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Record W3028922503 · doi:10.1123/japa.2019-0235

Accuracy of Thresholds Based on Cadence and Lifestyle Counts per Minute to Detect Outdoor Walking in Older Adults With Mobility Limitations

2020· article· en· W3028922503 on OpenAlexafffund
Sandra C. Webber, Francine Hahn, Lisa M. Lix, Brenda J. Tittlemier, Nancy M. Salbach, Ruth Barclay

Bibliographic record

VenueJournal of Aging and Physical Activity · 2020
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of ManitobaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCadenceInterquartile rangeConfidence intervalMedicinePreferred walking speedPhysical medicine and rehabilitationPopulationPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the optimal threshold, based on cadence and lifestyle counts per minute, to detect outdoor walking in mobility-limited older adults. METHODS: Older adults (N = 25, median age: 77.0 years, interquartile range: 10.5) wore activity monitors during 80 outdoor walks. Walking bouts were identified manually (reference standard) and compared with identification using cadence thresholds (≥30, ≥35, ≥40, ≥45, and ≥50 steps/min) and >760 counts per minute using low frequency extension analysis. RESULTS: Median walking bout duration was 10.5 min (interquartile range 4.8) and median outdoor walking speed was 0.70 m/s (interquartile range 0.20). Cadence thresholds of ≥30, ≥35, and ≥40 steps/min demonstrated high sensitivity (1.0, 95% confidence intervals [0.95, 1.0]) to detect walking bouts; estimates for specificity and positive predictive value were highest for ≥40 steps/min. CONCLUSION: A cadence threshold of ≥40 steps/min is recommended for detecting sustained outdoor walking in this population.

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)0.000
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 score0.384
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.035
GPT teacher head0.344
Teacher spread0.309 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

Explore more

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