presenting new challenges for medical students and educators, particularly in clerkship settings. Clerkship students face unique difficulties such as limited access to patients and diverse medical cases. EdTech, including virtual reality, simulators, and mobile apps, offers potential solutions by providing simulated experiences and remote access to learning resources. Objective: This review aims to:Identify the EdTech tools used in clerkship education from January 2020 to January 2023.Evaluate the outcomes of using such technology in clerkship education.Discuss the challenges associated with utilizing EdTech in this context​ Methods: A rapid review was conducted following guidelines by Tricco et al. (2017) and PRISMA standards. The search included databases like Medline, Embase, and Web of Science for English-language peer-reviewed empirical studies. Inclusion criteria focused on studies in clerkship environments involving medical students. A total of 1717 citations were screened, resulting in 35 studies included for analysis. Results: The review found that virtual reality (31.4%), learning platforms (14.2%), video-conferencing tools (11.4%), and mobile technologies (8.5%) were commonly used. These technologies served various purposes such as content delivery, interactive instruction, and assessment. Key advantages included authenticity, engagement, and remote learning. Challenges included the availability of technology, lack of hands-on experience, and high costs. Evaluation of these technologies showed positive learning outcomes, increased engagement, and improved learner satisfaction​. Conclusions: The integration of EdTech in clerkship education has shown promising results, enhancing learning outcomes and student satisfaction. However, challenges such as technological availability, cost, and lack of hands-on experience need to be addressed. Careful planning, collaboration, and alignment with curriculum objectives are essential for the effective implementation of EdTech in medical education. Future research should focus on long-term effects and broader implications of these technologies in clinical education.

• Predicting Prefecture−Level Well−Being Indicators in Japan Using Search Volumes in Internet Search Engines: an Infodemiology Study

  Date Submitted: Jul 23, 2024

  Open Peer Review Period: Jul 25, 2024 - Sep 19, 2024

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  Background: In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as Gross Domestic Product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation, but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offer the potential for more cost-effective, timely, and less biased assessments. Objective: Our study aimed to create a model using internet search data to predict well-being indicators at the regional level in Japan, providing policymakers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods: This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. As predictor variables, z-score normalized relative search volume (RSV) data from Google Trends for words relevant to each RWI domain collected for the same years were used. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to build a model to predict RWI using RSVs, where α balances between ridge and lasso regression effects, and λ regulates their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root Mean Square Errors (RMSE) and Coefficients of Determination (R2) were used to assess the model’s predictive accuracy and fit. Results: An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from −1.587 to 3.902, with standard deviations between 3.025 and 0.053. The optimized Elastic Net model, with parameters α = 0.2 and λ = 0.537, showed an RMSE of 1.504 and an R2 of 0.867, incorporating 1 to 11 variables per domain. Conclusions: This study demonstrates the effectiveness of using Internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost−efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence−based policymaking aimed at enhancing well−being. Clinical Trial: Not applicable.

• Evaluating Large Language Models for Sentiment Analysis and Hesitancy Analysis on Vaccine Posts from Social Media

  Date Submitted: Jul 24, 2024

  Open Peer Review Period: Jul 24, 2024 - Sep 18, 2024

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  Background: In the digital age, social media has become a crucial platform for public discourse on diverse health-related topics, including vaccines. Efficient sentiment analysis and hesitancy detection are essential for understanding public opinions and concerns. Large language models (LLMs) offer advanced capabilities for processing complex linguistic patterns, potentially providing valuable insights into vaccine-related discourse. Objective: To evaluate the performance of various LLMs in sentiment analysis and hesitancy detection related to vaccine discussions on social media and identify the most efficient, accurate, and cost-effective model for detecting vaccine-related public sentiment and hesitancy trends. Methods: We employed several LLMs—GPT-3.5, GPT-4, Claude-3 Sonnet, and Llama 2—to process and classify complex linguistic data related to human papillomavirus (HPV), measles, mumps, and rubella (MMR), and vaccines overall from X (formerly known as Twitter), Reddit, and YouTube. The models were tested across different learning paradigms: zero-shot, one-shot, and few-shot, to determine their adaptability and learning efficiency with varying amounts of training data. We evaluated the models' performance using accuracy, F1 score, precision, and recall. Additionally, we conducted a cost analysis focused on token usage to assess the computational efficiency of each approach. Results: GPT-4 (F1 score = 0.85, Accuracy = 0.83) outperformed GPT-3.5, Llama 2, and Claude-3 Sonnet across various metrics, regardless of the sentiment type or learning paradigm. Few-shot learning did not significantly enhance performance compared to the zero-shot paradigm. Moreover, the increased computational costs and token usage associated with few-shot learning did not justify its application, given the marginal improvement in model performance. The analysis highlighted challenges in classifying neutral sentiments and convenience, correctly interpreting sarcasm, and accurately identifying indirect expressions of vaccine hesitancy, emphasizing the need for model refinement. Conclusions: GPT-4 emerged as the most accurate model, excelling in sentiment and hesitancy analysis. Performance differences between learning paradigms were minimal, making zero-shot learning preferable for its balance of accuracy and computational efficiency. However, the zero-shot GPT-4 model is not the most cost-effective compared to traditional machine learning. A hybrid approach, using LLMs for initial annotation and traditional models for training, could optimize cost and performance. Despite reliance on specific LLM versions and a limited focus on certain vaccine types and platforms, our findings underscore the capabilities and limitations of LLMs in vaccine sentiment and hesitancy analysis, highlighting the need for ongoing evaluation and adaptation in public health communication strategies.

• Critical consideration towards broad consent by patient experts; Results of a semi-structured interview study on the secondary use of medical data.

  Date Submitted: Jul 24, 2024

  Open Peer Review Period: Jul 24, 2024 - Sep 18, 2024

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  Background: In health research, large databases and biobanks gain evermore in importance, especially against the background of the digital transformation of research through AI- and algorithms-based research innovations. Considering the health domain, some would argue that there is a moral obligation to make the personal health data gathered in databases and biobanks available for secondary research use. Yet, it is still unclear what ways to gain consent to use the stored data for secondary research purposes are both effective and respectful of the patient's autonomy. One prominent example under discussion is broad consent. As a form of consent in which specific study objectives are not defined, it is seen by many as an efficient alternative to the established consent of participants. Research on subjects' attitudes towards and their discursive reflections on broad consent is, however, limited. Objective: With our study, we aimed to gain deeper insights into the views and (normative) attitudes towards broad consent by members and representatives of patient organizations. Methods: Semi-structured interviews were conducted with members (N=13) and representatives (N=9) of German patient organizations. Subsequently, we evaluated the material using content analysis. Results: The results initially indicate a general agreement with broad consent. In contrast to the results of some existing studies on broad consent, our analysis reveals limitations in this regard: Positive assessments relate less to broad consent in particular, but rather to overarching regulations on secondary data use that deviate from broad consent. Broad consent is criticized for exactly what it was originally intended to regulate: reduced flow of information, lack of a concrete and communicated research objective, and coverage of (too) long periods of time. The interviewees often formulated specific ideas and wishes about appropriate procedures for consenting to secondary data use, i.e., specific governance structures, thus (implicitly) conceptualizing "informed consent" as the gold standard of consent procedures. Conclusions: The interviewees consider the provision of data for secondary use to be important for the improvement of treatment methods, freedom of research, and ethical considerations such as solidarity. However, these values are linked to conditions in the stakeholders' considerations and thus appear less absolute and universal than conditional and situated. Qualitative, empirical-ethical research can make this inherent complexity of ethical attitudes of stakeholders as negotiation processes tangible and fruitful for medical ethical practice.

• Quantifying public engagement with medical science, misinformation, and malinformation

  Date Submitted: Jul 23, 2024

  Open Peer Review Period: Jul 23, 2024 - Sep 17, 2024

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  Background: Medical journals are critical vanguards of research, and there is increased public interest in and engagement with medico-scientific findings. How findings propagate and are understood, and what harms erroneous claims might cause to public health remain unclear. Objective: To gauge the engagement of the public with medical science and quantify the propagation patterns of medico-scientific articles. Methods: Altmetric analysis of engagement with a decade of approximately 9.8 million articles from five leading medical journals. Comparative analysis with the proliferation of and sentiment of the article with the highest-ever Altmetric score, containing vaccine-negative malinformation in social media users and media outlets worldwide. Results: Potential scientific malinformation was much more likely to be engaged with and amplified by vaccine-negative twitter accounts than neutral ones (p < 0.00001), with negative editorialization alluding to the ostensible prestige of medical journals. Malinformation was invoked frequently invoked by conspiracy theory websites and non-news sources (39.2% of all citations) online to cast doubt on the efficacy of vaccination, who tended to use that information repeatedly. Conclusions: Our findings suggest growing public interest in medical science and presents evidence that medical and scientific journals need be aware of the harms of potential misinformation and malinformation. Clinical Trial: NA

• Randomized Controlled Trial of a Digital Therapeutics System for Treating New-onset Mild Hypertension

  Date Submitted: Jul 17, 2024

  Open Peer Review Period: Jul 23, 2024 - Sep 17, 2024

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  Background: Hypertension is the single most significant modifiable risk factor for all-cause morbidity and mortality worldwide.Lifestylechanges are the cornerstone of prevention and treatment of hypertension，Traditional lifestyle intervention depends on the hospital visit follow-up, patients with poor execution and poor compliance.However, the therapeutic effect of LIAN digital therapy with Internet technology in patients with mild hypertension is not yet established. Objective: We aim to evaluate the therapeutic efficacy of LIAN digital therapeutics in patients with new-onset mild hypertension who did not receive antihypertensive medication. Methods: This randomized controlled clinical trial included 1525 individuals with new-onset mild hypertension who were admitted to our Health Management Medicine Center between January 2022 and March 2023. They were randomly assigned 1:1 to the digital therapeutics group (n=756) or the control group (n = 769). The primary efficacy endpoint was the mean change in office blood pressure (BP) from baseline to 12 weeks. The key secondary efficacy endpoints were the mean changes in lipoprotein indices and lifestyle factors from baseline to 12 weeks. Results: The mean office BP decreased from 142.03 (SD 8.05)/90.39 (SD 5.69) to 136.19 (SD 10.60)/85.44 (SD 7.62) mmHg in the digital therapeutics group and from 141.91 (SD 7.25)/90.28 (SD 5.57) to 139.78 (SD12.33)/89.00 (SD 8.84) mmHg in the control group, with a mean difference in systolic BP and diastolic BP of –5.85 mmHg (95% CI –6.64 to –5.05 mmHg; P<0.001) and –4.95 mmHg (95% CI –5.50 to –4.40 mmHg; P<0.001), respectively. From baseline to 12 weeks, office BP control (<140/90 mmHg) was achieved in 423 (56.0%) patients in the digital therapeutics group and 308 (40.1%) patients in the control group (P<0.001). At 12 weeks, no group differences existed in high-density lipoprotein cholesterol (P=0.082), total cholesterol (P=0.055) or low-density lipoprotein (P=0.222) concentrations. However, changes in the total cholesterol/high-density lipoprotein cholesterol ratio and lifestyle factors were statistically significant (P<0.001). At 12 weeks, there were significant differences in lifestyle factors, including limiting oil consumption (P<0.001), limiting salt consumption (P<0.001), smoking status (P<0.001), drinking status (P<0.001), and exercise status (P<0.001), between the digital therapeutics group and the control group. Conclusions: Our study confirmed the superiority of digital therapeutics for improving BP and lifestyle factors in people with new-onset mild hypertension without antihypertensive medications.

