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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: J Behav Med. 2022 Apr 30;45(4):632–642. doi: 10.1007/s10865-022-00318-1

Activity-Induced Pain as a Predictor of Physical Activity Behavior among Individuals with Chronic Pain: The Role of Physical Activity Enjoyment

Lisa R LaRowe 1, David M Williams 1
PMCID: PMC9308740  NIHMSID: NIHMS1817374  PMID: 35501540

Abstract

Nearly 70% of adults with chronic pain experience increased pain during activity, and this may reduce enjoyment of physical activity (PA), and subsequent PA intention/behavior. The goal of this study was to examine increased pain during activity as a predictor of PA, via its effects on PA enjoyment. Participants included 178 overweight/obese midlife adults with chronic pain who completed an online prospective survey. Results indicated that greater increases in pain during activity were associated with less PA enjoyment, and, in turn, lower intention to exercise over the next week (p < .05). Activity-induced pain also predicted lower total volume of PA at 1-week follow-up, and this relationship was mediated by PA enjoyment (p < .05). These findings have the potential to inform the refinement of PA promotion interventions for individuals with chronic pain.

Keywords: pain, exercise, physical activity, intention, enjoyment

Introduction

Chronic pain is typically defined as pain that persists beyond the expected healing time, often lasting longer than 3 to 6 months (Treede et al., 2015). Over 50 million American adults suffer from chronic pain (Dahlhamer, 2018), engendering an annual economic impact of $635 billion in healthcare costs and lost productivity (Gaskin & Richard, 2012). The prevalence of chronic pain increases with age, and rates higher than 25% have been observed among midlife adults (Dahlhamer, 2018). There is also a well-documented positive correlation between chronic pain and overweight/obesity (e.g., Janke et al., 2007; Ray et al., 2011; Stone & Broderick, 2012), with data suggesting that the prevalence of chronic pain is up to 254% higher among obese (vs. low-normal weight) individuals (Stone & Broderick, 2012). Moreover, the association between chronic pain and obesity becomes stronger with advancing age (Stone & Broderick, 2012), and chronic pain has been shown to mediate obesity-induced impairment and the eventual decline of fitness and health-related quality of life (Heo et al., 2003). Thus, there is an urgent need to develop tailored intervention strategies for overweight/obese midlife adults with chronic pain.

Exercise is a promising intervention that can reduce pain, promote weight loss, and increase quality of life among overweight/obese mid-life adults with chronic pain (e.g., Geneen et al., 2017; Paley & Johnson, 2016; Zdziarski et al., 2015). Consequently, physical activity (PA) and exercise programs have increasingly been promoted and offered in numerous healthcare systems for various chronic pain conditions. Engagement in regular PA is associated with lower prevalence of chronic pain (e.g., Alzahrani, Mackey, et al., 2019; Alzahrani, Shirley, et al., 2019), and has been posited to reduce pain among overweight adults by reducing inflammation, decreasing mechanical loading, and improving psychological state (Zdziarski et al., 2015). However, adherence to formal exercise prescription is poor, with attrition rates as high as 50% (e.g., Linke et al., 2011).

There is an extensive literature focused on addressing barriers to PA adherence among individuals with chronic pain (e.g., Ambrose & Golightly, 2015; Kroll, 2015; Larsson et al., 2016; Paley & Johnson, 2016). Indeed, individuals with (versus without) chronic pain are less likely to be sufficiently active (e.g., Larsson et al., 2016), and are more likely to present with high levels of fear-avoidance behaviors (e.g., Kroll, 2015). In accordance with the fear-avoidance model of chronic pain, pain-related fear/anxiety activates escape mechanisms that lead to the avoidance of movement and activity, which, in turn, lead to increased levels of disability (Vlaeyen & Linton, 2000). A majority of work on PA adherence among individuals with chronic pain has focused on elucidating the role of fear and avoidance of pain in PA outcomes (e.g., L’Heureux et al., 2020; Larsson et al., 2016; Taulaniemi et al., 2020; Thompson et al., 2016). For example, longitudinal studies have demonstrated that higher levels of kinesiophobia (i.e., fear of pain and/or [re]-injury due to movement) predict lower levels of PA among individuals with chronic pain (Larsson et al., 2016), and researchers have repeatedly proposed that exercise promotion interventions for individuals with pain should include components aimed at reducing pain-related fear (e.g., via graded exposure and/or quota-based non-pain-contingent exercise; e.g., Booth et al., 2017; Rainville et al., 2004; Roditi & Robinson, 2011; Turk & Wilson, 2010). However, PA promotion interventions tailored for individuals with chronic pain have demonstrated mixed levels of efficacy, with one prior systematic review and meta-analysis finding that interventions aimed at increasing subjective PA levels among individuals with chronic pain are ineffective in the short-term, and only offer small effects medium- and long-term (Marley et al., 2017).

