Frontiers in digital health最新文献

{"title":"Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review.","authors":"Xiting Liu, Wenqian Chen, Wenwen Du, Pengmei Li, Xiaoxing Wang","doi":"10.3389/fdgth.2025.1583490","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1583490","url":null,"abstract":"<p><p>Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1583490"},"PeriodicalIF":3.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082558","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":"Weakly supervised text classification on free-text comments in patient-reported outcome measures.","authors":"Anna-Grace Linton, Vania Gatseva Dimitrova, Amy Downing, Richard Wagland, Adam W Glaser","doi":"10.3389/fdgth.2025.1345360","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1345360","url":null,"abstract":"<p><strong>Background: </strong>Free-text comments in patient-reported outcome measures (PROMs) data provide insights into health-related quality of life (HRQoL). However, these comments are typically analysed using manual methods, such as content analysis, which is labour-intensive and time-consuming. Machine learning analysis methods are largely unsupervised, necessitating post-analysis interpretation. Weakly supervised text classification (WSTC) can be a valuable analytical method of analysis for classifying domain-specific text data, especially when limited labelled data are available. In this paper, we applied five WSTC techniques to PROMs comment data to explore the extent to which they can be used to identify HRQoL themes reported by patients with prostate and colorectal cancer.</p><p><strong>Methods: </strong>The main HRQoL themes and associated keywords were identified from a scoping review. They were used to classify PROMs comments with these themes from two national PROMs datasets: colorectal cancer (<i>n</i> = 5,634) and prostate cancer (<i>n</i> = 59,768). Classification was done using five keyword-based WSTC methods (anchored CorEx, BERTopic, Guided LDA, WeSTClass, and X-Class). To evaluate these methods, we assessed the overall performance of the methods and by theme. Domain experts reviewed the interpretability of the methods using the keywords extracted from the methods during training.</p><p><strong>Results: </strong>Based on the 12 papers identified in the scoping review, we determined six main themes and corresponding keywords to label PROMs comments using WSTC methods. These themes were: Comorbidities, Daily Life, Health Pathways and Services, Physical Function, Psychological and Emotional Function, and Social Function. The performance of the methods varied across themes and between the datasets. While the best-performing model for both datasets, CorEx, attained weighted F1 scores of 0.57 (colorectal cancer) and 0.61 (prostate cancer), methods achieved an F1 score of up to 0.92 (Social Function) on individual themes. By evaluating the keywords extracted from the trained models, we saw that the methods that can utilise expert-driven seed terms and extrapolate based on limited data performed the best.</p><p><strong>Conclusions: </strong>Overall, evaluating these WSTC methods provided insight into their applicability for analysing PROMs comments. Evaluating the classification performance illustrated the potential and limitations of keyword-based WSTC in labelling PROMs comments when labelled data are limited.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1345360"},"PeriodicalIF":3.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082296","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":"Beyond the gender data gap: co-creating equitable digital patient twins.","authors":"Nora Weinberger, Daniela Hery, Dana Mahr, Stephan O Adler, Jean Stadlbauer, Theresa D Ahrens","doi":"10.3389/fdgth.2025.1584415","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1584415","url":null,"abstract":"<p><p>Digital patient twins constitute a transformative innovation in personalized medicine, integrating patient-specific data into predictive models that leverage artificial intelligence (AI) to optimize diagnostics and treatments. However, existing digital patient twins often fail to incorporate gender-sensitive and socio-economic factors, reinforcing biases and diminishing their clinical effectiveness. This (gender) data gap, long recognized as a fundamental problem in digital health, translates into significant disparities in healthcare outcomes. This mini-review explores the interdisciplinary connections of technical foundations, medical relevance, as well as social and ethical challenges of digital patient twins, emphasizing the necessity of gender-sensitive design and co-creation approaches. We argue that without intersectional and inclusive frameworks, digital patient twins risk perpetuating existing inequalities rather than mitigating them. By addressing the interplay between gender, AI-driven decision-making and health equity, this mini-review highlights strategies for designing more inclusive and ethically responsible digital patient twins to further interdisciplinary approaches.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1584415"},"PeriodicalIF":3.