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Article

Relationships between Obesity, Exercise Preferences, and Related Social Cognitive Theory Variables among Breast Cancer Survivors

by
Nashira I. Brown
1,*,
Dorothy W. Pekmezi
1,2,
Robert A. Oster
2,3,
Kerry S. Courneya
4,
Edward McAuley
5,6,
Diane K. Ehlers
7,
Siobhan M. Phillips
8,
Philip Anton
9 and
Laura Q. Rogers
2,3
1
Department of Health Behavior, University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
2
O’Neal Comprehensive Cancer Center at UAB, Birmingham, AL 35233, USA
3
Department of Medicine, Division of Preventive Medicine, Birmingham, AL 35294, USA
4
Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, AB T6G 2H9, Canada
5
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
6
The Cancer Center at Illinois, Urbana, IL 61801, USA
7
Department of Quantitative Health Sciences, Mayo Clinic, Phoenix, AZ 85054, USA
8
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
9
School of Human Sciences, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(5), 1286; https://doi.org/10.3390/nu15051286
Submission received: 1 February 2023 / Revised: 22 February 2023 / Accepted: 1 March 2023 / Published: 4 March 2023
(This article belongs to the Special Issue The Role of Diet and Physical Activity on Cancer Prevention and Control)
Versions Notes

Abstract

:
Breast cancer survivors with obesity have an increased risk of cancer recurrence, second malignancy, and comorbidities. Though physical activity (PA) interventions are needed, investigation of the relationships between obesity and factors influencing PA program aspects among cancer survivors remain understudied. Thus, we conducted a cross-sectional study examining associations amongst baseline body mass index (BMI), PA program preferences, PA, cardiorespiratory fitness, and related social cognitive theory variables (self-efficacy, exercise barriers interference, social support, positive and negative outcome expectations) from a randomized controlled PA trial with 320 post-treatment breast cancer survivors. BMI was significantly correlated with exercise barriers interference (r = 0.131, p = 0.019). Higher BMI was significantly associated with preference to exercise at a facility (p = 0.038), lower cardiorespiratory fitness (p < 0.001), lower walking self-efficacy (p < 0.001), and higher negative outcome expectations (p = 0.024), independent of covariates (comorbidity score, Western Ontario and McMaster Universities osteoarthritis index score, income, race, education). Those with class I/II obesity reported a higher negative outcome expectations score compared with class III. Location, walking self-efficacy, barriers, negative outcome expectations, and fitness should be considered when designing future PA programs among breast cancer survivors with obesity.

1. Introduction

The prevalence of breast cancer survivors continues to grow as diagnosis, treatment, and control advance. However, quality of life after diagnosis among this population is affected by modifiable lifestyle behaviors, including physical activity (PA) engagement [1]. American Cancer Society Guidelines for PA recommend at least 150 min of exercise per week and 2 days of strength training per week for cancer survivors to help reduce risk of cancer recurrence, second malignancy, and comorbidities (e.g., obesity) [2]. Notably, a minority (13.9%) of breast cancer survivors meet PA guidelines and only 35% maintain a healthy BMI [3]. Moreover, adult cancer survivors experience a more rapid rise in obesity [4], which has been associated with low cardiorespiratory fitness, a strong predictor of all-cause and cardiovascular mortality, following cancer treatment(s) [5], and thus are in need of effective lifestyle intervention [4].
Best practices for assisting this population in adopting a physically active lifestyle include using evidence-based theories [6] and program preferences [7] for PA program design. The social cognitive theory (SCT) is one of the most widely applied health behavior theories in PA research [8,9]. This framework posits that behavior is a dynamic interaction of a triad of factors (i.e., personal cognitive, the physical and social environment (socioenvironmental), and behavioral). SCT constructs that are commonly targeted in PA interventions are self-efficacy, exercise barriers interference, outcome expectations, and social support. Self-efficacy, or the confidence in one’s ability to take action and overcome obstacles and situations to reach a goal, is deemed as the significant primary personal factor that mediates behavior change [8], especially PA behaviors in women with breast cancer [10]. This construct has been associated with body mass index (BMI) in cancer survivors [11,12], along with other constructs, such as social support and exercise barriers interference [12].
In addition to SCT constructs, research focusing on what individuals prefer could be vital to optimizing participant engagement and acceptability when designing PA programs [12]. Although multiple studies have reported PA preferences for breast cancer survivors [13], very few have examined how preferences may vary by level of obesity [12,14]. Further, the few studies in populations without a history of cancer have shown that PA preferences (i.e., intervention delivery, supervision, and scheduling) differed among those with and without obesity [15,16]. Given the paucity of research on the relationship between obesity and potential differences in preferences among breast cancer survivors with cancer, further examination is warranted to develop effective PA programs.
Thus, to inform future PA promotion programs for breast cancer survivor populations with obesity, we performed a secondary analysis of baseline data from a randomized PA intervention trial [17]. Our study purpose was to examine the associations between BMI and factors influencing program content and delivery preferences (source, mode, structure, location, furthest distance willing to travel, furthest distance willing to travel if someone else paid for gas, price willing to pay for exercise program, program type, supervision, alone or with group), current PA (accelerometer), cardiorespiratory fitness, and related SCT constructs (i.e., self-efficacy (barriers and task), exercise barriers interference, social support, outcome expectations (negative outcome expectations and positive outcome expectations)) among the enrolled breast cancer survivors (n = 320). We hypothesized that BMI would be significantly associated with program preferences, PA, cardiorespiratory fitness, and related SCT constructs.

