Evidence map›Paper›PMID 38431553›Full record

ArticleBMC musculoskeletal disorders2024

Development of the intelligent knee osteoarthritis lifestyle app: a person-based approach.

Richard D M Stevenson, Enhad A Chowdhury, Victor B Inza, Max J Western, Nicola E Walsh, Simon L Jones, James L J Bilzon

Open access · goldAbstract read
In one paragraph

Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.7field-weighted citation impact, top 5% of its field
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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

7 authors at 4 institutions in 2 countries.

Richard D M StevensonDepartment for Health, The University of Bath, Bath, BA2 7AY, UK.
Enhad A ChowdhuryDepartment for Health, The University of Bath, Bath, BA2 7AY, UK.
Victor B InzaVisual Computing Centre, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Max J WesternDepartment for Health, The University of Bath, Bath, BA2 7AY, UK.
Nicola E WalshFaculty of Health and Applied Science, University of the West of England, Bristol, UK.
Simon L JonesDepartment of Computer Science, The University of Bath, Bath, BA2 7AY, UK.
James L J BilzonDepartment for Health, The University of Bath, Bath, BA2 7AY, UK. J.Bilzon@bath.ac.uk.
Versus Arthritis · GBUniversity of Bath · GBKing Abdullah University of Science and Technology · SAUniversity of the West of England · GB

Funding

Versus Arthritis 21595
6 · The paper itself

Abstract

backgroundKnee osteoarthritis is one of the most prevalent long term health conditions globally. Exercise and physical activity are now widely recognised to significantly reduce joint pain, improve physical function and quality of life in patients with knee osteoarthritis. However, prescribed exercise without regular contact with a healthcare professional often results in lower adherence and poorer health outcomes. Digital mobile health (mHealth) technologies offer great potential to support people with long-term conditions such as knee osteoarthritis more efficiently and effectively and with relatively lower cost than existing interventions. However, there are currently very few mHealth interventions for the self-management of knee osteoarthritis. The aim of the present study was to describe the development process of a mHealth app to extend the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through self-management, personalised physical activity, education, and social support.

methodsThe development of the intelligent knee osteoarthritis lifestyle application intervention involved an iterative and interconnected process comprising intervention 'planning' and 'optimisation' informed by the person-based approach framework for the development of digital health interventions. The planning phase involved a literature review and collection of qualitative data obtained from focus groups with individuals with knee osteoarthritis (n = 26) and interviews with relevant physiotherapists (n = 5) to generate 'guiding principles' for the intervention. The optimisation phase involved usability testing (n = 7) and qualitative 'think aloud' sessions (n = 6) with potential beneficiaries to refine the development of the intervention.

resultsKey themes that emerged from the qualitative data included the need for educational material, modifying activities to suit individual abilities and preferences as well as the inclusion of key features such as rehabilitation exercises. Following a user-trial further changes were made to improve the usability of the application.

conclusionsUsing a systematic person-based, development approach, we have developed the intelligent knee osteoarthritis lifestyle application to help people maintain physical activity behaviour. The app extends the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through personalised physical activity guidance, education, and social support.

Indexed as

Mobile ApplicationsOsteoarthritis, KneeSelf-ManagementHumansLife StyleQuality of LifeDigital healthKnee osteoarthritisMobile applicationPhysical activitySelf-management

Identifiers

PMID38431553
PMCPMC10908108
OpenAlexW4392348719

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.