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082591","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":"The quest for blood pressure markers in photoplethysmography and its applications in digital health.","authors":"Josep Sola, Andreu Arderiu, Tiago P Almeida, Sibylle Fallet, Sasan Yazdani, Serj Haddad, David Perruchoud, Olivier Grossenbacher, Jay Shah","doi":"10.3389/fdgth.2025.1518322","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1518322","url":null,"abstract":"<p><strong>Introduction: </strong>Photoplethysmography (PPG) sensors, capturing optical signals from arterial pulses, are debated for their potential in blood pressure (BP) measurement. This study employed the largest dataset to date of paired PPG and cuff BP readings to explore PPG signals for BP estimation.</p><p><strong>Methods: </strong>32,152 European residents (age 55.9% ± 11.8, 24% female, BMI 27.7 ± 4.6) voluntarily acquired and used a cuffless BP monitor (Aktiia SA, Switzerland) between March/2,021-March/2023. Systolic and diastolic BP (SBP, DBP) from an upper arm oscillometric cuff were collected simultaneously with wrist PPG (668,080 paired measurements). Six different machine learning models were developed to predict BP using cuff BP readings as reference (75%|15%|15% training|validation|testing): four baseline models [heart rate (HR), Age, Demography (DEM: Age + Gender + BMI), DEM + HR], and two models relying on the analysis of the PPG waveforms (PPG, PPG + DEM). Performance of each model was evaluated on the 4,823 subjects from the testing set using as metrics the Pearson's correlation (r) when comparing the estimated and the reference BP values, and the area under the receiver operating characteristic (AUROC) curves, and true positive and true negative rates (TPR, TNR) for the detection of high BP (reference SBP ≥ 140 or DBP ≥ 90 mmHg, applying a ± 8 mmHg exclusion zone to account for cuff measurement uncertainty).</p><p><strong>Results: </strong>Baseline models showed low correlation with cuff data and poor high BP detection (<i>r</i> < 0.35; AUROC < 0.65, TPR < 0.65, TNR < 0.58). PPG-based models excelled in correlating with cuff BP (SBP: <i>r</i> = 0.53 for PPG, <i>r</i> = 0.63 for PPG + DEM; DBP: <i>r</i> = 0.58 for PPG, <i>r</i> = 0.67 for PPG + DEM) and high BP detection (SBP: AUROC = 0.84, TPR = TNR = 0.75; DBP: AUROC = 0.89, TPR = TNR = 0.81 for PPG; SBP: AUROC = 0.89, TPR = TNR = 0.80; DBP: AUROC = 0.93, TPR = TNR = 0.86 for PPG + DEM).</p><p><strong>Discussion: </strong>This study demonstrated that PPG signals contain reliable markers of BP, and that BP values can be estimated using only markers found within PPG's optical pulsatility signals, outperforming models based solely on demographic data. These findings hold the potential to radically transform hypertension screening and global healthcare delivery, paving the way for innovative approaches in patient diagnosis, monitoring and treatment methodologies.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1518322"},"PeriodicalIF":3.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075524/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081888","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":"Comparison of the diagnostic concordance of tele-EMS and EMS physicians in the emergency medical service-a subanalysis of the TEMS-trial.","authors":"Patrick P Hess, Michael Czaplik, Johanna Hess, Hanna Schröder, Stefan K Beckers, Andreas Follmann, Mark Pitsch, Marc Felzen","doi":"10.3389/fdgth.2025.1519619","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1519619","url":null,"abstract":"<p><strong>Introduction: </strong>The emergency medical services (EMS) in Germany are facing several challenges in the near future. Due to the increasing number of emergency missions, the availability of EMS physicians is becoming more limited, resulting in longer response times. To maintain the high quality of EMS, telemedical support systems have shown potential as a valuable complement to the existing system for specific diagnoses. Since 2014, a tele-EMS system has been implemented in Aachen as an integrated telemedical solution alongside standard EMS. Accurate prehospital diagnosis plays a crucial role in ensuring appropriate hospital admission and reducing the time to clinical treatment for time-sensitive conditions. The main TEMS study demonstrated the overall non-inferiority of tele-EMS physicians compared to on-site EMS physicians. This sub-analysis focuses on comparing the diagnostic accuracy between these two groups.</p><p><strong>Methods: </strong>Up to four prehospital diagnoses were selected, coded according to the ICD-10 system, and compared with all admission and discharge diagnoses.</p><p><strong>Results: </strong>The comparison between diagnoses made by tele-EMS physicians and on-site EMS physicians with admission diagnoses showed no significant difference (<i>p</i> = 0.877). Additionally, no significant differences were found for the diagnoses of stroke (<i>p</i> = 0.385) and epileptic seizure (<i>p</i> = 0.738). However, patients from missions where paramedics decided to consult a tele-EMS physician had significantly longer hospital stays compared to those from missions where an on-site EMS physician was initially dispatched (<i>p</i> < 0.001).</p><p><strong>Discussion: </strong>This randomized controlled analysis demonstrated that there is no difference in diagnostic accuracy between on-site EMS physicians and remote tele-EMS physicians. The significantly longer hospital stays for patients treated by tele-EMS physicians suggest that EMS physicians may be called too frequently for non-severe cases.