Identification of additional factors that influence PA adherence among individuals with chronic pain may help to inform the ongoing refinement of PA promotion strategies among this population. One factor that remains relatively understudied, particularly in comparison to pain-related cognitive-affective factors (e.g., pain-related fear/anxiety, kinesiophobia), is activity-induced pain (i.e., acute increases in pain intensity during PA). Individual bouts of exercise can transiently exacerbate pain symptoms among adults with chronic pain (Lima et al., 2017; Sluka et al., 2018; Staud et al., 2005), and up to 70% of individuals chronic pain experience increased pain during activity (Damsgard et al., 2010). However, there has been almost no research to date examining the prospective association between activity-induced pain and future PA behavior. One study showed that greater increases in pain following exercise predicted increased drop-out rates among midlife/older adults with osteoarthritis engaging in a PA program, but associations between exercise-induced pain and total volume of PA among those who remained in the program were not reported (Beckwée et al., 2015). Clarifying the role of activity-induced pain in PA adherence is an important next step in this line of work, given that immediate consequences of behavior (i.e., how one feels during PA) are posited to be a salient predictor of future behavior (e.g., Neef et al., 1994).

Psychological hedonism posits that individuals are more likely to engage in behaviors that make them feel good and less likely to engage in behaviors that make them feel bad (Williams, 2018). Consistent with this notion, we hypothesize that increased pain during activity leads to lower levels of enjoyment of PA, and consequently reduces future PA intentions and behavior. Indeed, previous work has demonstrated that PA enjoyment is positively associated with PA intention (Focht, 2009), prospective studies have demonstrated that PA enjoyment is a robust predictor of PA behavior (e.g., Lewis et al., 2016; Ning et al., 2012; Rovniak et al., 2002; Ungar et al., 2016), and researchers have repeatedly suggested that enjoyment/pleasure play a key role in the maintenance of PA (e.g., Schutzer & Graves, 2004; Wankel, 1993; Williams, 2018; Williams et al., 2006).

To advance clinical practice for overweight/obese midlife adults with chronic pain, it is critical to determine the extent to which activity-induced pain serves as a barrier to PA via its effects on PA enjoyment. Therefore, the goal of this study was to test whether increased pain during activity is associated with less PA enjoyment, and subsequently leads to lower levels of PA intention and behavior (i.e., MET minutes) among overweight/obese midlife adults with chronic pain. To accomplish this goal, we used an online prospective survey to assess associations between past-week activity-induced pain (average pain intensity at rest subtracted from average pain intensity during activity), baseline PA enjoyment and intention, and total volume of leisure-time PA (MET minutes/week) at 1-week and 4-week follow-up assessments. We focused on leisure-time PA because of its documented role in reducing mortality (e.g., Kujala et al., 1998). We hypothesized that greater activity-induced pain over the past week would be associated with decreased PA enjoyment, and in turn, lead to lower intention to exercise during the next week. We further hypothesized that activity-induced pain would predict less past-week leisure-time PA (MET minutes/week) reported at follow-ups, via its serial effects on PA enjoyment and PA intention. More specifically, we expected that greater activity-induced pain would predict less PA enjoyment, which would be associated with lower PA intention, and subsequently lead to less leisure-time PA reported at follow-ups.

Method

Participants

Using Amazon Mechanical Turk (described below in Procedure/Online Survey), we recruited 200 adults with chronic pain who were aged 50–64, a current resident of the United States, screened positive for chronic pain (“Do you currently suffer from any type of chronic pain, that is, pain that occurs constantly or flares up frequently? Do not report aches and pain that are fleeting or minor.”; e.g., Kosiba et al., 2020), were overweight or obese (BMI > 25.0), and responded accurately to a response accuracy check. Participants were excluded if they were unable to read English. For the current analyses, we also excluded participants who reported an inability to exercise (n = 22), given that our primary outcome was total PA volume. Therefore, our final sample was comprised of N = 178 midlife overweight or obese adults with chronic pain.

