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PubMed2026

Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well-Being.

BACKGROUND: The pervasive use of social media has created a complex digital ecosystem where high connectivity coexists with significant challenges, including the rapid spread of misinformation, particularly regarding mental health, and documented negative impacts on psychological well-being. Platform architectures designed for engagement maximization have been identified as central factors in both issues. OBJECTIVE: This paper critically analyzes the interconnected relationships between social media use, misinformation dissemination, and mental health impacts, with particular attention to psychiatric misinformation across diagnostic categories (e.g., depression, anxiety, ADHD). A primary objective is to evaluate the potential of advanced critical digital literacy frameworks to serve as protective mechanisms against these dual threats. METHODS: A systematic search was conducted following PRISMA 2020 guidelines across APA PsycInfo, PubMed, JSTOR, and Google Scholar for literature published between January 2018 and March 2026 (updated from the original 2023 search). The search yielded 2672 records. After removing 624 duplicates, 2048 records underwent title and abstract screening, with 1802 excluded. The remaining 246 full-text articles were assessed for eligibility, resulting in 86 studies included in the final qualitative synthesis. Inter-rater reliability was established (Cohen's κ = 0.82). Quality assessment was conducted using the Joanna Briggs Institute Checklist, AXIS, and CASP tools, with findings weighted by methodological quality. A thematic analysis was undertaken to synthesize findings. RESULTS: The analysis reveals that core architectural features of social media platforms, algorithmic curation and engagement-based metrics, simultaneously foster environments ripe for misinformation spread and contribute to psychological distress, including anxiety, depression, and harmful social comparison. Psychiatric misinformation specifically (e.g., inaccurate claims about treatment effectiveness, diagnostic criteria, and medication side effects) represents a growing concern, particularly on image- and video-based platforms. The findings indicate that conventional media literacy approaches focused solely on fact-checking are insufficient. Instead, a critical digital literacy framework encompassing algorithmic awareness, data literacy, and emotional awareness is essential for building user resilience, with evidence from high-quality systematic reviews supporting this approach. CONCLUSIONS: Navigating the complexities of modern social media requires an integrated approach combining "pedagogies of play" for experiential skill development with advocacy for structural change (e.g., algorithmic transparency, well being by design principles). This dual strategy empowers individual users to critically engage with digital content while advocating for ethical platform design, thereby safeguarding both mental well-being and democratic discourse. Implications for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), policymakers, and platform designers are discussed.

Humans↗

PubMed2026

A pragmatic randomized controlled trial of self-directed online writing interventions for posttraumatic stress symptoms in a real-world digital setting.

Background: Public health and other large-scale crises, such as the COVID-19 pandemic, have intensified the global mental health burden, creating unprecedented demand for accessible interventions for posttraumatic stress symptoms (PTSS).Objective: We evaluated the feasibility and effectiveness of two self-directed online writing interventions embedded within China's WeChat ecosystem during the COVID-19 pandemic through a pragmatic randomised controlled trial.Methods: Between December 2021 and August 2022, 1,526 adults were screened for PTSS via a Tencent Medinfo Mini-Program. Eligible participants (n = 211) were randomised to Guided Narrative Technique-Writing (GNT-W, n = 100) or Expressive Writing (EW, n = 111). Both interventions comprised three self-directed daily writing sessions delivered entirely online without human support. Primary outcome was PTSD symptom severity (PTSD Checklist-Short), assessed at baseline, post-intervention, 2-week, and 1-month follow-ups.Results: While initial engagement followed typical digital health patterns (64.5% overall attrition), participants who initiated treatment showed strong adherence (77% completion). Both interventions were associated with significant within-group reductions in PTSS severity (GNT-W: b = -0.43, p = .023, d = -0.43; EW: b = -0.60, p = .001, d = -0.58), with no significant between-group difference (group × time: b = 0.18, p = .48). GNT-W did not confer additional benefit over EW protocol on PTSS severity.Conclusions: Both self-directed writing interventions were associated with within-group reductions in PTSS; without an inactive control condition, however, these changes cannot be firmly attributed to the interventions. GNT-W showed no advantage over the simpler EW protocol. These findings offer preliminary support for embedding scalable, low-barrier writing interventions in widely used digital platforms.Chinese Clinical Trial Registry: ChiCTR2000034836.

