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

  • Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and the Emergence of Neuroinformatics

    Date Submitted: Apr 15, 2024
    Open Peer Review Period: Apr 22, 2024 - Jun 17, 2024

    Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with burden of chronic neurodegenerative diseases doubled over the last two decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence and bioinformatic methods are changing this landscape. This review explores this transformative journey, emphasizing the critical role of bioinformatics in neurology, which we term as Neuroinformatics, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, the integration of biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. Additionally, telemedicine and virtual health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions like Multiple Sclerosis, Parkinson's Disease, Amyotrophic Lateral Sclerosis, Alzheimer’s Disease and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of information technology and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this review, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in neuroinformatics will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric healthcare.

  • Methodological Research on the Adaptation of Patient Decision Support Tools: A Scoping Review

    Date Submitted: Apr 16, 2024
    Open Peer Review Period: Apr 19, 2024 - Jun 14, 2024

    Background: In recent years, there have been many studies on the adaptation of patient decision support tools, but there is a lack of methodological research on the adaptation of patient decision support tools. Objective: This scoping review aims to summarize the steps for adapting patient decision aids (PDAs) based on current research and to explore related methodologies. Methods: A systematic search of the PubMed, Cochrane Library, EMBASE, CINAHL, Web of Science, CNKI, WANFANG, VIP, and SinoMed databases and grey literature was conducted up to January 2024. The search terms focused on patient decision aids and their adaptation. The results were integrated through statistical and thematic analysis. Results: Twenty-five studies were included. Eight steps for adapting PDAs were identified, including defining decision problems and options; assessing local cultural backgrounds; translating; adjusting PDA language style, content, and presentation; creating an initial version of the PDA; conducting acceptability testing; conducting feasibility testing; and PDA revisions. Only a few studies followed a rigorous process for adapting PDAs, and most research did not undertake steps such as local cultural background assessment and feasibility testing due to challenges related to sample size acquisition, cultural diversity, and complexity. Conclusions: This study focused on the steps of language style, content, presentation adjustment, and acceptability testing in the overall process of formulating the steps for adapting decision support tools, adapting these tools, and identifying specific methods for acceptability testing. This study enhanced the quality assessment indicators for PDA language style, content, and presentation adjustments to provide a reference for subsequent research. However, gaps still exist in the evaluation standards for the language style, content, and presentation of PDAs that should be addressed by future research.

  • Background: Cognitive behavioral therapy for psychosis (CBTp) is not widespread enough in clinical practice, although evidence has been presented. Objective: The purpose of this study was to determine whether cognitive behavioral therapy for psychosis using video-conferencing (vCBTp) was more effective than usual care (UC) treatment alone in improving psychiatric symptoms in patients with schizophrenia attending outpatient clinics. Methods: In this exploratory randomized controlled trial, patients with schizophrenia and schizoaffective disorders who were still taking medication in an outpatient clinic were randomly assigned to either the UC plus vCBTp group (n=12) or the UC group (n=12). The vCBTp was conducted once a week, with each section lasting for 50 min, for a total of seven sessions in real-time. The primary outcome was the Positive and Negative Syndrome Scale (PANSS) total score, which measures the difference in the mean change from baseline at week 0 to post-test at week 8. Results: Concerning the significant difference in the primary endpoint between the two groups, the mean change from baseline in the PANSS total score at week 8 in the vCBTp plus UC group (-9.5) was significantly greater (P<.001) than the mean change in the UC alone group (6.9). In addition, significant improvements were observed in positive symptoms, negative symptoms, and overall psychopathology subscales. No participants dropped out of the study, and no serious adverse events occurred. Conclusions: Summarily, all seven vCBTp sessions were effective in improving psychiatric symptoms. This approach is expected to improve the acceptance and accessibility of CBTp among outpatients with schizophrenia, potentially contributing to relapse prevention support and stepped care. Clinical Trial: University Hospital Medical Information Network Clinical Trials Registry: UMIN000043396; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000049544

  • Online Forum Discourse on Abortion: A Multifaceted Analysis of Medical, Emotional, and Legal Conversations

    Date Submitted: Apr 15, 2024
    Open Peer Review Period: Apr 18, 2024 - Jun 13, 2024

    Background: Abortion is one of the most common procedures worldwide. Despite this, access to abortion on demand is restricted in many countries, including Poland. As a result, many women undergo the procedure without medical supervision, exposing themselves to potential health consequences. Objective: The study aimed to qualitatively identify the themes present in abortion forums to analyze the problems faced by women. The forums were then quantitatively analyzed to determine which issues are potentially the most prevalent. Methods: The most popular forums on abortion were determined. An initial pilot study was conducted for qualitative analysis, followed by a manual quantitative investigation. Results: Analyzing 13,397 responses from 370 threads on four forums revealed "Abortion Process Progression" as the most discussed theme, signaling a high demand for information and support. "Emotional and Psychological Aspects" and "Medical and Pharmacological Aspects" were also significant, indicating a need for holistic care. Conclusions: This study highlights the critical need for information and support for women navigating abortion, particularly where access is restricted. It calls for addressing multifaceted challenges and promoting policy changes and support networks to enhance women's health and rights in abortion contexts. Further research is encouraged to refine support strategies.

  • Real-Time Delirium Prediction in Intensive Care Units: A Machine-Learning-Based Model Using Monitoring Data

    Date Submitted: Apr 15, 2024
    Open Peer Review Period: Apr 18, 2024 - Jun 13, 2024