• The effect of contextual factors on cybersecurity risk perception for assisted living technology and wearables – a mixed-methods study

  Date Submitted: Jul 17, 2024

  Open Peer Review Period: Jul 23, 2024 - Sep 17, 2024

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  Background: Over the last decade, the healthcare technology landscape has expanded significantly, introducing new and innovative solutions to address healthcare needs. The implications of cybersecurity incidents in the healthcare context extend beyond data breaches to potentially harming individuals' health and safety. Risk perception is influenced by various contextual factors, contributing to cybersecurity concerns that technological safeguards alone cannot address. Thus, it is imperative to study risk perceptions, contextual factors, and technological benefits to guide policy development, risk management, education, and implementation strategies. Objective: To investigate the differences in cybersecurity risk perception among various stakeholders in the healthcare sector in Norway and British Columbia (BC), Canada, and identify specific contextual factors that shape these perceptions. We expect to identify differences in risk perceptions for the explored healthcare technologies. Methods: Using a mixed-methods approach comprising surveys and interviews, we sampled healthcare-related wearable technology stakeholders, including healthcare workers, patients (adults and adolescents) and their families, health authorities and hospital staff (biomedical engineers, IT support, research), and device vendors/industry professionals in both Norway and BC. Surveys explored information security scenarios based on the Behavioural Cognitive Internet Security Questionnaire (BCISQ), risk perception, and contextualizing variables. We analyzed both survey datasets to summarize participants’ characteristics and responses to questions related to the BCISQ (behaviour and attitude) and risk perception. Interviews were analyzed thematically using an inductive-deductive approach to explore risk perception and contextual factors. Results: Data from 274 survey respondents were available for analysis: 185 from Norway, including 139 (75%) females, and 89 from BC, including 57 (64%) females. Forty-five respondents (31 in Norway and 14 in BC) participated in interviews. The BCISQ showed minor differences between locations; respondents demonstrated generally low-risk behaviour and robust information security awareness. However, password simulation demonstrated discrepancies between self-assessed and “real” behaviour by sharing or willingness to share passwords. Perceived risk is generally considered low, yet consequences of cybersecurity risks were evaluated as major but unlikely. Risk perception was stronger for assisted living and diabetes technologies than for smartwatches. The most important contextual factors shaping risk perceptions are human factors encompassing knowledge, competence, familiarity, feelings of dread, perceived benefit, and trust, as well as the technological factor of device functionality. Organizational and technological factors had lesser effects. Conclusions: We found minimal differences in behaviour and risk perception among Norwegian and BC participants. Human factors and device functionality were most influential in shaping cybersecurity risk perceptions. Considering the rising need for assisted living technologies and wearables, insight into risk perceptions can strengthen risk management, awareness, and competence building. Further, it can address potential concerns amongst stakeholders to enable quicker technology adoption.

• Differences in Telemedicine Use for Patients with Diabetes in an Academic Versus Safety Net Health System: A Retrospective Cohort Study

  Date Submitted: Jul 23, 2024

  Open Peer Review Period: Jul 22, 2024 - Sep 16, 2024

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  Background: The COVID-19 public health emergency (PHE) catalyzed widespread adoption of telemedicine, for both video and audio-only visits. This proliferation highlighted inequities in healthcare access by age, race, ethnicity, and preferred language. Few studies have investigated how differences in health system telemedicine implementation affected these inequities. Objective: To describe the characteristics of patients who utilized telemedicine during the PHE and identify predictors of telemedicine use across health systems with different telemedicine implementation. Methods: This retrospective cohort study included adults with diabetes receiving primary care 7/2020-3/2021 at two independent health systems in San Francisco. Participant sociodemographic characteristics, health information, and telemedicine utilization were derived from electronic health records. The primary outcome was visit type (any audio or video telemedicine vs. in-person only) during the study period. We used multivariable logistic regression to assess the association across health systems between visit type and key predictors associated with digital exclusion (age, race/ethnicity, preferred language, and neighborhood socioeconomic status), adjusting for baseline health information. We included an interaction term to estimate health system impact on each predictor, then stratified by health system (academic, which prioritized video-enabled visits, vs. safety-net, which prioritized audio-only visits). Results: Among 10,201 patients, results from our multivariable analyses demonstrated higher odds of telemedicine use in the safety-net system compared to the academic system (aOR 2.94, 95%CI 2.48-3.48). Overall, younger age (age 18-34: aOR 2.55, 95%CI 1.63-3.97; age 35-49: aOR 1.39, 95%CI 1.12-1.73 vs. age 75+) and Chinese language preference (aOR 2.04, 95%CI 1.66-2.5 vs. English) had higher odds of having a telemedicine visit. Conversely, patients had lower odds of having a telemedicine visit if they were non-Hispanic (NH) Asian (aOR 0.67, 95%CI 0.56-0.79), NH Black (aOR 0.83, 95%CI 0.68-1), Hispanic/Latine (aOR 0.76, 95%CI 0.61-0.95) compared to NH White patients. The model with the interaction term demonstrated significant interactions between health system and age, race and ethnicity, and preferred language.After stratifying by health system, several differences persisted in the academic health system: NH Asian patients and Hispanic/Latine patients had lower odds of a telemedicine visit (Asian aOR 0.57, 95%CI 0.46-0.70, Latine aOR 0.67, 95%CI 0.50-0.91) and younger age groups had higher odds (ages 18-34: aOR 3.97, 95%CI 1.99-7.93, ages 35-49: aOR 1.86, 95%CI 1.36-2.56). In the safety-net system, Chinese-speaking patients had a higher likelihood of having a telemedicine visit (aOR 2.52, 95% CI 1.85, 3.42). Conclusions: The study demonstrates disparities in telemedicine utilization by age, race and ethnicity, and language, primarily in the health system that utilized more video visits. While telemedicine has expanded rapidly during the PHE, certain populations remain at risk for digital exclusion. These findings suggest that system-level factors may significantly influence telemedicine adoption. Implementing both audio-only and video options may enhance accessibility for populations at risk for digital exclusion.

• Effectiveness of mobile health-based gamification interventions for improving physical activity in people with cardiovascular diseases: a systematic review and meta‐analysis of randomized controlled trials

  Date Submitted: Jul 16, 2024

  Open Peer Review Period: Jul 22, 2024 - Sep 16, 2024

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  Background: Gamification refers to using game design elements in nongame contexts. Using gamification to promote physical activity is a novel and promising avenue for improving lifestyle and mitigating the advancement of cardiovascular diseases. However, the literature provides mixed results regarding the effectiveness of gamification interventions for people with cardiovascular diseases. Objective: This systematic review and meta-analysis aim to evaluate the efficacy of gamification interventions in short-term and follow-up periods of physical activity in people with cardiovascular diseases and to explore the most effective game design elements. Methods: A comprehensive search was conducted across seven electronic databases for randomized controlled trials published in English from January 1st, 2010, to February 3rd, 2024. The studies were included when they used mobile health-based gamification interventions in people with cardiovascular diseases with control groups with or without gamification to promote physical activity or break a sedentary lifestyle and when they assessed relevant outcomes. Two independent reviewers screened the retrieved records for title, abstract, and full text, assessed the risk of bias, and extracted data. We conducted meta-analyses using a random-effects model approach. Sensitivity analysis and influence analysis were performed to examine the robustness of our results. All statistical analysis was performed using R Version 4.3.2. Results: A total of six randomized controlled trials were included. The meta-analysis of five studies revealed a small effect of gamification interventions on short-term physical activity (after sensitivity analysis: Hedges g = 0.32, 95% CI 0.19-0.45, 95% PI 0.02-0.62). The meta-analysis of four studies found the maintenance effect (measured with follow-up averaging 2.5 months after the end of the intervention) was small (Hedges g = 0.24, 95% CI 0.14-0.34, 95% PI -0.01-0.41). The meta-analysis of three studies with participants taking 696.96 more steps per day than the control group (95% CI 327.80-1066.12, 95% PI -121.39-1515.31). “Feedback” and “Avatar” are more important predictors. Conclusions: This meta-analysis provides evidence that gamification interventions effectively promote physical activity in people with cardiovascular disease. Importantly, this effect persists after intervention, indicating that this is not just a novel effect caused by the game nature of gamification. The 95% PI suggests that future implementation of gamification interventions in the same study population will lead to actual effects in promoting physical activity in the vast majority of cases. Clinical Trial: CRD42024518795 (PROSPERO).

• Large Language Models (LLMs) as Search Engine Alternatives for Improved Access and Quality of Health-related Information: A Qualitative Investigation into Experiences of Patients with Severe Congenital Scoliosis and Families

  Date Submitted: Jul 21, 2024

  Open Peer Review Period: Jul 22, 2024 - Sep 16, 2024

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  Background: In the digital age, access to health information has become a fundamental resource for patients managing chronic conditions such as severe congenital scoliosis. Traditional search engines often fall short in providing reliable, tailored information. Large Language Models (LLMs), with their advanced capabilities in natural language processing, offer a promising alternative by delivering personalized, context-sensitive information. Objective: To investigate the experiences and perception of patients with severe congenital scoliosis and families in using two selected Chinese LLMs with internet-browsing capability. Methods: In this qualitative phenomenological study, we introduced two pre-selected LLMs to 121 patients and family members during routine clinical care and interviewed 12 participants enrolled by purposive sampling. Face-to-face, semi-structured interviewing was conducted to solicit their experiences and perceptions. Data were analyzed using Colaizzi's method. Results: Four main themes were identified, including "replacement of conventional search engines," where participants reported a unanimous shift among participants from traditional search engines to LLMs driven by the LLMs’ superior ability to handle natural language queries and provide contextually relevant information; "improved access and quality of information" characterized by the 24/7 availability of LLMs and their capability to significantly improve the quality of information retrieved; "importance of prompting skills," where the effectiveness of LLMs was highly dependent on users' ability to formulate questions accurately, a skill that required learning and adaptation and influence user satisfaction and engagement; and "challenges with specificity and accuracy in LLM responses," which pointed to the limitations in the LLMs' performance, particularly when dealing with highly specific queries or when asked to recommend specific treatments, where errors were more likely and adherence to ethical guidelines restricted the provision of certain types of advice. Conclusions: Patients with severe scoliosis and families using LLMs for health-related information seeking tend to develop a clear preference for LLMs over conventional search engine, due to LLMs’ ability to provide accessible, personalized, and context-aware information on-demand. They demonstrate a significant behavioral shift towards more interactive and responsive forms of information retrieval. However, challenges such as inaccuracies in responses to specific queries and ethical limitations in providing treatment recommendations suggest the need for ongoing technological enhancements.

• Tele-nursing perceptions, needs, and related influences in T2DM patients: a qualitative study

  Date Submitted: Jul 21, 2024

  Open Peer Review Period: Jul 20, 2024 - Sep 14, 2024

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  Background: Type 2 Diabetes Mellitus (T2DM) is a growing global health concern, with increasing prevalence necessitating innovative management strategies. Tele-nursing, utilizing digital health technologies, offers a promising solution to enhance the management and care of T2DM patients. However, there is limited understanding of T2DM patients' awareness and acceptance of tele-nursing services, as well as their specific needs and the factors influencing their utilization of these services. Objective: To understand the perceptions and needs of T2DM patients regarding tele-nursing and to analyze the influencing factors, providing a scientific basis for the development and implementation of tele-nursing. Methods: A descriptive qualitative research method was employed. From June to August 2023, a purposive sampling method was used to select 20 T2DM patients from a tertiary hospital, following the principle of maximum variation. Semi-structured interviews were conducted, and the data were analyzed using thematic analysis. Results: Four main themes were identified: insufficient awareness and willingness to use tele-nursing, needs for tele-nursing services, facilitating factors for tele-nursing services, and barriers to tele-nursing services. Conclusions: The awareness of tele-nursing among T2DM patients needs to be further enhanced. Currently, the needs for tele-nursing are relatively singular, focusing mainly on health education and medication reminders, influenced by the accessibility of services, service items, and related costs. Tele-nursing should be guided by the specialized nursing needs of diabetes, combining remote technology with multidisciplinary teams to achieve diversified and remote diabetes care, gradually meeting the multi-level needs of patients.

