Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults
Bibliographic record
Abstract
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 ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".