Background: Social determinants of health (SDOH) are the conditions in which people are born, grow, live, work, and age, encompassing social and economic factors that shape health outcomes. There is an increasing call to leverage digital health technology (DHT) to address SDOH and health-related social needs and establish connections to resources and services.
Objective: This study aimed to (1) identify the DHT-related characteristics of DHT users with low socioeconomic status (SES), (2) determine the needs and preferences of DHT users with low SES, and (3) explore how current SDOH-DHT addresses these needs and preferences in addressing their health-related social needs.
Methods: We used a multiphase, mixed method, user-centered design approach. In phase 1, we developed a user profile based on a literature review, aggregate data, interviews with 26 low-SES individuals, and focus groups with 28 professionals. In phase 2, we conducted a landscape analysis of 17 existing SDOH-DHTs.
Results: DHT users of low SES had diverse social and technology characteristics. Five key themes emerged regarding user needs and preferences: (1) user-centered design, including multilingual support, visual guidance, and customization; (2) efficient, solution-based assessment of social risks, assets, and needs; (3) e-caring support features; (4) user education and feedback mechanisms; and (5) trust, privacy, and security. The landscape analysis revealed that current SDOH-DHT features do not adequately meet these needs.
Conclusions: Discrepancies between target user needs and current DHT features represent missed opportunities in developing user-centered tools for individuals of low SES. Findings underscore the importance of inclusive, empowering, and responsive design in SDOH-DHT to bridge health disparities and advance public health.
Background: Paramedics face frequent exposure to trauma and intense occupational stress, often under conditions of limited psychological support and ongoing stigma. Digital mental health interventions have the potential to offer accessible, confidential, and tailored support. However, their acceptability and design must be informed by the lived experiences of paramedics to ensure effectiveness.
Objective: This study aimed to explore the experiences of trauma exposure among UK paramedics in the workplace and their views on the design and delivery of digital mental health interventions.
Methods: Semi-structured interviews were conducted with 22 UK paramedics. Participants were recruited through purposive and snowball sampling. Interviews were transcribed verbatim and analyzed using reflexive thematic analysis. Ethical approval was obtained, and trauma-informed principles were applied throughout data collection and analysis.
Results: Five key themes were identified: (1) It Has to Feel Easy to Use: highlighting the need for digital tools that reduce cognitive burden and are accessible during unpredictable shifts; (2) Make It Fit My Needs: calling for interventions specifically designed for paramedics, with lived-experience-informed language and delivery; (3) We Need to Talk to Each Other: describing a strong desire for peer connection while recognizing barriers such as stigma and shift pressures; (4) I Need to Know It's Safe: emphasizes the importance of anonymity, data privacy, and psychological safety; and (5) Support Needs to Feel Human: reinforcing the value of integrating digital tools with human connection and professional services. Participants expressed strong support for an app-based solution that offers anonymity, rapid accessibility, and flexibility, while preserving opportunities for human interaction.
Conclusions: Paramedics face unique mental health challenges that are not adequately addressed by existing services. Digital mental health tools offer promise if they are carefully co-designed to reflect the realities of frontline work. Anonymity, usability, peer connection, and integration with existing support systems are critical to engagement. These findings offer actionable insights for the development of trauma-informed, context-sensitive digital mental health interventions for emergency service workers.
Background: Breast cancer treatment, particularly during the perioperative period, is often accompanied by significant psychological distress, including anxiety and uncertainty. Mobile health (mHealth) interventions have emerged as promising tools to provide timely psychosocial support through convenient, flexible, and personalized platforms. While research has explored the use of mHealth in breast cancer prevention, care management, and survivorship, few studies have examined patients' experiences with mobile interventions during the perioperative phase of breast cancer treatment.
Objective: This study aimed to explore the experiences of patients with breast cancer using iCareBreast, a mobile app designed to provide perioperative guidance and psychosocial support.
Methods: A qualitative approach was used to explore participant experiences. A total of 13 English- or Chinese-speaking participants from the intervention group of a clinical study were recruited via purposive sampling between April 2021 and February 2022. Semistructured individual phone interviews were conducted, audio-recorded, and transcribed verbatim. Thematic analysis was performed to identify key patterns of experience, focusing on usability, emotional impact, perceived value, and areas for future improvement.
Results: Overall, 4 main themes and 11 subthemes emerged from this study: (1) navigating the app with confidence and comfort, (2) making sense of treatment through relevant and evolving content, (3) finding emotional anchors in a time of uncertainty, and (4) advocating for broader use and continued motivation. Participants found the app user-friendly and appreciated its structure and locally relevant content, which helped reduce anxiety and enhance surgical preparedness. Features such as deep breathing exercises, motivational quotes, survivor stories, mindfulness practices, and peer support links offered emotional comfort and a sense of companionship. Participants strongly advocated for more personalized and adaptive content aligned with their treatment type and recovery progress. They also emphasized the value of interactive elements, such as video demonstrations and accessing messaging functions, to support sustained engagement. Many expressed the need for extended support throughout the adjuvant treatment phases, including chemotherapy and radiotherapy.
