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BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

BACKGROUND: Many clinical trials fail because of poor recruitment and enrollment which can directly impact the success of biomedical and clinical research outcomes. Options to leverage digital technology for improving clinical trial management are expansive, with potential benefits for improving access to clinical trials, encouraging trial diversity and inclusion, and potential cost-savings through enhanced efficiency. OBJECTIVES: This systematic review has two key aims: (1) identify and describe the digital technologies applied in clinical trial recruitment and enrollment and (2) evaluate evidence of these technologies addressing the recruitment and enrollment of racial and ethnic minority groups. METHODS: We conducted a cross-disciplinary review of articles from PubMed, IEEE Xplore, and ACM Digital Library, published in English between January 2012 and July 2022, using MeSH terms and keywords for digital health, clinical trials, and recruitment and enrollment. Articles unrelated to technology in the recruitment/enrollment process or those discussing recruitment/enrollment without technology aspects were excluded. RESULTS: The review returned 614 results, with 21 articles (four reviews and 17 original research articles) deemed suitable for inclusion after screening and full-text review. To address the first objective, various digital technologies were identified and characterized, which included articles with more than one technology subcategory including (a) multimedia presentations (19%, n = 4); (b) mobile applications (14%, n = 3); (c) social media platforms (29%, n = 6); (d) machine learning and computer algorithms (19%, n = 4); (e) e-consenting (24%, n = 5); (f) blockchain (5%, n = 1); (g) web-based programs (24%, n = 5); and (h) virtual messaging (24%, n = 5). Additionally, subthemes, including specific diseases or conditions addressed, privacy and regulatory concerns, cost/benefit analyses, and ethnic and minority recruitment considerations, were identified and discussed. Limited research was found to support a particular technologys effectiveness in racial and ethnic minority recruitment and enrollment. CONCLUSION: Results from this review illustrate that several types of technology are currently being explored and utilized in clinical trial recruitment and enrollment stages. However, evidence supporting the use of digital technologies is varied and requires further research and evaluation to identify the most valuable opportunities for encouraging diversity in clinical trial recruitment and enrollment practices.

Background: Current atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic health records (EHRs) could enhance clinical decision-making and patient engagement. The aim of this study was to develop a short-term ASCVD risk prediction model for hypertensive individuals using mHealth and EHR data and compare its performance to existing risk assessment tools. Methods: This is a retrospective cohort study including 51,127 hypertensive participants aged ≥18 years old who enrolled in the Hello Heart CV risk self-management program between January 2015 and January 2024. A machine learning (ML) model was derived from EHR data and mHealth measurements of blood pressure (BP) and heart rate (HR) collected via at-home BP monitors. Its performance was compared to that of PCE and PREVENT. Results: The XgBoost model incorporating 291 features outperformed the PCE and PREVENT scores in discriminating ASCVD risk for both prediction periods. For 90-day prediction, mean C-statistics were 0.81 (XgBoost) vs. 0.74 (PCE) and 0.65 (PREVENT). Similar findings were observed for 365-day prediction. mHealth measurements incrementally enhanced 365-day risk prediction (ROC-AUC 0.82 vs. 0.80 without mHealth). Conclusion: An EHR and mHealth-based ML model offers superior short-term ASCVD prediction compared to traditional tools. This approach supports personalized preventive strategies, particularly for populations with incomplete features for PCE or PREVENT. Further research should explore this novel risk prediction framework, and particularly additional mHealth data integration for broader applicability and increased predictive power. [ABSTRACT FROM AUTHOR]

Introduction: Parkinson's Disease affects over 8.5 million people and there are currently no medications approved to treat underlying disease. Clinical trials for disease modifying therapies (DMT) are hampered by a lack of sufficiently sensitive measures to detect treatment effect. Reliable digital assessments of motor function allow for frequent at-home measurements that may be able to sensitively detect disease progression. Methods: Here, we estimate the test-retest reliability of a suite of at-home motor measures derived from raw triaxial accelerometry data collected from 44 participants (21 with confirmed PD) and use the estimates to simulate digital measures in DMT trials. We consider three schedules of assessments and fit linear mixed models to the simulated data to determine whether a treatment effect can be detected. Results: We find at-home measures vary in reliability; many have ICCs as high as or higher than MDS-UPDRS part III total score. Compared with quarterly in-clinic assessments, frequent at-home measures reduce the sample size needed to detect a 30% reduction in disease progression from over 300 per study arm to 150 or less than 100 for bursts and evenly spaced at-home assessments, respectively. The results regarding superiority of at-home assessments for detecting change over time are robust to relaxing assumptions regarding the responsiveness to disease progression and variability in progression rates. Discussion: Overall, at-home measures have a favorable reliability profile for sensitive detection of treatment effects in DMT trials. Future work is needed to better understand the causes of variability in PD progression and identify the most appropriate statistical methods for effect detection. [ABSTRACT FROM AUTHOR]

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The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials. [ABSTRACT FROM AUTHOR]

