Participation in a digital self-management intervention for osteoarthritis and socioeconomic inequalities in patient-related outcomes

Abstract

Objective

To investigate changes in socioeconomic inequalities in patient-related outcomes and pain medication use, following participation in a digital self-management intervention for osteoarthritis (OA) in Sweden.

Method

Participants with hip/knee OA enrolled in the digital intervention were included. Self-reported outcomes collected were the numerical rating scale (NRS) pain, activity impairment, general health, Knee/Hip injury and Osteoarthritis Outcome Score (KOOS-12, HOOS-12) Pain, Function, and Quality of Life subscales, 5-level EuroQol 5 Dimensions (EQ-5D-5L), Patient Acceptable Symptom State (PASS) for function, walking difficulties, fear of movement, wish for surgery, pain medication use, physical function measured by the 30s chair-stand test, and level of physical activity. Educational attainment was used as a socioeconomic measure and the concentration index was used to assess the magnitude of inequalities at baseline and 3 month follow-up.

Results

The study included 21,688 participants (mean ± sd age 64.1 ± 9.1 years, 74.4% females). All outcomes except for PASS demonstrated inequalities in favour of highly educated participants at both time-points, with highly educated participants reporting better outcomes. At 3 month follow-up, the magnitude of inequality widened for activity impairment, but narrowed for NRS pain, EQ-5D-5L, KOOS-12/HOOS-12 Pain and Function, physical function, and wish for surgery. There were no statistically conclusive changes in the magnitude of inequalities for the remaining outcomes.

Conclusions

There were inequalities in patient-related outcomes in favour of those with higher education among participants of a digital self-management intervention for OA, although the magnitude of these pre-existing inequalities generally narrowed after the 3 month intervention.

Osteoarthritis (OA) affects more than 500 million individuals worldwide (1). OA exerts a substantial economic impact on the individual, society, and the healthcare system, manifesting in several ways, including diminished working hours, impaired work performance, and premature retirement (2, 3). OA often exhibits a higher prevalence, greater severity, and increased impact among socioeconomically disadvantaged individuals and communities (4, 5). Examining social determinants and their impact on OA outcomes is crucial for enhancing our understanding of how the social context influences health-related outcomes. This understanding can inform targeted interventions and healthcare systems for better management of OA and prevention of progression by prioritizing equality to improve overall well-being in this population.

Over the past few decades, there has been a growing focus on self-management for individuals with chronic health conditions such as OA (6–10). Digital technologies including smartphone apps have emerged as an economical and effective community-based model of care (11, 12). The first-line treatment for OA recommended by guidelines involves evidence-based interventions such as education, exercises, and weight control (13), which can all be implemented through digital technologies (6, 14). These treatments have been shown to have a demonstrated efficacy in reducing the impairments and disabilities caused by OA and a potential to delay joint replacement surgery (15, 16). Previous research has reported substantial inequalities in access to and use of digital interventions, also known as the ‘digital divide’ and ‘intervention-generated inequality’, across sociodemographic groups (17–19). These inequalities may be associated with factors such as language, being an immigrant, poor education, and financial hardship.

In the present study, we explored the socioeconomic disparities in self-reported outcomes of patients with knee or hip OA and the association of these disparities with participating in a digital first-line self-management intervention for OA. By focusing on individuals who have access to digital interventions, we aimed to understand how socioeconomic disparities manifest within this subgroup of the population and how participation in such interventions may influence these disparities.

Method

We analysed prospectively collected data from a digital intervention programme in Sweden, known as Joint Academy® (14). In brief, Joint Academy (www.jointacademy.com) is an online adaptation of the evidence-based, structured, first-line self-management programme specifically designed for individuals with hip or knee OA, known as ‘Better management of patients with OsteoArthritis’ (20). Participants joined the digital programme by recommendation from their physical therapist, orthopaedic surgeon, or insurance company, or via online search engines. Before participants were accepted into the programme, all had a clinical OA diagnosis of the knee or hip confirmed historically via a physical visit, and/or by online questionnaire responses and telephone or video consultation with their physical therapist in the digital programme. To participate, individuals must have a proficient understanding of the Swedish language, possess a Swedish social security number, and have a digital ID. They should also own a smartphone and be comfortable with handling digital information.