• Ubiquitous News Coverage and its Varied Effects in Communicating Protective Behaviors to American Adults in Infectious Disease Outbreaks: Evidence from a National Longitudinal Panel Survey

  Date Submitted: Jul 15, 2024

  Open Peer Review Period: Jul 19, 2024 - Sep 13, 2024

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  Background: Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them. Objective: This study aims to explore the role of different media platforms in shaping public discourse on preventive measures to infectious diseases such as quarantine and vaccination, and how media exposure influences individuals' intentions to adopt these behaviors. Methods: This study uses data from legacy media in the U.S., Twitter discussions, and a U.S. nationwide longitudinal panel survey from February 2020 to April 2021. We employed Intermedia Agenda Setting Theory and the Protective Action Decision Model to develop the theoretical framework. Results: We found a two-way interactive agenda flow between legacy media and social media platforms, particularly in controversial topics like vaccination (F = 16.39, p < .001 for newspapers; F = 44.46, p < .001 for Twitter). Exposure to media coverage increased individuals' perceived benefits of certain behaviors like vaccination but did not necessarily translate into behavioral adoption. For example, while individuals’ media exposure increased perceived benefits of mask-wearing (β=0.057, p<0.001 for household benefits; β=0.049, p<0.001 for community benefits), it was not consistently linked to higher intentions to wear masks (β = -0.026, p < .001). Conclusions: Our study integrates media flow across platforms with national panel survey data, offering a comprehensive view of communication dynamics during the early stage of an infectious disease outbreak. The findings caution against a one-size-fits-all approach in communicating different preventive behaviors, especially where individual and community benefits may not always align.

• Video Abstracts in Research: Open Access Approaches for Inclusion

  Date Submitted: Jul 11, 2024

  Open Peer Review Period: Jul 17, 2024 - Sep 11, 2024

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  Video abstracts condense the key messages of a research manuscript into a brief video format, providing an opportunity for enhanced public engagement, wider research dissemination, and greater policy impact. Open access tools provide a strong foundation for a do-it-yourself approach that is more inclusive and does not require subscription software. This methods piece provides practical suggestions for authors, either alone or as part of a team, to create their first video abstracts.

• Integrating Practitioners' Perspectives: Strengthening the MAST Framework for Evaluating Telemedicine Services

  Date Submitted: Jul 16, 2024

  Open Peer Review Period: Jul 16, 2024 - Sep 10, 2024

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  Background: Telemedicine is a digital substitute for in-person healthcare service delivery systems that has gained popularity amid the global COVID-19 pandemic. Objective: The objective of this study was to evaluate telemedicine compatibility from the perspective of healthcare practitioners to enhance the effectiveness and spectrum of the Model for Assessment of Telemedicine (MAST). Methods: Primary and Secondary Healthcare (PSHC) and King Edward Medical University (KEMU) extended their respective telemedicine services in 2020. In these initiatives, 24516 patients benefited from the telemedicine services provided by 1273 doctors from different specializations. A cross-sectional survey was conducted among 248 sampled telemedicine healthcare practitioners (HCPs) from 28.03.2020 to 30.3.2023. The responses with reference to the circulated online questionnaire were collected from HCPs designated at telemedicine portals in the public sector. Purposive sampling with descriptive analysis and Monte Carlo Feature Selection (MCFS) method were used to determine the significant features and interdependencies among various variables affecting the compatibility with the healthcare system as outcome. Results: HCP Perception was analysed explicitly and found significant in addition to the existing domains under multidisciplinary assessment in the MAST model. The subdomains related to integration with healthcare system (HIS), patient facilitation (PF), technology ease (TE), capacity building (CB), ethical integrity (EI), outcome assessment (OA) and communication gap (CG) under proposed HCP perception domain were found interdependent and of significant importance. The variables such as patient satisfaction, resource preservation, HCP satisfaction, digital connectivity, user-friendliness, and patient safety were found to be of higher importance (RI values). However, the compatibility of telemedicine with the healthcare system was also influenced by interdependencies (RI plot) and multifaceted interactions of variables derived from the HCP perception. Conclusions: The variables of HCP perception were exhibiting various weightages of importance and interdependencies in determining the compatibility of telemedicine with the healthcare system and recommended to be considered in the MAST framework.

• Benchmarking Nine State-of-the-Art Large Language Models on Real-World Neurosurgical Data

  Date Submitted: Jul 16, 2024

  Open Peer Review Period: Jul 16, 2024 - Sep 10, 2024

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  Background: Large Language Models (LLMs) have the capability to efficiently analyze extensive amounts of unstructured patient data. Utilizing these models for data mining can significantly enhance speed and throughput, potentially revolutionizing the future of large-scale retrospective chart reviews. Objective: To perform experimental evaluation of the potential of LLMs to extract structured information from patient records on real-world data and to provide actionable recommendations for utilizing LLMs for data mining, encompassing technical, clinical, legal and ethical aspects. Methods: Patients able to provide a written informed consent hospitalized in our institution within a one month period were included in the study. Total of 35 data points were manually extracted from the patient records based on the consensus of two authors. We asked various LLMs (GPT-4o, GPT-4, GPT-3.5, Google Bard, Claude 2, Cohere, neuroGPT-X, LLaMA-2, and LLaMA-3) to extract the same 35 data points from anonymized patient records. The models were instructed to respond in a machine-readable JSON format. Accuracy and machine-readability of the responses were compared among the models. Results: Total of 172 patients were included. The rate of agreement in manual data extraction was 92.6%. Claude 2 achieved the highest number of valid JSON formatted replies (99.4%). A complete response containing all data points was returned most consistently by GPT-4o (98.3%). The highest percentage of correct answers was achieved by GPT-4o (76.3%), followed by neuroGPT-X (68.9%), GPT-4 (68.5%), Claude 2 (68.3%), LLaMA-3 (38%), GPT-3.5 (30.8%), Bard (23.4%), Cohere (5%) and LLaMA-2 (0.3%). GPT-4o overperformed other models significantly (p < 0.001). The models performed the best on yes/no questions, and worst on textual questions. Conclusions: This study demonstrates considerable potential of LLMs in structured data extraction from patient records. It offers practical guidance on prompt engineering and model selection based on experimental results. The findings indicate a transformative impact LLMs could have on healthcare data processing, suggesting a promising direction for future explorations in medical data mining with advanced AI technologies.

• How is premature ovarian insufficiency information communicated on websites? A cross-sectional analysis of content, quality and health literacy.

  Date Submitted: Jul 14, 2024

  Open Peer Review Period: Jul 14, 2024 - Sep 8, 2024

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  Background: Introduction Premature ovarian insufficiency (POI) is the loss of ovarian function before the age of 40, presenting unique challenges to those affected. The internet serves as a key accessible source of information for patients seeking to learn more about POI. This study is the first to analyse websites about POI. Objective: To conduct a cross-sectional analysis of websites providing information on POI. This study will include a comprehensive examination of the content. The analysis will also assess whether these websites adhere to accessibility guidelines and promote health literacy effectively. Methods: Methods A structured search strategy was used to identify websites about POI on Google. From the top 100 search results, 25 websites were selected for comprehensive analysis. The evaluation criteria encompassed content, quality, accessibility, and readability. Results: Results The findings revealed that a large proportion of the websites were written by private healthcare providers. Websites varied in the content included and the quality. NHS associated websites scored highest in content analysis. Across the websites, there was a lack of information on specific domains, such as other treatment option available, hormonal replacement therapy and appeal for participation. Government websites scored the highest in terms of quality. The average accessibility score was 89.75 (out of 100) and for readability the average reading age was 18.3. Conclusions: Conclusions This study highlights areas where websites lack information about POI and the variablity in the quality of POI websites. Simple measures such as providing authorship, attribution and currency in health information may help boost reliability and trust of health information. Significant gaps in accessibility for minority groups can diminished by translation of information. Given the internet’s pivotal role as an information source for patients, the findings from this study can be leveraged by website developers to provide adequate information to increase knowledge and subsequently facilitate informed decision-making.

• Physicians’ perceptions of AI-based clinical decision support systems – influence of process design on trust and professional identity threat

  Date Submitted: Jul 12, 2024

  Open Peer Review Period: Jul 12, 2024 - Sep 6, 2024

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  Background: Artificial Intelligence (AI)-based systems in medicine like Clinical Decision Support Systems (CDSSs) have shown promising results in healthcare, sometimes outperforming human specialists. However, the integration of AI may challenge medical professionals' identities and lead to limited trust in technology, resulting in healthcare professionals rejecting AI-based systems. Objective: This study explores the impact of AI process design features on physicians' trust in the AI solution and on perceived threats to their professional identity. These design features involve the explainability of AI-based CDSS decision outcomes, the integration depth of the AI-generated advice into the clinical workflow, and the physician’s accountability for the AI system-induced medical decisions. Methods: We conducted a three-factorial online between-subject scenario-based experiment with 292 medical students in their medical training and experienced physicians across different specialties. The participants were presented with an AI-based CDSS for sepsis prediction and prevention for use in a hospital. Each participant was given a scenario in which the three design features of the AI-based CDSS were manipulated in a 2 x 2 x 2 factorial design. SPSS PROCESS macro was used for hypothesis testing. Results: The results suggest that the explainability of the AI-based CDSS was positively associated with both the trust in the AI system (.51, p < .001) and professional identity threat perceptions (.35, p < .05). Trust in the AI system was found to be negatively related to professional identity threat perceptions (-.14, p < .05), indicating a partially mediated effect on professional identity threat through trust. A deep integration of the AI-generated advice into the clinical workflow was positively associated with trust in the system (.26, p < .01). The accountability of the AI-based decisions, i.e., the system required a signature, was found to be positively associated with professional identity threat perceptions among the respondents (.34, p < .05). Conclusions: Our research highlights the role of process design features of AI systems used in medicine in shaping professional identity perceptions, mediated through increased trust in AI. An explainable AI-based CDSS and an AI generated system advice, which is deeply integrated into the clinical workflow, reinforce trust, thereby mitigating perceived professional identity threats. However, explainable AI and individual accountability of the system directly exacerbate threat perceptions. Our findings illustrate the complex nature of the behavioral patterns of AI in healthcare and have broader implications for supporting the implementation of AI-based CDSSs in context where AI systems may impact professional identity.

• Preventing adolescents' problematic social media use: Parents be on time!

  Date Submitted: Jul 12, 2024

  Open Peer Review Period: Jul 12, 2024 - Sep 6, 2024

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  Background: Concerned about adolescents' problematic social media use, many parents apply restrictive mediation. However, its effectiveness remains unclear. Objective: Therefore, this study aimed to provide insights into the specific groups and conditions under which restrictive mediation may effectively prevent adolescents' problematic social media use. Specifically, we investigated the prospective relationship between rules about amount, location and moment of Internet use and the onset of adolescents’ at-risk/problematic social media use. Additionally, we examined the moderating role of demographic and parenting factors, including adolescents’ age, adolescents’ gender, adolescent involvement in rule-setting, positive parenting, parental phubbing, and quality of co-parenting (two-way interactions). Furthermore, we explored whether the moderation effects of the parenting factors varied by adolescents’ age and gender (three-way interactions). Methods: Four wave survey data of 315 adolescents (T1: M age = 13.44 years, SD = 2.26, 46.3% girls) and their parents (T1: M age = 46.4 years, SD = 5.05, 55.4% mothers) were used. Results: Analyses revealed that setting internet-specific rules may prevent the development of problematic social media use symptoms in adolescents aged < 12.31 years, but may be counterproductive for adolescents aged > 15.70 years. No other significant two- and three-way interaction effects were found. Conclusions: These findings highlight the importance of age-appropriate parental mediation strategies to prevent problematic social media use.

