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MEASURING PHYSICAL ACTIVITY IN OLDER ADULT EXERCISERS WITH PEDOMETERS

2001· article· en· W2082011462 on OpenAlexaff
G. R. Jones, Catrine Tudor‐Locke, D. H. Paterson

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2001
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsSt Joseph's Health CareSt Joseph's Health Centre
Fundersnot available
KeywordsPhysical activityPhysical medicine and rehabilitationPhysical therapyPsychologyGerontologyMedicine

Abstract

fetched live from OpenAlex

The pedometer is reported to be a reliable and inexpensive tool to measure physical activity in independent living populations, however, previous studies have reported a large degree of variability between the accumulated steps on weekdays and weekends. An important question that arises concerning the use of pedometers is whether weekdays, weekends, or a combination of these days should be utilized to monitor physical activity. The purpose of this study was to determine on which days community-dwelling older adults should be monitored to provide an estimate of their daily physical activity. Eighteen subjects (6 males,12 females; mean age 69 ± SD 9, 95%CI = 65–73), self-monitored their physical activity by wearing a Digi-walker sw-200, during waking hours, for 9 consecutive days (two weekends framing five weekdays). The average number of steps/day were 6,559 ± 3,765, 95% CI = 4,820 - 8,299. A one-way analysis of variance indicated that there was a significant difference (p = 0.02) in the accumulated steps between days. Post-Hoc analysis revealed that pedometer values were higher on weekdays (7,463 ± 3,393, 95% CI = 5,880 - 9,046) than weekends (5,430 ± 3,922, 95% CI = 3,600 - 7,060) and highest on days attending exercise class (8,119 ± 2,912, 95% CI = 6,760 - 9,478), which were Monday, Wednesday and Friday. The results suggests that when using pedometers to assess physical activity in exercising, community-dwelling older adults both weekdays and weekends should be sampled.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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