</p><p><strong>Clinical trial registration: </strong>clinicaltrials.gov, identifier (NCT02617875).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1519619"},"PeriodicalIF":3.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082592","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":"Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning.","authors":"Lydia Helene Rupp, Akash Kumar, Misha Sadeghi, Lena Schindler-Gmelch, Marie Keinert, Bjoern M Eskofier, Matthias Berking","doi":"10.3389/fdgth.2025.1578917","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1578917","url":null,"abstract":"<p><strong>Introduction: </strong>The detrimental consequences of stress highlight the need for precise stress detection, as this offers a window for timely intervention. However, both objective and subjective measurements suffer from validity limitations. Contactless sensing technologies using machine learning methods present a potential alternative and could be used to estimate stress from externally visible physiological changes, such as emotional facial expressions. Although previous studies were able to classify stress from emotional expressions with accuracies of up to 88.32%, most works employed a classification approach and relied on data from contexts where stress was induced. Therefore, the primary aim of the present study was to clarify whether stress can be detected from facial expressions of six basic emotions (anxiety, anger, disgust, sadness, joy, love) and relaxation using a prediction approach.</p><p><strong>Method: </strong>To attain this goal, we analyzed video recordings of facial emotional expressions collected from n = 69 participants in a secondary analysis of a dataset from an interventional study. We aimed to explore associations with stress (assessed by the PSS-10 and a one-item stress measure).</p><p><strong>Results: </strong>Comparing two regression machine learning models [Random Forest (RF) and XGBoost], we found that facial emotional expressions were promising indicators of stress scores, with model fit being best when data from all six emotional facial expressions was used to train the model (one-item stress measure: MSE (XGB) = 2.31, MAE (XGB) = 1.32, MSE (RF) = 3.86, MAE (RF) = 1.69; PSS-10: MSE (XGB) = 25.65, MAE (XGB) = 4.16, MSE (RF) = 26.32, MAE (RF) = 4.14). XGBoost showed to be more reliable for prediction, with lower error for both training and test data.</p><p><strong>Discussion: </strong>The findings provide further evidence that non-invasive video recordings can complement standard objective and subjective markers of stress.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1578917"},"PeriodicalIF":3.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082593","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":"Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis.","authors":"Samuel Rubinstein, Aleenah Mohsin, Rahul Banerjee, Will Ma, Sanjay Mishra, Mary Kwok, Peter Yang, Jeremy L Warner, Andrew J Cowan","doi":"10.3389/fdgth.2025.1569554","DOIUrl":"10.3389/fdgth.2025.1569554","url":null,"abstract":"<p><strong>Background: </strong>Concise synopses of clinical evidence support treatment decision-making but are time-consuming to curate. Large language models (LLMs) offer potential but they may provide inaccurate information. We objectively assessed the abilities of four commercially available LLMs to generate synopses for six treatment regimens in multiple myeloma and amyloid light chain (AL) amyloidosis.</p><p><strong>Methods: </strong>We compared the performance of four LLMs: Claude 3.5, ChatGPT 4.0; Gemini 1.0 and Llama-3.1. Each LLM was prompted to write synopses for six regimens. Two hematologists independently assessed accuracy, completeness, relevance, clarity, coherence, and hallucinations using Likert scales. Mean scores with 95% confidence intervals (CI) were calculated across all domains and inter-rater reliability was evaluated using Cohen's quadratic weighted kappa.</p><p><strong>Results: </strong>Claude demonstrated the highest performance in all domains, outperforming the other LLMs in accuracy: mean Likert score 3.92 (95% CI 3.54-4.29); ChatGPT 3.25 (2.76-3.74); Gemini 3.17 (2.54-3.80); Llama 1.92 (1.41-2.43);completeness: mean Likert score 4.00 (3.66-4.34); GPT 2.58 (2.02-3.15); Gemini 2.58 (2.02-3.15); Llama 1.67 (1.39-1.95); and extentofhallucinations: mean Likert score 4.00 (4.00-4.00); ChatGPT 2.75 (2.06-3.44); Gemini 3.25 (2.65-3.85); Llama 1.92 (1.26-2.57). Llama performed considerably poorer across all the studied domains. ChatGPT and Gemini had intermediate performance. Notably, none of the LLMs registered perfect accuracy, completeness, or relevance.</p><p><strong>Conclusion: </strong>Claude performed at a consistently higher level than other LLMs, all tested LLMs required careful editing from a domain expert to become usable. More time will be needed to determine the suitability of LLMsto independently generate clinical synopses.