The intervention entails a comprehensive programme that includes customized neuromuscular exercises and patient education through an app. The neuromuscular exercises target specific muscle groups and neural pathways, emphasizing proprioception, balance, and functional movement patterns. Unlike conventional exercises, which often focus on muscle strengthening or cardiovascular fitness alone, these exercises prioritize neuromuscular coordination. Our digital self-management intervention incorporates customized neuromuscular exercises to enhance movement quality, joint stability, and functional capacity. Participants are provided with two daily personalized video exercises that adjust progressively over a period of 3 months. In addition, they receive educational text lessons on OA two to three times a week, which typically take 5–10 min to complete. After each text lesson, participants answer a quiz question to confirm their understanding of the material. A registered physiotherapist oversees each participant’s progress through video or phone calls and maintains ongoing communication through an asynchronous chat function. Participants can actively participate in a peer-support chat room integrated within the application.

Socioeconomic measure

We used educational attainment as a socioeconomic measure. Educational attainment is commonly regarded as a proxy measure for socioeconomic status owing to its association with various socioeconomic factors such as income, occupation, and access to resources (21). Moreover, educational attainment has been demonstrated to correlate with health outcomes independently of income or occupation, making it a valuable indicator in studies where comprehensive socioeconomic data may not be available (22). While household income is another commonly used metric in socioeconomic research, it may suffer from issues such as underreporting, particularly in self-reported surveys, and may not fully capture the complexity of socioeconomic status. Furthermore, it is important to highlight other advantages of using education as a measure of socioeconomic status: education is a more stable (having less fluctuation) measure of socioeconomic status compared with income and occupation, and education is generally a predictor of income and/or occupation.

The participants were surveyed about their highest level of completed schooling and any further higher level education. Participants were categorized into three educational groups: (i) low: less than high school (0–11 years), (ii) medium: high school (12 years), and (iii) high: college/university degree (> 12 years). Individuals who had completed some college but did not attain a college or university degree were categorized as medium.

Patient-related outcomes

We included the following self-reported outcomes, all entered through the smartphone:

• 11-point numerical rating scale (NRS) pain (0 = no pain and 10 = worst possible pain)

• Activity impairment (‘During the past 7 days, how much did knee/hip osteoarthritis affect your ability to do your regular daily activities, other than work at a job?’) (0 = no effect on daily activities and 10 = completely preventing conducting daily activities) (20)

• Current general health (0 = worst imaginable and 10 = best imaginable)

• Pain, Function, and Quality of Life (QoL) subscales of the Knee injury and Osteoarthritis Outcome Score 12 (KOOS-12) and the Hip disability and Osteoarthritis Outcome Score 12 (HOOS-12) (0–100, higher values indicating better status) (23)

• 5-level EuroQol 5 Dimensions (EQ-5D-5L) (UK index score −0.285 to 1, a higher score indicating better status) (24)

• Patient Acceptable Symptom State (PASS) (‘Considering your hip/knee function, do you feel that your current state is satisfactory? With hip/knee function, you should take into account all activities you have during your daily life, sport and recreational activities, your level of pain and other symptoms, and quality of life’: No/Yes) (25)

• Walking difficulties (No/Yes)

• Fear of movement (No/Yes) (26)

• Wish for surgery (‘Are your symptoms so severe that you wish to undergo surgery in your knee/hip?’: No/Yes)

• Pain medication use during the past month (‘In the past month, have you used any medication for the pain in your hip or knee joint?’: No/Yes). This question was added to the programme in a later stage and hence the responses are available for a smaller sample of the participants.

• Physical function, by the 30s chair-stand test (video-instructed in app, number self-reported) (27)

• Level of physical activity (‘How much time do you spend in a typical week on daily physical activity that is not exercise, such as walking, cycling or gardening?’: Less than 60 minutes, 61 to 150 minutes, 151 to 300 minutes, and more than 300 minutes).

Data analysis

We calculated standardized differences to compare the baseline characteristics of participants included in and excluded from the analyses, and applied a threshold of 0.1 to define a meaningful difference (28). Differences were accounted for using inverse probability of inclusion weighting in the analysis. We used the concentration index to quantify the magnitude of educational inequalities at both baseline and the 3 month follow-up (29). The concentration index is a widely recognized measure for quantifying socioeconomic inequalities in health outcomes, particularly in the context of research on health disparities (30). It provides a comprehensive assessment of the distribution of health outcomes across different socioeconomic levels, accounting for the entire distribution rather than just the association between two variables.

