Evidence map›Paper›PMID 28582250›Full record

ArticleJMIR rehabilitation and assistive technologies2016

A Personalized Self-Management Rehabilitation System with an Intelligent Shoe for Stroke Survivors: A Realist Evaluation.

Susan Mawson, Nasrin Nasr, Jack Parker, Richard Davies, Huiru Zheng, Gail Mountain

Abstract read
In one paragraph

Article in JMIR rehabilitation and assistive technologies, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

6 authors.

Susan MawsonRehabilitation and Assistive Technology Research Group, School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.ORCID http://orcid.org/0000-0003-2795-8983
Nasrin NasrRehabilitation and Assistive Technology Research Group, School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.ORCID http://orcid.org/0000-0002-3584-5906
Jack ParkerRehabilitation and Assistive Technology Research Group, School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.ORCID http://orcid.org/0000-0003-4684-7330
Richard DaviesComputer Science Research Institute, School of Computing and Mathematics, Ulster University, County Antrim, United Kingdom.ORCID http://orcid.org/0000-0002-2663-3979
Huiru ZhengComputer Science Research Institute, School of Computing and Mathematics, Ulster University, County Antrim, United Kingdom.ORCID http://orcid.org/0000-0001-7648-8709
Gail MountainRehabilitation and Assistive Technology Research Group, School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.ORCID http://orcid.org/0000-0002-5417-7691

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the United Kingdom, stroke is the most significant cause of adult disability. Stroke survivors are frequently left with physical and psychological changes that can profoundly affect their functional ability, independence, and social participation. Research suggests that long-term, intense, task- and context-specific rehabilitation that is goal-oriented and environmentally enriched improves function, independence, and quality of life after a stroke. It is recommended that rehabilitation should continue until maximum recovery has been achieved. However, the increasing demand on services and financial constraints means that needs cannot be met through traditional face-to-face delivery of rehabilitation. Using a participatory design methodology, we developed an information communication technology-enhanced Personalized Self-Managed rehabilitation System (PSMrS) for stroke survivors with integrated insole sensor technology within an "intelligent shoe.". The intervention model was based around a rehabilitation paradigm underpinned by theories of motor relearning and neuroplastic adaptation, motivational feedback, self-efficacy, and knowledge transfer.

objectiveTo understand the conditions under which this technology-based rehabilitation solution would most likely have an impact on the motor behavior of the user, what would work for whom, in what context, and how. We were interested in what aspects of the system would work best to facilitate the motor behavior change associated with self-managed rehabilitation and which user characteristics and circumstances of use could promote improved functional outcomes.

methodsWe used a Realist Evaluation (RE) framework to evaluate the final prototype PSMrS with the assumption that the intervention consists of a series of configurations that include the Context of use, the underlying Mechanisms of change and the potential Outcomes or impacts (CMOs). We developed the CMOs from literature reviews and engagement with clinicians, users, and caregivers during a series of focus groups and home visits. These CMOs were then tested in five in-depth case studies with stroke survivors and their caregivers.

resultsWhile two new propositions emerged, the second importantly related to the self-management aspects of the system. The study revealed that the system should also encourage independent use and the setting of personalized goals or activities.

conclusionsInformation communication technology that purports to support the self-management of stroke rehabilitation should give significant consideration to the need for motivational feedback that provides quantitative, reliable, accurate, context-specific, and culturally sensitive information about the achievement of personalized goal-based activities.

Indexed as

designequipment designrealist evaluationself-careself-managementsensor technologystroketelehealth

Identifiers

PMID28582250
PMCPMC5454551

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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.