Although Digital Health Technology is increasingly implemented in hospitals and clinics, physicians are not sufficiently equipped with the competencies needed to optimize technology utilization. Medical schools seem to be the most appropriate channel to better prepare future physicians for this development. The purpose of this research study is to investigate the extent to which top-ranked medical schools equip future physicians with the competencies necessary for them to leverage Digital Health Technology in the provision of care. This research work relied on a descriptive landscape analysis, and was composed of two phases: Phase I aimed at investigating the articulation of the direction of the selected universities and medical schools to identify any expressed inclination towards teaching innovation or Digital Health Technology. In phase II, medical schools' websites were analyzed to discover how innovation and Digital Health Technology are integrated in their curricula. Among the 60 medical schools that were analyzed, none mentioned any type of Digital Health Technology in their mission statements (that of the universities, in general, and medical schools, specifically). When investigating the medical schools' curricula to determine how universities nurture their learners in relation to Digital Health Technology, four universities covering different Digital Health Technology areas were identified. The results of the current study shed light on the untapped potential of working towards better equipping medical students with competencies that will enable them to leverage Digital Health Technology in their future practice and in turn enhance the quality of care. [ABSTRACT FROM AUTHOR]

Background: This study aims to create a comprehensive framework for the development and implementation of digital medication adherence technologies (DMATech), focusing on critical stages where engagement of medication users (MU) is considered meaningful, i.e. adds significant value, as agreed upon by participating stakeholders. Methods: Through a literature review and expert consensus, a framework was outlined covering key DMATech development and implementation phases and steps. An in-person workshop with MU representatives and adherence experts, using the Nominal Group Technique, further refined these stages for MU engagement. Results: The DMATech framework included three phases: 'Innovation,' 'Research and Development,' and 'Launch and Implementation,' each encompassing multiple steps. The workshop, attended by five MU representatives and nine adherence experts, identified critical stages for MU input including context analysis, ideation, proof of concept, prototype creation, DMATech's iteration, critical evaluation, healthcare implementation, real-world assessment, and improvement. Nevertheless, there was a divergence of consensus regarding the importance of MUs engagement in regulatory, financial, and marketing aspects. Conclusions: This study provides a holistic framework for DMATech development and implementation and underscores the necessity of MU engagement at various stages. Modes of MU engagement cannot be generalized; a case-by-case evaluation of engagement strategies is essential. [ABSTRACT FROM AUTHOR]

This study examines integrating Enterprise Resource Planning (ERP) systems with performance management (PM) practices in the UAE healthcare sector, identifying key factors for successful adoption. It addresses a critical gap by analyzing the interplay between ERP systems and PM to enhance operational efficiency, patient care, and administrative processes. A literature review identified thirty-six critical factors, refined through expert interviews to highlight nine weak integration areas and two new factors. An online survey with 81 experts, who rated the 38 factors on a five-point Likert scale, provided data to calculate the Relative Importance Index (RII). The results reveal that employee involvement in performance metrics and effective organizational measures significantly impact system effectiveness and alignment. Mid-tier factors such as leadership and managerial support are essential for integration momentum, while foundational elements like infrastructure, scalability, security, and compliance are crucial for long-term success. The study recommends a holistic approach to these factors to maximize ERP benefits, offering insights for healthcare administrators and policymakers. Additionally, it highlights the need to address the challenges, opportunities, and ethical considerations associated with using digital health technology in healthcare. Future research should explore ERP integration challenges in public and private healthcare settings, tailoring systems to specific organizational needs. [ABSTRACT FROM AUTHOR]

Through the conceptual framework of datafication, dataism and dataveillance, this study investigates beliefs and attitudes regarding datafication and the related privacy and security concerns among individuals with Type 1 diabetes. Qualitative research was conducted through interviews among fifty-two individuals with Type 1 diabetes in Poland and Italy. The findings reveal a dynamic interplay between self-discipline and empowerment. The majority of interviewees emphasized the benefits of technologies for gaining a better understanding of their health condition and for more effective disease management. However, a minority of interviewees perceived the negative effects of datafication, including dataveillance, which leads to hyper-control of the disease, and dataism, characterized by excessive reliance on and dependency on technology. Critical beliefs about technologies fuelled rejection attitudes, leading some interviewees to suspend or abandon their use. Lastly, reflexivity on privacy and security issues appears to be low, particularly among older individuals with lower levels of education and socioeconomic status. This results in a poor understanding and underestimation of the potential risks associated with security and privacy. The findings increase the understanding of the factors that can facilitate or hinder the adoption of technology among people with diabetes. [ABSTRACT FROM AUTHOR]

Purpose of Review: Timely treatment of status epilepticus (SE) improves outcomes, however gaps between recommended and implemented care are common. This review analyzes obstacles and explores interventions to optimize effective, evidence-based treatment of SE. Recent Findings: Seizure action plans, rescue medications, and noninvasive wearables with seizure detection capabilities can facilitate early intervention for prolonged seizures in the home and school. In the field, standardized EMS protocols, EMS education, and screening tools can address variability in SE definitions and treatment, particularly benzodiazepine dosing. In the emergency room and hospital, provider education, SE order sets and alerts, and rapid EEG technologies, can shorten time to first-line therapy, second-line therapy, and EEG initiation. Summary: Widespread, sustained improvement in SE care remains challenging. A multipronged approach including emphasis on pre-hospital intervention, treatment protocols adapted to local contexts, and SE databases to systematically collect process and outcome metrics have the potential to transform SE treatment and outcomes. [ABSTRACT FROM AUTHOR]

Abstract Although Digital Health Technology is increasingly implemented in hospitals and clinics, physicians are not sufficiently equipped with the competencies needed to optimize technology utilization. Medical schools seem to be the most appropriate channel to better prepare future physicians for this development. The purpose of this research study is to investigate the extent to which top-ranked medical schools equip future physicians with the competencies necessary for them to leverage Digital Health Technology in the provision of care. This research work relied on a descriptive landscape analysis, and was composed of two phases: Phase I aimed at investigating the articulation of the direction of the selected universities and medical schools to identify any expressed inclination towards teaching innovation or Digital Health Technology. In phase II, medical schools’ websites were analyzed to discover how innovation and Digital Health Technology are integrated in their curricula. Among the 60 medical schools that were analyzed, none mentioned any type of Digital Health Technology in their mission statements (that of the universities, in general, and medical schools, specifically). When investigating the medical schools’ curricula to determine how universities nurture their learners in relation to Digital Health Technology, four universities covering different Digital Health Technology areas were identified. The results of the current study shed light on the untapped potential of working towards better equipping medical students with competencies that will enable them to leverage Digital Health Technology in their future practice and in turn enhance the quality of care.

