BMC digital health最新文献

{"title":"Benchmarking Generative AI Tools for Interpretation of the WHO TB Mutation Catalogue.","authors":"Miguel Moreno-Molina, Anita Suresh, Rebecca E Colman, Timothy C Rodwell","doi":"10.1186/s44247-025-00225-w","DOIUrl":"10.1186/s44247-025-00225-w","url":null,"abstract":"<p><p>The World Health Organization (WHO) 2023 Mutation Catalogue for <i>Mycobacterium tuberculosis</i> is a crucial knowledgebase and tool for clinical interpretation of mutations associated with drug-resistant TB. However, the document's complexity and size pose challenges for many users. This study evaluated the potential of generative artificial intelligence (AI) models to facilitate natural language user interaction with the catalogue. This was a benchmarking study, not a clinical usability trial. Four prominent AI models-Google Gemini 2.5 Pro, OpenAI ChatGPT 4.1, Perplexity AI, and DeepSeek R1-were assessed through general test questions, mutation search and retrieval tasks using both full catalogue queries and antibiotic-specific tables, and the application of additional grading rules to score novel mutations. Performance was measured based on accuracy, completeness, clarity, source citation, and the presence of hallucinations. Google Gemini 2.5 Pro consistently demonstrated superior performance in accuracy, completeness, and avoidance of hallucinations across most evaluations, especially in general queries and large dataset searches. DeepSeek R1 excelled in applying grading rules to novel mutations and showed high accuracy in focused datasets, but exhibited some hallucinations. ChatGPT 4.1 was strong in clarity but lacked proper citations, and Perplexity AI showed variable performance with a higher frequency of hallucinations. The findings highlight the potential of AI tools to enhance the accessibility of complex knowledgebases like the WHO Mutation Catalogue, while emphasizing the need for rigorous benchmarking. While no model is yet suitable for direct clinical use, the results suggest that with further development, models like Google Gemini 2.5 Pro could form the basis of a custom AI agent to assist users in navigating this critical resource, ultimately contributing to improved TB control efforts.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147349800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can mHealth improve migrant wellness during public health emergencies? A community-engaged qualitative study during the COVID-19 pandemic.","authors":"Chad Abresch, Michelle Warren, Ellen Kerns, Alice Sato, Gleb Haynatzki, Jonathan Figliomeni, Fernando Sanchez, Gisela Marfileno, Lisvey Rivera, M Jana Broadhurst, Russell J McCulloh","doi":"10.1186/s44247-026-00253-0","DOIUrl":"https://doi.org/10.1186/s44247-026-00253-0","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic exposed significant healthcare access disparities for migrant communities, with infection rates up to three times higher than the general population. While mobile health technology offers potential solutions for reaching vulnerable populations during public health emergencies, implementation barriers persist. This study evaluates how mobile health interventions can effectively serve migrant communities through analysis of a digital health program implemented in rural Nebraska.</p><p><strong>Results: </strong>Analysis of semi-structured interviews with migrant family members between February 2022 and June 2024 revealed three key elements driving program success: culturally congruent support through community navigators, immediate access to testing and results, and program adaptability to meet broader community needs. Community navigators proved essential, expanding beyond technical support to address social needs ranging from food insecurity to domestic issues. The combination of digital tools with human support enabled families to make timely decisions about work and school attendance while accessing crucial social services.</p><p><strong>Conclusions: </strong>Digital health interventions can effectively serve migrant communities when designed with cultural sensitivity and supported by community navigators. While public health systems should prioritize technological infrastructure for emergency response, success requires concurrent investment in culturally responsive human support systems. The integration of digital tools with adaptable community navigation provides a model for reaching vulnerable populations during future public health emergencies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-026-00253-0.