• Development and validation of explainable machine learning models for sex-specific hip osteoporosis using electronic health records

  Date Submitted: Jul 11, 2024

  Open Peer Review Period: Jul 11, 2024 - Sep 5, 2024

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  Background: Hip fractures are associated with reduced mobility, morbidity, mortality and high healthcare costs, and approximately 90% of hip fractures in the elderly are associated with osteoporosis, making it particularly important to screen the population for hip osteoporosis and intervene early. Dual-energy X-ray absorptiometry (DXA) has limited accessibility, so predictive models for hip osteoporosis that do not use bone mineral density (BMD) data are essential. Objective: We aimed to develop and validate gender-specific hip osteoporosis prediction models using electronic health records without BMD data. Methods: This retrospective study used anonymized medical electronic records, from September 2013 to November 2023, of patients (≥50 years old) from the Health Management Center of the Second Xiangya Hospital. A total of 8039 women were included in the Derivation dataset. The set was then randomized into a 75% training dataset and a 25% testing dataset. Four algorithms for feature selection were used to identify predictors of osteoporosis. The identified predictors were then used to train and optimize eight machine learning models, including a light gradient boosting machine (LightGBM) model. The models were tuned using 5-fold cross-validation to assess model performance in the testing dataset and the independent validation dataset from the National Health and Nutrition Examination Surveys (NHANES). The SHapley Additive explanation (SHAP) method was used to rank the feature importance and explain the final model. Results: A combination of Boruta, LASSO, varSelRF, and RFE methods identified systolic blood pressure, red blood cell count, glycohemoglobin, alanine aminotransferase, aspartate aminotransferase, uric acid, age, and body mass index as the most important predictors of osteoporosis in women. The LightGBM model outperformed the other models, with an Area Under the Curve (AUC) of 0.808 (95%CI:0.782-0.834); the externally validated LightGBM model also had the highest AUC of 0.812 (95% CI:0.794-0.829). Conclusions: The LightGBM model demonstrates high identification performance even without questionnaire data, outperforming both the traditional LR model and the OSTA model. It can be integrated into routine clinical workflows to identify individuals at high risk of osteoporosis for female.

• Safety and efficacy of a modular digital psychotherapy for social anxiety: A randomized controlled trial

  Date Submitted: Jul 9, 2024

  Open Peer Review Period: Jul 9, 2024 - Sep 3, 2024

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  Background: Social anxiety disorder is a common mental health condition characterized by an intense fear of social situations which can lead to significant impairment in daily life. Cognitive behavioral therapy (CBT) has been recognized as an effective treatment; however, access to therapists is limited and the fear of interacting with therapists can delay treatment seeking. Furthermore, not all individuals respond. Tailoring modular treatments to individual cognitive profiles may improve efficacy. We developed a novel digital adaptation of CBT for social anxiety that is both modular and fully digital without therapist in the loop and implemented it in a smartphone app. Objective: To evaluate the safety, acceptability and efficacy of the new treatment in online participants with symptoms of social anxiety Methods: Two online randomized controlled trials comparing individuals with access to the treatment through the app to waitlist. Participants were recruited online and reported Social Phobia Inventory (SPIN) total scores >= 30. Primary outcomes were safety and efficacy over 6 weeks in 102 women aged 18-35 (RCT #1) and symptom reduction (Social Phobia Inventory total scores) after 8 weeks in 267 men and women aged 18-75 (RCT #2). Results: In RCT #1, active and control arm adverse event frequency and severity was not distinguishable. App acceptability was high. Secondary outcomes suggested greater symptom reduction in the active (-9.83 ± 12.80) than the control arm (-4.13 ± 11.59, t90 = -2.23, pFDR = .037, Cohen's d = 0.47). In RCT #2, there was a higher symptom reduction in the active arm (-12.89 ± 13.87) than the control arm (-7.48 ± 12.24, t227 = -3.13, pFDR = .008, Cohen's d = 0.42). Conclusions: The online-only, modular social anxiety CBT programme appears safe, acceptable and efficacious in an online patient group with self-reported symptoms of social anxiety. Clinical Trial: RCT #1: ClinicalTrials.gov NCT05858294, RCT #2: ClinicalTrials.gov NCT05987969

• Colorectal Cancer Racial Equity Post Volume, Content, and Exposure: Observational Study Using Twitter Data

  Date Submitted: Jul 8, 2024

  Open Peer Review Period: Jul 8, 2024 - Sep 2, 2024

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  Background: Racial inequity in health outcomes, especially in colorectal cancer (CRC), is among the most pressing issues in cancer communication. However, few studies have focused on the availability and potential reach of racial health equity content on social media. Objective: To examine the volume and content of, as well as exposure to, CRC racial health equity tweets from CRC equity disseminator accounts on Twitter (or X), defined as accounts that disseminate information related to racial equity concerning CRC outcomes. Methods: We identified Twitter accounts that posted CRC tweets between 2019 and 2021 and followed at least two CRC racial equity organization accounts as CRC equity disseminators. We analyzed the volume and content of racial equity related CRC tweets from these equity-disseminator accounts overall and by account types (experts vs. non-experts) and ascertained which types of accounts did the best job of exposing their followers to CRC racial equity content. Results: Only 5.8% of unique tweets from 798 CRC equity disseminators mentioned racially and ethnically minoritized groups. Of these tweets, most (57%) noted outcome disparities, but specific information about CRC symptoms (1.0%) and screening procedures (14.0%) were rare. Of the equity-information disseminators, expert accounts were more likely than non-experts to send CRC equity tweets. Broker accounts, or those with a significant portion of their followers subscribing to them as unique sources of equity content, were those that disseminated equity information most widely to community that would otherwise not learn about this topic. Conclusions: The analysis highlighted the disparate roles of expert and broker accounts in diffusing information with important implications for addressing cancer in racially minoritized groups in CRC. Public health practitioners should encourage CRC equity disseminators on social media to describe symptoms and screening procedures/benefits, and increase the reach of such content on social media.

• Development, reliability, and validity assessment of a numerical algorithm to detect centralization phenomenon and directional preference among spinal pain patients.

  Date Submitted: Jul 1, 2024

  Open Peer Review Period: Jul 8, 2024 - Sep 2, 2024

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  Background: Affecting millions of people, spinal pain is the leading cause of years lived with disability worldwide since the nineties. Centralization phenomenon (CP) and directional preference (DP) are common features in spinal pain patients, indicating a good clinical prognosis. CP is defined as a rapid and lasting migration of distal pain from the limb to the center of the spine after repeated movement tests. DP is a broader concept in which both the migration and decrease of pain are considered. While their detection has still been based on clinician decision, digital tool would provide objective measurements. Objective: We developed and assessed the reliability and validity of a new algorithm to detect CP and DP among spinal pain patients using quantitative a pain mapping software (PRISMAP). Methods: We designed a two-phase retrospective, cross-sectional, double-blinded diagnostic accuracy study. In Phase 1, using PRISMAP, we recorded and analyzed pain variations before and after a CP-focused physiotherapy session with specialized physiotherapy (PT). PTs classified patients as CP+ or CP-. We developed an algorithm to model changes in pain topography and intensity, identifying patients relative to PT classification. In Phase 2, PTs conducted four pain mappings. The initial two maps depicted a three-day overall patient pain profile, from which reliability and agreement were calculated using Intraclass Coefficient Correlation (ICC), Standard Error of Measurement (SEM), Coefficient of Variation (CV), and Bland-Altman analysis (BA). PTs then documented t-time pain mapping pre- and post-repeated movement test, classifying patients into CP-/CP+ and DP-/DP+. Validity parameters (sensitivity, specificity, positive and negative likelihood ratios (LR+/LR-)) were calculated from the latter two maps, using PT classification as the standard reference. Results: Twelve patients were included in Phase 1 and 49 in Phase 2. The algorithm demonstrated good reliability (ICC=0.993 [95%CI 0.988–0.996], SEM=0.211, CV=12.2%, and bias error with the BA of -0.041 representing 2.4% of the sample mean). Validity for CP was 92.0% [95%CI 73.7–99.02], 79.2% [95%CI 57.8–92.9], 4.42 [95%CI 2.01–9.71], 0.1 [95%CI 0.03–0.39] for sensitivity, specificity, LR+, and LR-. Validity for DP was 81.3% [95%CI 63.56–92.79], 88.2% [95%CI 63.6–98.5], 6.91 [1.86–25.66], and 0.21 [95%CI 0.1–0.45] for sensitivity, specificity, LR+, and LR-. Conclusions: The mathematical modeling of CP and DP is reliable and valid. This approach may enhance patient selection for future studies and serve as a clinical aid for practitioners.

• Systematic Identification of Caregivers of Patients Living with Dementia in the Electronic Health Record: Known Contacts and Natural Language Processing

  Date Submitted: Jul 1, 2024

  Open Peer Review Period: Jul 8, 2024 - Sep 2, 2024

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  Background: Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhancing care coordination, and advancing research and population health efforts in caregiving. Despite EHRs being effective in identifying patients through standardized data fields like demographics, lab results, medications, and diagnoses, identifying caregivers through the EHR is challenging in the absence of specific caregiver fields. Objective: Recognizing the complexity of identifying caregiving networks of people living with dementia (PLWD), this study aimed to systematically capture caregiver information by combining EHR structured fields and unstructured notes and free text. Methods: Among a cohort of PLWD aged ≥60 from Kaiser Permanente Colorado (KPCO) caregiver names were identified by combining structured patient contact fields, i.e. known contacts, with name-matching and natural language processing (NLP) techniques of unstructured notes and patient portal messages containing caregiver terms. Results: Among the cohort of N=789 PLWD, 95% had at least one caregiver name listed in structured fields (mean=2.1). Over 95% of the cohort had caregiver terms mentioned near a known contact name in unstructured encounter notes, with 35% having a full name match in unstructured patient portal messages. The NLP model identified 7,556 “new” names in the unstructured EHR text containing caregiver terms among 99% of the cohort with high accuracy and reliability (F1=.85, precision=.89, recall=.82). 87% of the cohort had a new name identified ≥2 near a caregiver term in their notes and portal messages. Conclusions: Analysis revealed significant patterns in caregiver-related information distributed across structured and unstructured EHR fields, emphasizing the importance of integrating both data sources for a comprehensive understanding of caregiving networks. A framework was developed to systematically identify potential caregivers across caregiving networks using structured and unstructured EHR data. This approach has the potential to improve health services for PLWD and their caregivers.

• Individuals’ perceptions of IoT in healthcare: A weight and meta-analytical review of theories and predictors of the adoption process

  Date Submitted: Jul 8, 2024

  Open Peer Review Period: Jul 8, 2024 - Sep 2, 2024

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  Background: The integration of the Internet of Things (IoT) into healthcare is revolutionizing the industry by enhancing acute disease care, managing chronic diseases, and supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices (WMDs), which offer real-time health monitoring and advanced remote health management. Globally, the integration and increased adoption of IoT in healthcare has led to enhanced efficiency, improved patient care, and generated significant economic value. Objective: This review aims to conduct a comprehensive meta and weight-analysis synthesizing findings from primarily quantitative articles to identify the most influential predictors and theories explaining the adoption process of IoT in healthcare Methods: A keyword search across electronic databases led us to the analysis of 68 papers with 72 datasets. We conducted a weight analysis, to identify the relationships with the most significant results. We also have conducted a meta-analysis by calculating the average beta values and their significance. Finally, we combined the results from both methods to visualize the most used theories. Results: A significant portion of studies are conducted in China, South Korea, and the United States. The technology acceptance model (TAM) and unified theory of use and acceptance of technology (UTAUT) were the most extensively used theories. The results highlight the importance of fostering positive perceptions toward IoT healthcare by mitigating perceived risks, emphasizing ease of use, and performance benefits. Leveraging performance impacts, the fun, and enjoyment derived from these technologies, the positive perceptions of family and doctors, and resource and support availability is going to promote intention to use. Promoting IoT healthcare technologies to innovative individuals and those motivated by health is more effective. Conclusions: Behavioral intention is the most studied variable, while attitude, performance, effort expectancy, and task-technology fit are less explored, indicating a gap in understanding their predictors. Adoption theories from the information systems field are predominantly used, but integrating health-specific theories can provide deeper insights into individual health motivations and threat perceptions. Future research should focus on understudied variables with conflicting results, predictors with fewer studies, and incorporate qualitative methods to gain deeper insights into the adoption process.

