Background: Osteoarthritis is one of the most common chronic joint diseases, mostly affecting the knee or hip through pain, joint stiffness and decreased physical functioning in daily life. Regular physical activity (PA) can help preserve and improve physical functioning and reduce pain in patients with osteoarthritis. Interventions aiming to improve movement behaviour can be optimized by tailoring them to a patients' starting point; their current movement behaviour. Movement behaviour needs to be assessed in its full complexity, and therefore a multidimensional description is needed. Objectives: The aim of this study was to identify subgroups based on movement behaviour patterns in patients with hip and/or knee osteoarthritis who are eligible for a PA intervention. Second, differences between subgroups regarding Body Mass Index, sex, age, physical functioning, comorbidities, fatigue and pain were determined between subgroups. Methods: Baseline data of the clinical trial 'e-Exercise Osteoarthritis', collected in Dutch primary care physical therapy practices were analysed. Movement behaviour was assessed with ActiGraph GT3X and GT3X+ accelerometers. Groups with similar patterns were identified using a hierarchical cluster analysis, including six clustering variables indicating total time in and distribution of PA and sedentary behaviours. Differences in clinical characteristics between groups were assessed via Kruskall Wallis and Chi2 tests. Results: Accelerometer data, including all daily activities during 3 to 5 subsequent days, of 182 patients (average age 63 years) with hip and/or knee osteoarthritis were analysed. Four patterns were identified: inactive & sedentary, prolonged sedentary, light active and active. Physical functioning was less impaired in the group with the active pattern compared to the inactive & sedentary pattern. The group with the prolonged sedentary pattern experienced lower levels of pain and fatigue and higher levels of physical functioning compared to the light active and compared to the inactive & sedentary. Conclusions: Four subgroups with substantially different movement behaviour patterns and clinical characteristics can be identified in patients with osteoarthritis of the hip and/or knee. Knowledge about these subgroups can be used to personalize future movement behaviour interventions for this population. Trial registration: Dutch clinical trial registration number of e-Exercise Osteoarthritis: NTR4224.
Keywords: Osteoarthritis; Physical activity; Sedentary behaviour; Movement behaviour
The incidence of osteoarthritis is increasing and is expected to continue so in the following years. This increase has been associated with an aging population and rising obesity numbers, which are risk factors for the development of the disease [[
Regular physical activity (PA) preserves and improves physical functioning and reduces pain symptoms in patients with osteoarthritis [[
PA is often assessed with either a single parameter or multiple independent parameters, such as time spent in PA, the intensity of performed activities, or amount of active periods in a certain intensity (also called bouts) per day [[
Interventions for patients with osteoarthritis often aim to improve movement behaviour. Even though these interventions have yielded positive results, effects are small and largely inconsequential for the longer term [[
Before health practitioners are able to measure and interpret multidimensional movement behaviour, the heterogeneity in movement behaviour of patients with osteoarthritis should be explored. With statistical methods such as cluster analysis, individuals can be grouped, based on similarities in multiple movement behaviour dimensions simultaneously. Recent research has identified patterns in movement behaviours of patients with COPD, chronic cancer-related fatigue and hemophilia [[
Exploring the differences between subgroups regarding clinical characteristics provides further insight into the characteristics and common problems of specific subgroups. This especially holds for factors that have been associated with decreased PA levels, such as physical functioning and comorbidity. Detailed, multidimensional descriptions of movement behaviour in patients with osteoarthritis of the hip and/or knee, as well as patient characteristics related to these behaviours, are lacking in current literature.
Therefore, this study aimed to identify subgroups based on movement behaviour patterns in patients with osteoarthritis of the hip and/or knee who are eligible for a PA intervention. The second aim was to determine the differences between subgroups regarding BMI, sex, age, physical functioning, comorbidities, fatigue and pain.
In this cross-sectional study, baseline data from the multicentre prospective trial 'e-Exercise Osteoarthritis' [[
Participants were eligible for inclusion in the trial e-Exercise Osteoarthritis if they were aged between 40 and 80 years and were diagnosed with hip and/or knee osteoarthritis according to the clinical criteria of the American College of Rheumatology [[
Patients were included in the current study if accelerometer data from at least three consecutive days of 8 h was available. This is in accordance with guidelines for accurate measuring of physical activity from accelerometer output [[
Movement behaviour was assessed with the GT3X or GT3X+ tri-axial accelerometer (ActiGraph LLC, Pensacola, FL, USA). All participants received the accelerometer with written instructions at baseline and were asked to wear the device for 5 consecutive days, with exception of sleep, showering and swimming, on an elastic belt around their waist. Participants were asked to document all time slots of 'wear-time' and 'non-wear time' in a diary. The device did not provide the participants with feedback regarding performed movement behaviour.
