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Activity Pacing Patterns In Adults With Disabilities

2022· article· en· W4294844992 on OpenAlexaff
Ioulia Barakou, Bregje L. Seves, Trynke Hoekstra, Femke Hoekstra, Leonie A. Krops, Pim Brandenbarg, L.H.V. van der Woude, Rienk Dekker, Florentina J. Hettinga

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMann–Whitney U testPhysical activityBody mass indexPhysical therapyMedicinePsychologyPhysical medicine and rehabilitationAudiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Activity pacing is a self-management strategy targeting inefficient activity patterns to help people with disabilities to engage in an active lifestyle. This study explored how two pacing patterns, even and fluctuating, are associated with personal characteristics in people with disabilities: personal factors, perceived fatigue, self-reported attitudes towards activity pacing, and physical activity. METHODS: 58 Participants wore an Actiheart activity monitor for one week and filled in the Fatigue Severity Scale assessing perceived fatigue, the Adapted-SQUASH assessing self-reported physical activity, and a questionnaire assessing perceived attitudes towards activity pacing. Based on a previously reported method, the median split over the week of the standard deviation of accelerometer-derived activity over the day was used (measure of activity pacing). Participants were defined as even or fluctuating pacers. The differences in personal characteristics for the two groups were examined using Independent samples t-test and Mann Whitney U test. RESULTS: Even pacers (N = 27) were older (60.4 ± 16.3 yr, t(df) = 2.3(56), p = .029) and had a higher Body Mass Index (28.9 ± 4.6 kg/m2, t(df) = 3.8(56), p = <.001) than fluctuating pacers (N = 31) (49.7 ± 19.5 yr), (24.9 ± 3.5 kg/m2). Even pacers also reported lower total minutes of self-reported physical activity per week (Median = 360 mins, IQR = 165-765 mins, Mann Whitney U = 276.5, p = .027) than fluctuating pacers (Median = 652.5 mins, IQR = 270-1435 mins). Even pacers had lower total minutes of accelerometer-derived physical activities per week (Median = 106 mins, IQR = 52-237 mins, U = 60, p = <.001) compared to fluctuating pacers (Median = 695.8 mins, IQR = 420-940 mins). Perceived activity pacing and fatigue were not different between the two groups (p= > .05). CONCLUSIONS: The comparison of the groups revealed differences in personal factors and physical activity, although they experienced similar fatigue levels. Those with an even pacing pattern showed lower physical activity levels, which may imply that pacing is used to regulate fatigue through lowering physical activity peaks as a reactive response to symptom occurrence. Future research could explore how pacing, physical activity, and fatigue management advice can contribute to increased physical activity.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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