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Self-Regulatory Strategies as Correlates of Physical Activity Behavior in Persons With Multiple Sclerosis
Accepted Manuscript Self-regulatory strategies as correlates of physical activity behavior in persons with multiple sclerosis Katie L. Cederberg, MS, Julia M. Balto, MS, Robert W. Motl, PhD PII:
S0003-9993(18)30088-1
DOI:
10.1016/j.apmr.2017.12.037
Reference:
YAPMR 57149
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 5 September 2017 Revised Date:
19 December 2017
Accepted Date: 28 December 2017
Please cite this article as: Cederberg KL, Balto JM, Motl RW, Self-regulatory strategies as correlates of physical activity behavior in persons with multiple sclerosis, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2018), doi: 10.1016/j.apmr.2017.12.037. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Self-regulatory strategies as correlates of physical activity behavior in persons
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with multiple sclerosis
Suggested Running Head: Self-regulation and physical activity in MS
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Authors: Katie L. Cederberg, MSa; Julia M. Baltob, MS; Robert W. Motl, PhDc*
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Departments of Physical and Occupational Therapy; University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, Alabama USA
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Department of Kinesiology and Community Health, University of Illinois at Urbana-
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Champaign, 906 S Goodwin Ave, Urbana, Illinois USA
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Department of Physical Therapy; University of Alabama at Birmingham, 1720 2nd Avenue
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South, Birmingham, Alabama USA
* Corresponding Author: Katie L. Cederberg; [email protected]; (205) 975-9321 +
Institution work was performed
All authors have reviewed and approved the manuscript Conflict of Interest: None
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Self-regulatory strategies as correlates of physical activity behavior in persons with Multiple
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Sclerosis. Objective: To examine self-regulation strategies as correlates of physical activity in persons
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with multiple sclerosis (MS). Design: Cross-sectional, or survey, study. Setting: University-based
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research laboratory. Participants: Convenience sample of 68 persons with MS. Interventions:
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Not applicable. Main Outcome Measures: Exercise Self-Efficacy Scale (EXSE), Physical Activity
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Self-Regulation Scale (PASR-12), and Godin Leisure-Time Exercise Questionnaire (GLTEQ).
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Results: Correlation analyses indicated that GLTEQ scores were positively and significantly
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associated with overall self-regulation (r=0.43), self-monitoring (r=0.45), goal-setting (r=0.27),
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reinforcement (r=0.30), time management (r=0.41), and relapse prevention (r=0.53) PASR-12
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scores. Regression analyses indicated that relapse prevention (B=5.01; SE B=1.74; β=0.51) and
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self-monitoring (B=3.65; SE B=1.71; β=0.33) were unique predictors of physical activity
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behavior, and relapse prevention demonstrated a significant association with physical activity
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behavior that was accounted for by EXSE. Conclusions: Our results indicate that self-regulatory
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strategies, particularly relapse prevention, may be important correlates of physical activity
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behavior that can inform the design of future behavioral interventions in MS.
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Keywords: multiple sclerosis, self-regulation, self-efficacy, physical activity
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BMI
Body Mass Index
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CNS
Central Nervous System
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ESS
Eliciting Social Support
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EXSE
Exercise Self-efficacy
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GLTEQ
Godin Leisure Time Exercise Questionnaire
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GS
Goal Setting
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HCS
Health Contribution Score
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METs
Metabolic Equivalents
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MS
Multiple Sclerosis
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PASR-12
Physical Activity Self-regulation 12-item Scale
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R
Reinforcements
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RP
Relapse Prevention
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SCT
Social Cognitive Theory
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SM
Self-monitoring
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TM
Time Management
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QOL
Quality of Life
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Abbreviations
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Introduction
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Multiple sclerosis (MS) is a chronic, disabling neurological disorder characterized by the
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demyelination and transection (i.e., lesions) of axons in the central nervous system (CNS) 1 with
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a prevalence of nearly 1 million people in the US and 2.5 million adults worldwide. The resulting
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damage in the CNS accrues over time and may result in neurological disability, ambulatory
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impairment, 2, 3 cognitive dysfunction, 4, 5 and symptoms of fatigue and depression 5 that all
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compromise quality of life (QOL). 6, 7
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Physical activity has beneficial effects on many of the aforementioned consequences of
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MS,8 including walking performance (i.e., walking endurance and speed) 9, 10, balance 11,
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cognition, 12 fatigue, 13 depressive symptoms, 14, 15 and QOL 16 in MS. Nevertheless, adults with
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MS do not engage in sufficient amounts of physical activity required for the health benefits
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associated with this behavior. One recent meta-analysis17 reported that persons with MS
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engaged in 1 standard deviation less physical activity than adults from the general population;
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this is problematic considering that the general population engages in very low amounts of
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physical activity, whereby 49% of people in the U.S. did not meet CDC guidelines for physical
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activity in 2009,18 and persons with MS are substantially less physical activity (i.e., ~84% based
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on 1 SD difference in the meta-analysis). Other research indicates that people with MS engage
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in 13.1 minutes per day less of moderate-to-vigorous physical activity than controls and fewer
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than 20% of adults with MS engaged in adequate amounts of health-promoting physical
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activity,19 based on the American College of Sports Medicine’s physical activity guidelines for
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adults 18-65 years of age.20
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The disconnect between the evidence of benefits and rates of participation has prompted interest in identifying correlates of physical activity that can inform the development
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and delivery of behavioral interventions.21 Such behavioral interventions are often grounded in
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theory and focus on teaching people the skills, strategies, and tools necessary for self-regulating
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physical activity behavior and its change over time.22 To date, few existing studies in people
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with disabilities, including MS, are actually designed based on such an approach nor consider a
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behavioral theory. 23 This may be associated with the limited amoung of research that has
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directly focused on the range of self-regulatory strategies that might correlate with physical
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activity behavior in MS, besides goal-setting.21 Such research may be important for informing,
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refining, and optimizing behavioral interventions for changing physical activity. This logic is
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based on self-regulatory strategies representing one’s ability to self-regulate goal directed
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behavior through (a) self-monitoring, (b) self-judgment, and (c) self-reaction.24 The focus on
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correlates is important for identifying possible “causal” factors of physical activity behavior that
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can be targeted by behavioral interventions.
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There are multiple self-regulatory strategies that might be associated with physical
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activity in MS, including self-monitoring, goal setting, eliciting social support, reinforcement,
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time management, and relapse prevention.25 Those strategies can be measured using the
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Physical Activity Self-Regulation scale.26 Importantly, the term relapse prevention involves a
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person’s efforts toward maintaining or preventing a deleterious change in a behavior (i.e.,
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physical activity), as opposed to preventing MS disease activity. Based on Social Cognitive
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Theory (SCT), self-regulatory strategies might be associated with physical activity via self-
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efficacy, 27 and self-efficacy has been identified as one of the most consistent correlates of
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physical activity in MS.21 If correct, persons who perceive greater strategic resources for
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engaging in physical activity might have greater self-efficacy for physical activity, and self-
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efficacy would presumably account for the association between self-regulatory strategies and
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physical activity behavior.
The present study was exploratory in nature, but informed by SCT, and involved a crosssectional design that examined self-regulatory strategies as correlates of physical activity
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behavior in persons with MS. We examined three questions: (1) What self-regulatory strategies
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(e.g., self-monitoring, goal setting, social support, reinforcement, time management, and
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relapse prevention) are associated with physical activity behavior?; (2) Of those self-regulatory
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strategies, is there a single strategy or combination of strategies that uniquely correlate with
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physical activity?; and (3) Are self-regulatory strategies associated with physical activity via self-
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efficacy? Such an examination of self-regulatory strategies is important for informing the design
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of behavioral interventions for changing physical activity in MS, a topic of considerable
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importance given the disconnect between rates of participation and evidence for benefits of
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physical activity in this population.28
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Participants
Method
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Over a six-month period, we recruited a convenience sample of persons with MS by
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advertising the study on the National MS Society local research page and our laboratory
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Facebook page, and contacting persons in our laboratory database. The inclusion criteria were:
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(1) 18-64 years of age; (2) relapse free during the past 30 days; and (3) ambulatory with or
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without assistance. Of the 92 persons with MS who underwent screening, 70 participants met
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inclusion criteria and were scheduled for testing; 8 persons were disqualified and 14 were
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qualified but dropped out prior to testing. Two participants did not complete the testing
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session, and this resulted in a final convenience sample of 68 persons with MS for data
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analyses. The sample size was considerable acceptable based on a power analysis performed in
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G*Power 3.1. The parameters for the power analysis were alpha = .05, beta = .20 (80% power),
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and a population effect size, rho = .30 (i.e., moderate effect size). This indicated that we needed
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a sample size of 64 participants in this study for adequate power.
