Evidence map›Paper›PMID 34504039›Full record

ArticleJournal of sport & exercise psychology2021

Burstiness and Stochasticity in the Malleability of Physical Activity.

Vincent Berardi, David Pincus, Evan Walker, Marc A Adams

Open access · bronzeAbstract read
In one paragraph

Article in Journal of sport & exercise psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Observational
  3. How do 24-h movement behaviours change during and after vacation? A cohort study.The international journal of behavioral nutrition and physical activity · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Vincent BerardiChapman University.
David PincusChapman University.
Evan WalkerChapman University.
Marc A AdamsArizona State University.
Chapman University · USArizona State University · US

Funding

WalkIT: Neighborhood walkability and moderation of adaptive walking interventionsR01CA198915 · NCI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI ADAMS, MARC A · 2015 to 2019
$2.8M
NCI NIH HHS R01 CA198915
6 · The paper itself

Abstract

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability.

Indexed as

AccelerometryExerciseWalkingAdultFemaleHealth PromotionHumansMaleMiddle Agedbehavioral interventionbehavior maintenanceinverse power lawself-organization

Identifiers

PMID34504039
PMCPMC9792373
OpenAlexW3196458535

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.