2. Materials and Methods

2.1. Study Design

This cross-sectional study examined relationships amongst BMI, PA program preferences, PA, cardiorespiratory fitness, and related SCT variables. Data for these secondary analyses were taken from the baseline survey for a randomized controlled PA behavior change trial with 320 post-treatment breast cancer survivors. Participants enrolled in the primary trial (Better exercise adherence after treatment for cancer (BEAT Cancer); n = 222) [18] were combined with participants enrolled to a trial supplement (accelerometer calibration sub-study entitled “Comparing doubly-labeled water to accelerometer to assess PA measurement error during and after a physical activity behavior change intervention” (COMPARE); n = 98).

2.2. Study Sample

Three hundred and twenty post-primary treatment breast cancer survivors were recruited through newspaper advertisements, cancer support groups, flyers posted in relevant locations (e.g., hospitals, physician offices, cancer centers/clinics), and areas frequented by women (e.g., retail stores, beauty salons). Eligible women met the following criteria: English speaking, between the ages of 18 and 70 years of age with a history of ductal carcinoma in situ (DCIS) or Stage I, II, or IIIA breast cancer and post-primary chemotherapy or radiation therapy, medically cleared for participation by their physician and underactive (participating in no more than 60 min of moderate intensity PA or no more than 30 min of vigorous intensity activity per week, on average, over the past 6 months).

3. Measures

3.1. Demographics and BMI

Self-reported participant demographics included age, race, ethnicity, years of education, annual household income, employment status, marital status, cancer stage at diagnosis, history of chemotherapy, history of radiation therapy, hormonal therapy type, functional comorbidity index score [19], and the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) [20]. The functional comorbidity index score, or the number of comorbidities, was assessed by totaling the number of “yes” responses to 18 diagnoses with possible scores of 0–18, 0 indicating no comorbidities and 18 indicating the highest number of comorbidities [19]. The WOMAC, 24-item scale, assessed lower extremity joint pain (5 items), stiffness (2 items), and physical dysfunction (17 items) [20]. Scores from the subscales were summed with possible score ranging from 0 to 68, with higher scores indicating greater pain, stiffness, and physical dysfunction [20]. Weight and height were measured in person by trained research staff using a calibrated scale and stadiometer. Brand and model were study site specific (University of Alabama at Birmingham (Detecto Model 439); Southern Illinois University(Continental Health-O-Meter #400 DML medical scale); University of Illinois (Seca 763 Digital Column Scale)) [21]. BMI was calculated using the measured weight and height (weight (kg)/height (m2)) [21].

3.2. Exercise Program Preferences

Exercise program preferences were assessed using a 15-item multiple choice self-administered survey that has been used in prior studies among breast cancer survivors [12,14,22,23]. The counseling preference items included queries regarding counseling source (i.e., cancer exercise physiologist, personal trainer, medical doctor, nurse, health club exercise specialist, cancer patient/survivor), mode of delivery (i.e., face-to-face, phone, video, written material, internet, audiotape, interactive workbook), and company (i.e., individual or with a group). Exercise training preference items focused on location (i.e., at home, outdoors, at work, health club, cancer exercise center), exercise type (i.e., walking, water, bike, jogging, resistance, yoga, Pilates), and supervision (i.e., supervised, or unsupervised). Programming preference items inquired about program type (i.e., aerobic, strength, or both), structure (i.e., flexible vs. scheduled), maximum price willing to pay for an exercise program (i.e., $0, $1–10/month, $11–20/month, $21–30/month, $31–40/month, $40+/month), farthest distance willing to travel to an exercise program (0 miles, 1–15 miles, 16–30 miles, 31–45 miles, 46–60 miles, 60+ miles), and the farthest distance willing to travel to an exercise program if cost of gas was covered (0 miles, 1–15 miles, 16–30 miles, 31–45 miles, 46–60 miles, 60+ miles).

