Detection of Potential Arbovirus Infections and Pregnancy Complications in Pregnant Women in Jamaica Using a Smartphone App (ZIKApp): Pilot Evaluation Study
Notice bibliographique
Résumé
BACKGROUND: There is growing evidence of the benefits of mobile health technology, which include symptom tracking apps for research, surveillance, and prevention. No study has yet addressed arbovirus symptom tracking in pregnancy. OBJECTIVE: This study aimed to evaluate the use of a smartphone app (ZIKApp) to self-report arbovirus symptoms and pregnancy complications and to assess compliance with daily symptom diaries during pregnancy in a cohort of women in an arbovirus-endemic, subtropical, middle-income country (Jamaica). METHODS: Pregnant women aged ≥16 years, having a smartphone, and planning on giving birth at the recruiting center were enrolled between February 2020 and July 2020. ZIKApp comprised a daily symptom diary based on algorithms to identify potential episodes of arbovirus infection and pregnancy complications. Sociodemographic, epidemiological, and obstetric information was collected at enrollment, with additional review of medical records, and users' perception was collected through an exit survey. Descriptive analyses and logistic regression analysis of possible factors associated with diary adherence were performed. RESULTS: Of the 173 women enrolled, 157 (90.8%) used ZIKApp for a median duration of 155 (IQR 127-173) days until pregnancy end, 6 (3.5%) used the app for <7 days, and 10 (5.8%) exited the study early. For each successive 30-day period from enrollment up to 150 days after enrollment, of these 157 women, 121 (77.1%) to 129 (82.2%) completed their daily symptom diary; 50 (31.8%) to 56 (35.7%) did so on the same day. Overall, 31.8% (50/157) of the women had good adherence to diary reporting (ie, they completed the task on the same day or 2 to 3 days later for ≥80% of the days enrolled). There were 3-fold higher odds of good adherence for participants aged >34 years versus those aged 25 to 29 years (adjusted odds ratio 3.14, 95% CI 1.10-8.98) and 2-fold higher odds for women with tertiary versus secondary education (adjusted odds ratio 2.26, 95% CI 1.06-4.83). Of the 161 women who ever made a diary entry, 5454 individual symptom reports were made (median 17 per woman; IQR 4-42; range 0-278); 9 (5.6%) women reported symptom combinations triggering a potential arbovirus episode (none had an adverse pregnancy outcome) and 55 (34.2%) reported painful uterine contractions or vaginal bleeding, mainly in the month before delivery. Overall, 51.8% (71/137) of the women rated the app as an excellent experience and were less likely to be poor diary adherers (P=.04) and 99.3% (138/139) reported that the app was easy to understand and use. CONCLUSIONS: This pilot found a high adherence to ZIKApp. It demonstrated the feasibility and usability of the app in an arbovirus-endemic region, supporting its future development to contribute to surveillance and diagnosis of arbovirus infections in pregnancy and to optimize maternal care.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».