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Predicting exercise adherence in cancer patients and survivors: a systematic review and meta-analysis of motivational and behavioural factors

Submitted by Anne M Lunde Husebø MSc, RN, PhD Candidate, Sindre M Dyrstad PhD Associate Professor, Jon A Søreide PhD, MD Attending Surgeon, Professor, Edvin Bru PhD Professor

Tags: adherence behavior cancer cancer patients chemotherapy exercise meta-analysis physical activity recruiting review treatment

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Aims and objectives

To examine research findings regarding predictors of adherence to exercise programmes in cancer populations.

The meta-analysis procedure was conducted using the software programme Comprehensive Meta Analysis Version 2 analysis (Borenstein et al. 2005). A meta-analysis is beneficial, allows for a more objective assessment of the review evidence and increases understanding of inconsistent results encountered in the reviewed studies (Egger et al. 1997). In the meta-analysis, correlation coefficients were weighted according to the number of cases in the studies. The authors of studies giving insufficient statistical information for meta-analysis were contacted to collect the information needed; however, we were not able to retrieve the necessary information for six correlations. In cases of missing information, the correlation coefficient was set to 0·00.

Courneya et al. (2010) reported correlations for two different exercise groups, which were combined in the meta-analysis. In two studies, the predictor ‘TPB-attitude’ was reported in both ‘instrumental attitude’ and ‘affective attitude’. An estimated mean coefficient of correlations was included in the meta-analyses.

Statistically significant heterogeneity was detected for associations of PBC and pretrial exercise behaviour with adherence. Heterogeneity was tested by using the I² estimation (Ioannidis 2008). In the meta-analysis random-effect model was implemented to compensate for between-studies heterogeneity (Deeks et al. 2008). Moreover, a complementary fixed-effect model was computed. According to Alderson and Green (2002), it is unlikely that there is important statistical heterogeneity if fixed-effect and random-effect meta-analysis give identical results. When comparing the results from the two models, substantial differences in coefficients and meta-correlation were not found (Table 6). There were therefore no indications of heterogeneity having a critical influence on the meta-analysis performed.

O'Connor et al. (2011) warn against excluding studies from systematic reviews just because they provide no ‘usable data’. We therefore found it relevant to also include findings concerning predictors of exercise adherence in studies not providing data suitable for the meta-analysis, but otherwise satisfied the quality criteria applied in the review. The assessment was based on the statistical significance of findings.

The quality assessment tool was adopted from Jack et al. (2010). We modified this tool by implementing a criterion for sample size calculation or power calculation as described by Ingram et al. (2006), and specified the outcome measures describing adherence scores and data regarding predictors of adherence. In this review, predictors of exercise adherence in exercise groups were the exclusive focus, and the ‘sample size’ criterion was altered from ≥300 to ≥50 subjects according to the rule-of-thumb for number of cases in analyses of correlation and regression given by Green (1991). Finally, the assessment tool consisted of 12 criteria, as presented in Table 2. Criterion A (selection bias) concerns issues such as percentage of selected subjects included and likelihood of representing the target population (Ingram et al. 2006). Sample size information gives N in the exercise groups only (criterion D). Randomisation procedure in randomized controlled trials (RCT) is not likely to affect the prediction of exercise adherence; therefore non-randomised design was not set as a quality criterion. Only two of the included studies applied single-group designs (Courneya et al. 2001, Peddle et al. 2009).

The methodological qualities of the studies were evaluated using score points; those meeting the standard were given a score of one, while those not meeting the standard were given a score of zero. Studies that met ≥50% of the criteria (scores ≥ 6) were rated as ‘acceptable quality’, while studies that met <50% of the criteria (scores < 6) were rated as ‘low quality’ (Jack et al. 2010). Disagreement on scoring the studies was solved by consensus.

The results of the quality assessment are presented in Table 4. The studies were categorised into levels with respect to their scores, with higher scores associated with higher quality. All studies were considered acceptable quality, with a median quality score of 9·0 (range, 7–11). The quality assessment process still revealed some limitations. Overall, the studies exhibited limitations regarding selection bias (criterion A), sample size (criterion D), information on drop-outs (criterion E) and power analysis (L). Accrual was the most reported problem concerning criterion A. Five studies reported accrual data; of these, four accrued <40% of eligible subjects (Courneya et al.2004a,b, 2008, Latka et al. 2009). McNeely et al. (2012) reported accrual rates between 33% and 57%, divided between two recruiting sites. One other study scored zero points on selection bias, because it used a single-group design and convenient recruiting from a special interest group, including 100% of eligible subjects (Courneya et al. 2001).

