Placing Design and Delivery at the Forefront of Physical Activity Intervention
Notice bibliographique
Résumé
As we move toward the fifth decade of intensive research on how to promote moderate and vigorous physical activity, our behavior change results thus far are very humbling (1). These modest results in behavior change clearly demonstrate room to improve. Past research has been focused highly on the specific content of the intervention — whether it is targeting the benefits and barriers of physical activity or strategies to improve self-regulation. In the current issue of the Journal, Morgan et al. (3) overview a conceptual model that places intervention design and delivery alongside intervention content and suggest that these factors are of equal importance to the success of the intervention because they foster sociocultural relevance. The concepts within the model are not novel, as the authors acknowledge throughout the article. Furthermore, cultural targeting within interventions has been a suggested focus in health behaviors for many years (4). What makes the model potentially useful for future interventions is the culmination of all of these concepts together in a simple integrated form for various phases of intervention testing. The separation of content as only one of four aspects in the model (i.e., from format, facilitator, and pedagogy) also helps position intervention delivery and design as critical factors to the success of intervention outcomes that interact with intervention content. This has direct relevance to the potential limitations of past research efforts, where null results in behavior change often are traced to a failure to change the putative mediator (6). Thus, our current theories, if delivered in a culturally benign format for the target group, are expected not to engender change. Morgan et al. (3) also highlight the critical importance of fieldwork, experience, and pedagogical aspects to intervention design and delivery and how these often are contrary to trial reporting. Indeed, in my experience (and from discussions with other trial researchers), many of the narratives behind the success or failure of a trial involve design and delivery aspects highlighted in the article. These factors often comprise the interesting elements that do not enter into the formal publication yet represent valuable lessons learned. This conceptual model may aid researchers in reporting on these delivery and design features. Despite the helpful integrated model proposed by Morgan et al. (3), there are limits to whether this approach will improve physical activity outcomes. First, as the authors note, the model is built mainly on subjective/experiential lessons learned. Although it seems reasonable to assume that factors such as intervention format and delivery are important to intervention success, the effectiveness of behavior change based on these elements has been relatively underwhelming in the limited research thus far (1). Continued experimental research on program delivery and intervention design is needed. We also need to continue to examine how cultural groups are formed and defined. As Morgan et al. (3) highlight, cultural targeting often is used interchangeably with ethnicity, but shared beliefs and values likely are the critical composites for targeting a group. For example, the very successful Football Fans in Training trial (2), delivered to Scottish soccer fans, demonstrates one possible form of more unconventional targeting. Cultural targeting relevant for engendering physical activity behavior change is a key continued research focus. Finally, although program design and delivery are the prominent features in the model, this does not alleviate the continued improvement needed to understanding physical activity determinants in the form of content. A move away from the more rational approaches to physical activity change, highlighted in traditional social cognitive theories, to more affective and reflexive/automatic bases for behavior change (5) likely is needed no matter how much we top-dress an intervention. Acknowledgments Disclosure of funding: R.E.R. is supported by a Canadian Cancer Society Senior Scientist Award and the Right to Give Foundation with additional funds from the Canadian Cancer Society, the Social Sciences and Humanities Research Council of Canada, and the Canadian Institutes for Health Research.
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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 | 0,072 | 0,071 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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 ».