Smartphone-Based Digital Peer Support for a Walking Intervention Among Public Officers in Kanagawa Prefecture: Single-Arm Pre- and Postintervention Evaluation
Notice bibliographique
Résumé
Background Digital peer support, defined as peer support delivered through technology such as smartphone apps, may be promising to promote activity in the form of step counts. Interactions among users have a positive impact on retention rates, and apps with social elements show significant improvements in daily step count. However, the feasibility of digital peer support in promoting physical activity (PA) is unknown; therefore, its effectiveness on step count and the clinical implications remain unconfirmed. Objective This study aimed to assess the feasibility of digital peer support over a 3-month intervention period using the retention rate as the outcome. Moreover, changes in daily step count and physical measurements were compared between pre- and postintervention. Methods The study design was a 3-month 1-arm intervention with participants from local government offices in Kanagawa, Japan. We used an available smartphone app, Minchalle, as the tool for the group intervention. Participants were required to report their daily step count to a maximum of 5 members composed exclusively of study participants. The primary outcome was the retention rate. Secondary outcomes included daily step count, the rate of achieving daily step goals, physical measurements, and lifestyle characteristics. Descriptive statistics and the Pearson coefficient were used to examine the relationship between goal achievement and step count, as well as changes in step count and various variables including physical measurements. Results Of the 63 participants, 62 completed the intervention. The retention rate was 98% (62/63). The average daily step count during the intervention was 6993 (SD 2328) steps, an 1182-step increase compared with the count observed 1 week before the intervention began. The rate of achieving the daily step count during the intervention was 53.5% (SD 26.2%). There was a significant correlation (r=0.27, P=.05) between achieving daily step goals and increasing daily step count. Comparative analyses showed that changes in weight (68.56, SD 16.97 kg vs 67.30, SD 16.86 kg; P<.001), BMI (24.82, SD 4.80 kg/m2 vs 24.35, SD 4.73 kg/m2; P<.001), somatic fat rate (28.50%, SD 7.44% vs 26.58%, SD 7.90%; P=.005), systolic blood pressure (130.42, SD 17.92 mm Hg vs 122.00, SD 15.06 mm Hg; P<.001), and diastolic blood pressure (83.24, SD 13.27 mm Hg vs 77.92, SD 11.71 mm Hg; P=.002) were significantly different before and after the intervention. Similarly, the daily amount of PA significantly improved from 5.77 (SD 3.81) metabolic equivalent (MET)–hours per day to 9.85 (SD 7.84) MET-hours per day (P<.001). Conclusions This study demonstrated that digital peer support is feasible for maintaining a high retention rate and can, therefore, effectively promote PA. It can be a promising tool to improve daily step count, subjective PA, and clinical outcomes, such as weight and somatic fat rate. Trial Registration UMIN Clinical Trials Registry UMIN000042520; https://tinyurl.com/46c4nm8z
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».