• Associations between device-measured physical activity and premature mortality in people with and without mental disorders: a population-based prospective cohort study

  Date Submitted: Jul 6, 2024

  Open Peer Review Period: Jul 6, 2024 - Aug 31, 2024

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  Background: While the impacts of physical activity (PA) on mental health and mortality are well-recognized, most existing evidence relies on subjective measures. Moreover, there is limited research on the association between device-measured PA and premature mortality among individuals with mental disorders. Objective: To explore the association between device-measured PA and the risk of premature mortality, as well as the disparities in life expectancy attributable to PA, among individuals with and without mental disorders. Methods: We included 84,982 adults 50 years and older from the UK Biobank (2013-2015, mean age 63.4 years), of whom 9.3% had mental disorders. Low-intensity PA (LPA), moderate-to-vigorous PA (MVPA), and vigorous PA (VPA) were assessed using wrist-worn accelerometers. Premature death, defined as mortality before age 75, was analyzed using Cox proportional hazards models, stratified by mental health status. Results: Over a median follow-up of 8.1 years, 1965 premature deaths (2.5%) occurred among individuals without mental disorders, while 352 premature deaths (4.4%) occurred among those with. Among individuals without mental disorders, high levels of LPA (>2374.2 min/week), MVPA (>332 min/week), and VPA (>30.2 min/week) were associated with reduced risk of premature mortality compared with those with the corresponding low PA levels (LPA: <1789.8 min/week, MVPA: <149 min/week, VPA: <10.1 min/week), with hazard ratios (HRs) (95% CI) of 0.74 (0.66-0.83), 0.69 (0.60-0.76), and 0.66 (0.58-0.75), respectively. Notably, the protective effect was stronger for individuals with mental disorders, with corresponding HRs (95% CI) were 0.62 (0.46-0.83), 0.50 (0.36-0.69), and 0.50 (0.35-0.73), respectively. Analysis of interactions showed that low LPA levels amplified the association between mental disorders and premature death (P for interaction=0.03). Furthermore, individuals with mental disorders who adhere to high PA levels gain greater life expectancy compared with the life expectancy gained by individuals without mental disorders who adhere to high PA levels. Conclusions: Greater levels of device-measured PA were inversely associated with premature mortality, with a more pronounced association observed among those with mental disorders. These findings suggest that maintaining higher PA levels may confer greater benefits for individuals with mental disorders than for those without, including a reduced risk of premature mortality and an extended lifespan.

• A Qualitative Analysis of Young People’s Experience of Mello: A Personalised, Transdiagnostic Smartphone App Targeting Repetitive Negative Thinking for Depression and Anxiety

  Date Submitted: Jun 30, 2024

  Open Peer Review Period: Jul 5, 2024 - Aug 30, 2024

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  Background: The increasing rates of mental health challenges among young people highlight an urgent need for accessible and effective treatment. However, current mental health systems face unprecedented demand, leaving the majority of young people globally with unmet mental health needs. Smartphones present a promising solution to this issue by offering in-the-moment support through innovative Just-In-Time Adaptive Interventions (JITAI), which provide support based on real-time data. Objective: This study explores young people's experiences with Mello, a JITAI which focused on the transdiagnostic mechanism of Repetitive Negative Thinking, a significant factor contributing to youth depression and anxiety. Methods: Semi-structured qualitative interviews were conducted with 15 participants aged 16 to 25 years, all of whom had previously participated in a pilot randomised controlled trial of Mello. Nine participants identified as female, four as male, and two as non-binary. Interviews focused on participants’ experiences with the Mello app, factors influencing engagement, perceived benefits and limitations, and suggestions for future improvements. Thematic analysis was used to analyse the data. Results: Three overarching themes were identified during the analysis: 1) Mello as a Tool for Intentional Engagement with Repetitive Negative Thinking, 2) Doing Therapy Your Own Way, and 3) Barriers to Engagement During Moments of Low Mood, Anxiety, and Repetitive Negative Thinking. Conclusions: Our findings underscore the value of Mello in promoting intentional engagement and reflection on Repetitive Negative Thinking, consistent with prior research that emphasises the effectiveness of tailored interventions. Although some users valued the self-guided nature of the application, others encountered difficulties with motivation. Future research should explore strategies to enhance engagement for young people with low mood and motivation, such as co-design methodologies, advanced personalisation features, and gamification techniques.

• Can Software Robot Enhance Cognitive Functions of Senior People?: An Longitudinal Exploratory Field Study with Korean Older Adults

  Date Submitted: Jun 30, 2024

  Open Peer Review Period: Jul 5, 2024 - Aug 30, 2024

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  Background: With dementia cases substantially increasing worldwide, adequate treatment tools are urgently needed. Considering the accessibility of cognitive interventions, non-pharmacological treatments such as digital cognitive training are gaining popularity. Past studies specifically focus on cognitive training itself which requires digital literacy and easy to use. To enhance digital literacy and facilitate ease of use, it is possible to incorporate a social aspect, provide rewards for app usage, and utilize AI conversational agents. Objective: We developed a 12-week digital cognitive training called ‘Care & Cure’, which consists of a chatbot service called ‘Saemi talk’ and a group chat service called ‘Our Town’. A field study was conducted, to answering two research questions. The primary objective is to determine whether ‘Care&Cure’ program can enhance cognitive function in older adults. The secondary objective is to determine if ‘Our Town,’ which acts as a catalyst to increase ‘Saemi talk’ usage and interaction between AI agent and the user, improves the social aspect element. Methods: A total of 133 participants (age range: 51–83 years; mean age: 64.75 years) who had not been diagnosed with dementia were recruited. All participants received the ‘Care & Cure’ program for three months. The primary outcomes were changes in Korean-Mini Mental State Examination-version 2 (K-MMSE 2) and Cognitive Impairment Screening Test (CIST) scores after 3 months. and the secondary outcomes were the participants' log data, degrees of social support (MOS-SSS), depression scores (SGDS-K), and engagement scores (TWEETS). Results: Overall, the Care & Cure intervention improved cognitive function, as measured by the K-MMSE pre-post score (Hedge’s g = 0.26, p < .001) and CIST pre-post scores (Hedge’s g = 0.35, p < .001). In secondary results, emotional/informational support showed the highest difference before and after use (t = -6.509, p < .001) with a moderate effect size (0.70; Hedge’s g). Willingness to participate was significantly different between the factors of cognition (t = 2.159, p < .05) with a weak effect size (0.43; Hedge’s g) and affect (t = 2.008, p < .05) with a moderate effect size (0.51; Hedge’s g). However, the behaviors did not show significant differences between pre- and post-use. Additionally, depression showed a significant difference between pre- and post-use (t = 3.093, p < .01) with a weak effect size (0.21; Hedge’s g). In addition, both the active user’s group and non-active user’s group showed a significant increase in CIST scores after treatment (paired t-test; p < 0.001 for active user’s group; p < 0.05, active user’s group). In addition, individuals who used ‘Our Town’ tended to use ‘Saemi talk’ more frequently (p < .001). And, those who used ‘Saemi talk’ more frequently had higher social support scores (p < .001), and individuals with higher social support tended to have higher K-MMSE scores (p < .05). Conclusions: Care & Cure successfully improved cognitive function, participation, social support, and emotional relief. We found that improving social support among participants was a key strategy for improving cognitive function.

• App-based training module on guiding physicians’ prescription for antibiotic treatment of gonorrhoea in China: a pilot cluster-randomised controlled trial

  Date Submitted: Jun 28, 2024

  Open Peer Review Period: Jul 4, 2024 - Aug 29, 2024

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  Background: High prevalence of physicians’ non-adherence to treatment recommendations for gonorrhoea is a concern in China and poor awareness of treatment recommendations has been documented as one of reasons for the non-adherence. Objective: To assess the impact of an App-based training tool on improvement of adherence to treatment recommendations for gonorrhoea in China. Methods: We did a cluster-randomised controlled trial. Clusters were hospitals from four provinces in China. We randomly allocated each hospital to receive an App-based training module (ATM) or continue the routine of training (ROT). In ATM hospitals, physicians were offered a free App-based training through their smartphones for access to the training anytime (intervention) for 6 months. In ROT hospitals, physicians participated in any training programmes as they routinely did (control). The primary outcome was comparison of changes in adherence rate between ATM and ROT. The secondary outcome was reasons for physicians to explain their non-adherence. Results: Among 72 hospitals from the 4 provinces (18 hospitals of each province), 36 each received ATM and ROT, respectively. Over 6 months, the adherence rate increased from 53.6% to 54.8% in ATM group while the rate decreased from 43.9% to 42.5% in ROT. The difference (5.5%, 95% CI -7-18, P=0.37) in changes of the adherence rate between ATM and ROT was not statistically significant (risk ratio 1.12, 95% CI 0.93- 1.35, P=0.23). A major reason for physicians’ non-adherence to the treatment recommendations was a concern on adequacy of the recommended dosage of currently available generic format of ceftriaxone for treatment of gonorrhoea in China. Conclusions: The findings indicate that sizeable improvement of adherence to the treatment recommendations through an App-based training tool might not be achievable over a 6-month period if concern on effectiveness of generic medicine still exits. Clinical Trial: https://www.chictr.org.cn/ ChiCTR2000029591

• Designing Clinical Decision Support Systems (CDSS): A User-Centred Lens of Design Characteristics, Challenges, and Implications—A Systematic Review

  Date Submitted: Jun 28, 2024

  Open Peer Review Period: Jul 4, 2024 - Aug 29, 2024

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  Background: Clinical Decision Support Systems (CDSS) have the potential to play a crucial role in enhancing healthcare quality by providing evidence-based information to clinicians at the point of care. Despite their increasing popularity, there is a lack of comprehensive research exploring their design characterisation and trends. This limits our understanding and ability to optimise their functionality, usability, and adoption in healthcare settings. Objective: This systematic review aims to analyse the design characteristics of CDSS, identify design-related challenges, and provide insight on the implications for future design. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) recommendations and used a Grounded Theory analytical approach to guide the conduct, data analysis, and synthesis. A search of five major electronic databases (PubMed, Web of Science, Scopus, IEEE Xplore, and the Journal of Decision Systems) was conducted for articles published between 2013 and 2023, using predefined design-focused keywords (design, user experience, implementation, evaluation, usability, and architecture). Out of 1922 initially identified articles, 40 passed screening and eligibility checks (by two researchers) for a full review and analysis. Results: Analysis of the studies revealed that User-Centred Design (UCD) is the most widely adopted approach for designing CDSS, with all design processes incorporating functional or usability evaluation mechanisms. The CDSS reported were mainly clinician-facing and mostly standalone systems, with their design lacking consideration for integration with existing clinical information systems and workflows. Through a UCD lens, four key categories of challenges relevant to CDSS design were identified: 1) usability and user experience, 2) reliability and effectiveness, 3) data access, and 4) context and clinical complexities. Conclusions: While CDSS show promise in enhancing health care delivery, identified challenges have implications for their future design, efficacy, and utilisation. Adopting pragmatic UCD design approaches that actively involve users is essential for enhancing usability and addressing identified user experience challenges. Integrating with clinical systems is crucial for interoperability and presents opportunities for AI-enabled CDSS that rely on large patient data. Incorporating emerging technologies like explainable AI can boost trust and acceptance. Enabling functionality for CDSS to support both clinicians and patients can create opportunities for effective use in virtual care settings.

