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Record W2110759991 · doi:10.1123/jpah.8.4.587

Actical Accelerometer Sedentary Activity Thresholds for Adults

2011· article· en· W2110759991 on OpenAlexafffundabout
Suzy L Wong, Rachel C. Colley, Sarah Connor Gorber, Mark S. Tremblay

Bibliographic record

VenueJournal of Physical Activity and Health · 2011
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsStatistics Canada
FundersInstitut pour la Recherche en Santé PubliquePublic Health Agency of Canada
KeywordsPhysical activityActivity monitorAccelerometerMedicinePhysical therapySedentary behaviorSedentary lifestylePhysical medicine and rehabilitationComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Actical accelerometer thresholds have been derived to enable objective measurement of time spent performing sedentary activity in children and adolescents, but not adults. Thus, the purpose of this study was to determine Actical accelerometer sedentary activity thresholds for adults. METHODS: Data were available from 3187 participants aged 6 to 79 years from a preliminary partial dataset of the Canadian Health Measures Survey, who wore an Actical for 7 days. Step count data were used to evaluate the use of 50, 100, and 800 counts per min (cpm) as sedentary activity thresholds. Minutes when no steps were recorded were considered minutes of sedentary activity. RESULTS: The use of higher cpm thresholds resulted in a greater percentage of sedentary minutes being correctly classified as sedentary. The percentage of minutes that were incorrectly classified as sedentary was substantially higher when using a threshold of 800 cpm compared with 50 or 100 cpm. Results were similar for children, adolescents, and adults. CONCLUSIONS: These findings suggest that a threshold of 100 cpm is appropriate for classifying sedentary activity of adults when using the Actical. As such, wear periods with minutes registering less than 100 cpm would be classified as time spent performing sedentary 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.184
GPT teacher head0.402
Teacher spread0.218 · 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

Citations184
Published2011
Admission routes3
Has abstractyes

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