Predictors of exercise participation in ambulatory and non-ambulatory older people with multiple sclerosis

Survey design

We accessed and performed secondary analysis of data collected from 743 people with MS as part of a national survey; the ‘Canadian Survey of Health, Lifestyle and Aging with MS’ (Ploughman et al., 2014) which was approved by 11 health research ethics boards across Canada. The database included health and lifestyle variables obtained from self-report questionnaires mailed to participants over the age of 55 years with MS symptoms for more than 20 years. Complete survey methods are described elsewhere (Ploughman et al., 2014).

Potential predictive variables

Based on previous research (Asano et al., 2013; Kayes et al., 2011; Motl et al., 2006; Stroud, Minahan & Sabapathy, 2009) and a model of healthy aging created using a qualitative approach (Ploughman et al., 2010; Ploughman et al., 2012a; Ploughman et al., 2012b; Ploughman et al., 2014) a list of ‘a priori’ factors potentially associated with exercise adherence were categorized into six domains; (1) demographic, (2) personal, (3) socioeconomic, (4) physical health, (5) exercise history and (6) healthcare support. Demographic information included age, gender, years of education, type of MS at diagnosis and years since MS diagnosis. Personal factors included stress, measured as part of the Simple Lifestyle Indicator Questionnaire (SLIQ) (Godwin et al., 2008), mood, measured using the Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983), and resilience (the Resilience Scale) (Wagnild, 2009). HADS and the Resilience Scale were separated into their subcomponents; HADS into anxiety (HADS-A) and depression (HADS-D) and resilience into five aspects (equanimity, perseverance, self-reliance, meaningfulness, and existential aloneness) (Wagnild, 2009).

Physical health variables included disability, measured by the Barthel Index (Mahoney & Barthel, 1965), fatigue (rated with a visual analogue scale), and co-morbid conditions determined using the Co-morbidity Questionnaire developed by Marie and Horwitz (Marrie & Horwitz, 2010). Socioeconomic variables included financial situation and social support. Level of social support was measured using the Personal Resource Questionnaire-2000 which consists of 15 items with a score range from a low of 7 to high of 105 (Weinert & Brandt, 1987). To determine health care support, participants identified and ranked the helpfulness of health care providers on a scale of 1 (not helpful) to 5 (very helpful). Participation in exercise and other lifestyle habits (smoking, alcohol, diet) was collected from responses to the Simple Lifestyle Indicator Questionnaire (SLIQ) (Godwin et al., 2008). In addition to describing their current level of exercise, respondents were also asked to describe the type and intensity of exercise they had engaged between the ages of 20 and 30 years (past exercise experience).

All the survey measures (and the survey itself) had been tested or validated among people with MS or community-living older adults. Complete information is provided elsewhere (Ploughman et al., 2014). The Barthel Index, SLIQ and Personal Resource Questionnaire 2000 were pretested using cognitive debriefing (Ploughman et al., 2010).

Data analysis

The statistical methods follow the steps outlined in Table 1. To create the outcome, descriptive physical activity information from SLIQ was transformed into metabolic equivalents (METS; a measure that quantifies exercise intensity based on the ACSM guidelines (ACSM, 2010)), and then MET-hours per week (MET intensity × number of 1 h intervals of exercise per week). As this measure was highly skewed, we classified respondents into ‘Exercisers’ (>6.75 MET-hours per week) or ‘Non-exercisers’ (<6.75 MET hours per week). These cut-off values were based on the ACSM recommendation that in order to improve or maintain fitness people with MS should be active three times weekly for 20–30 min at a moderate intensity (∼4.5 METs × 3 × 30 min sessions = 6.75 MET-hours/week) (ACSM, 2010).

In the first step of building an explanatory model, each ‘a priori’ variable (independent variable) was separately entered into a simple binary logistic regression with the dependent variable (exercise, no exercise). Continuous variables were modelled both linearly and as categories based on 25th, 50th and 75th quartiles or based on accepted cut-offs from the literature. Linear fit was examined by comparing Aikaike Information statistics (AIC) over models. When the AIC of the model with categorical variables was better than that with the continuous variable, that variable was retained only in the categorical format. When four levels were not required to categorize the variable, levels were combined and the variable retained in a binary format.

