Tag: Medical device

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STAT+: Zap in a cap: How one neurotech startup is using a hat to treat depression

Wake up. Brush your teeth. Wash your face. 

And put on your lifesaving baseball hat.

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.

Continue to STAT+ to read the full story…

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Determinants of Digital Health Literacy Among Patients With Serious Mental Illness: Cross-Sectional Survey

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.
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Early tinnitus burden and subjective hearing are candidate markers of 2-year quality of life after cochlear implantation in single-sided deafness

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.
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The Performance of Wearable Device–Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis

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
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ASD Wearables Feasibility Study

Conditions: Autism Spectrum Disorder; Wearable Device Feasibility

Interventions: Device: 14-Day Device Monitoring

Sponsors: Rady Pediatric Genomics & Systems Medicine Institute; Rady Children’s Hospital, San Diego; University of California, San Diego

Completed
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The Role of Technology in Mental Health Advances

This paragraph serves as an introduction to your blog post. Begin by discussing the primary theme or topic that you plan to cover, ensuring it captures the reader’s interest from the very first sentence. Share a brief overview that highlights why this topic is important and how it can provide value. Use this space to set the tone for the rest of the article, preparing readers for the journey ahead. Keep your language approachable, yet informative, to create a strong connection.

Sometimes, the simplest moments hold the deepest wisdom. Let your thoughts settle, and clarity will find you. Use this quote space to share something inspirational or reflective, perfectly aligned with the theme of your article.

Wrapping Up with Key Insights

In this concluding paragraph, summarize the key takeaways from your article, reinforcing the most important ideas discussed. Encourage readers to reflect on the insights shared, or offer actionable advice they can apply in their own lives. This is your chance to leave a lasting impression, so make sure your closing thoughts are impactful and memorable. A strong conclusion not only ties the article together but also inspires readers to engage further.

Appetite and ingestive regulation. Body-focused and impulse habits. Cognitive focus and executive control. Dissociation and identity integration. Fear and threat response. Mood and emotional regulation. Motor and impulse regulation. Reality testing and perceptual stability. Sensory processing. Sexual drive and regulation. Sleep and arousal regulation. Sleep-related parasomnias. Social and attachment drive. Speech and expression. Bipolar, schizophrenia, insomnia. A medical device would be good.