Abstract
Background
Pain is associated with reports of restricted physical activity (PA), yet the association between musculoskeletal pain characteristics and objectively measured PA quantities and patterns in late life is not well understood.
Methods
A total of 553 adults (mean age 75.8 ± 8.4 years, 54.4% women) in the Baltimore Longitudinal Study of Aging (BLSA) completed a health interview and subsequent 7-day wrist-worn ActiGraph assessment in the free-living environment between 2015 and 2020. Pain characteristics, including pain presence in 6x sites (ie, shoulders, hands/wrists, low back, hip, knees, and feet), pain laterality in each site, and pain distribution were assessed. PA metrics were summarized into total daily activity counts (TAC), activity fragmentation, active minutes/day, and diurnal patterns of activity. Linear regression models and mixed-effects models examined the association between pain characteristics and PA outcomes, adjusted for demographics and comorbidities.
Results
Unilateral knee pain was associated with 184 070 fewer TAC (p = .039) and 36.2 fewer active minutes/day (p = .032) compared to those without knee pain. Older adults with shoulder pain or hand/wrist pain had more active minutes compared to those without pain (p < .05 for all). For diurnal patterns of activity, participants with knee pain had fewer activity counts during the afternoon (12:00 pm to 5:59 pm). Analyses stratified by sex showed that these associations were only significant among women.
Conclusions
Our study highlights the importance of assessing pain laterality in addition to pain presence and suggests that pain interferes with multiple aspects of daily activity. Longitudinal studies are needed to assess the temporality of these findings.
Keywords: Accelerometer, Epidemiology, Mobility, Observational study, Pain
Over half of community-dwelling adults aged 65 and older suffer from pain (1). Musculoskeletal pain in the older population has been associated with adverse health outcomes such as poor functional status, cognitive impairment, and falls (2–7). Older adults with pain also experience psychological distress such as depression, fear of falling, and loneliness (8–10). These adverse health consequences may be partially attributable to restricted physical activity (PA) as a means to avoid inducing or aggravating pain (8,11).
Several studies have demonstrated a link between pain and lower levels of PA in older adults (8,12–17). However, most of these studies collected PA data using questionnaires which may not adequately capture time spent in light activities and may be biased by problems with recall, particularly in older adults (8,15–18). For the few studies that used objectively measured PA, limited metrics were used to characterize activity levels, such as daily activity volume and intensity (14,19). Recently, novel PA metrics such as activity fragmentation (active-to-sedentary transition probability [ASTP]) and diurnal patterns of activity have been linked with measures of functional status and mortality over and above traditional measures of total volume and intensity of activity in older adults (20–22). Activity fragmentation quantifies transitions from an active to a sedentary state and has been associated with fatigability and functional performance (20). These informative measures may thus provide novel insights into detailed daily patterns of PA in older adults living with pain and provide targets for future interventions.
Commonly used pain characteristics in previous studies examining the pain–activity relationship include pain intensity, duration, and location (8,15,16). Several epidemiological studies indicate that among older adults with pain, the majority of people suffer from pain in more than one site (1,3), and a growing body of evidence suggests that multisite pain is a strong predictor of mobility limitation and falls (3–5,9,23). Chronic multisite pain, identified as a geriatric syndrome, may thus directly or indirectly affect intensity and patterns of PA (24). Although a few studies found that pain is associated with kinesiophobia, the underlying mechanisms between pain and PA remain unclear (8,15). Previous studies have found that older adults with pain had reduced physical functioning, slowed foot reaction time, and increased fear of falling, and that these factors may be on the causal pathway from pain to PA (25–27). To better elucidate this relationship, more pain characteristics (eg, pain laterality, multisite pain) in addition to pain presence or severity and novel metrics of PA should be considered, in conjunction with detailed measures of PA.