The calculation of the concentration index is based on the concentration curve, which plots the cumulative percentage of the population ranked by a socioeconomic measure (horizontal axis) versus the cumulative percentage of a health outcome (vertical axis). If all the population experiences the same level of health outcome, the curve is a 45-degree line (perfect equality). Twice the area between the concentration curve and 45-degree line is defined as the concentration index. This index ranges from −1 to +1, with 0 reflecting perfect equality and higher absolute values indicating higher inequality. The index is negative (positive) when the outcome is disproportionately concentrated among the lowest (highest) socioeconomic groups. For bounded variables (having lower and upper bounds with no true zero), we applied Erreygers’ concentration index (31) in the study (this was the case for all variables except for physical function). We used bootstrapping with 1000 replications to calculate 95% confidence intervals for the concentration index for each time-point, as well as the difference between time-points. The concentration index was computed using the ‘conindex’ function in Stata (32). We also computed polychoric and polyserial correlations to assess the strength and direction of relationships between educational attainment and other variables.

Results

All consecutive participants aged ≥ 20 years with hip or knee OA who enrolled in the digital programme between January 2019 and April 2022, and who provided their informed consent for research at enrolment, were eligible for the current study (n = 27,169). Of these, we excluded 64 people (0.2%) who did not answer the baseline question regarding their educational attainment and 5417 (19.2%) who did not respond to the 3 month follow-up. In total, 21 688 participants, with mean ± sd age of 64.1 ± 9.1 years, of whom 74.4% were female, were included in our analysis. While the baseline characteristics of excluded participants (n = 5,481) were generally comparable to those of included participants, there were some meaningful differences, which disappeared after weighting by the inverse probability of inclusion (Table 1). Of the included participants, 11.6%, 32.7%, and 55.7% had low, medium, and high educational level, respectively. Participants with a low level of education were older and had a higher proportion of reported coexisting conditions compared to the other two groups (Table 2). The strength and direction of relationships between educational attainment and other variables are also presented in Table 2, which shows predominantly lower values in the high education group (negative correlations), with all correlations being weak (< 0.3).

Table 1. Absolute standardized difference in baseline characteristics of participants included and excluded before and after applying inverse probability weighting.

Characteristic	Included (n = 21,688)	Excluded (n = 5,481)	Absolute standardized difference*
			Before weighting	After weighting
Age (years)	64.1 ± 9.1	63.0 ± 10.8	0.11	0.06
Female	16,146 (74.5)	3,901 (71.2)	0.07	0.00
Body mass index (kg/m^2)	27.2 ± 4.7	27.4 ± 5.0	0.04	0.01
Education
Less than high school	2,505 (11.6)	998 (18.2)	0.19	0.01
High school	7,084 (32.7)	1,314 (24.0)	0.19	0.01
College/university	12,099 (55.8)	3,106 (56.7)	0.03	0.01
Missing	0	63 (1.1)
Diabetes	1,233 (5.7)	399 (7.3)	0.07	0.00
Lung diseases	2,241 (10.3)	603 (11.0)	0.03	0.00
Balance troubles	681 (3.1)	251 (4.6)	0.08	0.00
Rheumatoid arthritis	961 (4.4)	310 (5.7)	0.06	0.00
Cardiovascular diseases	1,618 (7.5)	448 (8.2)	0.03	0.00
Pain in other joints	16,446 (75.8)	3,951 (72.1)	0.07	0.06
NRS pain	5.1 (1.9)	5.3 (2.0)	0.10	0.01
Activity impairment	4.0 (2.4)	4.3 (2.5)	0.13	0.01
General health	6.6 (1.8)	6.4 (1.9)	0.11	0.01
UK EQ-5D-5L index score	0.74 (0.18)	0.71 (0.21)	0.16	0.03
KOOS-12/HOOS-12 Pain	52.7 (16.3)	51.1 (17.4)	0.10	0.01
KOOS-12/HOOS-12 Function	62.3 (19.2)	60.8 (20.7)	0.08	0.02
KOOS-12/HOOS-12 QoL	45.5 (17.0)	44.2 (18.1)	0.08	0.02
Physical function	12.9 (4.3)	13.0 (4.8)	0.01	0.03
Walking difficulties	14,358 (66.2)	3,657 (66.7)	0.03	0.00
Fear of movement	3,471 (16.0)	1,122 (20.5)	0.12	0.01
Wish for surgery	2,899 (13.4)	535 (9.8)	0.12	0.08
PASS	3,684 (17.0)	935 (17.1)	0.01	0.03
Physical activity
< 60 min	5,019 (23.1)	1,655 (30.2)	0.17	0.01
61–90 min	3,301 (15.2)	753 (13.9)	0.04	0.00
91–150 min	3,912 (18.0)	930 (17.2)	0.02	0.01
151–300 min	4,978 (23.0)	1107 (20.2)	0.06	0.00
> 300 min	4,478 (20.7)	973 (17.8)	0.07	0.00
Pain medication use†	2,053 (62.3)	1,810 (64.2)	0.04	0.01