Six studies contained at least 50 subjects in the exercise group (Courneya et al. 2002, 2004a,b, 2008, 2010, McNeely et al. 2012). Only three of these studies reported to have conducted power analysis (Courneya et al. 2002, 2004a, 2010).

Five studies reported data on drop-outs or withdrawals (Courneya et al. 2004a,b, Peddle et al. 2009, Pinto et al. 2009, Swenson et al.2010), with completion rates between 79% and 91%. Adherence rates did not differ significantly between studies with a high or low drop-out rate in the exercise groups.

The quality criteria – prospective design (criterion C), defining adherence (criterion F), data presenting adherence score (criterion G), data presenting predictors of adherence (criterion H) and standardised or valid measurements (criterion I) —obtained the highest assessment scores. All of the reviewed studies provided sufficient information about these criteria. However, irregularities in defining and calculating adherence, and inconsistencies in the analysis and presentation of data regarding correlations between exercise adherence scores and predictors of adherence were observed.

One aspect of exercise adherence examined in all trials was training volume. Training volume included the total amount of exercise performed, average minutes or steps performed per week, quantity of prescribed exercise achieved by the intervention group and total amount of exercise divided by the expected amount of exercise. Two studies (Latka et al. 2009, Pinto et al. 2009) referred to adherence meaning prescribed exercise goals. Seven studies assessed adherence by using objective attendance records to determine adherence to the exercise protocol (Courneya et al. 2001, 2004b, Daley et al. 2007, Courneya et al. 2008, Peddle et al. 2009, Courneya et al. 2010, McNeely et al. 2012), while in five studies exercise adherence was assessed by self-reporting methods, such as exercise logs, weekly surveys and weekly phone calls to the participants (Courneya et al. 2002, 2004a, Latka et al. 2009, Pinto et al. 2009, Swenson et al. 2010). Slightly higher adherence was observed in studies employing supervised exercise programmes than in those using home-based programmes (70·5% vs. 67·5%, respectively).

Exercise stage of change drawn from TTM of behaviour change was examined as an exercise adherence predictor in five of the reviewed studies, two of which were included in the meta-analysis. In both studies stage of change was measured with the same scale. Courneyaet al. (2004a,b) established statistically significant correlations between exercise adherence and exercise stage in cancer survivors, and the meta-correlation was found to be statistically significant and moderately strong. The two studies did not report which stage of change corresponded to the correlation with adherence. Latka et al. (2009) observed significant univariate associations between a higher readiness for change and adherence to the exercise prescription. The final two studies observed non-significant correlations between adherence and exercise stage of change (Daley et al. 2007, Pinto et al. 2009). The TTM construct decisional balance for exercise, associated with stage of exercise adoption, was examined as the only motivational variable in Pinto et al. (2009). It was not statistically significant in relation to a home-based exercise programme.

The association between pretrial exercise behaviour and adherence to programme exercise was investigated in eight of the studies. Pretrial exercise yielded quite heterogeneous results between studies included in the meta-analysis, resulting in a non-significant meta-correlation. Reviewed narratively this predictor achieved a contradictory result. Pinto et al. (2009) report baseline PA as a significant predictor of weekly pedometer steps (p < 0·05) among breast cancer patients. Swenson et al. (2010) observed baseline activity levels to be inversely correlated with exercise adherence, and sedentary subjects recorded significantly fewer steps per day. Daley et al. (2007) and Latka et al. (2009) report no significant correlations between pretrial exercise behaviour and adherence to programme exercise.

Smoking behaviour was included as a behavioural variable in six studies, of which five provided sufficient data for the meta-analysis (Courneya et al. 2004b, 2008, 2010, Peddle et al. 2009, McNeely et al. 2012). Smoking status exhibited no significant associations with exercise behaviour in the meta-analysis (r = −0·06). Drinking habits as a determinant of exercise adherence was examined in two studies (Courneya et al. 2004b, McNeely et al. 2012). In these two trials, alcohol consumption emerged as a weak but still statistically significant determinant of exercise adherence. McNeely et al. (2012) reported alcohol intake as the strongest predictor of adherence, establishing significant correlations between high exercise adherence and low alcohol consumption. Courneya et al. (2004b) also report alcohol consumption to be significantly and inversely associated with exercise adherence. Drinking habit, examined in the meta-analysis, did not predict exercise adherence (r = −0·06).