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"4 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12992342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An iterative mindset approach as an adjunct to the national diabetes prevention program: a randomized trial assessing retention, engagement and weight loss.","authors":"Joshua W Leichter, Michael J Cannon, Yvonne Mensa-Wilmot, Zena W Belay, Anyssa S Garza, Abby M Schmalz, Keenan I Walch, Kyra Bobinet","doi":"10.1186/s44247-026-00247-y","DOIUrl":"https://doi.org/10.1186/s44247-026-00247-y","url":null,"abstract":"<p><strong>Background: </strong>Retention and engagement continue to be a challenge for the National Diabetes Prevention Program lifestyle change program (LCP). This challenge is especially stark for persons living in medically-underserved, economically-disadvantaged communities. In the current work, we sought to investigate if a digital solution-an adjunct habit formation app (Fresh Tri^®) grounded in neuroscience theory around iteration (i.e., changing one's approach to a problem when encountering challenges)-could improve these metrics, as well as weight loss, in these populations.</p><p><strong>Methods: </strong>We conducted a randomized study using a three-level, nested hierarchical structure, with 364 participants (212 intervention; 152 control) across 33 sites from February 1, 2021-October 13, 2022. We examined retention, engagement (number of sessions attended) and ≥ 5% body weight loss for participants in the intervention condition (standard LCP curriculum plus the Fresh Tri iterative mindset app) compared to the control condition (standard LCP curriculum) and also to a nationally representative group (National DPP LCP data from the United States Centers for Disease Control and Prevention (CDC)).</p><p><strong>Results: </strong>Intervention participants reported higher retention than controls at 6 months (79.7% vs. 67.1%; OR = 2.06; 95% CI = 1.15-3.68; <i>P</i>=.01) and 12 months (78.3% vs. 53.1%; OR = 3.14; 95% CI = 1.11-8.88; <i>P</i>=.03). Engagement at 6 and 12 months was not statistically different. Compared to the National DPP LCP participants overall, both the intervention and control arms had higher retention (<i>P</i><.001) and engagement (intervention <i>P</i><.001; control <i>P</i>=.02) at 6 months. We found no differences in ≥ 5% body weight loss between intervention and control groups at 6 months (26% vs. 27%; OR = 0.99; 95% CI = 0.57-1.73; <i>P</i>=.97, <i>P</i>=.90), but a statistically significant higher proportion of those in the intervention arm having ≥ 5% weight loss after 12 months (41.3% vs. 30.6%, OR = 1.72; 95% CI = 1.01-2.92; <i>P</i>=.05).</p><p><strong>Conclusions: </strong>Overall, these results show promise for the applicability of a digital habit formation app, based on an iterative mindset, as an adjunct to the LCP for retention and weight loss.</p><p><strong>Trial registration: </strong>This clinical trial (ClinicalTrials.gov Identifier: NCT06656273) was retrospectively registered on October 18, 2024.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"4 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Older adults' preferences for features of medication adherence technologies: a preference elicitation study.","authors":"Ghada Elba, Bincy Baby, Ryan H Griffin, Tejal Patel","doi":"10.1186/s44247-025-00222-z","DOIUrl":"10.1186/s44247-025-00222-z","url":null,"abstract":"<p><strong>Introduction: </strong>Older adults are at risk of medication non-adherence due to complex medication regimens and medication management challenges. Medication adherence technologies can help, but previous research demonstrates variability in usability and preferences of their features among older adults. Therefore, our objective is to examine older adults' preferences for medication adherence technology features and their trade-offs to guide the development of these technologies. This will facilitate serving older adults better by addressing their needs and preference.</p><p><strong>Methods: </strong>Guided by the Patient-Centered Benefit-Risk Framework, we conducted a questionnaire based preference elicitation study where older adults ranked 10 medication adherence technology features identified through qualitative interviews, then identified acceptable trade-offs. Recruitment of our sample was based on convenience sampling, and the inclusion criteria was older adults above 60 years and older and able to speak and read English. The ranking was evaluated by calculating the relative importance using relative importance index (RII) and the trade-offs were assessed using win rate analysis. Statistical significance was assessed using Kruskal Wallis analysis.