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Self-Regulatory Processes
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Physical activity self-regulatory strategies were assessed using the Physical Activity Self-
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Regulation Scale (PASR-12).26 The PASR-12 contains 12 items, with 2 items per subscale of self-
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monitoring (PASR SM), goal setting (PASR GS), eliciting social support (PASR ESS), reinforcement
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(PASR R), time management (PASR TM), and relapse prevention (PASR RP). The items are rated
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on an ordinal scale of 1 (Never use strategy) through 5 (Use strategy very often). The scores per
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subscale were calculated by summing the individual item scores, and range between 2 and 10,
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and self-regulation subscale scores were summed for a measure of overall self-regulation (PASR
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Overall), providing a total score that ranges between 12 and 60. Higher scores indicated greater
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use of self-regulatory strategies for physical activity.26 We are unaware of research that has
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validated the PASR-12 in MS. The internal consistency reliability of scores for the overall scale
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and 6 subscales in our sample were acceptable (≥ 0.70) with values of 0.94 for the overall scale,
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0.84 for PASR SM, 0.88 for PASR GS, 0.90 for PASR ESS, 0.73 for PASR R, 0.83 for PASR TM, and
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0.87 for PASR RP.
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Exercise Self-Efficacy
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Self-efficacy was measured with the Exercise Self-Efficacy scale (EXSE).29 This scale has 6 items that assess an individual’s beliefs in their ability to engage in moderate-to-vigorous
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physical activity without stopping across increasing increments of time.29 The scores from the 6
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items are rated on a scale from 0 (Not at all confident) through 100 (Highly confident), and
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averaged into a total score ranging between 0 and 100. There is evidence for the reliability and
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validity of scores from the EXSE in persons with MS.30 The internal consistency of the EXSE in
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our sample was 0.96.
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Physical Activity Level
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The Godin Leisure-Time Exercise Questionnaire (GLTEQ) measures the frequency of strenuous, moderate, and mild leisure-time physical activity performed for periods of 15
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minutes or more over a typical week.31 This questionnaire has evidence for the reliability of its
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scores over time32 and the scores have been validated based on the pattern of association with
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other measures of physical activity, including self-report measures, pedometers, and
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accelerometers.33-35 Scores from this measure have captured the effects of physical activity and
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exercise training interventions.36-38 Such evidence supports the use of the GLTEQ as an
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appropriate measure of physical activity in MS. We do note that there are other options
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available for objectively measuring physical activity (e.g., accelerometers or pedometers), but
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there are a number of limitations with such measures including behavioral reactivity, non-
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compliance for wearing the device, and specificity for primarily ambulatory-based physical
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activity.
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Recently, a new scoring method has been proposed for generating an overall GLTEQ
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score that aligns with current recommendations for physical activity and the dose–response
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association between the volume of physical activity and health benefits (i.e., health
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contribution score or HCS31, 39). The HCS is based on only strenuous and moderate physical
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activity and is computed by multiplying the frequencies of strenuous and moderate activities by
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nine and five metabolic equivalents (METs), respectively, and then adding the resultant scores.
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The HCS ranges between 0 and 98 and can be converted into one of three categories, namely
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insufficiently active (i.e., score < 14 units that is the equivalent of < 7 kcal/kg/week), moderately
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active (i.e., score between 14 and 23 units that is the equivalent of between 7 and 13.9
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kcal/kg/week), and active (i.e., score ≥ 24 units that exceeds 13.9 kcal/kg/week). The HCS has
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been validated in MS.40
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Procedures
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This study was approved by a University Institutional Review Board, and participants provided written informed consent. Participants underwent a neurological evaluation by
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a Neurostatus-certified examiner for generation of an Expanded Disability Status Scale (EDSS)
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score,41 measurements of height and weight using a scale-stadiometer for generation of body-
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mass index (BMI), and then provided sociodemographic (age, sex, race, BMI, marital status,
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employment status, education status, and annual household income) and clinical characteristics
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(disease duration and MS type). Participants further completed the PASR-12 and EXSE, and then
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the GLTEQ.
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Statistical Analysis
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All data were analyzed in SPSS Statistics, Version 24 (IBM Corporation, Armonk, NY), and
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descriptive statistics are reported as mean and standard deviation, unless otherwise noted
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(e.g., median or number and percentage). We examined skewness and kurtosis estimates and
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frequency distributions for establishing the normality of the variables. We examined
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correlations among GLTEQ HCS scores, overall PASR-12 scores, PASR-12 subscales scores, and
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EXSE scores using Pearson product-moment correlations (r); correlation coefficients of 0.1, 0.3,
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and 0.5 were interpreted as small, moderate, and large, respectively.42 This was followed by
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Spearman rho rank-order correlations for confirming that outliers and non-normality were not
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biasing the correlations, and partial correlations whereby we controlled for disability status,
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disease duration, and MS type (0=relapsing-remitting MS, 1=progressive MS). We then
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performed a series of regression analyses for examining PASR-12 scores as correlates of
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physical activity. The first regression analysis involved a direct entry of all six PASR-12 subscales
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for explanation of variance in physical activity; this analysis identified self-regulatory strategies
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that were uniquely associated with physical activity. We performed additional analyses of self-
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efficacy as a possible mediator of the potential relationship between self-regulation strategies
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and physical activity consistent with the Baron and Kenny approach.43 This required as a
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precondition that self-efficacy, self-regulatory strategies, and physical activity all demonstrated
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significant bivariate correlations. If so, we performed hierarchical regression analysis with step-
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wise entry wherein we regressed physical activity levels on overall self-regulation (Step 1), and
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self-regulation plus exercise self-efficacy (step 2). This analysis was repeated with self-
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regulatory subscales that were identified as independent correlates of physical activity in the
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first regression analysis. The attenuation in standardized beta-coefficients between Step 1 and
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Step 2 for self-regulatory strategies and physical activity would suggest a possible mediation
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role for self-efficacy.
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Results
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Sample Demographic and Clinical Characteristics The sample was largely female (69.6%), Caucasian (94.2%), and married (59.4%), with a mean age of 50.5 (SD=8.9) years. Nearly half of participants were employed (49.3%), 85.5%
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reported some college education, and 68.1% reported an annual household income of greater
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than $40,000. The mean BMI was 28.7 (SD=6.6) kg/m2. Participants mostly had relapsing-
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remitting MS (RRMS; 84.1%), mild to moderate disability (median (IQR) EDSS score of 4.0 (2.0)),
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and a mean disease duration of 14.4 (10.5) years. These sample characteristics are comparable
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with national estimates for sex (72.7% female44), race (90.1% Caucasian44), age (47.1 years44),
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employment status (50.8%45), MS type (i.e., 84.3% RRMS46), disability status (67.7% mild to
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moderate47), and disease duration (13.8 years since symptom onset45).
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Self-Regulatory Strategies Across Levels of Physical Activity
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for the overall sample are provided in Table 1. The table further provides actual ranges of
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scores and skewness and kurtosis estimates for the variables; the skewness and kurtosis
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estimates along with inspection of frequency distributions did not identify problems with
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normality for the variables. Regarding physical activity status classification based on GLTEQ HCS
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scores, there were 32 persons who were insufficiently active (47%), 11 who were moderately
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active (16%), and 25 who were active (37%).
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Correlation Analysis of Self-regulation and Physical Activity
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The bivariate correlations among physical activity behavior, self-efficacy, and self-
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regulatory strategy scores are presented in Table 2. Our results indicated that physical activity
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behavior was significantly correlated with self-efficacy and relapse prevention (large
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correlations), overall self-regulation (moderate correlation), and self-monitoring, goal setting,
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reinforcement, and time-management (moderate correlations). Physical activity behavior was
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not significantly correlated with eliciting social support. Self-efficacy was associated with overall
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self-regulation and relapse prevention (large correlations) as well as self-monitoring, goal
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setting, reinforcement, and time management (moderate correlations); self-efficacy was not
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correlated with eliciting social support. Those correlations were not changed in a secondary
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analysis using Spearman rho rank order correlation coefficients (values not provided), and this
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supports that non-normality and outliers were not biasing the patterns of associations. Those
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correlations were further not changed in partial correlation analyses that controlled for EDSS
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scores, disease duration, and type of MS, and this suggested that clinical characteristics were
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not driving the pattern of associations; the partial correlations are provided in parentheses
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after the bivariate correlation coefficients in Table 2.