Procedure/Online Survey

All study procedures were completed using Amazon Mechanical Turk, which is a crowdsourcing web service that is commonly used to sample participants for health-related and psychological research (e.g., Paolacci et al., 2010; Shapiro et al., 2013; Strickland & Stoops, 2019; Walters et al., 2018). Using Mechanical Turk, “requesters” (in this case, researchers) can hire “workers” (in this case, participants) to complete “human intelligence tasks” (HITs; in this case, surveys) for a small cost. Mechanical Turk offers advantages that can reduce costs and increase recruitment feasibility, and thus has been increasingly used to generate convenience samples (Ipeirotis, 2010; Strickland & Stoops, 2019). Moreover, there is evidence that Mechanical Turk samples are more diverse than traditional convenience samples (e.g., undergraduate psychology participant pools; Strickland & Stoops, 2019). Mechanical Turk also provides tools to increase data quality (e.g., response accuracy checks; Amazon Mechanical Turk, 2011), and previous work has found that the accuracy of data collected from Mechanical Turk is similar to that of data collected from field and laboratory experiments (Paolacci et al., 2010; Strickland & Stoops, 2019). There is also evidence that the prevalence of chronic pain among Mechanical Turk users is comparable to rates observed in the general population (Shapiro et al., 2013).

A recruitment post was used to invite Mechanical Turk “workers” to participate in a survey on chronic pain and physical activity. Prospective participants completed a screening questionnaire to determine eligibility. Eligible participants were invited to complete a brief (~30 minutes) online baseline survey. Consistent with recommendations (e.g., Keith et al., 2017), a response accuracy check was embedded within this survey (i.e., “To monitor quality, please respond with a two for this item.”), and only those participants who responded accurately to this item were invited to continue with the study. Indeed, such checks can increase the quality of the data by monitoring careless or insufficient effort responding (Keith et al., 2017). Participants who responded correctly to the baseline response accuracy check were re-contacted to complete two brief (~15 minutes) follow-up surveys at 1-week and 4-weeks post-baseline via Amazon Mechanical Turk. Identical response accuracy checks were included in each follow-up survey.

Measures

Activity-Induced Pain.

Participants were asked to respond to two items: (1) “Please mark the box beside the number that best describes your pain when at rest over the past week” and (2) “Please mark the box beside the number that best describes your pain during activity over the past week”, at baseline using a numerical rating scale (NRS) ranging from 0 (no pain) to 10 (worst possible pain). Consistent with previous research (Damsgard et al., 2010), activity-induced pain was assessed by subtracting each participant’s score on the NRS for “pain at rest” from the score for “pain during activity”.

Physical Activity.

At baseline, current engagement in regular PA was assessed by asking participants to indicate whether or not (yes/no) they “currently engage in regular physical activity.” At the 1-week and 4-week follow-up assessments, we assessed past-week leisure-time PA using Part 4 of the International Physical Activity Questionnaire (IPAQ; Craig et al., 2003). Participants were asked to answer questions about the PA that they engaged in over the last seven days solely for recreation, sport, exercise, or leisure. Specifically, they were asked to indicate time spent walking, engaging in moderate PA (e.g., bicycling at a regular pace, swimming at a regular pace, doubles tennis), and engaging in vigorous PA (e.g., aerobics, running, fast bicycling, fast swimming) during leisure-time. Consistent with IPAQ data processing rules and scoring instructions (International Physical Activity Questionnaire Research Committee, 2005), only activities performed for at least 10 consecutive minutes were recorded, and each activity (walking, moderate intensity, vigorous intensity) was truncated to a maximum of 180 minutes per day. Total volume of past-week leisure-time PA was computed as MET minutes ([3.3 * walking minutes * walking days] + [4.0 * moderate intensity activity minutes * moderate intensity days] + [8.0 * vigorous intensity activity minutes * vigorous intensity days]).

Physical Activity Enjoyment.

The Physical Activity Enjoyment Scale (PACES; Kendzierski & DeCarlo, 1991) was used to measure PA enjoyment at baseline. The PACES asks participants to indicate how they feel “at the moment” about PA using 18 bipolar 7-point rating scales (e.g., “I enjoy it… I hate it”). After reverse-scoring the 12 negatively phrased items, an overall PA enjoyment score was computed by summing individual item scores. Higher scores reflected greater PA enjoyment. Prior work has demonstrated good reliability and validity of the PACES measure (Kendzierski & DeCarlo, 1991), and internal consistency was excellent in the current sample (α = .92).

Behavioral Intention.

At baseline, participants were asked to rate their agreement with the statement, “I intend to exercise over the next week,” using a 7-point response scale ranging from 1 (false) to 7 (true; Ajzen, 2017).

Clinical Pain Variables.

