Frontiers in digital health最新文献

{"title":"The externalization of internal experiences in psychotherapy through generative artificial intelligence: a theoretical, clinical, and ethical analysis.","authors":"Yuval Haber, Dorit Hadar Shoval, Inbar Levkovich, Dror Yinon, Karny Gigi, Oori Pen, Tal Angert, Zohar Elyoseph","doi":"10.3389/fdgth.2025.1512273","DOIUrl":"10.3389/fdgth.2025.1512273","url":null,"abstract":"<p><strong>Introduction: </strong>Externalization techniques are well established in psychotherapy approaches, including narrative therapy and cognitive behavioral therapy. These methods elicit internal experiences such as emotions and make them tangible through external representations. Recent advances in generative artificial intelligence (GenAI), specifically large language models (LLMs), present new possibilities for therapeutic interventions; however, their integration into core psychotherapy practices remains largely unexplored. This study aimed to examine the clinical, ethical, and theoretical implications of integrating GenAI into the therapeutic space through a proof-of-concept (POC) of AI-driven externalization techniques, while emphasizing the essential role of the human therapist.</p><p><strong>Methods: </strong>To this end, we developed two customized GPTs agents: VIVI (visual externalization), which uses DALL-E 3 to create images reflecting patients' internal experiences (e.g., depression or hope), and DIVI (dialogic role-play-based externalization), which simulates conversations with aspects of patients' internal content. These tools were implemented and evaluated through a clinical case study under professional psychological guidance.</p><p><strong>Results: </strong>The integration of VIVI and DIVI demonstrated that GenAI can serve as an \"artificial third\", creating a Winnicottian playful space that enhances, rather than supplants, the dyadic therapist-patient relationship. The tools successfully externalized complex internal dynamics, offering new therapeutic avenues, while also revealing challenges such as empathic failures and cultural biases.</p><p><strong>Discussion: </strong>These findings highlight both the promise and the ethical complexities of AI-enhanced therapy, including concerns about data security, representation accuracy, and the balance of clinical authority. To address these challenges, we propose the SAFE-AI protocol, offering clinicians structured guidelines for responsible AI integration in therapy. Future research should systematically evaluate the generalizability, efficacy, and ethical implications of these tools across diverse populations and therapeutic contexts.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1512273"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451156","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":"Navigating the landscape of remote patient monitoring in Canada: trends, challenges, and future directions.","authors":"Khayreddine Bouabida, Breitner Gomes Chaves, Enoch Anane, Navaal Jagram","doi":"10.3389/fdgth.2025.1523401","DOIUrl":"10.3389/fdgth.2025.1523401","url":null,"abstract":"<p><p>Remote Patient Monitoring (RPM) has driven significant advancements in Canadian healthcare, especially during the transformative period from 2018 to 2023. This perspective article explores the state of play and examines the current landscape of RPM platforms adopted across Canada, detailing their functionalities and measurable impacts on healthcare outcomes, particularly in chronic disease management and hospital readmission reduction. We explore the regulatory, technical, and operational challenges that RPM faces, including critical issues around data privacy, security, and interoperability, factors essential for sustainable integration. Additionally, this article provides a balanced analysis of RPM's potential for continued growth within Canadian healthcare, highlighting its strengths and limitations in the post-2023 context and offering strategic recommendations to guide its future development. Keywords: Remote Patient Monitoring, Digital Health, Virtual Care, Canadian Healthcare, Healthcare Technology, AI, Perspectives.