    Background: Delirium in intensive care units (ICUs) poses a significant challenge and affects not only global patient outcomes but also healthcare efficiency. The development of an accurate, real-time prediction model for delirium represents a crucial advancement in critical care and addresses the need for timely intervention and resource optimization in ICUs worldwide. Objective: This study aimed to create a novel machine-learning model for real-time delirium prediction in ICUs using the random forest method. Methods: Distinct from existing approaches, the model integrated routinely available clinical data such as age, sex, and patient monitoring device outputs to ensure its practicality and adaptability in diverse clinical settings. Using these data, we trained a random forest model to predict the occurrence of delirium in patients. Retrospective data were used for training and internal validation. Retrospective data were used for training and internal validation. Prospective data were used to confirm the reliability of the delirium determination. CAM-ICU records assessed by ICU nurses were collected and validated by qualified investigators performing CAM-ICU measurements prospectively on the same patients and then determining Cohen's kappa coefficient. In addition, we additionally verified the performance of the model using a temporal validation cohort and performed external validation using data from an external hospital. Results: The Kappa coefficient between labels generated by ICU nurses and prospectively verified by qualified researchers was 0.81. This indicates that the recorded CAM-ICU results were reliable. The model showed robust performance in internal validation (area under the receiver operating characteristic curve [AUROC]: 0.82, area under the precision–recall curve [AUPRC]: 0.62) and maintained its accuracy in temporal validation (AUROC: 0.73, AUPRC: 0.85), confirming its reliability over time. External validation across various patient populations and time frames further confirmed its effectiveness (AUROC: 0.84, AUPRC: 0.77). Conclusions: Our model represents a significant breakthrough in the management of delirium in ICUs and offers a real-time, data-driven approach for improving patient care. The proven accuracy and adaptability of this model in various clinical scenarios underscore its potential to substantially improve patient outcomes and operational efficiency in ICUs. The integration of this model into current healthcare practices may lead to major advancements in early delirium detection and treatment, thereby reducing the ICU stay and improving the recovery rate.

  • Measuring the Digital Skills in nursing degree students

    Date Submitted: Apr 15, 2024
    Open Peer Review Period: Apr 18, 2024 - Jun 13, 2024

    Background: In the healthcare field the digital revolution has changed the way in which healthcare is provided through the prevention, diagnosis and monitoring of people's health. In Catalonia, work has been conducted to evaluate the digital competence (DC) of healthcare professionals using a validated test based on the ACTIC (accreditation of competence in information and communication technologies) model. It is therefore important Objective: To determine the DC level of nursing students as future healthcare professionals. Methods: The aim of this study is to analyze the current DC level of students taking the Degree in Nursing at the Terres de l’Ebre Campus, Universitat Rovira i Virgili (URV), Catalonia, Spain. We have used a questionnaire to evaluate the DC level of these students. Results: Our sample comprised 147 students from various modules of the Degree in Nursing. Our results show differences in levels of self-perception and DC but also differences in the competence of students across the various academic years. Conclusions: Evaluating the level of competence of nursing degree students is fundamental for practising and enhancing the most important digital competencies required in the field of professional nursing

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

  • Background: With the increasing popularity of electronic devices, the longitudinal effects of daily prolonged electronic device usage on brain health and the aging process remain unclear. Objective: The aim of this study was to investigate the impact of daily use of electronic devices on the risk of neurodegenerative diseases and brain structure. Methods: This longitudinal population-based cohort study analyzed the impact of mobile phone usage duration, weekly usage time, and other electronic device habits on future brain structure and the future risk of various neurodegenerative diseases, including all-cause dementia (ACD), Alzheimer's disease (AD), vascular dementia (VD), all-cause parkinsonism (ACP), and Parkinson's disease (PD). Results: The study included over 277,000 participants for risk analysis, with a mean baseline age of approximately 55.9 years. The average follow-up duration was approximately 13.9 years. Longer years of mobile phone usage were associated with a reduced risk of ACD, AD, and VD compared to rarely using mobile phones. Additionally, longer years of mobile phone usage were linked to a decreased risk of ACP and PD in participants aged older than 60 years. Neuroimaging analysis involved 35,643 participants, with an average duration of approximately 9.0 years between baseline and neuroimaging scans. Longer years of mobile phone usage were correlated with thicker cortex in various areas. Conclusions: Longer years of mobile phone usage were associated with a reduced risk of neurodegenerative diseases and improved brain structure compared to minimal usage. However, higher weekly usage time did not confer additional risk reduction compared to lower weekly usage.

  • Revolutionizing Healthcare: The Transformative Impact of LLMs in Medicine

    Date Submitted: Apr 1, 2024
    Open Peer Review Period: Apr 17, 2024 - Jun 12, 2024

    Large language models (LLMs) are rapidly advancing medical AI, offering revolutionary changes in healthcare. These models excel in natural language processing, enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs like GPT-4 and BERT demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in healthcare.

  • Background: HIV infections have caused severe public health and economic burdens to the world. Adolescents and young people continue to constitute a large proportion of newly diagnosed HIV cases. Online health interventions will be the trend to prevent the rising HIV epidemic. Objective: HIV infections have caused severe public health and economic burdens to the world. Adolescents and young people continue to constitute a large proportion of newly diagnosed HIV cases. Online health interventions will be the trend to prevent the rising HIV epidemic. Methods: Four databases were searched and studies from 2008 to January 2023 were screened. Eligible randomized controlled trials (RCTs) were included. Basic study characteristics, intervention strategies and study results were extracted and compared for data analysis. BCTs used were identified according to the definitions from the BCT taxonomy by Michie and Abraham in 2008. Frequencies of BCTs were counted to uncover the commonly used techniques in online HIV prevention programs. Results: A total of 27 RCTs are included in this review. Commonly used BCTs identified include prompt intention formation (100%, 27/27), provide information about behavior-health link (96%, 26/27), provide general encouragement (96%, 26/27), provide information on consequences (89%, 24/27), and provide instruction (85%, 23/27). BCTs with a provision nature are more observed in interventions with significant improvement in HIV knowledge, while more use of BCTs with the nature of initiating actions are observed in studies with significant increase in condom-use self-efficacy. Studies with a significant increase in condom use have used BCTs that manage and maintain behavior the most among the three outcomes. Conclusions: This is the first systematic review that examined the use of BCTs in online HIV prevention interventions for adolescents and young people. Commonly used BCTs found in this review can serve as important references for constructing more effective online interventions in the future. Clinical Trial: N/A

