Acceptability of the Pregnancy, Exercise, and Nutrition Research Study With Smartphone App Support (PEARS) and the Use of Mobile Health in a Mixed Lifestyle Intervention by Pregnant Obese and Overweight Women: Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Dietary interventions can improve pregnancy outcomes among women with increased BMI. Although the interest in mobile health interventions is growing, little is known about the acceptability of smartphone apps to support lifestyle interventions in such a cohort. OBJECTIVE: We aimed to assess the acceptability of the pregnancy, exercise, and nutrition research study with smartphone app support (PEARS) and the use of mobile health in a mixed lifestyle intervention delivered to overweight and obese pregnant women. METHODS: PEARS was a randomized controlled trial of a low glycemic index dietary intervention with exercise prescription and a smartphone app, which was delivered to pregnant women who were overweight or obese. Acceptability questionnaires were completed by the intervention group at 28 weeks of gestation (n=149) and at postintervention (n=123). Maternal characteristics were recorded (ie, age, ethnicity, BMI, socioeconomic status). Associations between maternal characteristics and acceptability of the intervention and app were analyzed using two-tailed t tests, Mann-Whitney U tests, chi-square test, and logistic regression. One-on-one semistructured interviews were conducted with a subcohort of the intervention participants (n=28) at 34 weeks of gestation, in which the participants shared their experiences of the PEARS intervention. RESULTS: The intervention was generally accepted, with respondents agreeing that the diet was easy to follow (98/148, 68.5%), enjoyable (106/148, 74.1%), and affordable (110/148, 76.9%). Qualitative and quantitative results were consistent with each another, both demonstrating that app acceptability was high. The participants agreed that the app was enjoyable (96/120, 80.0%) and easy to use (116/119, 97.5%). Compared to those with tertiary education, those with lower education levels were more likely to enjoy the dietary changes (P=.04). Enjoyment of the app was associated with disadvantaged neighborhood deprivation index (P=.01) and higher BMI (P=.03). CONCLUSIONS: The PEARS intervention and use of a supportive smartphone app were accepted by pregnant women, particularly by those from vulnerable subgroups of this population. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 29316280; https://www.isrctn.com/ISRCTN29316280.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".