Conclusions: The iCareBreast app was perceived as a supportive tool during the perioperative period, helping patients navigate both informational and emotional challenges. However, the findings underscore the importance of extending content across the treatment continuum and enhancing personalization and interactivity. mHealth interventions should be responsive to patients' evolving needs and integrated into clinical care pathways to provide timely, comprehensive, tailored, and ongoing support for women with bre
Background: Telerehabilitation is a promising solution to provide continuity of care. Most existing telerehabilitation platforms focus on rehabilitating upper limbs, balance, and cognitive training, but exercises improving cardiovascular fitness are often neglected.
Objective: The objective of this study is to evaluate the acceptability and feasibility of a telerehabilitation intervention combining cognitive and aerobic exercises.
Methods: A virtual reality-based dual-task exercise exploiting a cycle ergometer was designed, developed, and integrated with a commercially available telerehabilitation platform. Patients with different conditions were enrolled and administered subjective questionnaires investigating attitudes toward technology, usability, technology acceptance, and subjective workload. Their therapists were interviewed, and adherence and performance data were analyzed.
Results: In total, 26 patients with neurological or post-COVID symptoms were included. Their attitude toward technology (range: 0-5) did not change after the training period (pre: 3.44 [IQR 0.63]; post: 3.50 [IQR 0.48]); the platform was rated usable and acceptable. Frustration and physical and mental workload were present, especially among younger participants. The adherence was moderate, but individual differences were present (0.59 [IQR 0.54]). The therapists highlighted the potential of remote rehabilitation programs but also identified some limitations.
Conclusions: This study proved the feasibility and acceptability of a customized virtual reality-based telerehabilitation program allowing for the safe implementation of aerobic cycling-based dual-task training. The solution was judged meaningful for dehospitalized patients, although some environmental and technical barriers should be overcome to implement telerehabilitation more effectively.
Background: As information and communication technologies and artificial intelligence (AI) become deeply integrated into daily life, the focus on users' digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective: This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review. Using the stimulus-organism-response framework as a guiding lens, this study aims to develop a comprehensive model for designing human-centered AI systems that promote digital well-being.
Methods: A systematic review of 240 multidisciplinary publications was conducted to explore the intersection of AI, digital well-being, and human-centered design. The analysis involved identifying key themes, frameworks, and approaches, with the stimulus-organism-response model serving as an overarching perspective to organize findings and inform the model development.
Results: The review led to the development of a human-centered artificial intelligence model for digital well-being, a conceptual framework that consolidates current knowledge on designing AI systems to support digital well-being and positively influence human behavior. The proposed model integrates insights from cross-disciplinary research, providing a structured understanding of how AI system features (stimuli) affect users' internal states such as perceptions and emotions (organisms) and lead to attitudinal or behavioral changes (responses). Additionally, this paper highlights emerging challenges and opportunities, including ethical considerations, scalability, and practical guidelines for applying the model in long-term research and practice.
Conclusions: This study contributes to advancing the field by presenting an overarching framework for fostering digital well-being through human-centered AI systems. By addressing gaps in the fragmented literature and proposing a unifying model, the findings offer insights for researchers and practitioners. The human-centered artificial intelligence for digital well-being model serves as a foundation for future exploration and practical application in creating intelligent computing systems that improve users' digital well-being in everyday life.
Background: Sleep duration plays a crucial role in cognitive health and is closely linked to cognitive decline. However, the relationship between sleep duration and cognitive function in the Chinese population remains poorly understood.
Objective: This study aims to evaluate the association between sleep duration and cognitive function among middle-aged and older adults in China.
Methods: Using data from 15,526 participants in the 2020 China Health and Retirement Longitudinal Study, we used 3 composite indicators, encompassing episodic memory, mental acuity, and overall cognitive function to evaluate cognitive performance. Sleep duration per night, self-reported through face-to-face interviews, was also obtained. Adjustments were made using multiple generalized linear regression models, accounting for demographic, lifestyle, and health-related covariates.
Results: Among the 15,526 respondents analyzed, 53.02% (8232/15,526) were female and 46.98% (7294/15,526) were male, with an average age of 61.5 (SD 9.27) years. Those reporting sleep durations of 4 hours or less (β=-1.85, 95% CI -2.07 to -1.62), 5 hours (β=-0.55, 95% CI -0.78 to -0.33; P<.001), 9 hours (β=-1.78, 95% CI -2.17 to -1.39), and 10 hours or more (β=-3.01, 95% CI -3.39 to -2.63) per night had a significant negative relationship with cognitive function. In the adjusted model, the negative impact of long sleep (≥10 hours) on overall cognitive function became more pronounced (β=-3.01, 95% CI -3.39 to -2.63; P<.001), followed closely by extremely short sleep (≤4 hours; β=-1.85, 95% CI -2.07 to -1.62; P<.001).
Conclusions: This study reveals an inverted U-shaped relationship between sleep duration and global cognitive decline, indicating that cognitive function should be closely monitored in individuals with both short and long sleep durations. Consequently, public health strategies should prioritize the promotion of moderate sleep to mitigate the cognitive risks associated with aging, particularly in culturally specific contexts.
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