The review and meta-analysis revealed some significant, although weak, correlations between adherence to exercise protocols and motivational and behavioural factors. A relatively strong predictor of adherence to exercise in the cancer populations studied was exercise stage of change. The transtheoretical model of behaviour change (TTM) suggests that a behaviour change intervention might be more successful if it were individualised and aimed at the individual's stage of change (Taylor 2003). Rogers et al. (2005ab) observed that breast cancer patients who exercised regularly (i.e. action or maintenance stage participants) reported exercise barriers less frequently. For cancer patients and survivors to maintain recommended exercise levels, barriers to exercise should be addressed when planning exercise interventions (Rogers et al. 2005b). Pinto et al. (2005) report significant increases in motivational readiness to meet exercise guidelines and adhere to the intervention programme among breast cancer patients who received tailored counselling.

Regular exercise over a long period of time is believed to increase the chance that this behaviour will become habitual (Taylor 2003). Additionally, the ability to overcome barriers to exercise may be stronger in cancer patients who exercise regularly before diagnosis. While most cancer patients decrease their physical activity levels after diagnosis, the importance of a positive attitude towards exercise including an established exercise behaviour before the cancer diagnosis has been made, increase the adherence to exercise intervention programmes (Midtgaard et al. 2009). In a review of 38 studies including 1088 healthy adults, Trost et al. (2002) found exercise habits to be a consistent predictor of current exercise behaviour. Findings in this review contrast somewhat with the above. Results for pretrial exercise levels varied considerably across studies included in the meta-analysis, a non-significant meta-correlation. This finding was also confirmed in the review of studies explored narratively. Because of the contrast with previous studies and also heterogeneity in findings between the reviewed studies, no firm conclusions should be made. The result may reflect that getting cancer might profoundly influence people, leading to radical change in behavioural patterns including exercise behaviour. Given this interpretation, this is a positive finding which indicates that a history of sedentary behaviour does not hinder cancer patients from becoming more physical active. However, more research is needed to test this hypothesis.

The meta-analysis identified intention as the most significant predictor among theory of planned behaviour (TPB) constructs. Godin and Kok (1996) claim that a combined effect of intention and PBC can explain one-third of variations in health behaviour, with intention being the key construct.

Current literature on health psychology emphasises self-efficacy as an important predictor for exercise adherence, and claims that people with high self-efficacy are more likely to adhere to exercise programmes, and also have a stronger belief in the benefits of regular exercise (Taylor 2003). This opinion is shared by authors who claim that self-efficacy is a significant determinant of exercise behaviour in cancer patients (Rogers et al. 2005a). However, in contrast to this view, self-efficacy showed only weak correlations with exercise adherence in the present meta-analysis, and obtained no firm conclusion concerning significance in the complementary studies. Martin and Sinden (2001) postulate problems related to the choice of efficacy measurement as reasons for non-correlation between adherences and previous exercise behaviours.

The meta-analysis provides partial support for the opinion that TPB constructs are important motivational variables related to exercise, both in general (Hagger et al. 2002) and in cancer populations (Blanchard et al. 2002). This finding might be partly explained by the suggestion that external motivational factors are more important than internal motivation with respect to cancer patients' adherence to exercise programmes. A critique of both the TPB and social-cognitive theory is that they try to explain decision-making processes when individuals adopt a new health behaviour, without addressing how to maintain the behaviour (Martin & Sinden 2001). Varying strengths of relationships between TPB motivational variables and exercise adherence can also be explained by ceiling effects caused by a biased sample of highly motivated subjects and by little variability in the variables at baseline.

The finding that smoking status is not a significant contributor is somewhat surprising. Smoking is known to be negatively associated with adherence to health behaviour recommendations, and is significantly and inversely related to exercise adherence (Martin & Sinden 2001).

As a complex phenomenon, exercise adherence is also influenced by the exercise programme offered and by the exercise environment (WHO 2003), a claim supported by this review as it reveals large variations in adherence rates among the reviewed studies. This finding may be particularly important for assessing the quality of the studies and for calculating and interpreting correlation variables. Further research is needed to clarify the importance of external elements as determinants of exercise adherence. Supervised trials applying behavioural change techniques may counter the effects of individual internal motivation and result in increased exercise adherence (Courneya et al. 2008, 2010, McNeely et al. 2012).

