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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: Psychol Health Med. 2019 Oct 19;25(3):331–343. doi: 10.1080/13548506.2019.1679849

Changing Multiple Health Behaviors in Cancer Survivors: Smoking and Exercise

Emily Cox-Martin a, Matthew G Cox b, Karen Basen-Engquist c, Cathy Bradley d, Janice A Blalock c
PMCID: PMC7033014  NIHMSID: NIHMS1541405  PMID: 31630537

Abstract

Research regarding cross-behavioral relationships between the cognitive mechanisms motivating health behavior change is lacking for cancer survivors. The objective of this study was to investigate these relationships to inform the development of multiple health behavior change (MHBC) interventions for this at-risk population. Eligible participants included cancer survivors attending an intake appointment for smoking cessation services at a comprehensive cancer center. This cross-sectional survey study assessed participants’ self-efficacy and motivation (stage of change) for smoking cessation and exercise, as well as self-reported health behaviors. Analyses evaluated cross-behavioral associations between cognitive mechanisms, and their relationships with smoking and exercise behaviors. Seventy-six participants completed the survey questionnaire. The correlation between self-efficacy scores for smoking cessation and exercise was statistically significant (r = .45, 95% CI [.09, .67]), as were correlations between self-efficacy and reported levels of exercise ((r = .44, 95% CI [.20, .65]) strenuous); ((r = .36, 95% CI [.12, .59]) moderate), exercise self-efficacy and smoking behavior (r = −.27, 95% CI [−.46, −.05]), and smoking self-efficacy and smoking behavior (r = −0.41, 95% CI [−.61, −.18]). For cancer survivors, associations between exercise self-efficacy and smoking cessation self-efficacy may offer an opportunity to leverage MHBC; specifically, this positive association may facilitate exercise intervention in survivors seeking smoking cessation services. MHBC interventions could improve overall behavior change and subsequent health outcomes by incorporating behavior change techniques that optimize these cognitive mechanisms, such as action planning and reinforcement.

Keywords: cancer survivors, multiple health behavior change, exercise, smoking cessation

Introduction

Modifiable risk behaviors account for a large proportion of preventable deaths in the United States (Danaei et al., 2009; Spring, Moller, & Coons, 2012). Epidemiological data shows that major risk behaviors contributing to morbidity and mortality rarely occur in isolation (Ezzati et al., 2002; Fine, Philogene, Gramling, Coups, & Sinha, 2004); a public health perspective that has bolstered research focusing on multiple health behavior change (MHBC). MHBC interventions offer an efficient opportunity for disease prevention, and may capitalize on an individual’s motivation to change unhealthy behaviors through potential mechanisms of coaction, such as self-efficacy (Johnson et al., 2014).

Prochaska and Prochaska’s (2011) review of MHBC research supports interventions addressing multiple risk behaviors associated with cancer (e.g., smoking, physical inactivity) across various samples. However, these studies focused on primary prevention and did not include individuals with cancer diagnoses. MHBC may be particularly useful in secondary and tertiary cancer prevention, given the frequent overlap of health risk behaviors in cancer patients (Colditz, Wolin, & Gehlert, 2012;Prochaska & Prochaska, 2011) and the unique motivating impetus that a cancer diagnosis can serve for behavior change (Bluethmann et al., 2015; Prochaska & Prochaska, 2011). However, in a systematic review of interventions targeting multiple lifestyle risk behaviors, only five out of 220 articles focused on cancer survivors (King et al., 2015). Another review focusing on MHBC in adults with cancer or at risk for developing cancer identified only seven intervention studies including survivors (Green, Hayman, & Cooley, 2015). A recent review identified 27 intervention studies for MHBC in cancer survivors, but these were limited to studies promoting healthy eating and exercise only (Amireault, Fong, & Sabiston, 2018). These studies highlight the need for increased research in MHBC for cancer survivors, as well as more diversity in the health behaviors targeted, beyond the commonly paired addiction (i.e., alcohol intake and smoking cessation) and energy balance (i.e., exercise and diet) behaviors.

