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PubMed2026

Implementing a novel digital health platform for self-management of postmenopausal osteoporosis: A qualitative study of user experiences, perspectives and implementation outcomes.

BACKGROUND: Osteoporosis self-management requires scalable support, and digital health platforms may meet this need. This study aimed to characterise the experiences and perspectives of postmenopausal women who participated in a 12-month randomised controlled trial (RCT) of a digital voice assistant (DVA) delivered osteoporosis self-management intervention, and to assess key implementation outcomes. METHODS: This was a qualitative analysis of interviews with postmenopausal women from the intervention arm (DVA group) of the RCT. The DVA program broadcast education videos, medication reminders, home-based exercise, nutrition advice and monthly quizzes through a DVA device. Semi-structured interviews were recorded, transcribed and managed in NVivo through reflexive thematic analysis, guided by the Practical Planning for Implementation and Scale-Up and Proctor's implementation outcome taxonomy frameworks. Evidence weighting summarised participant coverage and code density. RESULTS: Twenty-two of 25 (88%) DVA group participants completed semi-structured interviews. Thematic analysis identified seven themes mapped to Proctor's implementation outcomes. Evidence weighting indicated strong support for the intervention's appropriateness and acceptability, moderate support for its adoption, fidelity, feasibility and sustainability, and limited support for costs. Participants valued clear audiovisual guidance, conversation-based interactions with natural language, and flexible home-based access to self-management. CONCLUSION: Digital health platforms for osteoporosis self-management appear feasible, acceptable and sustainable among postmenopausal women. Findings indicate that these platforms are approaching readiness for evaluation in implementation-focused settings, contingent on streamlined content, reliable delivery modalities, accessible user support, clear privacy regulations and pragmatic pricing models.

Humans↗

PubMed2026

Digital health interventions for diabetes management in the eastern mediterranean region: A systematic review of types and effectiveness.

AIM: The aim of this study was to systematically review and evaluate the types and effectiveness of digital health interventions used for diabetes management in the Eastern Mediterranean Region (EMRO). METHODS: This systematic review, conducted according to PRISMA guidelines, searched PubMed, Web of Science, and Scopus up to May 2025 to identify studies on digital interventions for diabetes management in EMRO countries. Methodological quality of the included studies was evaluated using the EPHPP tool, and findings were categorized by intervention type, outcome measures, and intervention effectiveness. RESULTS: A total of 46 studies were included, mainly from Iran and Saudi Arabia. Phone calls and SMS were the most common digital tools. Digital interventions significantly improved HbA1c, fasting blood sugar, and several behavioral outcomes such as physical activity, medication adherence, and self-efficacy, while effects on psychological outcomes were mixed. CONCLUSION: Digital health interventions, especially phone calls and SMS, effectively improve glycemic control and self-care behaviors, though their impact on psychological outcomes remains inconsistent.

Humans↗

PubMed2026

The effectiveness of digital health interventions for type 2 diabetes in underserved populations: A systematic review and meta-analysis.

This systematic review and meta-analysis of 12 randomized controlled trials (1835 participants) evaluated whether digital health interventions (DHIs) improve glycemic control among underserved adults with type 2 diabetes (T2D), including racial/ethnic minority, low-income, Medicaid-insured, rural, and low-health-literacy populations. Searches of PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception to December 20, 2025 identified eligible parallel-group randomized controlled trials reporting change in hemoglobin A1c (HbA1c). Two reviewers independently screened studies, extracted data, and assessed risk of bias using the revised Cochrane Risk of Bias 2 tool. Random-effects meta-analysis showed that DHIs produced a modest but statistically significant HbA1c reduction versus control (mean difference, -0.37 %age points; 95% CI, -0.44 to -0.30; P < .0001; equivalent to -4.0 mmol/mol). Heterogeneity was moderate-to-substantial (I² = 69.9%). Subgroup analyses suggested directionally similar effects by population group and intervention modality, but interpretation was limited by study-level data and the small number of trials. Funnel-plot inspection and Egger's test (P = .31) did not suggest major small-study effects, although power was limited. Overall certainty for HbA1c was moderate. DHIs may support more equitable diabetes care when implemented with cultural tailoring, language access, digital-literacy support, and technology-access safeguards.

Humans↗

PubMed2025

Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study.