The raw output of the accelerometer provides three dimensional activity counts per minute and a vector magnitude derived from combined activity counts from the three axes. Activity counts are derived from the frequency and intensity of accelerations and indicate intensity of performed activity, where higher counts stand for higher intensity. Accelerometer counts of the GT3X+ are found appropriate for quantifying activity [[
Extracted variables - see also Table 2 - included total minutes spent in moderate to very vigorous (MV) intensity, sedentary and prolonged sedentary activity per day. Furthermore, the variables 'number of MV bouts per day', 'average length of MV bouts' and 'number of sedentary bouts per day' were assessed to measure the distribution of physical activity and sedentary behaviour over the day. The minimum bout length of all variables was 2 min. One exception was the variable 'total time in sedentary bouts' with a minimum bout length of 30 min [[
Physical functioning was assessed with the subscale 'function in daily living' of the Hip Osteoarthritis Outcome Score (HOOS) [[
Weight (in kg), length (in meters), age, sex and comorbidities were assessed in the baseline questionnaire. BMI was categorized in (a) < 25 kg/m
Statistical analyses were performed with IBM SPSS Statistics for Windows (version 24, IBM Corp., Armonk, N.Y., USA). Prior to the statistical analyses, the data was checked for missing values and outliers. Correlations between clustering variables were assessed to check for multicollinearity. A Pearson's correlation coefficient r >.90 was deemed problematic for the clustering procedure.
A hierarchical cluster analysis following Ward's linkage method with Euclidean distances was performed. Clustering variables included the movement behaviour variables (Table 1). Based on visual inspection of the dendrogram and interpretability of the clusters, a choice was made for a number of clusters (subgroups) that described the distinct movement behaviours best. Because the data within the identified subgroups was not normally distributed, a Kruskall Wallis test was performed to determine if subgroups were significantly different on all clustering variables. In the secondary analysis, Chi
Participant characteristics
Variable Category Included participants ( Excluded participants ( Age in years, mean (SD) . 63.0 (8.6) 63.4 (10.0) Sex, n (%) Male 60 (33.0) 7 (26.9) Female 122 (67.0) 19 (73.1) Body Mass Index in kg/m2, n (%) < 25 59 (32.4) 3 (11.5) 25–29.9 76 (41.8) 10 (38.5) > 30 47 (25.8) 13 (50.0) Location of osteoarthritis, n (%) Knee 119 (65.4) 19 (73.1) Hip 35 (19.2) 3 (11.5) Both 28 (15.4) 4 (15.4) Time since diagnosis, n (%) < 1 year 39 (21.4) 2 (7.7) 1 to 5 years 69 (37.9) 11 (42.3) > 5 years 74 (40.7) 13 (50.0) Comorbidities, n (%) None 111 (61.0) 13 (50.0) Single 33 (18.1) 7 (26.9) Multiple 38 (20.9) 6 (23.1) Physical functioning [0–100], mean (SD) . 58.5 (19.7) 58.8 (22.4) Fatigue [0–10], mean (SD) . 5.1 (2.7) 5.0 (3.4) Pain [0–10], mean (SD) . 5.3 (2.3) 5.6 (2.4) Accordance with health norma, n (%) . 28 (15.4) – –
SD standard deviation, n number of individuals, [...] = scale range
There are no strict guidelines to determine the required sample size for a cluster analysis. The rule-of-thumb n ≥ 2
One participant was excluded because the accelerometer data were invalid due to incorrect settings. Twenty-four participants were excluded because less than 3 days of 8 h of accelerometer data were recorded. One outlier was excluded because one outcome was distanced 6.7 standard deviations from the mean, and therefore it severely influenced the clustering procedure. Finally, 182 participants were included in this study. Characteristics of in- and excluded participants are shown in Table 1. Some minor differences exist between in- and excluded participants but there was no strong indication for sampling bias. In Table 2, total group means on all clustering variables are shown. In total, data of 122 females and 60 males, with an average age of 63.0 years (sd = 8.6 years), were analysed. All correlation coefficients between clustering variables were within acceptable limits, defined as Pearson's r <.9.