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

  • Background: The monitoring of free-living physical activity by wearable devices can offer real-time assessment of activity features associated with health outcomes or provide suggestions for treatment recommendations and adjustments. The conclusions of studies on physical activity (PA) and health depend crucially on reliable statistical analyses of the digital data. Data analytics, however, are challenging due to the various metrics adopted to measure PA, different aims of studies, and complex temporal variation within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. Objective: This research reviews studies that adopted analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addresses three questions: (1) What are the metrics used to describe an individual’s free-living daily PA? (2) What are the current analytical tools to analyze PA data, under the aims of classification, association with health outcomes, and prediction of health events? (3) What are the challenges in the analyses and what recommendations for future research are suggested regarding use of statistical methods in response to various tasks? Methods: This scoping review is conducted following the Arksey and O'Malley framework to map research studies by exploring the information about physical activity. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were queried in February 2024 to identify related publications. Eligible articles were either classification, association, or prediction studies involving human PA monitored by wearable accelerometers. Results: After screening 1312 articles, 428 eligible studies were identified and categorized into at least one of the three thematic issues: classification (75/428), association (342/428), and prediction (32/428). Most articles (414/428) adopted a PA variable derived from three acceleration dimensions, rather than from a single-dimensional acceleration. All eligible articles considered PA metrics represented in the time domain (428/428); a small fraction also considered PA metrics in the frequency domain (16/428). Studies evaluating the influence of PA on health conditions have increased greatly. Among those in our study, regression-type models were most prevalent (373/428). The machine learning approach for classification research is gaining popularity as well (32/75). In addition to summary statistics of PA, several recent studies utilized tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements or functional data analysis with PA as a continuum for time-varying association (68/428). Conclusions: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals’ overall physical activity. If the distribution and profile of PA are to be evaluated or detected, taking the PA metrics as longitudinal or functional data can bring in more information and improve comprehension of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

  • Background: The aging population in China is becoming increasingly severe, and there is a health inequality phenomenon among urban and rural elderly. With the development of ICT, eHealth has become one of the important factors affecting health. Urban elderly could more conveniently access health information and medical services, while rural elderly may have difficulty enjoying the digital dividends brought by eHealth, highlighting the phenomenon of the “digital health divide”. Objective: This study analyzes the digital health divide and determinants among urban and rural elderly from the perspective of capital theory. Methods: The model for analyzing the digital health divide among urban and rural elderly is constructed based on capital theory. Analysis of variance is used to verify the digital health divide among urban and rural elderly. Structural equation modeling is used to analyze the factors, and the Blinder-Oaxaca decomposition method is used to analyze the main causes. Results: There are three levels of digital health divide among urban and rural elderly, namely digital access divide (F=11.39, P<.01), digital usage divide (F=39.53, P<.001), and digital outcome divide (F=30.20, P<.001). The influence of different levels of divide is transmitted along the digital chain, the impact coefficient of digital access divide on digital usage divide is β=0.060 (P<.05), and digital usage divide on digital outcome divide is β=0.363 (P<.001). The digital usage divide is the most significant level, and cultural capital (β=0.221, P<.001), social support (β=0.361, P<.001), economic capital (β=0.111, P<.01), and habits (β=0.248, P<.001) are most dominant factors contributing to the rural-urban digital usage divide. The Blinder-Oaxaca decomposition results further indicate that cultural capital (33.9%) and social capital (22.5%) are the main factors influencing digital usage divide among urban and rural elderly. Conclusions: here exists digital health divide among urban and rural elderly, and the influence of three level of divide is transmitted along the digital chain. The digital usage divide is the main level, cultural capital and social capital are the main reasons for its formation. To against these divides among urban and rural elderly, interventions in policy, society, technology, and economics are recommended.

  • Assessing Long-Term Engagement: A Novel Index Comparison Using Survival Analysis and Multiple Linear Regression

    Date Submitted: Apr 12, 2024
    Open Peer Review Period: Apr 12, 2024 - Jun 7, 2024

    Background: No established tools are currently capable of quantitatively measuring engagement for health promotion tools, including Digital Therapeutics. Objective: We evaluate the engagement index (EI) in commercial health management app for long term use by comparing it with the new EI created by the researchers based on the original EI. Methods: Participants were recruited from cancer survivors enrolled in a randomized controlled trial evaluating the impact of mHealth apps on recovery. We used 240 of these patients who were randomly assigned the Noom app. The study validated a new EI compared to an existing EI, with data analysis performed for long-term use. The new EI was calculated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices. Results: The old EI demonstrated limited predictive ability for EI values between 6 to 9 months, with a mean squared error (MSE) of .10 and r-squared of .05. However, the new EI displayed enhanced predictive performance. All three new EIs, with different combinations of features, exhibited a lower MSE and higher r-squared compared to the old EI. Cox regression analysis revealed the old EI presented significant hazard ratios (HR) for click depth and loyalty indices, while the new EI consistently demonstrated significant HRs for loyalty and recency indices. Conclusions: We evaluated the effectiveness of the EI and proposes potential enhancements due to the ongoing need for a standardized index to measure patient compliance with mHealth applications. We emphasize the importance of log data and suggest avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation.

  • Background: Frailty and sarcopenia are more prevalent among LTCF residents than community dwellers, with exercise, especially multicomponent and PRT, being essential for management. However, LTCFs, particularly in rural areas, face challenges in implementing structured exercise programs due to healthcare professional shortages. The Nintendo Switch RingFit Adventure (RFA) exergame (exergame-RFA), which combines resistance, aerobic, and balance exercises, offers a potential solution by boosting motivation and reducing staff intervention needs. Objective: We aimed to evaluate the clinical effectiveness of the exergame-RFA in improving muscle mass and functional performance among elderly LTCF residents. Methods: This was a randomized controlled trial, conducted from August 2022 to September 2023, involved elders ≥60 years in rural southern Taiwan LTCFs. Participants were randomized into an intervention group (exergame-RFA plus standard care) or a control group (standard care alone). The intervention, conducted seated with arm fit skills and trunk control exercises via RFA, lasted 30 minutes, twice weekly for 12 weeks. Primary outcomes measured were the study of osteoporotic fracture index, appendicular skeletal muscle mass index, handgrip strength, and gait speed. Secondary outcomes included box and block test, (BBT), maximal voluntary isometric contraction of dominant upper extremity (MVIC), muscle thickness under sonography (sono-thickness), ADLs by the Kihon Checklist, quality of life by the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and the cognition by brain health test (BHT). We employed an intention-to-treat analysis, incorporating a simple imputation technique in statistical analysis. A mixed analysis of variance, with time as a within-subject factor and intervention as a between-subject factor, was used to compare the training effects on outcomes. Results: The study recruited 96 individuals, with 60 undergoing randomization and 55 completing the study. Significant group X time interactions were observed in the exergame-RFA group in all primary outcomes (all p< .01, except p= .011 for HGS) and most secondary outcomes, including MVIC of biceps and triceps muscle, sono-thickness of biceps muscle, BBT, Kihon checklist, and BHT. Conclusions: Exergame-RFA significantly improve muscle mass, strength, and functional performance among elderly residents of rural LTCFs, offering a novel approach to addressing frailty and sarcopenia. Clinical Trial: This trial was registered at ClinicalTrials.gov (NCT05360667)

