Effect of a Tailored eHealth Physical Activity Intervention on Physical Activity and Depression During Postpartum: Randomized Controlled Trial (The Postpartum Wellness Study)
Notice bibliographique
Résumé
Background: Strong evidence suggests physical activity (PA) can ameliorate postpartum depression (PPD) symptoms; however, many postpartum individuals do not meet PA guidelines. Electronic health (eHealth) interventions are a promising approach to address common barriers to PA during postpartum. Objective: To test the effectiveness of a tailored eHealth PA intervention for increasing PA and decreasing depressive symptoms in individuals at high risk for PPD. Methods: We conducted a randomized controlled trial within the Kaiser Permanente Northern California integrated health care delivery system. From November 2020 to September 2022, individuals 2-6 months postpartum at high risk for PPD were randomized to an eHealth PA intervention (n=50) or usual care (n=49). The eHealth PA intervention group received access to an online library of 98 ten-minute workout videos developed for postpartum individuals, including interaction with their infants. At baseline, 3 months, and 6 months postrandomization, surveys were used to assess depressive symptoms, PA, sleep quality, anxiety symptoms, perceived stress, and mother-infant bonding. PA was also measured using a wrist-worn accelerometer for 7 days at each timepoint. Primary outcomes were depressive symptoms and device-measured moderate-to-vigorous intensity PA (dm-MVPA) at 3 months postrandomization. Secondary outcomes were self-reported MVPA (sr-MVPA) at 3 and 6 months postrandomization and depressive symptoms and dm-MVPA at 6 months postrandomization. Intent-to-treat and modified intent-to-treat (excluding participants in the intervention group who did not watch at least 1 video) analyses were conducted using linear regression adjusted for variables used in the randomization procedure and using multiple imputation to account for missing data. Results: Participants were 4 months postpartum at baseline with moderately severe depressive symptoms (mean PHQ-8 [Patient Health Questionnaire-8] score=12.6), on average. Intent-to-treat analyses indicated no association between the intervention and change in depressive symptoms (mean difference=-0.9; 95% CI -3.3 to 1.5), dm-MVPA per day (mean difference=-4.5 minutes; 95% CI -23.5 to 14.5), or sr-MVPA per week (mean difference=3.8; 95% CI -1.9 to 9.5) at 3 months postrandomization or 6 months postrandomization (depressive symptoms: mean difference=-1.3; 95% CI -3.7 to 1.1; dm-MVPA: mean difference=1.3 minutes; 95% CI -18.9 to 21.5; sr-MVPA: mean difference=-1.8 MET-hours; 95% CI -7.7 to 4.2). Engagement with the intervention was suboptimal; although 52% (n=26) of participants allocated to the intervention group logged on to the intervention website and watched at least 1 video, the median minutes watched per week peaked at 10 minutes 2 weeks postrandomization, then fell to zero for the rest of the follow-up period. Results from modified intent-to-treat analyses were similar to those from intent-to-treat analyses. Conclusions: An eHealth PA intervention tailored to postpartum individuals did not affect depressive symptoms or PA among those at high risk for PPD. Additional research to develop effective and engaging PA interventions is needed to help alleviate PPD symptoms and decrease PPD risk.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».