It was our hypothesis that predictors of exercise would differ among people with different degrees of MS disability. A variable indicating ambulation was therefore created from the response to the ambulation question in the Barthel Index. Those who scored 0 or 5 (answers walk independently with or without a cane >150 m) were categorized as ‘Ambulators’. Respondents who scored 10 or 15 on Barthel Index ambulatory question (answers use of wheelchair/walking aid indoors only) were categorized as ‘Non-Ambulators’. All predictor variables were tested for interaction with ambulation (step 3; Table 1): a main effect of and an interaction with ambulatory status were added to each of the univariate models to determine if the association with exercising was different for persons who were ambulatory and those who were not.

In step 4, all variables (linear or categorical, with or without an associated interaction) that predicted exercise, (p < 0.10) were entered into a multivariate logistic regression model. Variables with p-values above 0.05 were removed from the model one by one, based on their p-values (step 5; Table 1). Once all remaining variables were statistically significant, all variables that had been removed were re-entered one by one as well as with an interaction for ambulation to verify that none was eligible to re-enter the model (step 6; Table 1).

Because it was hypothesized, ‘a priori’, that predictors of exercising would vary between ambulators and non-ambulators, an internal cross validation (Kleinbaum, Kupper & Muller, 1988) was also done to examine whether a model created among ambulators would also fit non-ambulators. To do this, betas from a logistic regression model created among ambulators was applied to non-ambulators and c-statistics of the two compared.

Differences between exercisers and non-exercisers were compared using ANOVA and chi-square; significance set at p < 0.05. Statistical analyses were performed using SAS 9.3.

Participant characteristics

Respondents were on average 64.6 (SD6.2) years of age and had been diagnosed with MS on average 24.8 (SD10.0) years prior. The ratio of females to males was 3.5:1. In comparing the characteristics of Exercisers (n = 404) and Non-exercisers (n = 339), the Non-exercisers were older and more disabled (as measured by the Barthel Index) (Table 2). They were more likely to be initially diagnosed with primary or secondary progressive disease and less likely to be diagnosed with relapsing-remitting MS (Table 2). When describing their past exercise experience, 368/743 (50%) respondents reported that they were previously active but are now inactive. One hundred and four respondents (14%) reported being inactive for most of their life (not active during the ages 20–30 and not currently active) and 271 (36.5%) reported that they were active when they were young and are still currently active. Those who described their current exercise reported participating in activities such as swimming and water fitness (n = 82), gardening and housework (n = 114), yoga, stretching or Tai Chi (n = 84) and most commonly, walking (n = 266).

Predictors of exercise

Of the independent variables considered, many had a significant univariate association with exercising, though only meaningfulness (part of the Resilience Scale), fatigue and financial situation had associations that varied by ambulatory status (p < 0.10).There were expected correlations between predictor variables (>0.3); resilience, depressive symptoms, anxiety symptoms, fatigue and social support (data not shown). A final predictive model was created (Table 3). Ambulators had a greater odds of exercising at moderate to vigorous levels (OR 1.90, 95% CI [1.24–2.91]), as did people with lower levels of disability generally (OR 1.50, 95% CI [1.34–1.68] for each 10-point difference in the Barthel Index). Similarly, the odds of exercise were higher among persons with higher levels of perseverance.17, 95% CI [1.08–1.26] for each additional point on the scale of 0 to 14), less fatigue (OR 2.01, 95% CI [1.32–3.07] for those in the lowest quartile), fewer years since diagnosis of MS (OR 1.58, 95% CI [1.11–2.23] for those diagnosed within the past 23 years compared to 24 years ago or more), and less cardiovascular comorbidity (OR 1.55, 95% CI [1.02–2.35] for no more than one compared to two or more). As perseverance has a standard deviation of 2.3, a 1-point difference on this scale is close to a clinically significant difference based on a half a standard deviation. For easier interpretation, compared to the worst quartile of perseverance (scores from 2 to 9), odds ratios for the second (10–11), third (12) and best (13–14) quartiles were 1.50 (95% CI [0.89–2.51]), 2.04 (95% CI [1.19–3.49]) and 2.71 (95% CI [1.62–4.48]) respectively. Similarly, modelling the Barthel Index as quartiles compared to the worst quartile (scores of 0–60), results in odds ratios of 4.11 (95% CI [2.39–7.06]), 7.04 (95% CI [3.91–12.68]) and 10.1 (95% CI [5.06–20.28]) for the second (scores 65–80), third (scores 85–90) and best (a score of 100) quartiles, respectively. This model had good fit based on AIC statistics of other models considered. Furthermore, the c-statistic of 0.81 is considered strong (Hosmer & Lemeshow, 1989; Hosmer & Lemeshow, 2000): a c-statistic of 0.5 indicates allocation no better than chance and a value of 1.0 perfect assignment.