BACKGROUND: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. OBJECTIVE: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). METHODS: We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. RESULTS: We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46%), rural dwelling individuals (n=49,118, 8%), those over the age of 65 years (n=190,333, 31%), and individuals with low socioeconomic status (n=122,795, 20%). CONCLUSIONS: We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research.

Humans↗

PubMed2025

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

A number of seismic shifts are expected to reshape the future of medicine. The global population is rapidly aging, significantly impacting the global disease burden. Medicine is undergoing a paradigm shift, defining and diagnosing diseases at earlier stages and shifting the health care focus from treating diseases to preventing them. The application and purview of digital medicine are expected to broaden significantly. Furthermore, the COVID-19 pandemic has further accelerated the shift toward predictive, preventive, personalized, and participatory (P4) medicine, and has identified health care accessibility, affordability, and patient empowerment as core values in the future digital health era. This "left shift" toward preventive care is anticipated to redefine health care, 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, and telemedicine. Breast cancer and diabetes serve as case studies to demonstrate how these technologies such as personalized risk assessment, artificial intelligence-assisted and app-based technologies, have been developed and commercialized to provide personalized preventive care, identifying those at a higher risk and providing instructions and interventions for healthier lifestyles and improved quality of life. Overall, preventive medicine and the use of advanced technology will hold great potential for improving health care outcomes in the future.

Humans↗

PubMed2026

Efficacy of prescription-eligible digital health applications for depression and generalized anxiety disorder in Germany: a systematic review and meta-analysis.

In Germany, prescription-eligible digital mental health applications (DiGA) were introduced in 2020 as promising interventions to address, among others, depression and anxiety disorders, two of the most prevalent mental health conditions worldwide. Despite growing interest in DiGAs, their overall efficacy remains uncertain. This study aimed to systematically evaluate and quantify the efficacy of prescription-eligible digital interventions for depression and generalized anxiety disorder by synthesizing evidence from randomized controlled trials (19 trials; total N = 4,078; pooled mean age = 38.7 years, SD = 12.1). Here we show that prescription-eligible digital applications for depression and generalized anxiety disorder reduce symptom severity compared with control conditions. For depression, effects were observed both immediately after the intervention (number of apps = 5; k = 17; SMD = - 0.49; 95% CI - 0.65 to - 0.32) and at follow-up (number of apps = 1; k = 4; SMD = - 0.35; 95% CI - 0.46 to - 0.29), while evidence for generalized anxiety disorder was limited due to a small number of available studies (number of studies = 2). These findings support the integration of evidence-based digital tools into mental health treatment strategies in Germany. However, the available evidence is currently dominated by a small number of applications, particularly Deprexis, and should therefore not be interpreted as equally representative of all DiGAs currently listed for depression in Germany. The findings also highlight methodological limitations of current research and underscore the need for real-world evaluations, which address not only efficacy but also the effectiveness, content, quality and implementation.

Generalized Anxiety Disorder↗

PubMed2026

Effectiveness of digital health technologies for post-discharge follow-up and management in older adults: a systematic review.

Older adults (≥65 years) are a rapidly growing population that are experiencing a higher number of hospitalisation admissions, longer hospital stays, and greater hospitalisation-related costs than younger adults. There is an important gap in post-discharge care for older adults, and digital technologies, such as video visits, mobile health apps, and remote patient monitoring, may support follow-up and management after hospital discharge. This systematic review examined the effectiveness, feasibility, acceptability, and impact (ie, effects on rehospitalisation, quality of life, mental health, adherence, and patient satisfaction) of technology-based interventions used for the follow-up and management of older adults after hospital discharge. MEDLINE (via PubMed), Scopus, and Web of Science were searched from database inception to January, 2026. The search identified 1972 records, of which 46 studies met the inclusion criteria: older adult populations (aged ≥65 years), a technology-based intervention, post-discharge follow-up or management, and empirical data. Overall, digital post-discharge interventions were reported to be feasible, with good engagement, adherence, compliance, and retention; low dropout rates; and positive patient satisfaction. However, mixed findings were reported regarding rehospitalisation rates and mental health outcomes for virtual care compared with those for traditional care. Digital health technologies might represent a promising step towards improving post-discharge health care and continuity of care for older adults.