</p><p><strong>Results: </strong>Thirty older adults were recruited, of which twenty-three (mean age 73 years, 47.8% males) participated. The 10 reported features were button size, screen size, device size, compartment division, setting time and alarm, alarm sound, user-friendly leaflet, battery operated, locking features, and number of steps to set up the device. Screen size was ranked highest with relative importance index (RII) of 0.75. Win-rate analysis of trade-offs revealed that a user-friendly leaflet was the most frequently selected feature with p value < 0.001 (Kruskal Wallis test).</p><p><strong>Conclusion: </strong>This study highlights the importance of understanding the preferences of older adults to guide selecting the medication adherence technology that better meet their needs, as well as developing tools supporting medication management and adherence.</p><p><strong>Clinical trial number: </strong>Not applicable.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-025-00222-z.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":"86"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12641034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145607387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthy at Home for COPD: An Integrated Digital Monitoring, Treatment, and Pulmonary Rehabilitation Intervention.","authors":"Laurel O'Connor, Stephanie Behar, Seanan Tarrant, Pamela Stamegna, Caitlin Pretz, Biqi Wang, Brandon Savage, Thomas Scornavacca, Jeanne Shirshac, Tracey Wilkie, Michael Hyder, Adrian Zai, Shaun Toomey, Marie Mullen, Kimberly Fisher, Emil Tigas, Steven Wong, David D McManus, Eric Alper, Peter K Lindenauer, Eric Dickson, John P Broach, Vik Kheterpal, Apurv Soni","doi":"10.1186/s44247-024-00142-4","DOIUrl":"10.1186/s44247-024-00142-4","url":null,"abstract":"<p><strong>Background: </strong>Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality in the United States. Frequent exacerbations result in higher use of emergency services and hospitalizations, leading to poor patient outcomes and high costs. The objective of this study is to demonstrate the feasibility of a multimodal, community-based intervention in treating acute COPD exacerbations.</p><p><strong>Results: </strong>Over 18 months, 1,333 patients were approached and 100 (7.5%) were enrolled (mean age 66, 52% female). Ninety-six participants (96%) remained in the study for the full enrollment period. Fifty-five (55%) participated in tele-pulmonary-rehabilitation. Participants wore the smartwatch for a median of 114 days (IQR 30-210) and 18.9 hours/day (IQR16-20) resulting in a median of 1034 minutes/day (IQR 939-1133). The rate at which participants completed scheduled survey instruments ranged from 78-93%. Nearly all participants (85%) performed COPD ecological momentary assessment at least once with a median of 4.85 recordings during study participation. On average, a 2.48-point improvement (p=0.03) in COPD Assessment Test Score was observed from baseline to study completion. The adherence and symptom improvement metrics were not associated with baseline patient activation measures.</p><p><strong>Conclusions: </strong>A multimodal intervention combining preventative care, symptom and biometric monitoring, and MIH services was feasible in adults living with COPD. Participants demonstrated high protocol fidelity and engagement and reported improved quality of life.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>\"Nothing is going to replace an in-person visit\"</i>: Canadian long-term care providers' and recipients' perspectives on when telehealth for physician visits is not appropriate.","authors":"Tyler R Cole, Valorie A Crooks, Janice Sorensen, Sherin Jamal, Akber Mithani, Lillian Hung, Jeremy Snyder, Catherine Youngren","doi":"10.1186/s44247-025-00219-8","DOIUrl":"10.1186/s44247-025-00219-8","url":null,"abstract":"<p><strong>Background: </strong>Within long-term care (LTC) homes, telehealth use has been found to reduce unnecessary emergency department transfers, support the care needs of rural and underserved communities, and supplement in-person physician care. Despite these benefits, it is not well understood when telehealth is not an appropriate medium for providing physician care to residents with complex health needs. This knowledge gap must be addressed given the recent rise in telehealth use in LTC homes in many health systems following the COVID-19 pandemic, when virtual care use increased in many health care sectors to limit travel and in-person exposure risks, that is expected to be maintained going forward.