  • Background: Breast cancer is prevalent among women in the United States. Non-metastatic disease is treated by partial or complete mastectomy procedures. However, the rates of those procedures vary across practices. Generating real-world evidence on breast cancer surgery could lead to improved and consistent practices. Objective: The paper aims to determine whether All of Us data are fit for use in generating real-world evidence on mastectomy procedures. Methods: Our mastectomy phenotype consisted of adult female participants who had CPT4 or SNOMED codes for a partial or complete mastectomy procedure. We evaluated the phenotype with a novel data quality framework that consisted of five elements: conformance, completeness, concordance, plausibility, and temporality. Also, we used a previously developed adjectival rating matrix with categories of poor (providing little to no data), fair (using only internal EHR data), and good (using internal and external benchmark/data) to evaluate each data quality dimension (DQD). Our subgroup analysis compared partial to complete mastectomy procedure phenotypes. Results: There were 3,704 participants in the partial or complete mastectomy cohort. The geospatial distribution of our cohort varied substantially across states. For example, our cohort consisted of 817 (22.1%) participants from Massachusetts but fewer than 20 participants from multiple other states. We compared the sociodemographics of the partial (n = 2,445) and complete (n = 1,259) mastectomy subgroups. Those groups differed in the distribution of education (P = .02) and income (P < .001) levels using chi-square analysis. The DQD conformance was rated as good. A total of 3,216 (86.7%) participants in our cohort had CPT4 codes for a mastectomy that did not conform to a SNOMED standard. The DQD completeness was rated as fair. The prevalence of breast cancer related concepts was higher in our cohort compared to adult female participants who did not have a mastectomy procedure (P < .001). The DQD concordance was rated as fair. In both the partial and complete mastectomy subgroups, the correlations among concepts were consistent with the clinical management of breast cancer. The DQD plausibility was rated as fair. Although we did not have external benchmark comparisons, the distributions of concepts by age group and time were consistent with expectations. The DQD temporality was rated as fair. The median time between biopsy and mastectomy was seven weeks. Conclusions: Our data quality framework was implemented successfully on a mastectomy phenotype. Moreover, the framework allowed us to differentiate breast-conserving therapy and complete mastectomy subgroups in the All of Us data. The results of our analysis could be informative for future breast cancer studies with the OMOP CDM.

  • Factors Influencing Electronic Health Literacy Among Colorectal Cancer Patients in Lifestyle Medicine

    Date Submitted: Apr 11, 2024
    Open Peer Review Period: Apr 11, 2024 - Jun 6, 2024

    Background: The level and the influence factors of the e-health literacy among colorectal cancer patients are not clear. Objective: Explore the level and the influence factors of e-health literacy among colorectal cancer patients Methods: Colorectal cancer patients were invited to complete a survey on general characteristics, lifestyle and e-health literacy. Lifestyle evaluation include nutrition, exercise, sleep, staying away from risk factors, stress management, and social relationships. Mann-Whitney U test, Kruskal-Wallis test and generalized linear model were employed to explore the influence factor of the e-health literacy of the colorectal cancer patients. Results: A total of 184 individuals were evaluated, The score of the e-health literacy among colorectal cancer patients is 24.00(19.50, 29.00). The generalized linear model demonstrated that younger age, higher level of education attainment, monthly family income, surgical history, higher self-rated health, frequent searching for online health information, owning more electronic devices, perceiving online health resources as helpful, having reading habits, higher frequency of children's social interactions per week, and higher integrative values were associated with higher levels of e-health literacy. Conclusions: The e-health literacy score among colorectal cancer patients was modest, influenced by factors such as age, education attainment, income, surgical history, self-rated health, online health information habits, device ownership, perceived usefulness of online health resources, reading habits, frequency of children's social interactions, and integrative values. Clinicians should consider these factors when designing interventions to improve e-health literacy tailored to individual values and preferences. Clinical Trial: No

  • Smart Contracts and Shared Platforms in Sustainable Healthcare: Systematic Review

    Date Submitted: Apr 5, 2024
    Open Peer Review Period: Apr 9, 2024 - Jun 4, 2024

    Background: The benefits of SC (smart contracts) for sustainable healthcare are a relatively recent topic that has gathered attention given its relationship with trust and the advantages of decentralization, immutability, and traceability that could be introduced in healthcare. Nevertheless, there is a lack of studies exploring the role of SC in this sector based on the frameworks propounded by the literature that reflect the business logic -that has been customized, automatized, and prioritized- and the system trust. This study will address this lacuna Objective: This study aims to provide a comprehensive understanding of SC in healthcare based on reviewing the frameworks propounded by the literature. Methods: A structured literature review based on PRISMA principles was performed. A quantitative assessment of the studies, based on machine learning and data reduction methodologies, is complemented by a qualitative, in-depth, detailed review of the frameworks propounded by the literature. Results: A total of 70 studies, which depicted 19% of studies about this subject, met the selection criteria and were analyzed. Three factors depicted the advances in the topic. Two of them were referred to the leading roles of SC: (a) healthcare process enhancement and (b) assurance of patients’ privacy protection. The first role included six themes, and the second one three themes. The third factor encompassed the technical features that improve systems’ efficiency. The in-depth review of these three factors and the identification of stakeholders allowed us to characterize the system trust in healthcare SC. Conclusions: This comprehensive review allows us to understand the relevance of SC and the potentiality of their use in patient-centric healthcare that considers more than technical aspects. It also provides insights for further research based on specific stakeholders, locations, and behaviors.

  • How to embed a choice experiment in an online decision aid or tool: a scoping review

    Date Submitted: Apr 5, 2024
    Open Peer Review Period: Apr 9, 2024 - Jun 4, 2024