Journal Article↗

PubMed2026

Effects of digital health-based exercise interventions on older adults with sarcopenia: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Sarcopenia, the progressive loss of muscle mass and function, impairs independence in older adults. Digital health exercise interventions are scalable solutions for older adults with sarcopenia. This systematic review and meta-analysis aimed to synthesize the evidence on their efficacy in populations with clinically diagnosed sarcopenia and identify influential intervention characteristics associated with treatment outcomes. METHODS: We systematically searched PubMed, EMBASE, Web of Science, Cochrane Library, CINAHL, CNKI, and Wanfang on May 20, 2026, with no date restrictions. We included randomized controlled trials involving adults aged ≥60 with sarcopenia receiving digital exercise interventions. Two reviewers independently screened studies, extracted data, and assessed risk of bias; analyses were performed using R and Review Manager. RESULTS: Fourteen trials (n = 927) were included. Digital interventions showed potential improvements in muscle mass (MD = 0.25, 95%CI:0.03-0.46, 95% PI:-0.36 to 0.85), muscle strength (MD = 2.14, 95% CI:1.18-3.11, P < 0.001), balance ability (SMD = 0.31, 95% CI:0.12-0.51, P = 0.001), walking performance (SMD = 0.55, 95% CI:0.21-0.89, 95% PI:-0.70 to 1.79), and physical function (SMD = 0.89, 95% CI:0.08-1.71, 95% PI:-2.33 to 4.12), but not quality of life (SMD = 0.08, 95% CI:-0.19 to 0.35, P = 0.53). Exploratory subgroup analyses suggested that factors such as supervision, program duration, and measurement tools may influence outcomes; however, formal tests for subgroup differences were generally non-significant, and consistent patterns across all metrics were not observed. CONCLUSION: Digital exercise interventions show potential for managing sarcopenia in older adults, though the very low to moderate certainty of evidence indicates that true effects may differ substantially from observed estimates. This review explores potential roles of intervention design, supervision, and multimodal delivery. Future research should adopt rigorous designs and longer follow-up to validate results and enhance clinical application. TRIAL REGISTRATION: PROSPERO CRD420251135174.

Humans↗

PubMed2026

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P=.003), injunctive norms (β=0.078; P=.009), perceived susceptibility (β=0.051; P=.03), perceived benefits (β=0.253; P<.001), and trust (β=0.33; P<.001), and negatively associated with perceived severity (β=-0.047; P=.049) and privacy concerns (β=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans↗

PubMed2026

Effects of Digital Mental Health Screening Alone and With the Online MINDBODYSTRONG CBT-Based Program on Burnout, Depression, Anxiety, Healthy Behaviors, and Suicidal Ideation in at-Risk Nurses at 3- and 6-Months Post-Intervention: An RCT.

BACKGROUND: Burnout and mental distress among nurses are global public health epidemics that adversely affect nurse well-being and healthcare quality. Evidence-based, scalable mental health interventions are urgently needed. AIMS: To evaluate the 3- and 6-month outcomes of a randomized controlled trial (RCT) comparing a psychologically safe, digital mental health screening and referral program alone versus the same screening and referral program combined with the video-based online MINDBODYSTRONG (MBS) cognitive behavioral therapy (CBT)-based skills-building program among nurses at risk for mental distress. METHODS: 501 nurses were recruited from professional organizations and healthcare systems across the United States by email and randomized to either mental health screening and referral (standard care) or standard care plus the MBS cognitive behavioral skills-building intervention (the intervention). All study activities were conducted remotely. Follow-up surveys administered at 3- and 6-months assessed anxiety, depression, suicidal ideation, burnout, healthy lifestyle beliefs, and healthy lifestyle behaviors using valid and reliable scales. RESULTS: Compared with the screening and referral only group, participants in the intervention group had greater reductions in anxiety and depression and significantly greater increases in healthy lifestyle beliefs and behaviors at 3 and 6 months post-intervention. After controlling baseline risk, the intervention group had a lower risk of suicidal ideation than the screening and referral group at 3 months (relative risk ratio [RRR] = 0.717; 95% CI: 0.320-1.606) and 6 months (RRR = 0.329; 95% CI: 0.101-1.072). The intervention group also had a significantly lower risk of burnout at 6 months (RRR: 0.698, 95% CI: 0.528, 0.929, p = 0.012). Nurses who completed more MBS sessions had less suicidal ideation at 6 months and those who completed more MBS skills-building activities had less burnout at 3 and 6 months. LINKING ACTION TO EVIDENCE: Integrating psychologically safe mental health screening combined with the scalable online CBT-based intervention, MBS, can produce sustained improvements in burnout, mental health symptoms, including suicidality, and healthy lifestyle beliefs and behaviors among nurses experiencing mental distress.