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

Post-intervention effectiveness of a computerized personalized cognitive stimulation program adapted according to cognitive reserve in older adults without cognitive impairment in Primary Care: A randomized clinical trial.

BACKGROUND: Cognitive reserve may influence responsiveness to cognitive interventions, yet it is rarely used to tailor computerized stimulation. OBJECTIVE: To evaluate the effectiveness of a computerized cognitive stimulation program personalized according to cognitive reserve on cognition, reserve-related activities, and digital competence in community-dwelling older adults without cognitive impairment in Primary Care. METHODS: In this randomized clinical trial, 102 adults aged ≥65 years with normal cognitive performance were recruited from three primary care centers in Zaragoza, Spain, and stratified by cognitive reserve level before random allocation to intervention or control. The intervention comprised digital literacy sessions followed by 8 weeks of home-based computerized cognitive stimulation tailored to participants' cognitive reserve profiles and life history. Controls received a single group-based health education session focused on maintaining everyday cognitive activity. Outcomes were assessed at baseline and post-intervention using global cognition (MEC-35), the Cognitive Reserve Questionnaire, the Mobile Device Proficiency Questionnaire-16, and domain-specific neuropsychological tests. A total of 100 participants completed the final evaluation and were included in complete-case analyses. RESULTS: Compared with controls, the intervention group showed greater adjusted post-intervention improvements in global cognition (MEC-35 between-group difference: 1.8 points) and several cognitive measures, including temporal orientation, calculation, attention, praxis, verbal fluency, processing speed, executive functions, and verbal learning. CRQ scores and digital competence also improved, with small-to-large effect sizes. CONCLUSIONS: A computerized cognitive stimulation program adapted according to cognitive reserve appears feasible in Primary Care and may improve cognition, engagement in reserve-related activities, and digital competence in older adults without cognitive impairment.

Humans↗

PubMed2026

USleep: efficacy of app-based audio interventions to improve sleep disturbance in working adults, a multi-arm randomized controlled trial.

STUDY OBJECTIVES: To evaluate the efficacy of three categories of standalone, audio-based sleep interventions (Bedtime Stories, Sleep Sounds, Sleep Skills) delivered via mental health application (MHapp) in improving sleep among working adults with sleep disturbance. METHODS: A multi-arm, parallel randomized controlled trial was conducted. Adults with self-reported sleep disturbances were recruited online and randomly allocated to Bedtime Stories, Sleep Sounds, Sleep Skills, or digital control. Participants completed self-report questionnaires on sleep disturbance and other related outcomes at baseline (t0) and after the 4-week intervention (t1). The primary analysis followed an intention-to-treat approach using mixed-effects models. RESULTS: A total of 495 working adults (mean age = 32.7 years; 55.8% female) were randomized. For sleep disturbance (primary outcome), the between-group Hedges' g effect sizes were very small and not statistically significant (Bedtimes stories vs. control: g = 0.12, 95% CI -0.13 to 0.37, Sleep Sounds vs. control: g = 0.14, 95% CI -0.11 to 0.39, Sleep Skills 0.07, 95% CI -0.07 to 0.29), with slightly greater reductions in sleep disturbance for the intervention groups than control. The same pattern was observed for sleep-related impairment, mental health, well-being, and pre-sleep arousal. CONCLUSION: Audio-based sleep interventions delivered via a MHapp did not demonstrate superior efficacy over a digital control condition in reducing self-reported sleep disturbance among working adults. Although safe and well-tolerated, their use as standalone treatments for sleep disturbance is not supported by these findings. Future research should explore effectiveness in real-world settings, including user content choice across categories, and use objective sleep measures. CLINICAL TRIAL REGISTRATION: Registered at https://www.isrctn.com/ under "Evaluating the efficacy of audio-based digital tools to improve sleep on the Unmind workplace well-being platform"; https://www.isrctn.com/ISRCTN13426045; registration number: 13426045.