The Graded Chronic Pain Scale (GCPS) is a reliable and valid measure of chronic pain in both clinical and non-clinical samples (Von Korff, 2011; Von Korff et al., 1992) that provides a categorical classification of chronic pain by grade (severity) ranging from Grade 1 (low intensity, low interference) to Grade 4 (severe interference). The GCPS also provides a measure of characteristic pain intensity (GCPS-CPI). Specifically, characteristic pain intensity was assessed by asking participants to rate their pain “right now,” “on average,” and at its “worst” in the past three months on an 11-point scale ranging from 0 (no pain) to 10 (pain as bad as it could be). Responses on these items were summed to generate a characteristic pain intensity score (GCPS-CPI) ranging from 0–30. In addition to completing the GCPS at baseline, we also asked all participants to indicate the location of their pain.

Sociodemographic Variables.

Participants were asked to report sociodemographic information, including age, gender, race, ethnicity, education, employment status, and annual income at baseline.

Data Analysis

First, we tested cross-sectional associations between activity-induced pain and (1) PA enjoyment (PACES scores) and (2) intention to exercise using separate hierarchical linear regression models. Baseline engagement in regular PA (yes/no) and pain intensity (GCPS-CPI score) were included as covariates in the models, given that these constructs have previously demonstrated associations with PA/exercise behavior among individuals with chronic pain (e.g., Arensman et al., 2020; Larsson et al., 2016; Macías-Hernández et al., 2020). Covariates were entered at Step 1, and activity-induced pain was entered at Step 2. We also tested the indirect association between activity-induced pain and intention to exercise via PA enjoyment using the PROCESS Macro for SPSS, when controlling for the same variables.

Second, we tested prospective associations between activity-induced pain and leisure-time IPAQ scores (total MET min/week, moderate intensity MET min/week, vigorous intensity MET min/week) at the 1-week and 4-week follow-ups, when controlling for baseline engagement in regular PA and characteristic pain intensity. Consistent with previous research (e.g., Ekelund et al., 2006; Galioto Wiedemann, Calvo, Meister, & Spitznagel, 2014; Van Dyck, Cardon, Deforche, Owen, & De Bourdeaudhuij, 2011), a logarithmic transformation was applied to all IPAQ scores (which were positively skewed and leptokurtic) prior to analysis to approximate the normal distribution. We also tested two serial multiple mediator models examining the indirect effects of activity-induced pain on total leisure-time MET min/week at the 1-week and 4-week follow-ups via PA enjoyment and intention for exercise (assessed at baseline). Each model tested the direct effect of activity-induced pain on PA at follow-up (when controlling for the mediator variables), and three indirect effects: (1) activity-induced pain → PA enjoyment → PA behavior (total leisure-time MET min/week at either 1-week or 4-week follow-up), (2) activity-induced pain → intention to exercise → PA behavior, and (3) activity-induced pain → PA enjoyment → intention to exercise → PA behavior. We generated 95% confidence intervals (CI) of the indirect effects using 10,000 bootstrap samples. All analyses controlled for baseline engagement in regular physical activity and characteristic pain intensity.

Of note, only participants who responded accurately to response accuracy checks (described in Procedure/Online Survey) given at each follow-up appointment were included in subsequent analyses. More specifically, although 80% (n = 141) participants completed the 1-week follow-up assessment, n = 22 of these did not respond accurately to the response accuracy check given at this assessment. Therefore, a total of n = 119 participants were included in 1-week follow-up analyses. Of these, 73% (n = 87) completed the 4-week follow-up assessment, and an additional n = 16 were excluded from analysis due to responding incorrectly to the response accuracy check. Therefore, a total of n = 71 participants were included at 4-week follow-up.

Results

Participant Characteristics

Participants included 178 midlife overweight/obese adults with current chronic pain (35.4% female; 39.9% Hispanic; Mage = 55.49, SD = 4.03, range: 50–64) who endorsed an ability to exercise. Participants were generally well-educated (89.9% completed at least a 4-year college degree), and over two-thirds (67.9%) reported a total household income greater than $50,000. The majority of participants (83.1%) endorsed either Grade 3 or Grade 4 chronic pain, indicating high levels of pain-related disability, and the most commonly endorsed pain locations were back/neck (49%), lower extremities (21%), and head/face (17%). More than half of participants (54.5%) reported increased pain during activity at baseline. Additional baseline participant characteristics are presented in Table 1.

Table 1.