    Background: Decision aids empower patients to understand how treatment options match their preferences. Choice experiments, a valuable method to clarify values used within decision aids, present patients with hypothetical scenarios to reveal their preferences for treatment characteristics. Given the rise in research embedding choice experiments in decision tools and the emergence of novel developments in embedding methodology, a scoping review is warranted. Objective: This scoping review examines how choice experiments are embedded into decision tools and how these tools are evaluated, to identify best practices. Methods: This scoping review was conducted following best practices in line with the PRISMA extension for scoping reviews. The searchers were conducted on MEDLINE, PsycInfo, and Web of Science databases using key search terms. Data were extracted using data charting tables created in Excel. A narrative synthesis was used to summarize the data and illustrations were used to visualise the results using tables and figures. Results: Overall, 22 tools were included in the scoping review. The methodology, development and evaluation details of tools were extracted from 33 papers. These tools were developed for a variety of health conditions including musculoskeletal conditions, oncological conditions, and chronic conditions. Most tools (78%) originated in the USA. The primary purpose (91%) of these tools was to assist patients in comparing or choosing treatments. The most commonly included attributes in the choice tasks were efficacy and side effects. Adaptive conjoint analysis was the most frequent (10 tools) design approach. Conjoint analysis designs used a higher number of tasks (16 -20) while DCEs and adaptive conjoint analysis designs used low (6) to moderate (12) number of tasks. Sawtooth software was commonly used to embed choice tasks in the tools. After completing the choice tasks patients received tailored information in the form of attribute importance scores, highlighting which treatment characteristics mattered most to the patient based on their choices (16 tools), and/or a "best match" treatment recommendation aligned with the patient's preferences (5 tools). A high degree of heterogeneity was observed in the evaluation methodologies and outcome measures used to assess the decision tools. The decisional conflict scale emerged as the most frequently employed outcome measure. Conclusions: This study highlights several methodological challenges that require further investigation. Future research should focus on determining the most effective methods for embedding choice tasks in decision tools, presenting balanced information, and selecting suitable outcome measures to evaluate these tools.

  • The Application of Preventive Medicine in the Future Digital Health Era

    Date Submitted: Apr 4, 2024
    Open Peer Review Period: Apr 9, 2024 - Jun 4, 2024

    A number of seismic shifts is expected to reshape the future of medicine. The global population is rapidly aging, significantly impacting global disease burden. Medicine is undergoing a paradigm shift, defining and diagnosing diseases to earlier stages and shifting the healthcare focus from treating diseases to preventing them. The application and purview of digital medicine is expected to broaden significantly. Furthermore, the COVID-19 pandemic further accelerated the shift towards predictive, preventive, personalized, and participatory (P4) medicine, and identified healthcare accessibility, affordability and patient empowerment as core values in the future digital health era. This “left shift” towards preventive care is anticipated to redefine healthcare, emphasizing health promotion over disease treatment. In the future, the traditional triad of preventive medicine--primary, secondary, and tertiary prevention--will be realized with technologies such as genomics, artificial intelligence, bioengineering and wearable devices, wearables, and telemedicine. Breast cancer and diabetes serve as case studies to demonstrate how these technologies such as personalized risk assessment, AI-assisted and app-based technologies have been developed and commercialized to provide personalized preventive care. Overall, preventive medicine and the use of advanced technology will hold great potential for improving healthcare outcomes in the future.

  • A 25-Years Retrospective of Health Information Technologies Infrastructuring: the Example of Catalonia Region

    Date Submitted: Mar 28, 2024
    Open Peer Review Period: Apr 7, 2024 - Jun 2, 2024

    Background: The evolution of healthcare systems has been driven by aging populations, chronic diseases, and rising expectations for quality care. This evolution coincides with significant advancements in information and communication technologies (ICT), which offer the potential to enhance healthcare accessibility, efficiency, and patient outcomes through tools such as telemedicine, electronic health records, and mobile health apps. Objective: This article aims to explore the integration of health information technologies (HIT) in healthcare delivery, focusing on the experiences of Catalonia, a region with universal healthcare coverage and a single public health insurer. It discusses the transition from paper to digital systems, the development of health information registries and platforms, and the challenges and successes encountered over the past twenty-five years. Methods: We systematically analyse the implementation processes of HIT in Catalonia, detailing the architectures and usage trends of these platforms over time. We also provide a qualitative analysis of the organizational, political, and cultural barriers to integrating HIT and evaluate the impact of these technologies on creating more personalized, data-driven care. Results: Catalonia's experience highlights the potential of HIT to facilitate integrated care pathways and improve health outcomes. However, challenges such as organizational resistance, political hurdles, and cultural differences, along with a fragmented approach to health information management, have impeded the full realization of these benefits. Conclusions: The evolution of HIT in Catalonia over the past quarter-century provides valuable insights and lessons for other regions aiming to navigate the complex landscape of digital healthcare transformation. However, the effectiveness of such strategies is contingent on a robust primary care network and a unified healthcare model. The future of healthcare digitalization in Catalonia, and potentially elsewhere, hinges on overcoming legacy data fragmentation and moving towards a genuinely integrated health and social information system that streamlines the strategic use of health data. This transition is not just about adopting new technologies but also about a paradigm shift in healthcare delivery, moving from referrals and transferability of responsibility towards a collaboration emphasizing a holistic approach to patient care for improved health outcomes. Clinical Trial: N/A

  • Background: Electronic Health Records (EHRs) have revolutionized the accessibility and sharing of patient data among healthcare providers, fostering a more coordinated and efficient delivery of care. Over the past 25 years, the evolution of EHRs has significantly contributed to scientific achievements in healthcare, improving the accuracy and efficiency of patient care and supporting better health outcomes. Despite their numerous benefits, EHRs face challenges including interoperability issues, common data models, system compatibility, privacy concerns, and data cleaning complexities. Objective: The objective of our study was to examine the evolution of EHRs over the past 25 years, focusing on their advancements in technology, interoperability, and the impact on healthcare delivery and research. We aimed to identify the challenges and limitations of EHRs in facilitating disease management and understanding, as well as their contribution to epidemiological studies, pragmatic clinical trials, and health economic studies. Methods: We conducted a comprehensive review of literature from PubMed database pertaining to the development and implementation of EHRs over the past quarter-century. Studies from January 2000 to February 2024 were included. Finally, 1,377 studies were selected for the analysis and presentation. Results: Studies that utilized EHR data were for various research purposes, including epidemiological studies, clinical trials, cost-effective studies, and policy studies. We highlighted significant advancements in EHR technology that facilitated improved management and understanding of diseases through comprehensive data collection and analysis over the past 25 years. However, challenges related to data interoperability, privacy, and inconsistencies were also identified. The studies underscored the importance of EHRs in creating more accurate representations of clinical practices and patient populations. We also saw great efforts in incorporating data from different sources and formats with the EHRs, as well as new analytic tools and platforms. Conclusions: EHRs have emerged as a pivotal component of modern healthcare systems, enhancing the efficiency and accuracy of patient care and supporting advanced clinical research. Despite facing interoperability and data management challenges, the benefits of EHRs in improving healthcare delivery and facilitating significant scientific achievements are undeniable. To maximize their potential, there is a critical need for improved resource sharing, collaborations among healthcare providers, and the development of consistent data formats and policies in healthcare networks. Clinical Trial: NA