Data are shown as mean ± sd or n (%).

*Values ≥ 0.10 are considered as meaningful differences.

†For those who responded to the pain medication use question.

NRS, numerical rating scale; KOOS, Knee injury and Osteoarthritis Outcome Score; HOOS, Hip disability and Osteoarthritis Outcome Score; QoL, quality of life; PASS, Patient Acceptable Symptom State.

Table 2. Descriptive characteristics of the participants at enrolment.

Characteristic	Participants with low education (n = 2,505)	Participants with medium education (n = 7,084)	Participants with high education (n = 12,099)	Correlation coefficient
Age (years)	67.2 ± 8.4	62.7 ± 8.7	64.3 ± 9.3	−0.041
Female	1,791 (71.5)	4,982 (70.3)	9,373 (77.5)	0.109
Body mass index (kg/m^2)	28.0 ± 4.6	28.0 ± 4.9	26.5 ± 4.5	−0.169
Affected joint				0.002
Hip	942 (37.6)	2,843 (40.1)	4,722 (39.0)
Knee	1,563 (62.4)	4,241 (59.9)	7,377 (61.0)
Diabetes	202 (8.1)	464 (6.6)	567 (4.7)	−0.119
Lung diseases	280 (11.2)	731 (10.3)	1,230 (10.2)	−0.017
Balance troubles	86 (3.4)	228 (3.2)	367 (3.0)	−0.022
Rheumatoid arthritis	140 (5.6)	340 (4.8)	481 (4.0)	−0.068
Cardiovascular diseases	248 (9.9)	516 (7.3)	854 (7.1)	−0.057
Pain in other joints	1,895 (75.7)	5,416 (76.5)	9,135 (75.5)	−0.010

Data are shown as mean ± sd or n (%).

Patient-related outcomes at baseline and 3 month follow-up, stratified by education, are reported in Table 3 for all three groups. All educational groups demonstrated improvements in the outcomes from the initial assessment to the 3 month follow-up period. The magnitude of these changes is reported in Table 4.

Table 3. Mean patient-reported outcome measures at baseline and 3 month follow-up weighted by inverse probability of inclusion, stratified by education.

Outcome measure	Baseline			3-month follow-up
	Low	Medium	High	Low	Medium	High
NRS Pain	5.6 ± 1.8	5.4 ± 1.9	4.9 ± 1.9	3.8 ± 2.2	3.5 ± 2.1	3.2 ± 2.0
Activity impairment	4.4 ± 2.3	4.2 ± 2.4	3.9 ± 2.4	3.5 ± 2.3	3.2 ± 2.3	2.9 ± 2.3
General health	6.4 ± 1.8	6.4 ± 1.9	6.6 ± 1.8	6.5 ± 1.9	6.5 ± 1.9	6.7 ± 1.8
UK EQ-5D-5L index score	0.7 ± 0.2	0.7 ± 0.2	0.8 ± 0.2	0.8 ± 0.2	0.8 ± 0.2	0.8 ± 0.2
KOOS-12/HOOS-12 Pain	48.1 ± 15.7	50.5 ± 16.4	54.6 ± 16.2	56.7 ± 18.1	58.9 ± 18.0	62.6 ± 17.1
KOOS-12/HOOS-12 Function	56.1 ± 18.7	59.3 ± 19.3	64.9 ± 18.8	63.5 ± 19.7	66.6 ± 19.7	71.2 ± 18.5
KOOS-12/HOOS-12 QoL	43.1 ± 16.5	44.4 ± 17.3	46.2 ± 17.1	49.8 ± 18.4	50.2 ± 18.4	52.0 ± 18.1
Physical function	12.4 ± 4.2	12.7 ± 4.2	13.3 ± 4.4	17.5 ± 5.3	18.0 ± 5.4	18.6 ± 5.7
Walking difficulties	72.6	68.1	64.1	62.7	59.5	55.0
Fear of moving	15.8	17.2	17.0	4.9	7.0	7.7
Wish for surgery	18.8	17.5	13.8	15.8	14.4	11.9
PASS	20.7	17.8	15.3	45.1	42.5	39.6
Physical activity
< 60 min	36.1	28.3	19.9	32.1	25.7	17.5
61–150 min	30.7	32.6	33.5	33.2	34.1	34.1
151–300 min	17.7	20.5	24.6	19.4	21.1	26.1
> 300 min	15.5	18.7	22.0	15.3	19.2	22.3
Pain medication use	68.8	60.3	60.6	55.1	46.4	48.7