The aim of this study is to examine the cross-sectional association between musculoskeletal pain characteristics (ie, pain presence, pain laterality, pain distribution, number of pain sites) and objectively measured PA using both traditional (ie, daily activity counts, time spent in active states) and novel metrics (ie, activity fragmentation, diurnal patterns) in older adults in the Baltimore Longitudinal Study of Aging (BLSA). We hypothesized that older adults with pain, especially unilateral pain or multisite pain will have lower activity levels, less active time, and more fragmented and shifted activity patterns than those without pain. Understanding these associations may provide an evidence basis for future interventions to promote PA and improve mobility among older adults living with pain.
Method
The BLSA, a longitudinal cohort study conducted by the National Institute on Aging Intramural Research Program, aims to explore the independence of aging and disease processes and their mutual impact on physical and cognitive performance. The study was established in 1958 and continuously recruits community-dwelling volunteers without major chronic conditions or cognitive or functional impairments at the time of enrollment. Detailed descriptions of the study design are published elsewhere (28). Once enrolled, participants are followed for health characteristics, cognitive assessments, and physical function tests every 4 years if aged <60 years, every 2 years if aged 60–79 years, and annually if aged ≥80 years. The study protocol was approved by the National Institutes of Health Intramural Program Institutional Review Board. Informed consent was obtained from all participants at each study visit. In the current study, participants aged ≥60 years with complete health interview data and accelerometer data were included.
Musculoskeletal Pain Characteristics
At each visit, participants were asked to report pain that lasted at least 1 month in the past year in the shoulders, hands/wrists, low back, hip, knees, and feet. Pain laterality was determined based on participants’ reported pain sites at the left or right side of the shoulder, hand/wrist, hip, knee, and foot as (1) no pain, (2) unilateral pain, or (3) bilateral pain. The distribution of musculoskeletal pain sites was classified based on the number of pain sites as (1) no pain, (2) single-site pain, or (3) multisite pain (≥2 sites).
Physical Activity
Objective PA was assessed using the triaxial wrist-worn Actigraph GT9X Link (Actigraph, Pensacola, FL) accelerometer on the nondominant wrist with a sampling frequency of 80 Hz. On the last day of each clinic visit, participants were fitted with the accelerometer and instructed to wear it for the next 7 days, 24 h/d in the free-living environment. After completing the 7-day data collection, participants returned the accelerometer to the clinical research center by prepaid mailer. Data were downloaded and preprocessed using the ActiLife software (version 6.13.4) to derive activity counts (unitless quantities of movement) in 1-minute epochs. Participants with at least 3 valid days of accelerometer data were included in the analysis. A valid day was defined as having ≤10% of missing data and nonvalid days were excluded from the analysis. For valid days, missing values were imputed as the average activity counts per minute during the same minute across all other valid days for each participant (22).
PA metrics were summarized into (1) total activity counts (TAC), (2) activity fragmentation index (defined as the ASTP) (20), and (3) active minutes. TAC was derived from the sum of activity counts for each minute and averaged across valid days. To calculate ASTP and active minutes, each minute was labeled as active if activity counts reached ≥1 853/min (29). Minutes with activity counts <1 835 were labeled as nonactive time which was used as sedentary minutes to calculate ASTP. Activity bouts were defined as consecutive minutes spent in either an active or sedentary state. ASTP was then calculated as the reciprocal of the average activity bout duration for each participant. A higher activity fragmentation index indicates bouts of activity are more “broken up” or fragmented, and that the person is more likely to transition to a rest/sedentary state during activity (20,21). Total time spent in active states per day was calculated as the sum of active minutes averaged across valid days.
Covariates Assessments
Data on sociodemographic (eg, age, sex, race/ethnicity, and education) and health characteristics were collected from a health interview. Height and weight were assessed using a stadiometer and a calibrated scale, respectively. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Chronic conditions including cardiovascular disease, diabetes, stroke, hypertension, osteoarthritis, and peripheral neuropathy were self-reported by participants. Other covariates include 6-meter usual gait speed (meters/second) (30), cognitive function measured by Mini-Mental State Examination (MMSE) (31), and depression defined as the Center for Epidemiological Studies—Depression Scale (CES-D) ≥16 (32).