While it is common for multiple risk behaviors to cluster together, research demonstrates that cigarette smoking often overlaps with physical inactivity, with smokers generally having lower levels of exercise than non-smokers (Chiolero, Wietlisbach, Ruffieux, Paccaud, & Cornuz, 2006; Fine et al., 2004). Research also shows that regular exercise can aid smoking cessation efforts (Ussher, Taylor, & Faulkner, 2014), can attenuate weight gain following cessation attempts (Gennuso et al., 2014; Luo et al., 2019), and improves smoking related pulmonary health complications (Fuertes et al., 2018; Garcia-Aymerich, Lange, Benet, Schnohr, & Antó, 2007).

Successful engagement in either smoking cessation or exercise is related to the key constructs of self-efficacy and motivation (e.g., stage of change). Self-efficacy, defined as one’s belief in one’s own ability to succeed with a particular goal, is a central construct of multiple theories of health behavior change, but figures most prominently in Social Cognitive Theory (Bandura, 1982). Smoking cessation self-efficacy is predictive of smoking cessation outcomes; however, the strength of this relationship depends on the timing of the self-efficacy measurement in relation to the actual quit attempt (i.e., pre- or post-quit attempt) (Gwaltney, Metrik, Kahler, & Shiffman, 2009). Likewise, exercise self-efficacy is associated with higher levels of exercise (Anderson, Wojcik, Winett, & Williams, 2006), especially in lifestyle modification trials, and is an important mediator of exercise in both cancer survivors (Cox et al., 2015), as well as at-risk populations for whom exercise could be preventive (Anderson-Bill, Winett, & Wojcik, 2011). Motivation for health behavior change, as understood through the Transtheoretical Model (TTM) (Prochaska & Velicer, 1997), is classified by the progression through six distinct stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Extensive reviews and meta-analyses for smoking cessation and exercise support the use of TTM for understanding change in these behaviors.

More research is needed regarding a theory-based understanding of MHBC in individuals with previous cancer diagnoses. This study investigates relationships between self-efficacy and stage of change for smoking cessation and exercise cancer survivors. Due to the lack of previous research in this area, this study is exploratory and uses an observational design. Importantly, this study responds to calls to better understand relationships across different health behaviors and their relevant motivating mechanisms (Geller, Lippke, & Nigg, 2017), to design MHBC interventions that address the prevalence of non-communicable diseases such as cancer.

Methods

Participants and procedures

Participants included outpatients with a cancer diagnosis arriving for an initial smoking cessation appointment at a large comprehensive cancer center in Southeast Texas. Participants were 18 years or older, current smokers or had recently quit, and ambulatory. Participants were recruited in the waiting room of a smoking cessation clinic, and provided informed consent according to hospital Institutional Review Board standards. Participants with scheduled appointments were initially screened for eligibility using the electronic health record. Eligibility criteria were confirmed in person with the participant

Surveys were paper and pencil. Participants could complete the survey in person, returning it directly to hospital staff, or return it via mail within one week. If surveys were not returned, the participant received a reminder telephone call at one week and two weeks from initial consent. Ninety-nine participants were approached for the study; one was ineligible and four declined. Ninety-four participants consented with two withdrawing from the study. Seventy-six participants returned completed surveys (Figure 1).

Figure 1:

Figure 1:

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Consort diagram

Measures

Exercise self-efficacy

Exercise self-efficacy was assessed using an 18-item scale (Benisovich, Rossi, Norman, & Nigg, 1998; Marcus, Selby, Niaura, & Rossi, 1992) measuring participants’ confidence in their ability to engage in exercise in certain situations (e.g. “[when] I feel I don’t have the time”). Specifically, items reflect different barriers to exercise that may arise, and the measure evaluates self-efficacy in the context of those barriers. Participants ranked their confidence on a 5-point Likert scale ranging from one (not at all confident) to five (completely confident). The scale is comprised of 6 subscales, including negative affect, excuse making, must exercise alone, inconvenient to exercise, resistance from others, and bad weather, all which have previously demonstrated adequate internal consistency (Cronbach’s alphas ranging from 0.77–0.87) (Benisovich et al., 1998; Marcus et al., 1992). Higher scores indicate a greater level of self-efficacy for exercise behaviors. Average scores range from 1–5.

