Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. Objective: This study aims to synthesize determinants of adults’ initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. Methods: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users’ experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. Results: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. Conclusions: Engagement with DMHTs is jointly determined by users’ beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
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A next-generation device that detects signs of stress could have wide-ranging applications, from investigating sleep disorders to detecting signs of sepsis.
The polygraph detector, described in Science Advances, is worn on the chest and can even sense when a person is lying.
It allows psychophysiological states to be continuously monitored through a combination of multimodal sensing and wireless data transmission.
The gadget offers an alternative to current approaches such as such as polygraphy and polysomnography (PSG), which involve cumbersome wired sensors that limit their practicality.
“By uncovering mechanistic links between autonomic imbalance, stress reactivity, and health outcomes, these devices have the potential to transform diagnostic workflows, optimize educational programs, and enable personalized therapeutic monitoring across stress medicine, pediatrics, and behavioral health,” reported Sun Hong Kim, PhD, from the University of Seoul in South Korea, and co-workers.
Subtle physiological variations in cardiac, respiratory, electrodermal, and thermal activity often serve as indicators of compromised health or heightened stress responses.
These can be reflected in many scenarios, from pediatric sleep disorders that disrupt neurodevelopment to the psychological strain experienced in high-stakes clinical settings or during polygraph examinations.
Accurate monitoring of psychophysiological states is therefore essential for understanding how stress and autonomic dysfunction manifest across a wide spectrum of medical conditions.
However, most existing devices monitor only one or two parameters or rely on electrochemical sensors that detect sweat biomarkers, thereby failing to reflect the complex and dynamic interplay between multiple physiological systems.
Wearable polygraph device in the palm of a hand for scale. [John A. Rogers/Northwestern University]
Kim and co-workers therefore designed a single platform to enable comprehensive assessment of autonomic and stress-related physiology in real time.
The device continuously measures changes in heartbeat, skin temperature, and breathing, which are then converted using machine learning into measures of psychological strain.
The device had high fidelity with gold standard systems in quantifying the complex psychological stress induced by polygraph interviews and complex cognitive load tasks as well as the physical stress caused by repeatedly putting a hand in an iced water.
During overnight monitoring of children, it reliably identified arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures among infants with Down syndrome.
Real-world deployment during emergency simulation training showed that multimodal stress signatures correlate inversely with performance, reflecting its value for medical education.
Machine learning analyses across all studies confirmed that multimodal features outperformed single-signal approaches in detecting stress and clinical events with high sensitivity and specificity.
“A particularly notable contribution lies in pediatric sleep medicine,” the authors noted.
“Simultaneous comparison with PSG confirms the ability to detect arousals, hypopnea, and apnea while also providing mechanistic insights into autonomic regulation.
“In infants with Down syndrome, multimodal analysis reveals attenuated sympathetic responsiveness and parasympathetic dominance, consistent with known vulnerabilities in airway patency and autonomic control.
“Such disease-specific autonomic signatures may serve as valuable biomarkers for risk stratification, early diagnosis, and targeted intervention in neurodevelopmental disorders.”
That’s right. If you have treatment-resistant depression, this could be the regular morning routine in your future. The hat would activate a blueberry-sized device implanted in your skull that sends a pulse of electricity into your brain.
This is Jacob Robinson’s vision — and it got closer to reality on Friday after the Food and Drug Administration approved a request from Robinson’s startup, Motif Neurotech, to start an initial feasibility trial to test the efficacy of their device in treating depression that hasn’t responded to other treatments. Scientists have been zapping brains to alleviate depression for decades through a method called transcranial magnetic stimulation, or TMS. Motif wants to do the same thing, but with a twist.