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1523401"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451155","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":"Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors.","authors":"Enzo G Plaitano, Daniel McNeish, Sophia M Bartels, Kathleen Bell, Jesse Dallery, Michael Grabinski, Michaela Kiernan, Hannah A Lavoie, Shea M Lemley, Michael R Lowe, David P MacKinnon, Stephen A Metcalf, Lisa Onken, Judith J Prochaska, Cady Lauren Sand, Emily A Scherer, Luke E Stoeckel, Haiyi Xie, Lisa A Marsch","doi":"10.3389/fdgth.2025.1467772","DOIUrl":"10.3389/fdgth.2025.1467772","url":null,"abstract":"<p><strong>Introduction: </strong>Smoking, obesity, and insufficient physical activity are modifiable health risk behaviors. Self-regulation is one fundamental behavior change mechanism often incorporated within digital therapeutics as it varies momentarily across time and contexts and may play a causal role in improving these health behaviors. However, the role of momentary self-regulation in achieving behavior change has been infrequently examined. Using a novel momentary self-regulation scale, this study examined how targeting self-regulation through a digital therapeutic impacts adherence to the therapeutic and two different health risk behavioral outcomes.</p><p><strong>Methods: </strong>This prospective interventional study included momentary data for 28 days from 50 participants with obesity and binge eating disorder and 50 participants who smoked regularly. An evidence-based digital therapeutic, called Laddr™, provided self-regulation behavior change tools. Participants reported on their momentary self-regulation via ecological momentary assessments and health risk behaviors were measured as steps taken from a physical activity tracker and breathalyzed carbon monoxide. Medical regimen adherence was assessed as daily Laddr usage. Bayesian dynamic mediation models were used to examine moment-to-moment mediation effects between momentary self-regulation subscales, medical regimen adherence, and behavioral outcomes.</p><p><strong>Results: </strong>In the binge eating disorder sample, the perseverance [<i>β</i> ₁ = 0.17, 95% CI = (0.06, 0.45)] and emotion regulation [<i>β</i> ₁ = 0.12, 95% CI = (0.03, 0.27)] targets of momentary self-regulation positively predicted Laddr adherence on the following day, and higher Laddr adherence was subsequently a positive predictor of steps taken the same day for both perseverance [<i>β</i> ₂ = 0.335, 95% CI = (0.030, 0.717)] and emotion regulation [<i>β</i> ₂ = 0.389, 95% CI = (0.080, 0.738)]. In the smoking sample, the perseverance target of momentary self-regulation positively predicted Laddr adherence on the following day [<i>β</i> = 0.91, 95% CI = (0.60, 1.24)]. However, higher Laddr adherence was not a predictor of CO values on the same day [<i>β</i> ₂ = -0.09, 95% CI = (-0.24, 0.09)].</p><p><strong>Conclusions: </strong>This study provides evidence that a digital therapeutic targeting self-regulation can modify the relationships between momentary self-regulation, medical regimen adherence, and behavioral health outcomes. Together, this work demonstrated the ability to digitally assess the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and pro-health behavioral outcomes.</p><p><strong>Clinical trial registration: </strong>ClinicalTrials.gov, identifier (NCT03774433).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1467772"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470109","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":"Use of a wearable device to improve sleep quality.","authors":"Susan L Moore, Evan P Carey, Kristyna Finikiotis, Kelsey L Ford, Richard D Zane, Katherine K Green","doi":"10.3389/fdgth.2024.1384173","DOIUrl":"10.3389/fdgth.2024.1384173","url":null,"abstract":"<p><strong>Objectives: </strong>The present study aimed to analyze the effects of the use of a digital wellness device on improving sleep through reducing environmental noise.</p><p><strong>Methods: </strong>Fifty-five self-reported light or moderate sleepers with difficulty falling or staying asleep due to environmental noise participated in the study. Objective sleep architecture data were collected via a wireless electroencephalogram (EEG) sleep monitor and subjective data were obtained through analysis of daily sleep diaries and responses to study-specific user experience surveys. Four primary outcomes specified <i>a priori</i> were analyzed for statistical significance: objectively measured sleep onset latency (SOL), wake after sleep onset (WASO), number of awakenings, and perceived SOL. Exploratory analysis through descriptive statistics was conducted for an additional 36 secondary outcomes.</p><p><strong>Results: </strong>Use of the digital wellness device was associated with reduced SOL both objectively and subjectively. Perceived SOL was 32.5% reduced (<i>p</i> < 0.001, difference in means 7.5 min, 95% CI 22.3%-41.4% faster), and objectively measured SOL was 13.3% reduced (<i>p</i> = 0.030, difference in means 2.7 min, 95% CI = 1.4%-23.8% faster). No statistically significant differences were found for other primary outcomes. Among the subjective secondary outcomes, 100% of participants felt the device blocked environmental noise, 86% reported falling asleep more easily, 76% felt they stayed asleep longer, and 82% felt overall sleep quality was improved. No differences were observed among objectively measured secondary outcomes.