Data are shown as mean ± sd or percentage.

NRS, numerical rating scale; EQ-5D-5L, 5-level EuroQol 5 Dimensions; KOOS, Knee injury and Osteoarthritis Outcome Score; HOOS, Hip disability and Osteoarthritis Outcome Score; QoL, quality of life; PASS, Patient Acceptable Symptom State.

Table 4. Mean change in patient-reported outcome measures weighted by inverse probability of inclusion, stratified by education.

	Educational level
Outcome measure	Low	Medium	High
NRS Pain	−1.8 (−1.9, −1.8)	−1.9 (−1.9, −1.8)	−1.7 (−1.7, −1.7)
Activity impairment	−0.9 (−1.0, −0.8)	−0.9 (−1.0, −0.9)	−1.0 (−1.0, −1.0)
General health	0.1 (0.0, 0.2)	0.1 (0.1, 0.2)	0.1 (0.1, 0.1)
UK EQ-5D-5L index score	0.05 (0.05, 0.06)	0.05 (0.05, 0.05)	0.04 (0.04, 0.04)
KOOS-12/HOOS-12 Pain	8.6 (7.9, 9.2)	8.4 (8.1, 8.8)	7.9 (7.6, 8.2)
KOOS-12/HOOS-12 Function	7.4 (6.7, 8.1)	7.3 (6.9, 7.7)	6.3 (6.0, 6.6)
KOOS-12/HOOS-12 QoL	6.7 (6.1, 7.3)	5.8 (5.5, 6.2)	5.8 (5.5, 6.1)
Physical function	5.1 (4.9, 5.3)	5.3 (5.2, 5.4)	5.3 (5.2, 5.4)
Walking difficulties	−9.8% (−11.7, −7.9)	−8.6% (−9.8, −7.5)	−9.1% (−10.0, −8.3)
Fear of moving	−9.4% (−10.8, −8.0)	−10.3% (−11.1, −9.4)	−9.3% (−9.9, −8.6)
Wish for surgery	−3.0% (−5.0, −1.1)	−3.1% (−4.0, −2.1)	−1.8% (−2.5, −1.1)
PASS	24.4% (22.3, 26.5)	24.6% (23.4, 25.8)	24.3% (23.4, 25.2)
Physical activity
< 60 min	−4.0% (−5.9, −2.0)	−2.6% (−3.8, −1.5)	−2.4% (−3.1, −1.6)
61–150 min	2.5% (0.2, 4.8)	1.5% (0.1, 2.9)	0.6% (−0.4, 1.7)
151–300 min	1.7% (−0.2, 3.6)	0.6% (−0.5, 1.8)	1.4% (0.4, 2.4)
> 300 min	−0.2% (−1.8, 1.3)	0.5% (−0.4, 1.5)	0.3% (−0.5, 1.1)
Pain medication use	−13.7% (−16.9, −10.4)	−13.9% (−18.8, −9.0)	−11.8% (−13.9, −9.7)

SD = Standard Deviation; NRS = Numerical Rating Scale; KOOS = Knee injury and Osteoarthritis Outcome Score; HOOS = Hip disability and Osteoarthritis Outcome Score.

The sign of the concentration index suggested that at both baseline and the 3 month follow-up, for all outcomes except for fear of movement and PASS, there were educational inequalities in favour of highly educated people; that is, people with a higher level of education reported better outcomes (Table 5). Following participation in the intervention, while the magnitude of inequalities widened for activity impairment, it narrowed for NRS Pain, EQ-5D-5L, KOOS-12/HOOS-12 Pain, KOOS-12/HOOS-12 Function, physical function, and wish for surgery. For the remaining outcomes, there were no statistically conclusive changes in the magnitude of inequalities (Table 5).