The meta-analysis was based on relatively few trials. One explanation is the inconsistent use of statistics for estimating the prediction of adherence in the reviewed studies. This made reviewing a challenge and complicated the conclusions drawn from the review data, a problem pointed out with respect to the methodology of meta-analysis (Egger et al. 1997, Martin & Sinden 2001). Due to this critical issue, four out of 12 studies had to be excluded, and accordingly were 150 subjects omitted from the meta-analysis. For two of the variables correlation coefficients were available in two studies only. Combining data on predictors from only two studies in a meta-analysis can be considered a weakness in the methodology and raises the question of how many studies are required for a meta-analysis (Deeks et al.2008). Valentine et al. (2010) claims that two studies are enough, stating: ‘because all other synthesis techniques are less transparent and/or are less likely to be valid’ (p. 245). The results should nonetheless be interpreted with care.

A total of six correlations were reported as non-significant in the primary studies, and the exact coefficients were not given. In cases of missing information, the correlation coefficient was set to 0·00. If both negative and positive correlations were expected, 0·00 would be the median non-significant coefficient. This could be considered as a conservative estimate in this context, because positive correlations are more likely than negative correlations. Therefore, a replacement with 0·00 may have resulted in an underestimation of meta-correlations (Blue 1995, Armitage & Conner 2001, Hagger et al. 2002).

Inclusion of few studies in the meta-analysis may increase the possibility for publication bias and heterogeneity among studies. Heterogeneity may compromise conclusions regarding patients' ability to be physically active and potentially influence the reliability of the data (Egger et al. 1997). Significant heterogeneity between studies was detected for two of the predictions for which meta-analysis were conducted. However, random- and fixed-effect yielded only moderately different coefficients of meta-correlation for these variables. Thus, indicating that heterogeneity is a modest problem in the present study. Nonetheless, heterogeneity might indicate publication bias (Deekset al. 2008), and conclusions should be made with caution.

Studies reporting low accrual rates highlight the issue of selection bias and generalisation. Low accrual rates may reduce the representativeness of the sample and compromise the strength of the research results, affecting generalisation and external validity (Oldervoll et al. 2005).

In addition, the relatively small sample sizes and large number of predictors in the studies considered might indicate artificially strong correlations and provide less robust data regarding the relationship between exercise adherence and its determinants (Green 1991). Green (1991) recommends that researchers estimate effect size based on the characteristics of their study and employ the rules-of-thumb that include effect size or conduct power analyses.

Few studies reported data regarding dropouts or with-drawals, which made it difficult to draw conclusions about the feasibility of multivariate analysis and its results, especially when multiple variables and small samples were involved.

The studies varied in their use of adherence measurements, which complicated comparison between the characteristics of high-adherence studies to low-adherence studies. Patients who report their exercise achievements in diaries may compromise the validity of adherence data through over-reporting or recall vagueness (WHO 2003). Objective observation of the participants' attendance at exercise classes might be a more accurate measure of adherence and could affect patients' motivation and attitude towards exercise behaviour (Armitage & Conner 2001).

The present review contributes to the knowledge of factors that motivate or form barriers to exercise in cancer populations. It identifies exercise stage of change, intention and PBC as statistically significant, although moderately strong predictors of adherence to exercise intervention programmes. Findings give some support to TPB and TTM as relevant frameworks for the understanding of what motivates cancer patients to engage in exercise behaviour. Predictors of exercise adherence in cancer populations are seldom reported on. Few studies and small sample sizes increase risk of biased conclusions made in reviews and meta-analysis. To meet this challenge more research is warranted. Moreover, relatively weak predictions were identified, and, more research is needed to identify predictors that could be of greater importance.

The results of this review suggest that more attention should be paid to what will improve cancer survivors' adherence to exercise and to how motivational and behavioural predictors can play a substantial role when cancer survivors take part in health promotion behaviour, which calls for research based on motivational and behavioural theory. Our findings entail the necessity of substantial basic work for a nursing practice that focuses on what motivates survivors to take on exercise, and what will enhance their belief in managing their exercise programme. A change in lifestyle toward initiating physical health behaviour can be a challenge to cancer survivors during and after treatment. The time around diagnosis and treatment has been hypothesised as a ‘teachable moment’, which may increase the individuals' motivation to change their lifestyle. Using the findings of our review that exercise stage of change represents important predictors of exercise adherence, nurses should establish strategies for surveying patients' motivation and readiness to engage in exercise programmes. More attention should be given to the significance of external motivational factors and how these can be applied clinically in health behaviour change programmes.

Study design: AMLH, EB; data collection and analysis: AMLH, EB and manuscript preparation: AMLH, SMD, JAS, EB.

The authors have no financial, personal, political, academic or other relations that could lead to a conflict of interest.

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