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Regression Analysis of Self-regulation and Physical Activity
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The summary of regression analysis for self-regulatory subscales as unique predictors of self-reported physical activity behavior is presented in Table 3. The overall model was
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statistically significant (F=5.80, p < 0.05), and the variables explained 36% of the variance in
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physical activity; this is consistent with previous research in the area.21 Self-monitoring (B=3.65;
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SE B=1.71; β=0.33) and relapse prevention (B=5.01; SE B=1.74; β=0.51) explained unique
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variance in physical activity and were the only predictors of physical activity behavior in our
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sample of persons with multiple sclerosis. The Variance Inflation Factor values were less than
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5.0 for all variables in this model, and the Tolerance values all exceeded 0.20; this suggests that
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multicollinearity was not biasing the results of this regression analysis.
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We further conducted two hierarchical regression analyses for overall self-regulation and self-efficacy as well as self-monitoring, relapse prevention, and self-efficacy predicting
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physical activity behavior (Table 4 and Table 5, respectively). The first model indicated that
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overall self-regulation had a significant association with physical activity behavior based on the
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standardized beta-coefficient (Step 1), and the standardized beta-coefficient diminished in
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magnitude with the addition of self-efficacy (Step 2). The second model indicated that relapse
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prevention, but not self-monitoring, had a significant association with physical activity behavior
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based on the standardized beta-coefficient (Step 1), but this was attenuated with the addition
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of self-efficacy (Step 2). The observed reduction in standardize beta-coefficients between self-
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regulation strategies and physical activity toward zero between Step 1 and Step 2 of the
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regression analyses would be suggestive of mediation of the association by self-efficacy
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consistent with Baron and Kenny.43
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Discussion
We provide novel data on self-regulatory strategies as correlates of physical activity in persons with MS. Our results indicate that (a) overall self-regulatory strategies and self-
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monitoring, goal setting, reinforcement, time management, and relapse prevention strategies
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were correlated with physical activity; (b) relapse prevention and self-monitoring uniquely
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explained variance in physical activity; and (c) self-efficacy accounted for the associations
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between overall self-regulation and physical activity and relapse prevention and physical
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activity. Such results are consistent with predictions from Bandura’s SCT and the theory of self-
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regulation.24, 25 These results further extend a recent systematic review that identified self-
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efficacy and self-regulatory strategies, specifically goal setting, as important correlates of
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physical activity in persons with MS.21 However, our results are unique in that we identify (a)
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the self-regulatory sub-components of self-monitoring, reinforcement, time management, and
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particularly relapse prevention as significant correlates of physical activity and (b) the possible
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mediation pathway between self-regulation and physical activity, namely self-efficacy. Such
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results may inform future research on behavioral interventions for physical activity in MS and
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persons with neurological disabilities, as noted as critical in a recent scoping review.23
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Our results suggest that, beyond self-efficacy as a commonly identified correlate of physical activity in MS,21 the self-regulatory strategies of relapse prevention and self-monitoring
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are important correlates of physical activity in persons with MS. Indeed, regarding the six
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components of self-regulation that we examined as correlates, relapse prevention and self-
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monitoring were the only unique predictors of physical activity. Importantly, the association
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between those self-regulatory strategies, particularly relapse prevention, and physical activity
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was attenuated when accounting for self-efficacy. These results highlight that relapse
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prevention is an important target for possibly increasing physical activity in MS, and this may
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work through self-efficacy expectations. This is consistent with expectations derived from
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Bandura’s theory of self-regulation,27 and suggests that relapse prevention strategies works via
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the mediator variable of self-efficacy for explaining variation in physical activity in MS.
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Importantly, other variables may be involved in explaining physical activity in MS, including
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symptoms of MS and/or physical or social barriers. When we controlled for disability status,
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disease duration, and MS type, the bivariate correlations were unchanged in pattern or
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magnitude. Future research should consider other variables that could explain physical activity
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and the association between self-regulation and physical activity such as MS symptoms
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including fatigue and depression. Additionally, our results suggested relapse prevention, or the perceived ability to cope
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with situations that might undermine participation in physical activity, is tightly related with
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one’s self-efficacy, or the perceived ability to overcome situations that might destabilize regular
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participation in physical activity.48 Most high-risk situations that could lead to the decline of
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physical activity participation threaten one’s perception of control over the situation and can
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reduce self-efficacy. There are many approaches for relapse prevention that could be included
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in a behavioral intervention including: (a) skills training on recognizing high-risk situations (e.g.,
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vacation) that could lead to decreased participation in physical activity; (b) skills training of
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coping mechanisms in high-risk situations (e.g., rehearsal of common situations that would
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challenge one’s participation in regular physical activity); (c) education about immediate and
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delayed effects of decreasing physical activity participation; (d) tools on what to do after a
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setback in participation and education of individuals on how to limit the extent of the setback
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and reinstall regular physical activity in one’s routine.48 Importantly, the relationship between
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physical activity and self-regulatory strategies could be reciprocal, such that low physical
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activity could explain low self-regulatory processes, and low self-regulatory strategies could
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explain low physical activity participation. Additionally, in a generally insufficiently active
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population, relapse prevention strategies may be particularly salient in helping inactive people
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become more active and maintain that behavior over time. Future research should include
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longitudinal and experimental designs to evaluate these relationships in frameworks that
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permit a closer understanding of causality.
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Study Limitations There are important limitations of our results. We did not include a non-MS control group for understanding the uniqueness or generalizability of our results outside of MS. Our
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sample size was relatively small and homogeneous regarding demographic and clinical
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characteristics limiting the generalizability of our results amongst all people with MS.
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Additionally, the cross-sectional design of the study precludes inferences of temporal sequence
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and causality. Future research should examine the influence of self-regulatory strategies on
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changes in physical activity participation over time in persons with MS. We are unaware of any
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studies that have validated the PASR-12 in persons with MS, but our data suggest that the scale
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has acceptable internal consistency. Future research should further evaluate the validity and
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reliability of the PASR-12 in persons with MS. Although the GLTEQ is an unobtrusive, time and
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cost efficient measure of physical activity, it is a self-report measure and is therefore subject to
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various participant biases (e.g., recall bias). If the results were driven by recall bias, however,
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one might expect a more consistent or homogeneous pattern of associations among self-
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efficacy, self-regulation, and physical activity scores. We further note that the GLTEQ has
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received strong support as a valid and reliable measure of physical activity in MS, 33-35 and
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further has captured the effects of physical activity and exercise interventions (i.e.,
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responsiveness).36-38
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Conclusions
To our knowledge, this was the first study that examined a range of self-regulatory
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strategies as correlates of physical activity behavior in MS. Our results indicate that relapse
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prevention strategies and self-efficacy may be key determinants of physical activity behavior in
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persons with MS. Importantly, the use of self-regulation, namely physical activity behavior
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relapse prevention strategies, represents an important, previously unidentified correlate of
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physical activity behavior, and its association with physical activity may be mediated by self-
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efficacy in persons with MS. These findings should be considered when designing, refining, and
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optimizing behavioral interventions that target physical activity behavior change in persons
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with MS. Such behavioral interventions might target self-regulatory strategies, particularly
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relapse preventions for increasing self-efficacy, as an approach for changing physical activity
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participation in MS.