Humans↗

PubMed2026

From Infection Control to Healthcare System Resilience: Lessons Learned from SARS-CoV-2 Research in Healthcare Workers.

The COVID-19 pandemic placed unprecedented pressure on healthcare systems and exposed healthcare workers (HCWs) to biological hazards, organizational pressures, and psychological strain. Evidence generated during the emergency shows that HCW protection cannot rely on isolated measures, but requires an integrated framework combining epidemiological surveillance, contact tracing, infection prevention and control, vaccination, occupational health, and workforce support. Contact tracing helped identify occupational exposures and clarify how duration, proximity, and inadequate use of personal protective equipment jointly shaped infection risk. Subsequent studies of reinfection showed that susceptibility reflected the interaction of viral circulation, individual immunity, and vaccination status. Vaccination reduced the clinical impact of SARS-CoV-2 and supported service continuity, although uptake depended on trust, communication, and management of adverse event concerns. The pandemic also highlighted substantial economic consequences and a high burden of psychological distress and burnout among HCWs. Building on this evidence, future preparedness should translate these lessons into permanent, adaptable infrastructure rather than temporary emergency arrangements, integrating interoperable, AI-assisted surveillance capable of combining occupational, diagnostic, vaccination, and genomic data to detect emerging risks early, while ensuring robust data governance and human oversight. Equally central is the need to address long-term workforce vulnerabilities, including Long COVID, attrition, and burnout, through early identification, rehabilitation, flexible return-to-work models, and sustained psychosocial support. Achieving this requires structured multidisciplinary collaboration among occupational medicine, infection control, epidemiology, mental health, and digital health specialists, moving from fragmented infection-control protocols to an integrated, proactive, and learning-oriented preparedness strategy. Protecting HCWs is therefore not only an occupational safety priority but a foundational prerequisite for safe, equitable, and sustainable healthcare delivery during future infectious threats.

Humans↗

PubMed2026

Multicenter randomized effectiveness/implementation trial of a digital self-management support tool to improve the quality of life during adjuvant hormonal therapy for patients with early breast cancer: The HOPE trial.

BACKGROUND: For patients with hormone receptor (HR) positive early breast cancer (BC), adjuvant endocrine therapy (ET) represents the cornerstone of treatment. However, 75% of patients experience ET-related symptoms that negatively affect their quality of life (QOL). Despite their high prevalence, these symptoms are often underestimated and under-addressed during consultations. As a result, non-adherence to ET is common and remains a major barrier for optimal disease and survival outcomes. METHODS: National, prospective, randomized, open-label hybrid type 1 effectiveness/implementation trial conducted in France comparing a personalized digital health pathway plus standard of care (SoC) vs. SoC alone in patients with HR+ early BC reporting ET-related symptoms. 180 patients will be randomized 1:1 to receive either 12 weeks of the digital health pathway or 12 weeks of SoC. The intervention is anchored by the Resilience© digital companion including remote symptom and needs assessment, an introductory nurse-navigator phone call, and access to personalized, symptom-specific online educational and self-management programs (physical activity, yoga, meditation or cognitive behavioral therapy). In both arms, patients will be invited to wear a wearable device to objectively monitor behavioral parameters. The primary endpoint is the ET symptoms scale of the European Organization for Research and Treatment of Cancer (EORTC) QLQ-BR45 over 12-weeks. Secondary endpoints include other QOL domains, self-reported ET adherence, eHealth literacy, self-efficacy, and evaluation of the implementation process. DISCUSSION: This study should provide evidence on the effectiveness and real-world implementation of a personalized digital health pathway to improve QOL in patients experiencing ET-related symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT06781996; Protocol version 3.0.

