Musculoskeletal health is one of the leading public health problems world-wide (1). In Denmark it is estimated that 30% of the population suffers from musculoskeletal health problems and accounts for 12% of the total burden of disease (2, 3). It is the most frequent reason for seeking health care and 15% of all consultations in general practice are due to musculoskeletal health problems (4). As the population ages it is expected that this burden will continue to grow (1). In treatment of musculoskeletal pain, therapeutic exercise and physical activity are considered high-value and low-cost interventions to improve physical functioning and reducing pain (5). To obtain training programs and guidance to therapeutic exercises, patients are usually referred from general practice to a physiotherapist (6). To remedy the pressure of patients with musculoskeletal health problems in the Danish health care sector, the mobile app “TrainAwayPain” (TAP) was introduced in November 2019. The TAP mobile app includes three main components: 1) information and advice 2) videos for home exercises instructed by a physiotherapist and 3) monitoring of symptoms and activity by a validated scale (7) and feed-back mechanisms. TAP was developed by Sundhed.dk in cooperation with specialists from the Central Denmark Region, the North Denmark Region and the Doctor’s Handbook. It is free to download from App Store or Google Play (8).
In the last 20 years digital solutions has become an integrated part of the Danish health care system (9). One of the digital devices with a lot of utilization potential for use in the health sector is the smartphone, as many people are always close to their phone (10). WHO defines medical and public health practises supported by mobile phones as “mHealth” (11). Previous studies have examined the use of smartphones to improve self-management of musculoskeletal health problems (12-19). The studies found that for most people, the use of mHealth supports self-management of musculoskeletal health problems. A challenge facing the use of mHealth and other digital health care solutions, is that not all people are able to use and understand the solutions equally. A new report from WHO states that there is a great inequality in the ability to use digital health care (20). None of the previous studies have examined whether a socioeconomic difference in using mHealth for musculoskeletal health problems exists.
The TAP mobile app was developed with knowledge from behaviour change theories, among other Bandura’s concept of self-efficacy from the Social Cognitive Theory (21). The theory argues that accomplishment of a new behaviour depends on one’s degree of self-efficacy (22). A previous study has found this to be an important factor in the self-management of musculoskeletal pain (23). Bandura argues that self-efficacy among other things is formed by social relations, support and persuasions. Especially persons with high authority can affect one’s self-efficacy, e.g. a general practitioner (GP) (21).
The objective of this study was to investigate the association between socioeconomic status and the use of TAP. The study furthermore examines whether the use of the mobile app was modified when it was recommended by a GP.
Our hypothesis was that those who had the app recommended by a GP would have a greater degree of self-efficacy, and thus use the app to a greater extent, compared to those who found the app in another way.
We conducted a cross-sectional study in accordance with the STROBE statement (24) based on data from TAP, The Danish Civil Registration System (25) and The National Health Insurance Service Registry (26) provided by Statistics Denmark. The data from TAP included information on the use of the mobile app, pain site, pain duration and MSK-HQ score (7) at the time of download. Data from the Danish Civil Registration System included sociodemographic information on education level, age, ethnicity, civil status and place of residence. Additionally, we gained information on visits to a GP or other health care professionals from The National Health Insurance Service Registry. Data from TAP was merged to data from the Danish Civil Registration System and The National Health Insurance Service Registry by a personal identification number.
Measures
Use of TAP
Determination of whether a participant had used TAP actively was based on activity data and log-data from TAP. In TAP it is possible to register one’s activity, e.g. which exercise has been completed. It is also possible to register other physical activities than those presented in TAP, e.g. running, swimming etc. It was assumed that a participant had actively used TAP if they had registered any activity, no matter which kind. Data from TAP also included information on when a participant had last opened TAP. If a participants latest registered opening of TAP was on the same day as registration, it was assumed that they had not actively used TAP. If a participant had opened TAP after the day of registration it was assumed that they more than once had used TAP. The study thus considers use of TAP as having either registered an activity, no matter which one, or opening TAP any day after registration. A dichotomous variable was created and coded 0 if participants were non-users and 1 if they were users.