Smoking cessation self-efficacy

Smoking cessation self-efficacy/confidence was assessed using a 20-item scale (Velicer, Diclemente, Rossi, & Prochaska, 1990) measuring participants’ confidence in their ability to not smoke in certain situations (e.g. “with friends at a party”). Participants ranked their confidence for each situation on a 5-point Likert scale ranging from one (not at all confident) to five (extremely confident). Higher scores indicate a greater level of self-efficacy for not smoking. This measure previously demonstrated predictive validity for relapse as well as cessation at long-term follow up (Condiotte & Lichtenstein, 1981) and good internal consistency (Cronbach’s alpha ranging from 0.93–0.95).

Exercise stage of change

Exercise stage of change was assessed through participants’ endorsement of current exercise behavior according to guidelines by the American College of Sports Medicine (Marcus et al., 1992). Participants who reported that they did not currently exercise and did not intend to exercise in the next 6 months were categorized in the “precontemplation stage.” Participants who reported that they did not currently exercise but intended to in the next 6 months were categorized in the “contemplation stage.” Participants who reported that they did not currently exercise but intended to do so in the next 30 days were categorized in the “preparation stage.” Additionally, participants who reported that they currently exercise regularly and had done so for less than 6 months, were categorized in the “action stage,” while those who had been doing so for over 6 months were categorized in the “maintenance stage.” Research supports the construct validity of stages of change applied to exercise, and the reliability of this measure over a 2-week period has produced a k index of 0.78, indicating good reliability (Marcus et al., 1992; Schumann et al., 2002).

Smoking cessation stage of change

Participants’ smoking cessation stage of change was assessed through self-reported behavior in response to three questions regarding current smoking behavior. Participants were categorized in the “precontemplation stage” for smoking cessation if they were not considering quitting at the time of assessment, or in the “contemplation stage” if they were considering quitting smoking within the next 6 months following assessment. Those who had at least one 24-hour quit attempt in the past year were categorized in the “preparation stage,” while those who had fully quit smoking within the last 6 months were considered in the “action stage,” and those who had been abstinent for 6 months or more were in the “maintenance stage.” Previous research has demonstrated predictive validity for this stage classification through its ability to predict future quit attempts and success of cessation (DiClemente et al., 1991).

Exercise behavior

Participants’ weekly leisure time exercise behaviors were assessed using the Godin Leisure-Time Exercise Questionnaire (GLTEQ) (Godin & Shephard, 1997), a four item self-report measure. The first three items asked participants to indicate how many times in the previous 7-day period they engaged in a) strenuous exercise, b) moderate exercise and c) mild exercise for more than 15 minutes. Qualitative descriptions and example behaviors for each exertion level were provided. The fourth item asked patients to indicate how often in the previous 7-day period they engaged in any regular activity long enough to work up a sweat. In addition to providing estimates of exercise behavior for each intensity level, data collected by the first three items of the GLTEQ can be used to calculate the Leisure Score Index (LSI), a summary score weighted by intensity level, calculated with the following formula: (frequency of mild × 3) + (frequency of moderate × 5) + (frequency of strenuous × 9). In the development study, the 2-week test-retest reliability coefficients for this measure were .83 and .85 (Godin & Shephard, 1985).

Smoking behavior

Participants’ smoking behavior was measured through a 7-day time line follow back which is reliable and valid for evaluating smoking behaviors over time (Brown et al., 1998).