To determine if separate statistical models were needed to predict exercising among ambulatory and non-ambulatory persons, internal cross-validation methodology was performed by fitting a logistic regression model to ambulators, applying the regression coefficients for the explanatory variables (β) to the observed data on these variables among the non-ambulators, and comparing fit via c-statistics (Kleinbaum, Kupper & Muller, 1988). For ambulators alone, fatigue was replaced with a measure of depressive symptoms. Fatigue and depressive symptoms were correlated, but while fatigue was a better fit for the model of ambulators and non-ambulators combined, depression improved fit among ambulators only. The c-statistics among ambulators was 0.71 (95% CI [0.65–0.78]), and that of non-ambulators 0.74, supporting the determination that only a single model was needed to determine predictors of exercise.

Level of disability a major predictor

Level of disability was the major predictor of exercise in both ambulatory and non-ambulatory groups. Respondents scoring 100/100 on Barthel Index (highest quartile; least disability) were 10 times more likely and those scoring 85–90, seven times more likely to exercise than those scoring less than 60 (lowest quartile). Ambulatory people with MS were 1.9 times more likely to exercise than those who were non-ambulatory (walked indoors only, used a wheelchair or were bedridden). Furthermore, respondents diagnosed for less than 24 years were 1.6 times more likely to exercise than those diagnosed longer than 24 years. Taken together, our results suggest that respondents who need assistance or who have walking disability have few exercise options. Development of seated and modified exercise programs followed by effectiveness research is required. Programs that are individually tailored, guided by qualified personnel such as a physiotherapist, focused on goal-setting and independence using remote technology such as Blue Prescription, hold promise (Hale et al., 2013). Several systematic reviews examining exercise in MS suggest a critical need for rehabilitation research among more disabled groups (Latimer-Cheung et al., 2013; Rietberg et al., 2005). Although our sample were on average 64 years of age with MS symptoms for about 33 years, our finding of the critical role of disability in exercise concurs with findings from a sample of 417 ambulatory participants who were on average 43 years old with symptoms for about 8 years (Asano et al., 2013) and among 68 people with relapsing-remitting MS (Suh et al., 2014).

Our findings also suggest that the predictors of exercise participation in MS differ somewhat from those of people with SCI (Ginis et al., 2012). Martin Ginis and group showed that physical independence and injury severity were not strongly predictive of exercise participation in middle-aged people with chronic spinal cord injury (Ginis et al., 2012), suggesting that interventions to promote exercise compliance in SCI may not be entirely applicable to MS.

Perseverance

A novel finding in this study was the role of resilience, specifically perseverance, in predicting exercise participation. Respondents scoring in the highest quartile of perseverance (scores of 13 or 14 out of 14) were 2.7 times more likely to exercise than those scoring in the lowest quartile (less than 10). In our previous qualitative research (Ploughman et al., 2012a; Ploughman et al., 2012b), perseverance, or commitment to an outcome despite challenges, had previously emerged as a characteristic of people who maintained exercise into old age despite their MS-related symptoms. Previous results of the ‘Canadian Survey of Health Lifestyle and Aging with MS’ database showed that this sample of older people with MS exercise more than other older Canadians (Ploughman et al., 2014).