Sociodemographic and Pain Characteristics

Baseline (N = 178) 1-Week Follow-Up (N = 119) 4-week Follow-up (N = 71)
N (%) N (%) N (%)
Gender
 Female 63 (35.4%) 4 (33.6%) 25 (35.2%)
Race
 White 109 (61.2%) 71 (59.7%) 43 (60.6%)
 Black or African American 39 (21.9%) 28 (23.5%) 15 (21.1%)
 Asian 27 (15.2%) 19 (16.0%) 13 (18.3%)
 American Indian/Alaska Native 3 (1.7%) 1 (0.8%) 0 (0.0%)
Ethnicity
 Hispanic 71 (39.9%) 52 (43.7%) 31 (43.7%)
Marital Status
 Single 8 (4.5%) 6 (5.0%) 4 (5.6%)
 Married 163 (91.6%) 106 (89.1%) 62 (87.3%)
 Divorced/Separated 5 (2.8%) 5 (4.2%) 3 (4.2%)
 Widowed 2 (1.1%) 2 (1.7%) 2 (2.8%)
Education
 High school graduate or GED 4 (2.2%) 3 (2.5%) 2 (2.8%)
 Some college/Technical school/Associates degree 14 (7.9%) 11 (9.2%) 5 (7.0%)
 4-year college degree 111 (62.4%) 76 (63.9%) 48 (67.6%)
 Some school beyond 4-year college degree 16 (9.0%) 8 (6.7%) 5 (7.0%)
 Professional degree (e.g., MD, JD, PhD) 33 (18.5%) 21 (17.6%) 11 (15.5%)
Household Income
 < $10,000 5 (2.8%) 4 (3.4%) 2 (2.8%)
 $10,000 - $49,999 52 (29.2%) 36 (30.3%) 25 (35.2%)
 $50,000 - $100,000 111 (62.4%) 74 (62.2%) 39 (54.9%)
 > $100,000 10 (5.6%) 5 (4.2%) 5 (7.0%)
Chronic Pain Grade a
 Grade 1 19 (10.7%) 14 (11.8%) 11 (15.5%)
 Grade 2 11 (6.2%) 8 (6.7%) 5 (7.0%)
 Grade 3 47 (26.4%) 35 (29.4%) 20 (28.2%)
 Grade 4 101 (56.7%) 62 (52.1%) 35 (49.3%)
Primary Pain Location
 Back/neck 87 (48.9%) 60 (50.4%) 40 (56.3%)
 Head/face 31 (17.4%) 18 (15.1%) 6 (8.5%)
 Upper extremities 13 (7.3%) 10 (8.4%) 5 (7.0%)
 Lower extremities 38 (21.3%) 26 (21.8%) 18 (25.4%)
 Chest/breast 5 (2.8%) 3 (2.5%) 2 (2.8%)
 Stomach/abdomen 4 (2.2%) 2 (1.7%) 0 (0.0%)
Current Engagement in Regular PA
 Yes 127 (71.3%) 82% (68.9%) 49 (69.0%)
M (SD)
Age 55.49 (4.03) 55.24 (3.74) 55.13 (3.64)
Characteristic Pain Intensity a 20.02 (4.99) 19.64 (4.64) 18.75 (5.10)*
Activity-Induced Pain .49 (1.94) .64 (2.05) .73 (1.88)
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Note.

a

Graded Chronic Pain Scale.

*

indicates that participants who completed the follow-up survey differed from participants who did not complete the follow-up (p < .05).

Participants who did not complete the 1-week follow-up assessment (n = 37) reported greater baseline characteristic pain intensity (M = 21.46, SD = 5.61), compared to those who completed the 1-week follow-up assessment (n = 141; M = 19.64, SD = 4.76, F(1, 176) = 3.972, p = .048). However, once those who did not respond accurately to the response accuracy check were excluded, no differences in any sociodemographic, pain, or PA characteristics were observed between those who provided complete/accurate data at the 1-week follow-up (n = 119) and those who did not (n = 59; all ps > .05). Participants who did not complete the 4-week follow-up assessment (n = 32) similarly reported greater baseline characteristic pain intensity (M = 21.09, SD = 3.59), compared to those who completed the 4-week follow-up assessment (n = 87; M = 19.10, SD = 4.67, F(1, 117) = 4.37, p = .039). Even when those who did not respond accurately to the response accuracy check were excluded, those who provided complete/accurate data at the 4-week follow-up (n = 71) reported lower baseline characteristic pain intensity (M = 18.75, SD = 5.10), than those who did not (n = 48; M = 20.96, SD = 3.60, F(1, 117) = 6.74, p = .011). No other differences in sociodemographic, pain, or PA characteristics were observed. Taken together, participants who completed follow-ups and were included in analyses had slightly lower levels of characteristic pain intensity than those who were not included, but did not differ on any other baseline characteristics.