Data analysis

Pearson product-moment correlations were used to evaluate the linear relationships between measures of self-efficacy and measures of health behaviors. To deal with any non-normal distributions among variables, a non-parametric bootstrap method was used. Specifically, a bias corrected accelerated bootstrap method was used to assess statistical significance with 10,000 bootstrap samples. A Chi-square was used to investigate any possible relationship between stages of change for each behavior. Analysis of variance (ANOVA) was used to examine the relationship between self-efficacy and stage of change for each behavior and Tukey’s HSD was used to make within group comparisons and control for Type I error. Separate models were estimated for the two dependent variables, smoking cessation self-efficacy and exercise self-efficacy. Missing data analyses were also conducted. T-tests and chi-squares were used to evaluate differences in demographic variables between those who had missing data and those who did not. Analyses were conducted with R statistical software version 3.3.1.

Results

Participant characteristics

Table 1 reports demographic and disease characteristics for the sample. Participants (N = 76) were, on average, age 57.0 years, mostly female (57.9%) and white (78.9%). The most frequent disease group reported was breast (22.67%). Participants reported smoking a median of 10.29 (IQR = 6.50–15.82) cigarettes per day. Additionally, they reported a median of 0 (IQR = 0.00–0.00) bouts of strenuous intensity exercise, 0 (IQR = 0–3.00) bouts of moderate intensity exercise, and 3.00 (IQR = 1.00–7.00) bouts of mild intensity exercise per week. Median GLTEQ LSI score was 24 (IQR = 6.0–51.12). Of those who returned the survey, the average missingness for all variables was 10.2% and the range was 1.3% to 26.3%. No significant differences were found between those with missing data and those without on any measured demographic variables (all p’s > .05).

Table 1.

Sample demographic variables (n = 76)

Variable Number (%) or mean (SD)
(n = 76)
 Age in years, mean (SD) 57.03 (10.08)
 Sex, female, n (%) 44 (57.90)
Education, n (%) (n = 75)
 </= High school diploma/GED 25 (32.89)
 Technical/vocational degree 4 (5.26)
 Some college/vocational degree 29 (38.16)
 College graduate 21 (27.63)
Race/Ethnicity, n (%) (n = 76)
 NH White 56 (73.68)
 Other 20(26.32)
Income per year, n (%) (n = 75)
 ≤$60,000 33 (43.42)
 ≥$60–001 42 (55.26)
Employment Status, n (%) (n = 74)
 Employed full time 34 (44.74)
 Not employed (not seeking employment, homemaker, student, retired) 42 (55.26)
Marital Status, n (%) (n = 76)
 Not married/not partnered 34 (44.74)
 Married/partnered 42 (55.26)
Cancer Diagnosis, n (%) (n = 75)
 Breast 17 (22.67)
 Genitourinary 10 (13.33)
 Head and Neck 9 (12.00)
 Blood Cancers 7 (9.00)
 Other 32 (43.00)
Time since diagnosis, median yrs (IQR) 2 (0–5)
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Smoking cessation and exercise stage of change

Stage of change for smoking cessation was reported as 20% (n = 15) of participants in contemplation, 70.67% (n = 53) in preparation, and 9.33% (n = 7) in action. Because the sample was selected from a cessation clinic, none were in precontemplation. Exercise stage of change was reported as 16.22% (n = 12) of participants in precontemplation, 14.86% (n = 11) in contemplation, 37.84% (n = 28) in preparation, 21.62% (n = 16) in action, and 9.46% (n = 7) in maintenance.

Self-efficacy for smoking cessation and exercise

Measures for smoking cessation and exercise self-efficacy demonstrated excellent internal consistency, α = 0.95 and α = 0.96 respectively within this sample. The average smoking cessation self-efficacy score was (M = 1.85, SD = 0.98), and the average exercise self-efficacy was (M = 1.79, SD = 1.02), with higher scores indicating greater self-efficacy for engaging in exercise behavior and for smoking cessation, respectively; average scores could range from 1–5.

