Evidence map›Paper›PMID 31249902›Full record

ArticleContemporary clinical trials communications2019

Examining conscious motor processing and the effect of single-task, dual-task and analogy training on walking during rehabilitation by older adults at risk of falling in Hong Kong: Design and methodology of a randomized controlled trial.

Thomson W L Wong

Erratum issuedAbstract read
In one paragraph

Article in Contemporary clinical trials communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Thomson W L WongSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Walking is a common activity among older adults. Fallers were discovered to have a higher propensity to conscious motor processing (reinvestment) than age-matched non-fallers, leading to movement disruption, possibly by constraining or interfering with automatic motor control mechanisms. However, gait rehabilitation programs by implicit motor learning methodologies may ameliorate reinvestment propensity while improving gait ability in older adults. One hundred and five older adults, with moderate to high risk of falling, will be recruited from different community centres in Hong Kong to investigate the effect of single-task, dual-task and analogy training (implicit motor learning methodologies) on walking during rehabilitation in the context of reinvestment propensity, balance, walking ability and fear of falling. Participants will be randomly allocated and received 12 training sessions in either a Single-task Walking Group (STWG), a Dual-task Walking Group (DTWG) or an Analogy Walking Group (AGWG). Reinvestment propensity, walking ability, balance and fear of falling will be assessed by the Chinese version Movement Specific Reinvestment Scale (MSRS-C), 10-meter Walk Test, Tinetti Balance Assessment Tool, Timed 'Up & Go' Test (TU&G), Berg Balance Scale (BBS) and 13-item Falls Efficacy Scale (FES-13), respectively at baseline before training (T0), just after completion of all training sessions (T1), and 6 months after completion of all training sessions (T2). Participants' number of falls between T1 and T2 will also be recorded. The results could establish a solid foundation for further development and implementation of a novel and effective gait rehabilitation program in fall prevention for older adults at risk of falling.

Indexed as

Analogy trainingDual-task trainingGait rehabilitationOlder adultsSingle-task training

Identifiers

PMID31249902
PMCPMC6584477

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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.