Associations between Activity-Induced Pain, Physical Activity Enjoyment, and Intention to Exercise at Baseline

There was a borderline significant association between activity-induced pain and PA enjoyment (β = −.141, p = .051; ΔR2 = .020, p = .051), such that participants who endorsed greater increases in pain during activity reported enjoying PA less. Although there was no direct association between activity-induced pain and intention to exercise (β = −.091, p = .184; ΔR2 = .008, p = .184), there was a significant indirect association between these variables via PA enjoyment (indirect association; b = −.04 [SE = .03], 95% CI [−.101, −.001]). Specifically, greater increases in pain during PA over the past week were associated with less enjoyment of PA, which in turn, was associated with lower intention to exercise in the next week (see Table 2).

Table 2.

Indirect Association between Activity-Induced Pain and Intention to Exercise via Enjoyment of Physical Activity

Model Coefficients
Antecedent Consequent
M (Enjoyment) Y (Intention)
Coeff. SE p Coeff. SE p
X (Activity-induced pain) a −1.3472 .6846 .0507 c’ −.0204 .0422 .6288
M (Enjoyment) b .0310 .0046 <.001
Characteristic pain intensity i 1 −.3179 .2672 .2358 i 2 .0800 .0163 <.001
Engagement in regular PA constant 12.1812 2.9421 .0001 .5524 .1878 .0037
76.8246 5.7191 <.001 1.1314 .4971 .0241
R2 =.1064 R2 = .3602
F (3,174) = 6.9071, p < .001 F (4,173) = 24.3502, p < 0.001
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Activity-Induced Pain as a Predictor of Physical Activity at Follow-Ups

One-Week Follow-Up

Activity-induced pain predicted less engagement in leisure-time PA (total MET minutes in the previous week) reported at the 1-week follow-up (β = −.188, p = .038; ΔR2 = .034, p = .038). More specifically, participants who reported greater increases in pain during activity at baseline subsequently reported less engagement in moderate (β = −.184, p = .037; ΔR2 = .033, p = .037) and vigorous (β = −.189, p = .024; ΔR2 = .035, p = .024) intensity PA.

We also tested a serial mediation model in which we hypothesized that activity-induced pain at baseline would be negatively associated with PA enjoyment, which would, in turn, be associated with lower intention to engage in exercise, and subsequently predict fewer MET minutes reported in the previous week at the 1-week follow-up. After accounting for the mediator variables (PA enjoyment, intention to exercise), the effect of activity-induced pain on PA was no longer statistically significant (direct effect; b = −.09, SE = .06, 95% CI [−.2084, .0216]). Results of this model further revealed that PA enjoyment mediated the relationship between activity-induced pain and engagement in PA at the 1-week follow-up (indirect effect; b = −.03 [SE = .02], 95% CI [−.0796, −.0007]), such that participants who endorsed greater increases in pain during activity at baseline reported enjoying PA less, and subsequently reported fewer past-week MET minutes at the 1-week follow-up. Intention did not mediate the relationship between activity-induced pain and subsequent PA behavior (indirect effect; b = −.001 [SE = .01], 95% CI [−.0274, .0146]), and the serial mediation effect of PA enjoyment and intention to exercise on the relationship between activity-induced pain and subsequent PA behavior was not significant (indirect effect; b = −.01 [SE = .01], 95% CI [−.0295, .0015]). Moreover, the total indirect effect was not statistically significant (total indirect effect; b = −.03 [SE = .02], 95% CI [−.0845, .0015]). Full results of this model are presented in Table 3.

Table 3.

Indirect Effect of Activity-Induced Pain on Physical Activity via Enjoyment of Physical Activity and Intention to Exercise

One-Week Follow-Up Four-Week Follow-Up
Path Coeff. SE 95% CI Coeff. SE 95% CI
Activity-induced pain → Enjoyment → MET/week −.0259 .0182 −.0796, −.0007 −.0055 .0146 −.0676, .0082
Activity-induced pain → Intention → MET/week −.0007 .0096 −.0274, .0146 .0010 .0168 −.0239, .0413
Activity-induced pain → Enjoyment → Intention → MET/week −.0052 .0071 −.0295, .0015 −.0045 .0101 −.0504, .0045
Total effect −.1252 .0595 −.2431, −.0073 −.0288 .0756 −.1796, .1221
Direct effect −.0934 .0581 −.2084, .0216 −.0198 .0756 −.1707, .1312
Total indirect effect −.0318 .0216 −.0845, .0015 −.0090 .0226 −.0627, .0274
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Four-Week Follow-Up

There was a negative association between activity-induced pain and engagement in moderate intensity PA at 4-week follow-up (β = −.221, p = .045; ΔR2 = .048, p = .045). Activity-induced pain was not associated with vigorous PA (β = −.179, p = .112; ΔR2 = .031, p = .112) or total MET minutes in the previous week reported at the 4-week follow-up (β = −.045, p = .705; ΔR2 = .002, p = .705). There was no indirect effect of activity-induced pain on PA reported at the 4-week follow-up via PA enjoyment and/or intention to exercise (total indirect effect; b = −.01 [SE = .02], 95% CI [−.0627, .0274]). Full results of the serial mediation model are presented in Table 3.