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References
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1. Criste G, Trapp B, Dutta R. Axonal loss in multiple sclerosis: causes and mechanisms. Handb Clin Neurol 2014;122:101-13. DOI: 10.1016/b978-0-444-52001-2.00005-4 2. Bjartmar C, Wujek JR, Trapp BD. Axonal loss in the pathology of MS: consequences for understanding the progressive phase of the disease. J Neurol Sci 2003;206(2):165-71. 3. Motl RW, Learmonth YC. Neurological disability and its association with walking impairment in multiple sclerosis: brief review. Neurodegener Dis Manag 2014;4(6):491-500. DOI: 10.2217/nmt.14.32 4. Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurol 1991;41(5):685-91. 5. Crayton H, Heyman RA, Rossman HS. A multimodal approach to managing the symptoms of multiple sclerosis. Neurol 2004;63(11 Suppl 5):S12-8. 6. Kes VB, Cengic L, Cesarik M et al. Quality of life in patients with multiple sclerosis. Acta Clin Croat 2013;52(1):107-11. 7. Salehpoor G, Rezaei S, Hosseininezhad M. Quality of life in multiple sclerosis (MS) and role of fatigue, depression, anxiety, and stress: A bicenter study from north of Iran. Iran J Nurs Midwifery Res 2014;19(6):593-9. 8. Motl RW, Sandroff BM. Benefits of exercise training in multiple sclerosis. Curr Neurol Neurosci Rep 2015;15(9):1-9. DOI: 10.1007/s11910-015-0585-6 9. Latimer-Cheung AE, Pilutti LA, Hicks AL et al. Effects of exercise training on fitness, mobility, fatigue, and health-related quality of life among adults with multiple sclerosis: A systematic review to inform guideline development. Arch Phys Med Rehabil 2013;94(9):1800-28. DOI: 10.1016/j.apmr.2013.04.020 10. Motl RW, Pilutti LA. The benefits of exercise training in multiple sclerosis. Nat Rev Neurol 2012;8(9):487-97. DOI: 10.1038/nrneurol.2012.136 11. Paltamaa J, Sjogren T, Peurala SH, Heinonen A. Effects of physiotherapy interventions on balance in multiple sclerosis: A systematic review and meta-analysis of randomized controlled trials. J Rehabil Med 2012;44(10):811-23. 10.2340/16501977-1047 12. Sandroff BM, Motl RW, Scudder MR, DeLuca J. Systematic, evidence-based review of exercise, physical activity, and physical fitness effects on cognition in persons with multiple sclerosis. Neuropsychol Rev 2016;26(3):271-94. DOI: 10.1007/s11065-016-9324-2 13. Heine M, van de Port I, Rietberg MB, van Wegen EE, Kwakkel G. Exercise therapy for fatigue in multiple sclerosis. Cochrane Database Syst Rev 2015(9):CD009956. DOI: 10.1002/14651858.CD009956.pub2 14. Adamson BC, Ensari I, Motl RW. Effect of exercise on depressive symptoms in adults with neurologic disorders: A systematic review and meta-analysis. Arch Phys Med Rehabil 2015;96(7):1329-38. 10.1016/j.apmr.2015.01.005 15. Dalgas U, Stenager E, Sloth M, Stenager E. The effect of exercise on depressive symptoms in multiple sclerosis based on a meta-analysis and critical review of the literature. Eur J Neurol 2015;22(3):443-e34. 10.1111/ene.12576 16. Motl RW, Gosney JL. Effect of exercise training on quality of life in multiple sclerosis: A metaanalysis. Mult Scler 2008;14(1):129-35. DOI: 10.1177/1352458507080464 17. Kinnett-Hopkins D, Adamson B, Rougeau K, Motl RW. People with MS are less physically active than healthy controls but as active as those with other chronic diseases: An updated meta-analysis. Mult Scler Relat Disord 2017;13:38-43. DOI: 10.1016/j.msard.2017.01.016 18. Centers for Disease Control and Prevention. BRFSS Prevalence & Trends Data. 2009. [Access Date: December 19, 2017]. Available from: https://www.cdc.gov/brfss/brfssprevalence/index.html.
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19. Klaren RE, Motl RW, Dlugonski D, Sandroff BM, Pilutti LA. Objectively quantified physical activity in persons with multiple sclerosis. Arch Phys Med Rehabil 2013;94(12):2342-8. DOI: 10.1016/j.apmr.2013.07.011 20. Garber CE, Blissmer B, Deschenes MR et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 2011;43(7):1334-59. DOI: 10.1249/MSS.0b013e318213fefb 21. Casey B, Coote S, Shirazipour C et al. Modifiable psychosocial constructs associated with physical activity participation in people with multiple sclerosis: a systematic review and meta-analysis. Arch Phys Med Rehabil 2017;98(7):1453-75. DOI: 10.1016/j.apmr.2017.01.027 22. Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health 2010;31:399-418. 10.1146/annurev.publhealth.012809.103604 23. Lai B, Young HJ, Bickel CS, Motl RW, Rimmer JH. Current trends in exercise intervention research, technology, and behavioral change strategies for people with disabilities: A scoping review. Am J Phys Med Rehabil 2017. 10.1097/phm.0000000000000743 24. Bandura A. Social Cognitive Theory of Self-Regulation. Academic Press 1991;50:248-87. 25. Bandura A. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall; 1986. 26. Umstattd MR, Motl R, Wilcox S, Saunders R, Watford M. Measuring physical activity self-regulation strategies in older adults. J Phys Act Health 2009;6 Suppl 1:S105-12. 27. Bandura A. Health promotion by social cognitive means. Health Educ Behav 2004;31(2):143-64. DOI: 10.1177/1090198104263660 28. Motl RW, Sandroff BM, Kwakkel G et al. Exercise in patients with multiple sclerosis. Lancet Neurol 2017;16(10):848-56. DOI: 10.1016/S1474-4422(17)30281-8 29. McAuley E. Self-efficacy and the maintenance of exercise participation in older adults. J Behav Med 1993;16(1):103-13. 30. Motl RW, Snook EM, McAuley E, Scott JA, Douglass ML. Correlates of physical activity among individuals with multiple sclerosis. Ann Behav Med 2006;32(2):154-61. DOI: 10.1207/s15324796abm3202_13 31. Godin G. The Godin-Shephard leisure-time physical activity questionnaire. Health Fit J Can 2011;4:18-22. 32. Motl RW, McAuley E, Klaren R. Reliability of physical-activity measures over six months in adults with multiple sclerosis: implications for designing behavioral interventions. Behav Med 2014;40(1):29-33. DOI: 10.1080/08964289.2013.821966 33. Motl RW, McAuley E, Snook EM, Scott JA. Validity of physical activity measures in ambulatory individuals with multiple sclerosis. Disabil Rehabil 2006;28(18):1151-6. DOI: 10.1080/09638280600551476 34. Gosney JL, Scott JA, Snook EM, Motl RW. Physical activity and multiple sclerosis: validity of selfreport and objective measures. Fam Community Health 2007;30(2):144-50. 35. Kinnett-Hopkins D, Grover SA, Yeh EA, Motl RW. Physical activity in pediatric onset multiple sclerosis: Validating a questionnaire for clinical practice and research. Mult Scler Relat Disord 2016;10:269. DOI: 10.1016/j.msard.2016.08.010 36. Motl RW, Dlugonski D, Wojcicki TR, McAuley E, Mohr DC. Internet intervention for increasing physical activity in persons with multiple sclerosis. Mult Scler 2011;17(1):116-28. DOI: 10.1177/1352458510383148
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37. Pilutti LA, Dlugonski D, Sandroff BM, Klaren R, Motl RW. Randomized controlled trial of a behavioral intervention targeting symptoms and physical activity in multiple sclerosis. Mult Scler 2014;20(5):594-601. DOI: 10.1177/1352458513503391 38. Carter A, Daley A, Humphreys L et al. Pragmatic intervention for increasing self-directed exercise behaviour and improving important health outcomes in people with multiple sclerosis: a randomised controlled trial. Mult Scler 2014;20(8):1112-22. DOI: 10.1177/1352458513519354 39. Amireault S, Godin G. The Godin-Shephard leisure-time physical activity questionnaire: Validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Percept Mot Skills 2015;120(2):604-22. DOI: 10.2466/03.27.PMS.120v19x7 40. Motl RW, Bollaert RE, Sandroff BM. Validation of the Godin Leisure-Time Exercise Questionnaire classification coding system using accelerometry in multiple sclerosis. Rehab Psychol 2017. DOI: 10.1037/rep0000162 41. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33(11):1444-52. 42. Cohen J. Statistical power analysis for the behavioral sciences, 2nd Ed. Mahwah, NJ: Lawrence Erlbaum Associates 1988:20-6. 43. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51(6):1173-82. 44. Kister I, Chamot E, Salter AR, Cutter GR, Bacon TE, Herbert J. Disability in multiple sclerosis: A reference for patients and clinicians. Neurol 2013;80:1018-24. 45. Kister I, Bacon TE, Chamot E et al. Natural history of multiple sclerosis symptoms. Int J MS Care 2013;15(3):146-58. DOI: 10.7224/1537-2073.2012-053 46. Federation Multiple Sclerosis International. Atlas of MS: Mapping multiple sclerosis around the world. 2013. Available from: https://www.msif.org/wp-content/uploads/2014/09/Atlas-ofMS.pdf. 47. Salter A, Thomas N, Tyry T, Cutter G, Marrie RA. Employment and absenteeism in working-age persons with multiple sclerosis. J Med Econ 2017;20(5):493-502. DOI: 10.1080/13696998.2016.1277229 48. Marlatt GA, Gordon JR. Relapse Prevention. New York, NY: The Guilford Press; 1985.