Humans↗

PubMed2026

Effect of brewers' yeast or beta-glucan derived from Saccharomyces cerevisiae on breast milk supply following preterm birth: the BLOOM randomised controlled trial.

OBJECTIVE: Breast milk is the optimal source of nutrition for preterm infants; however, low breast milk production is common following a preterm birth. This study aimed to determine if taking brewers' yeast or beta-glucan improves daily expressed breast milk volume. DESIGN: Randomised, blinded, parallel, placebo-controlled trial. SETTING: Three Australian tertiary-level neonatal units. PATIENTS: Mothers with a singleton or twin pregnancy who gave birth at <34 weeks' gestation. INTERVENTIONS: Mothers were randomised within 72 hours of birth into three parallel groups in a 1:1:1 ratio to receive either brewers' yeast, beta-glucan or placebo capsules for 7 days. MAIN OUTCOME MEASURE: Total expressed breast milk volume over a 24-hour period on day 7 of intervention. RESULTS: A total of 105 mothers underwent randomisation between August 2022 and April 2024 (36 brewers' yeast, 35 beta-glucan and 34 placebo). The adjusted mean difference in daily expressed breast milk volume was 94 mL/day (95% CI -51 mL/day to 239 mL/day) between the brewers' yeast and placebo groups and -25 mL/day (95% CI -173 mL/day to 123 mL/day) between the beta-glucan and placebo groups. Maternal side effects were similar across groups. CONCLUSION: We found no clear effect of short-term administration of brewers' yeast or beta-glucan on breast-milk production following preterm birth; both interventions were well tolerated. Given the small sample size, these findings do not rule out the possibility of a clinically meaningful benefit of brewers' yeast and suggest further research with a larger sample size may be warranted to clarify the potential clinical impact. TRIAL REGISTRATION NUMBER: ACTRN12622000968774.

Intensive Care Units, Neonatal↗

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

Association of media use with sleep of children and adolescents: an umbrella review.

Adequate sleep is essential for child and adolescent development, driving extensive research across scientific disciplines. This umbrella review provides a comprehensive overview of existing evidence on media consumption and sleep and thereby lays the foundation for identifying key concepts and gaps. An inclusive systematic search for reviews reporting literature searches was conducted in 02/2021 and updated last in 09/2024. Methodological quality of the included reviews was assessed using AMSTAR-2. We included 84 reviews reporting on the association between media use and sleep in individuals aged 0-18 years. The field is dominated by reviews of low methodological quality, mainly including original cross-sectional studies with subjective measures in older children and adolescents. A total of 475 original articles were covered by the reviews; only 10 of them appeared in at least seven and at most nine reviews. Screen time generally had a negative impact on sleep, though evidence varied from very low to strong. Evidence for effects of conventional books on sleep remains inconclusive. High quality systematic reviews are needed to evaluate robust studies using objective measures of sleep and contemporary media use across all age groups up to adolescence and to explore the impact of non-digital media use on sleep, considering age and gender differences.

Humans↗

PubMed2026

Nurse-Led Home-Based Mobile Health Cardiac Rehabilitation Program for Patients With Chronic Heart Failure: A Randomized Controlled Trial.

This 12-week randomized controlled trial evaluated a nurse-led mHealth intervention for patients with chronic heart failure, conceptually informed by Riegel's middle-range theory of self-care of chronic illness. The program integrated wearable activity tracking with weekly nurse-led behavioral coaching, reflecting the core self-care processes of monitoring, maintenance, and management. Compared with usual care, the intervention significantly improved daily step count, 6-minute walk distance, metabolic equivalents, and left ventricular ejection fraction. Findings highlight the effectiveness of theory-informed, nurse-delivered mHealth strategies in enhancing physical activity and cardiopulmonary function, while underscoring the critical role of advanced practice nurses in home-based chronic disease management.

Aged↗

PubMed2026

Seventeen-year follow-up of hypophosphatasia diagnosed in middle-aged siblings harboring a novel intronic and a rare missense ALPL gene mutation.

