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Record W2176717484 · doi:10.1177/0898264315609905

Associations of Daily Pedometer Steps and Self-Reported Physical Activity With Health-Related Quality of Life

2015· article· en· W2176717484 on OpenAlexaff
Jeff K. Vallance, Dean T. Eurich, Paul A. Gardiner, Lorian Taylor, Steven T. Johnson

Bibliographic record

VenueJournal of Aging and Health · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of AlbertaAlberta Health ServicesAthabasca University
Fundersnot available
KeywordsPedometerWaistMedicineQuality of life (healthcare)Physical therapyBody mass indexGerontologyRandom digit dialingConfidence intervalPopulationPhysical activityEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this article is to examine associations of self-reported physical activity (PA) and pedometer steps with health-related quality of life (HRQoL) among a population-based sample of older adults. METHOD: Adults ≥55 years (N = 1,296) were recruited through random-digit dialing and responded to a questionnaire via computer-assisted telephone interviewing methods. Questionnaires assessed demographic variables and validated measures of PA and HRQoL. Participants received a step pedometer and waist circumference tape measure via post. RESULTS: Compared with participants in the low-step group (0-6,999 steps/day), participants in the high-step group (>10,000 steps/day) had significantly higher scores on mental health (Mdiff = 3.1, p < .001, confidence intervals [CI] = [1.8, 4.3]), physical health (Mdiff = 3.5, p < .001, CI = [2.2, 4.7]), and global health (Mdiff = 3.5, p < .001, CI = [2.3, 4.7]). Waist circumference and body mass index did not moderate any associations of pedometer steps and PA with HRQoL. CONCLUSION: Older adults exceeding established step thresholds reported significantly higher HRQoL indices compared with those achieving lower thresholds.

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)0.002
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 score0.127
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.186
GPT teacher head0.426
Teacher spread0.241 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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