Jul 2026· Journal of Medical Internet Research· Vol 28, pp. e93779-e93779· 0 citations· 72 references
Medicine
TL;DR
Unlike existing reviews prioritizing algorithmic performance metrics over usability, clinical workflow integration, and patient trust, this study systematically maps these essential sociotechnical factors and innovatively applies the HCAI framework to the sleep AI lifecycle.
Abstract
Abstract Background Sleep disorders represent a significant public health burden associated with cardiovascular and neurocognitive morbidities. While AI technologies offer potential for personalized sleep medicine, clinical integration remains limited. This translational disparity is often attributed to a lack of human-centered design, specifically insufficient stakeholder engagement in the development and implementation of these technologies. Current research frequently prioritizes algorithmic performance over usability and patient trust. Objective This scoping review systematically maps the extent and nature of human-centered AI (HCAI) research within sleep medicine across different AI modalities, evaluating how diverse stakeholders are involved in the design, validation, and implementation of AI tools, including patients, clinicians, and technologists. Methods Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched 8 databases (PubMed, Web of Science, Embase, Scopus, IEEE Xplore, ACM Digital Library, APA PsycINFO, and CINAHL) for literature published up to June 18, 2026. We identified primary research describing the design, development, or evaluation of AI technologies for sleep health with explicit human-centered components. Included studies (n=34) were categorized based on AI technology type and the method of stakeholder engagement. Data were extracted and synthesized using a thematic analysis approach. Results Based on the included studies, the analysis reveals an uneven distribution of research focus across technological domains as descriptive patterns rather than definitive trends. Research on generative AI (GenAI) is predominantly restricted to downstream expert auditing of output accuracy (comprising 7/11, 64% of GenAI studies), with a noticeable gap in upstream participatory design involving patients. Conversely, deep learning research primarily focuses on technical explainable AI methods to address algorithmic opacity for clinicians, yet lacks progression to real-world clinical implementation. Mobile health and wearable technologies (17/34, 50%) demonstrate the most balanced HCAI ecosystem, evidencing a complete translational cycle from upstream co-design to downstream clinical implementation. Furthermore, an emerging trend is observed where AI is evolving from an automated diagnostic tool into an interactive therapeutic agent, with recent studies indicating that lay users may perceive responses from large language models as more empathetic than those from physicians. Conclusions Lacking formal quality appraisal, our findings reflect research activity patterns rather than confirmed clinical effectiveness. Nevertheless, this scoping review innovatively applies the HCAI framework to the sleep AI lifecycle. Unlike existing reviews prioritizing algorithmic performance metrics over usability, clinical workflow integration, and patient trust, this study systematically maps these essential sociotechnical factors. It contributes to the field by revealing distinct methodological disparities and the urgent need for upstream participatory design, particularly for GenAI. In the real world, establishing standardized protocols for human-AI interaction, ensuring algorithmic transparency, and addressing demographic biases are essential to foster the clinical trust required for effective AI adoption.
This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.
Haoran Chen, Shenglan Xiao, Tong Wan et al.· Journal of Medical Internet...· 0 citations
BACKGROUND
Mental health disorders contribute to disability worldwide. Chronic stress can lead to cardiovascular diseases, metabolic syndrome, and immune dysfunction, posing a major public health challenge. Meditation-based approaches improve stress resilience and overall wellbeing. Sahaj Samadhi Meditation (SSM) is a mantra-based meditative practice, with a limited but emerging evidence base. This scoping review aimed to summarise the existing evidence.
METHODS
Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute methodology (JBI), we searched PubMed, PsycINFO, Web of Science, and Scopus (from inception to July 2026), along with clinical trial registries. Studies assessing SSM as the primary intervention were included. Screening and data extraction were conducted independently by two reviewers. The protocol was registered in the Open Science Framework (https://osf.io/2rckd).
RESULTS
Eleven studies were included: five experimental studies, one community survey, two clinical intervention studies, and three registered protocols. Experimental studies showed consistent differences in attentional processing, perceptual stability, and neurophysiological markers, including higher frontal theta coherence and increased mismatch negativity responses among SSM practitioners. Patients with glaucoma and chronic pain reported modest improvements in sleep quality and depressive symptoms. Protocols evaluate late-life depression, opioid-related outcomes and chronic pain in SSM practitioners. All studies were small with substantial heterogeneity.
