Background: Maternal obesity increases risks of adverse pregnancy outcomes and long-term diseases for mothers and child. Digital lifestyle interventions show promise, but their effectiveness depends on meeting the specific needs of pregnant women with obesity and healthcare providers (HCPs). Objectives: To explore perspectives and practices on healthy lifestyle and care for pregnant women with obesity, and to identify needs and preferences for digital lifestyle intervention development and implementation. Methods: A qualitative study using focus groups and interviews was conducted with 13 HCPs and 13 pregnant women with obesity. Sessions were audio-recorded, transcribed and analysed thematically. Women viewed a healthy lifestyle as multidimensional, encompassing nutrition, physical activity, mental well-being, and rest, but faced barriers such as pregnancy discomfort, limited knowledge, and stigma. Both women and HCPs emphasized child health as a motivator and valued goal setting and practical advice. Existing care was seen as inconsistent and generic, with HCPs constrained by time and unclear roles. Participants preferred a personalized, user-friendly mobile app with modular, evidence-based content tailored to individual goals, pregnancy stage, and medical status. Features such as self-monitoring, goal setting, and a supportive, non-judgmental tone were important. Integration into routine obstetric care was considered key for engagement and effectiveness. If designed accordingly, such tools could provide accessible, tailored support between appointments, reinforce positive behaviour change, improve patient-provider communication, and reduce HCP time pressures. Conclusions: Co-designing digital lifestyle tools with women and HCPs is vital. Personalized, feasible interventions integrated in obstetric care can support behaviour change and improve outcomes for mothers and children. Trial registration number: not applicable.

Large language model (LLM)-based conversational systems are increasingly proposed as personalized digital well-being tools (DWTs) for youth, yet current personalization approaches often fail to reflect heterogeneous lived experiences. We present a participatory approach for eliciting personalization requirements for LLM-based DWTs, with youth well-being as a case study. In a multi-stage co-creation study with youth, parents, and youth care professionals (N=38), we combined data-driven personas with participatory persona co-creation and follow-up stakeholder interviews to examine what meaningful LLM-based DWT personalization should entail. Our preliminary findings suggest that stakeholders prioritize person-centered personalization beyond demographic or clinical labels, emphasizing identity, relational dynamics, routines, literacy needs, and changing lived context. Participants also highlighted the importance of dialogic mechanisms, particularly reflective questioning and ongoing updating of contextual representations over time. We argue that participatory personas can serve as reflexive scaffolds for surfacing community-grounded personalization needs and translating them into emerging design requirements for adaptive LLM systems. These findings position personalization in DWTs as an ongoing dialogic, person-centered process that warrants further investigation in research on human-AI personalization.

Background: The transition from adolescence to young adulthood (age 10‐25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time using ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not been systematically reviewed. Objective: This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological reporting quality to inform future precision health intervention development. Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and SWiM (Synthesis Without Meta-Analysis) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies focused on participants aged 10 to 25 years and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using the Joanna Briggs Institute checklists. AYA coauthors contributed to all phases. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively, and no meta-analysis was conducted. Results: A total of 61 unique interventions were included. JITAIs for AYAs addressed substance use (n=24, 39.3%), mental health (n=23, 37.7%), and physical health or chronic conditions (n=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold–based, and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting the interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. Conclusions: This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also integrating AYA perspectives into the interpretation of findings and recommendations. Unlike prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. These findings highlight the need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts.

BACKGROUND: Maternal obesity affects approximately 16% of pregnancies worldwide and is a major concern for public health. This condition is associated with elevated risks of adverse outcomes, such as gestational diabetes, hypertensive disorders of pregnancy, and cardiovascular and metabolic disease (CVMD) during the life course. Our long-term experience with the (cost-)effective web-based Smarter Pregnancy coaching program is promising to mitigate these risks and improve lifestyle behaviours as well as maternal and neonatal health outcomes. Here our aim is to describe the rationale and study protocol for a randomised controlled trial (RCT) to investigate the clinical and cost-effectiveness, and implementation potential of Smarter Pregnancy Plus (SP+) from early pregnancy onwards in women with obesity. METHODS: The HYGEIA study is designed as a multicentre, two-armed RCT. A total of 930 women with a body mass index ≥30 kg/m² and a singleton pregnancy of ≤14 weeks gestation will be recruited and randomised 1:1 to the intervention (n=465) and control group (n=465). The intervention group will receive six months of digital lifestyle coaching via the SP+ application, supported by at least one video consultation. SP+ is an extended, co-designed and tailored app version for pregnant women with obesity based on the validated SP program. It provides evidence-based coaching on nutrition, physical activity, mental health, folic acid and vitamin D supplement use, and smoking and alcohol cessation. The control group will receive usual care. Data will be collected through validated questionnaires, electronic medical records, financial records, and qualitative stakeholder sessions, guided by the Non adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS)-framework. The primary outcome is the incidence of maternal CVMD during pregnancy, assessed using a composite measure that includes gestational diabetes, hypertensive disorders of pregnancy, and worsening of pre-existing hypertension or diabetes. Secondary outcomes include changes in lifestyle behaviours, maternal and neonatal outcomes, quality of life, and cost-effectiveness. Tertiary outcomes involve patient satisfaction, provider feasibility and a roadmap for nationwide implementation. DISCUSSION: The HYGEIA study will provide evidence on the clinical impact, cost-effectiveness, and implementation potential of SP+ as a digital lifestyle intervention for pregnant women with obesity. If successful, SP+ could further enhance routine obstetric care and contribute to improving maternal and neonatal health while reducing healthcare costs. TRIAL REGISTRATION: Dutch Trial Registration number NL-OMON57196 (URL: https://onderzoekmetmensen.nl/nl/node/57196/pdf ), date of registration 19-12-2024.

