Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults
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Letters18 October 2016Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese AdultsMitesh S. Patel, MD, MBA, MS, David A. Asch, MD, MBA, and Kevin G. Volpp, MD, PhDMitesh S. Patel, MD, MBA, MSFrom Perelman School of Medicine at the University of Pennsylvania and Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania., David A. Asch, MD, MBAFrom Perelman School of Medicine at the University of Pennsylvania and Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania., and Kevin G. Volpp, MD, PhDFrom Perelman School of Medicine at the University of Pennsylvania and Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L16-0280 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Drs. Weinstock and Petry note that behavior change is complex and reinforcement is an important component of interventions to increase physical activity. Although we agree with these comments, our study focused on the effect of different ways to frame financial incentives. Our findings show that, holding reinforcement constant, financial incentives framed as a loss were most effective. A prior study by Drs. Weinstock and Petry revealed important insights but should be compared with our study with caution, because theirs was smaller, did not target overweight and obese adults, used a different step goal, and had a different primary ...References1. Petry NM, Andrade LF, Barry D, Byrne S. A randomized study of reinforcing ambulatory exercise in older adults. Psychol Aging. 2013;28:1164-73. [PMID: 24128075] doi:10.1037/a0032563 CrossrefMedlineGoogle Scholar2. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al; American College of Sports Medicine. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43:1334-59. [PMID: 21694556] doi:10.1249/MSS.0b013e318213fefb CrossrefMedlineGoogle Scholar3. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007;298:2296-304. [PMID: 18029834] CrossrefMedlineGoogle Scholar4. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313:625-6. [PMID: 25668268] doi:10.1001/jama.2014.17841 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Perelman School of Medicine at the University of Pennsylvania and Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1635. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoFraming Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults Mitesh S. Patel , David A. Asch , Roy Rosin , Dylan S. Small , Scarlett L. Bellamy , Jack Heuer , Susan Sproat , Chris Hyson , Nancy Haff , Samantha M. Lee , Lisa Wesby , Karen Hoffer , David Shuttleworth , Devon H. Taylor , Victoria Hilbert , Jingsan Zhu , Lin Yang , Xingmei Wang , and Kevin G. Volpp Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults Jeremiah Weinstock and Nancy M. Petry Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults Marc S. Mitchell and Paul I. Oh Metrics Cited byInvestigating Rewards and Deposit Contract Financial Incentives for Physical Activity Behavior Change Using a Smartphone App: Randomized Controlled TrialSmartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagementApplying Behavioral Economics to Improve Adolescent and Young Adult Health: A Developmentally-Sensitive ApproachEngineering a mobile platform to promote sleep in the pediatric primary care settingGoalkeeper: A Zero-Sum Exergame for Motivating Physical ActivityMaking a Dent in the Trillion-Dollar Problem: Toward Zero DefectsFinancial incentives for physical activity in adults: systematic review and meta-analysisA randomized, controlled, behavioral intervention to promote walking after abdominal organ transplantation: results from the LIFT studyBeActivePhysical Activity after Commitment Lotteries: Examining Long-Term Results in a Cluster Randomized TrialEvaluating the Carrot Rewards App, a Population-Level Incentive-Based Intervention Promoting Step Counts Across Two Canadian Provinces: Quasi-Experimental StudyExponential or Hyperbolic? Identifying and Testing the Predictive Power of Time Preference Over Unhealthy Behaviours 18 October 2016Volume 165, Issue 8Page: 600KeywordsBehaviorBehavioral economicsDisclosureExerciseMotivationOverweightSports and exercise medicine ePublished: 18 October 2016 Issue Published: 18 October 2016 Copyright & PermissionsCopyright © 2016 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».