Patterns of Engagement With the mHealth Component of a Sexual and Reproductive Health Risk Reduction Intervention for Young People With Depression: Latent Trajectory Analysis
Notice bibliographique
Résumé
Background: Mobile health (mHealth) interventions are increasingly used to reduce risk and promote health in real-time, real-life contexts. Engagement is critical for effectiveness of mHealth interventions but may be challenging for young people experiencing depressive symptoms. Objective: We examined engagement with the 4-week mHealth component of a counseling-plus-mHealth intervention to reduce sexual and reproductive health (SRH) risk among young people with depression (Momentary Affect Regulation - Safer Sex Intervention [MARSSI]) to determine (1) mHealth engagement patterns over time and (2) how sociodemographic characteristics, SRH risks, and depressive symptom severity were associated with these engagement patterns. Methods: We undertook secondary analysis of data collected from June 2021 to September 2023 in a randomized controlled trial of MARSSI versus a breast health podcast. Eligibility included age 16-21 years, ability to become pregnant, smartphone ownership, English fluency, past-3-month penile-vaginal sex ≥1x/week and ≥1 SRH risk, and Patient Health Questionnaire-8 item score ≥8. Intervention participants received one-on-one telehealth counseling and then used an app for 4 weeks, responding to surveys (3 prompted at quasi-random, 1 scheduled daily) about affect, effective contraception and condom use self-efficacy, sexual and pregnancy desire, and recent sex, and receiving tailored messages reinforcing the counseling. We computed mHealth engagement days (responding to ≥1 app survey) by week and overall. Latent trajectory analysis identified engagement patterns over the 4 mHealth weeks among participants with any engagement. Using regression analysis, we examined the associations of sociodemographic characteristics, SRH risks, and depressive symptom severity with mHealth engagement patterns and examined moderation by depressive symptom severity. Of the 201 intervention participants, 194 (96.5%) enrolled in the app. Results: Among those responding to app surveys (167/194, 86.1%), the median engagement was 14 (IQR 4-23) days; 32.9% (55/167) responded on ≥20 days. Overall app engagement (median) declined from 5 (IQR 3-7) days in week 1 to 1 (IQR 0-5) day in week 4. On latent trajectory analysis, 4 patterns of app engagement emerged: high-throughout (48/167, 28.7%), high-then-declining (40/167, 23.9%), mid-then-declining (47/167, 28.1%), and low-throughout (33/167, 19.7%). Participants identifying gender other than female and those perceiving higher socioeconomic status were more likely to have high-throughout or high-then-declining engagement. Asian or Black non-Hispanic participants and those using low-effectiveness contraception were more likely to have no engagement. In the multivariable model, Asian (adjusted odds ratio [AOR] 0.28, 95% CI 0.10-0.81), Black non-Hispanic (AOR 0.28, 95% CI 0.12-0.66), and higher perceived socioeconomic status (AOR 1.24, 95% CI 1.05-1.48) remained significantly associated with engagement. Engagement patterns showed no differences by depressive symptom severity and no significant moderation. Conclusions: Young people with depressive symptoms showed initial high engagement with the intervention's mHealth app to reduce adverse SRH outcomes. Methods to increase and sustain mHealth engagement and differences in engagement by sociodemographic characteristics warrant further studies to optimize the reach of mHealth interventions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».