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Record W2800317144 · doi:10.2196/mhealth.9471

Using Digital Health Technologies to Understand the Association Between Movement Behaviors and Interstitial Glucose: Exploratory Analysis

2018· article· en· W2800317144 on OpenAlexvenueno aff
Andrew P. Kingsnorth, Maxine Whelan, James P Sanders, Lauren B. Sherar, Dale Esliger

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNIHR Leicester Biomedical Research CentreLoughborough UniversityUniversity of LeicesterNational Institute for Health and Care ResearchUniversity Hospitals of Leicester NHS Trust
KeywordsPostprandialGlycemicMedicinePhysical activityInternal medicineEndocrinologyBody mass indexDiabetes mellitusPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Acute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings. OBJECTIVE: The objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults. METHODS: This study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1]) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations. RESULTS: After adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P<.001), SD of glucose (mmol/L; beta=0.0006; 95% CI 0.00037-0.00081; P<.001), and MAGE (mmol/L; beta=0.002; 95% CI 0.00131-0.00273; P<.001) for those of a lower fitness. Additionally, light activity was inversely associated with mean glucose (mmol/L; beta=-0.0004; 95% CI -0.00078 to -0.00006; P=.02), SD of glucose (mmol/L; beta=-0.0006; 95% CI -0.00085 to -0.00039; P<.001), and MAGE (mmol/L; beta=-0.002; 95% CI -0.00285 to -0.00146; P<.001) for those of a lower fitness. Moderate to vigorous physical activity was only inversely associated with mean glucose (mmol/L; beta=-0.002; 95% CI -0.00250 to -0.00058; P=.002). CONCLUSIONS: Evidence of an acute stimulus-response model was observed between sedentary time, physical activity, and glucose variability in low fitness individuals, with sedentary time and light activity conferring the most consistent changes in glucose variability. Further work is required to investigate the coupling of movement behaviors and glucose responses in larger samples and whether providing these rich data sources as feedback could induce lifestyle behavior change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.125
GPT teacher head0.418
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2018
Admission routes1
Has abstractyes

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