3.3. PA

Weekly minutes of moderate-plus-vigorous intensity PA were assessed with ActiGraph accelerometer (model: GT3X, Pensacola, FL, USA). Participants were instructed (orally and written) to wear the device for at least 10 waking hours for seven (primary trial) or 10 (COMPARE) consecutive days [24]. The parameters used to validate the minimum wear time of 4 days was comprised of wear time ≥10 waking hours. The cut points that were used to establish moderate-to-vigorous activity intensity were: moderate (1952–5724 counts/min) and vigorous (5725+ counts/min) [21]. Minutes of vigorous intensity activity were doubled prior to adding minutes of moderate intensity activity and calculating weekly minutes of moderate-to-vigorous activity.

3.4. Cardiorespiratory Fitness

Following the American College of Sports Medicine guidelines for testing [25], cardiorespiratory fitness (relative VO2 peak) was estimated with submaximal treadmill testing [26] in which speed and elevation were gradually increased until the participant achieved 85% of age-predicted maximal heart rate. Following the modified Naughton protocol, tests were begun at a slower speed and progressed at lower increments, as in past studies with individuals who are sedentary, older, fatigued, or have balance complications [21,25]. The oxygen cost of walking at the treadmill grade and speed achieved at 85% of predicted heart rate was estimated using published regression equations and is expressed in mL/kg/min [27].

3.5. PA Related SCT Variables

3.5.1. Self-Efficacy

Both barriers and task self-efficacy were assessed. One’s confidence in his/her ability to act and overcome obstacles and situations to reach a goal was assessed using a reliable (α = 0.97–0.98) 9-item scale, Barriers Self-Efficacy, designed for breast cancer patients [28]. Walking task self-efficacy was assessed using a valid and reliable (r = 0.89 and α = 0.96) 6-item scale, Self-Efficacy for Walking, to measure confidence in walking at a moderate pace for six different intervals of time (i.e., 5, 10, 15, 20, 25, and 30 min) [29]. Both measures of self-efficacy asked participants to indicate their confidence (0–100%, at 10% intervals (i.e., not at all confident, 0–20%; slightly confident, 20–40%; moderately confident, 40–60%; very confident, 60–80%; extremely confident, 80–100)) [28,30]. Responses were averaged separately for barriers and task self-efficacy with a range of possible scores (0–100).

3.5.2. Exercise Barriers Interference

Perceived barriers (or barriers interference), or how often recognized obstacles (i.e., lack of time, fear of injury, fatigue, lack of energy, lack of company, cost of exercising, lack of enjoyment, lack of equipment, family responsibilities, inconvenient exercise schedule, lack of interest, lack of knowledgeable exercise staff, feeling nauseated, no facilities/space, not a priority, procrastination, pain/discomfort, not in routine, lack of self-discipline, lack of skills, weather) interfered with exercise, was assessed using a 21-item, 5-point Likert scale (1 = rare to 5 = very often) measure, Exercise Barriers Interference, that has demonstrated reliability (α = 0.92) among breast cancer survivors [31]. Responses were summed for a total exercise barriers interference score [31,32,33] with a range of possible scores (21–105).

3.5.3. Social Support

Social support, or the perception of encouragement to engage in PA, from other sources (i.e., friends and family) [31] was measured via a 4-item (friends, 2-items, family, 2-items), 5-point Likert scale (0 = none to 4 = very often), Social Support for Physical Activity, with an internal consistency of 0.80 [31]. Responses were summed for a total social support score with possible scores ranging from 0 to 16 [34,35].

3.5.4. Outcome Expectations

Outcome expectations, or the anticipated positive and/or negative consequences of engaging in a behavior (e.g., exercise) was evaluated using a reliable (α = 0.79 and 0.70, respectively) 17-item (14 positive expectations and 3 negative expectations), 5-point Likert scale (1 = strongly disagree to 5 = strongly agree); responses were summed for positive and negative outcomes separately (i.e., higher score indicates greater perceived benefit (positive expectations) or greater perceived risk (negative expectations)) with possible scores ranging from 14 to 70 and 3 to 15, respectively [32].

