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 AdultsMarc S. Mitchell, PhD and Paul I. Oh, MDMarc S. Mitchell, PhDFrom University Health Network, Toronto, Ontario, Canada.Search for more papers by this author and Paul I. Oh, MDFrom University Health Network, Toronto, Ontario, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L16-0282 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Patel and colleagues (1) found that the upfront allocation of financial incentives followed by subsequent loss (loss-framed incentives) stimulated physical activity to a greater extent than other incentive designs (gain-framed and lottery). Although this important finding is consistent with behavioral economics, we believe that it should be interpreted with caution. Without baseline step data, whether the generic 7000-step target was even appropriate is unclear. For many, it may have been unrealistically high or, conversely, far too easy. Tying incentives to a more tailored approach to goal-setting (for example, 2000 steps greater than baseline) may have yielded different ...References1. Patel MS, Asch DA, Rosin R, Small DS, Bellamy SL, Heuer J, et al. Framing financial incentives to increase physical activity among overweight and obese adults: a randomized, controlled trial. Ann Intern Med. 2016;164:385-94. [PMID: 26881417]. doi:10.7326/M15-1635 LinkGoogle Scholar2. Orr K, Howe HS, Omran J, Smith KA, Palmateer TM, Ma AE, et al. Validity of smartphone pedometer applications. BMC Res Notes. 2015;8:733. [PMID: 26621351] doi:10.1186/s13104-015-1705-8 CrossrefMedlineGoogle Scholar3. Schrack J, Zipunnikov V, Crainiceanu C. Electronic devices and applications to track physical activity [Letter]. JAMA. 2015;313:2079-80. [PMID: 26010643] doi:10.1001/jama.2015.3877 CrossrefMedlineGoogle Scholar4. De Cocker KA, De Meyer J, De Bourdeaudhuij IM, Cardon GM. Non-traditional wearing positions of pedometers: validity and reliability of the Omron HJ-203-ED pedometer under controlled and free-living conditions. J Sci Med Sport. 2012;15:418-24. [PMID: 22483530] doi:10.1016/j.jsams.2012.02.002 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University Health Network, Toronto, Ontario, Canada.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L16-0282. 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 Mitesh S. Patel , David A. Asch , and Kevin G. Volpp Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults Jeremiah Weinstock and Nancy M. Petry Metrics Cited bySticky Goals: Understanding Goal Commitments for Behavioral Changes in the WildGoldenTime: Exploring System-Driven Timeboxing and Micro-Financial Incentives for Self-Regulated Phone Use 18 October 2016Volume 165, Issue 8Page: 599-600KeywordsBehaviorBehavioral economicsDisclosureExerciseForecastingMotivationOverweightReproducibility ePublished: 18 October 2016 Issue Published: 18 October 2016 Copyright & PermissionsCopyright © 2016 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".