Discussion

The current study is the first to explore associations between activity-induced pain, PA enjoyment, and PA intention and behavior (i.e., total volume of leisure-time PA measured in MET minutes). Results indicated that participants who reported greater increases in pain during activity over the past week also reported less PA enjoyment, and, in turn, endorsed lower intention to exercise over the next week. Results further demonstrated that activity-induced pain was a significant predictor of total volume of PA at the 1-week follow-up assessment, and that this relationship was mediated by PA enjoyment. More specifically, greater activity-induced pain was associated with less enjoyment of PA, which, in turn, predicted lower total volume of PA at the 1-week follow-up. Activity-induced pain also predicted moderate (but not vigorous) intensity PA at the 4-week follow-up assessment. The relationship between activity-induced pain and total volume of PA at the 4-week follow-up was not mediated by PA enjoyment and/or PA intention.

Regular PA can improve pain severity, physical function, and quality of life among overweight and obese individuals with chronic pain (Geneen et al., 2017), and higher levels of PA during midlife have been shown to predict less bodily pain and functional impairment over time (Dugan et al., 2009). However, PA is often poorly tolerated among adults with chronic pain (Clauw & Crofford, 2003; Paley & Johnson, 2016), and, consistent with previous research (e.g., Lima et al., 2017; Sluka et al., 2018; Staud et al., 2005), more than half of participants in the current sample reported increased pain during activity. The current results also provide the first prospective evidence that activity-induced pain may lead to poor adherence to PA via its effects on PA enjoyment. Pain is inherently aversive and previous research has demonstrated that increased pain leads to a negative shift in affective valence (e.g., Ditre & Brandon, 2008; Gaskin et al., 1992; Zhang, 2006). Thus, activity-induced pain may preclude individuals from experiencing pleasure during exercise and developing positive attitudes towards PA, and may reduce the likelihood that individuals engage in future PA. This is consistent with psychological hedonism, which suggests that individuals are more likely to engage in PA that makes them feel good and less likely to engage in PA that makes them feel bad (Williams, 2018). Moreover, in accordance with the fear-avoidance model of pain, the escape and avoidance of increased pain during activity (via discontinuing the activity and/or avoiding future activity) is likely a powerful behavioral reinforcer of physical inactivity among individuals with chronic pain. Over time, avoidance of activity-induced pain and subsequent physical inactivity may lead to greater pain-related disability and other deleterious health outcomes (e.g., increased cardiovascular disease risk) among this population (e.g., Leeuw et al., 2007; Rodríguez-Sánchez et al., 2021; Vlaeyen & Linton, 2000; Zale et al., 2013).

To date, clinical research has focused largely on the role of fear-avoidance in PA adherence among individuals with chronic pain, and has recommended that patients gradually confront feared activities (e.g., via graded exposure) in order to modify maladaptive and inaccurate beliefs about the consequences of PA. However, the current findings suggest that PA/exercise programs for individuals with chronic pain may further benefit from tailoring to account for the antithetical influence of activity-induced pain, perhaps by acting on immediate reward/benefit derived from exercise. In other words, even if strategies like graded exposure increase patients’ self-efficacy and willingness to initiate exercise (e.g., Turk & Wilson, 2010), our results suggest that acute increases in pain during exercise may still lead to less enjoyment in PA, and, consequently, lower volume of total PA. One strategy that may offer promise for decreasing activity-induced pain and increasing PA enjoyment is encouraging individuals to self-select PA intensity (vs. conforming to national guidelines which recommend 150 min/week of moderate intensity PA). This may allow them to better regulate activity-induced pain by providing the opportunity to reduce PA intensity when pain increases (Mauger, 2014). Indeed, there is initial evidence that this approach may reduce pain intensity (Van Oosterwijck et al., 2012), and promote a more positive affective response during PA (e.g., Ekkekakis & Lind, 2006; Parfitt et al., 2006; Rose & Parfitt, 2007). It may also be particularly important for clinicians to encourage individuals with chronic pain to try a variety of activities to determine which forms of PA are most enjoyable.