• Social Media and Web-based Advertising Improve Recruitment in an SJS/TEN Community-based Study

  Date Submitted: Jun 27, 2024

  Open Peer Review Period: Jul 3, 2024 - Aug 28, 2024

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  Background: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are genetically mediated life-threatening reactions that in adults are usually caused by a medication. These genetic associations promise an opportunity for pre-prescription screening and prevention. However, the study of genetic risk and long-term sequelae of SJS/TEN across racially diverse populations has been hampered by many factors that include its rarity, social disparities, and trust in healthcare and providers, which impact access to hospital and clinic-based research studies. Objective: To explore the utility of multiple social media and web-based search tools with the goal of increasing numbers, diversity, and inclusivity of study enrollment. Methods: The SJS survivor study is a community-based retrospective cohort study which remotely recruits drug-induced SJS/TEN survivors in the United States to help determine genetic risk and long-term outcomes. Baseline recruitment included advertisem*nts on the SJS Foundation website and American Burn Association newsletter. Social media ads were later introduced by Vanderbilt University Medical Center (VUMC) Facebook and Instagram accounts, where posts were created using flyers and 60-second SJS/TEN survivor video vignettes. Finally, a national Google Ad campaign was launched. We measured the change in registration of both study interest and effectiveness of implementation of specific social media and web-based search tools before and after their implementation. Results: Since the introduction of social media and Google Ads in March 2022, we report a 48.6% increase in enrollment overall and a 289.5% increase in participation interest. We noticed the ads were inclusive to all age groups and saw a more even age distribution of enrolled participants from 18 through 74 years old was seen, with an average of 15% enrolled into each age category. The most significant increase in both enrollment and diversity of responses came from Google Ads with a total of 201 expressions of interest, 33% of which self-identified as non-White and 56 participants enrolled. Conclusions: Social media and web-based search tools differ in their enrollment effectiveness. In this community-based study, social media and web-based strategies increased numbers, diversity, and inclusion of enrollment and show promise as tools to increase both diversity and enrollment in rare diseases such as SJS/TEN.

• Electronic implementation of patient-reported outcome measures in primary health care - a mixed method systematic review.

  Date Submitted: Jun 27, 2024

  Open Peer Review Period: Jul 3, 2024 - Aug 28, 2024

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  Background: Managing chronic diseases remains a significant challenge in OECD countries. Digital tools, especially electronic PROMs (ePROMs), have shown potential in improving data collection and healthcare delivery but their implementation in primary health care is still scarce. Objective: We aimed to describe the implementation and effectiveness of ePROMs in chronic disease management in primary health care settings and to identify associated barriers and facilitators. Methods: We conducted a mixed-method systematic review following Cochrane Methods and PRISMA guidelines, including studies that implemented ePROMs among adults to manage chronic diseases. We extracted outcomes related to patient health, provider workflow, costs, and implementation factors. We used the RE-AIM Framework to assess the reach, efficacy, adoption, implementation, and maintenance of ePROMs. Results: Our search yielded 12,168 references, from which 22 studies were included after screening and exclusions. These studies, conducted mainly in the United States and Canada, covered various chronic diseases and utilized diverse ePROMs tools, primarily mobile applications. While some studies reported improvements in patient health outcomes and self-management, others indicated no significant change. Barriers included digital literacy and integration into existing workflows, whereas facilitators involved personalized approaches and existing patient-provider relationships. Conclusions: Success in implementing ePROMs in primary health care appears to hinge on addressing digital literacy, ensuring personalization and meaningful patient-provider interactions, carefully integrating technology into clinical workflows, and conducting thorough research on their long-term impacts and cost effectiveness. Future efforts should focus on these areas to fully realize the benefits of digital health technologies for patients, providers, and healthcare systems. Clinical Trial: PROSPERO Systematic Review Registry (ID: CRD42022333513).

• The Application of Artificial Intelligence to Ecological Momentary Assessment Data in Suicide Research: A Systematic Review

  Date Submitted: Jun 27, 2024

  Open Peer Review Period: Jul 3, 2024 - Aug 28, 2024

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  Background: Ecological Momentary Assessment (EMA) can capture highly dynamic processes and intense variability patterns suitable to the study of suicidal ideation and behaviors. Artificial Intelligence (AI), and in particular Machine Learning (ML) strategies, have increasingly been applied to EMA data in suicide research. Objective: The review aims to (1) synthesize empirical research applying AI strategies to EMA data in the study of suicidal ideation and behaviors, (2) identify methodologies used, data collection procedures employed, suicide outcomes studied, AI applied, results reported, and (3) develop a standardized reporting framework for researchers applying AI to EMA data in future. Methods: PsycINFO, PubMed, SCOPUS and EMBASE were searched for articles published until June 2024. Studies that applied AI to EMA data in the investigation of suicide outcomes (suicidal ideation, suicide attempt, suicide death), collected across devices (Smartphone, Personal Digital Assistant, PC, tablet) and settings (clinical, community), were included. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were used to identify relevant studies while minimizing bias. Specific EMA data reported included EMA sampling method, monitoring period, prompt latency, compliance, attrition, and treatment of missing data. Quality appraisal was performed using an adapted checklist for reporting EMA studies (CREMAS). Results: 1,201 records were identified across databases. After full text review, 12 articles, comprising 4398 participants were included. Studies were conducted in psychiatric hospitals (n = 5), emergency departments (n = 2), outpatient clinics (n = 2), medical residency programs (n = 1), and university mental health clinics (n = 1), with some conducted across settings. Design features reported (sampling strategy, prompting frequency, response latency, device used, compliance, and treatment of missing data) varied across studies. In the application of AI to EMA data to predict suicidal ideation, studies reported mean AUCs (0.74 to 0.86), sensitivity (0.64 to 0.81), specificity (0.73 to 0.86), and positive predictive values (0.72 to 0.77). Conclusions: The application of AI to EMA data within suicide research is a small but burgeoning area with high heterogeneity apparent in data collection and reporting standards. Findings indicate some promise in the application of ML to self-report EMA data in the prediction of near-term suicidal ideation. The development by the research team of a reporting framework aims to standardize reporting on the application of AI to EMA data in mental health research going forward. Clinical Trial: PROSPERO: CRD42023440218Open Science Framework: https://doi.org/10.17605/OSF.IO/NZWUJ

• Cost-effectiveness analysis of an artificial intelligence-based eHealth system to predict and reduce emergency department visits and unscheduled hospitalizations of older people living at home: a retrospective study.

  Date Submitted: Jun 27, 2024

  Open Peer Review Period: Jul 3, 2024 - Aug 28, 2024

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  Background: Dependent older people or those losing their autonomy are risk of emergency hospitalization. digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. However, few studies have assessed the medico-economic impact of these systems, particularly for older people. Objective: The objective of this study was to analyze the clinical and economic impacts of an eHealth device in real life compared to the usual monitoring of older people living at home. Methods: This study is a single-center, retrospective and controlled trial on data collected between May 31, 2021 and May 31, 2022 in one health care and home nursing center located in Brittany, France. Participants had to be older than 75 years, living at home, and receiving assistance from the home care service for at least 1 month. We implemented an eHealth system that produces an alert for a high risk of emergency department visits or hospitalization. After each home visit, the Home aides completed a questionnaire on participants’ functional status, using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an emergency visit within 7 to14 days. In case of risk, the eHealth system alerted a coordinating nurse who could then inform the family carer and the patient’s nurses or general practitioner. Results: The use of the Presage Care device reduced the number emergency hospitalization by nearly 32 % compared to the control arm. In total, the use of the Presage Care system reduced hospital costs by 45.72%. This result is a minority result in view of all the additional costs: in home care, in post-hospitalizations consultations and in the major risk of re-hospitalization.In the intervention arm, among the 726 visits not followed by an alert, only 4(0.56%) hospitalizations followed the visit (P<.001), which confirm the relevance of the alerts issued by the system Conclusions: This study shows encouraging results on the impact of a remote medical monitoring system for the older adults, both in terms of reducing the number of emergency department visits and the cost of hospitalization. Clinical Trial: NCT05221697

• Africa’s Digital Health Revolution: Leapfrogging Challenges to Deliver Healthcare for All

  Date Submitted: Jun 24, 2024

  Open Peer Review Period: Jul 2, 2024 - Aug 27, 2024

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  Proposed Article Abstract: In the African context, digital health solutions can leapfrog numerous health systems challenges to delivering effective and high quality care. However, the last 25 years of digital health innovation for Africa has resulted in numerous prototypes with very few implemented and sustained at large scales. In this viewpoint, we discuss opportunities for [HB2] and challenges in developing , evaluating and scaling digital technologies that are context specific to sub-Saharan Africa. We then explore a ‘people, process, tech’ approache to empower stakeholders to effectively navigate this complex landscape: bundling interventions, committing to open source, focusing on systems and technology integration[HB4] , and becoming the platform. We will illustrate these strategic approaches with case studies from the African continent.

• The performance of large language models in managing abnormal results of cervical cancer screening: Comparative Study

  Date Submitted: Jun 25, 2024

  Open Peer Review Period: Jul 2, 2024 - Aug 27, 2024

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  Background: Cervical cancer remains the fourth leading cause of female death globally. Screening for cervical cancer is an effective preventative strategy. However, its impact is lessened in environments with scarce medical resources due to poor clinical decision-making and improper resource allocation. Large Language Models (LLMs) could significantly enhance medical systems in these settings by improving decision-making processes. Objective: This study aims to evaluate the performance of LLMs in managing abnormal cervical cancer screening results. Methods: Models are selected from AlpacaEval leaderboard version 2.0 and the capability of our computer. Questions inputted to models are designed in accordance to CSCCP and ASCCP guidelines. Two experts review the response from each model for accuracy, guideline compliance, clarity, and practicality by grading as A, B, C and D weighted as 3, 2, 1 and 0 scores, respectively. Effective rate is calculated as the ratio of the number of A and B to the number of all designed questions. Results: Nine models are included in this study, while 33 questions are specifically designed. Seven models (ChatGPT 4.0 Turbo, Claude 2, Gemini Pro, Mistral-7B-v0.2, Starling-LM-7B alpha, HuatuoGPT and BioMedLM 2.7B) provide stable responses. Among all included models, ChatGPT 4.0 Turbo and Claude 2 ranked in first level with mean score 2.30[2.0, 2.60] (effective rate: 88.81%) and 2.21[1.88, 2.54] (effective rate: 78.79%) when compared to the other seven models (P<0.001). Conclusions: Proprietary LLMs, particularly ChatGPT 4.0 Turbo and Claude 2, show promise in clinical decision-making involving logical analysis. However, this study underscores the need for further research to explore the practical application of LLMs in medicine.

• Date Submitted: Jun 30, 2024

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

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  Background: Postpartum depression, a common mental health disorder with a complex etiology, seriously affects family relationships and social stability. Stigmatization and labeling of patients with postpartum depression persist in social media, a phenomenon that further aggravates the psychological burden of patients. Therefore, analyzing the differences in public perceptions and attitudes toward postpartum depression in order to develop targeted intervention strategies is particularly important. Objective: This study aimed to uncover the popular issues and emotional evolution of public discourse on postpartum depression, as well as the perceptual and emotional differences regarding postpartum depression between various user groups. Methods: A web crawler tool was used to obtain comments posted by users in the postpartum depression community of Zhihu from January 1, 2015 to October 18, 2023. Machine learning analysis techniques, including TextCNN model, topic modeling, and sentiment analysis, were then used to uncover the salient topics and sentiment differences among various user groups. Results: Our findings showed that public concern about postpartum depression continued to rise exponentially in 2020, a phenomenon closely linked to the outbreak of COVID-19. Topic modeling results showed that public discourse covered five key topics: parental role growth, reflections on marriage and parenting, family support and understanding, parenting experience, and postpartum mental health. Significant differences in the focus of attention on topics related to postpartum depression were observed among various user groups. In addition, significant differences in sentiment were observed among various topics, user groups, and years of public discourse. Conclusions: Currently, public attitudes toward postpartum depression showed a positive trend, but the overall emotional expression still skewed negative. Hence, multiple parties must work together to create a more inclusive social environment and build a better social support system. The government should intensify publicity and education efforts to raise public awareness of postpartum depression in order to eliminate public stigmatization of people with postpartum depression. Social media should positively disseminate scientific knowledge about postpartum depression and provide links to specialized services to prevent extreme events. The public health sector should constantly monitor changes in the social environment and take timely measures to guide public opinion.