Hypophosphatasia (HPP) is the rare inborn-error-of-metabolism that features impaired mineralization of the skeleton and teeth due to a deactivating mutation or mutations of the gene ALPL which encodes the tissue-nonspecific isoenzyme of alkaline phosphatase (TNSALP). We report 17-year follow-up of twin sisters and a brother referred in middle-age for painful proximal femoral "stress fractures" and then diagnosed with HPP. They reported generalized muscle and bone pain, metatarsal fractures, arthropathy and, since childhood, tooth loss. Their concordant findings were explained by compound heterozygosity in ALPL for a rare maternal missense mutation (c.1403C > T, p.Ala468Val) in exon 12, together with a novel presumably paternal change (c.863-14G > A) predicting a cryptic mRNA splice site in intron 8. Fractures continued during follow-up until one sister received a three-and-one-half-year course of hydroxyapatite-targeted TNSALP supplementation therapy (asfotase alfa) during which substantial improvement occurred in her clinical, biochemical, and functional parameters as well as quality of life. Following subsequent unplanned treatment cessation she suffered significant clinical deterioration, including new fractures and loss of mobility. Her bone histopathology documented osteomalacia. Treatment resumption restored its benefits. Among ten asymptomatic family members evaluated in this four-generation kindred, eight were carriers heterozygous for either ALPL mutation. Those harboring the maternal missense defect manifested mild hypophosphatasemia, suggesting a dominant-negative mutation effect. This experience underscores the importance of in-depth phenotyping and then clinical follow-up to characterize ALPL variant combinations, and for maintaining effective asfotase alfa treatment.

Humans↗

PubMed2026

Effectiveness of Embedded Social Media Content on E-cigarette Attitudes and Behaviors: Results from a Randomized Control Trial.

BACKGROUND: This longitudinal randomized controlled trial examined the effects of anti-vaping social media content on e-cigarette attitudes and intentions among U.S. young adults (ages 18-24; n=3,400). METHODS: Targeted content was embedded directly into participants' social media feeds, with varying levels of impressions. RESULTS: Results indicate that increased exposure to anti-vaping messages significantly elevated perceived risk of harm (β=0.11, 95% CI: 0.03-0.20, p < .01) and social unacceptability of e-cigarette use (β=0.10, 95% CI: 0.03-0.18, p < .01), while decreasing intentions to vape (RRR = 0.59, 95% CI: 0.36-0.97, p < .05). Notably, these attitudinal shifts occurred even with relatively low ad exposure and over an extended intervention period, while controlling for the e-cigarette use status (never, former, or current) of participants. Discussion These findings support the effectiveness of digital media campaigns in influencing health-related behaviors and attitudes among young adults. Specifically, embedding anti e-cigarette content directly in the feed of young adults is shown to be effective in shifting attitudes in a space where these young adults are already engaged. Limitations include limited ad impressions and minimal change in ad awareness recall, suggesting future research should explore longer interventions and broader nicotine product messaging.

Humans↗

PubMed2026

Routine methods misidentify Serratia spp.: Limitations of MALDI-TOF MS revealed by whole-genome sequencing.

Accurate species-level identification within the genus Serratia remains challenging due to extensive phenotypic overlap and high genomic relatedness among closely related and recently described taxa. This study presents an evaluation of routine and genome-based identification approaches applied to clinical Serratia isolates, integrating phenotypic assays, MALDI-TOF MS (Bruker Daltonics), 16S rRNA gene sequencing, and Whole-Genome Sequencing (WGS). A total of 103 isolates collected from a teaching hospital were analyzed. WGS was performed on a subset of isolates. Conventional biochemical methods classified all isolates as Serratia marcescens, whereas MALDI-TOF MS identified 60.1% as S. marcescens, 11.6% as S. ureilytica, and 28.1% just at the genus level. Peak analysis from MALDI-TOF MS revealed specific peaks associated with S. marcescens and S. ureilytica, but limited discriminatory power. WGS of six isolates initially identified as S. ureilytica by MALDI-TOF MS revealed reclassification as Serratia sarumanii (n = 5) and Serratia montpellierensis (n = 1), supported by Average Nucleotide Identity (ANI), Average Amino Acid Identity (AAI), and Digital DNA-DNA Hybridization (dDDH) thresholds. In contrast, 16S rRNA analysis showed limited species-level resolution. Phylogenomic and SNP-based analyses confirmed these classifications with strong support. Overall, this study underscores the critical role of high-resolution genomic approaches for precise species identification and highlights the need for continuous expansion and curation of MALDI-TOF MS reference databases to support reliable clinical diagnostics and epidemiological surveillance of emerging Serratia species.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Mobile health apps improve Health-Related Quality of Life in Type 2 Diabetes Mellitus by enhancing medication adherence: A multicentre randomised controlled trial with mediation analysis.