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Table 1. Values of physical activity, self-efficacy, and self-regulatory variables in multiple sclerosis (N=68) Mean Standard Actual Range Skewness Kurtosis Score Deviation of Scores Value Value GLTEQ 21.7 25.2 0-98 1.4 1.5 EXSE 39.7 27.7 0-96.7 0.3 ‒1.0 PASR Overall 34.2 11.7 12-60 0.2 ‒0.7 PASR SM 6.3 2.2 2-10 ‒0.3 ‒0.7 PASR GS 5.7 2.3 2-10 0.0 ‒0.9 PASR ESS 4.4 2.3 2-10 0.7 ‒0.6 PASR R 7.1 2.1 2-10 ‒0.6 0.1 PASR TM 5.4 2.5 2-10 0.3 ‒0.9 PASR RP 4.9 2.5 2-10 0.6 ‒0.6 Note. GLTEQ, Godin Leisure-Time Exercise Questionnaire; EXSE, exercise self-efficacy scale; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention.
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Table 2. Correlations among physical activity, self-efficacy, and self-regulatory variables in multiple sclerosis (N=68)
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Variable 1 2 3 4 5 6 7 8 9 1. GLTEQ --2. EXSE .69(.70)* --3. PASR Overall .43(.47)* .50(.50)* --4. PASR SM .45(.53)* .34(.41)* .72(.74)* --5. PASR GS .27(.30)** .32(.37)* .76(.78)* .69(.73)* --6. PASR ESS .13(.18)* .17(.18)* .65(.64)* .35(.36)* .51(.48)** --7. PASR R .30(.36)* .36(.35)* .79(.80)* .66(.69)* .69(.74)* .45(.44)* --8. PASR TM .41(.41)* .38(.37)* .84(.83)* .54(.58)* .61(.60)* .62(.62)* .61(.62)* --9. PASR RP .53(.54)* .52(.53)* .83(.83)* .58(.60)* .58(.59)* .54(.55)* .62(.63)* .79(.79)* --Note. Values are bivariate (partial) correlation coefficients. Partial correlation coefficients control for disability status, disease duration, and MS type. GLTEQ, Godin Leisure-Time Exercise Questionnaire; EXSE, exercise self-efficacy scale; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention. *p<.05.
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Table 3. Summary of regression analysis for self-regulatory subscales predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variables B SE B β PASR SM 3.65 1.71 .33* PASR GS ‒1.32 1.74 ‒.13 PASR ESS ‒2.31 1.47 ‒.21 PASR R ‒1.50 1.89 ‒.13 PASR TM 1.14 1.80 .11 PASR RP 5.01 1.74 .51* 2 Note. R =.36; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention. B, unstandardized beta-coefficient; SE B, standard error of unstandardized betacoefficient; β, standardized beta-coefficient; *p<.05.
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Table 4. Summary of hierarchical regression analysis for overall self-regulation and self-efficacy predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variable B SE B β Step 1 PASR Overall 0.90 0.24 .43* Step 2 PASR Overall 0.27 0.22 .13* EXSE 0.56 0.10 .61* Note. R2 = .18 for Step 1; ΔR2 = .28 for Step 2 (p<.05); PASR Overall, Physical Activity Self-Regulation Scale Overall; EXSE, Exercise Self-Efficacy. B, unstandardized beta-coefficient; SE B, standard error of unstandardized beta-coefficient; β, standardized beta-coefficient; *p<.05.
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Table 5. Summary of hierarchical regression analysis for self-monitoring, relapse prevention, and selfefficacy predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variable B SE B β Step 1 PASR SM 2.32 1.40 .21* PASR RP 3.99 1.26 .40* Step 2 PASR SM 2.00 1.18 .18* PASR RP 1.39 1.17 .14* EXSE 0.49 0.09 .54* Note. R2 = .31 for Step 1; ΔR2 = .21 for Step 2 (p<.05); PASR SM, Physical Activity Self-Regulation Scale Self-monitoring; PASR RP, Physical Activity Self-Regulation Scale Relapse Prevention; EXSE, Exercise SelfEfficacy; B, unstandardized beta-coefficient; SE B, standard error of unstandardized beta-coefficient; β, standardized beta-coefficient; *p<.05.
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Highlights Self-regulatory strategies are important correlates of physical activity in MS.
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Relapse prevention was a unique predictor of physical activity behavior.
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Self-efficacy was an important determinant of physical activity behavior in MS.
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Relapse prevention presumably works via self-efficacy in persons with MS.
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S0003-9993(18)30088-1
DOI:
10.1016/j.apmr.2017.12.037
Reference:
YAPMR 57149
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 5 September 2017 Revised Date:
19 December 2017
Accepted Date: 28 December 2017
Please cite this article as: Cederberg KL, Balto JM, Motl RW, Self-regulatory strategies as correlates of physical activity behavior in persons with multiple sclerosis, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2018), doi: 10.1016/j.apmr.2017.12.037. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Self-regulatory strategies as correlates of physical activity behavior in persons
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with multiple sclerosis
Suggested Running Head: Self-regulation and physical activity in MS
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Authors: Katie L. Cederberg, MSa; Julia M. Baltob, MS; Robert W. Motl, PhDc*
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Departments of Physical and Occupational Therapy; University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, Alabama USA
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Department of Kinesiology and Community Health, University of Illinois at Urbana-
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Champaign, 906 S Goodwin Ave, Urbana, Illinois USA
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Department of Physical Therapy; University of Alabama at Birmingham, 1720 2nd Avenue
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South, Birmingham, Alabama USA
* Corresponding Author: Katie L. Cederberg; [email protected]; (205) 975-9321 +
Institution work was performed
All authors have reviewed and approved the manuscript Conflict of Interest: None
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Self-regulatory strategies as correlates of physical activity behavior in persons with Multiple
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Sclerosis. Objective: To examine self-regulation strategies as correlates of physical activity in persons
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with multiple sclerosis (MS). Design: Cross-sectional, or survey, study. Setting: University-based
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research laboratory. Participants: Convenience sample of 68 persons with MS. Interventions:
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Not applicable. Main Outcome Measures: Exercise Self-Efficacy Scale (EXSE), Physical Activity
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Self-Regulation Scale (PASR-12), and Godin Leisure-Time Exercise Questionnaire (GLTEQ).
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Results: Correlation analyses indicated that GLTEQ scores were positively and significantly
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associated with overall self-regulation (r=0.43), self-monitoring (r=0.45), goal-setting (r=0.27),
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reinforcement (r=0.30), time management (r=0.41), and relapse prevention (r=0.53) PASR-12
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scores. Regression analyses indicated that relapse prevention (B=5.01; SE B=1.74; β=0.51) and
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self-monitoring (B=3.65; SE B=1.71; β=0.33) were unique predictors of physical activity
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behavior, and relapse prevention demonstrated a significant association with physical activity
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behavior that was accounted for by EXSE. Conclusions: Our results indicate that self-regulatory
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strategies, particularly relapse prevention, may be important correlates of physical activity
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behavior that can inform the design of future behavioral interventions in MS.