Abstract The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials.

BackgroundYouth in the child welfare system (child welfare-involved [CWI] youth) have high documented rates of mental health symptoms and experience significant disparities in mental health care services access and engagement. Adolescence is a developmental stage that confers increased likelihood of experiencing mental health symptoms and the emergence of disorders that can persist into adulthood. Despite a high documented need for evidence-based mental health services for CWI youth, coordination between child welfare and mental health service systems to increase access to care remains inadequate, and engagement in mental health services is low. Navigator models developed in the health care field to address challenges of service access, fragmentation, and continuity that affect the quality of care provide a promising approach to increase linkage to, and engagement in, mental health services for CWI youth. However, at present, there is no empirically supported mental health navigator model to address the unique and complex mental health needs of CWI youth and their families.ObjectiveUsing a randomized controlled trial, this study aims to develop and test a foster care family navigator (FCFN) model to improve mental health service outcomes for CWI adolescents (aged 12-17 years).MethodsThe navigator model leverages an in-person navigator and use of adjunctive digital health technology to engage with, and improve, care coordination, tracking, and monitoring of mental health service needs for CWI youth and families. In total, 80 caregiver-youth dyads will be randomized to receive either the FCFN intervention or standard of care (clinical case management services): 40 (50%) to FCFN and 40 (50%) to control. Qualitative exit interviews will inform the feasibility and acceptability of the services received during the 6-month period. The primary trial outcomes are mental health treatment initiation and engagement. Other pre- and postservice outcomes, such as proportion screened and time to screening, will also be evaluated. We hypothesize that youth receiving the FCFN intervention will have higher rates of mental health treatment initiation and engagement than youth receiving standard of care.ResultsWe propose enrollment of 80 dyads by March 2024, final data collection by September 2024, and the publication of main findings in March 2025. After final data analysis and writing of the results, the resulting manuscripts will be submitted to journals for dissemination.ConclusionsThis study will be the first to produce empirically driven conclusions and recommendations for implementing a family mental health navigation model for CWI youth with long-standing and unaddressed disparities in behavioral health services access. The study findings have potential to have large-scale trial applicability and be feasible and acceptable for eventual system implementation and adoption.Trial registrationClinicalTrials.gov NCT04506437; https://www.clinicaltrials.gov/study/NCT04506437.International registered report identifier (irrid)DERR1-10.2196/49999.

Abstract Background Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people’s chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person’s health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem. Methods A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day’s discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. Results Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. Conclusions The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software.

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility. Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings. Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated “slight tremor” or worse. Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

Background: The Konectom™ smartphone-based cognitive processing speed (CPS) test is designed to assess processing speed and account for impact of visuomotor function on performance. Objective: Evaluate reliability and validity of Konectom CPS Test, performed in clinic and remotely. Methods: Data were collected from people with multiple sclerosis (PwMS) aged 18–64 years and healthy control participants (HC) matched for age, sex, and education. Remote test–retest reliability (intraclass correlation coefficients, ICC); correlation with established clinical measures (Spearman correlation coefficients); group analyses between cognitively impaired/unimpaired PwMS; and influence of age, sex, education, and upper limb motor function on CPS Test measures were assessed. Results: Eighty PwMS and 66 HC participated. CPS Test measures from remote tests had good test–retest reliability (ICC of 0.67–0.87) and correlated with symbol digit modalities test (highest |ρ| = 0.80, p < 0.0001). Remote measures were stable (change from baseline < 5%) and correlated with MS disability (highest |ρ| = 0.39, p = 0.0004) measured by Expanded Disability Status Scale. CPS Test measures displayed sensitivity to cognitive impairment (highest d = 1.47). Demographics and motor function had the lowest impact on CPS Test substitution time, a measure accounting for visuomotor function. Conclusion: Konectom CPS Test measures provide valid, reliable remote measurements of cognitive processing speed in PwMS. [ABSTRACT FROM AUTHOR]

Background: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem. Methods: A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day's discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. Results: Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. Conclusions: The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software. [ABSTRACT FROM AUTHOR]

