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Record W6945231699 · doi:10.25384/sage.c.4649402

A Location-Based Objective Assessment of Physical Activity and Sedentary Behavior in Ambulatory Hemodialysis Patients

2019· other· en· W6945231699 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2019
Typeother
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsnot available
Fundersnot available
KeywordsSedentary behaviorPhysical activityEnergy expenditureAmbulatoryHemodialysisSedentary lifestyleMetabolic equivalentActivity monitor

Abstract

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Background:Dialysis patients have reduced moderate to vigorous physical activity, and light physical activity. This has been shown in self-reported surveys and objective accelerometer studies. Less attention has been directed toward sedentary behavior, which is characterized by low energy expenditure (≤1.5 metabolic equivalents). Furthermore, locations where physical activity and sedentary behavior occur are largely unknown for dialysis patients.Objectives:The objectives of this study were (1) to determine the minutes per day of moderate to vigorous physical activity, light physical activity, and sedentary behavior for hemodialysis patients; (2) to describe differences in moderate to vigorous physical activity, light physical activity, and sedentary behavior comparing dialysis versus nondialysis days; and (3) to describe the locations where moderate to vigorous physical activity, light physical activity, and sedentary behavior occur using global positioning system (GPS) data.Design:Cross-sectional study.Setting:The study was performed at a tertiary care hospital in Nova Scotia, Canada.Patients:A total of 50 adult in-center hemodialysis patients consented to the study.Measurements:Physical activity and sedentary behavior were measured with an Actigraph-GT3X accelerometer. Location was determined using a Qstarz BT-Q1000X GPS receiver.Methods:Minutes of daily activity were described as was percentage of wear time for each activity level across different locations during waking hours. Physical activity intensity, quantity, and location were also analyzed according to dialysis vs nondialysis days.Results:Forty-three patients met requirements for accelerometer analysis, of whom 42 had GPS data. Median wear time was 836.5 min/day (interquartile range [IQR]: 788.3-918.3). Median minutes of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 636 minutes (IQR: 594.1-730.1), 178 minutes (IQR: 144-222.1), and 1.6 minutes (IQR: 0.6-7.7), respectively. Proportion of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 78.4% (IQR: 70.7-84.0), 21.5% (IQR: 16.0-26.9), and 0.2% (IQR: 0.1-1.1), respectively. Home was the dominant location for total linked accelerometer-GPS time (59.4%, IQR: 46.9-69.5) as well as for each prespecified level of activity. Significantly more sedentary behavior and less light physical activity occurred on dialysis days compared with nondialysis days (<i>P</i> ≤ .01, respectively). Moderate to vigorous physical activity did not differ significantly between dialysis and nondialysis days.Limitations:Small sample size from a single academic center may limit generalizability. Difficult to engage population as less than half of eligible dialysis patients provided consent. Physical activity may have been underestimated as devices were not worn for all waking hours or aquatic activities, and hip-based accelerometers may not capture stationary exercise.Conclusions:Ambulatory, in-center hemodialysis patients exhibit substantial sedentary behavior and minimal physical activity across a limited range of locations. Given the sedentary tendencies of this population, focus should be directed on increasing physical activity at any location frequented. Home-based exercise programs may serve as a potential adjunct to established intradialytic-based therapies given the amount of time spent in the home environment.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.355
Teacher spread0.324 · 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.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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