Surprisingly, activity-induced pain did not predict engagement in vigorous intensity PA at the 4-week follow-up, however, this may be due to low statistical power (only 71 participants were included in the 4-week follow-up analyses), and future research should include larger sample sizes that are more adequately powered to detect an effect. Of note, the current pattern of results trended in the expected direction, such that participants who reported increased pain during activity at baseline subsequently reported engaging in vigorous intensity PA for a median of 60 minutes at the 4-week follow-up, whereas those who did not report increased pain during activity reported a median of 90 minutes of PA. It is possible that activity-induced pain is a more robust predictor of proximal (vs. distal) outcomes, and that changes in activity-induced pain throughout the course of the study influenced PA behavior at the 4-week follow-up. Future work should examine changes in activity-induced pain over time, as well as subsequent effects on PA intention/behavior. Also contrary to expectation, the hypothesized serial relationship (activity-induced pain → PA enjoyment → PA intention → total volume of PA) was nonsignificant, and after controlling for all other variables in the mediation model, PA intention was not associated with PA behavior at either the 1-week or 4-week follow-ups. One potential explanation for this pattern of findings stems from prior work suggesting that, although global conceptual models suggest that intention is the direct antecedent of behavior, intention is often a weak predictor of future behavior and/or behavior change (e.g., André & Laurencelle, 2020). Indeed, there is widely documented discordance between intention and behavior, which often results when individuals do not report strong habits towards the target behavior (André & Laurencelle, 2020). This may help to explain why intention did not predict PA behavior in the current study.

In addition to elucidating the need for novel strategies to increase PA enjoyment among individuals with chronic pain, results of the current study also suggest that tailored treatments for individuals with chronic pain may benefit from including strategies to decrease pain during activity. For example, including pain coping skills training (e.g., attention diversion methods, such as imagery and distraction) prior to initiating an exercise/PA program may help individuals manage acute increases in pain during activity. Treatments could also provide psychoeducation regarding the beneficial longer-term effects of PA on chronic pain to aid in the development of discrepancy between current physical inactivity and stated goals for reducing pain.

Several limitations of this study and directions for future research are worth noting. First, all participant characteristics, including chronic pain status and engagement in PA, were assessed via self-report. Future work should include additional verification of chronic pain status (e.g., medical records, physician report), and objective measurements of total volume of PA (e.g., via accelerometry). Second, activity-induced pain was assessed via recall and was not specific to pain during exercise. The incorporation of real-time reporting of pain during exercise is likely to be superior to retrospective reports of pain during ‘activity’, more generally, over extended periods of time. Thus, future work would benefit from using ecological momentary assessment (EMA) methodology to obtain repeated pain intensity assessments during exercise (Stone et al., 2007). Third, we did not assess the type (e.g., neuropathic vs. nociceptive) or source of participants’ pain (beyond pain location), or the presence/severity of other medical conditions, and future research is needed to determine whether these factors may influence the effects of activity-induced pain on PA behavior. Fourth, participants in this study were not engaging in a PA/exercise promotion intervention and/or asked to change their activity level in any way, and the extent to which these results may generalize to individuals attempting to adopt an exercise program remains unclear. Fifth, we observed attrition rates of 20% and 27% at the 1-week and 4-week follow-up assessments, respectively, and participants who did not complete follow-up assessments reported slightly worse pain at baseline. Although the retention rates observed in this study are largely consistent with prior research findings that the average completion rates for longitudinal Mechanical Turk studies are 62.72% for the initial follow-up and 79.45% after the initial follow-up (Keith et al., 2017), low retention may bias the sample (e.g., Keith et al., 2017) and future research should employ additional strategies to enhance retention (e.g., increased compensation when the follow-up time frame increases).

Finally, although Amazon Mechanical Turk is a convenient, feasible, and cost-effective way to gather data during the early stages of hypothesis testing and has been shown to result in more demographically diverse samples when compared to more traditional convenience sampling methods (e.g., undergraduate psychology participant pools; e.g., Paolacci et al., 2010; Strickland & Stoops, 2019), questions remain regarding the generalizability of these results. For example, the current sample was generally well-educated (nearly 90% of the sample completed at least a 4-year college degree), which is surprising given that the prevalence of chronic pain is typically significantly lower among adults with at least a bachelor’s degree (Dahlhamer, 2018). Moreover, there is some evidence that Mechanical Turk users may differ from nationally representative samples in terms of health status and behaviors (Walters et al., 2018). Thus, additional work is needed to generalize these results across larger and more diverse samples that are recruited via a variety of sampling methods (e.g., pain treatment centers).

Despite these limitations, results of the current study represent an important step towards better understanding the role of activity-induced pain and PA enjoyment in PA behavior among individuals with chronic pain. This and future work has the potential to inform the refinement of PA promotion interventions for individuals with chronic pain, and, ultimately, improve pain outcomes and optimize healthy aging among this population.

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