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Record W3164642450 · doi:10.1177/10731911211017637

A Comparison of Associations Between Self-Reported and Device-Based Sedentary Behavior and Obesity Markers in Adults: A Multi-National Cross-Sectional Study

2021· article· en· W3164642450 on OpenAlexaff
Gérson Ferrari, Marianella Herrera‐Cuenca, Ioná Zalcman Zimberg, Viviana Guajardo, Georgina Gómez, Dayana Quesada, Attilio Rigotti, Lilia Yadira Cortés, Martha Cecilia Yépez García, Rossina G. Pareja, Miguel Peralta, Adilson Marques, Ana Carolina Barco Leme, Irina Kovalskys, Scott Rollo, Mauro Fisberg

Bibliographic record

VenueAssessment · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of Guelph
FundersCoca-Cola
KeywordsWaistSittingSedentary behaviorBody mass indexAnthropometryObesityScreen timeSedentary lifestyleCircumferenceCross-sectional studyPsychologyPhysical therapyDemographyMedicinePhysical activityGerontologyMathematicsInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the associations between self-reported and device-based measures of sedentary behavior (SB) with obesity markers in adults from Latin American countries. Sitting time and total time spent in different SBs were self-reported using two different questionnaires. Accelerometers were used to assess total sedentary time. Body mass index, waist, and neck circumferences were assessed. The highest self-reported sitting time was in Argentina, the highest total time spent in different SBs was in Brazil and Costa Rica, and the highest device-based sedentary time was observed in Peru. Neither self-reported sitting time, total time spent in different SBs or device-based sedentary time were associated with body mass index. Device-based sedentary time was positively associated with waist circumference and self-reported sitting time was positively associated with neck circumference. Caution is warranted when comparing the associations of self-reported and device-based assessments of SB with anthropometric variables.

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 score0.030
Threshold uncertainty score0.592

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 head0.061
GPT teacher head0.421
Teacher spread0.361 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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