Many people with Type 2 diabetes (T2D) who could benefit from digital health technologies (DHTs) are either not using DHTs or do use them, but not for long enough to reach their behavioral or metabolic goals. We aimed to identify subgroups within DHT adopters and non-adopters and describe their unique profiles to better understand the type of tailored support needed to promote effective and sustained DHT use across a diverse T2D population. We conducted latent class analysis of a sample of adults with T2D who responded to an internet survey between December 2021 and March 2022. We describe the clinical and psychological characteristics of DHT adopters and non-adopters, and their attitudes toward DHTs. A total of 633 individuals were characterized as either DHT "Adopters" (n = 376 reporting any use of DHT) or "Non-Adopters" (n = 257 reporting never using any DHT). Within Adopters, three subgroups were identified: 21% (79/376) were "Self-managing Adopters," who reported high health activation and self-efficacy for diabetes management, 42% (158/376) were "Activated Adopters with dropout risk," and 37% (139/376) were "Non-Activated Adopters with dropout risk." The latter two subgroups reported barriers to using DHTs and lower rates of intended future use. Within Non-Adopters, two subgroups were identified: 31% (79/257) were "Activated Non-Adopters," and 69% (178/257) were "Non-Adopters with barriers," and were similarly distinguished by health activation and barriers to using DHTs. Beyond demographic characteristics, psychological, and clinical factors may help identify different subgroups of Adopters and Non-Adopters. [ABSTRACT FROM AUTHOR]

Sensor-based digital health technology (DHT) has emerged as a promising means to assess patient functioning within and outside clinical trials. Sensor-based functional outcomes (SBFOs) provide valuable insights that complement other measures of how a patient feels or functions to enhance understanding of the patient experience to inform medical product development. This perspective paper provides recommendations for defining SBFOs, discusses the core evidence required to support SBFOs to inform decision-making, and considers future directions for the field. The clinical outcome assessment (COA) development process provides an important starting point for developing patient-centered SBFOs; however, given the infancy of the field, SBFO development may benefit from a hybrid approach to evidence generation by merging exploratory data analysis with patient engagement in measure development. Effective SBFO development requires combining unique expertise in patient engagement, measurement and regulatory science, and digital health and analytics. Challenges specific to SBFO development include identifying concepts of interest, ensuring measurement of meaningful aspects of health, and identifying thresholds for meaningful change. SBFOs are complementary to other COAs and, as part of an integrated evidence strategy, offer great promise in fostering a holistic understanding of patient experience and treatment benefits, particularly in real-world settings. [ABSTRACT FROM AUTHOR]

Over the last decade increasing examples indicate opportunities to measure patient functioning and its relevance for clinical and regulatory decision making via endpoints collected through digital health technologies. More recently, we have seen such measures support primary study endpoints and enable smaller trials. The field is advancing fast: validation requirements have been proposed in the literature and regulators are releasing new guidances to review these endpoints. Pharmaceutical companies are embracing collaborations to develop them and working with academia and patient organizations in their development. However, the road to validation and regulatory acceptance is lengthy. The full value of digital endpoints cannot be unlocked until better collaboration and modular evidence frameworks are developed enabling re-use of evidence and repurposing of digital endpoints. This paper proposes a solution by presenting a novel modular evidence framework -the Digital Evidence Ecosystem and Protocols (DEEP)- enabling repurposing of measurement solutions, re-use of evidence, application of standards and also facilitates collaboration with health technology assessment bodies. The integration of digital endpoints in healthcare, essential for personalized and remote care, requires harmonization and transparency. The proposed novel stack model offers a modular approach, fostering collaboration and expediting the adoption in patient care [ABSTRACT FROM AUTHOR]

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD. [ABSTRACT FROM AUTHOR]

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes. [ABSTRACT FROM AUTHOR]

Objective: Through interviews with older adults and professionals in Chongqing, China, this paper explores the challenges and opportunities faced by smart cities that undertake to better support ageing‐in‐place. Methods: We undertook a qualitative multi‐methods approach, engaging 46 professional stakeholders and 64 older people to examine the role of smart cities in enabling older people to age‐in‐place in Chongqing, China. Results: With the development of smart cities, technology has the potential to facilitate ageing‐in‐place by creating opportunities for heathy and active ageing. This study identified specific priorities in delivering age‐friendly smart cities, including the following: shaping smart environments around the everyday lives of older people, designing inclusive and equitable smart cities and ensuring strong forms of institutional governance, trust and relationship building with older people. Conclusions: The age‐friendly city and communities movement (AFCC) and smart city policy instruments have potential for realising active ageing by supporting mobility, access to services and civic participation. However, there exists a disconnect between smart city policy and practice in delivering tangible well‐being outcomes for older people. Addressing this requires greater cross‐sectoral working, reconciling smart city policy priorities with AFCC domains and creating the institutional and governance framework to enable socially sustainable cities to support ageing‐in‐place. [ABSTRACT FROM AUTHOR]

BACKGROUND: Angelman syndrome (AS) is a rare neurodevelopmental disorder characterized by the absence of a functional UBE3A gene, which causes developmental, behavioral, and medical challenges. While currently untreatable, comprehensive data could help identify appropriate endpoints assessing meaningful improvements in clinical trials. Herein are reported the results from the FREESIAS study assessing the feasibility and utility of in-clinic and at-home measures of key AS symptoms. METHODS: Fifty-five individuals with AS (aged

BackgroundCurrent atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic health records (EHRs) could enhance clinical decision-making and patient engagement. The aim of this study was to develop a short-term ASCVD risk prediction model for hypertensive individuals using mHealth and EHR data and compare its performance to existing risk assessment tools.MethodsThis is a retrospective cohort study including 51,127 hypertensive participants aged ≥18 years old who enrolled in the Hello Heart CV risk self-management program between January 2015 and January 2024. A machine learning (ML) model was derived from EHR data and mHealth measurements of blood pressure (BP) and heart rate (HR) collected via at-home BP monitors. Its performance was compared to that of PCE and PREVENT.ResultsThe XgBoost model incorporating 291 features outperformed the PCE and PREVENT scores in discriminating ASCVD risk for both prediction periods. For 90-day prediction, mean C-statistics were 0.81 (XgBoost) vs. 0.74 (PCE) and 0.65 (PREVENT). Similar findings were observed for 365-day prediction. mHealth measurements incrementally enhanced 365-day risk prediction (ROC-AUC 0.82 vs. 0.80 without mHealth).ConclusionAn EHR and mHealth-based ML model offers superior short-term ASCVD prediction compared to traditional tools. This approach supports personalized preventive strategies, particularly for populations with incomplete features for PCE or PREVENT. Further research should explore this novel risk prediction framework, and particularly additional mHealth data integration for broader applicability and increased predictive power.