  • Background: It is reported over 90% of formal smokers in China have achieved quitting without assistance. Social media platforms such as TikTok and Bilibili in China are flooded with a number of short videos related to smoking cessation. However, the quality, usefulness and personal expertise in uploading smoking cessation related content on these platforms have not been evaluated. Objective: This study aims to analyze the usefulness and quality of Chinese Short Video on smoking cessation on TikTok and Bilibili, trying to answer whether social media in China can be a useful source of information for hundreds of millions of smokers to quit smoking. Methods: In October 2023, we searched TikTok and Bilibili for the keywords "quit smoking(戒烟)" and "how to quit smoking on your own(如何自己戒烟)". After eliminating duplicate, unrelated videos, non-Chinese videos, no audio and no video content, we got a total of 400 videos,400 videos were independently reviewed and encoded by 4 reviewers. Video quality and content were assessed using three types of informational appeal(fear, social, and humor),a global quality score(GQS, on a scale of 1-5),and usefulness score (categorizing videos as "useless", "slightly useful", "moderately useful" and "very useful"). Results: Videos on TikTok are more popular than Bilibili. There was no significant difference in GQS and usefulness scores on different platforms (GQS P=0.831, usefulness score P=0.114). The quality and usefulness of videos related to smoking cessation on TikTok and Bilibili were average, with most videos scoring 3(Bilibili 56.5%, TikTok 55.5%) and most of the usefulness scores being slightly useful, lying in the interval 1-4 (Bilibili 53.5%, TikTok 59%).Research shows that videos from Health professionals and Goverment/Professional institutions are better in quality and usefulness than videos from other sources. Videos that are classified as Help from a professional are of higher quality and more useful than videos in other categories. No video variables were significantly correlated with GQS and usefulness of videos on Bilibili; video duration, likes, comments, favorites, and retweets were significantly correlated with GQS and usefulness of videos on TikTok. Conclusions: The overall quality of the videos related to the two platforms is poor, and the quality of the videos uploaded by Health professionals and Goverment/Professional institution is slightly better,which has certain reference value after screening. As an information source of smoking cessation,the platform is not scientific and quality enough. If short videos are to be employed as a new approach for effective health education in the future, it becomes imperative to systematically review the latest findings from smoking cessation intervention research and conduct further research on the characteristics of widely disseminated and influential videos on social media to strike a balance between scientific accuracy and popular appeal.

  • Background: Conventional neuropsychological screening tools for mild cognitive impairment (MCI) have been threatened by their burdensomeness and inaccurate at detecting MCI. From a digital healthcare perspective, smartphone interaction, longitudinally and unobtrusively acquired behavior data in a non-clinical setting, alleviate these limitations. Objective: This study aimed to investigate the discriminant powers of digital biomarkers, drawn from smartphone-derived keystroke dynamics using the Neurokeys keyboard application. Methods: 64 healthy controls (HCs) and 47 patients with MCI producing 3,530 typing sessions within a month, performing the Korean version of the Montreal Cognitive Assessment (MoCA-K), were tested. A total of 2,740 were finally analyzed using the receiving operant curve analysis to investigate sensitivity and specificity. Results: Patients with MCI had significantly higher keystroke latency than controls. In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=-.468, P<0.001; flight time: r=-0.497, P<0.001). Conclusions: The current findings shed new light on the potential of smartphone-derived keystroke dynamics as an ecological surrogate for a laboratory-based conventional screening tool. Clinical Trial: Thaiclinicaltrial.org TCTR20220415002, https:// https://www.thaiclinicaltrials.org/show/TCTR20220415002

  • Background: E-cigarette use has been a public health issue in the US. On June 23, 2022, the FDA issued marketing denial orders (MDOs) to Juul Labs Inc. for all of their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media Objective: To better understand the response of the general public to the policy, and the life-cycle of public health-related policy on social media. Methods: 6,023 tweets and 22,288 reply/retweets were collected from Twitter (rebrand as X) between Jun 2022 and October 2022. We conducted a descriptive analysis, topic modelling utilizing the state-of-the-art BERTopic technique, and sentiment analysis. Results: We found that the life cycle of reactions to the FDA’s ban on Juul lasted no longer than a week on Twitter. Not only the news related to the announcement itself but the surrounding discussions (the 6 topics presented in the study) diminished shortly after June 23rd, 2022—the date when the ban was officially announced. Of the top 50 most retweeted tweets, we found posters responded from neutral (23/45, 51.11%) to more negatively (19/45, 42.22%) on the corresponding topics. Conclusions: We observed a short life-cycle for this news announcement with more negative sentiment toward the FDA’s ban on JUUL. Policymakers could employ tactics such as ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help sustain the conversation.

  • Background: Patients undergoing major surgery are at risk of complications and delayed recovery. Prehabilitation has shown promise in improving postoperative outcomes. Offering prehabilitation by means of mHealth can help overcome barriers for participating in prehabilitation and empower patients prior to major surgery. We developed the Be Prepared mHealth application, which has shown potential in an earlier pilot study. Objective: The aim of this study was to evaluate the effectiveness of the Be Prepared application on postoperative recovery of physical functioning in patients undergoing major elective surgery. Methods: This study was a multicenter RCT with two arms. Adults scheduled for major elective surgery were randomly assigned to the control (usual care) or intervention group (Be Prepared application in addition to usual care). The Be Prepared application is a stand-alone smartphone application with pre- and postoperative information and instructions on changing risk behavior for patients undergoing major elective surgery. All outcome measures were collected using online questionnaires and electronic health records. The primary outcome was recovery of postoperative physical functioning up to 12 weeks after hospital discharge measured with the CAT PROMIS Physical Functioning. Secondary outcomes included social participation, self-reported recovery, health-related quality of life, postoperative outcomes and patient satisfaction. Measurements were performed at five time points: before random assignment, and one, three, six and twelve weeks after hospital discharge. Results: A total of 369 patients were analyzed, 181 in the control group and 188 in the intervention group. Of the 188 patients in the intervention group, 146 patients (78%) used the app at least once after first login. The between-group difference in recovery of physical functioning was 2.97 (95% CI 0.90 to 5.02) in favor of the intervention group. However, this effect was negated by the significantly lower physical functioning score one week after hospital discharge in the intervention group (MD -1.72, 95% CI -3.38 to -0.07). Most secondary outcome measures did not show significant greater improvements in the intervention group compared to the control group. Patient satisfaction with overall perioperative care was significantly higher in the intervention group compared to the control group and satisfaction with the Be Prepared app was high. Conclusions: The use of the Be Prepared application as a stand-alone intervention does not seem beneficial for improving postoperative recovery in patients undergoing major surgery. However, satisfaction with perioperative care was higher in patients using the application. Given the advantages of digital technology in healthcare, it can be considered a basis for prehabilitation care pathways, complemented by guidance from health care professionals as needed. Clinical Trial: Netherlands Trial Register NL8623