Clustering variables and group means
Variable name Minimum bout length in minutes VM3 counts/min Total group mean (SD) 2 < 100 6.2 (1.5) 30 < 100 2.2 (1.4) 2 < 100 53.2 (10.0) 10 ≥ 2690 10.0 (14.3) 2 ≥ 2690 5.3 (4.6) 2 ≥ 2690 3.8 (2.0)
MV moderate to very vigorous, VM3 triaxial vector magnitude
Table 3 shows the mean scores on the clustering variables and Fig. 1 shows the normalized mean outcomes per subgroup. The dendrogram, illustrating the clustering procedure, can be found in Additional file 1. All clustering variables contributed significantly to the identification of clusters (p <.001).
Mean scores of pattern groups on clustering variables (N = 182)
6.7 (0.8) 7.7 (1.1) 4.5 (0.9) 5.6 (0.7) 2.3 (0.7) 3.7 (1.1) 1.2 (0.7) 1.1 (0.7) 57.5 (6.9) 52.3 (9.2) 44.7 (8.5) 59.3 (9.2) 0.7 (1.5) 13.0 (11.7) 5.9 (6.1) 23.0 (22.0) 2.1 (1.7) 4.9 (3.5) 6.2 (3.7) 8.7 (6.5) 2.8 (1.0) 5.0 (2.6) 3.1 (0.9) 4.5 (1.6)
SD standard deviation, n number of individuals
Graph: Fig. 1 Standardized group means on clustering variables
The first pattern was called inactive & sedentary. Firstly because the average time spent in MV bouts was close to zero and the number and average length of MV bouts per day was lower compared to the other patterns. Second, the time spent in sedentary bouts and prolonged sedentary bouts, and the number of sedentary bouts were all above the total group average.
The group with the second pattern spent more minutes in sedentary bouts and prolonged sedentary bouts compared to all other patterns. At the same time, the number of sedentary bouts was lower compared to that of the inactive & sedentary, indicating fewer breaks in sedentary time. This pattern was therefore named prolonged sedentary. The group with this pattern spent more time in MV activity compared to the inactive & sedentary and the third pattern (light active) but less compared to the fourth (active). This pattern had the highest average length of MV bouts.
The third pattern showed the least minutes in sedentary behaviour and lowest number of sedentary bouts compared to the other patterns. Time spent in prolonged sedentary behaviour was less compared to the inactive & sedentary pattern and to the prolonged sedentary pattern, though comparable with the fourth (active) pattern. The time spent in MV activity and the average duration of MV bouts were below the total group average while the amount of moderate to very vigorous bouts was above average. Likely, this group is mostly active in light intensity activities during the day and therefore the pattern of this group was called light active.
The last pattern was characterized by more time in MV activity compared to all other patterns and was called the active pattern. Individuals with this pattern on average spent more time in sedentary behaviour compared to people with the light active pattern, though less compared to the prolonged sedentary pattern and the inactive & sedentary pattern. The active pattern showed the most sedentary bouts and the least minutes in prolonged sedentary bouts, indicating more breaks in sedentary time.
Chi
Differences in clinical characteristics between subgroups
63.4 (8.7) 65.1 (8.2) 60.9 (8.3) 62.1 (8.9) 50.9 (3.0)a 67.0 (2.3) 53.6 (2.7)a 61.8 (3.1)b 6.0 (0.3)a 4.7 (0.3) 5.8 (0.3) 4.8 (0.4) 5.8 (0.4)a 4.1 (0.4) 5.7 (0.4)a 4.9 (0.4) Male 6 (12.8)c 30 (55.6)b 6 (13.3)c 18 (50)b Female 41 (87.2) 24 (44,4) 39 (86.7) 18 (50) < 25 kg/m2 6 (12.8) 18 (33.3) 11 (46.7) 14 (38.9) 25–29.9 kg/m2 27 (57.4) 22 (40.7) 13 (28.9) 14 (38.9) > 30 kg/m2 14 (29.8) 14 (25.9) 11 (24.4) 8 (22.2) None 29 (61.7) 33 (61.1) 27 (60.0) 22 (61.1) Single 12 (25.5) 8 (14.8) 6 (13.3) 7 (19.4) Multiple 6 (12.8) 13 (24.1) 12 (26.7) 7 (19.4)
SD standard deviation, n number of individuals
The Kruskall Wallis test did not indicate significant differences in age between groups. Significant differences were identified for levels of physical functioning, pain and fatigue. Post-hoc tests with Bonferroni correction for multiple testing showed the following:
The group with the prolonged sedentary pattern had a higher level of physical functioning compared to group with the light active pattern (adjusted p <.05) and compared to the group with the sedentary non active pattern (adjusted p <.001). Second, they had lower pain levels compared to the groups with the inactive & sedentary pattern (adjusted p <.05) and the light active pattern. The group with the prolonged sedentary pattern was less fatigued compared to the groups with the light active pattern (adjusted. p <.05) and the inactive & sedentary pattern (adjusted p <.05). The group with the active pattern had significantly higher levels of physical functioning compared to the group with the inactive & sedentary pattern (adjusted. p <.001).