Table 5. Magnitude of the concentration index for different outcomes at baseline and at 3 months following participation in a digital self-management programme for osteoarthritis, and its change between these two time-points.

Outcome measure	N	Baseline	3-month follow-up	Change/direction
NRS Pain	21 688	−0.061 (−0.066, −0.055)	−0.046 (−0.052, −0.039)	0.015 (0.009, 0.020)/N
Activity impairment	21 618	−0.041 (−0.048, −0.034)	−0.048 (−0.055, −0.041)	−0.007 (−0.013, −0.0004)/W
General health	21 618	+0.023 (+0.018, +0.029)	+0.021 (+0.016, +0.027)	−0.001 (−0.007, 0.004)/IN
UK EQ-5D-5L index score	21 618	+0.031 (+0.026, +0.035)	+0.023 (+0.019, +0.027)	−0.008 (−0.012, −0.005)/N
KOOS-12/HOOS-12 Pain	20 197	+0.053 (+0.048, +0.057)	+0.047 (+0.042, +0.052)	−0.005 (−0.010, −0.001)/N
KOOS-12/HOOS-12 Function	20 197	+0.071 (+0.066, +0.076)	+0.060 (+0.054, +0.066)	−0.010 (−0.015, −0.006)/N
KOOS-12/HOOS-12 QoL	20197	+0.024 (+0.019, +0.029)	+0.020 (+0.015, +0.026)	−0.004 (−0.008, 0.000)/IN
Physical function	17 482	+0.014 (+0.011, +0.017)	+0.011 (+0.008, +0.013)	−0.003 (−0.005, −0.001)/N
Walking difficulties	21 618	−0.060 (−0.073, −0.048)	−0.060 (−0.075, −0.046)	0.001 (−0.012, 0.014)/IN
Fear of movement	21 618	+0.005 (−0.005, +0.016)	+0.011 (+0.003, +0.018)	0.006 (−0.005, 0.016)/IN
Wish for surgery	15 014	−0.041 (−0.055, −0.030)	−0.029 (−0.042, −0.018)	0.012 (0.001, 0.024)/N
PASS	21 688	−0.038 (−0.049, −0.029)	−0.040 (−0.054, −0.027)	−0.002 (−0.017, 0.011)/IN
Physical activity	21 618	+0.088 (+0.078, +0.097)	+0.084 (+0.075, +0.093)	−0.004 (−0.013, 0.004)/IN
Pain medication use	3 296	−0.067 (−0.100, −0.033)	−0.053 (−0.079, −0.029)	0.014 (−0.019, 0.046)/IN

A + (−), sign reflects that the outcome is disproportionately concentrated among persons with higher (lower) educational attainment. N: Narrowed; W: Widened; IN: Inconclusive.

NRS = Numerical Rating Scale; KOOS = Knee injury and Osteoarthritis Outcome Score; HOOS = Hip disability and Osteoarthritis Outcome Score.

Discussion

We used data from a digital intervention programme in Sweden to investigate whether individuals with knee or hip OA and socioeconomic disadvantages, defined based on educational attainment, have poorer outcomes compared to their counterparts without socioeconomic disadvantages. Our results suggest that at both baseline and 3 month follow-up, socioeconomic inequalities favoured adults with higher levels of education for all outcomes except for PASS and fear of movement, signifying that those with higher education reported better outcomes. Following 3 months’ participation in the digital self-management intervention, while the magnitude of inequalities widened for activity impairment, it narrowed for NRS Pain, EQ-5D-5L, KOOS-12/HOOS-12 Pain, KOOS-12/HOOS-12 Function, physical function, and wish for surgery. The degree of this disparity varied among different outcome measures at both baseline and follow-up, but the magnitude of inequalities generally narrowed post-intervention.

