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Record W2765703393 · doi:10.1101/211425

Behavioral and neural evidence of the rewarding value of exercise behaviors: A systematic review

2017· review· en· W2765703393 on OpenAlexaff
Boris Cheval, Rémi Radel, Jason L. Neva, Lara A. Boyd, Stephan P. Swinnen, David Sander, Matthieu P. Boisgontier

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of British Columbia
FundersVlaamse regeringFonds Wetenschappelijk Onderzoek
KeywordsPsychologyPhysical activityAffect (linguistics)Cognitive psychologySedentary behaviorPhysical exerciseBrain activity and meditationValue (mathematics)Physical medicine and rehabilitationNeuroscienceMedicineComputer sciencePhysical therapyCommunicationMachine learning

Abstract

fetched live from OpenAlex

Abstract Background In a time of physical inactivity pandemic, attempts to better understand the factors underlying the regulation of exercise behavior are important. The dominant neurobiological approach to exercise behavior considers physical activity to be a reward. However, negative affective responses during exercise challenge this idea. Objective Our objective was to systematically review studies testing the automatic reactions triggered by stimuli associated with different types of exercise behavior (e.g., physical activity, sedentary behaviors) and energetic cost variations (e.g., decreased energetic cost, irrespective of the level of physical activity). Methods Two authors systematically searched, screened, extracted, and analyzed data from articles in the MEDLINE database. Results We included 26 studies. Three outcomes of automatic processes were tested: Affective reactions, attentional capture, and approach tendencies. Behavioral results show that physical activity can become attention-grabbing, automatically trigger positive affect, and elicit approach behaviors. These automatic reactions explain and predict exercise behaviors. However, the use of a wide variety of measures prevents drawing solid conclusions about the specific effects of automatic processes. Brain imaging results are scarce but show that stimuli associated with physical activity and, to a lesser extent, sedentary behaviors activate regions involved in reward processes. Studies investigating the rewarding value of behaviors driving energetic cost variations such as behaviors minimizing energetic cost are lacking. Conclusion Reward is an important factor in exercise behavior. The literature based on the investigation of automatic behaviors seems in line with the suggestion that physical activity is rewarding, at least for physically active individuals. Results suggest that sedentary behaviors could also be rewarding, although this evidence remains weak due to a lack of investigations. Finally, from an evolutionary perspective, behaviors minimizing energetic cost are likely to be rewarding. However, no study has investigated this hypothesis. In sum, additional studies are required to establish a strong and complete framework of the reward processes underlying automatic exercise behavior. Key points - Behavioral and brain imaging studies using different outcomes of automatic behavior show that physical activity and, to a weaker extent, sedentary behaviors are rewarding. - Behaviors minimizing energetic cost have been essential to evolutionary survival and are likely to be rewarding. However, experimental evidence is still lacking. - The dominant neuropsychological approaches to exercise behavior are incomplete, which may partly explain our current inability to counteract the pandemic of physical inactivity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0140.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.133
GPT teacher head0.414
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2017
Admission routes1
Has abstractyes

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