Relationships between cognitive and behavioral variables

A significant positive correlation was found between smoking cessation and exercise self-efficacy (r = .45, 95% CI [.09, .67]). Both smoking cessation self-efficacy and exercise self-efficacy were negatively correlated with number of cigarettes smoked per week; however, only exercise self-efficacy was associated with exercise behavior. Specifically, exercise self-efficacy was significantly correlated with strenuous and moderate intensity exercise, but not mild. Table 2 reports correlations between all variables. No statistically significant relationship was found between the stages of change for the two behaviors χ2(8, N = 73) = 4.81, p = .778.

Table 2.

Pearson correlations between cognitive and behavioral variables

Variable 1 2 3 4 5 6
1. Godin strenuous (n = 70) - 0.52* 0.23 −0.14 0.44* 0.01
2. Godin moderate (n = 69) - 0.34* −0.17 0.36* 0.07
3. Godin mild (n = 70) - 0.24 0.22 0.09
4. Cigarettes smoked (n = 72) - −0.27* −0.41*
5. Exercise self- efficacy (n = 60) - 0.45*
6. Smoking cessation self-efficacy (n = 56) -
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*

<.05

Note. Variables 1, 2, & 3 are continuous measures of separate Godin intensity items.

Note. Significance tested via bias corrected accelerated bootstrap.

Note. Sample sizes for individual variables are included.

Separate ANOVAs estimated the relationship between stages of change for each behavior and both self-efficacy constructs (Table 3). There was no statistically significant relationship between smoking cessation self-efficacy and stage of change for either behavior (all p’s > .05). Participants in the precontemplation stage of change for exercise had higher exercise self-efficacy scores than those in preparation, action, or maintenance. Figures 2 and 3 demonstrate the levels of each type of self-efficacy across the stages of change for exercise and smoking cessation.

Table 3.

Self-efficacy means and standard deviations in relation to stage of change for smoking cessation and exercise behaviors (n = 75)

Exercise Stage of Change Smoking cessation self-efficacy1 Exercise self-efficacy2
 Precontemplation, n = 12 1.82 (1.21) 2.58 (1.20)
 Contemplation, n = 11 1.84 (0.74) 2.09 (0.79)
 Preparation, n = 28 1.70 (0.83) 1.57 (0.79)a
 Action, n = 16 1.71 (0.89) 1.36 (0.76)b
 Maintenance, n = 7 2.00 (0.55) 1.17 (1.04)c
Smoking Stage of Change Smoking cessation self-efficacy3 Exercise self-efficacy4
 Contemplation, n = 15 1.53 (0.70) 1.75 (0.69)
 Preparation, n = 53 1.70 (0.84) 1.71 (1.06)
 Action, n = 7 2.25 (0.92) 1.64 (0.68)
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a

Precontemplation > Preparation, p < 0.05;

b

Precontemplation > Action, p < 0.05;

c

Precontemplation > Maintenance, p < 0.05

1

F(2, 56) = 0.027, p = .974

2

F(4, 55) = 4.058, p = .006

3

F(2, 52) = 1.399, p = .256

4

F(4, 50) = 0.131, p = .970

Figure 2.

Figure 2.

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Relationship of exercise and smoking cessation self-efficacies to exercise stage of change

Figure 3.

Figure 3.

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Relationship of exercise and smoking cessation self-efficacies to smoking stage of change

Discussion

This study investigated the relationships between self-efficacy and stage of change related to smoking cessation and exercise in a sample of cancer survivors seeking smoking cessation services. Smoking cessation and exercise self-efficacy were positively correlated. That is, survivors reporting more confidence in their ability to engage in smoking cessation also reported more confidence in their ability to engage in exercise. This finding suggests a cross-behavioral relationship between these cognitive variables. Both self-efficacy measures demonstrated significant negative associations with smoking behavior, such that higher self-efficacy scores for both smoking cessation and exercise were associated with fewer cigarettes smoked. Exercise self-efficacy demonstrated a positive and significant relationship with both strenuous and moderate exercise, although no significant association was found between smoking cessation self-efficacy and exercise behaviors. Johnson and colleagues’ (Johnson et al., 2014) construct of coaction suggests that taking action in one domain of behavior change may increase the likelihood of taking action in a second domain of behavior change. While evidence of the effectiveness of MHBC interventions supports this construct, there is less evidence regarding potential mechanisms (Johnson et al., 2014). These findings shed light on self-efficacy as a potential cross-behavioral mechanism for MHBC in cancer survivors. Overall, the correlations in our study, including between self-efficacies, were mostly moderate in size. In Boudreaux et al (2013) and King et al (1996), the effect sizes for the correlation between self-efficacies were small, indicating that the degree of association may be different across patient populations; more specifically, stronger in a sample of cancer survivors who smoke. Bandura states that self-efficacy is both behavior specific, as well as specific to a particular situation. Our data do not refute this claim but indicate that self-efficacy constructs may be interrelated across behaviors.

