Assessment of App-Based Versus Conventional Survey Modalities for Reproductive Health Research in India, South Africa, and the United States: Comparative Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: There is a widely acknowledged global need for more research on reproductive health (including contraception, menstrual health, sexuality, and maternal morbidities) and its impact on overall well-being. However, several factors-notably, high costs, considerable effort, and the sensitivity of these topics-impede the collection of the necessary data, especially in less accessible and lower-income populations. The burgeoning ownership of smartphones and growing use of menstrual tracking apps (MTAs) may present an opportunity to conduct reproductive health research with fewer impediments than those associated with conventional survey methods. OBJECTIVE: The main objective was to ascertain the feasibility, potential usefulness, and limitations of conducting reproductive health research using a mainstream MTA. METHODS: In each of the 3 countries, we evaluated questionnaire responses from (1) current users of an MTA (Clue) and (2) participants surveyed using conventional survey modalities (in-person interviews, SMS text messaging, and web-based questionnaires). We compared these responses with published data collected from large nationally representative benchmark samples (the United States Census and the Demographic and Health Surveys for South Africa and India). RESULTS: Given a sufficiently large user base, app-distributed surveys were able to quickly capture large samples on par with other methods and at low cost, with the additional advantage of being able to deploy remotely and simultaneously across countries. In each country, neither the app nor the conventional modality sample emerged as a consistently closer match to the distributions of the demographic attributes and the patterns of contraceptive use reported for the respective benchmark sample. Despite efforts to obtain representative samples, the conventional modality samples sometimes over- and other times underrepresented some subgroups (eg, underrepresentation of married persons in the United States and overrepresentation of rural residents in India). In all 3 countries, app users were younger, more educated, more likely to be urban residents, and more likely to use nonhormonal rather than hormonal contraceptive methods compared with the respective national benchmark. App users, compared with the conventional modality samples, consistently reported being more comfortable discussing their menstrual periods with other persons (eg, family, friends, and health care providers), suggesting that MTA users may be more likely to respond truthfully to questions on sensitive or taboo health topics. The app samples' consistency across countries regarding users' demographic profiles, contraceptive choices, and personal attitudes toward menstruation supports the validity of making cross-country comparisons of survey findings for a given app's users. CONCLUSIONS: MTAs such as Clue can provide a quick, scalable, and cost-effective method for collecting health data, including on sensitive topics, across a wide variety of settings and countries. With expanding global access to technology and the increasing use of these tools, consumer MTAs can be a viable survey modality to strengthen reproductive health research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,031 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| 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 ».