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Record W2957984414 · doi:10.3389/fnagi.2019.00203

Revisiting the MotionWatch8©: Calibrating Cut-Points for Measuring Physical Activity and Sedentary Behavior Among Adults With Stroke

2019· article· en· W2957984414 on OpenAlexafffund
Ryan S. Falck, John R. Best, Michael C. R. Li, Janice J. Eng, Teresa Liu‐Ambrose

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2019
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British Columbia
FundersCanadian Institutes of Health ResearchFondation pour la Recherche sur AlzheimerVancouver Coastal Health Research InstituteUniversity of British Columbia
KeywordsStroke (engine)ActigraphyMedicinePhysical therapyPhysical medicine and rehabilitationQuality of life (healthcare)CognitionInternal medicineCircadian rhythmPsychiatry

Abstract

fetched live from OpenAlex

Reduced moderate-to-vigorous physical activity (MVPA) and increased sedentary behavior (SB) are common following stroke, which can limit stroke recovery and contribute to greater cognitive decline. Hence, the MVPA and SB of adults with stroke should be measured concurrently using objective methods. One currently available method for objectively measuring MVPA and SB is the MotionWatch8© (MW8). However, adults with stroke can have significant mobility restrictions (depending on stroke severity) and thus it is important to determine separate MVPA and SB cut-points for adults with stroke, as well as validate separate cut-points: (1) when the MW8 is worn on the stroke affected side compared to the non-affected side; and (2) for adults with mild stroke versus adults with moderate-to-severe stroke. In the current study, we concurrently measured MW8 actigraphy (worn on both the stroke affected side and the non-affected side) and indirect calorimetry during 10 different activities of daily living for 43 adults with stroke (aged 55-87 years). Using intra-class correlations (ICC), we first investigated the agreement of the MW8 when placed on the affected side as compared to the non-affected side for: (1) all participants irrespective of stroke severity; (2) participants with mild stroke, classified as a Fugl Meyer motor score of ≥79/100; and (3) participants with moderate-to-severe stroke (i.e., Fugl Meyer < 79/100). We then determined cut-points for all participants-as well as separate cut-points based on stroke severity-on both the stroke affected side and non-affected side for SB and MVPA using receiver operating characteristic curves. The results of our analyses indicate that the agreement in MW8 output between the stroke affected and non-affected sides was moderate across all participants (ICC = 0.67), as well as for each sub-group (mild stroke: ICC = 0.64; moderate-to-severe stroke: ICC = 0.77). Additionally, the results of our cut-point analyses support using different cut-points for different levels of stroke severity and also for the stroke affected side. We determined the following cut-points: (1) for the affected side, adults with mild stroke have cut-points of SB ≤134 counts per minute (CPM) and MVPA ≥704 CPM, while adults with moderate-to-severe stroke have cut-points of SB ≤281 CPM and MVPA ≥468 CPM; and (2) the non-affected side, adults with mild stroke have cut-points of SB ≤162 CPM and MVPA ≥661 CPM, while adults with moderate-to-severe stroke have cut-points of SB ≤281 CPM and MVPA ≥738 CPM. Hence, these data provide a new measure for concurrently examining the dynamic relationships between MVPA and SB among adults with stroke.

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)0.008
metaresearch head score (Gemma)0.013
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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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