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Record W4283320623 · doi:10.2196/38101

Mobility-Focused Physical Outcome Measures Over Telecommunication Technology (Zoom): Intra and Interrater Reliability Trial

2022· article· en· W4283320623 on OpenAlexaffvenue
Marie‐Louise Bird, Felix Peel, Matthew Schmidt, Natalie A. Fini, Emily Ramage, Brodie M. Sakakibara, Dawn B. Simpson, Carey Mather, Dominique A. Cadilhac, Kdk Ahuja, Heather Bridgman, Coralie English

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIntraclass correlationInter-rater reliabilityStopwatchReliability (semiconductor)Physical therapyTelehealthPhysical medicine and rehabilitationIntra-rater reliabilityPsychologyRehabilitationMedicinePsychometricsTelemedicineStatisticsHealth careRating scaleMathematics

Abstract

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BACKGROUND: Rehabilitation provided via telehealth offers an alternative to currently limited in-person health care. Effective rehabilitation depends on accurate and relevant assessments that reliably measure changes in function over time. The reliability of a suite of relevant assessments to measure the impact of rehabilitation on physical function is unknown. OBJECTIVE: We aimed to measure the intrarater reliability of mobility-focused physical outcome measures delivered via Zoom (a commonly used telecommunication platform) and interrater reliability, comparing Zoom with in-person measures. METHODS: In this reliability trial, healthy volunteers were recruited to complete 7 mobility-focused outcome measures in view of a laptop, under instructions from a remotely based researcher who undertook the remote evaluations. An in-person researcher (providing the benchmark scores) concurrently recorded their scores. Interrater and intrarater reliability were assessed for Grip Strength, Functional Reach Test, 5-Time Sit to Stand, 3- and 4-Meter Walks and Timed Up and Go, using intraclass correlation coefficients (ICC) and Bland-Altman plots. These tests were chosen because they cover a wide array of physical mobility, strength, and balance constructs; require little to no assistance from a clinician; can be performed in the limits of a home environment; and are likely to be feasible over a telehealth delivery mode. RESULTS: A total of 30 participants (mean age 36.2, SD 12.5 years; n=19, 63% male) completed all assessments. Interrater reliability was excellent for Grip Strength (ICC=0.99) and Functional Reach Test (ICC=0.99), good for 5-Time Sit to Stand (ICC=0.842) and 4-Meter Walk (ICC=0.76), moderate for Timed Up and Go (ICC=0.64), and poor for 3-Meter Walk (ICC=-0.46). Intrarater reliability, accessed by the remote researcher, was excellent for Grip Strength (ICC=0.91); good for Timed Up and Go, 3-Meter Walk, 4-Meter Walk, and Functional Reach (ICC=0.84-0.89); and moderate for 5-Time Sit to Stand (ICC=0.67). Although recorded simultaneously, the following time-based assessments were recorded as significantly longer via Zoom: 5-Time Sit to Stand (1.2 seconds), Timed Up and Go (1.0 seconds), and 3-Meter Walk (1.3 seconds). CONCLUSIONS: Untimed mobility-focused physical outcome measures have excellent interrater reliability between in-person and telehealth measurements. Timed outcome measures took approximately 1 second longer via Zoom, reducing the reliability of tests with a shorter duration. Small time differences favoring in-person attendance are of a similar magnitude to clinically important differences, indicating assessments undertaken using telecommunications technology (Zoom) cannot be compared directly with face-to-face delivery. This has implications for clinicians using blended (ie, some face-to-face and some via the internet) assessments. High intrarater reliability of mobility-focused physical outcome measures has been demonstrated in this study.

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)0.027
metaresearch head score (Gemma)0.041
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.037
GPT teacher head0.374
Teacher spread0.338 · 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".

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Citations13
Published2022
Admission routes2
Has abstractyes

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