Statistical Analysis
Data distributions were checked for normality. One outlier was detected with extremely low TAC active minutes, and high activity fragmentation based on 99th percentiles and was removed from the analytic sample. Sociodemographic characteristics, health conditions, pain measures, and accelerometer metrics were summarized using mean (standard deviation [SD]) or frequency and percentage. The differences in sociodemographic characteristics, health conditions, and other covariates by pain distribution were compared using 1-way analysis of variance (ANOVA) for continuous variables or Chi-squared tests for categorical variables. Linear regression models were used to examine the association between pain characteristics (ie, pain presence, pain laterality, pain distribution, number of pain sites) and PA metrics (ie, TAC, ASTP, and active minutes) in separate models. Multivariable models were adjusted for age, sex, race, education years, BMI, stroke, diabetes, and fall history (Model 1). We additionally adjusted for usual gait speed (Model 2) and depression (Model 3), respectively, to test the potential mediation roles of these factors in the relationship between pain and PA. As sex differences in PA and chronic pain have been reported in previous studies (33,34), we further stratified the sample by sex to examine whether the association between pain and PA differed by sex.
Multiple linear mixed-effects models tested differences in TAC across four 6-hour time intervals of the day by pain characteristics, adjusting for demographics and health characteristics. An unstructured correlation matrix was used to account for within-participant clustering of time intervals and restricted maximum-likelihood (REML) estimation was applied to fit the models.
All significance tests were conducted using two-sided tests. The significance level α was set as 0.05. All p values were additionally adjusted to control the false discovery rate (FDR). All statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC) and R (version 4.2.1).
Results
Among a total of 553 participants included in the analytic sample, the average age was 75.8 (SD = 8.4) years. Over half (54.4%) of the participants were women and around two-thirds (69.6%) were White. The characteristics of all participants and stratified by pain distribution are presented in Table 1. Participants classified as having multisite pain tended to be younger (p = .045). Other factors associated with multisite pain were higher BMI, hypertension, osteoarthritis, peripheral neuropathy, falls, depression, a lower MMSE score, and slow gait speed (Table 1). The percentage of pain characteristics in all participants is shown in Figure 1. Back pain was (41.5%) the most prevalent pain site among all 6 musculoskeletal pain sites, followed by shoulder pain (17.6%), hand/wrist pain (14.0%), and knee pain (12.9%). A total of 212 (38.3%) participants did not report pain in any musculoskeletal site. Thirty-five percent of participants had pain in only 1 site and 26% had multisite pain.
Table 1.
Demographic and Health Characteristics in 553 Adults Aged ≥60 Years in the Baltimore Longitudinal Study of Aging
| Characteristics |
Total, N = 553 | Pain Distribution | |||
|---|---|---|---|---|---|
| No Pain, n = 212 (38.3%) | Single-site Pain, n = 196 (35.4%) | Multisite pain, n = 145 (26.2%) | p Value* | ||
| Mean (SD) or n (%) | Mean (SD) or n (%) | ||||
| Age, mean (SD) | 75.8 ± 8.4 | 76.9 ± 8.3 | 75.4 ± 8.4 | 74.8 ± 8.5 | .045 |
| Sex | .097 | ||||
| Male | 252 (45.6) | 102 (48.1) | 94 (48.0) | 56 (38.6) | |
| Female | 301 (54.4) | 110 (51.9) | 102 (52.0) | 89 (61.4) | |
