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Record W2802175611 · doi:10.1111/aphw.12126

Comparing Types of Financial Incentives to Promote Walking: An Experimental Test

2018· article· en· W2802175611 on OpenAlexaff
Rachel J. Burns, Alexander J. Rothman

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsCarleton University
FundersUniversity of Minnesota
KeywordsIncentiveTest (biology)BusinessFinancePsychologyEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Offering people financial incentives to increase their physical activity is an increasingly prevalent intervention strategy. However, little is known about the relative effectiveness of different types of incentives. This study tested whether incentives based on specified reinforcement types and schedules differentially affected the likelihood of meeting a walking goal and explored if observed behavioural changes may have been attributable to the perceived value of the incentive. METHODS: A 2 (reinforcement type: cash reward, deposit contract) × 2 (schedule: fixed, variable) between-subjects experiment with a hanging control condition was conducted over 8 weeks (n = 153). RESULTS: Although walking was greater in the incentive conditions relative to the control condition, walking did not differ across incentive conditions. Exploratory analyses indicated that the perceived value of the incentive was associated with the likelihood of meeting the walking goal, but was not affected by reinforcement type or schedule. CONCLUSIONS: The reinforcement type and schedule manipulations tested in this study did not differentially affect walking. Given that walking behaviour was associated with perceived value, designing incentive strategies that optimise the perceived value of the incentive may be a promising avenue for future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.415
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2018
Admission routes1
Has abstractyes

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