Stages of change for both behaviors were not significantly associated, consistent with previous research (Boudreaux et al., 2003; T. K. King et al., 1996). There were no statistically significant cross-behavioral relationships between self-efficacies and stages of change. For exercise, significant differences in self-efficacy were found between precontemplation and later stages of change, with participants in precontemplation reporting significantly higher self-efficacy than those in preparation, action, or maintenance. Prior to engaging in a behavior change process for exercise, an individual may not be aware of barriers involved in the task. As that individual moves through stages of change for exercise, barriers for behavioral engagement may become more salient, negatively impacting self-efficacy levels. Previous literature indicates that levels of self-efficacy may differ across stages of change, and that for exercise change in self-efficacy might be more important in the early phases of adoption as opposed to maintenance (Baldwin et al., 2006; McAuley & Blissmer, 2000; Sutton, 2000).

This study is unique in that it specifically investigates the interrelationships of cognitive and motivational variables in a sample of cancer survivors who are also smokers, a pointedly at-risk group. It also directly addresses a gap in the literature by focusing on improving our understanding of the possible mechanisms motivating MHBC (Geller et al., 2017). A majority of MHBC research clusters on either addiction behaviors or energy balance behaviors; this study looks at smoking cessation and exercise given the unique overlap of these two behaviors. In terms of clinical implications, this study identifies constructs that may be most important in terms of potential mechanisms of change. With cancer survivors, MHBC interventions may want to utilize behavior change techniques (BCTs) that optimize self-efficacy (e.g. action planning, self-monitoring) (French, Olander, Chisholm, & Mc Sharry, 2014; Olander et al., 2013; Williams & French, 2011). Selecting BCTs known to influence identified mechanisms of behavior change is critical for effective intervention.

The study’s cross-sectional design limits causal interpretations of change in cognitive and behavioral constructs over time. Future studies using longitudinal designs would allow for improved understanding of how these relationships function over time and how cross-behavioral mechanisms may influence future behavior change. Self-report measures were used for cognitive and behavioral variables, introducing the potential for bias. Objective measures for behavioral variables would strengthen the validity of this research in the future. While a variety of cancer diagnoses were included in this sample, this heterogeneity improves the generalizability of these results and better represents a “real world” clinical setting; although generalization is limited to the oncology/smoking cessation clinical context. The sample size is small. Nonetheless, this study is a first step in better understanding these relationships, and so future studies replicating or building on these initial results should seek a larger sample size.

This study demonstrates the associations between specific cognitive variables for smoking cessation and exercise in cancer survivors seeking smoking cessation services. Interventions for MHBC in this population may be able to capitalize on self-efficacy across behaviors, particularly at the initiation phase, to improve adherence and outcomes. These results can be used to guide further research on possible associations between cognitive variables not included in this study, as well as the identification of behavioral techniques used in future MHBC interventions.

Acknowledgements

The first author’s work was supported by the National Cancer Institute grant R25T CA057730. This research was also supported by the PROSPR shared resource (CA016672) and the Center for Energy Balance in Cancer Prevention and Survivorship, which is supported by the Duncan Family Institute for Cancer Prevention and Risk Assessment.

Footnotes

Conflict of interest: No conflicts of interest are reported.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent: Informed consent was obtained from all study participants.

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