Despite availability of multiple FDA approved therapies, many children with juvenile idiopathic arthritis (JIA) suffer pain and disability due to uncontrolled disease. The term JIA includes a heterogeneous set of conditions unified by chronic inflammatory arthritis, collectively affecting 1:1,000 children. When reviewing treatment options with families the rheumatologist currently refers to the experience of the average patient in relatively small controlled clinical trials, to consensus-based treatment plans, or increasingly the choice is dictated by the formulary restrictions of insurance payers. The current paradigm for treatment selection does not incorporate real-world evidence of treatment effectiveness centered to the individual patients with whom decisions are to be made. Treatment decisions based on the evidence of the average patient are not optimized to reflect the unique clinical characteristics of an individual with JIA and their disease course, nor does it account for heterogeneous treatment effects. To guide treatment choices centered around each patient, we describe a novel concept of utilizing digital health technology to bring patient-centered information into shared decision-making discussions based on comparative effectiveness analysis of electronic health record or observational clinical registry data of patients with similar characteristics. The envisioned digital tool will organize and present data relevant to the individual patient and enable evidence-based individualized treatment decision making when used in a collaborative manner with the patient family and rheumatologist. Capabilities in digital health technology, data capturing, and analytical methodologies are ripe for this endeavor. This brings the concept of a learning health system directly to the point of care.

IntroductionParkinson's Disease affects over 8.5 million people and there are currently no medications approved to treat underlying disease. Clinical trials for disease modifying therapies (DMT) are hampered by a lack of sufficiently sensitive measures to detect treatment effect. Reliable digital assessments of motor function allow for frequent at-home measurements that may be able to sensitively detect disease progression.MethodsHere, we estimate the test-retest reliability of a suite of at-home motor measures derived from raw triaxial accelerometry data collected from 44 participants (21 with confirmed PD) and use the estimates to simulate digital measures in DMT trials. We consider three schedules of assessments and fit linear mixed models to the simulated data to determine whether a treatment effect can be detected.ResultsWe find at-home measures vary in reliability; many have ICCs as high as or higher than MDS-UPDRS part III total score. Compared with quarterly in-clinic assessments, frequent at-home measures reduce the sample size needed to detect a 30% reduction in disease progression from over 300 per study arm to 150 or less than 100 for bursts and evenly spaced at-home assessments, respectively. The results regarding superiority of at-home assessments for detecting change over time are robust to relaxing assumptions regarding the responsiveness to disease progression and variability in progression rates.DiscussionOverall, at-home measures have a favorable reliability profile for sensitive detection of treatment effects in DMT trials. Future work is needed to better understand the causes of variability in PD progression and identify the most appropriate statistical methods for effect detection.

Introduction: While detecting active TB is central to public health efforts, modelling indicates that reducing latent TB through preventive therapy is crucial. Current regimens mitigate risk but are lengthy and have side effects, necessitating support for uninterrupted treatment. This paper presents the development and evaluation of a digital health platform designed to enhance adherence among LTBI patients. Methods: A Quasi-experimental study was conducted among LTBI patients in Delhi. A total of 163 participants were allocated to intervention (n=82) and control (n=81) groups. Participants were followed up for 6 months post recruitment. Effectiveness of mobile application was evaluated through quantitative tools. Results: Intervention group participants showed slightly higher treatment completion rates (65.91%), in comparison to participants in control group (63%). The analysis demonstrated no co-relation of gender, age, education and employment with treatment completion rates in intervention group. While text and video-based interventions have shown success, there remains a need for more user-centric digital health interventions in this area, given the limited number of studies to date. Conclusion: The mobile health applications can be useful for LTBI care. However, there is a need of involving users during development so that continued interest of users can be ensured.