AIMS: This study evaluated whether a gamified mHealth application (CareAide®) improves Health-Related Quality of Life (HRQoL) in Type 2 Diabetes Mellitus (T2DM) and whether this effect is mediated by medication adherence. METHODS: Prespecified secondary analysis of the T2DM cohort from a 6-month multicentre RCT (NCT06068309; N = 663; three Malaysian hospitals). Participants were randomised 1:1 to standard care or CareAide®. Adherence (MMAS-8), EQ-5D-5L utility (Malaysian value set), and AQoL-6D were assessed at baseline and 6 months. Simple mediation analysis (PROCESS Model 4; 5000 bootstraps) adjusted for baseline HRQoL. RESULTS: CareAide® significantly predicted higher MMAS-8 scores (mean difference +1.756; d = 1.638; p < 0.001). Higher MMAS-8 scores significantly predicted improved AQoL-6D utility (b = 0.024; p < 0.001). The direct effect on AQoL-6D was non-significant (p = 0.248). Bootstrapped indirect effect confirmed full mediation via AQoL-6D (0.042; 95% CI [0.024, 0.060]). A sensitivity analysis adjusting for baseline HbA1c confirmed full mediation (indirect = 0.034; 95% CI [0.015, 0.052]; n = 563). EQ-5D-5L utility showed a significant direct between-group difference at 6 months (p = 0.012) but did not operate as a mediation outcome. CONCLUSIONS: Medication adherence fully mediates the AQoL-6D HRQoL benefit of a gamified mHealth intervention in T2DM, as confirmed by both the primary and HbA1c-adjusted sensitivity analyses. These findings support integration of behaviourally informed digital adjuncts into routine primary diabetes care.

Humans↗

PubMed2026

Use of wearable technologies for physical activity promotion in older adults: A systematic review.

This systematic review, conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251055299), examined the use of wearable technologies for promoting physical activity (PA) in adults aged 60 years and older. Searches across five databases (PubMed, Scopus, Web of Science, CINAHL, Cochrane) identified 2438 records, of which only six randomized controlled trials published between 2021 and 2025 met inclusion criteria, with sample sizes ranging from 36 to 551 participants and mean ages between 65 and 79 years. Given the small number of included studies, findings should be interpreted as preliminary. The studies ranged from the standalone use of commercial trackers (Fitbit, Polar, ActiGraph) to multicomponent interventions combining wearables with physiotherapist feedback, telephone counseling, web-based platforms, or interactive cognitive-motor training. Wearables used alone, as in the REACT trial, produced small or non-significant PA effects. In contrast, interventions integrating devices with personalized feedback, professional support, or digital platforms, such as PROMOTE and TASMANIA, were associated with more consistent improvements in PA, physical function, and cognitive outcomes. Multicomponent programs, such as PEER and ICMT, reported broader benefits, including cognition, balance, and reductions in sedentary behavior, though these findings derive from individual trials and require replication. Risk of bias, assessed with the Cochrane Risk of Bias tool version 2 (RoB 2.0), was rated as "some concerns" for five studies and low for only one, mainly due to gaps in randomization reporting, missing data, and lack of preregistration. Tentatively, and based on a very limited evidence base, wearables may have greater impact when embedded within broader behavioral systems, incorporating feedback, coaching, or interactive components, rather than when used in isolation as passive monitoring tools. Adherence and psychosocial outcomes appeared related to comfort and perceived usefulness among older adults, though larger and more robust trials are needed to confirm these patterns.

Humans↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Teaching Acute Coronary Syndrome High-Risk ECG Interpretation and Clinical Decision-Making Through FOAMed Videos and Podcast Versus Print-Based Materials Among Emergency Care Providers: Randomized Controlled Mixed Methods Trial.