  • The Association of Cadence and the Weather Across Healthy Adults and Mobility Impaired Disease Groups

    Date Submitted: Mar 14, 2024
    Open Peer Review Period: Mar 18, 2024 - May 13, 2024

    Background: Real-world walking speed is being proposed as the sixth vital sign to track functional health; this requires consideration of how the context of activity affects the individual. For example, as it gets colder outside healthy adults walk faster and increase their cadence (steps/min). Environmental factors (weather conditions, time of year, time of day) and the individual (disease-group, gender, height, BMI) may have a varied impact on walking speed across disease cohorts. Objective: The aim of the paper is to examine the relationship between temperature and wind speed with measures of outdoor walking speed (cadence) across disease groups and healthy adult controls. Expected differences between disease groups and healthy controls were expected to be found. Methods: As a part of a technical validation study assessing worn mobility trackers (Mobilise-D), participants were asked to carry a smartphone with an installed tracking app recording GPS location, step activity and local temperature and wind speed, of walking bouts for seven consecutive days. A total of 57 participants were assessed included: 16 healthy adults, 12 patients with Multiple Sclerosis, 17 patients with Chronic Obstructive Pulmonary disease and 12 with Parkinson’s disease. Results: Pearson and Spearman Rank correlation coefficients were obtained for associations between hourly temperature and wind speed against hourly measurements of cadence and physical activity. Temperature and wind speed were found to be positively correlated with cadence (CI: -.437, -.228). Fisher’s r groupwise comparison established significant differences for associations of weather and mobility between health adults and COPD (p <.001), MS (p =.032), and PD (p = .007). The opposite effect of temperature on physical activity was observed in MS compared against COPD, PD and HA groups. Conclusions: Mobility impaired disease groups are affected by the weather in a dissimilar pattern to that of healthy adults. This should be accounted for when measuring digital mobility outcomes of various patient groups. Smartphone apps for real world cadence appear to be very similar to those recorded by wearable sensors for both healthy adults and disease groups

  • Background: Teaching is often characterized as a stressful profession, with a significant proportion of teachers experiencing chronic stress and burnout. Research indicates that teachers’ stress commonly originates upon their entry into workforce, leading to negative effects on their health, occupational well-being and work performance and also impacting students’ outcomes. While meta-analytical evidence shows the efficacy of internet-based stress management interventions (iSMIs) for both experienced employees and university students, there is a gap in research on the efficacy of iSMIs tailored to teachers as well as career starters. We investigated whether an iSMI that was tailored to beginning teachers is effective in reducing their perceived stress. Additionally, this iSMI, developed according to transactional stress theory, explored problem-solving ability and emotion-regulation skills as potential mechanisms of change for the first time. Methods: Participants were 200 highly stressed beginning teachers undergoing German teacher induction, randomized to either an intervention group or a wait-list control group. Outcomes were assessed at baseline, post-intervention, 3- and an extended 6-month follow-up. Results: In the intention-to-treat analysis, the intervention group reported significant, practically meaningful and sustained improvements in perceived stress (d = 0.52) as well as in secondary outcomes. Furthermore, a parallel mediation analysis showed that the iSMI exerted its effect on reduced perceived stress via both improved problem-solving ability and emotion-regulation skills. Conclusions: The present study contributes to the growing evidence on iSMIs for early career teachers during a highly demanding transition phase and advocates for their integration into beginning teachers’ training programs. Furthermore, the results of the mediation analysis highlight the importance of teaching both coping strategies in stress management interventions and strengthening the interventions program theory that is based on transactional stress theory. These results help in further approaching to understand how iSMIs work and lay the groundwork for further exploration of conditions affecting change. Trial registration: This trial was registered in the German Clinical Trials Registry on 23/02/2018 (DRKS00013880).

  • Background: The development of adverse lifestyle behaviors increases the risk of a variety of chronic age-related diseases, including cardiovascular disease, obesity, and Alzheimer's disease. There is limited evidence regarding the effectiveness of eHealth-based multiple health behavior change (MHBC) for lifestyle risk behaviors. Objective: The purpose of this systematic evaluation is to assess the effectiveness of eHealth intervention in changing two or more major lifestyle risk behaviors in people aged 50 and older. Methods: The literature search was conducted in six electronic databases (PubMed, Embase, Web of Science, Scopus, Cochrane Library, and SPORT Discus) up to July 8, 2023. Eligible studies were randomized controlled trials of eHealth interventions targeting two or more of six behaviors of interest, including alcohol use, smoking, diet, physical activity (PA), sedentary behavior (SB), and sleep. Results: Thirty-two articles with 33 studies were included. eHealth-based MHBC interventions significantly increased the number of people who quit smoking (OR= 2.13 (95% CI 1.64 to 2.76), P<0.001), fruit intake (SMD=0.18 (95% CI 0.04 to 0.32), P= 0.01), vegetable intake (SMD=0.17 (95% CI 0.05 to 0.28), P=0.003), self-reported total PA (TPA) (SMD=0.22 (95% CI 0.02 to 0.43), P=0.03) and objectively measured moderate-to-vigorous physical activity (MVPA) (SMD=0.25 (95% CI 0.09 to 0.41), P= 0.002), and decreased the fat intake (SMD=-0.23 (95% CI -0.33 to -0.13), P<0.001). No effects were seen for alcohol use or SB or sleep. A sensitivity analysis was conducted to test the robustness of the pooled results. Moreover, the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment. Conclusions: eHealth-based MHBC interventions may be a promising strategy to increase PA, improve diet, and reduce smoking in older adults. However, the effects were small. Further high-quality, older adult-oriented research is needed to develop eHealth interventions that can change multiple behaviors. Clinical Trial: PROSPERO CRD42023444418