Summary: Background: The Narrative Experiences Online (NEON) Intervention provides self-managed web-based access to mental health recovery narratives (n = 659). We evaluated effectiveness and cost-effectiveness in improving quality of life for adults resident in England with mental health problems and recent psychosis experience. Methods: Prospectively registered pragmatic parallel-group randomised trial controlling for usual care, recruiting from statutory mental health services and through community engagement activities, with a 52-week primary endpoint (ISRCTN11152837). All trial procedures and the NEON Intervention were delivered by an integrated web-application. Randomisation was through an independently generated list (no stratification). Allocation was masked for statistical staff and the Chief Investigator but not participants. Intervention arm participants received immediate NEON Intervention access. Control arm participants received access after completing primary endpoint questionnaires. The primary outcome was quality of life through the Manchester Short Assessment (MANSA). Serious Adverse Events (SAEs) were collected through web-based safety report forms and identified from health service usage data. The primary analysis was by a prospectively described Intention To Treat principle excluding participants who had registered multiple times, with multiple imputation for missing data. Findings: Between 9 March 2020 and 1 March 2021, 739 participants were randomised (intervention:370; control: 369), providing more than 90% power to detect a baseline-adjusted difference of 0.25 in the MANSA score. Mean age was 34.8 years (standard deviation (SD) 12.0), 561 (75.9%) were white British, 443 (59.9%) were female, 609 (82.4%) had accessed specialist care mental health services, and 698 (94.5%) had accessed primary care mental health services. Mean baseline MANSA score was 3.7 for control and intervention arms (SD 0.9 and 1.0). 565 (76.5%) participants provided primary endpoint MANSA data with a mean score of 4.1 (SD 1.0) for both arms. We found no significant difference in Quality of Life between the two arms at the primary endpoint (baseline-adjusted difference 0.07, 95% CI −0.07 to 0.21, p = 0.35). The incremental cost-effectiveness ratio (£110,501 per quality-adjusted life-year (QALY)) exceeded the prospectively defined cost-effectiveness threshold (£30,000 per QALY). 158 (42.8%) control arm and 194 (52.4%) intervention arm participants accessed narratives outside of the NEON Intervention. There were no related serious adverse events (SAEs). 116 unrelated SAEs were reported by control arm participants, and 107 by intervention arm participants. Interpretation: Our findings do not indicate NEON Intervention access for all people with psychosis experience. Future research should consider a) evaluation with current mental health services users; b) optimisation to enable users to find hope-promoting narratives. Funding: National Institute for Health and Care Research (NIHR).

AbstractPurposeMethodsResultsConclusions\nIMPLICATION FOR REHABILITATIONDigital health technologies have the potential to advance rehabilitation. The Virtual Engagement Rehabilitation Assistant (VERA) is a digital technology, co-designed to increase service-user engagement and promote self-management. This qualitative study explored staff and service-user perceptions of implementing VERA on a UK complex inpatient rehabilitation ward.Purposively sampled service-users were allocated to VERA for up to six weeks. The Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework underpinned service-user post-intervention interviews and staff focus groups, and structured analysis of the data. Seven service-users were interviewed. Nine staff contributed to focus groups.A framework analysis identified themes (and subthemes) structured by the NASSS framework domains: 1. Nature of Clinical Condition, 2. Technology (Ease of Use, Holding Information/Resources in a single Digital Location, Appointments), 3. Value Proposition (Structuring Time, Feedback, Unexpected Benefits) 4. Adopters (Confidence in using Technology, Usefulness), 5. Wider Organisation. Ease of use and storage of key information in a single location were beneficial. Reliability, and provision of accurate and timely feedback to staff and service-users, were identified as essential.A blended approach is required to meet staff and service-user needs. The potential for VERA in a community setting was identified and requires further investigation. Learning from VERA will support development of other digital technologies and their implementation.Digital health technologies have the potential to positively impact rehabilitation but may not be suitable for all service-users.Digital health technologies for rehabilitation must be easy to use and reliable.Relevant and informative feedback from the digital health technology was considered essential by both staff and service-users.Utilising a theoretical framework that focuses on key components of implementation was instrumental for development and evaluation of Virtual Engagement Rehabilitation Assistant (VERA).Digital health technologies have the potential to positively impact rehabilitation but may not be suitable for all service-users.Digital health technologies for rehabilitation must be easy to use and reliable.Relevant and informative feedback from the digital health technology was considered essential by both staff and service-users.Utilising a theoretical framework that focuses on key components of implementation was instrumental for development and evaluation of Virtual Engagement Rehabilitation Assistant (VERA). [ABSTRACT FROM AUTHOR]

Eye-tracking is deemed a promising methodology for usability evaluation studies in healthcare, however clear theoretical guidance and practice remains lacking. A rapid review was performed on current use of eye tracking as a usability evaluation method on digital health technologies in the period of 2019 to 2024. Usability evaluation studies were included when they described a digital health technology intervention in which eye-tracking technologies were applied. To gain insight into how eye-tracking technologies contributed to measuring digital health technologies’ usability, data was extracted on the use of eye-tracking for usability and key study findings. Seventeen papers were included in the review. Findings show that eye-tracking is frequently combined with other usability evaluation methods, with high methodological diversity, to test the usability of DHT. Future research is needed to enhance understanding of the effectiveness of eye-tracking outcomes in DHT usability testing when combined with other usability evaluation methods in order to provide (usability) researchers theoretical guidance on its application. [ABSTRACT FROM AUTHOR]

Background: Digital Health technologies (DHT) have potential to deliver intensive, novel and engaging rehabilitation for people with neurological conditions, yet health services lack a strong track record in embedding DHT into practice. The aim of this review was to synthesise factors that have been shown to influence implementation of DHT into neurological rehabilitation. Method: An integrative review was undertaken. An extensive search of MEDLINE, CINAHL, AMED, EMBASE was undertaken. The title and abstract of all retrieved sources were screened against pre-defined criteria. Retained sources underwent full text review. The quality of all included sources was assessed. A meta-ethnographic synthesis explored commonalities and contradictions of the included studies. Results: Fourteen studies (1 quantitative, 8 qualitative and 5 mixed methods) were included. Eleven implementation theories/models/frameworks were used across the 14 studies. Five themes were identified: (i) individual factors; (ii) user experience of the technology; (iii) the content of the intervention; (iv) access to the technology and (v) supporting use. Conclusions: Key factors which appear to influence the implementation of DHT into clinical settings are highlighted. Implementation theories, models and frameworks are under-utilised in DHT rehabilitation research. This needs to be addressed if DHT are to realise their potential in neurological rehabilitation. Registration: The protocol was registered and is available from PROSPERO (CRD42021268984). [ABSTRACT FROM AUTHOR]

Telehealth is a great tool that makes accessing healthcare easier for those incarcerated and can help with reentry into the the community. Justice impacted individuals face many hardships including adverse health outcomes which can be mitigated through access to telehealth services and providers. During the federally recognized COVID-19 pandemic the need for accessible healthcare was exacerbated and telehealth use surged. While access to telehealth should be considered a necessity, there are many challenges and barriers for justice impacted individuals to be able to utilize this service. This perspective examines aspects of accessibility, pandemic, policy, digital tools, and ethical and social considerations of telehealth in correctional facilities. Carceral facilities should continue to innovate and invest in telehealth to revolutionize healthcare delivery, and improve health outcomes for justice impacted individuals.