Introduction: Mental health issues among young people have surged post-COVID-19. Mental health apps can offer accessible preventive support on a large scale, yet the perspective of minoritized youth–such as those from low socioeconomic and ethnic/racial backgrounds–are underexplored. This risks low uptake and effectiveness, and exacerbating health inequities. This study aimed to understand the needs and concerns of minoritized youth in the Netherlands using a participatory approach. Methods: We conducted 3 co-creation sessions with 17 adolescents (16 females, majority Dutch Moroccan background) aged 11–22 years, recruited through community centers in lower-income neighborhoods in The Netherlands, with the help of community workers. We also organized a discussion session with 26 preventive youth workers to explore their perspectives regarding implementation. A subset of youth (n = 10) analyzed the data in 2 co-thematic analysis workshops. We compared youth and researcher themes. Results: Youth saw data-driven mental health apps as useful for short-term stress relief through motivational quotes, social activity suggestions, and homework support, but unable to solve more severe issues. In the co-analysis, youth analyzed based on emotion and functions, whereas researchers employed a more technical lens. Key themes included identity-based (such as religion, gender, and age) and contextual tailoring (to school/home schedules), compassionate communication as opposed to fake support (robots), safety, and the role of social media. Conclusion: These findings highlight the need to examine how app design for young people can prioritize authentic, compassionate communication, safety–including transparency about data–tailoring to identify aspects, adapting the timing and frequency of notifications, and integrating social connections and social media. Participatory approaches are promising to better understand the needs of youth from minoritized backgrounds for digital mental health technologies, with the aim of equitable digital solutions.

Adolescents and young adults (AYA) often face mental health challenges and are heavily influenced by technology. Digital health interventions (DHIs), leveraging smartphone data and artificial intelligence, offer immense potential for personalized and accessible mental health support. However, ethical guidelines for DHI research fail to address AYA’s unique developmental and technological needs and leave crucial ethical questions unanswered. This gap creates risks of either over- or under-protecting AYA in DHI research, slowing progress and causing harm. This Perspective examines ethical gaps in DHI research for AYA, focusing on three critical domains: challenges of passive data collection and artificial intelligence, consent practices, and risks of exacerbating inequities. We propose an agenda for ethical guidance based on bioethical principles autonomy, respect for persons, beneficence and justice, developed through participatory research with AYA, particularly marginalized groups. We discuss methodologies to achieve this agenda, ensuring ethical, youth-focused and equitable DHI research for the mental health of AYA.

StayWell is a 60-day CBT/DBT-based text messaging intervention which leverages reinforcement learning algorithms to support mental health. Participants were randomly assigned to receiving personalized messaging (adaptive arm), static messaging (random arm) or mood-monitoring only messages (control arm). A diverse sample of 1121 adults participated in a fully remote trial between December 2021 and July 2022. Across study arms, participants showed a 25% reduction in depression symptoms (PHQ-8) and 24% reduction in anxiety symptoms (GAD-7) following the intervention. We did not find statistically significant differences in PHQ-8 and GAD-7 reductions between intervention arms. Participants in the control arm had higher mood-monitoring messages response rates than those in other conditions. Finally, post-hoc exploratory analysis assessing outcomes by condition indicated that patients with minimal to mild depression symptoms (PHQ-8 < 10) benefitted from the reinforcement learning algorithm. The results of this trial suggest that StayWell is a promising text-messaging intervention to achieve reductions in depression and anxiety among diverse populations.