CONCLUSIONS
SSM may influence psychological, neurophysiological, and cognitive parameters. However, these findings are based on small samples with narrow evidence base. Large trials with standardised protocols are required to recommend SSM for clinical use or integrate into community-based health programmes.
Archana Balasubramaninan, S. Varadarajan, Mathangi Damal Chandrasekar et al.· Complementary Therapies in M...· 0 citations
BACKGROUND
Wearable devices are increasingly used in cardiovascular rehabilitation, rhythm monitoring, and long-term self-management. Although clinical benefits have been widely discussed, less attention has been paid to potential negative psychological and behavioral effects. This scoping review aimed to map evidence on wearable device-related psychological and behavioral effects in cardiovascular patients and summarize implications for nursing assessment and patient education.
METHODS
A scoping review was conducted in accordance with the Joanna Briggs Institute methodology and the PRISMA Extension for Scoping Reviews. PubMed, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure, Wanfang, VIP, and SinoMed were searched for studies published from January 2014 to April 2026. Titles, abstracts, and full texts or complete available publication records were screened and charted. Records without retrievable full text or sufficient information for standardized data charting were excluded and documented. Data were synthesized using descriptive and thematic approaches.
RESULTS
A total of 110 publications were included after revised eligibility and data-charting verification. Four categories of negative psychological and behavioral effects were identified: health anxiety and heightened symptom perception, alert-related concerns and repeated reassurance seeking, excessive self-monitoring and device dependence, and impaired self-management confidence or patient experience. Preliminary nursing assessment clues were summarized for patients using wearable devices in cardiovascular care, including fear of rehabilitation exercise, catastrophic interpretation of symptoms, device dependence with weakened adherence to medical advice, and excessive reassurance seeking.
CONCLUSIONS
Wearable devices may support cardiovascular rehabilitation and self-management, but continuous monitoring, abnormal alerts, data inaccuracies, and insufficient interpretation may also contribute to worry, avoidance, dependence, or repeated reassurance seeking in some patients. Because direct post-PCI evidence remains limited, the nursing assessment clues proposed in this review should be interpreted as preliminary, evidence-informed prompts rather than validated screening tools.
Abstract Background AI adoption in health care has accelerated rapidly, with ambient documentation tools, diagnostic imaging AI, and clinical decision support systems (CDSSs) entering routine practice. However, the cognitive demands placed on clinicians supervising these systems remain understudied. Specifically, the concept of verification burden requires closer examination. Consequently, institutional decision-makers lack a structured, certainty-graded evidence base regarding the true impact of AI on clinician workload and burnout. Objective This study aimed to systematically review evidence on cognitive workload and burnout in health care professionals that use AI-powered clinical tools, quantify pooled effects under a conservative inferential framework, and assess certainty of evidence by AI category. Methods The study was registered in PROSPERO (CRD420261284298) and reported per PRISMA 2020 and PRISMA-S guidelines. We searched MEDLINE, Embase, Web of Science, and Cochrane CENTRAL (January 2015-2026) for studies measuring cognitive workload or burnout using validated instruments (NASA Task Load Index [NASA-TLX] and Professional Fulfillment Index [PFI]) among health care professionals using clinical AI. Risk of bias was assessed using ROB 2.0 and ROBINS-I; certainty was rated using GRADE. Meta-analyses applied Hartung-Knapp-Sidik-Jonkman adjustment with restricted maximum likelihood estimation, incorporating prediction intervals (PIs). Results We included 21 studies representing 2885 health care professionals across 7 countries. The synthesis demonstrated that the cognitive impact of clinical AI varies according to its specific application. Pooled analyses of ambient AI documentation showed statistically significant reductions in NASA-TLX temporal demand (SMD −1.46, 95% CI −2.81 to −0.11; k=2; I2=31.1%) and effort (SMD −1.29, 95% CI −2.16 to −0.42; k=2; I2=0%), PFI work exhaustion (MD −0.35, 95% CI −0.58 to −0.12; k=3; I2=0%; 95% PI −1.03 to 0.33), and burnout prevalence (OR 0.47, 95% CI 0.25-0.86; k=3; I2=0%; 95% PI 0.06-3.82). Two pools favored ambient AI but did not reach significance at k=2: NASA-TLX mental demand (SMD −1.29, 95% CI −3.64 to 1.07) and documentation time (SMD −0.24, 95% CI −1.10 to 0.61). Diagnostic imaging AI and CDSS showed mixed or paradoxically increased workload. GRADE certainty was moderate for cognitive workload reduction with ambient AI, low for burnout reduction with ambient AI, and very low for imaging AI and CDSS outcomes. Conclusions This review combines validated workload instruments, meta-analysis, and PIs in health care AI, delivering a GRADE certainty assessment across 5 AI categories that prior accuracy- or efficiency-focused reviews have not provided. Ambient AI documentation was associated with reduced cognitive workload and burnout, but only in voluntary early-adopter cohorts and based on few studies; the conservative CIs were wide and, where estimable, PIs crossed the null. Findings inform institutional pilots with prospective workload measurement, regulatory human-factors evaluation of AI medical devices, and human-centered AI design. Net benefit on the health care workforce remains an open empirical question.
