Claire L. Little, Katie L. Druce, William G. Dixon, David M. Schultz , Thomas House, John McBeth
Abstract
Because people with chronic pain feel uncertain about their future pain, a pain-forecasting model could support individuals to manage their daily pain and improve their quality of life. We conducted two patient and public involvement activities to design the content of a pain-forecasting model by learning participants’ priorities in the features provided by a pain forecast and understanding the perceived benefits that such forecasts would provide.
Introduction
Chronic pain (i.e., pain lasting at least three months) is experienced by an estimated 43% of adults in the United Kingdom [1, 2]. Chronic pain conditions are associated with significant individual and societal burden. They are among the leading causes of disability globally [3], with an estimated 568 million global cases of lower-back pain alone in 2019 [4]. Individuals report that pain interferes with their professional and social lives, affects their relationships, and decreases their quality of life, mood and sleep [5]. In the UK, 13.4% of sickness days were due to musculoskeletal conditions in 2021 [6]. Although up-to-date figures are scarce, the economic costs of chronic pain are considerable. For example, chronic pain conditions cost 1.5–3% of European GDP in 2012 [7].
Materials and method
Two PPI activities were conducted with individuals with chronic pain. The first PPI activity was a focus group to inform the second PPI activity, a survey of people living with chronic pain. The aim of the focus group was to identify potential pain features that could be produced by a pain forecast and a list of potential benefits of a pain forecast.
Results
Demographic data of the 12 participants are provided in Table 3. There were nine females and three males, with all age brackets (18–25, 26–45, 46–65 or 66+) represented. Six participants had been living with chronic pain for at least five years, and five participants had two or more chronic-pain conditions. Chronic-pain conditions of the participants included osteoarthritis, chronic headache, fibromyalgia, neuropathic pain, rheumatoid arthritis, and spondyloarthritis.
Discussion
There are limitations to the PPI activities that should be considered. The representation of different conditions in our survey may have been impacted by the charities that shared the advertisements with their members, perhaps explaining the high prevalence of fibromyalgia among our respondents.
Conclusions
To understand whether individuals with chronic pain would be interested in forecasts of their pain, we conducted two patient and public involvement activities: a focus group of 12 participants and a survey of 148 other participants.
Citation: Little CL, Druce KL, Dixon WG, Schultz DM, House T, McBeth J (2023) What do people living with chronic pain want from a pain forecast? A research prioritization study. PLoS ONE 18(10): e0292968. https://doi.org/10.1371/journal.pone.0292968
Editor: Shadia Hamoud Alshahrani, King Khalid University, SAUDI ARABIA
Received: May 9, 2023; Accepted: October 3, 2023; Published: October 12, 2023
Copyright: © 2023 Little et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets generated and analysed during the current study are not publicly available as data were collected for patient and public involvement activities and consent was obtained for the sharing of anonymous quotes and aggregated data only. The data within the Cloudy with a Chance of Pain study is detailed health data for a national population and is both sensitive and special category data. Given the detailed nature of the dataset, it is not possible to provide a minimal de-identified dataset that retains the necessary data utility to replicate our study’s findings and be considered anonymised. Anonymisation of the Cloudy study data (whilst retaining data utility) is only possible through a combination of measures [i.e., de-identification, data minimisation related to the use case and the provision of access via a Secure Data Environment (SDE)]. These measures are in line with the UK Anonymisation Network guidance (Elliot, Mackey, & O’Hara, 2020). We are currently working towards establishing the processes for supporting access and sharing via an SDE and anticipate the data
will be more widely sharable sometime in 2024. Elaine Mackey is the contact for dataset access ([email protected]).
Funding: This work was supported by infrastructure support from the Centre for Epidemiology Versus Arthritis (grant number 21755; https://www.versusarthritis.org/research/our-current-research/our-research-centres/ [versusarthritis.org]). TH receives funding from the Royal Society (grant number INF/R2/180067; https://royalsociety.org/ [royalsociety.org]) and the Alan Turing Institute for Data Science and Artificial Intelligence (https://www.turing.ac.uk/ [turing.ac.uk]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Coauthor Will Dixon has received consultancy fees from Google, and David Schultz has received consultancy fees from Palta, both unrelated to this work. All other authors have no conflicts of interest to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292968#sec009