Background: Individuals with serious mental illness increasingly use digital devices and the internet to access health information and services but often face challenges when navigating digital tools, which may limit the benefits they receive from online health resources and digital health care services. Objective: The objective of our study was to assess digital health literacy among individuals with serious mental illness and identify factors influencing this literacy. Methods: Participants were recruited, using convenience sampling, from 2 psychiatric clinics, 1 day-care center, and 4 halfway houses in Taipei, Taiwan, between May 2024 and February 2025. Self-reported data were collected using a survey that incorporated the eHealth Literacy Scale, the Attitudes Toward Computer/Internet Questionnaire, and the Mobile Device Proficiency Questionnaire. Generalized linear modeling was applied to identify factors associated with digital health literacy. Results: Among 255 participants included in the analysis, 83.5% (n=213) reported owning at least 1 digital device. Digital health literacy was significantly lower among individuals who reported greater perceived difficulty in using digital tools (=−1.533, 95% CI −2.350 to −0.717; <.001) and higher distrust in online information (=−0.986, 95% CI −1.916 to −0.056; =.04). By contrast, greater mobile device proficiency (=0.144, 95% CI 0.008‐0.281; =.04) and self-efficacy (=1.777, 95% CI 0.376‐3.177; =.01) were positively associated with digital health literacy. Conclusions: Despite widespread device ownership, digital health literacy was varied and generally suboptimal among patients with serious mental illness. Perceived difficulty and distrust emerged as major barriers; proficiency and self-efficacy facilitated higher literacy. These findings highlight the need for mental health professionals to integrate tailored digital skills training, confidence-building strategies, and ongoing support into digital health interventions for individuals with serious mental illnesses.
BackgroundCochlear implantation is a common treatment for adults with single-sided deafness (SSD), but patient-reported benefits vary. The relationships among tinnitus burden, perceived hearing ability, psychological distress, disease-specific health-related quality of life, and whether early postoperative outcomes predict later results are not well understood.ObjectiveThis study explores how disease-specific quality of life relates to tinnitus burden, hearing, stress, depression, and anxiety after cochlear implantation in SSD. It also seeks early markers linked to 2-year outcomes.MethodsThis secondary complete-case analysis was based on a previously reported prospective longitudinal SSD cohort. Of 70 adults with postlingual SSD, 36 (51.4%) had complete Nijmegen Cochlear Implant Questionnaire (NCIQ) data at baseline and at 6 months, 1 year, and 2 years after unilateral cochlear implantation and were included. Additional measures included the Tinnitus Questionnaire (TQ), Oldenburg Inventory (OI), PerceivFed Stress Questionnaire (PSQ), General Depression Scale (ADS-L), Generalized Anxiety Disorder 7-item scale (GAD-7), and Freiburg Monosyllable Test (FMT) at 65 dB. Timepoint-specific correlations with the NCIQ were analyzed using Spearman’s rank correlations. Exploratory multivariable analyses employed linear regression on rank-transformed variables to assess whether baseline and 6-month patient-reported profiles were associated with 2-year NCIQ outcomes. Longitudinal within-patient comparisons were conducted as a secondary descriptive analysis.ResultsHigher NCIQ scores were linked to lower tinnitus burden and better hearing across all assessments. Associations with depression and anxiety persisted, while connections with perceived stress emerged after surgery. At baseline, higher tinnitus burden was associated with lower 2-year NCIQ scores. At 6 months, higher tinnitus is still associated with lower 2-year NCIQ scores, whereas better hearing is associated with higher 2-year NCIQ scores. Early postoperative improvement was followed by stabilization over 2 years.ConclusionImprovement in health-related quality of life after cochlear implantation in adults with SSD is complex and extends beyond hearing alone. Tinnitus was the most consistent negative factor, while improved subjective hearing at 6 months was associated with better outcomes at 2 years. These results support a structured, multidimensional approach to patient-reported follow-up after cochlear implantation in SSD and suggest that early postoperative patient-reported status may serve as an early candidate marker for later quality-of-life outcomes.
Background: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression. Objective: This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device–based AI models for detecting depression and predicting depressive episodes and explored factors influencing outcomes. Methods: Following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy) guidelines, the PubMed, Embase, Web of Science, and PsycINFO databases were searched from inception to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) were pooled using a bivariate random effects model. Risk of bias was assessed using Prediction Model Risk of Bias Assessment Tool plus artificial intelligence (PROBAST+ AI), and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. Results: We included 16 studies (32 datasets) with 1189 patients and 13,593 samples. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI 0.83‐0.93) and 0.93 (95% CI 0.87‐0.96), with a diagnostic odds ratio of 110.47 (95% CI 33.33‐366.17) and AUC of 0.96 (95% CI 0.94‐0.98). Random forest models showed the best performance (sensitivity=0.89, specificity=0.91, AUC=0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy (<.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI 0.80‐0.91), and pooled specificity was 0.65 (95% CI 0.59‐0.71). The overall risk of bias was low to moderate, with no evidence of publication bias. Conclusions: Wearable device–based AI models achieved high accuracy for detecting depression and moderate utility in predicting episodes. However, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limited generalizability. Trial Registration: PROSPERO CRD420251070778; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251070778