Goal: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials’ data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.

Background: There is significant unmet need for effective and efficiently delivered care for people with Parkinson's disease (PwP). We undertook a service improvement initiative to co-develop and implement a new care pathway, Home Based Care (HBC), based on supported self-management, remote monitoring and the ability to trigger a healthcare contact when needed. Objective: To evaluate feasibility, acceptability and safety of Home Based Care. Methods: We evaluated data from the first 100 patients on HBC for 6 months. Patient monitoring, performed at baseline and 6-monthly, comprised motor (MDS-UPDRS II and accelerometer), non-motor (NMSQ, PDSS-2, HADS) and quality of life (PDQ) measures. Care quality was audited against Parkinson's UK national audit standards. Process measures captured feasibility. Acceptability was assessed using a mixed-methods approach comprising questionnaires and semi-structured interviews. Results: Between October 2019 and January 2021, 108 PwP were enrolled onto HBC, with data from 100 being available at 6 months. Over 90% of all questionnaires were returned, 97% were complete or had < 3 missing items. Reporting and communications occurred within agreed timeframes. Compared with baseline, after 6m on HBC, PD symptoms were stable; more PwP felt listened to (90% vs. 79%) and able to seek help (79% vs. 68%). HBC met 93% of national audit criteria. Key themes from the interviews included autonomy and empowerment. Conclusions: We have demonstrated acceptability, feasibility and safety of our novel remotely delivered Parkinson's care pathway. Ensuring scalability will widen its reach and realize its benefits for underserved communities, enabling formal comparisons with standard care and cost-effectiveness evaluation. [ABSTRACT FROM AUTHOR]

Objectives: Hardly any value frameworks exist that are focused on provider-facing digital health technologies (DHTs) for managing chronic disease with diverse stakeholder participation in their creation. Our study aimed to 1) understanding different stakeholder opinions on where value lies in provider-facing technologies and 2) create a comprehensive value assessment framework for DHT assessment. Methods: Mixed-methods comprising both primary and secondary evidence were used. A scoping review enabled a greater understanding of the evidence base and generated the initial indicators. Thirty-four indicators were proposed within 6 value domains: health inequalities (3), data rights and governance (6), technical and security characteristics (6), clinical characteristics (7), economic characteristics (9), and user preferences (3). Subsequently, a 3-round Web-Delphi was conducted to rate the indicators' importance in the context of technology assessment and determine whether there was consensus. Results: The framework was adapted to 45 indicators based on participant contributions in round 1 and delivered 16 stable indicators with consensus after rounds 2 and 3. Twenty-nine indicators showed instability and/or dissensus, particularly the data rights domain, in which all 5 indicators were unstable, showcasing the novelty of the concept of data rights. Significant instability between important and very important ratings was present within stakeholder groups, particularly clinicians and policy experts, indicating they were unsure how different aspects should be valued. Conclusions: Our study provides a comprehensive value assessment framework for assessing provider-facing DHTs incorporating diverse stakeholder perspectives. Instability for specific indicators was expected due to the novelty of data and analytics integration in health technologies and their assessment. Further work is needed to ensure that, across all types of stakeholders, there is a clear understanding of the potential impacts of provider-facing DHTs. Highlights: Current health technology assessment (HTA) methods may not be well suited for evaluating digital health technologies (DHTs) because of their complexity and wide-ranging impact on the health system. This article adds to the literature by exploring a wide range of stakeholder opinions on the value of provider-facing DHTs, creating a holistic value framework for these technologies, and highlighting areas in which further discussions are needed to align stakeholders on DHTs' value attributes. A Web-based Delphi co-creation approach was used involving key stakeholders from throughout the digital health space to generate a widely applicable value framework for assessing provider-facing DHTs. The stakeholders include patients, health care professionals, supply-side actors, decision makers, and academia from the United States, United Kingdom, and Germany. High levels of instability among stakeholders and value domains are demonstrated, indicating the novelty of assessing provider-facing DHTs and their impact on the health system. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Digital technologies ranging from biosensors to virtual reality have revolutionized the healthcare landscape by offering innovations that hold great promise in addressing the challenges posed by rapidly aging populations. To optimize healthcare experiences for older people, it is crucial to understand their user experience (UX) with digital health technologies. This systematic review, covering articles published from 2013 to 2023, aimed to explore frequently used questionnaires for assessing digital healthcare UX among older people. The inclusion criteria were original studies assessing UX in digital health for individuals aged ≥65 years. Of 184 articles identified, 17 were selected after rigorous screening. The questionnaires used included the System Usability Scale (SUS), the User Experience Questionnaire (UEQ), and the Post-Study System Usability Questionnaire. Customized questionnaires based on models such as the Technology Acceptance Model and the Almere model were developed in some studies. Owing to its simplicity and effectiveness in assessing digital health UX among older people, the SUS emerged as the go-to tool (52.9%). Combining the SUS with the UEQ provided comprehensive insights into UX. Specialized questionnaires were also used, but further research is needed to validate and adapt these tools for diverse cultural contexts and evolving technologies. [ABSTRACT FROM AUTHOR]

