Evidence map›Paper›PMID 42682613›Full record

ArticleHong Kong journal of occupational therapy : HKJOT2026

Quantifying Electric-Powered Wheelchair (EPW) Driving Performance Using Jerk Metrics: A Pilot Study.

Serena S W Ng, Chung Hang Tang, Stephen C H Tse, Alfred C H Yip, Winnie W T Lam

Abstract read
In one paragraph

Article in Hong Kong journal of occupational therapy : HKJOT, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Serena S W NgDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.ORCID https://orcid.org/0009-0006-4783-7627
Chung Hang TangDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
Stephen C H TseDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
Alfred C H YipDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
Winnie W T LamDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Current assessments of electric-powered wheelchair (EPW) driving, such as the Wheelchair Skills Test (WST), rely on observational scoring and lack validation in outdoor environments. Occupational Therapists require standardized, quantitative measures to evaluate driving performance and monitor training progress. Objective: This pilot study examined: (1) the influence of cognitive, physical, and sensory components on EPW driving, (2) the validity of sensor-derived jerk metrics relative to WST scores, and (3) the feasibility of these metrics as outcome measures in an EPW training program. Methods: Seven adults with mobility impairments (six novice wheelchair drivers; mean age 59.4 years) completed baseline assessments. Sensors mounted on the EPW and joystick captured jerk during seven WST tasks with Apps displayed. Three metrics-median jerk, time above threshold, and the Jerk Index-were computed and correlated with WST scores. Pre/post comparisons were conducted following four training sessions. Results: Sensor-derived metrics demonstrated strong inverse correlations with WST scores (ρ = -0.750 to -0.857, Conclusions: Jerk-based metrics, particularly the Jerk Index, show promise as objective measures of EPW driving performance. They demonstrated concurrent validity with WST scores and applicability in outdoor contexts. Larger studies are warranted to confirm reliability, establish clinical thresholds, and support integration into rehabilitation practice.

Indexed as

driving performanceelectric-powered wheelchairjerk indexrehabilitationwheelchair skills test

Identifiers

PMID42682613
PMCPMC13530034

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.