Background: Hypersensitivity to punishment is one of the core features of major depressive disorder (MDD). Hypersensitivity to punishment has been proposed to originate from aberrant aversive learning. One of the key areas in aversive learning is the habenula. Although evidence for dysfunctional aversive learning in patients with depression is well established, whether this dysfunction and its neural correlates persist during symptomatic remission of depression remains largely unexplored. Methods: Functional magnetic resonance imaging data from 36 medication-free remitted patients with recurrent MDD and 27 healthy control participants participating in a Pavlovian classical conditioning task were assessed within a computational modeling framework to evaluate temporal difference–related activation of the habenula during aversive learning. Furthermore, generalized psychophysiological interaction analyses were performed to assess functional connectivity of the temporal difference signal with the habenula as an a priori region of interest. Results: Relative to healthy control participants, patients showed significantly increased temporal difference–related aversive learning activation in the bilateral habenula. This activation was correlated with residual symptoms in the remitted MDD group. Furthermore, patients exhibited decreased functional connectivity between the habenula and the ventral tegmental area compared with control participants. Conclusions: The increased habenula activity during aversive learning, particularly during the expectation of punishment, together with decreased functional habenula–ventral tegmental area connectivity in remitted patients with MDD, reflect hypersensitivity to and/or inability to regulate the impact of aversive environmental cues and punishment.

Introduction Health behaviours such as exercise and diet strongly influence well-being and disease risk, providing the opportunity for interventions tailored to diverse individual contexts. Precise behaviour interventions are critical during adolescence and young adulthood (ages 10-25), a formative period shaping lifelong well-being. We will conduct a systematic review of just-in-time adaptive interventions (JITAIs) for health behaviour and well-being in adolescents and young adults (AYAs). A JITAI is an emerging digital health design that provides precise health support by monitoring and adjusting to individual, specific and evolving contexts in real time. Despite demonstrated potential, no published reviews have explored how JITAIs can dynamically adapt to intersectional health factors of diverse AYAs. We will identify the JITAIs' distal and proximal outcomes and their tailoring mechanisms, and report their effectiveness. We will also explore studies' considerations of health equity. This will form a comprehensive assessment of JITAIs and their role in promoting health behaviours of AYAs. We will integrate evidence to guide the development and implementation of precise, effective and equitable digital health interventions for AYAs. Methods and analysis In adherence to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, we will conduct a systematic search across multiple databases, including CENTRAL, MEDLINE and WHO Global Index Medicus. We will include peer-reviewed studies on JITAIs targeting health of AYAs in multiple languages. Two independent reviewers will conduct screening and data extraction of study and participant characteristics, JITAI designs, health outcome measures and equity considerations. We will provide a narrative synthesis of findings and, if data allows, conduct a meta-analysis. Ethics and dissemination As we will not collect primary data, we do not require ethical approval. We will disseminate the review findings through peer-reviewed journal publication, conferences and stakeholder meetings to inform participatory research. PROSPERO registration number CRD42023473117.

Background: Digital and mobile health interventions using personalization via reinforcement learning algorithms have the potential to reach large number of people to support physical activity and help manage diabetes and depression in daily life. Objective: The Diabetes and Mental Health Adaptive Notification and Tracking Evaluation (DIAMANTE) study tested whether a digital physical activity intervention using personalized text messaging via reinforcement learning algorithms could increase step counts in a diverse, multilingual sample of people with diabetes and depression symptoms. Methods: From January 2020 to June 2022, participants were recruited from 4 San Francisco, California–based public primary care clinics and through web-based platforms to participate in the 24-week randomized controlled trial. Eligibility criteria included English or Spanish language preference and a documented diagnosis of diabetes and elevated depression symptoms. The trial had 3 arms: a Control group receiving a weekly mood monitoring message, a Random messaging group receiving randomly selected feedback and motivational text messages daily, and an Adaptive messaging group receiving text messages selected by a reinforcement learning algorithm daily. Randomization was performed with a 1:1:1 allocation. The primary outcome, changes in daily step counts, was passively collected via a mobile app. The primary analysis assessed changes in daily step count using a linear mixed-effects model. An a priori subanalysis compared the primary step count outcome within recruitment samples. Results: In total, 168 participants were analyzed, including those with 24% (40/168) Spanish language preference and 37.5% (63/168) from clinic-based recruitment. The results of the linear mixed-effects model indicated that participants in the Adaptive arm cumulatively gained an average of 3.6 steps each day (95% CI 2.45-4.78; P<.001) over the 24-week intervention (average of 608 total steps), whereas both the Control and Random arm participants had significantly decreased rates of change. Postintervention estimates suggest that participants in the Adaptive messaging arm showed a significant step count increase of 19% (606/3197; P<.001), in contrast to 1.6% (59/3698) and 3.9% (136/3480) step count increase in the Random and Control arms, respectively. Intervention effectiveness differences were observed between participants recruited from the San Francisco clinics and those recruited via web-based platforms, with the significant step count trend persisting across both samples for participants in the Adaptive group. Conclusions: Our study supports the use of reinforcement learning algorithms for personalizing text messaging interventions to increase physical activity in a diverse sample of people with diabetes and depression. It is the first to test this approach in a large, diverse, and multilingual sample.

