Experiences and Perceptions of Using Smartphone Ecological Momentary Assessment for Reporting Knee Osteoarthritis Pain and Symptoms

Author:

Overton Mark1,Swain Nicola1,Falling Carrie1,Gwynne-Jones David2,Fillingim Roger3,Mani Ramakrishnan1

Affiliation:

1. Centre for Health, Activity and Rehabilitation Research, School of Physiotherapy, University of Otago

2. Department of Surgical Sciences, Otago School of Medicine, University of Otago, Dunedin, New Zealand

3. Department of Community Dentistry and Behavioural Science, Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL

Abstract

Background: Knee osteoarthritis (OA) is a prevalent, painful, and disabling musculoskeletal condition. One method that could more accurately monitor the pain associated with knee OA is ecological momentary assessment (EMA) using a smartphone. Objectives: The aim of this study was to explore participant experiences and perceptions of using smartphone EMA as a way of communicating knee OA pain and symptoms following participating in a 2-week smartphone EMA study. Materials and Methods: Using a maximum variation sampling method, participants were invited to share their thoughts and opinions in semistructured focus group interviews. Interviews were recorded and transcribed verbatim before thematic analysis using the general inductive approach. Results: A total of 20 participants participated in 6 focus groups. Three themes and 7 subthemes were identified from the data. Identified themes included: user experience of smartphone EMA, data quality of smartphone EMA, and practical aspects of smartphone EMA. Discussion: Overall, smartphone EMA was deemed as being an acceptable method for monitoring pain and symptoms associated with knee OA. These findings will assist researchers in designing future EMA studies alongside clinicians implementing smartphone EMA into practice. Perspective: This study highlights that smartphone EMA is an acceptable method for capturing pain-related symptoms and experiences of those expereiencing knee OA. Future EMA studies should ensure design features are considered that reduce missing data and limit the responder burden to improve data quality.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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