</p><p><strong>Methods: </strong>This analysis contributes to a broader evaluative study investigating care provider and care recipient experiences and preferences for physician telehealth in LTC homes within the Fraser Health region in British Columbia, Canada. For data collection, semi-structured interviews and focus groups were undertaken with seventy care providers (staff, physicians) and recipients (residents, family caregivers). Using a thematic approach, transcripts were analyzed to find common instances when using telehealth for physician care was seen as not appropriate across participant groups.</p><p><strong>Results: </strong>Three types of patient care activities were identified as not appropriate to be conducted via physician visits using telehealth. First, new patient visits were thought to benefit from an interpersonal and conversational familiarity that could not be supported by telehealth. Second, difficult in-depth conversations that required conversational nuance (e.g., eye contact, supportive body language), such as palliative care planning, were thought to be inappropriate for telehealth appointments. Finally, instances where LTC staff would need to perform hands-on clinical assessments on behalf of physicians who were attending virtually via telehealth were not seen as desirable.</p><p><strong>Conclusions: </strong>This analysis highlights perspectives surrounding when telehealth is not appropriate for providing physician services for residents in LTC based on the preferences and experiences shared by both care recipients and care providers. The findings present an opportunity to develop and implement guidelines on appropriate use of telehealth in LTC to support best care practices.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":"77"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145403027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and usability testing of an online platform for provider training and implementation of cognitive-behavioral therapy guided self-help for eating disorders.","authors":"Laura D'Adamo, Agatha A Laboe, Jake Goldberg, Carli P Howe, Molly Fennig Steinhoff, Bianca DePietro, Marie-Laure Firebaugh, Zafra Cooper, Denise E Wilfley, Ellen E Fitzsimmons-Craft","doi":"10.1186/s44247-024-00140-6","DOIUrl":"10.1186/s44247-024-00140-6","url":null,"abstract":"<p><strong>Background: </strong>Most individuals with eating disorders (EDs) do not receive treatment, and those who do receive care typically do not receive evidence-based treatment, partly due to lack of accessible provider training. This study developed a novel \"all-in-one\" online platform for disseminating training for mental health providers in cognitive-behavioral therapy guided self-help (CBTgsh) for EDs and supporting its implementation. The aim of the study was to obtain usability data from the online platform prior to evaluating its effects on provider training outcomes and patient ED symptom outcomes in an open pilot trial.</p><p><strong>Methods: </strong>Nine mental health provider participants (<i>n</i> = 4 in Cycle 1; <i>n</i> = 5 in Cycle 2) and 9 patient participants (<i>n</i> = 4 in Cycle 1; <i>n</i> = 5 in Cycle 2) were enrolled over two cycles of usability testing. In Cycle 1, we recruited providers and patients separately to complete brief platform testing sessions. In Cycle 2, we recruited provider-patient dyads; providers completed training using the platform and subsequently delivered CBTgsh to a patient for three weeks. Usability was assessed using the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and semi-structured interviews.</p><p><strong>Results: </strong>Interview feedback converged on two themes for providers (applicability of program for real-world use, platform structure and function) and two themes for patients (barriers and facilitators to engagement, perceived treatment effects). SUS and USE scores were in the \"average\" to \"good\" ranges across cycles.