| Race | <.001 | ||||
| White | 385 (69.6) | 149 (70.3) | 147 (75.0) | 89 (61.4) | |
| Black | 136 (24.6) | 45 (21.2) | 38 (19.4) | 53 (36.5) | |
| Other | 32 (5.8) | 18 (8.5) | 11 (5.6) | 3 (2.1) | |
| Education level | .137 | ||||
| <College graduation | 79 (14.3) | 23 (10.8) | 27 (13.8) | 29 (20.0) | |
| College graduation | 109 (19.7) | 44 (20.8) | 45 (23.0) | 20 (13.8) | |
| >College graduation | 365 (66.0) | 145 (68.4) | 124 (63.3) | 96 (66.2) | |
| BMI | <.001 | ||||
| <25 | 192 (34.7) | 93 (43.9) | 67 (34.2) | 32 (22.1) | |
| 25–29.9 | 224 (40.5) | 75 (35.4) | 85 (43.4) | 64 (44.1) | |
| ≥30 | 137 (24.8) | 44 (20.7) | 44 (22.4) | 49 (33.8) | |
| Stroke | 23 (4.2) | 7 (3.3) | 5 (2.6) | 11 (7.6) | .069 |
| Diabetes | 92 (16.6) | 36 (17.0) | 32 (16.3) | 24 (16.6) | .902 |
| Hypertension | 277 (50.1) | 97 (45.8) | 88 (44.9) | 92 (63.5) | .002 |
| Osteoarthritis | 315 (57.0) | 79 (37.3) | 125 (63.8) | 111 (76.6) | <.001 |
| Peripheral neuropathy | 59 (10.7) | 16 (7.6) | 22 (11.2) | 21 (14.5) | .035 |
| Fall in the past year | 132 (23.9) | 38 (18.0) | 43 (21.9) | 51 (35.2) | <.001 |
| Depression (CES-D ≥ 16) | 25 (4.6) (547/553) | 8 (3.9) | 4 (2.1) | 13 (9.0) | .043 |
| MMSE score | 28.5 ± 1.4 (512/553) | 28.4 ± 1.4 | 28.7 ± 1.1 | 28.4 ± 1.6 | .013 |
| Usual gait speed (m/s) | 1.12 ± 0.24 (551/553) | 1.13 ± 0.23 | 1.13 ± 0.23 | 1.06 ± 0.26 | .008 |
| TAC (x1000 counts) | 2109. 3 ± 550.4 | 2089.5 ± 561.8 | 2098.2 ± 551.1 | 2153.4 ± 533.6 | .527 |
| ASTP (%) | 24.6 ± 5.7 | 24.4 ± 5.6 | 25.1 ± 6.1 | 24.2 ± 5.2 | .339 |
| Active time (mins) | 420.4 ± 101.6 | 417.5 ± 102.6 | 418.4 ± 102.6 | 427.2 ± 99.0 | .638 |
Notes: ASTP = active-to-sedentary transition probability; BMI = body mass index; CES-D = Center for Epidemiological Studies Depression; MMSE = Mini-Mental State Examination; SD = standard deviation; TAC = total daily activity counts.
*One-way analysis of variance (ANOVA) was used for continuous variables and chi-square tests were used for categorical variables. The bold p-values indicate statistically significant results (p < .05).
Figure 1.
Percentage of pain characteristics by body location and distribution: (A) pain presence, (B) pain laterality, (C) pain distribution, (D) number of pain sites. In panel B, no pain group for each site was omitted from the figure.
Among the 6 pain sites explored, we found that older adults who reported hand/wrist pain had more active time (24.8 minutes) compared to those without pain (p = .035), after adjusting for sociodemographic, health characteristics, and fall history (Model 1; Table 2). For pain laterality, participants with bilateral shoulder pain had 2.4% lower activity fragmentation (p = .049) and spent 47.2 more minutes/day active (p = .031) compared to those without shoulder pain. Older adults with only left or right hand/wrist pain had 35.4 more active minutes/day compared to those without hand/wrist pain (p = .043). Unilateral knee pain was associated with 184 070 fewer activity counts per day (p = .039) and 36.2 fewer active minutes/day (p = .032). There were no substantial changes in coefficients after additionally adjusting for usual gait speed (Model 2; Supplementary Table 2). In models additionally adjusted for depression, the associations of unilateral knee pain with TAC and active time lost statistical significance (Model 3; Supplementary Table 4). When we changed the comparison group to those reporting no pain, the association of knee pain with activity counts or active minutes lost significance (Supplementary Tables 1, 3, and 5). None of the associations remained significant after controlling for FDR.
Table 2.