After publication of this article [1], the authors reported that the disclaimer statement in the backmatter was missing and should have read ‘Disclaimer: The ideas in this article are those of the authors and do not necessarily represent policy of the American Medical Association.’ The original article [1] has been corrected. 

(International Journal for Equity in Health, (2023), 22, 1, (265), 10.1186/s12939-023-02077-0)

The StayWell at Home intervention, a 60-day text-messaging program based on Cognitive Behavioral Therapy (CBT) principles, was developed to help adults cope with the adverse effects of the global pandemic. Participants in StayWell at Home were found to show reduced depressive and anxiety symptoms after participation. However, it remains unclear whether the intervention improved mood and which intervention components were most effective at improving user mood during the pandemic. Thus, utilizing a micro-randomized trial (MRT) design, we examined two intervention components to inform the mechanisms of action that improve mood: 1) text messages delivering CBT-informed coping strategies (i.e., behavioral activation, other coping skills, or social support); 2) time at which messages were sent. Data from two independent trials of StayWell are included in this paper. The first trial included 303 adults aged 18 or older, and the second included 266 adults aged 18 or older. Participants were recruited via online platforms (e.g., Facebook ads) and partnerships with community-based agencies aiming to reach diverse populations, including low-income individuals and people of color. The results of this paper indicate that participating in the program improved and sustained self-reported mood ratings among participants. We did not find significant differences between the type of message delivered and mood ratings. On the other hand, the results from Phase 1 indicated that delivering any type of message in the 3 pm-6 pm time window improved mood significantly over sending a message in the 9 am-12 pm time window. The StayWell at Home program increases in mood ratings appeared more pronounced during the first two to three weeks of the intervention and were maintained for the remainder of the study period. The current paper provides evidence that low-burden text-message interventions may effectively address behavioral health concerns among diverse communities.

Introduction After COVID-19, a global mental health crisis affects young people, with one in five youth experiencing mental health problems worldwide. Delivering mental health interventions via mobile devices is a promising strategy to address the treatment gap. Mental health apps are effective for adolescent and young adult samples, but face challenges such as low real-world reach and under-representation of minoritised youth. To increase digital health uptake, including among minoritised youth, there is a need for diversity, equity and inclusion (DEI) considerations in the development and evaluation of mental health apps. How well DEI is integrated into youth mental health apps has not been comprehensively assessed. This scoping review aims to examine to what extent DEI considerations are integrated into the design and evaluation of youth mental health apps and report on youth, caregiver and other stakeholder involvement. Methods and analysis We will identify studies published in English from 2009 to 29 September 2023 on apps for mental health in youth. We will use PubMed, Global Health, APA PsycINFO, SCOPUS, CINAHL PLUS and the Cochrane Database and will report according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Papers eligible for inclusion must be peer-reviewed publications in English involving smartphone applications used by adolescents or young adults aged 10–25, with a focus on depression, anxiety or suicidal ideation. Two independent reviewers will review and extract articles using a template developed by the authors. We will analyse the data using narrative synthesis and descriptive statistics. This study will identify gaps in the literature and provide a roadmap for equitable and inclusive mental health apps for youth. Ethics and dissemination Ethics approval is not required. Findings will be disseminated through academic, industry, community networks and scientific publications.

Background: Women are less physically active, report greater perceived barriers for exercise, and show higher levels of depressive symptoms. This contributes to high global disability. The relationship between perceived barriers for physical activity and depressive symptoms in women remains largely unexplored. The aims of this cross-sectional analysis were to examine the association between physical activity barriers and depressive symptoms, and identify types of barriers in physically inactive community-dwelling women. Methods: Three hundred eighteen physically inactive women aged 25–65 years completed the Barriers to Being Active Quiz (BBAQ) developed by the Centers for Disease Control and Prevention, and the Center for Epidemiological Studies Depression Scale at the baseline visit of the mobile phone-based physical activity education trial. The BBAQ consists of six subscales (lack of time, social influence, lack of energy, lack of willpower, fear of injury, lack of skill, and lack of resources). We used multivariate regression analyses, correcting for sociodemographics. Results: Higher physical activity barriers were associated with greater depressive symptoms scores (linear effect, estimate = 0.75, 95% confidence interval [CI]: 0.39–1.12, p < 0.001). This effect appeared to taper off for the higher barrier scores (quadratic effect, estimate: -0.02, 95% CI: -0.03 to -0.01, p = 0.002). Exploratory analyses indicated that these associations were most driven by the social influence (p = 0.027) and lack of energy subscales (p = 0.017). Conclusions: Higher depression scores were associated with higher physical activity barriers. Social influence and lack of energy were particularly important barriers. Addressing these barriers may improve the efficacy of physical activity interventions in women with higher depressive symptoms. Future research should assess this in a randomized controlled trial.