Smartphone App–Based Exercise for Pregnant Women in Indonesia: Quasi-Experimental Study on Physical Activity, Fear of Childbirth, and Quality of Life
Notice bibliographique
Résumé
BACKGROUND: Pregnancy, a vital phase in a woman's life, entails immense physical, psychological, and emotional alterations that might affect maternal health. Physical activity during pregnancy improves health outcomes; however, adherence to the recommendation is low. Moreover, fear of childbirth (FoC) has a negative impact on maternal mental health and quality of life (QoL). Mobile health (mHealth) interventions, especially those delivered through smartphone-based exercise management apps, provide a scalable solution to improve maternal health outcomes. OBJECTIVE: This study aims to assess the impact of an exercise management intervention based on a smartphone app on physical activity, FoC, and QoL in pregnant women in Indonesia. METHODS: We used a quasi-experimental design with repeated measures, conducted at public health centers in West Java, Indonesia. A total of 240 pregnant women were recruited through convenience sampling and allocated to either an intervention group (n=120), which received a smartphone app-based exercise and behavioral program, or a control group (n=120), which received standard prenatal care. Data were collected at 3 time points: baseline measurement (T0), postintervention measurement (T1), and 1-month follow-up measurement (T2). The intervention targeted improvements in physical activity, reduction of FoC, and enhancement of QoL. Validated instruments were used to assess outcomes, including the Pregnancy Physical Activity Questionnaire (PPAQ), the Wijma Delivery Expectation Questionnaire Version A (WDEQ-A), and the Quality-of-Life Gravidarum (QOL-GRAV) scale. Statistical analyses were performed using repeated measures ANOVA with Bonferroni post hoc tests, and effect sizes were calculated using Cohen d. RESULTS: The intervention group had significant increases in physical activity levels from T0 to T1 (Cohen d=0.65; P<.001) and from T0 to T2 (Cohen d=0.72, P<.001), whereas there were no significant changes in the control group. FoC scores were significantly lower at T1 (Cohen d=0.52; P<.001) and T2 (Cohen d=0.56; P<.001) compared to T0 in the intervention group, but no changes were observed in the control group. QoL scores increased significantly in the intervention group from T0 to T1 (Cohen d=0.60; P<.001) and from T0 to T2 (Cohen d=0.68; P<.001), while there were no significant changes noted in the control group. CONCLUSIONS: The exercise management intervention using the smartphone app was effective in increasing physical activity, reducing FoC, and improving QoL among pregnant women in Indonesia. The intervention represents a scalable and accessible mechanism through which maternal health can be improved in limited-resource contexts. Large-scale, long-term studies are needed to evaluate the sustainability of the benefits observed and the incorporation of mHealth solutions in standard prenatal management.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».