Pain Characteristics and Physical Activity Outcomes in 553 Adults Aged ≥60 Years in the Baltimore Longitudinal Study of Aging (Model 1)
| TAC (×1 000 counts) | ASTP (%) | Active Time (min) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| β | SE | p Value | β | SE | p Value | β | SE | p Value | |
| Pain presence | |||||||||
| Shoulder pain | 41.16 | 57.61 | .475 | –0.57 | 0.62 | .354 | 11.35 | 10.93 | .300 |
| Hand/wrist pain | 113.16 | 61.87 | .068 | –0.65 | 0.66 | .325 | 24.76 | 11.72 | .035 |
| Back pain | 30.56 | 43.92 | .487 | 0.09 | 0.47 | .843 | 3.14 | 8.34 | .707 |
| Hip pain | –48.25 | 78.43 | .539 | 0.01 | 0.84 | .994 | –10.49 | 14.88 | .481 |
| Knee pain | –90.09 | 65.23 | .168 | 0.93 | 0.70 | .184 | –15.71 | 12.37 | .205 |
| Foot pain | –11.89 | 77.66 | .878 | –0.50 | 0.83 | .549 | –3.71 | 14.80 | .802 |
| Pain laterality | |||||||||
| Shoulder pain | |||||||||
| No | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Unilateral | 4.73 | 63.87 | .941 | –0.19 | 0.68 | .783 | 2.80 | 12.10 | .817 |
| Bilateral | 192.67 | 115.15 | .095 | –2.43 | 1.23 | .049 | 47.19 | 21.82 | .031 |
| Hands/wrists pain | |||||||||
| No | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Unilateral | 134.96 | 92.29 | .144 | –1.12 | 0.99 | .255 | 35.36 | 17.47 | .043 |
| Bilateral | 93.89 | 82.66 | .257 | –0.36 | 0.88 | .686 | 18.21 | 15.64 | .245 |
| Hip pain | |||||||||
| No | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Unilateral | –35.81 | 92.04 | .697 | –0.09 | 0.98 | .931 | –5.00 | 17.46 | .775 |
| Bilateral | 10.28 | 162.48 | .950 | –1.05 | 1.73 | .546 | –8.38 | 30.83 | .786 |
| Knee pain | |||||||||
| No | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Unilateral | –184.07 | 89.05 | .039 | 1.49 | 0.94 | .113 | –36.21 | 16.87 | .032 |
| Bilateral | 43.27 | 93.51 | .644 | –0.72 | 0.98 | .465 | 11.38 | 17.71 | .521 |
| Foot pain | |||||||||
| No | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Unilateral | –79.80 | 102.23 | .435 | –1.15 | 1.09 | .294 | –8.54 | 19.49 | .662 |
| Bilateral | 127.01 | 131.57 | .335 | 0.11 | 1.41 | .939 | 13.89 | 25.09 | .580 |
| Pain distribution | |||||||||
| No pain | ref | ref | ref | ref | ref | ref | ref | ref | ref |
| Single-site pain | –10.76 | 50.08 | .830 | 0.70 | 0.53 | .194 | –0.09 | 9.51 | .993 |
| Multisite pain | 50.98 | 56.25 | .365 | –0.26 | 0.60 | .663 | 8.90 | 10.68 | .405 |
| Number of pain sites | 10.96 | 20.41 | .591 | –0.07 | 0.22 | .756 | 2.23 | 3.87 | .566 |
Notes: Multivariable linear regression models adjusted for age, sex, race, education years, body mass index, stroke, diabetes, and fall history. The bold p-values indicate statistically significant results (p < .05). ASTP = active-to-sedentary transition probability; SE = standard error; TAC = total daily activity counts.
Diurnal patterns of daily activity differed by pain characteristics based on results from linear mixed models adjusted for demographic and health characteristics (Table 3). Older adults with hand/wrist pain had 46 754 more activity counts during the afternoon from 12 pm to 6 pm (p = .046) compared to those without hand/wrist pain. Hip pain was associated with 65 419 fewer activity counts during 6 am to 12 pm (p = .014). Compared to those without knee pain, older adults with knee pain were significantly less active during daytime from 6 am to 6 pm (p < .05). In terms of pain laterality, older adults with bilateral shoulder pain had 100 204 more activity counts from 6 pm to 12 am compared to those without shoulder pain (p = .040). Unilateral hand/wrist pain was associated with 73 819 more activity counts during afternoon (p = .034). Older adults with unilateral knee pain but not bilateral knee pain had significantly 88 665 fewer activity counts during afternoon (p = .009). We found similar findings for differences in diurnal patterns by pain characteristics when we changed the comparison group to those reporting no pain (Supplementary Table 10).
