Factors Associated with Use of the LETSGO mHealth App in Routine Follow-Up After Gynecologic Cancer Treatment: An Observational Study (Preprint)
Notice bibliographique
Résumé
Background: Mobile health (mHealth) apps offer new opportunities to support cancer survivors in managing their health, adopting healthier lifestyles, and increasing awareness of recurrence symptoms. However, despite their potential, little is known about survivors' use of such tools in routine follow-up care, or which factors are associated with their engagement. Objective: The aim of the present study was to identify survivor characteristics associated with the use of the Lifestyle and Empowerment Techniques in Survivorship of Gynecologic Oncology (LETSGO) app. In addition, we aimed to describe engagement patterns among gynecologic cancer survivors during the first year of follow-up after treatment. Methods: App data from 378 participants in the intervention group of the LETSGO multicenter clinical trial were included in the analysis. Using multiple logistic regression, we analyzed app data from the first year of LETSGO app use to identify participant characteristics associated with use. In addition, we described the frequency of user interactions with app features and identified the most frequently used features during the first year of the intervention in a subset of 49 participants with complete app log data. Results: Of the 378 participants in the intervention group, 267 (70.6%) used the LETSGO app at least twice and were defined as app users. The median age of app users was 63 (53-70) years vs 70 (60-78) years among app nonusers (P<.001). App use was associated with younger age (odds ratio [OR] 0.96 per year, 95% CI 0.93-0.99; P=.003), higher education (OR 2.2, 95% CI 1.1-4.6; P=.03), and having ovarian cancer (OR 2.9; 95% CI 1.1-7.5; P=.03). The LETSGO app included a monthly reminder for symptom self-registration, and monthly peaks in the symptom monitoring feature corresponded with the timing of this reminder. App log data from the full first year of the intervention were available for 49 participants. Among these, user engagement was highest for the physical activity and activity goal-setting features, whereas the disease information feature showed steady but less frequent use. Conclusions: Our findings indicate that LETSGO app use was associated with survivor characteristics such as age, education level, and cancer type. Considering user characteristics when tailoring mHealth apps may support user engagement in digital follow-up care and inform the development of mHealth tools that better align with survivors' needs and preferences. Engagement with the LETSGO app during the first year of follow-up among a subset of participants with complete app log data was highest for the physical activity and activity goal-setting features. The pattern of symptom registration following reminders indicates that scheduled prompts may support regular app engagement, while steady use of the disease information feature suggests an ongoing need for accessible health information during follow-up.
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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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».