Smartphone App–Based Exercise for Pregnant Women in Indonesia: Quasi-Experimental Study on Physical Activity, Fear of Childbirth, and Quality of Life
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".