</p><p><strong>Conclusions: </strong>Findings from this study demonstrate preliminary feasibility and acceptability of the online platform. Data collected in this study will inform further refinements to the online platform. The platform's effects on provider training outcomes and patient ED symptom outcomes will be evaluated in an open pilot trial. Given the wide treatment gap for EDs and barriers to dissemination and implementation of evidence-based treatments, the online platform represents a scalable solution that could improve access to evidence-based care for EDs.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malaria RDT (mRDT) interpretation accuracy by frontline health workers compared to AI in Kano state, Nigeria.","authors":"Sasha Frade, Shawna Cooper, Sam Smedinghoff, David Hattery, Yongshao Ruan, Paul Isabelli, Nirmal Ravi, Megan McLaughlin, Lynn Metz, Barry Finette","doi":"10.1186/s44247-025-00190-4","DOIUrl":"10.1186/s44247-025-00190-4","url":null,"abstract":"<p><strong>Background: </strong>Although malaria is preventable and treatable, it continues to be a significant cause of illness and death. Early diagnosis through testing is critical in reducing malaria-related morbidity and mortality. Malaria rapid diagnostic tests (mRDTs) are preferred for their ease of use, sensitivity, and rapid results, yet misadministration and misinterpretation errors persist. This study investigated whether pairing an existing application with an AI-based software could enhance interpretation accuracy among Frontline Healthcare Workers (FHWs) in Kano State, Nigeria.</p><p><strong>Methods: </strong>A comparative analysis was conducted, examining mRDT interpretations by FHWs, trained expert mRDT reviewers (Panel Readers), and AI-based computer vision algorithms. The accuracy comparisons included: (1) AI interpretation versus Panel Read interpretation, (2) FHW interpretation versus Panel Read interpretation, (3) FHW interpretation versus AI interpretation, and (4) AI performance on faint positive lines. Accuracy was reported as a weighted F1 score, reflecting the harmonic mean of recall (sensitivity) and precision (positive predictive value).</p><p><strong>Results: </strong>The AI algorithm demonstrated high accuracy, matching Panel Read interpretations correctly for positives 96.38% of the time and negatives 97.12%. FHW interpretations agreed with the Panel Read 96.82% on positives and 94.31% on negatives. Comparison of FHW and AI interpretations showed 97.52% agreement on positives and 93.38% on negatives. The overall accuracy was higher for AI (weighted F1 score of 96.4) compared to FHWs (95.3). Notably, the AI accurately identified 90.2% of 163 faint positive mRDTs, whereas FHWs correctly identified 76.1%.</p><p><strong>Conclusion: </strong>AI-based computer vision algorithms performed comparably to trained and experienced FHWs and exceeded FHW performance in identifying faint positives. These findings demonstrate the potential of AI technology to enhance the accuracy of mRDT interpretation, thereby improving malaria diagnosis and reporting accuracy in malaria-endemic, resource-limited settings.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-025-00190-4.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":"50"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of cognitive testing and ecological momentary assessments using smartphones in middle aged and older adults with insomnia.","authors":"Sara Ghadimi, Jason Ereso, Alexander J Kaula, Nick Taptiklis, Francesca Cormack, Cathy Alessi, Jennifer L Martin, Joseph M Dzierzewski, Arash Naeim, Sarah Kremen, Tue Te, Constance H Fung","doi":"10.1186/s44247-025-00158-4","DOIUrl":"10.1186/s44247-025-00158-4","url":null,"abstract":"<p><strong>Background: </strong>Older adults with insomnia who use benzodiazepine receptor agonists (BZAs) may be at increased risk of cognitive impairment. Cognitive testing outside of clinical settings may yield results that are more reflective of individuals' cognition in their natural environment, where they experience fluctuations in mental state (e.g. drowsiness). We assessed the feasibility of self-administered cognitive testing via smartphone apps for collecting in-moment, in-context data about a person's current state (ecological momentary assessment, EMA).</p><p><strong>Methods: </strong>Participants (<i>n</i> = 20; median age 66 years; 14 females, 18 white) aged ≥ 55 years who were recruited from a BZA deprescribing trial were invited to complete (over a 28 day period) daily drowsiness assessments on an EMA app (cued by smartwatch alerts) and weekly self-administered digit span (DGS) forward/backward (2 [minimum] - 9 [maximum]), verbal paired associates (VPA; 0 [best]-24 [worst] total errors), and cued delayed recall of VPA (VPA-DR; 0 [best] - 8 [worst] errors) tests on a cognitive app. We assessed the completion of EMA (0-28 days) and cognitive sessions (# of participants per # sessions completed). We performed thematic analysis of the participant interviews.