Table 3.
Interaction Effect of 6-Hour Time Intervals and Pain Characteristics On Total Daily Activity Counts (TAC) for Each Time Interval
| 12:00 am to 5:59 am | 6:00 am to 11:59 am | 12:00 pm to 5:59 pm | 6:00 pm to 11:59 pm | |
|---|---|---|---|---|
| Beta Coefficient (×1 000 counts) (SE) | ||||
| Pain presence | ||||
| Shoulder pain | 6.41 (17.79) | –1.62 (19.36) | 16.97 (21.51) | 24.38 (24.08) |
| Hand/wrist pain | 8.15 (19.33) | 25.70 (21.06) | 46.75 (23.41) * | 41.24 (26.23) |
| Back pain | –10.65 (13.63) | 7.20 (14.86) | 14.80 (16.53) | 27.68 (18.51) |
| Hip pain | 5.59 (24.41) | –65.42 (26.63) * | –7.62 (29.67) | 11.03 (33.28) |
| Knee pain | 7.19 (20.10) | –52.55 (21.91) * | –52.70 (24.38) * | –0.91 (27.35) |
| Foot pain | 9.06 (24.22) | –21.45 (26.39) | 25.03 (29.37) | –13.26 (32.92) |
| Pain laterality | ||||
| Shoulder pain | ||||
| No | ref | ref | ref | ref |
| Unilateral | –0.41 (19.74) | 3.65 (21.47) | 4.20 (23.84) | 5.40 (26.66) |
| Bilateral | 30.00 (35.93) | –14.94 (39.17) | 77.29 (43.57) | 100.20 (48.81) * |
| Hands/wrists pain | ||||
| No | ref | ref | ref | ref |
| Unilateral | 6.18 (28.74) | 56.38 (31.32) | 73.82 (34.84) * | 22.35 (39.04) |
| Bilateral | 10.53 (25.81) | 9.39 (28.15) | 22.80 (31.33) | 52.93 (35.13) |
| Hip pain | ||||
| No | ref | ref | ref | ref |
| Unilateral | –5.12 (28.65) | –53.65 (31.27) | 6.25 (34.82) | 18.66 (39.06) |
| Bilateral | 38.02 (50.41) | –84.97 (54.98) | 11.67 (61.19) | 14.21 (68.59) |
| Knee pain | ||||
| No | ref | ref | ref | ref |
| Unilateral | 3.82 (27.80) | –56.49 (30.31) | –88.66 (33.72) ** | –49.21 (37.79) |
| Bilateral | 0.87 (28.94) | –30.71 (31.49) | 15.06 (34.97) | 57.20 (39.13) |
| Foot pain | ||||
| No | ref | ref | ref | ref |
| Unilateral | –31.26 (31.75) | –18.41 (34.59) | 31.79 (38.47) | –26.81 (43.11) |
| Bilateral | 68.32 (41.07) | 17.32 (44.80) | 29.75 (49.88) | 1.28 (55.95) |
| Pain distribution | ||||
| No pain | ref | ref | ref | ref |
| Single-site pain | –4.37 (15.61) | –10.56 (17.02) | 6.30 (18.93) | 4.74 (21.22) |
| Multisite pain | –0.64 (17.29) | –3.26 (18.80) | 24.63 (20.85) | 34.22 (23.31) |
Notes: Linear mixed-effects model adjusted for age, sex, race, education years, body mass index, stroke, diabetes, and fall history. The bold values indicate statistically significant results (p < .05). SE = standard error.
* p < .05.
