Evidence map›Paper›PMID 40228292›Full record

ArticleJMIR rehabilitation and assistive technologies2025

Understanding Patient and Physiotherapist Requirements for a Personalized Automated Smartphone Telemonitored App for Posttotal Knee Arthroplasty Rehabilitation: Qualitative Study.

Eleanor Shuxian Chew, Aileen Eugenia Scully, Samanth Shi-Man Koh, Ee-Lin Woon, Juanita Krysten Miao-Shi Low, Yu-Heng Kwan, John Wei-Ming Tan, Yong-Hao Pua, Celia Ia-Choo Tan, Luke Jonathan Haseler

Abstract read
In one paragraph

Article in JMIR rehabilitation and assistive technologies, 2025. 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

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

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

10 authors.

Eleanor Shuxian Chew *Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0002-8801-5041
Aileen Eugenia Scully *Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore, Singapore.ORCID http://orcid.org/0000-0002-4578-2048
Samanth Shi-Man KohDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0002-1218-7736
Ee-Lin WoonDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0002-9927-1569
Juanita Krysten Miao-Shi LowDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0002-7204-8025
Yu-Heng KwanProgramme in Health Services and System Research, Duke-NUS Medical School, Singapore, Singapore.ORCID http://orcid.org/0000-0001-7802-9696
John Wei-Ming TanDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0003-1838-0868
Yong-Hao PuaDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0003-2313-9665
Celia Ia-Choo TanDepartment of Physiotherapy, Singapore General Hospital, Singapore, Singapore.ORCID http://orcid.org/0000-0002-3886-8222
Luke Jonathan HaselerCurtin School of Allied Health, Curtin University, Perth, Australia.ORCID http://orcid.org/0000-0003-1607-4402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Total knee arthroplasty (TKA) is a cost-effective surgical intervention for painful knee osteoarthritis in older adults, but postsurgery rehabilitation access is limited. Telerehabilitation offers a solution, but existing models require significant therapist involvement and a costly setup. A personalized smartphone-based automated program could be a cost-effective alternative. Objective: This study aimed to understand the requirements of both patients and physiotherapists in developing an automated telemonitored rehabilitation smartphone app for individuals undergoing TKA. To ensure uptake and long-term sustainability, this study adopted a person-based approach. Methods: A multistakeholder qualitative study of user needs was conducted. Physiotherapists and patients who underwent TKA were recruited via purposive sampling. Individual in-depth, hour-long interviews were conducted via Zoom by an experienced, trained female interviewer with a Master of Arts in Sociology. Data were audio-recorded and transcribed by the same interviewer. Two reviewers (ESC and SSK) independently analyzed the data using thematic analysis, with data triangulation achieved through cross-checking of data sources by 3 reviewers (ESC, SSK, and AES). Interviews were conducted to data saturation. Results: Six patients and 4 physiotherapists participated. For the patient interface, patients emphasized ease of use and specified features like a search function and multilingual options. For the physiotherapist interface, physiotherapists stated ease of accessing patient data and outcome measures for effective monitoring as important. Both patients and physiotherapists highlighted the need for timely, condition-specific information, supplemented by visual aids to support exercises, pain management, and recovery goals. They also stressed the significance of progress tracking, feedback, and the ability to access health care professionals for reassurance. Motivational features, including reminders, prompts, and exercise logs, were recommended to improve adherence. Both groups similarly identified the need for initial training to ensure confident use of the app. Conclusions: This study provided insights into the requirements of potential end users of a smartphone app for automated telemonitored rehabilitation following TKA. This is useful for steering the development of a user-centric smartphone app.

Indexed as

applicationinterviewknee arthroplastyknee replacementmobilemobile phonesmartphone rehabilitationtelemonitored

Identifiers

PMID40228292
PMCPMC12011314

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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.