</p><p><strong>Results: </strong>The median number of days that EMA was completed was 24.5. Twelve (60%) individuals participated in 4 sessions; 2 (10%) individuals participated in 3 sessions; 2 (10%) individuals participated in 2 sessions; and 4 (20%) individuals participated in 1 session. No drowsiness was reported 36% of the time, whereas 38% of the responses reflected feeling \"a little bit\" drowsy and 26% at least \"somewhat\" drowsy. Mean cognitive test scores were DGS-Forward = 7 (SD 1.3), DGS-Backward = 5.6 (SD 1.0), VPA total errors = 9.9 (SD 3.7), and VPA-DR = 2.2 (SD 1.9). Three themes emerged from the participant interviews: 1) concern for one's own cognitive abilities, 2) strategies employed for optimizing scores (including strategies that would invalidate results), and 3) ease of use of the applications.</p><p><strong>Conclusions: </strong>Our findings indicate that mobile cognitive tests and EMAs are feasible in this older population. Further work is needed to understand how scores are influenced by the setting, mood, and behaviors.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-025-00158-4.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12208955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the performance of an automated respiratory rate counter in detecting fast breathing pneumonia in children using a reference video expert panel.","authors":"Ahad Mahmud Khan, Md Shafiqul Islam, Nabidul Haque Chowdhury, Salahuddin Ahmed, Rezwana Tabassum, Sadia Afrin, Zannatul Ferdush Amin, Kazi Sazzadul Haque, Afroza Yeasmin Rumi, Jawata Rahman, Rakib Bhuiyan, Rizouan Ur Rashid, Kamrun Nahar, Robynne Simpson, Ayaz Ahmed, Md Mozibur Rahman, Ting Shi, Abdullah H Baqui, Steve Cunningham, Eric D McCollum, Harry Campbell","doi":"10.1186/s44247-025-00175-3","DOIUrl":"10.1186/s44247-025-00175-3","url":null,"abstract":"<p><strong>Background: </strong>According to the World Health Organization's Integrated Management of Childhood Illness (IMCI) guidelines, childhood pneumonia diagnosis relies on counting respiratory rate (RR). Counting RR by health workers is frequently inaccurate, leading to misdiagnosis and poor outcomes. Automated RR counters could potentially overcome these limitations. To address this gap, we introduced an automated RR counter and developed a reference video expert panel (VEP) to evaluate its performance.</p><p><strong>Methods: </strong>We conducted a cross-sectional study involving children aged 0-59 months with suspected pneumonia in Bangladesh. The RR of children was counted using an automated counter (ChARM) and chest movements were simultaneously videotaped. These videos were interpreted by the VEP, trained to a standard procedure. We assessed ChARM's accuracy in comparison to the RR generated by the VEP and summarised the time taken to count RR by ChARM.</p><p><strong>Results: </strong>Among 339 enrolled children, ChARM successfully counted the RR of 294 children (86.7%). The VEP reached a consensus (i.e., RR count difference within two breaths per minute (bpm) between two VEP members) in 257 of the 294 children (87.4%). ChARM and the VEP agreed on RR counts within two bpm in 68.1% of children (<i>n</i> = 175/257), with a mean difference of 1.7 bpm and limits of agreement ranging from -6.7 to 10.2 bpm. ChARM classified age-adjusted fast and normal breathing with a sensitivity of 95.8% (95% CI: 88.1-99.1) and a specificity of 93.5% (95% CI: 89.0-96.6), demonstrating high agreement (kappa = 0.86). The median time to count the RR by ChARM was 66 s (interquartile range: 61-73 s).</p><p><strong>Conclusions: </strong>ChARM counted RR accurately against a VEP reference, indicating a potential role in supporting health workers to diagnose pneumonia. However, it was unsuccessful for 1 in 8 cases, typically those more clinically challenging, suggesting a possible systematic bias. Further research is needed to address these issues and confirm ChARM's reliability for broader use in real-world settings.</p><p><strong>Trial registration: </strong>Current Controlled Trials ISRCTN14120515, registered retrospectively on 19 September 2024.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-025-00175-3.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"3 1","pages":"32"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}