Other studies have shown that self-efficacy (Kasser & Kosma, 2012; Kosma & Kasser, 2012; Nickel et al., 2014; Schmitt et al., 2014), perceived exercise benefits (Kosma & Kasser, 2012; Suh et al., 2014) and positive exercise intentions (Ginis et al., 2012) are associated with exercise participation in people with MS and SCI-related mobility disability. Self-efficacy, resilience and perseverance are overlapping constructs that impact health behaviors (Pilutti et al., 2014; Sinnakaruppan et al., 2010). Resilience is believed to be an innate characteristic but research suggests that it can also be learned (McAllister & McKinnon, 2009). Researchers have used focused cognitive behavioural techniques to encourage optimism and dispute pessimistic thinking as a method to improve individual resilience and self-efficacy (Graziano et al., 2014). Resilience can be improved using optimism training, as well as teaching control and empowerment, educating individuals about their illness, and involving patients in support groups (Ng et al., 2013). These techniques are clearly important in promoting exercise participation and should be considered in the design of exercise trials especially among people with significant MS-related barriers.

Fatigue or depression as predictors?

We found that participants who reported less fatigue (top quartile in 0-100 scale) were twice as likely to exercise. Asano and group (Asano et al., 2013) have also reported fatigue (feeling too tired) as an exercise barrier among people with MS. It is important to consider that MS fatigue and depressive symptoms overlap and in fact, fatigue is an attribute of depression suggesting a complex relationship between the two (Feinstein et al., 2014). Presence of depressive symptoms provided a slightly better model fit than fatigue in the ambulatory group during our analysis. Motl et al. also showed that depression was a symptom inversely associated with exercise in a group of ambulatory subjects with relapsing–remitting MS (Suh, Motl & Mohr, 2010). Whether fatigue or depression or both, better symptom management is required in order to foster exercise participation in the long term.

Comorbid conditions

As our study cohort were older, we expected participants to have more comorbid conditions, however, to our knowledge this is the first report that the number of cardiovascular comorbid conditions impacted exercise behaviour in MS. It was interesting that cardiovascular conditions (diabetes, hypertension and hyperlipidemia) but not musculoskeletal conditions (arthritis, joint replacement etc.) predicted exercise. Participants reporting fewer than two cardiovascular comorbidities were 1.5 times more likely to exercise than those reporting more. As reported by Marrie and Hanwell, comorbid conditions are associated with increased MS disability progression and lower adherence to treatment which could be linked to low exercise participation (Marrie & Hanwell, 2013). Exercise participation, adherence to a healthy diet and avoidance of smoking and excessive alcohol are likely parts of an overall MS self-management program to enhance healthy aging with MS (Ploughman et al., 2012a; Ploughman et al., 2012b; Ploughman et al., 2014).

Factors that do not predict exercise

Previous research suggests that as individuals age they experience a progressive loss of cognitive and physical skills and abilities, which act as barriers for engagement in healthy lifestyle practices, like physical activity (Widerstrom-Noga & Finlayson, 2010; Motl et al., 2006; Prakash et al., 2010). Our findings did not support age as an exercise predictor.

Based on previous qualitative and quantitative we had expected that gender (Anens et al., 2014), social support (Ploughman et al., 2012a), financial resources (Ginis et al., 2012), previous exercise behaviors and the support of health care professionals (Ploughman et al., 2012b) would be predictive of exercise participation but they were not. When subjected to rigorous analysis in a large cohort with MS-related disability, the influence of these factors were negligible and even absent. On the other hand, our cohort of older people with MS was unique so the impact of gender as reported by others (Anens et al., 2014) and other differences may not be as applicable in this group.

Limitations

Although this unique cohort may provide new insights into maintaining exercise participation among people with MS as they age, there are some study limitations. The cross-sectional design limits our ability to assess change and the effects of variables on predicting exercise participation over time. As the data analysed was obtained through surveys, the responses to the questions were subjective which may or may not align with objective measures. Furthermore, questions such as past exercise experience and diagnosis of co-morbid conditions can be affected by recall bias and memory difficulties. By nature of the volunteer survey design, our sample may be biased in that active participants and those without cognitive impairment may have been more likely to respond. We did not examine cognitive impairment, nor did we have access to data about sleep patterns and pain; potential moderators of exercise and physical activity.