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JMIR Hum Factors. 2025 Jun 13;12:e60205. doi: 10.2196/60205.
ABSTRACT
BACKGROUND: Chatbots, as dialog-based platforms, have the potential to transform health education and behavior-change interventions. Despite the growing use of chatbots, qualitative insights into user and provider experiences remain underexplored, particularly with respect to experiences and perceptions, adoption factors, and the role of theoretical frameworks in design.
OBJECTIVE: This systematic review of qualitative evidence aims to address three key research questions (RQs): (RQ1) user and provider experiences; (RQ2) facilitators and barriers to adoption; and (RQ3) role of theoretical frameworks.
METHODS: We systematically searched PubMed, the Cochrane Library, and ScienceDirect from January 1, 2018, to October 1, 2023, for English- or German-language, peer-reviewed qualitative or mixed methods studies. Studies were included if they examined users' or providers' experiences with chatbots in health education or behavior-change contexts. Two reviewers independently screened titles, abstracts, and full texts (Cohen κ=0.82). We used the Joanna Briggs Institute Critical Appraisal Checklist for quality assessment and conducted a reflexive thematic analysis following Braun and Clarke's framework.
RESULTS: Among the 1754 records identified, 27 studies from 10 countries met the inclusion criteria, encompassing 241 qualitative-only participants and 10,802 mixed method participants (657 contributing qualitative data). For RQ1, users emphasized empathy and emotional connection. For RQ2, accessibility and ease of use emerged as facilitators, whereas trust deficits, technical glitches, and cultural misalignment were key barriers. For RQ3, the integration of behavior-change theories emerged as underutilized despite their potential to increase motivation.
CONCLUSIONS: Chatbots demonstrate strong potential for health education and behavior-change interventions but must address privacy and trust issues, embed robust theoretical underpinnings, and overcome adoption barriers to fully realize their impact. Future directions should include evaluations of cultural adaptability and rigorous ethical considerations in chatbot design.
PMID:40513000 | DOI:10.2196/60205
BMC Prim Care. 2025 Jun 9;26(1):196. doi: 10.1186/s12875-025-02785-2.
ABSTRACT
BACKGROUND: Artificial Intelligence (AI) has significantly reshaped Primary Health Care (PHC), offering various possibilities and complexities across all functional dimensions. The objective is to review and synthesize available evidence on the opportunities, challenges, and requirements of AI implementation in PHC based on the Primary Care Evaluation Tool (PCET).
METHODS: We conducted a systematic review, following the Cochrane Collaboration method, to identify the latest evidence regarding AI implementation in PHC. A comprehensive search across eight databases- PubMed, Web of Science, Scopus, Science Direct, Embase, CINAHL, IEEE, and Cochrane was conducted using MeSH terms alongside the SPIDER framework to pinpoint quantitative and qualitative literature published from 2000 to 2024. Two reviewers independently applied inclusion and exclusion criteria, guided by the SPIDER framework, to review full texts and extract data. We synthesized extracted data from the study characteristics, opportunities, challenges, and requirements, employing thematic-framework analysis, according to the PCET model. The quality of the studies was evaluated using the JBI critical appraisal tools.
RESULTS: In this review, we included a total of 109 articles, most of which were conducted in North America (n = 49, 44%), followed by Europe (n = 36, 33%). The included studies employed a diverse range of study designs. Using the PCET model, we categorized AI-related opportunities, challenges, and requirements across four key dimensions. The greatest opportunities for AI integration in PHC were centered on enhancing comprehensive service delivery, particularly by improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction. However, the most challenges emerged within the stewardship and resource generation functions, with key concerns related to data security and privacy, technical performance issues, and limitations in data accessibility. Ensuring successful AI integration requires a robust stewardship function, strategic investments in resource generation, and a collaborative approach that fosters co-development, scientific advancements, and continuous evaluation.
CONCLUSIONS: Successful AI integration in PHC requires a coordinated, multidimensional approach, with stewardship, resource generation, and financing playing key roles in enabling service delivery. Addressing existing knowledge gaps, examining interactions among these dimensions, and fostering a collaborative approach in developing AI solutions among stakeholders are essential steps toward achieving an equitable and efficient AI-driven PHC system.
PROTOCOL: Registered in Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/HG2DV ).
PMID:40490689 | PMC:PMC12147259 | DOI:10.1186/s12875-025-02785-2
Front Digit Health. 2025 May 22;7:1581285. doi: 10.3389/fdgth.2025.1581285. eCollection 2025.
ABSTRACT
BACKGROUND: Wearable devices offer innovative solutions for chronic pain (CP) management by enabling real-time monitoring and personalized pain control. Although they are increasingly used to monitor pain-related parameters, their potential for predicting CP progression remains underutilized. Current studies focus mainly on correlations between data and pain levels, but rarely use this information for accurate prediction.
OBJECTIVE: This study aims to review recent advancements in wearable technology for CP management, emphasizing the integration of multimodal data, sensor quality, compliance with data security standards, and the effectiveness of predictive models in identifying CP episodes.
METHODS: A systematic search across six major databases identified studies evaluating wearable devices designed to collect pain-related parameters and predict CP. Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.
RESULTS: Wearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. Additionally, the integration of multimodal data, including physiological, psychological, and demographic factors, remains underexplored, presenting an opportunity to improve prediction accuracy.
CONCLUSIONS: Future research should prioritize developing robust predictive models, standardizing data protocols, and addressing security and privacy concerns to maximize wearable devices' potential in CP management. Enhancing real-time capabilities and fostering interdisciplinary collaborations will improve clinical applicability, enabling personalized and preventive pain management.
PMID:40475225 | PMC:PMC12137249 | DOI:10.3389/fdgth.2025.1581285