</p><p><strong>Conclusions: </strong>Participants fell asleep faster when using the wearable wellness device. Participants also perceived sleep quality improvements with the intervention, although no objective differences were measured. These findings show promise for using noise-masking digital wellness devices in noisy environments to improve sleep quality.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1384173"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11834000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451154","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 machine learning methodology for the systematic extraction of chronic kidney disease comorbidities from abstracts.","authors":"Eszter Sághy, Mostafa Elsharkawy, Frank Moriarty, Sándor Kovács, István Wittmann, Antal Zemplényi","doi":"10.3389/fdgth.2025.1495879","DOIUrl":"10.3389/fdgth.2025.1495879","url":null,"abstract":"<p><strong>Background: </strong>Chronic Kidney Disease (CKD) is a global health concern and is frequently underdiagnosed due to its subtle initial symptoms, contributing to increasing morbidity and mortality. A comprehensive understanding of CKD comorbidities could lead to the identification of risk-groups, more effective treatment and improved patient outcomes. Our research presents a two-fold objective: developing an effective machine learning (ML) workflow for text classification and entity relation extraction and assembling a broad list of diseases influencing CKD development and progression.</p><p><strong>Methods: </strong>We analysed 39,680 abstracts with CKD in the title from the Embase library. Abstracts about a disease affecting CKD development and/or progression were selected by multiple ML classifiers trained on a human-labelled sample. The best classifier was further trained with active learning. Disease names in question were extracted from the selected abstracts using a novel entity relation extraction methodology. The resulting disease list and their corresponding abstracts were manually checked and a final disease list was created.</p><p><strong>Findings: </strong>The SVM model gave the best results and was chosen for further training with active learning. This optimised ML workflow enabled us to discern 68 comorbidities across 15 ICD-10 disease groups contributing to CKD progression or development. The reading of the ML-selected abstracts showed that some diseases have direct causal effect on CKD, while others, like schizophrenia, has indirect causal effect on CKD.</p><p><strong>Interpretation: </strong>These findings have the potential to guide future CKD investigations, by facilitating the inclusion of a broader array of comorbidities in CKD prognostic models. Ultimately, our study enhances understanding of prognostic comorbidities and supports clinical practice by enabling improved patient monitoring, preventive strategies, and early detection for individuals at higher CKD development or progression risk.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1495879"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470121","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 review of human factors and infusion pumps: lessons for procurement.","authors":"Laura Herrero, Marina Cano, Raj Ratwani, Laura Sánchez, Blanca Sánchez, Ramón Sancibrián, Galo Peralta","doi":"10.3389/fdgth.2025.1425409","DOIUrl":"10.3389/fdgth.2025.1425409","url":null,"abstract":"<p><p>Integrating advanced technologies like medical devices in healthcare is crucial for addressing critical challenges, but patient safety must remain the top priority. In modern clinical settings, medical devices, such as infusion devices used to administer fluids and drugs, carry risks from use errors, requiring a focus on usability and human factors engineering (HFE). Despite the significance of integrating HFE into technology selection processes, it is often overlooked. A review of five key articles demonstrates how applying HFE principles in procurement strategies can enhance device usability and patient safety. Although designed to reduce medication errors, infusion devices can still cause over-infusion or delays, indicating the need for improved safety features that must be considered in the context of sociotechnical systems. The reviewed studies suggest incorporating HFE in design, purchasing, and implementation to address these issues. The studies highlight various HFE methodologies, showing a wide variation in design, deployment, interpretation, and reporting. This comprehensive examination underscores the importance of standardised evaluations to ensure safer and more effective medical devices, emphasizing the essential role of HFE in advancing patient