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

BackgroundSensor-based monitoring tools fill a critical gap in multiple sclerosis (MS) research and clinical care.ObjectiveThe aim of this study is to assess performance characteristics of the Floodlight Proof-of-Concept (PoC) app.MethodsIn a 24-week study (clinicaltrials.gov: NCT02952911), smartphone-based active tests and passive monitoring assessed cognition (electronic Symbol Digit Modalities Test), upper extremity function (Pinching Test, Draw a Shape Test), and gait and balance (Static Balance Test, U-Turn Test, Walk Test, Passive Monitoring). Intraclass correlation coefficients (ICCs) and age- or sex-adjusted Spearman's rank correlation determined test-retest reliability and correlations with clinical and magnetic resonance imaging (MRI) outcome measures, respectively.ResultsSeventy-six people with MS (PwMS) and 25 healthy controls were enrolled. In PwMS, ICCs were moderate-to-good (ICC(2,1) = 0.61-0.85) across tests. Correlations with domain-specific standard clinical disability measures were significant for all tests in the cognitive (r = 0.82, p < 0.001), upper extremity function (|r|= 0.40-0.64, all p < 0.001), and gait and balance domains (r = -0.25 to -0.52, all p < 0.05; except for Static Balance Test: r = -0.20, p > 0.05). Most tests also correlated with Expanded Disability Status Scale, 29-item Multiple Sclerosis Impact Scale items or subscales, and/or normalized brain volume.ConclusionThe Floodlight PoC app captures reliable and clinically relevant measures of functional impairment in MS, supporting its potential use in clinical research and practice.

BackgroundYoung adults involved in the justice system have high rates of substance use disorders and low rates of treatment engagement. Most justice-involved young adults are supervised in the community-not incarcerated in jail or prison-where they have ongoing access to substances and experience significant barriers to care. When they do engage in treatment, they tend to have worse outcomes than justice-involved adolescents and older adults. Despite the need to develop targeted treatments, there are unique challenges in recruiting this population into clinical research. Digital health technology offers many novel avenues for recruiting justice-involved young adults into clinical research studies and disseminating substance use disorder treatments to justice-involved young adults. Because the vast majority of young adults regularly use one or more social media platforms, social media may offer a cost-effective and efficient way to achieve these goals.ObjectiveThis study aimed to describe the process and feasibility of using social media platforms (Facebook and Reddit) to recruit justice-involved young adults into clinical research. Justice-involved young adults recruited from these platforms completed a survey assessing the acceptability of digital health interventions to address substance use in this population.MethodsJustice-involved young adults (aged 18-24 years) were recruited through paid advertisements placed on Facebook and Reddit. Participants responded to a web-based survey focused on their substance use, treatment use history, and acceptability of various digital health interventions focused on substance use.ResultsA national sample of justice-involved young adults were successfully enrolled and completed the survey (N=131). Participants were racially diverse (8/131, 6.1% American Indian individuals; 27/131, 20.6% Asian individuals; 23/131, 17.6% Black individuals; 26/131, 19.8% Latinx individuals; 8/131, 6.1% Pacific Islander individuals; 49/131, 37.4% White individuals; and 2/131, 1.5% individuals who identified as "other" race and ethnicity). Advertisements were cost-effective (US $0.66 per click on Facebook and US $0.47 per click on Reddit). More than half (72/131, 54.9%) of the participants were on probation or parole in the past year and reported hazardous alcohol (54/131, 51.9%) or drug (66/131, 57.4%) use. Most of the participants (103/131, 78.6%) were not currently participating in substance use treatment. Nearly two-third (82/131, 62.6%) of the participants were willing to participate in one or more hypothetical digital health interventions.ConclusionsSocial media is a feasible and cost-effective method for reaching justice-involved young adults to participate in substance use research trials. With limited budgets, researchers can reach a broad audience, many of whom could benefit from treatment but are not currently engaged in care. Proposed digital health interventions focusing on reducing substance use, such as private Facebook groups, SMS text message-based appointment reminders, and coaching, had high acceptability. Future work will build on these findings to develop substance use treatment interventions for this population.

Through the conceptual framework of datafication, dataism and dataveillance, this study investigates beliefs and attitudes regarding datafication and the related privacy and security concerns among individuals with Type 1 diabetes. Qualitative research was conducted through interviews among fifty-two individuals with Type 1 diabetes in Poland and Italy. The findings reveal a dynamic interplay between self-discipline and empowerment. The majority of interviewees emphasized the benefits of technologies for gaining a better understanding of their health condition and for more effective disease management. However, a minority of interviewees perceived the negative effects of datafication, including dataveillance, which leads to hyper-control of the disease, and dataism, characterized by excessive reliance on and dependency on technology. Critical beliefs about technologies fuelled rejection attitudes, leading some interviewees to suspend or abandon their use. Lastly, reflexivity on privacy and security issues appears to be low, particularly among older individuals with lower levels of education and socioeconomic status. This results in a poor understanding and underestimation of the potential risks associated with security and privacy. The findings increase the understanding of the factors that can facilitate or hinder the adoption of technology among people with diabetes.