  • Background: The Portfolio Diet is a plant-based dietary pattern for cardiovascular disease (CVD) risk reduction. To enhance implementation of the Portfolio Diet, we developed the PortfolioDiet.app. Objective: The objective of this study was to evaluate the effect of PortfolioDiet.app on dietary adherence and its acceptability among adults with a high risk of CVD over 12-weeks. Methods: Forty-one participants in an ongoing study (ClinicalTrials.gov Identifier: NCT02481466) were invited to participate in an ancillary study and be randomized to the PortfolioDiet.app or a control for 12 weeks. Adherence to the Portfolio Diet was assessed by weighed 7-day diet records at baseline and 12-weeks using the clinical-Portfolio Diet Score (c-PDS). Acceptability of the app was evaluated using a multifaceted approach, including usability through the System Usability Scale (SUS), with a score higher than 70 being considered acceptable, and a qualitative analysis of open-ended questions using Nvivo 12. Results: Fourteen participants were randomized (8 intervention, 6 control) and completed the ancillary study. There was a tendency for an increase in adherence to the Portfolio Diet by 1.25±2.8 (5.0%) and 0.19±4.4 (0.8%) points in the app and control group, respectively, with no difference between groups (P>0.05). Participants used the app on average for 18±14 days per month and rated the app as usable (SUS of 80.9±17.3). Qualitative analyses identified four main themes (User Engagement, Usability, External Factors, and Added Components), which complemented the quantitative data obtained. Conclusions: This study demonstrates the PortfolioDiet.app is usable and reinforces dietitian advice to follow the Portfolio Diet in adults at high risk of CVD. A randomized controlled trial investigating the PortfolioDiet.app on health-related outcomes is warranted. Clinical Trial: ClinicalTrials.gov Identifier: NCT02481466

  • Background: The need for quick, holistic processing of information is critical in multiple domains and particularly in healthcare. Whilst numerous guidelines exist for a range of visualisation representations, there are currently no guidelines for the design of holistic integrated information visualisations enabling quick information processing and holistic comprehension, of multi-dimensional data, in time critical contexts. Objective: To establish a theoretical underpinning to the argument that holistic integrated visualisations are a distinct type of visualisation for time critical contexts and identifying applicable design principles and guidelines which can be used to design for such cases. Methods: We systematically searched the literature for peer-reviewed research on visualisation strategies, guidelines and taxonomies. The literature selection followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. The search was performed using six databases: ACM, Google Scholar, IEEE Xplore, PubMed, SCOPUS and Web of Science (WoS). The search was up to March 2023 using the terms (“visualisations” OR “visualizations) AND (“guidelines OR “taxonomy” OR “taxonomies”), with studies restricted to the English language. Results: Out of a total of 863 papers, 46 were included in the final review. 22 papers related to visualising complexity to provide an overview or holistic understanding of multi-dimensional data; 13 papers focused on integrated presentation; integrating or combining multi-dimensional data into a single visual representation; and 16 papers pertained to time and designing for rapid information processing. 30 papers presented general information visualisation or visual communication guidelines and principles. At present no specific guidelines or principles exist wholly for holistic, integrated visualisations in time-critical contexts. Conclusions: Holistic integrated information visualisation (HI-viz) in time-critical domains is a unique use-case requiring a unique set of design guidelines. We propose 5 main guidelines, derived from existing design theories and guidelines, that can serve as a starting point to enable both holistic and rapid processing of information, facilitating better informed decisions in time-critical contexts.

  • Background: Healthcare instructions are important for patients who are discharged from the emergency department (ED). Objective: his study aimed to evaluate the digital shift using Short Message Service (SMS) in ED discharge instructions and patient education. Methods: A questionnaire survey for ED-discharged patients who had febrile or respiratory symptoms was conducted from August 1st, 2021 through July 31st, 2022. The 29-item questionnaire was on a 5-point Likert scale and contained five categories: disease and healthcare knowledge, care confidence, extant anxiety trajectories, contentment with discharge-bound health directives, and fundamental demographic matrices. Statistical significance was set at p < 0.05. Results: A total of 618 validated questionnaires were included in the analysis. Knowledge significantly correlated with care confidence (r= 0.17, p<0.01) and satisfaction (r=0.11, p<0.01). Care confidence significantly correlated with satisfaction (r=0.65, p <0.01) and showed significant negative correlation with anxiety (r=-0.21, p<0.01). Anxiety had significant negative correlations with satisfaction (r = -0.22, p <0. 01). After instruction, the anxiety score decreased from 3.8±3.0 to 2.8±2.6 (p<0. 01)in the airway infection group (n=380) and from 3.8±3.0 to 2.7±2.5 (p<0.01) in the fever group (n=238). Conclusions: Using SMS for ED discharge instructions and patient education is feasible and effective.

  • Endometriosis Online Communities: A Quantitative Analysis

    Date Submitted: Mar 3, 2024
    Open Peer Review Period: Mar 3, 2024 - Apr 28, 2024

    Background: Endometriosis is a chronic condition that affects 10% of people with a uterus. Due to the complex social and psychological impacts caused by the condition, people with endometriosis often turn to online health communities (OHCs) for support. Objective: Prior work identifies a lack of large-scale analyses of endometriosis patient experiences and of OHCs. Our study fills this gap by investigating aspects of the condition and aggregate user needs that emerge from two endometriosis OHCs, r/Endo and r/endometriosis. Methods: We leverage topic modeling and supervised machine learning to identify associations between a post's subject matter ("topics"), the people and relationships ("personas") mentioned, and the type of support the post seeks ("intent"). Results: The most discussed topics in posts are medical stories, medical appointments, sharing symptoms, menstruation, and empathy. In addition, when discussing medical appointments, users are more likely to mention the endometriosis OHCs than medical professionals. Furthermore, medical professional is the least likely of any persona to be associated with empathy. Posts that mention partner or family are likely to discuss topics from the life issues category, in particular fertility. Lastly, we find that while users seek experiential knowledge regarding treatments and healthcare processes, they also wish to vent and to establish emotional connections about the life-altering aspects of the condition. Conclusions: Endometriosis OHCs provide members a space where they can discuss care pathways, learn to manage symptoms, and receive validation. Our results emphasize the need for greater empathy within clinical settings, easier access to appointments, more information on care pathways, and further support for patient loved ones. In addition, this study demonstrates the value of quantitative analyses of OHCs: they can support and extend findings from small-scale studies about patient experiences and provide insight into hard-to-reach groups. Lastly, analyses of OHCs can help design interventions to improve care, as argued in previous studies.