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Keywords: multiple sclerosis, self-regulation, self-efficacy, physical activity
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BMI
Body Mass Index
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CNS
Central Nervous System
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ESS
Eliciting Social Support
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EXSE
Exercise Self-efficacy
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GLTEQ
Godin Leisure Time Exercise Questionnaire
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GS
Goal Setting
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HCS
Health Contribution Score
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METs
Metabolic Equivalents
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MS
Multiple Sclerosis
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PASR-12
Physical Activity Self-regulation 12-item Scale
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R
Reinforcements
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RP
Relapse Prevention
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SCT
Social Cognitive Theory
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SM
Self-monitoring
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TM
Time Management
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QOL
Quality of Life
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Abbreviations
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Introduction
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Multiple sclerosis (MS) is a chronic, disabling neurological disorder characterized by the
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demyelination and transection (i.e., lesions) of axons in the central nervous system (CNS) 1 with
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a prevalence of nearly 1 million people in the US and 2.5 million adults worldwide. The resulting
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damage in the CNS accrues over time and may result in neurological disability, ambulatory
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impairment, 2, 3 cognitive dysfunction, 4, 5 and symptoms of fatigue and depression 5 that all
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compromise quality of life (QOL). 6, 7
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Physical activity has beneficial effects on many of the aforementioned consequences of
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MS,8 including walking performance (i.e., walking endurance and speed) 9, 10, balance 11,
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cognition, 12 fatigue, 13 depressive symptoms, 14, 15 and QOL 16 in MS. Nevertheless, adults with
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MS do not engage in sufficient amounts of physical activity required for the health benefits
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associated with this behavior. One recent meta-analysis17 reported that persons with MS
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engaged in 1 standard deviation less physical activity than adults from the general population;
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this is problematic considering that the general population engages in very low amounts of
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physical activity, whereby 49% of people in the U.S. did not meet CDC guidelines for physical
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activity in 2009,18 and persons with MS are substantially less physical activity (i.e., ~84% based
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on 1 SD difference in the meta-analysis). Other research indicates that people with MS engage
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in 13.1 minutes per day less of moderate-to-vigorous physical activity than controls and fewer
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than 20% of adults with MS engaged in adequate amounts of health-promoting physical
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activity,19 based on the American College of Sports Medicine’s physical activity guidelines for
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adults 18-65 years of age.20
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The disconnect between the evidence of benefits and rates of participation has prompted interest in identifying correlates of physical activity that can inform the development
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and delivery of behavioral interventions.21 Such behavioral interventions are often grounded in
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theory and focus on teaching people the skills, strategies, and tools necessary for self-regulating
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physical activity behavior and its change over time.22 To date, few existing studies in people
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with disabilities, including MS, are actually designed based on such an approach nor consider a
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behavioral theory. 23 This may be associated with the limited amoung of research that has
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directly focused on the range of self-regulatory strategies that might correlate with physical
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activity behavior in MS, besides goal-setting.21 Such research may be important for informing,
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refining, and optimizing behavioral interventions for changing physical activity. This logic is
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based on self-regulatory strategies representing one’s ability to self-regulate goal directed
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behavior through (a) self-monitoring, (b) self-judgment, and (c) self-reaction.24 The focus on
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correlates is important for identifying possible “causal” factors of physical activity behavior that
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can be targeted by behavioral interventions.
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There are multiple self-regulatory strategies that might be associated with physical
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activity in MS, including self-monitoring, goal setting, eliciting social support, reinforcement,
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time management, and relapse prevention.25 Those strategies can be measured using the
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Physical Activity Self-Regulation scale.26 Importantly, the term relapse prevention involves a
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person’s efforts toward maintaining or preventing a deleterious change in a behavior (i.e.,
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physical activity), as opposed to preventing MS disease activity. Based on Social Cognitive
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Theory (SCT), self-regulatory strategies might be associated with physical activity via self-
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efficacy, 27 and self-efficacy has been identified as one of the most consistent correlates of
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physical activity in MS.21 If correct, persons who perceive greater strategic resources for
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engaging in physical activity might have greater self-efficacy for physical activity, and self-
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efficacy would presumably account for the association between self-regulatory strategies and
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physical activity behavior.
The present study was exploratory in nature, but informed by SCT, and involved a crosssectional design that examined self-regulatory strategies as correlates of physical activity
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behavior in persons with MS. We examined three questions: (1) What self-regulatory strategies
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(e.g., self-monitoring, goal setting, social support, reinforcement, time management, and
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relapse prevention) are associated with physical activity behavior?; (2) Of those self-regulatory
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strategies, is there a single strategy or combination of strategies that uniquely correlate with
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physical activity?; and (3) Are self-regulatory strategies associated with physical activity via self-
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efficacy? Such an examination of self-regulatory strategies is important for informing the design
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of behavioral interventions for changing physical activity in MS, a topic of considerable
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importance given the disconnect between rates of participation and evidence for benefits of
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physical activity in this population.28
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Method
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Over a six-month period, we recruited a convenience sample of persons with MS by
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advertising the study on the National MS Society local research page and our laboratory
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Facebook page, and contacting persons in our laboratory database. The inclusion criteria were:
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(1) 18-64 years of age; (2) relapse free during the past 30 days; and (3) ambulatory with or
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without assistance. Of the 92 persons with MS who underwent screening, 70 participants met
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inclusion criteria and were scheduled for testing; 8 persons were disqualified and 14 were
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qualified but dropped out prior to testing. Two participants did not complete the testing
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session, and this resulted in a final convenience sample of 68 persons with MS for data
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analyses. The sample size was considerable acceptable based on a power analysis performed in
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G*Power 3.1. The parameters for the power analysis were alpha = .05, beta = .20 (80% power),
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and a population effect size, rho = .30 (i.e., moderate effect size). This indicated that we needed
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a sample size of 64 participants in this study for adequate power.
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Self-Regulatory Processes
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Physical activity self-regulatory strategies were assessed using the Physical Activity Self-
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Regulation Scale (PASR-12).26 The PASR-12 contains 12 items, with 2 items per subscale of self-
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monitoring (PASR SM), goal setting (PASR GS), eliciting social support (PASR ESS), reinforcement
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(PASR R), time management (PASR TM), and relapse prevention (PASR RP). The items are rated
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on an ordinal scale of 1 (Never use strategy) through 5 (Use strategy very often). The scores per
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subscale were calculated by summing the individual item scores, and range between 2 and 10,
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and self-regulation subscale scores were summed for a measure of overall self-regulation (PASR
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Overall), providing a total score that ranges between 12 and 60. Higher scores indicated greater
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use of self-regulatory strategies for physical activity.26 We are unaware of research that has
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validated the PASR-12 in MS. The internal consistency reliability of scores for the overall scale
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and 6 subscales in our sample were acceptable (≥ 0.70) with values of 0.94 for the overall scale,
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0.84 for PASR SM, 0.88 for PASR GS, 0.90 for PASR ESS, 0.73 for PASR R, 0.83 for PASR TM, and
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0.87 for PASR RP.
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Exercise Self-Efficacy
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Self-efficacy was measured with the Exercise Self-Efficacy scale (EXSE).29 This scale has 6 items that assess an individual’s beliefs in their ability to engage in moderate-to-vigorous
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physical activity without stopping across increasing increments of time.29 The scores from the 6
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items are rated on a scale from 0 (Not at all confident) through 100 (Highly confident), and
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averaged into a total score ranging between 0 and 100. There is evidence for the reliability and
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validity of scores from the EXSE in persons with MS.30 The internal consistency of the EXSE in
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our sample was 0.96.
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Physical Activity Level
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The Godin Leisure-Time Exercise Questionnaire (GLTEQ) measures the frequency of strenuous, moderate, and mild leisure-time physical activity performed for periods of 15
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minutes or more over a typical week.31 This questionnaire has evidence for the reliability of its
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scores over time32 and the scores have been validated based on the pattern of association with
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other measures of physical activity, including self-report measures, pedometers, and
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accelerometers.33-35 Scores from this measure have captured the effects of physical activity and
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exercise training interventions.36-38 Such evidence supports the use of the GLTEQ as an
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appropriate measure of physical activity in MS. We do note that there are other options
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available for objectively measuring physical activity (e.g., accelerometers or pedometers), but
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there are a number of limitations with such measures including behavioral reactivity, non-
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compliance for wearing the device, and specificity for primarily ambulatory-based physical
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activity.
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Recently, a new scoring method has been proposed for generating an overall GLTEQ
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score that aligns with current recommendations for physical activity and the dose–response
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association between the volume of physical activity and health benefits (i.e., health
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contribution score or HCS31, 39). The HCS is based on only strenuous and moderate physical
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activity and is computed by multiplying the frequencies of strenuous and moderate activities by
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nine and five metabolic equivalents (METs), respectively, and then adding the resultant scores.
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The HCS ranges between 0 and 98 and can be converted into one of three categories, namely
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insufficiently active (i.e., score < 14 units that is the equivalent of < 7 kcal/kg/week), moderately
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active (i.e., score between 14 and 23 units that is the equivalent of between 7 and 13.9
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kcal/kg/week), and active (i.e., score ≥ 24 units that exceeds 13.9 kcal/kg/week). The HCS has
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been validated in MS.40
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Procedures
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This study was approved by a University Institutional Review Board, and participants provided written informed consent. Participants underwent a neurological evaluation by
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a Neurostatus-certified examiner for generation of an Expanded Disability Status Scale (EDSS)
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score,41 measurements of height and weight using a scale-stadiometer for generation of body-
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mass index (BMI), and then provided sociodemographic (age, sex, race, BMI, marital status,
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employment status, education status, and annual household income) and clinical characteristics
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(disease duration and MS type). Participants further completed the PASR-12 and EXSE, and then
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the GLTEQ.