BACKGROUND: Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)-related patterns, and interprofessional emergency care underrepresented. OBJECTIVE: This study aimed to compare the effectiveness of FOAMed podcast and videos versus traditional print-based materials for teaching high-risk ACS ECG patterns and related clinical decision-making in emergency providers. METHODS: We conducted a prospective, interprofessional, controlled mixed methods trial across 5 training sites in Germany. Paramedics, prehospital emergency physicians, and emergency department clinicians received either a FOAMed multimedia module or print-based materials through concealed allocation; deviations from the intended 1:1 ratio resulted from participant no-shows. The intervention consisted of a 30-minute supervised self-learning session. In total, 103 participants were allocated to FOAMed (n=45) or print-based materials (n=58). Two coprimary outcomes were assessed: ECG interpretation accuracy and text-based ACS clinical decision-making. Secondary outcomes included subjective confidence, learning experience, and exploratory qualitative free-text responses. Outcome assessment was automated and blinded; mixed ANOVA was the primary analysis. The study was not prospectively registered because it assessed educational outcomes in health care professionals rather than patient health outcomes. RESULTS: All 103 participants completed the study. Both groups improved, with greater gains in the FOAMed group: ECG interpretation increased from 55% to 65.5% and text-based ACS clinical decision-making from 45% to 68%, versus 57% to 60% and from 47% to 63%, respectively, in the print-based group. Effect sizes were η²=0.055 for ECG interpretation and η²=0.044 for clinical decision-making. Exploratory subgroup analyses provided no evidence of differential effects across age, gender, or professional background and were likely underpowered. Qualitative responses (46 and 37 entries) provided contextual insights into perceived clarity, engagement, and practical relevance supporting the quantitative findings. CONCLUSIONS: This study is innovative in directly comparing a curated FOAMed multimedia module with selected print-based materials in an interprofessional emergency care population. It differs from existing research by focusing on subtle, emerging ischemic patterns and evaluating realistic, time-limited self-learning formats. The findings provide evidence that curated FOAMed resources can produce greater short-term improvements in ECG interpretation and text-based ACS clinical decision-making than traditional print-based materials in this setting. Although implications for clinical performance remain hypothetical, concise, high-quality digital modules may represent a practical supplement to structured continuing education in emergency care.

Humans↗

PubMed2026

Assessing perinatal depression identifying abilities among maternal and child health workers in rural China using smartphone-based virtual patients: a multi-center cross-sectional study.

OBJECTIVE: To assess rural maternal and child health (MCH) workers' virtual patients (VPs)-assessed performance in identifying perinatal depression (PND) using smartphone-based VPs, and to identify factors associated with this performance in rural Hunan, China. METHODS: A multicentre cross-sectional study was conducted in Hunan Province, China. A standardized questionnaire collected demographic and work-related characteristics of rural MCH workers. Smartphone-based VPs were used to assess PND identification performance in a simulated clinical scenario. An overall score ≥60 was used as a prespecified operational benchmark across consultation, ancillary assessment, diagnosis, management, and health education domains. Data were analyzed using SPSS 26.0. RESULTS: A total of 375 rural MCH workers participated, yielding an effective response rate of 90.4%. Only 25.9% met the prespecified operational benchmark for VP-assessed PND identification performance. The mean accuracy scores for consultation, ancillary assessment, diagnosis, management, and health education were 94%, 48%, 64%, 58%, and 74%, respectively. Complete consultation accuracy was higher among MCH workers from township health centers than among those from county-level MCH hospitals. MCH workers aged 18-39 years showed higher odds of complete diagnostic accuracy for PND than those aged ≥40 years. CONCLUSIONS: Smartphone-based VP assessment was feasible in rural MCH settings and revealed suboptimal PND identification performance. Mobile VPs may help identify frontline performance gaps and inform targeted training, but further validation against real-world clinical performance, or standardized patient encounters is needed before large-scale implementation. These findings may support targeted capacity-building for rural MCH workers and more equitable perinatal mental health care.

Humans↗