Socioeconomic status
The most frequently used measures of socioeconomic status are education, income and employment. The measures are interdependent, yet they provide different information. Income and employment are a snapshot of a person's socioeconomic status. Income and employment can change over time, resulting in a higher or lower socio-economic status, depending on how one’s income and job situation change. Employment as a goal has become more difficult to quantify, as types of jobs have changed significantly in recent years, and the forms of work in the different social classes are more like each other. Education as a measure has the advantage that causality problems often are avoided as you typically train while you are young and healthy, whereas employment and income can depend on one's state of health. Illness often means reduced employment and income, which affects the measure of one's socio-economic status (27). Due to this, this study uses education level as a measure of socioeconomic status. Information on education level was obtained from Statistics Denmark and participants were classified by their longest obtained education in the year they downloaded TAP. The variable was operationalized into four categories: “no education”, “low education”, “medium-long education” and “long education”.
Sociodemographic factors
Sociodemographic information included age, sex, civil status, ethnicity and place of residence. Information on sociodemographic factors was obtained from Statistics Denmark, where information from the population register is settled yearly. The information from the population register is thus linked to the participants according to which year they downloaded TAP.
Information on age at the time of download was operationalized into five categories: “<30 years”, “30-44 years”, “45-59 years”, “60-75 years” and “>75 years”. Participant’s sex was determined as what sex they are registered as in the Danish Civil Registration system, either “male” or “female”. Information on ethnicity was operationalized as “Danish” if participants were registered as Danish, and “Non-Danish” if participants were registered as immigrants or descendants of immigrants. Civil status was operationalized as “cohabiting” if participants were married, registered partners or cohabiting, and as “living alone” if they were single, widower or divorced. Place of residence was operationalized into five categories: “Capital”, “Zealand”, “Southern Denmark”, “Central Jutland” and “Northern Jutland”.
Recommendation of TAP from a GP
To assess whether participants had TAP recommended by a GP, we collected data from The National Health Insurance Service Registry. We obtained information on when the participants had seen a GP between 2019-2022. A consultation with a GP was included if it was a consultation by phone, email or physical. If the participants had a consultation up to 7 days before the day they downloaded TAP, it was assumed that the download of TAP was by recommendation of a GP.
Recommendation of TAP from other health care professionals
To make a sensitivity analysis we included visits to other health care professionals who would be likely to recommend TAP besides a GP. We assumed that this would either be a physiotherapist or a chiropractor. If the participants had a consultation up to 7 days before the day, they downloaded TAP with either a physiotherapist, chiropractor or GP it was assumed that the download of TAP was by recommendation of a health care professional.
Statistical analysis
The statistical analysis included a descriptive analysis, a main analysis, a stratified analysis and a sensitivity analysis to the stratified analysis.
The descriptive analysis described the characteristics of the participants by education level. The analysis was performed without any missing values on education level or use of TAP. A Pearson’s Chi2 was performed to compare the characteristics across the groups.
The main analysis included a logistical regression to examine the association between education level and the use of TAP. The association was both examined in an unadjusted model and in a model adjusted for age, sex, civil status, ethnicity and place of residence. A likelihood-ratio test was performed to test the hypothesis of no difference between the unadjusted and the adjusted model. Estimates were presented as odds ratios (OR) with 95% confidence intervals and p-values with a 0.05 level of significance.
An analysis was performed to examine the hypothesis of whether the association between education level and the use of TAP was modified by having TAP recommended by a GP. The model was adjusted for the same variables as the adjusted model in the main analysis.
The results were presented as relative risks (RR) and presented with the principles of M. Knol and T. VanderWeele (28).
A sensitivity analysis to the stratified analysis was performed to examine the results if we included recommendations from chiropractors and physiotherapists. The model was adjusted for the same variables as the previous analysis.
All statistical analysis was performed using STATA 17.