safety within healthcare settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1425409"},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470125","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":"Comparative analysis of ChatGPT and Gemini (Bard) in medical inquiry: a scoping review.","authors":"Fattah H Fattah, Abdulwahid M Salih, Ameer M Salih, Saywan K Asaad, Abdullah K Ghafour, Rawa Bapir, Berun A Abdalla, Snur Othman, Sasan M Ahmed, Sabah Jalal Hasan, Yousif M Mahmood, Fahmi H Kakamad","doi":"10.3389/fdgth.2025.1482712","DOIUrl":"10.3389/fdgth.2025.1482712","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence and machine learning are popular interconnected technologies. AI chatbots like ChatGPT and Gemini show considerable promise in medical inquiries. This scoping review aims to assess the accuracy and response length (in characters) of ChatGPT and Gemini in medical applications.</p><p><strong>Methods: </strong>The eligible databases were searched to find studies published in English from January 1 to October 20, 2023. The inclusion criteria consisted of studies that focused on using AI in medicine and assessed outcomes based on the accuracy and character count (length) of ChatGPT and Gemini. Data collected from the studies included the first author's name, the country where the study was conducted, the type of study design, publication year, sample size, medical speciality, and the accuracy and response length.</p><p><strong>Results: </strong>The initial search identified 64 papers, with 11 meeting the inclusion criteria, involving 1,177 samples. ChatGPT showed higher accuracy in radiology (87.43% vs. Gemini's 71%) and shorter responses (907 vs. 1,428 characters). Similar trends were noted in other specialties. However, Gemini outperformed ChatGPT in emergency scenarios (87% vs. 77%) and in renal diets with low potassium and high phosphorus (79% vs. 60% and 100% vs. 77%). Statistical analysis confirms that ChatGPT has greater accuracy and shorter responses than Gemini in medical studies, with a <i>p</i>-value of <.001 for both metrics.</p><p><strong>Conclusion: </strong>This Scoping review suggests that ChatGPT may demonstrate higher accuracy and provide shorter responses than Gemini in medical studies.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1482712"},"PeriodicalIF":3.2,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442881","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":"Unlocking the potential of serious games for rehabilitation in low and middle-income countries: addressing potential and current limitations.","authors":"Diriba Dereje, Dheeraj Lamba, Teklu Gemechu Abessa, Chala Kenea, Cintia Ramari, Muhammad Osama, Oyéné Kossi, Paul Muteb Boma, Jules Panda, Anna Kushnir, Joanna Mourad, Jean Mapinduzi, Maryam Fourtassi, Kim Daniels, Judith Deutsch, Bruno Bonnechère","doi":"10.3389/fdgth.2025.1505717","DOIUrl":"10.3389/fdgth.2025.1505717","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1505717"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434332","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":"START-the Swiss tele-assisted rehabilitation and training program to support transition from inpatient to outpatient care in the subacute phase after a stroke: feasibility, safety and performance evaluation.","authors":"Szabina Gäumann, Carina Ziller, Nele Paulissen, Frank Behrendt, Zorica Suica, Björn Crüts, Luana Gammerschlag, Katrin Parmar, Hans Ulrich Gerth, Leo H Bonati, Corina Schuster-Amft","doi":"10.3389/fdgth.2024.1496170","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1496170","url":null,"abstract":"<p><strong>Introduction: </strong>Effective rehabilitation is essential to prevent physical and cognitive decline, but many stroke patients face challenges to maintain rehabilitation efforts after hospital discharge. Telerehabilitation, delivered via digital platforms, represents a promising approach for intensive continuation of stroke rehabilitation after discharge. The Swiss tele-assisted rehabilitation and training program (START), delivered through the Blended Clinic mobile application, seeks to support patients to start during inpatient rehabilitation, continue during the transition to the home environment, continue until outpatient rehabilitation starts and beyond. The study aims to evaluate feasibility, safety and performance of the START program on the Blended Clinic platform during inpatient, transition, and outpatient rehabilitation with patients in the early and late subacute phase after a stroke. Furthermore, patients' functional status, mobility and activity level, and health-related quality of life are monitored.