Humans↗

PubMed2026

Digital Remote Assessment of Motor and Speech Changes in Amyotrophic Lateral Sclerosis: Longitudinal Observational Study.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with an active trial landscape that relies on the sensitivity of selected clinical trial endpoints. Traditional clinical outcome assessments perform well in trials but lack strong psychometric properties and may not detect small but clinically meaningful disease progression. Digital health technologies offer a promising alternative for tracking ALS disease progression. OBJECTIVE: This study assessed the feasibility of remote digital monitoring in ALS using a comprehensive battery of prescribed home-based assessments via a smartphone, a wearable device, and a computer-based mouse-clicking task. METHODS: Participants completed weekly remote assessments, including motor and speech tasks via a smartphone app and a computer mouse-clicking task for 24 weeks. They also participated in 3 remote telephone visits in weeks 1, 13, and 25. Reliability, minimal detectable change, and correlations with self-reported ALS Functional Rating Scale-Revised subdomain scores were calculated for 8 features across the speech, fine motor, and gross motor smartphone app tasks and for all 32 features from the computer mouse-clicking task. Sensitivity to longitudinal change was assessed for the 8 smartphone-derived features and for a representative subset of 8 computer mouse-clicking features. RESULTS: Forty-two participants (19 with ALS and 23 controls) completed 10,237 smartphone assessments and 459 computer mouse-clicking sessions. Baseline discriminative models differentiated ALS from controls with AUC values of 0.75-0.92. Digital measures correlated strongly with self-reported ALS Functional Rating Scale-Revised subdomain scores. Both participants with ALS and controls demonstrated improvement in fine motor and speech measures, with the exception of nondominant-hand pegboard performance, which declined in the ALS group. Improvements were smaller in participants with ALS, leading to increasing group differences over time, although only one feature showed a statistically significant separation over the 24 weeks. Gait and balance performance declined in both groups, with greater but nonsignificant separation observed for balance measures. CONCLUSIONS: These findings support the feasibility of digital remote assessments in ALS, demonstrate the ability to discriminate between ALS and controls based on certain features collected from speech, fine, and gross motor tasks, and in some cases, quantify functional decline over time. Further research is necessary to explore the natural history of these features longitudinally in larger cohorts of participants with ALS over extended periods to enable their potential integration into clinical trials.

Adult↗

PubMed2025

Urban mental health: a position paper of the European psychiatric association.

BACKGROUND: Urbanization, the shift of a growing population into urban areas, is shaping global development across infrastructure, health, and sustainability. Although it brings economic growth, innovation, and improved access to services, it may also impact mental health. METHODS: The present article was prepared on behalf of the European Psychiatric Association and explores the complexity of associations between urbanization and mental health, highlighting both potential risks and opportunities for improvement. RESULTS: Urban growth often leads to increased population density, social fragmentation, and environmental stressors, including noise, pollution, and reduced green spaces, all of which might account for worsening mental health. Urban residents might be at risk of various mental disorders due to these stressors, accompanied by the risk of social disconnection. Moreover, socioeconomic disparities in urban settings can lead to unequal healthcare access, further contributing to these challenges. However, urbanization also offers unique opportunities to improve mental health through better resource allocation, innovative healthcare solutions, and community-building initiatives. Indeed, cities might serve as areas for mental health promotion by integrating mental health services into primary care, utilizing digital health technologies, and fostering environments that promote social interactions and well-being. Urban planning that prioritizes green spaces, safe housing, and accessible public transportation holds the potential to mitigate some risks related to urban living. CONCLUSIONS: While urbanization presents significant challenges to mental health, it also provides grounds for transformative interventions. Addressing the mental health needs of urban populations requires a multifaceted approach that includes policy reform, community engagement, and sustainable urban planning.

Humans↗

PubMed2026

Self-selected goals outperform assigned goals in reducing mobile phone usage: Evidence from a randomized controlled trial.