The aim of this study was to identify patterns in movement behaviours of patients with osteoarthritis of the hip and/or knee and second, to examine the differences in clinical characteristics between groups with these patterns. The analysis resulted in the distinction of four different movement behaviour patterns: inactive & sedentary, prolonged sedentary, light active and active. Second, differences were found between groups regarding sex, physical functioning, pain and fatigue.
This study highlights the heterogeneity in movement behaviours of patients with hip and/or knee osteoarthritis, resulting in the distinction of four groups with substantially different movement behaviour patterns and clinical characteristics. The results confirm that high levels of sedentary behaviour and high MV activity are not mutually exclusive [[
In order to improve movement behaviour effectively, multiple dimensions should be targeted [[
The secondary analyses of this study highlighted other clinically relevant differences in characteristics between groups. First, men were predominantly represented in the groups with the prolonged sedentary pattern and the active pattern. Interestingly, these two groups both spent more time in MV activity compared to the other groups while their sedentary behaviours were dissimilar. These results seem to be in line with an earlier finding that men are more likely to participate in MV activity compared to women [[
To our knowledge, this is the first study that has identified patterns in movement behaviour of patients with hip and/or knee osteoarthritis. Patterns in movement behaviour have recently been defined in patients with COPD [[
Some limitations of this study should be mentioned. First, because the GT3X and GT3X+ are not water resistant, time spent swimming could not be recorded. For the 19 participants who reported a swimming activity, measured activity was therefore underestimated. Furthermore, differences in movement behaviour may exist between week- and weekend days, especially among workers. Unfortunately, we did not manage to control for this in the analysis and this might therefore have influenced the results.
Also, based on best available literature, a choice was made for the cut-points for sedentary behaviour and MV activity. It is known that this choice affects measures of movement behaviour [[
Notably, based on our data 28 participants (15,4%) met the WHO guidelines for PA, while people meeting this norm in their daily life were excluded from our study. There can be various reasons for this discrepancy between self-reported PA during the anamnesis and PA assessed with the accelerometer. First, participants were aware that their PA was being recorded and this may have led to an increase in daily PA. Another reason may be that it is challenging for physical therapists to estimate daily PA based on self-reported information of the patient. Consequently, the movement behaviour patterns identified in this study apply to this population of individuals with knee and/or hip osteoarthritis in the physical therapy setting. Further research is needed to confirm the presence of these patterns in larger populations.
Another point to consider is that large variances are often seen in movement behaviour measures of patients with hip and knee osteoarthritis [[
In conclusion, four patterns in movement behaviours of patients with hip and/or knee osteoarthritis were identified. Differences between groups with these patterns exist regarding sex, physical functioning, pain and fatigue. Treatment for patients with osteoarthritis of the hip and/or knee can be personalized by incorporating knowledge on movement behaviour interventions and target the dimensions that will benefit the individual most.
The E-Exercise Osteoarthritis trial was funded by ZonMW, the Dutch Rheumatoid Arthritis Foundation and the Royal Dutch society for Physiotherapy (KNGF). There was no additional funding for the current study.
We thank dr. Rob Zwitserlood for writing assistance and general support.
All authors (AH, CK, MP and CV) worked on the conception and design of the study, and interpretation of the results and editing of the manuscript. AH wrote the manuscript and performed the data transformations and statistical analyses. CK was the daily supervisor and advisor of AH in the study as a whole. MP contributed with statistical expertise. All authors gave approval of the version that was submitted and take responsibility for the integrity of this study as a whole.
The data that support the findings of this study are available on request from the corresponding author AH. The data are not publicly available due to them containing information that could compromise research participant privacy or consent.
The current study lied in the scope of the e-Exercise Osteoarthritis trial, which was approved by the Medical Ethical Committee of the St. Elisabeth hospital Tilburg, the Netherlands (Dutch Trial Register NTR4224). All participants gave written informed consent before participation.
Not applicable.
The authors (AH, CK, MP and CV) declare that they have no competing interests.
Graph: Additional file 1: Dendrogram.
Supplementary information accompanies this paper at 10.1186/s12891-020-03644-0.
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