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1569554"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058044","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":"A novel transformer-based approach for cardiovascular disease detection.","authors":"Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N Arvanitis","doi":"10.3389/fdgth.2025.1548448","DOIUrl":"10.3389/fdgth.2025.1548448","url":null,"abstract":"<p><p>According to the World Health Organization, cardiovascular diseases (CVDs) account for an estimated 17.9 million deaths annually. CVDs refer to disorders of the heart and blood vessels such as arrhythmia, atrial fibrillation, congestive heart failure, and normal sinus rhythm. Early prediction of these diseases can significantly reduce the number of annual deaths. This study proposes a novel, efficient, and low-cost transformer-based algorithm for CVD classification. Initially, 56 features were extracted from electrocardiography recordings using 1,200 cardiac ailment records, with each of the four diseases represented by 300 records. Then, random forest was used to select the 13 most prominent features. Finally, a novel transformer-based algorithm has been developed to classify four classes of cardiovascular diseases. The proposed study achieved a maximum accuracy, precision, recall, and F1 score of 0.9979, 0.9959, 0.9958, and 0.9959, respectively. The proposed algorithm outperformed all the existing state-of-the-art algorithms for CVD classification.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1548448"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058600","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":"Co-creating Wellby-a mobile app and wearable for student well-being management guided by a needs assessment and co-design.","authors":"Justin Laiti, Jennifer Donnelly, Elaine Byrne, Pádraic J Dunne","doi":"10.3389/fdgth.2025.1560541","DOIUrl":"10.3389/fdgth.2025.1560541","url":null,"abstract":"<p><strong>Background: </strong>Adolescents need additional well-being support, particularly in stressful periods such as during the final years of secondary school. These students are growing up in an increasingly digital world, however there is a lack of mobile applications specifically designed to support adolescent students' well-being. Because of this, there is a need for co-created digital tools that are built to promote thriving in this population. The aim of this study was to explore how digital tools, such as a mobile app and wearable, can be used to address Irish secondary school student well-being needs through a collaborative co-design process with students.</p><p><strong>Methods: </strong>Groups of students at four schools were sent a needs assessment to understand student's most pressing well-being needs. Co-design sessions were conducted with a group of students at each school, following the confirmation of stress and sleep as students' main well-being priorities and their interest in digital support tools.</p><p><strong>Results: </strong>Students' conversations and designs from these sessions helped to uncover important elements of a well-being toolkit that they named, Wellby. The Wellby toolkit is comprised of a bespoke mobile app and wearable device for use by individuals. Participating students identified requisite elements of Wellby support that included self-tracking tools, supports for stress, and customizable features.</p><p><strong>Discussion: </strong>These insights from Irish secondary school students helped to shape a student-centered well-being support tool and provide an example of co-created positive technology.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1560541"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052029","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, adherence and usability of an observational digital health study built using Apple's ResearchKit among adults aged 18-84 years.","authors":"B Brady, S Zhou, D Ashworth, L Zheng, R Eramudugolla, K J Anstey","doi":"10.3389/fdgth.2025.1520971","DOIUrl":"10.3389/fdgth.2025.1520971","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the Labs Without Walls app and paired Apple Watch devices for remote research among Australian adults aged 18-84.</p><p><strong>Methods: </strong>The study app, built using Apple's open-source ResearchKit frameworks, uses a multi-timescale measurement burst design over 8-weeks. Participants downloaded the app, completed tasks over 8 weeks, and wore Apple Watch devices. Feasibility was assessed by recruitment, remote consent, and data collection without training. Adherence was measured by task completion rates. Usability was assessed by response times, a post-study survey, and qualitative feedback.</p><p><strong>Results: </strong>228 participants (mean age 53, age range 18-84; 62.7% female) were recruited nationwide, consented remotely, and provided data. 201 (88.16%) completed the 8-week protocol. Task adherence ranged from 100% to 70.61%. Health, environmental, and sleep data were collected passively. Usability feedback was excellent, with 84% rating the app as \"extremely\" or \"a lot\" user-friendly, 88% finding alert frequency \"just right,\" and 95.7% finding the schedule manageable. Few age or sex differences were found.</p><p><strong>Conclusions: </strong>The Labs Without Walls app and paired Apple Watch devices are user-friendly and enable adults aged 18-84 to complete surveys, cognitive and sensory tasks, and provide passive health and environmental data. The app can be used without formal training by males and females living in Australia, including older adults. Future iterations should consider gamification and strategies to improve daily-diary survey user experience.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1520971"},"PeriodicalIF":3.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014233","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}