Evaluating Feasibility and Acceptability of the “My HeartHELP” Mobile App for Promoting Heart-Healthy Lifestyle Behaviors: Mixed Methods Study
Notice bibliographique
Résumé
Background: Few mobile apps have strategies for self-monitoring multiple heart-healthy behaviors simultaneously, as well as automated and tailored feedback on individual behavioral outcomes for cardiovascular health. An app named "My HeartHELP" was developed for the general adult population to promote 6 heart-healthy lifestyle behaviors-physical activity, nonsedentary behaviors, healthy eating behaviors, nonsmoking, no alcohol binge drinking, and self-assessment of body weight. Three behavioral strategies were used: (1) text messaging the users for information on cardiovascular health, (2) self-monitoring of 6 heart-healthy behaviors to fill out the blanks of behavioral items, and (3) automated and tailored feedback messaging to users for behavioral outcomes obtained from self-monitoring. objectives: This study aimed to evaluate the feasibility and acceptability of the "My HeartHELP" app. Methods: The participants were 29 community residents in Seoul, South Korea, who met at least 1 criterion of metabolic syndrome. To evaluate the feasibility, we assessed 3 records, which are as follows: First, the "record for self-monitoring" was determined as feasible if an average percentage for each of the 6 behaviors over 4 weeks was 75% or higher based on percentages of participants who completed to record each of 6 heart-healthy behaviors. Second, the "record for access to the app" was determined as feasible if users accessed at least once a day on average per week. Third, "records for behavioral changes" over 4 weeks were collected via a self-reported questionnaire. To evaluate acceptability, we used an assessment tool comprising 12 items that included subscales for comprehensibility, ease, health benefits, technical completeness, overall satisfaction, and recommendation to others on a 5-point Likert scale. Acceptability was determined as acceptable if the average scores for the total scale and each subscale were 3.5 points or greater. Second, qualitative data were collected through 2 focus groups, each consisting of 14 or 15 participants. All data were collected in June and July 2022. Results: During the 4 weeks, 95.6% (range: 85.8%-97.4%) of the participants adhered to more than 75% of "completion of daily self-monitoring of each heart-healthy behavior," having met the criterion. The participants accessed the app on average 1.8 (SD 1.70) times per day, meeting the criteria. Participants had positive behavioral changes in all 6 behaviors, of which nonsedentary behavior (10%-28%; χ21=1.76; P<.001) and non-fast-food intake were especially statistically significant (72%-93%; χ21=5.64; P=.03) over 4 weeks. Participants reported 3.8 points for a total score of acceptability and more than 3.5 points for all subscales, which met the criterion. Qualitative data obtained from focus groups indicated that automated and tailored feedback messages motivated participants to promote healthy lifestyles. Conclusions: The "My HeartHELP" app may be a feasible and acceptable mobile app to promote self-monitoring and possibly behavioral changes in heart-healthy lifestyle behaviors.
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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 | 0,021 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».