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Statistical Analysis
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All data were analyzed in SPSS Statistics, Version 24 (IBM Corporation, Armonk, NY), and
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descriptive statistics are reported as mean and standard deviation, unless otherwise noted
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(e.g., median or number and percentage). We examined skewness and kurtosis estimates and
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frequency distributions for establishing the normality of the variables. We examined
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correlations among GLTEQ HCS scores, overall PASR-12 scores, PASR-12 subscales scores, and
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EXSE scores using Pearson product-moment correlations (r); correlation coefficients of 0.1, 0.3,
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and 0.5 were interpreted as small, moderate, and large, respectively.42 This was followed by
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Spearman rho rank-order correlations for confirming that outliers and non-normality were not
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biasing the correlations, and partial correlations whereby we controlled for disability status,
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disease duration, and MS type (0=relapsing-remitting MS, 1=progressive MS). We then
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performed a series of regression analyses for examining PASR-12 scores as correlates of
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physical activity. The first regression analysis involved a direct entry of all six PASR-12 subscales
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for explanation of variance in physical activity; this analysis identified self-regulatory strategies
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that were uniquely associated with physical activity. We performed additional analyses of self-
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efficacy as a possible mediator of the potential relationship between self-regulation strategies
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and physical activity consistent with the Baron and Kenny approach.43 This required as a
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precondition that self-efficacy, self-regulatory strategies, and physical activity all demonstrated
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significant bivariate correlations. If so, we performed hierarchical regression analysis with step-
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wise entry wherein we regressed physical activity levels on overall self-regulation (Step 1), and
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self-regulation plus exercise self-efficacy (step 2). This analysis was repeated with self-
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regulatory subscales that were identified as independent correlates of physical activity in the
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first regression analysis. The attenuation in standardized beta-coefficients between Step 1 and
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Step 2 for self-regulatory strategies and physical activity would suggest a possible mediation
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role for self-efficacy.
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Results
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Sample Demographic and Clinical Characteristics The sample was largely female (69.6%), Caucasian (94.2%), and married (59.4%), with a mean age of 50.5 (SD=8.9) years. Nearly half of participants were employed (49.3%), 85.5%
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reported some college education, and 68.1% reported an annual household income of greater
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than $40,000. The mean BMI was 28.7 (SD=6.6) kg/m2. Participants mostly had relapsing-
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remitting MS (RRMS; 84.1%), mild to moderate disability (median (IQR) EDSS score of 4.0 (2.0)),
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and a mean disease duration of 14.4 (10.5) years. These sample characteristics are comparable
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with national estimates for sex (72.7% female44), race (90.1% Caucasian44), age (47.1 years44),
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employment status (50.8%45), MS type (i.e., 84.3% RRMS46), disability status (67.7% mild to
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moderate47), and disease duration (13.8 years since symptom onset45).
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Self-Regulatory Strategies Across Levels of Physical Activity
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for the overall sample are provided in Table 1. The table further provides actual ranges of
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scores and skewness and kurtosis estimates for the variables; the skewness and kurtosis
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estimates along with inspection of frequency distributions did not identify problems with
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normality for the variables. Regarding physical activity status classification based on GLTEQ HCS
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scores, there were 32 persons who were insufficiently active (47%), 11 who were moderately
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active (16%), and 25 who were active (37%).
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Correlation Analysis of Self-regulation and Physical Activity
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The bivariate correlations among physical activity behavior, self-efficacy, and self-
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regulatory strategy scores are presented in Table 2. Our results indicated that physical activity
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behavior was significantly correlated with self-efficacy and relapse prevention (large
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correlations), overall self-regulation (moderate correlation), and self-monitoring, goal setting,
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reinforcement, and time-management (moderate correlations). Physical activity behavior was
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not significantly correlated with eliciting social support. Self-efficacy was associated with overall
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self-regulation and relapse prevention (large correlations) as well as self-monitoring, goal
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setting, reinforcement, and time management (moderate correlations); self-efficacy was not
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correlated with eliciting social support. Those correlations were not changed in a secondary
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analysis using Spearman rho rank order correlation coefficients (values not provided), and this
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supports that non-normality and outliers were not biasing the patterns of associations. Those
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correlations were further not changed in partial correlation analyses that controlled for EDSS
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scores, disease duration, and type of MS, and this suggested that clinical characteristics were
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not driving the pattern of associations; the partial correlations are provided in parentheses
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after the bivariate correlation coefficients in Table 2.
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Regression Analysis of Self-regulation and Physical Activity
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The summary of regression analysis for self-regulatory subscales as unique predictors of self-reported physical activity behavior is presented in Table 3. The overall model was
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statistically significant (F=5.80, p < 0.05), and the variables explained 36% of the variance in
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physical activity; this is consistent with previous research in the area.21 Self-monitoring (B=3.65;
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SE B=1.71; β=0.33) and relapse prevention (B=5.01; SE B=1.74; β=0.51) explained unique
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variance in physical activity and were the only predictors of physical activity behavior in our
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sample of persons with multiple sclerosis. The Variance Inflation Factor values were less than
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5.0 for all variables in this model, and the Tolerance values all exceeded 0.20; this suggests that
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multicollinearity was not biasing the results of this regression analysis.
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We further conducted two hierarchical regression analyses for overall self-regulation and self-efficacy as well as self-monitoring, relapse prevention, and self-efficacy predicting
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physical activity behavior (Table 4 and Table 5, respectively). The first model indicated that
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overall self-regulation had a significant association with physical activity behavior based on the
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standardized beta-coefficient (Step 1), and the standardized beta-coefficient diminished in
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magnitude with the addition of self-efficacy (Step 2). The second model indicated that relapse
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prevention, but not self-monitoring, had a significant association with physical activity behavior
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based on the standardized beta-coefficient (Step 1), but this was attenuated with the addition
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of self-efficacy (Step 2). The observed reduction in standardize beta-coefficients between self-
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regulation strategies and physical activity toward zero between Step 1 and Step 2 of the
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regression analyses would be suggestive of mediation of the association by self-efficacy
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consistent with Baron and Kenny.43
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Discussion
We provide novel data on self-regulatory strategies as correlates of physical activity in persons with MS. Our results indicate that (a) overall self-regulatory strategies and self-
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monitoring, goal setting, reinforcement, time management, and relapse prevention strategies
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were correlated with physical activity; (b) relapse prevention and self-monitoring uniquely
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explained variance in physical activity; and (c) self-efficacy accounted for the associations
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between overall self-regulation and physical activity and relapse prevention and physical
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activity. Such results are consistent with predictions from Bandura’s SCT and the theory of self-
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regulation.24, 25 These results further extend a recent systematic review that identified self-
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efficacy and self-regulatory strategies, specifically goal setting, as important correlates of
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physical activity in persons with MS.21 However, our results are unique in that we identify (a)
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the self-regulatory sub-components of self-monitoring, reinforcement, time management, and
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particularly relapse prevention as significant correlates of physical activity and (b) the possible
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mediation pathway between self-regulation and physical activity, namely self-efficacy. Such
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results may inform future research on behavioral interventions for physical activity in MS and
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persons with neurological disabilities, as noted as critical in a recent scoping review.23
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Our results suggest that, beyond self-efficacy as a commonly identified correlate of physical activity in MS,21 the self-regulatory strategies of relapse prevention and self-monitoring
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are important correlates of physical activity in persons with MS. Indeed, regarding the six
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components of self-regulation that we examined as correlates, relapse prevention and self-
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monitoring were the only unique predictors of physical activity. Importantly, the association
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between those self-regulatory strategies, particularly relapse prevention, and physical activity
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was attenuated when accounting for self-efficacy. These results highlight that relapse
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prevention is an important target for possibly increasing physical activity in MS, and this may
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work through self-efficacy expectations. This is consistent with expectations derived from
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Bandura’s theory of self-regulation,27 and suggests that relapse prevention strategies works via
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the mediator variable of self-efficacy for explaining variation in physical activity in MS.