Excessive smartphone use is increasingly recognized as a public-health concern, yet scalable approaches to help individuals regulate daily use remain limited. We examine whether allowing individuals to self-select reduction goals improves behavioral and psychological outcomes when incentives and average goal levels are held constant across conditions. In a twelve-week randomized controlled trial, (N = 149; over 9000 person-day observations), participants were assigned to (i) a self-selected condition (choosing a 10%, 20%, or 30% reduction in daily phone use), (ii) an assigned condition (assigned a 14% reduction goal), or (iii) a no-goal control condition. Participants who selected their own goals reduced phone use by 26 min more per day (73% larger reduction) and achieved their goals 11 percentage points more often than those assigned goals, despite identical incentives and average goal levels. Reductions in phone use and higher goal achievement were associated with improvements in perceived addiction, depressive, and anxiety symptoms. These psychological outcomes were secondary endpoints. Although the between-group estimates generally followed the same directional pattern as the behavioral outcomes, the sample size for these analyses was limited and the between-group differences were not statistically significant. These findings should therefore be interpreted with caution. Overall, the results provide causal field evidence that self-selection under this goal-setting design can improve behavioral outcomes. Allowing individuals to choose their own goals may strengthen engagement and support healthier digital behavior. Incorporating opportunities for goal-selection may represent a simple addition to digital-health and public-health interventions aimed at helping individuals moderate smartphone use and improve well-being.

Humans↗

PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗

PubMed2026

A Smartphone-Based Ecological Momentary Intervention for Workplace Mental Health: Randomized Controlled Trial.

BACKGROUND: Work-related stress has been widely associated with an increased risk of various mental disorders and poor mental well-being. The fast-growing mobile health services industry has provided new opportunities for workplace mental health. OBJECTIVE: This randomized controlled trial examined the effectiveness of Neurum (Neurum Limited), a smartphone-based intervention tool featuring ecological momentary assessments and interventions that aims to reduce workplace stress in real-time and real-world settings. METHODS: A total of 201 working adults were recruited for a 4-week smartphone-based intervention that incorporated cognitive behavioral therapy, mindfulness exercises, and self-regulation exercises delivered on Neurum. A simple randomization procedure was used. Participants in the intervention group were encouraged to log mood journals, complete mental health exercises, and provide user feedback whenever applicable. The key outcome was measured by the Depression, Anxiety, and Stress Scale-21 items (DASS-21; Cronbach α=0.87). RESULTS: The intervention group consisted of 102 participants, while the control group consisted of 99 participants. More participants dropped out from the intervention group (n=21) than from the control group (n=2; χ21=18.259; P<.001). The final sample consisted of 178 participants (male: 85/178, 47.8%; female: 93/178, 52.2%; mean age of 34.65, SD 7.67 y). Analyses revealed that after the 4-week intervention, the DASS-21 scores decreased in the intervention group (mean difference [MD]post-pre intervention=-14.518) but increased in the control group (MDpost-pre intervention=3.319; F1,176=59.358, P<.001; η2=0.252). This effect was largely led by stress reduction (F1,176=64.679, P<.001; for the intervention group, MDpost-pre intervention=-6.692, while for the control group, MDpost-pre intervention=2.000). On average, participants completed 6.27 (SD 9.4) exercises and provided 9.74 (SD 18.2) mood journal logs, with a daily engagement of 4.95 (SD 6.89) minutes. However, the associations between the changes in DASS-21 scores and the numbers of exercises or mood journal logs did not reach statistical significance. CONCLUSIONS: This study primarily established the effectiveness of Neurum in alleviating depression, anxiety, and stress symptoms in noninstitutionalized working adults, with a satisfactory user retention rate. Despite potential health-related culture differences, Neurum contributed to evidence-based digital health in nonclinical settings for timely needs and general accessibility as an alternative to traditional, face-to-face, and high-cost mental health services. Future directions involving a personalized approach in online mental health services were discussed.

Humans↗

PubMed2026

Facilitating Thought Progression via a Gamified Mobile Application for Depression: Possible Mediators of Outcomes.

Mobile health interventions represent a scalable and accessible alternative to traditional therapy, which often is out of reach due to high costs and societal stigma. Rumination is considered a key mechanism of emotional disorders and represents a potential treatment target for digital health interventions. The current study investigated the role of rumination as a mediator of the reduction in depression and anxiety reported after the use of a gamified mobile app based on the Facilitating Thought Progression (FTP) framework. One hundred-one adults with mild to moderate depression were randomized to the FTP intervention or a waitlist control group and completed weekly assessments of depression, anxiety, and rumination over 8 weeks. Multilevel structural equation modeling revealed that reduction in rumination significantly mediated decreases in depression and anxiety in the intervention group but not in the waitlist condition. These findings suggest that the FTP app targeted rumination and further highlights its role as a critical target for interventions for depression and anxiety.

Adult↗