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Record W2096421670 · doi:10.1123/japa.2013-0002

GT3X+ Accelerometer, Yamax Pedometer, and SC-StepMX Pedometer Step Count Accuracy in Community-Dwelling Older Adults

2014· article· en· W2096421670 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Aging and Physical Activity · 2014
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPedometerInterquartile rangeMedicineAccelerometerPhysical activityPhysical therapyPhysical medicine and rehabilitationSurgeryComputer science

Abstract

fetched live from OpenAlex

The purpose was to compare step count accuracy of an accelerometer (ActiGraph GT3X+), a mechanical pedometer (Yamax SW200), and a piezoelectric pedometer (SC-StepMX). Older adults (n = 13 with walking aids, n = 22 without; M = 81.5 years old, SD = 5.0) walked 100 m wearing the devices. Device-detected steps were compared with manually counted steps. We found no significant differences among monitors for those who walked without aids (p = .063). However, individuals who used walking aids exhibited slower gait speeds (M = 0.83 m/s, SD = 0.2) than non-walking aid users (M = 1.21 m/s, SD = 0.2, p < .001), and for them the SC-StepMX demonstrated a significantly lower percentage of error (Mdn = 1.0, interquartile range [IQR] = 0.5-2.0) than the other devices (Yamax SW200, Mdn = 68.9, IQR = 35.9-89.3; left GT3X+, Mdn = 52.0, IQR = 37.1-58.9; right GT3X+, Mdn = 51.0, IQR = 32.3-66.5; p < .05). These results support using a piezoelectric pedometer for measuring steps in older adults who use walking aids and who walk slowly.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.930

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.361
Teacher spread0.328 · 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