ClinicalTrials.gov identifier: NCT00248105.
Relationship between physical activity and health-related utility among knee osteoarthritis patients†
Article first published online: 26 JUN 2012
Copyright © 2012 by the American College of Rheumatology
Arthritis Care & Research
Volume 64, Issue 7, pages 1094–1098, July 2012
How to Cite
Manheim, L. M., Dunlop, D., Song, J., Semanik, P., Lee, J. and Chang, R. W. (2012), Relationship between physical activity and health-related utility among knee osteoarthritis patients. Arthritis Care Res, 64: 1094–1098. doi: 10.1002/acr.21639
- Issue published online: 26 JUN 2012
- Article first published online: 26 JUN 2012
- Accepted manuscript online: 10 FEB 2012 04:36PM EST
- Manuscript Accepted: 3 FEB 2012
- Manuscript Received: 26 MAY 2011
- Arthritis Foundation
- NIH. Grant Numbers: R01-AR055287, P60-AR48098
To estimate the relationship between physical activity and health-related utility for people with knee osteoarthritis (OA) and implications for designing cost-effective interventions.
We used generalized estimating equation regression analysis to estimate partial association of accelerometer-measured physical activity levels with health-related utility after controlling for demographics, health status, knee OA severity level, pain, and functioning.
Moving from the lowest to the middle tertile of physical activity level was associated with a 0.071 (P < 0.01) increase in health-related utility after controlling for demographics and a 0.036 (P < 0.05) increase in utility after controlling for demographics, health status, knee OA severity level, weight, pain, and functional impairments.
Intervention programs that move individuals out of the lowest tertile of physical activity have the potential to be cost effective.
Knee osteoarthritis (OA) is a major health problem that affects 6% of adults (1). Exercise programs for persons with arthritis can increase strength and functional status and decrease pain, depressive symptoms, fatigue, and quality of life without adversely affecting joint status (2–4). There is evidence that these physical activity–associated benefits can also be realized by interventions that encourage increased physical activity without utilizing formal exercise programs, which can be difficult to sustain long term (5). The need for increased activity in this population is highlighted by the findings of studies showing that 44% of adults with physician-diagnosed arthritis were inactive (6) and that 41.1% of men and 56.5% of women with knee OA were inactive (7).
When evaluating the relative value of programs directed at improving overall physical activity of people with knee OA, cost-effectiveness calculations are increasingly being used to evaluate if the net gain to society is worth the additional costs of the intervention. A generally accepted measure of effectiveness is the effect of an intervention on health-related utility, which is then used to construct changes in quality-adjusted life years (QALYs) (8). A QALY weights one's remaining lifetime or, alternatively, the period evaluated following an intervention by the value of health-related utility observed during that time period, where utility is normed to range from death (utility = 0) to perfect health (utility = 1). Interventions shown to improve the number of QALYs of an individual have been used to justify a given intervention cost (8). The use of QALYs as an outcome has been formalized in evaluating drug coverage decisions in countries such as the UK, Australia, and Canada (9).
The 2 objectives of this study were to assess the extent to which increased physical activity is positively associated with health-related utility and to then discuss whether this association is large enough to potentially justify interventions that are successful in increasing physical activity as cost effective. To assess the magnitude of changes in physical activity levels necessary to achieve utility gains, we estimated the association between physical activity levels and health-related utility in a sample of adults with symptomatic knee OA confirmed by radiograph (Kellgren/Lawrence grade ≥2) recruited for a counseling intervention to improve their level of physical activity. While the association between health-related utility measures and physical activity levels among participants in a randomized controlled trial (RCT) may differ from the relationship in the general community, it can suggest the extent to which an intervention might change the level of QALYs if it can substantially affect physical activity levels. This information, in turn, suggests limits on the cost of interventions aimed at improving physical activity, if such interventions are to be cost effective.
Significance & Innovations
We examined the relationship between physical activity and health-related utility for individuals with knee osteoarthritis and implications for designing cost-effective interventions.
A significant increase in health-related utility was observed when individuals moved out of the lowest tertile of physical activity.
Interventions that target the individuals with the lowest physical activity levels to increase their level of nonvigorous activity have the potential to be cost effective.
Subjects and methods
The sample of knee OA participants in this study came from an RCT of adults with knee OA conducted to assess the efficacy of a tailored health promotion intervention to increase physical activity. The 155 knee OA participants were recruited from clinical practices (8%), research registries (40%), and the community (52%). Participants were excluded from the study if they were planning to undergo total joint replacement in the subsequent 12 months; had a contraindication to physical activity due to comorbid conditions, including a history of peripheral vascular disease, spinal stenosis, residual lower extremity neuromuscular effects of stroke, and major signs or symptoms compatible with pulmonary and cardiovascular disease; were unable to perform basic self-care activities; had plans to relocate away from the Chicago area within 24 months; or were missing baseline data for any of the measures described below.