• Urban-rural difference in the association between internet use trajectories and depressive symptoms in Chinese adolescents: Longitudinal observational study

  Date Submitted: Jun 30, 2024

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

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  Background: Internet use exhibits diverse trajectories during adolescence, which may contribute to depressive symptoms. Currently, there is a lack of evidence on whether the association between online use trajectories and depressive symptoms varies between urban and rural areas. Objective: This study aims to investigate the association of internet use trajectories with adolescent depressive symptoms and to explore variation in this association between urban and rural areas. Methods: This longitudinal study used three-wave data from the 2014-2018 cycle of the China Family Panel Study. Weekly hours of internet use and depressive symptoms were measured using self-reported questionnaires. Latent class growth modeling was performed to identify the trajectories of internet use. Multivariable logistic regressions were conducted to examine the association between internet use trajectories and depressive symptoms, stratified for rural and urban adolescents. Results: Participants were 2,237 adolescents aged 10-15 years at baseline (average age =12·46±1·73). Two latent trajectory classes of internet use were identified: the low growth group (89·8%) and the high growth group (10·2%). Adolescents in the high growth group had a higher risk of developing depressive symptoms (OR = 1·486 [95%CI: 1·065–2·076]) compared to those in the low growth group. In the stratified analysis, the association between internet use trajectories and depressive symptoms was significant solely among rural adolescents (OR = 1·856 [95%CI: 1·164–2·959]). Conclusions: This study elucidates the urban-rural difference in the associations between trajectories of internet use and adolescent depressive symptoms. Our findings underscore the importance of prioritizing interventions targeting rural adolescents’ internet use behaviors, ultimately aimed at reducing the negative impact on their mental health.

• eHealth use and psychological health improvement among older adults: The sequential mediating roles of social support and self-esteem

  Date Submitted: Jun 22, 2024

  Open Peer Review Period: Jun 28, 2024 - Aug 23, 2024

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  Background: As the trend of population aging increases in many countries around the world, the mental health of the elderly has become an increasing concern. Psychological health is widely recognized as a crucial factor in enhancing older adults' quality of life. Concurrently, Internet use among older adults has risen in recent years, and the impact of these technologies may have a significant influence on the psychological health of older adults. Objective: China is one of the countries with a typical aging trend. The current study aims to reveal the mechanism of how eHealth use influences older adults’ psychological health in China. Furthermore, this study aims to examine the roles of social support and self-esteem in the relationship between eHealth use and psychological health based on the Stimulus-Organism-Response (SOR) framework and Social Cognitive Theory (SCT). Methods: The empirical data for this study were drawn from all 31 provinces of China. The study targeted adults aged 18 and above who resided in China. Mediation and moderation analyses were conducted to test the hypotheses proposed in this study. From the total sample of 7,019 participants, 898 older adults aged 60 and above were selected for the analysis. Results: The findings reveal that eHealth use is positively associated with psychological health through the serial mediation of social support and self-esteem. Additionally, the study also identified eHealth can increase both perceived family and friends support among older adults, and both family and friends support can increase older adults’ self-esteem. Conclusions: To promote better psychological health among older adults, it is essential for them to access eHealth tools. eHealth use can improve older adults’ psychological health by increasing their perceived family and friends’ support, which can then facilitate their self-esteem and eventually enhance their psychological health.

• Effects of web-based single-session growth mindset interventions for reducing adolescent anxiety: A four-armed randomised controlled trial

  Date Submitted: Jun 21, 2024

  Open Peer Review Period: Jun 28, 2024 - Aug 23, 2024

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  Background: Anxiety disorders are the most common mental disorders worldwide. However, 65% of them do not access services. The high prevalence of anxiety and low intervention uptake indicate a pressing need to develop timely, scalable, and potent interventions suitable for adolescents. Objective: Adapting the existing single-session interventions (SSIs), the study further developed SSI on growth mindset about negative emotion for adolescent mental health. This study aimed to compare the effectiveness of four SSIs: Single-session Intervention of Growth Mindset for Anxiety (SIGMA), SIGMA with boosters, SSI of Growth Mindset of Personality (SSI-GP) and active control (ST, support therapy), in reducing adolescent anxiety. Methods: Classes were randomised to one of four intervention conditions: the SIGMA, SIGMA with boosters, SSI-GP, or ST. Each intervention took approximately 45 minutes online. Participants reported anxiety symptoms (primary outcome), depressive symptoms, suicidal/self-hurting thoughts, perceived control, hopelessness, attitude toward help-seeking and psychological well-being (secondary outcomes) at pre-intervention, the 2-week and 8-week follow-up. Participants also filled out the feedback scale at post-intervention. Generalized Estimating Equations (GEE) was used to examine the effectiveness of the SSIs. Results: 731 adolescents from seven secondary schools were randomised. The intent-to-treat analysis found a significant decrease in anxiety symptoms (the mean and 95% confidence interval(CI) at baseline: SIGMA-Booster: 6.8 [6.0, 7.6], SIGMA: 6.5 [5.8, 7.3], SSIGP: 7.0 [6.2, 7.7], ST: 6.9 [6.1, 7.7]) in two-week (the mean and 95%CI at two-week follow-up: SIGMA-Booster: 5.9 [5.1, 6.7], SIGMA: 5.7 [4.9, 6.5], SSIGP: 5.4 [4.6, 6.2], ST: 5.7 [4.9, 6.4]) and eight-week follow-ups (the mean and 95%CI at eight-week follow-up: SIGMA-Booster: 5.9 [5.1, 6.7], SIGMA: 5.3 [4.5, 6.0], SSIGP: 5.6 [4.8, 6.4], ST: 5.8 [5.1, 6.6]) in all four groups. Moderation analysis found participants with higher motivation for change and higher baseline anxiety scores and fixed mindsets had more improvements in anxiety symptoms. More than 70% of participants were positive about the feasibility and acceptability of the SSIs. Conclusions: The single-session interventions were effective at reducing anxiety and depression among adolescents over 8 weeks. Our data revealed the potential benefits of brief web-based intervention for adolescents, which may be scalable destigmatized and cost-effective alternative to school-based programs. Clinical Trial: ClinicalTrials.gov NCT05027880; https://clinicaltrials.gov/ct2/show/NCT05027880

• ReproSchema: Enhancing Research Reproducibility through Standardized Survey Data Collection

  Date Submitted: Jun 21, 2024

  Open Peer Review Period: Jun 28, 2024 - Aug 23, 2024

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  Background: Inconsistencies in survey-type assessments (e.g., questionnaires) data collection across biomedical, clinical, behavioral, and social sciences pose challenges to research reproducibility. ReproSchema offers a schema-centric framework and comprehensive tools to standardize survey (e.g., assessment) design and facilitate reproducible data collection in multiple scenarios. Objective: This study illustrates ReproSchema’s impact on enhancing research reproducibility and reliability. We first introduce ReproSchema’s conceptual and practical foundations, then compare it against twelve platforms, assessing its contributions in resolving inconsistencies in data collection. Three use cases detail ReproSchema’s application in standardizing required mental health common data elements, tracking changes in longitudinal data collection, and creating interactive checklists for neuroimaging research. Methods: We describe ReproSchema’s foundation and practical implementation before selecting twelve platforms for comparison, including CEDAR, former, Kobo Toolbox, LORIS, MindLogger, OpenClinica, Pavlovia.org, PsyToolkit, Qualtrics, REDCap, SurveyCTO, SurveyMonkey. Our comparison focuses on adapted FAIR principles (i.e., Findability, Accessibility, Interoperability, and Reusability) and survey-platform-generic functions (i.e., shared assessment, multilingual, multimedia, validation, branch logic, scoring logic, adaptability, and non-code). We then present three use cases of survey design—NIMH-Minimal, the Adolescent Brain Cognitive Development (ABCD) and HEALthy Brain and Child Development Study (HBCD), and the Committee on Best Practices in Data Analysis and Sharing Checklist (eCOBIDAS)—to demonstration ReproSchema’s versatile applications. Results: ReproSchema standardizes survey-based data collection through a central schema and other synergistic components (e.g., a library of assessments, a toolkit for format conversion and schema validation, a user interface for data collection, and a template for multi-assessment research protocol creation). In the platform comparisons, ReproSchema is one of the few platforms that meet all criteria related to the adapted FAIR principles and six out of eight functionalities. Additionally, three use cases highlight ReproSchema’s effectiveness in streamlining data collection, enforcing version control, and facilitating data harmonization post-collection. Conclusions: ReproSchema contributes to reproducible data collection through the standardized creation and usage of assessments in diverse research settings while being equipped with the general functions of other survey platforms. ReproSchema’s existing limitations and plan enhancements, including ontology mappings and semantic search capabilities, demonstrate ongoing refinement in utility for the research community.

• Analysis of Virtual Standardized Patients for Assessing Clinical Fundamental Skills of Medical Students: A Prospective Study

  Date Submitted: Jun 28, 2024

  Open Peer Review Period: Jun 28, 2024 - Aug 23, 2024

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  Background: Background: Standardized patients (SP) are crucial resources for traditional clinical training, but their limited numbers make one-on-one training challenging to guarantee. With the advancement of artificial intelligence, virtual standardized patients (VSP) offer a promising solution to the scarcity of physical SP. However, the accuracy of VSP needs further investigation. Specifically, we aim to assess whether VSP can serve as viable substitutes for traditional SP in history-taking teaching by studying their accuracy. Objective: Objective: This article aims to investigate the accuracy of virtual standardized patient applications and evaluate whether the enhanced system's precision can support history-taking teaching and performance evaluation. Methods: Methods: This study was a prospective study, data collected from the application of VSP to medical students and residents. VSP was used in a human-machine collaborative mode, with VSP providing synchronous scoring while students conducted SP history-taking assessments. Each subject had SP and VSP scores. Results: Results: The study found significant differences in scoring between different versions of VSP and SP (p < 0.001). Across various clinical cases, there were differences in application accuracy for different versions of VSP (p < 0.001). Among different training groups, the diarrhea case showed significant differences in speech recognition accuracy (Z = -2.719, p = 0.007) and intent recognition accuracy (Z = -2.406, p = 0.016). Scoring and intent recognition accuracy improved significantly after system upgrades. Conclusions: Conclusion: VSP has a comprehensive and detailed scoring system and demonstrates good scoring accuracy, which can serve as a valuable tool for history-taking training.

• Blended Teaching in Undergraduate Dental Education During and Post-COVID-19 Pandemic

  Date Submitted: Jun 20, 2024

  Open Peer Review Period: Jun 27, 2024 - Aug 22, 2024

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  Background: The COVID-19 pandemic has caused severe challenges while providing a unique opportunity to drive the rapid evolvement of higher education, including dentistry. Dental education needs transition toward an increasingly flexible and adaptable approach, such as blended teaching, to better prepare for the current pandemic and beyond. Objective: This study provides perspectives of senior undergraduate dental students toward blended teaching during and post the COVID-19 pandemic and identifies modifiable factors influencing their potential engagement in blended dental education. Methods: A survey on blended teaching was conducted among final-year undergraduate students at the leading dental school in mainland China during the fall semesters of 2019–2020 and 2022–2023. Thus, the survey items and instruments evaluated students’ satisfaction toward blended teaching during and post the COVID-19 pandemic, assessed their self-perceived preference to participate in blended teaching, compared its strengths to offline or online teaching only, and identified modifiable factors influencing students’ potential engagement in the post-COVID-19 times. Results: Final-year undergraduate dental students exhibited a generally positive attitude toward blended teaching during and post-COVID-19 pandemic. Moreover, certain types of online/offline learning materials, face-to-face instruction for specific learning activities, and the implementation of active learning approaches were correlated with higher levels of satisfaction with blended teaching. Students preferred incorporating blended teaching into their education in both periods due to its distinct advantages over offline or online teaching only. Factors such as the instability of technical support, poor online–offline integration, and a lack of learning motivation diminished students’ potential engagement. Meanwhile, the factors that enhanced students’ potential engagement included providing high-quality learning materials, improving technical environment, and employing teacher incentives. Conclusions: During and post-COVID-19, final-year dental students exhibit high satisfaction with blended teaching and prefer participating in blended education. This finding underscores the importance of tailored teaching materials, in-person interactions, and active learning methods. Furthermore, high-quality learning materials, technical environment, teacher incentives, learning process evaluation, effective classroom procedures and routines, and ongoing teacher–student interactions must be further improved to address issues of the students’ potential engagement in blended teaching for the COVID-19 pandemic and beyond.