Eun Jeong Gong, Chang Seok Bang, J. Lee· Journal of Medical Internet...· 0 citations
BACKGROUND
Obesity is a major public health challenge worldwide, driving chronic disease and health care costs. Primary health care (PHC) is a key setting for prevention, early identification, and management of obesity, yet implementation of evidence-based interventions remains limited. Identifying the barriers and enablers that influence adoption of obesity care in PHC is critical to strengthen delivery.
OBJECTIVE
This scoping review will systematically map and synthesize barriers and enablers to implementing obesity interventions in PHC, considering behavioral, nutritional, pharmacological, digital, and organizational approaches, across both adult and pediatric populations.
METHODS
The review will follow the Joanna Briggs Institute (JBI) methodology and be reported according to PRISMA-ScR guidelines. Searches will be conducted in PubMed, Cochrane Library, Scopus, Web of Science, and ScienceDirect, complemented by gray literature from OpenGrey, ResearchGate, Google Scholar, and organizational websites. Eligible sources will include peer-reviewed and gray literature published between 2019 and 2025 in English, French, German, Italian, Portuguese, Spanish, or Turkish. Two reviewers will independently screen, extract, and analyze data, with discrepancies resolved by consensus. Data will be synthesized descriptively and thematically, supported by NVivo, and results presented in tables, thematic maps, and conceptual diagrams. Expected Results Anticipated barriers include time constraints, limited provider training, stigma, scarce resources, and inadequate reimbursement models. Enablers are expected to include supportive health policies, training, patient engagement strategies, and team-based care models.
CONCLUSIONS
This review will synthesize implementation factors affecting obesity care in PHC, informing scalable strategies and policy to improve obesity management worldwide.
Sandra León-Herrera, Ileana Gefaell, M. Guisado-Clavero et al.· Obesity Reviews· 0 citations
Background and Purpose: Healthy aging has become a global priority in response to increasing life expectancy and the rising prevalence of age-related health challenges. This review aimed to identify and synthesize evidence-based components of interventions promoting healthy aging.
Methodology: A scoping review was conducted following the PRISMA-ScR (PRISMA Extension for Scoping Reviews) guidelines. Comprehensive searches across databases, including Scopus, Cochrane, and PubMed, for studies published between 2020 and 2025.
Findings: 30 studies that met the inclusion criteria were included for final analysis. The synthesis of findings identified six key components that consistently support the effectiveness of healthy aging interventions: (i) diet, (ii) prevention and management of multimorbidity, (iii) physical activity, (iv) social support, (v) mental health management, and (vi) cognitive well-being.
Contributions: These components highlight the multidimensional aspects of healthy aging and the need to adopt an integrative approach when designing interventions. The findings provide critical insights for policymakers, healthcare practitioners, and researchers in developing frameworks and programs to promote health, well-being, and quality of life (QoL) among older adults.
Keywords: Healthy aging, quality of life, health, well-being, lifestyle.
Cite as: Mohd Fauzy, N. K., Harith, S., Yahaya, R., Kamaludin, M., Abdul Rahman, H., & Lee, D.-Y. (2026). Defining evidence-based components of healthy aging interventions: An integrative scoping review. Journal of Nusantara Studies, 11(2), 243-261. http://dx.doi.org/10.24200/jonus.vol11iss2pp243-261
Nur Kamilah Mohd Fauzy, S. Harith, Rosliza Yahaya et al.· Journal of Nusantara Studies...· 0 citations