An examination of user and household characteristics associated with adolescents' adoption and usage of a lifestyle behaviour modification app
Notice bibliographique
Résumé
BACKGROUND: Adolescent obesity continues to be a major public health problem within Canada; therefore, effective solutions are required. E-health interventions can provide Canadian adolescents (13-17 years old) with personalized support to help them modify their obesogenic behaviours. However, predictors of app adoption and usage among adolescents have not been extensively examined. OBJECTIVE: This study aimed to examine user and household characteristics associated with adolescents’ adoption and usage of the Aim2Be© app; a health behaviour modification intervention delivered through a smartphone app. METHODS: 371 adolescent-parent dyads completed baseline assessment and were provided with access to the Aim2Be© app. Mean adolescent age was 14.9 years and 50.1% were male (n=186). Mean adult age was 44.1 years and 34.7% were male (n=129). Multivariable logistic and linear regressions, along with path analyses, were used to determine characteristics that were significantly associated with app adoption and usage, respectively. Additionally, analyses were stratified by parent’s sex. Univariable analyses were conducted in Stata (v.13.1), while path analyses were conducted in Mplus (v.8). All models were adjusted for adolescent’s age and sex, and a significance level of 5% was used. RESULTS: 79.2% of adolescents (n=294) adopted the Aim2Be© app. When examining user characteristics, adolescent engagement in healthy behaviours was directly associated with increased odds of app adoption (OR=1.08; 95%CI=1.01-1.14). Autonomous motivation was indirectly associated with app adoption (OR=1.02; 95%CI=1.00-1.04). When examining parenting practices, mediated through user characteristics, autonomy supportive practices were associated with increased app usage (β=0.21; 95%CI=0.07-0.36), while structure practices were indirectly associated with increased odds of app adoption (OR=1.02; 95%CI=1.00-1.04). When the analyses were stratified by parent’s sex, differences in the associations emerged. CONCLUSIONS: Both user characteristics and parenting practices were significantly associated with adolescents’ app adoption and/or usage. The findings of this study will help inform future e-health interventions increase user engagement by identifying the characteristics of individuals who are not accessing the intervention, as well as identifying factors of the household environment that support long-term intervention use. This information will fill an important gap within the literature, as high attrition rates are commonly reported among e-health interventions and can consequently jeopardize program effectiveness.
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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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 ».