• Development of a predictive dashboard for falls prevention in residential aged care: An architecture towards prescriptive decision support

  Date Submitted: Jun 26, 2024

  Open Peer Review Period: Jun 25, 2024 - Aug 20, 2024

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  Background: Falls are a prevalent and serious health condition among older people in residential aged care facilities (RACFs) causing significant health and economic burden. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current falls prevention programs in RACFs rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety. Objective: Our aim was to develop a predictive, dynamic, dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies employed to overcome them during the development of the dashboard. Methods: A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, falls incidents, and falls risk assessments were utilised. A dynamic falls risk prediction model and personalised rule-based falls prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems. Results: The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill through functionality was utilised to navigate through different dashboard views. Resident level change in daily risk of falling, along with risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support. Conclusions: This study emphasises the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amidst underlying data system changes. The development process utilised an iterative dashboard co-design process, ensuring successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard's impact on health processes and economic outcomes.

• An Online Tool for Monitoring and Understanding COVID-19 Based on Self-reporting Tweets and Large Language Models

  Date Submitted: Jun 12, 2024

  Open Peer Review Period: Jun 19, 2024 - Aug 14, 2024

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  Background: We built a publicly available database of COVID-19-related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We presented the results of our analysis of infection, reinfection, recovery, and long-term effects of COVID-19 on a weekly- refreshing visualization website. Objective: We built a publicly available database of COVID-19-related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We presented the results of our analysis of infection, reinfection, recovery, and long-term effects of COVID-19 on a weekly- refreshing visualization website. Methods: We used X (formerly Twitter) to collect COVID-related data, from which 9 native-English-speaking annotators annotated a training dataset of COVID-positive self-reporters. We then used large language models to identify positive self-reporters from other unannotated tweets. We employed the Hibert transform to calculate the lead of the prediction curve ahead of the reported curve. Finally, we presented our findings on symptoms, recovery, reinfections, and long-term effects of COVID-19 on the website Covlab. Results: We collected 9.8 million tweets related to COVID-19 between January 1, 2020, and April 1, 2024, including 469,491 self-reported cases. The predicted number of infection cases by our model is 7.63 days ahead of the official report. In addition to common symptoms, we identified some symptoms that were not included in the list from the Centers for Disease Control and Prevention, such as lethargy and hallucinations. Repeat infections were commonly occurring, with rates of second and third infections at 7.49% and 1.37%, respectively, whereas 0.45% also reported that they had been infected more than 5 times. The average time to recovery has decreased over the years. Conclusions: Although with some biases and limitations, self-reported tweet data serve as a valuable complement to clinical data, especially in the post-pandemic era dominated by mild cases. Our online analytic platform can play a significant role in continuously tracking COVID-19, finding new uncommon symptoms, detecting and monitoring the manifestation of long-term effects, and providing necessary insights to the public and decision-makers.

• Digital Rehabilitation Programme for Breast Cancer Survivors on Adjuvant Hormonal Therapy: A Feasibility Study

  Date Submitted: Jun 18, 2024

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

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  Background: Breast cancer survivors often face physical and psychological challenges, including weight gain, metabolic syndrome, and reduced quality of life. To address these concerns, a mobile app-based rehabilitation program called "THRIVE" was developed to improve physical activity, medication adherence, and health-related quality of life (HRQoL) in this population. Objective: This prospective, single-arm study evaluated the feasibility and effectiveness of the "THRIVE" app among breast cancer survivors receiving hormonal therapy. Methods: Participants were recruited from Queen Mary Hospital in Hong Kong between December 2022 and June 2023. Eligible survivors had completed radical surgery within the past 5 years and were currently on hormonal treatment.Participants used the "THRIVE" mobile app and a FitWatch activity tracker to monitor their exercise, medication adherence, and self-care for 16 weeks. Outcomes measured at baseline and week 16 included physical activity intensity, HRQoL, psychological stress, body composition, and drug compliance. Results: Total 50 participants, median age 53 years old, completed the study. The retention rate was 90%, and 74% of participants completed the recommended exercises 3-4 times per week.The primary outcome of physical activity intensity showed no significant changes from baseline to week 16 (MET-total: 1904.1 vs. 2296.7 MET-minutes/week, p=0.24). Several secondary outcomes improved significantly, including cognitive function (p=0.021), future perspective (p=0.044), arm symptoms (p=0.042), depression (p=0.01) and anxiety scores (p=0.0003). All participants reported perfect medication compliance (100%) using the app's drug reminder feature. Conclusions: This digital rehabilitation program demonstrated good feasibility and meaningful quality of life benefits for breast cancer survivors on hormonal therapy.

• Digital Information Exchange Between the Public and Researchers in Health Studies: Scoping Review

  Date Submitted: Jun 18, 2024

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

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  Background: Information exchange regarding scope and content of health studies is becoming increasingly important. Digital methods, including study websites can facilitate such exchange. Objective: This study is a scoping review that aimed to describe how digital information exchange occurs between public and researchers in health studies. Methods: This scoping review was prospectively registered in Open Science Framework. It adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guideline. Eligibility was defined based on the PCC (Population, Concept, and Context) framework as follows: (1) public or researchers (population), (2) digital information exchange using any methods (concept), and (3) health studies (context). Bibliographic databases (MEDLINE, PsycINFO, CINAHL and Web of Science), bibliographies of included studies and Google Scholar were searched up to February 2024. Two researchers screened the records to identify primary studies or reports published in peer-reviewed journals and extracted the data. Data items included bibliographic and PCC characteristics, facilitators and barriers associated with digital information exchange, and evidence gaps. Data were synthesized using descriptive statistics or narratively described. Results: Overall, 4072 records were screened and 18 studies published between 2010 and 2021 were included. All studies evaluated or assessed the preferences for digital information exchange. The addressed populations included the public (mainly adults with any or specific diseases), the researchers, or both. The digital information exchange methods included websites, emails, forums, platforms, social media, or portals. Interactivity (ie, if digital information exchange is or should be active or passive) was addressed in half of the studies. Exchange content included health information or data with the aim to inform, to recruit, to link or to gather innovative research ideas from participants in health studies. We identified 7 facilitators and 9 barriers of digital information exchange. The main facilitators were: (1) consideration of any stakeholder perspectives and needs, (2) use of modern or low-cost communication technologies, (3) use of public-oriented language, and (4) continuous communication of health study process. The main barriers were: (1) information exchange not planned or not feasible due to inadequate resources, (2) too complex technical language, and (3) ethical concerns. Evidence gaps indicate that new studies should assess the methods and the receiver (ie, the public) preferences and needs that are required to deliver and facilitate (interactive) digital information exchange. Conclusions: Only few studies addressing digital information exchange in health studies could be identified in this review. There was little focus on interactivity in such exchange. Digital information exchange was associated with more barriers than facilitators suggesting that more effort is required to improve such exchange between the public and researchers. Future studies should investigate interactive digital methods and the receiver preferences and needs required for such an exchange.

• Discussions of Cannabis Over Patient Portal Secure Messaging: A Content Analysis

  Date Submitted: Jun 16, 2024

  Open Peer Review Period: Jun 16, 2024 - Aug 11, 2024

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  Background: Patient portal secure messaging allows patients to describe health-related behaviors in ways that may not be sufficiently captured in standard electronic health record (EHR) documentation, but little is known about how cannabis is discussed on this platform. Objective: We aimed to identify patient and provider secure messages that discussed cannabis and contextualize these discussions over periods prior to and after its legalization for medical purposes in Pennsylvania. Methods: We examined 382,982 secure messages sent by 15,340 patients and 6101 providers from an integrated health delivery system in Pennsylvania, USA from 2012 to June 2022. We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. We then conducted a qualitative content analysis on a random sample of identified messages to understand the medical reasons behind patients’ use, the primary purposes of the cannabis-related discussions, and changes in these purposes over time. Results: We identified 1782 messages sent by 1098 patients (7.2% of total patients in the study), and 800 messages sent by 430 providers (7.0% of total providers in the study) as explicitly discussing cannabis. The most common medical reasons for use stated by patients in 190 sampled messages included pain or a pain-related condition (50.5% of messages), anxiety (13.7% of messages), and sleep (11.1% of messages). We coded 56 different purposes behind the mentions of cannabis in patient messages, and 33 purposes in 100 sampled provider messages. In years prior to the legalization (2012-2016), patient and provider messages (n=20 for both) were primarily driven by discussions about cannabis screening results (38.9% and 76.5% of messages, respectively). In the years following legalization (2017-2022), patient messages (n=170) primarily involved seeking assistance to facilitate medical use (35.2% of messages) and reporting current use (25.3% of messages). Provider messages (n=80) were driven by giving assistance with medical marijuana access (27.5% of messages) and stating that they were unable to refer, prescribe or recommend medical marijuana (26.3% of messages). Conclusions: Patients showed a willingness to discuss cannabis use over patient portal secure messages and expressed interest in use after the legalization of medical marijuana. Some providers responded to patient inquiries with assistance in obtaining access to medical marijuana, while others cautioned patients on the risks of use. Insight into cannabis-related discussions through secure messages can help health systems determine opportunities to improve care processes around patients’ cannabis use, and providers should be supported to communicate accurate and consistent information.

• A Qualitative Study of Electronic Health Record Data Collection Practices: Path to Standardization and Interoperability of the Interpreter Needed Data Element

  Date Submitted: Jun 3, 2024

  Open Peer Review Period: Jun 10, 2024 - Aug 5, 2024

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  Background: Poor health outcomes are well documented among patients with limited English proficiency (LEP). The use of interpreters can improve the quality of care for patients with LEP. Despite a growing and unmet need for interpretation services in the U.S. health care system, rates of interpreter use in the care setting are consistently low. Standardized collection and exchange of patient interpretation needs can improve access to appropriate language services. Objective: This paper examines current practices for collecting, documenting, and exchanging interpreter needed data in the electronic health record (EHR). The paper identifies data collection workflows, use cases for interpreter needed data, challenges to data collection and use, and potential opportunities to advance the standardized collection and use of interpreter needed data to facilitate patient-centered care. Methods: We conducted a targeted literature scan to identify current data standardization efforts for stakeholders, including EHR developers, health systems, clinicians, a practice-based research organization, a national standards collaborative, a professional health care association, and Federal agency representatives to fill in gaps from the literature review. Results: The findings indicate that key informants value standardized collection and exchange of patient language service needs and preferences. Key use cases for interpreter needed data identified from the discussions include: 1) person-centered care; 2) transitions of care; and 3) health care administration. The discussions revealed that EHR developers provide a data field for documenting interpreter needed data, and that this data is routinely collected across health care organizations through commonly used data collection workflows. However, this data element is not mapped to standard terminologies, such as Logical Observation Identifiers Names and Codes (LOINC®) or Systematized Medical Nomenclature for Medicine–Clinical Terminology (SNOMED-CT®), consequently limiting the opportunities to electronically share this data between health systems and community-based organizations. Key informants described three potential challenges to using interpreter needed data for person-centered care and quality improvement: 1) lack of adoption of available data standards; 2) limited electronic exchange; and 3) patient mistrust. Conclusions: Collection, documentation, and use of interpreter needed data can improve the quality of services provided, patients care experiences, and health equity in care delivery without invoking a significant burden on the health care system. Although there is routine collection and documentation of patient interpretation needs, the lack of standardization limits exchange of this information among health care and community-based organizations.