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Importantly, other variables may be involved in explaining physical activity in MS, including
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symptoms of MS and/or physical or social barriers. When we controlled for disability status,
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disease duration, and MS type, the bivariate correlations were unchanged in pattern or
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magnitude. Future research should consider other variables that could explain physical activity
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and the association between self-regulation and physical activity such as MS symptoms
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including fatigue and depression. Additionally, our results suggested relapse prevention, or the perceived ability to cope
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with situations that might undermine participation in physical activity, is tightly related with
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one’s self-efficacy, or the perceived ability to overcome situations that might destabilize regular
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participation in physical activity.48 Most high-risk situations that could lead to the decline of
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physical activity participation threaten one’s perception of control over the situation and can
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reduce self-efficacy. There are many approaches for relapse prevention that could be included
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in a behavioral intervention including: (a) skills training on recognizing high-risk situations (e.g.,
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vacation) that could lead to decreased participation in physical activity; (b) skills training of
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coping mechanisms in high-risk situations (e.g., rehearsal of common situations that would
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challenge one’s participation in regular physical activity); (c) education about immediate and
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delayed effects of decreasing physical activity participation; (d) tools on what to do after a
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setback in participation and education of individuals on how to limit the extent of the setback
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and reinstall regular physical activity in one’s routine.48 Importantly, the relationship between
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physical activity and self-regulatory strategies could be reciprocal, such that low physical
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activity could explain low self-regulatory processes, and low self-regulatory strategies could
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explain low physical activity participation. Additionally, in a generally insufficiently active
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population, relapse prevention strategies may be particularly salient in helping inactive people
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become more active and maintain that behavior over time. Future research should include
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longitudinal and experimental designs to evaluate these relationships in frameworks that
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permit a closer understanding of causality.
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Study Limitations There are important limitations of our results. We did not include a non-MS control group for understanding the uniqueness or generalizability of our results outside of MS. Our
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sample size was relatively small and homogeneous regarding demographic and clinical
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characteristics limiting the generalizability of our results amongst all people with MS.
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Additionally, the cross-sectional design of the study precludes inferences of temporal sequence
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and causality. Future research should examine the influence of self-regulatory strategies on
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changes in physical activity participation over time in persons with MS. We are unaware of any
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studies that have validated the PASR-12 in persons with MS, but our data suggest that the scale
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has acceptable internal consistency. Future research should further evaluate the validity and
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reliability of the PASR-12 in persons with MS. Although the GLTEQ is an unobtrusive, time and
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cost efficient measure of physical activity, it is a self-report measure and is therefore subject to
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various participant biases (e.g., recall bias). If the results were driven by recall bias, however,
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one might expect a more consistent or homogeneous pattern of associations among self-
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efficacy, self-regulation, and physical activity scores. We further note that the GLTEQ has
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received strong support as a valid and reliable measure of physical activity in MS, 33-35 and
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further has captured the effects of physical activity and exercise interventions (i.e.,
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responsiveness).36-38
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Conclusions
To our knowledge, this was the first study that examined a range of self-regulatory
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strategies as correlates of physical activity behavior in MS. Our results indicate that relapse
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prevention strategies and self-efficacy may be key determinants of physical activity behavior in
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persons with MS. Importantly, the use of self-regulation, namely physical activity behavior
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relapse prevention strategies, represents an important, previously unidentified correlate of
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physical activity behavior, and its association with physical activity may be mediated by self-
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efficacy in persons with MS. These findings should be considered when designing, refining, and
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optimizing behavioral interventions that target physical activity behavior change in persons
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with MS. Such behavioral interventions might target self-regulatory strategies, particularly
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relapse preventions for increasing self-efficacy, as an approach for changing physical activity
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participation in MS.
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Table 1. Values of physical activity, self-efficacy, and self-regulatory variables in multiple sclerosis (N=68) Mean Standard Actual Range Skewness Kurtosis Score Deviation of Scores Value Value GLTEQ 21.7 25.2 0-98 1.4 1.5 EXSE 39.7 27.7 0-96.7 0.3 ‒1.0 PASR Overall 34.2 11.7 12-60 0.2 ‒0.7 PASR SM 6.3 2.2 2-10 ‒0.3 ‒0.7 PASR GS 5.7 2.3 2-10 0.0 ‒0.9 PASR ESS 4.4 2.3 2-10 0.7 ‒0.6 PASR R 7.1 2.1 2-10 ‒0.6 0.1 PASR TM 5.4 2.5 2-10 0.3 ‒0.9 PASR RP 4.9 2.5 2-10 0.6 ‒0.6 Note. GLTEQ, Godin Leisure-Time Exercise Questionnaire; EXSE, exercise self-efficacy scale; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention.
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Table 2. Correlations among physical activity, self-efficacy, and self-regulatory variables in multiple sclerosis (N=68)
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Variable 1 2 3 4 5 6 7 8 9 1. GLTEQ --2. EXSE .69(.70)* --3. PASR Overall .43(.47)* .50(.50)* --4. PASR SM .45(.53)* .34(.41)* .72(.74)* --5. PASR GS .27(.30)** .32(.37)* .76(.78)* .69(.73)* --6. PASR ESS .13(.18)* .17(.18)* .65(.64)* .35(.36)* .51(.48)** --7. PASR R .30(.36)* .36(.35)* .79(.80)* .66(.69)* .69(.74)* .45(.44)* --8. PASR TM .41(.41)* .38(.37)* .84(.83)* .54(.58)* .61(.60)* .62(.62)* .61(.62)* --9. PASR RP .53(.54)* .52(.53)* .83(.83)* .58(.60)* .58(.59)* .54(.55)* .62(.63)* .79(.79)* --Note. Values are bivariate (partial) correlation coefficients. Partial correlation coefficients control for disability status, disease duration, and MS type. GLTEQ, Godin Leisure-Time Exercise Questionnaire; EXSE, exercise self-efficacy scale; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention. *p<.05.
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Table 3. Summary of regression analysis for self-regulatory subscales predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variables B SE B β PASR SM 3.65 1.71 .33* PASR GS ‒1.32 1.74 ‒.13 PASR ESS ‒2.31 1.47 ‒.21 PASR R ‒1.50 1.89 ‒.13 PASR TM 1.14 1.80 .11 PASR RP 5.01 1.74 .51* 2 Note. R =.36; PARS, Physical Activity Self-Regulation Scale; PARS SM, Self-monitoring; PASR GS, Goal Setting; PASR ESS, Eliciting Social Support; PASR R, Reinforcement; PASR TM, Time Management; PASR RP, Relapse Prevention. B, unstandardized beta-coefficient; SE B, standard error of unstandardized betacoefficient; β, standardized beta-coefficient; *p<.05.
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Table 4. Summary of hierarchical regression analysis for overall self-regulation and self-efficacy predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variable B SE B β Step 1 PASR Overall 0.90 0.24 .43* Step 2 PASR Overall 0.27 0.22 .13* EXSE 0.56 0.10 .61* Note. R2 = .18 for Step 1; ΔR2 = .28 for Step 2 (p<.05); PASR Overall, Physical Activity Self-Regulation Scale Overall; EXSE, Exercise Self-Efficacy. B, unstandardized beta-coefficient; SE B, standard error of unstandardized beta-coefficient; β, standardized beta-coefficient; *p<.05.
ACCEPTED MANUSCRIPT
Table 5. Summary of hierarchical regression analysis for self-monitoring, relapse prevention, and selfefficacy predicting self-reported physical activity levels in multiple sclerosis (N=68)
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Variable B SE B β Step 1 PASR SM 2.32 1.40 .21* PASR RP 3.99 1.26 .40* Step 2 PASR SM 2.00 1.18 .18* PASR RP 1.39 1.17 .14* EXSE 0.49 0.09 .54* Note. R2 = .31 for Step 1; ΔR2 = .21 for Step 2 (p<.05); PASR SM, Physical Activity Self-Regulation Scale Self-monitoring; PASR RP, Physical Activity Self-Regulation Scale Relapse Prevention; EXSE, Exercise SelfEfficacy; B, unstandardized beta-coefficient; SE B, standard error of unstandardized beta-coefficient; β, standardized beta-coefficient; *p<.05.
ACCEPTED MANUSCRIPT
Highlights Self-regulatory strategies are important correlates of physical activity in MS.
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Relapse prevention was a unique predictor of physical activity behavior.
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Self-efficacy was an important determinant of physical activity behavior in MS.
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Relapse prevention presumably works via self-efficacy in persons with MS.
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