** p < .01.
Figure 2 depicts the diurnal patterns of daily activity by pain characteristics. Similar to the results from linear mixed-effects models, older adults with hip or knee pain tended to have lower activity levels during the day (6 am to 6 pm). Participants with unilateral knee pain had dramatically diminished diurnal patterns compared to those without knee pain or with bilateral knee pain. Bilateral hip pain was associated with a shifted diurnal pattern, with less PA in the morning and a peak activity level in the afternoon compared to others who performed peak activities in the morning. Similar trends were found in diurnal patterns when adding a group for those reporting no pain (Supplementary Figure 1).
Figure 2.
Smoothed 24-hour median activity counts per minute according to pain characteristics: (A) pain presence, (B) pain laterality, and (C) pain distribution.
Additional analyses stratified by sex suggested that these significant associations were only present among women (Supplementary Tables 6 and 8). Older women with hand/wrist pain spent 29.1 min/d more in an active state (p = .044) than those without hand/wrist pain. Bilateral shoulder pain was associated with 56.1 min/d more active time (p = .043) compared to no shoulder pain. Unilateral hand/wrist pain was associated with 42.8 more active minutes (p = .033) compared to no hand/wrist pain. Older women with unilateral knee pain had 299.6 fewer activity counts per day (p = .011), 2.6% higher activity fragmentation (p = .025), and 53.7 minutes fewer active minutes (p = .014; Supplementary Table 6). We found similar results when we changed the comparison group to those reporting no pain (Supplementary Tables 7 and 9.
Discussion
This study examined the cross-sectional association between pain characteristics and accelerometer-measured PA outcomes including total activity volume, active minutes/day, activity fragmentation, and diurnal activity patterns. Although none of the associations remained significant after adjustment for multiple testing, our results suggest that older adults with knee pain and hip pain tended to have lower PA levels than those without knee or hip pain. Specifically, unilateral knee pain but not bilateral knee pain was associated with both fewer activity counts and fewer active minutes per day. Diurnal patterns of activity also differed by pain status; knee pain was associated with fewer activity counts in the afternoon whereas hip pain appeared to interfere with activity during the morning. However, those with hand/wrist pain had more active minutes compared to those without hand/wrist pain. Our study extends previous evidence by demonstrating the associations between musculoskeletal pain and daily activities (12,14) and highlights the importance of assessing pain laterality in addition to pain presence and pain severity in older adults.
Our findings extend prior evidence by showing that musculoskeletal pain, particularly knee pain and hip pain, are associated with accelerometer-measured activity volume and time spent in an active state. The results showing more active time in participants with hand/wrist pain suggest that upper extremity pain may not appear to be detrimental to activity performance compared to lower extremity pain. A growing body of evidence has shown that older adults with knee pain have significantly poorer physical function and gait performance (26,35), which may be attributable to restricted daily activity. Other studies have also found an association between hip pain and reduced walking ability (36). Over the past decade, evidence has suggested multisite musculoskeletal pain may be more detrimental to mobility and physical performance than single-site pain in older adults (4,7,14,37). Although our study did not find lower activity levels among older adults with multisite musculoskeletal pain, we characterized objectively measured activity levels and patterns by pain status and location; results suggest that lower extremity pain but not upper extremity pain may interfere with daily activity.
Older adults with unilateral knee pain but not bilateral knee pain had fewer activity counts and spent less time in an active state. Although several studies have demonstrated the associations of unilateral knee pain with lower knee extension strength and poorer physical performance, few studies have explored the impact of pain laterality on daily activity levels (38,39). One study found that older adults with unilateral knee osteoarthritis had significantly poorer performance in chair stand time and lower physical function scores compared to those with bilateral osteoarthritis (39). It has also been reported that older adults with unilateral knee osteoarthritis had poorer balance control and poorer performance in sit-to-stand testing than those without knee osteoarthritis (40). These studies suggest that older adults with unilateral knee pain may experience muscle weakness, gait abnormality, and restricted daily activity and this effect may be greater than with bilateral knee pain. In addition, in the current study, these associations were diminished after adjusting for depression, suggesting that depression may partially explain the potential effects of unilateral knee pain on activity restriction (9). Future studies are warranted to investigate the temporal relationship and associated factors between unilateral knee pain and PA levels, and pain management strategy and exercise programs should consider the role of depression in older adults with unilateral knee pain.
