Exploring co-participation in health: strategies and initiatives towards inclusive well-being

International Journal of Health Governance, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Purpose This article explores the main elements of co-participation in health, examining how community engagement can improve health outcomes and services’ overall efficiency. It aims to discuss identify key features that facilitate strategies service delivery program implementation. Design/methodology/approach The authors conducted a general literature review comprehensively explore role drawing on scientific real-world examples factors contribute successful interventions. A total 50 published resources were included, descriptive analysis was performed, focusing summarizing existing highlighting themes practical strategies. Documents selected from publications dated between 2004 2024. Findings Community participation is presented as critical factor improving population outcomes. examined initiatives promote idea integration into design implementation programs increases treatment adherence, users' perception improved Several approaches are tools adequately integrate such empowerment, government decentralization incorporation technology, among others. Practical implications Coparticipation improves promotes greater equity social justice. Involving citizens decision-making contributes quality life well-being community. Empowering patients’ not only builds one’s self-agency but also simultaneously facilitates closing gaps healthcare due large shortages workforce around world. has further for systems’ financing, efficiency sustainability. Social research it underscores essential fostering equity, justice inclusivity within systems. Originality/value offers an innovative perspective partnership achieving good outcomes, importance adapting interventions local contexts, need sustainable financing inclusion wide range actions toward participation.

Language: Английский

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Idiographic Lapse Prediction with State Space Modeling: Algorithm Development and Validation (Preprint)

Published: March 6, 2025

Many mental health conditions (e.g., substance use or panic disorders) involve long-term patient assessment and treatment. Growing evidence suggests that the progression presentation of these may be highly individualized. Digital sensing predictive modeling can augment scarce clinician resources to expand personalize care. This manuscript discusses techniques process data into risk predictions, for instance lapse a with alcohol disorder (AUD). Of particular interest are idiographic approaches fit personalized models each patient. bridges two active research areas in health: prediction time-series modeling. Existing work has focused on machine learning (ML) classifier approaches, typically trained at population level. In contrast, psychological explanatory relied techniques. The authors propose state space (SSMs), an framework, as alternative ML classifiers prediction. used 3-month observational study participants (N=148) early recovery from AUD. Using once-daily ecological momentary assessments (EMA), SSMs compared their performance logistic regression gradient-boosted classifiers. Performance was evaluated using area under receiver operating characteristic curve (auROC) three tasks: same-day lapse, within 3 days, 7 days. To mimic real-world use, changes auROC when were given access increasing amounts participant’s EMA (15, 30, 45, 60, 75 days). Bayesian hierarchical compare benchmark techniques, specifically analyzing posterior estimates mean model auROC. Posterior strongly suggested had best all tasks 30+ days participant data. With 15 data, results varied by task. probabilities (first quartile, median, third quartile), (.877, .997, .999), (.992, .999, (.995, .998, .999). .732, <.001, <.001. compelling traditional support fitting, even rare outcomes, offer better than existing approaches. Further, estimate patient’s behavior, making them ideal stepping beyond frameworks optimal treatment selection administered digital therapeutics platform). While AUD is case study, this SSM framework readily applied other conditions.

Language: Английский

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Idiographic Lapse Prediction with State Space Modeling: Algorithm Development and Validation (Preprint)

JMIR Formative Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

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A mobile health intervention for emerging adults with regular cannabis use: A micro-randomized pilot trial design protocol

Contemporary Clinical Trials, Journal Year: 2024, Volume and Issue: 145, P. 107667 - 107667

Published: Aug. 17, 2024

Language: Английский

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The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024

Journal of Behavioral Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 28, 2024

Language: Английский

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Who engages in well-being interventions? An analysis of a global digital intervention study

The Journal of Positive Psychology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: Oct. 22, 2024

Despite growing interest in interventions aimed at enhancing emotional well-being, little research has addressed the question of engagement. This study explored engagement a 7-day online well-being intervention involving 24,180 participants from 195 countries/territories (78% female, Mage = 49, 62% White). Following an onboarding survey, completed morning practice and evening follow-up survey for week. Overall, 76% initiated (i.e. returned to platform after enrollment start intervention), completing average four daily practices. Several demographic (e.g. being older, White) psychological variables lower financial strain, higher life satisfaction) emerged as common predictors initiating more Age was particularly important predictor across outcomes. These findings offer novel insights into how individual characteristics relate have implications both designing interpreting findings.

Language: Английский

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Patterns of Engagement with the mHealth Component of a Sexual/Reproductive Health Risk Reduction Intervention for Young People with Depression: Latent Trajectory Analysis (Preprint)

Published: Dec. 24, 2024

Mobile health (mHealth) interventions are increasingly used to reduce risk and promote in real-time, real-life contexts. Engagement is critical mHealth intervention effectiveness yet may be challenging for young people experiencing depressive symptoms. We examined engagement with the 4-week component of a counseling-plus-mHealth sexual/reproductive (SRH) among depression (“MARSSI”) determine 1) patterns over time 2) how sociodemographic characteristics, SRH risks, symptom severity were associated these patterns. undertook secondary analysis data collected 6/2021–9/2023 multi-state randomized controlled trial MARSSI vs. breast podcast. Eligibility included age 16-21 years; ability become pregnant; smartphone ownership; English fluency; past-3-month penile-vaginal sex ≥1x/week ≥1 risk; PHQ-8 score≥8. Intervention participants received 1-on-1 telehealth counseling, then an app 4 weeks, responding surveys (3 prompted at quasi-random, 1 scheduled daily) about affect, effective contraception condom use self-efficacy, sexual pregnancy desire, recent sex, receiving tailored messages reinforcing counseling. computed days (responding survey) by week overall. Latent trajectory identified four weeks any engagement. Using regression analysis, we associations (p<.05) moderation severity. Of 201 participants, 194 (96.5%) enrolled app. Among those (n=167, 86%), median (IQR) was 14 (4-23); 33% responded on ≥20 days. App declined 1-4: 5 (3-7), 3 (1-6), (0-6), (0-5). On latent emerged: high-throughout (29%), high-then-declining (24%), mid-then-declining (28%), low-throughout (20%). Participants identifying gender other than female perceiving higher socioeconomic status more likely have and/or Asian or Black non-Hispanic using low-effectiveness no In multivariable model, remained significantly lower perceived SES There differences significant moderation. Young symptoms showed initial high during adverse outcomes. Methods increase sustain characteristics warrant further study optimize reach interventions. ClinicalTrials.gov NCT04798248

Language: Английский

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Patterns of Engagement with the mHealth Component of a Sexual/Reproductive Health Risk Reduction Intervention for Young People with Depression: Latent Trajectory Analysis (Preprint)

JMIR mhealth and uhealth, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Language: Английский

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