Measures collected for variables of interest.
Physical activity was monitored in all study participants using a GT1M accelerometer (ActiGraph), which measures vertical acceleration and deceleration (10). Accelerometer data were collected at 1-minute intervals and transformed to activity counts per day. An activity count is the weighted sum of the number of accelerations measured at each minute, where the weights are proportional to the magnitude of measured acceleration. Participants were instructed to wear the accelerometer upon rising in the morning, and to wear it continuously (except for water activities) until going to bed at night for 7 consecutive days. Skipped days, which were reported in a daily log, were excluded from the analysis. Consistent with Troiano et al (11), days with <10 hours of wear time were excluded from the analysis, where the transformation algorithm converting raw accelerometer readings to wear time was determined using methods validated for knee OA populations (12).
The amount of preintervention physical activity was estimated from the mean daily activity counts, which represented the summed activity counts for all wear hours divided by the number of valid days (days having >10 hours of wear time) monitored during the 7-day period. We then divided the sample into tertiles, with 3 groups representing the lowest, middle, and highest physical activity levels at baseline.
Health-related utility was measured using the Short Form 36 (SF-36) 6-dimension utility measure (SF-6D), which was based on the version of the SF-6D that Brazier et al derived from the SF-36 (13). More specifically, the SF-6D measures 6 health domains physical functioning, role limitations, social functioning, pain, mental health, and vitality. For example, the pain domain varies from having no pain to having pain that extremely interferes with work (both outside the home and housework). Because the SF-36 responses were collected at baseline and 3-month, 6-month, and 12-month followup interview periods, we had multiple measures of the SF-36 for each individual, which were then converted to SF-6D utility scores using the preference weights estimated from a random sample of community dwellers by Brazier et al (13). Each individual had between 1 and 4 measures of health-related utility. Also collected during the baseline interviews were demographic information (age and whether the participant was female, nonwhite, and without any college education), clinical health factors (Kellgren/Lawrence knee OA severity grade 2, 3, or 4), the number of comorbidities ascertained from baseline medications, if the participant was overweight (body mass index [BMI] >25 and ≤30 kg/m2) or obese (BMI >30 kg/m2), Western Ontario and McMaster Universities Osteoarthritis Index pain subscale, and disability measures (instrumental activities of daily living and activities of daily living limitations) (14). Design variables included membership in the intervention or control group and the followup interview at which utility was assessed.
A repeated-measures hierarchical generalized estimating equation regression analysis related health-related utility at baseline and 3-month, 6-month, and 12-month followup periods to baseline physical activity tertile levels (using the lowest tertile as the reference). This association of physical activity levels with utility level controlled for design factors and demographics, then added clinical health factors, and then pain and disability.
Of the 155 individuals with knee OA enrolled in the study, 142 had complete baseline data. These 142 individuals formed the analysis sample. There was an average of 3.27 SF-6D utility observations per individual. Figure 1 shows the distribution of baseline utility scores for each of the 3 physical activity tertiles, indicating approximately normal distributions and no significant floor or ceiling effects on the dependent variable. While the utility scale was normed to range from 0–1 (death to perfect health), Figure 1 shows that, for this sample, utility ranged from 0.4–1. It is notable that higher utility scores (>0.8) occurred more frequently among the 2 upper tertiles. However, the overall relationship of physical activity tertiles with health-related utility cannot be determined from the data shown in Figure 1.
We defined our physical activity tertiles based on the average daily total accelerometer counts during the measurement week. Actual physical activity counts for the middle and highest tertiles were approximately double and more than triple those for the lowest physical activity group, respectively. The means and range of average daily counts for each tertile are shown in Table 1.
|Variable||Tertile 1||Tertile 2||Tertile 2 vs. tertile 1, P†||Tertile 3||Tertile 3 vs. tertile 1, P†|
|Sample size, no.||47||49||–||46||–|
|SF-6D utility, mean ± SD||0.74 ± 0.11||0.76 ± 0.10||0.352||0.73 ± 0.13||0.69|
|Physical activity counts, mean ± SD (range)||113,273 ± 30,893 (36,498–163,024)||207,398 ± 28,738 (163,412–250,553)||< 0.001||346,176 ± 88,993 (255,077–607,090)||< 0.001|
|Age, mean ± SD years||72.36 ± 11.14||61.48 ± 12.89||< 0.001||56.54 ± 9.49||< 0.001|
|WOMAC pain, mean ± SD||18.68 ± 10.62||15.55 ± 11.98||0.18||19.29 ± 12.57||0.80|
Table 1 also shows the baseline levels of the demographic, health, and pain/functional limitation scores for each tertile. Higher levels of physical activity were significantly correlated with age, male sex, and nonwhite race. Table 2 shows the hierarchical regression results.