To the best of our knowledge, this is the first study delineating diurnal patterns of accelerometer-measured activity by pain status. Daily activities tend to increase rapidly and reach their peak before mid-day, gradually decreasing during the afternoon and evening hours (22). Adding to previous research, we found fewer activity counts in the morning among participants with hip pain. These effects appear to be stronger among participants with bilateral hip pain, where we found a shifted pattern of activity during the morning and peak activity counts in the early afternoon, indicating that individuals with bilateral hip pain initiate daily activities later in the day compared to their counterparts. One explanation is that older adults with hip arthritis may experience joint stiffness and weakness in the morning, which leads to a reduced and/or delayed ability to perform daily activities (41). Knee pain, especially unilateral knee pain, was also associated with diminished patterns of daily activity, with fewer activity counts during the afternoon and slightly less activity during morning time, which may suggest increasing pain as the day progresses. Collectively, these results suggest that older adults with knee or hip pain may intentionally limit their daily activities to avoid pain. Further studies are warranted to examine whether tailored pain management and exercise programs could help increase activities by time-of-day in community-dwelling older adults.
Analyses stratified by sex showed that the associations between pain and PA were significant only among women, suggesting that pain may be more detrimental in women than in men. It has been found that women are at elevated risk of developing numerous chronic pain conditions including fibromyalgia and headaches, and that they report more widespread pain and suffer greater pain-related distress (42–44). Several experimental studies suggest that women have higher sensitivity to noxious stimulation and demonstrate lower pain thresholds and tolerance for a variety of stimuli than men (44,45). Collectively, these results suggest older women with pain may intentionally restrict daily activities as a pain avoidance strategy, leading to a more sedentary lifestyle with more fragmented activity patterns, which in the long run may further aggravate physical deconditioning and disability. Given the high prevalence of pain-related disorders in older women, more effective pain management strategies are needed to alleviate the effects of pain on reduced PA and decline in physical functioning in this vulnerable population.
Several limitations need to be considered in the interpretation of our findings. First, due to the cross-sectional study design, temporality could not be determined. Daily activity can also lead to musculoskeletal pain which in turn impacts daily activity. It has been shown that PA behavior may predict pain modulatory function and exercise has been identified as a nonpharmacological strategy for pain management (46–48). Second, we have limited data on the severity of pain. Previous studies have found that activity levels differed by severity of pain but not presence of pain. Further studies with more detailed information on pain characteristics may help elucidate the relationship between pain and PA, particularly among upper- and lower-body locations. There is also a lack of information about when pain occurred in the past 12 months. The time between pain occurrence and accelerometer assessment may differ by individuals. Ecological momentary assessment for pain along with accelerometer assessment may be needed in future studies. Third, BLSA is a relatively healthy and well-functioning cohort, which may limit the generalizability of the study findings. Future studies are warranted to characterize PA levels and patterns by pain status in other older populations.
In conclusion, upper extremity and lower extremity pain may differentially impact activity quantities and patterns. Although the significant associations of pain laterality with TAC and active minutes were only observed among women, diurnal patterns of daily activity significantly differ by pain characteristics in all participants. Our study findings highlight the association of pain with multiple aspects of daily activity, which may inform future interventional studies to promote PA among older adults with pain especially those with unilateral lower extremity pain.
Supplementary Material
Contributor Information
Yurun Cai, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania, USA.
Fangyu Liu, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Amal A Wanigatunga, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Center on Aging and Health, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Jacek K Urbanek, Center on Aging and Health, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Division of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Eleanor M Simonsick, Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA.
Luigi Ferrucci, Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA.
Jennifer A Schrack, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Center on Aging and Health, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Lewis A Lipsitz, (Medical Sciences Section).
Funding
This research was supported by the Intramural Research Program of the National Institute on Aging (NIA), National Institutes of Health (NIH). J.A.S. is funded by U01AG057545. LF and E.M.S. were supported by the Intramural Research Program of the NIA, NIH.
Conflict of Interest
E.M.S., L.F., and J.A.S. currently serve on the editorial board of The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences (JGMS). The other authors declare no conflict.
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