A Remote Acceptance-Based Affect Regulation Intervention to Promote Physical Activity Among Early Career Professionals: A Mixed Methods Examination of Feasibility
Notice bibliographique
Résumé
Background: The benefits of physical activity (PA) are well-established, yet much of the population is insufficiently active to reap optimal health effects. Early career professionals (ECPs) comprise one transitional group at-risk for inactivity and therefore a critical target for PA promotion. A web-based intervention utilizing online modules and podcasts represents an innovative delivery format for this time-pressed population; however, theoretical mechanisms of action and corresponding behaviour change techniques need to be honed to effectively increase PA. Affective state (e.g., challenging emotion or mood) is one factor that contributes to an established intention-behaviour gap and is especially pertinent among ECPs who face many demands and stressors. As such, an intervention designed to foster intention translation, strengthen emotion regulation, and mitigate the effect of incidental affect (e.g., work-related stress on PA engagement) to assist with PA initiation is warranted. This study uses a parallel randomized controlled design to explore the feasibility of a web-based intervention grounded in the Multi-Process Action Control (M-PAC) Framework and with a specific focus on Acceptance and Commitment Therapy (ACT) principles to promote PA among ECPs. Objectives: 1) To examine primary outcomes related to the feasibility and acceptability of a six-week web-based intervention and 2) explore the effects of the intervention on secondary outcomes of interest including moderate-to-vigorous PA (MVPA), emotion regulation, M-PAC constructs, and ACT constructs (acceptance, valued living, and mindfulness). Methods: Adults aged 25-44 residing in Canada who were employed at least part-time in a desk-based job and identifying as not meeting PA guidelines (<150 min MVPA) were recruited. Participants were randomized into a 6-week online intervention or a wait-list control group using a mixed block design. The intervention group gained access to 6-weekly self-guided online modules incorporating select M-PAC iii iv constructs and integrating ACT principles with an emphasis on affect regulation strategies. Short podcast episodes were offered as a complement to the lesson concepts. Primary feasibility outcomes were descriptive and included recruitment, retention, engagement and adherence. Satisfaction and acceptability were measured via self-report and through qualitative interviews. Secondary outcomes of MVPA, emotion regulation, M-PAC constructs, and ACT constructs were assessed via self-report at baseline and post-intervention at 6 weeks using questionnaires. Effect sizes were calculated using analysis of covariance to control for baseline values. Results: Twenty-six adults were recruited and randomized to the web-based intervention (n=14) and waitlist control (n=12) groups. The recruitment rate was 35%, retention was 73%, engagement was 63%, and satisfaction was high (M = 2.68/4; M = 4.07/5). Qualitative feedback was highly positive and suggestions for intervention improvement were themed around ideas for strengthening engagement, increasing podcast awareness, and addressing minor technical issues. Participants logged in 4.57 times (SD = 3.30) and spent 31.6 minutes (SD = 18.25) per week on the intervention. Participants allocated to the intervention improved MVPA (ηp2 = 0.53), emotion regulation (ηp2 = 0.42), M-PAC action control constructs of behavioural regulation (ηp2 = 0.48), affective attitude (ηp2 = 0.26), identity (ηp2 = 0.11), and ACT-related constructs of mindfulness (ηp2 = 0.47), valued living (ηp2 = 0.20), and acceptance and action (ηp2 = 0.07), Conclusion: The recruitment, retention, and engagement rates were adequate while satisfaction was favourable, suggesting a full-scale randomized controlled trial is feasible with minor modifications. Secondary outcomes showed movement in the hypothesized direction suggesting intervention fidelity. A large-scale study is warranted to establish intervention effectiveness.
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 | 0,016 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,004 | 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 ».