|Baseline measures||Model 1||Model 2||Model 3|
|Physical activity tertile 2||0.071†||0.057†||0.036‡|
|Physical activity tertile 3||0.058‡||0.035||0.027|
|Age (centered on 60 years)||0.003†||0.002†||0.001‡|
|K/L grade 3||−0.032§||−0.022|
|K/L grade 4||−0.020||−0.013|
|WOMAC pain (range 0–21)||−0.004†|
Controlling only for demographic characteristics and design variables, the middle activity tertile had a utility score that was 0.071 (P < 0.01) higher than the lowest activity tertile and the highest activity tertile had a utility score that was 0.058 (P < 0.05) higher than the lowest activity tertile. Adding the clinical health factors reduced the effects somewhat, and further adding pain and disability reduced the coefficients on the middle and high activity tertiles to 0.036 (P < 0.05) and 0.027 (P not significant), respectively. Therefore, while moving from the lowest to the middle physical activity tertile was associated with a significant increase in utility level, no further increase was associated with moving from the middle to the highest physical activity tertile. There was no significant difference between the middle and highest tertile coefficients. Significant health factors (model 2) were being overweight or obese, which had a negative effect on utility. In the final model, the only other significant predictors of utility were age and pain.
The evaluation of interventions to improve physical activity is often put to a cost-effectiveness test, determining whether the outcomes justify their costs. The standard metric to evaluate cost-effectiveness is the change in QALYs, a measure based on health-related utility. One might therefore wonder if an intervention that moved knee OA individuals from the lowest tertile of physical activity to higher tertiles can be justified in terms of improved health-related utility and at what cost, given current guidelines that rate programs as cost effective if they are below $50,000–100,000 per QALY (15).
We found that when using a standard health-related utility score based on the SF-36 health survey, individuals in the middle physical activity tertile had health-related utility scores that were 0.071 (P < 0.01) higher than those in the lowest tertile after controlling for demographics. Further controlling for clinical factors, pain, and disability reduced this difference to 0.036 (P = 0.032). Interestingly, individuals in the highest tertile appeared to have utility levels similar to (actually, insignificantly lower than) those in the middle tertile. Therefore, targeting those with the least active lifestyles and, on average, doubling their physical activity counts would potentially improve their QALYs significantly, as measured by a standard cost-effectiveness measure.
Would such an intervention be cost effective? If one considers the 0.036 difference in utility we found after controlling for demographic, health, pain, and disability differences and supposes an intervention could obtain such an effect after 1 year, then starting at 0 difference and attaining the 0.036 utility difference at 12 months yields an average annual QALY gain of 0.018, if utility gains showed a linear trend over the year. If the intervention cost is $450, the related cost-effectiveness ratio is $25,000 per QALY, well within the cost-effectiveness range. Under a more conservative assumption where a program is only 33% effective in moving individuals into the higher physical activity tertile zone, then this cost-effectiveness ratio becomes $75,000 per QALY. Lowering the cost of the intervention to $300 reduces the cost-effectiveness ratio to $50,000 per QALY.
Of course, the associations found in this study do not necessarily translate to what would be observed if the physical activity levels were changed since they are not necessarily causal. Unobserved baseline differences in health status correlated with baseline physical activity levels might explain observed associations between physical activity and utility levels; however, we were conservative in our investigation by controlling for pain levels, overweight/obesity levels, and disability in estimating expected differences between the lowest activity level and the other activity levels. In fact, if a physical activity intervention improved weight, disability, and pain levels, the overall utility levels could have greater improvement.
In conclusion, while we cannot say whether a controlled intervention would reproduce the observational results reported here, there is the potential for a physical activity intervention to be cost effective at existing cost-effectiveness standards. Limiting the cost of such an intervention, targeting it to individuals with very low levels of physical activity, and substantially increasing physical activity counts would appear to be necessary ingredients for a successful intervention; however, achieving these goals would not appear to require substantial increases in bouts of moderate/vigorous activity, which are often associated with successful exercise programs and are required in order to meet current Department of Health and Human Services physical activity guidelines (16).
All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Manheim had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study conception and design. Manheim, Dunlop, Song, Chang.
Acquisition of data. Manheim, Dunlop, Song, Chang.
Analysis and interpretation of data. Manheim, Dunlop, Song, Semanik, Lee, Chang.
- 8Cost-effectiveness in health and medicine. Oxford: Oxford University Press; 1996., , , .
- 11Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2010; 42: 181–8., , , , , .
- 14Documentation of physical functioning measured in the Health and Retirement Study and the Asset and Health Dynamics Among the Oldest Old Study. Ann Arbor (MI): University of Michigan; 2004., .
- 16Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee report. Washington, DC: US Department of Health and Human Services; 2008.