</p><p><strong>Methods: </strong>This single-center feasibility trial with three measurement sessions will include 40 patients, who will be introduced to START during their inpatient rehabilitation. Patients will continue for 12 weeks post-discharge. For the feasibility assessment, process-, training- and mHealth-related parameter will be evaluated, which include recruitment rate, process-evaluation, safety, adherence, drop-out rate, stability and maintenance of the system, usability, quality, satisfaction, user and program experience, and perceived change. Secondary outcomes will focus on motor function, mobility, quality of life, activity level, heart rate, blood pressure, and performance-based measures.</p><p><strong>Discussion: </strong>The study's strengths include its foundation in previous usability analyses, which informed refinements to the START program. The study's design is based on the ISO 14155 standard, ensuring high standards for medical device research and supporting the future certification of the START program on the Blended Clinic platform. Potential challenges include patient self-reporting via the mobile application and barriers related to technology use among older adults and older mobile devices. Additionally, the availability of coaching is limited to business hours, which may affect adherence. Despite these challenges, the study's findings will provide insights into the feasibility of mobile-based telerehabilitation and guide the design of a future randomized controlled trial.</p><p><strong>Clinical trial registration: </strong>The study is registered with the Swiss National Clinical Trial Portal (SNCTP000005943), EUDAMED (CIV-CH-24-05-046954), and clinicaltrils.gov (NCT06449612).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1496170"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442885","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 current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.","authors":"Claire R van Genugten, Melissa S Y Thong, Wouter van Ballegooijen, Annet M Kleiboer, Donna Spruijt-Metz, Arnout C Smit, Mirjam A G Sprangers, Yannik Terhorst, Heleen Riper","doi":"10.3389/fdgth.2025.1460167","DOIUrl":"10.3389/fdgth.2025.1460167","url":null,"abstract":"<p><strong>Background: </strong>Just-In-Time Adaptive Interventions (JITAIs) are interventions designed to deliver timely tailored support by adjusting to changes in users' internal states and external contexts. To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. Given the idiosyncratic, dynamic, and context dependent nature of mental health symptoms, JITAIs hold promise for mental health. However, the development of JITAIs is still in the early stages and is complex due to the multifactorial nature of JITAIs. Considering this complexity, Nahum-Shani et al. developed a conceptual framework for developing and testing JITAIs for health-related problems. This review evaluates the current state of JITAIs in the field of mental health including their alignment with Nahum-Shani et al.'s framework.</p><p><strong>Methods: </strong>Nine databases were systematically searched in August 2023. Protocol or empirical studies self-identifying their intervention as a \"JITAI\" targeting mental health were included in the qualitative synthesis if they were published in peer-reviewed journals and written in English.</p><p><strong>Results: </strong>Of the 1,419 records initially screened, 9 papers reporting on 5 JITAIs were included (sample size range: 5 to an expected 264). Two JITAIs were for bulimia nervosa, one for depression, one for insomnia, and one for maternal prenatal stress. Although most core components of Nahum-Shani's et al.'s framework were incorporated in the JITAIs, essential elements (e.g., adaptivity and receptivity) within the core components were missing and the core components were only partly substantiated by empirical evidence (e.g., interventions were supported, but the decision rules and points were not). Complex analytical techniques such as data from passive monitoring of individuals' states and contexts were hardly used. Regarding the current state of studies, initial findings on usability, feasibility, and effectiveness appear positive.</p><p><strong>Conclusions: </strong>JITAIs for mental health are still in their early stages of development, with opportunities for improvement in both development and testing. For future development, it is recommended that developers utilize complex analytical techniques that can handle real-or near-time data such as machine learning, passive monitoring, and conduct further research into empirical-based decision rules and points for optimization in terms of enhanced effectiveness and user-engagement.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1460167"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400748","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}