Disparities in preoperative pain, disability, and quality of life among patients undergoing total knee arthroplasty have been reported (33), and the current findings suggest that these inequalities extend to participants in the non-surgical management programmes earlier in the OA disease process. Consistent with the findings of the present study, two recent studies using data from the Good Life with Osteoarthritis in Denmark (GLA:D) (an OA management programme) and ‘Better Management of People with Osteoarthritis’ in Sweden reported inequalities in patient-reported outcome measures in favour of participants with socioeconomic advantages (34, 35). However, in contrast to the present findings, they reported that attendance in face-to-face OA management programmes widened outcome inequalities, particularly during longer follow-up periods (34, 35). Our results showed a widening of the inequality only for the activity impairment, while for the remaining outcomes, either the gap was narrowed or there were no statistically conclusive changes in the magnitude of inequalities. The varying results could be due to the convenience, flexibility, and accessibility advantages of digital interventions, self-selection among participants, and better possibility of regular monitoring compared to the face-to-to-face programmes (36–38). Another report indicated higher adherence among individuals with lower institutionally based education and living outside the largest Swedish cities (39), raising the possibility that individuals with lower educational levels who participated in the programme had better technical knowledge, were better informed, and had higher motivation to participate, resulting in more substantial improvements in their outcomes.

Previous studies have emphasized the lower rates of access and engagement among lower socioeconomic groups in both OA management programmes (19) and other chronic disease self-management programmes (40). While access and engagement rates were not explicitly reported in the present study, it is noteworthy that although individuals with lower levels of education reported poorer outcomes, they were still more likely to express satisfaction with their current state. Given more severe pain among people with lower educational levels, this finding is in line with previous evidence suggesting that individuals with more severe pain are considering poorer outcomes as acceptable (41, 42).

Some limitations of this study should be taken into account. First, educational attainment was the only socioeconomic measure in this study, which may limit the generalization of the results to other socioeconomic measures such as income and wealth. While our study investigated the role of educational attainment as a socioeconomic variable, we acknowledge that other important factors, such as language proficiency, immigrant status, and financial hardship, were not systematically explored. These factors may contribute towards health inequalities and could potentially influence the outcomes observed in our study. Future research should consider the inclusion of these variables to provide a more comprehensive understanding of the socioeconomic determinants of health outcomes. Also, the classification of education attainment into low, medium, and high categories based on specified cut-offs lacks a universally validated standard. While similar categorizations have been used in various studies examining socioeconomic disparities, the absence of standardized validation may introduce variability in interpretation across different research contexts. Secondly, the findings of this study are specific to exercise-based self-management programmes in OA, and caution should be applied when attempting to apply these results to other musculoskeletal conditions or alternative forms of management. The generalizability of the current findings is limited to individuals who are proficient in the Swedish language and are comfortable with handling digital information. These eligibility criteria narrow down the pool of potential participants and therefore make the current results not generalizable to the broader Swedish population, either to immigrants or to asylum seekers in Sweden who do not speak Swedish. Although our study investigated individuals with access to digital interventions, it is essential to recognize persistent disparities in access across various sociodemographic groups. Despite efforts to enhance accessibility, significant discrepancies remain documented in the literature, underscoring the limitations of our focus on a subgroup with digital access. We acknowledge the need to directly address individuals lacking access to digital interventions to provide a comprehensive understanding of socioeconomic disparities in health outcomes. Finally, our findings are based on self-reported data and primarily include participants with (on average) higher educational levels, potentially limiting the generalizability of the findings.

Conclusion

Our findings suggest that while there were socioeconomic inequalities in most outcomes in favour of people with higher education, participation in a digital self-management intervention was generally associated with narrowing the pre-existing socioeconomic inequalities in the outcomes reported. Although the improvements observed suggest that the intervention benefited individuals across different educational levels, the fact that baseline and follow-up disparities favoured those with higher education underscores the importance of addressing socioeconomic inequalities. To effectively address the increasing number of individuals affected by OA and to ensure equitable access to care and improved outcomes, it is crucial that policymakers acknowledge and address socioeconomic inequalities among individuals with OA. Failure to carefully customize and directly target barriers to self-management can potentially worsen the social gradient observed in chronic disease outcomes when implementing self-management support programmes (40). Lastly, the significant disparities in patient-related outcomes observed before participation in the OA self-management programme highlight the overarching requirement for earlier implementation of coordinated public health actions, with a specific focus on equality.

Ethics and consent

The study was approved by the Swedish Ethical Review Authority (Dnr: 2021-01713, 2021-06-16) and performed in accordance with the Declaration of Helsinki. Digital informed consent was obtained from participants at enrolment.

Acknowledgement

We thank Dr Majda Misini Ignjatovic for data acquisition.

Disclosure statement

AK and LSL act as part-time scientific advisors for Joint Academy®, and LED is co-founder and chief medical officer at Joint Academy®. All other authors declare that they have no competing interests.