3.5.5. Statistical Analyses

Descriptive analyses were conducted to summarize participant characteristics. BMI was analyzed as a continuous variable and as a 3-level categorical outcome (i.e., non-obese (BMI ≤ 29.9 kg/m2), obese classes I/II (BMI = 30–39.9 kg/m2), and obese class III (BMI ≥ 40 kg/m2)). A 3-level BMI was created to facilitate identifying potentially important differences at a higher BMI that may be missed when analyzing with a continuous BMI.
The associations with continuous BMI were analyzed using Pearson correlation coefficients (continuous correlates) and independent groups t-test (continuous correlates). The associations with 3-level categorical BMI were analyzed using one-way analysis of variance (ANOVA), followed by the Tukey post-hoc test (continuous correlates), and also the chi-square test (categorical or dichotomous correlates). Continuous study variables were examined for normality of distribution using box plots, stem-and-leaf plots, normal probability plots, and the Kolmogorov–Smirnov test. Variables that were determined as being non-normally distributed were also examined using the non-parametric Kruskall–Wallis test. Since the parametric were similar to the non-parametric results, we report the parametric results for ease of interpretation, and previous studies have reported parametric results obtained from these or similar variables. Follow-up multiple linear or logistic regression were performed based on the type of dependent variable.
No imputations were performed for missing data since the amount of missing data were very small (<1%); there were no missing data for most of the study variables. All analyses were performed using IBM SPSS Statistics software (SPSS) Version 28 (Armonk, NY, USA: IBM Corp), and p < 0.05 was deemed as statistically significant.
Exercise program preferences were dichotomized due to the small stratum-specific sample sizes with an emphasis on preferences more likely to alter program design (e.g., facility vs. other). Hence, each preference was reviewed based on the number of participants preferring each option within a specific preference question and then collapsed based on preference with greater potential to alter a program design (e.g., facility vs. other options not requiring a facility).
Accelerometer-measured PA was analyzed as dichotomous, categorical outcome (met PA recommendations: >150 min of moderate-to-vigorous PA vs. did not meet PA recommendations: <150 min of moderate-to-vigorous PA) based on the American Cancer Society guidelines for PA for cancer survivors and to simplify interpretation [2].

3.6. Covariates and Adjusted Analyses

The variables investigated as potential covariates included age, race, ethnicity, education, income, employment, marital status, cancer stage at diagnosis, months since diagnosis, history of chemotherapy, history of radiation, hormonal therapy, number of comorbidities, and WOMAC score. A variable was considered a covariate if it was statistically significantly associated with both BMI and one or more of the correlates of interest (PA preferences, PA, cardiorespiratory fitness, and SCT variables). The identified covariates were then used for adjusted multiple variable regression analyses performed as indicated, with linear regression analysis performed for continuous outcomes and logistic regression analysis performed for dichotomous outcomes. All regression coefficients were tested for statistical significance. The R2 value was examined as goodness of fit measure.

4. Results

4.1. Participants

The participant characteristics are summarized in Table 1. Overall, participants were post-treatment breast cancer survivors with over half (52.2%) having obesity (mean BMI of 31.1 ± 7.34 kg/m2). Most participants were white (83%), non-Hispanic (98.7%), employed (67.5%) with an annual household income greater than $50,000 (67.6%), and a history of chemotherapy (61.6%) or radiation (65.6%). At cancer diagnosis, most were stage 1 (39.1%).

4.2. BMI and Program Preferences

A statistically significant relationship between BMI and a dichotomized program preference was found for only one preference, regardless of whether BMI was analyzed as a continuous variable (p = 0.009) or as a 3-level outcome (p = 0.038). Participants who preferred to exercise at a facility had a higher BMI (versus lower) (32.8 ± 8.6 vs. 30.3 ± 6.6 respectively). No other program preferences were associated with the continuous or 3-level BMI outcomes.

4.3. BMI, Current PA, and Cardiorespiratory Fitness

There were no statistically significant differences or associations with levels of current PA (moderate-to-vigorous PA or meets recommendations) when BMI was analyzed as a 3-level categorical outcome (Table 2 and Table 3). Similarly, no statistically significant association between PA and continuous BMI was noted (Table 4). However, there was a significant inverse correlation between BMI and cardiorespiratory fitness (r = −0.414, p < 0.001) in which higher BMI was related to lower cardiorespiratory fitness (Table 4). Results from a one-way ANOVA yielded statistically significant differences in cardiorespiratory fitness between levels of BMI (Table 2). A Tukey post-hoc test indicated that there was a significant difference in cardiorespiratory fitness between all levels of BMI (all p < 0.001). Specifically, cardiorespiratory fitness was significantly lower among obese class III vs. non-obese and obese class I/II.

4.4. BMI and SCT Variables

Pearson correlation coefficients between continuous BMI and PA-related SCT variables are provided in Table 4. Higher BMI was significantly correlated with higher exercise barriers interference (r = 0.131, p = 0.019) and lower walking self-efficacy (r = −0.364, p < 0.001). No other SCT variables were significantly correlated with BMI.
The one-way ANOVA showed statistically significant differences in walking self-efficacy and negative outcome expectations as reflected in Table 2. A Tukey post-hoc test indicated that there was a significant difference in walking self-efficacy scores between all levels of BMI (all p < 0.001). Walking self-efficacy scores were significantly lower among obese class III vs. non-obese and obese class I/II. As for the negative outcome expectations, obese class I/II reported significantly higher negative outcome expectations than non-obese (p = 0.024). The remaining constructs did not show any statistically significant differences.

4.5. Adjusted Associations

Multiple variable linear regression analyses were performed to examine the independent relationships between BMI (dependent variable) and fitness, exercise barriers interference, walking self-efficacy, and identified covariates (comorbidity score, WOMAC, income, race, and education). The model was statistically significant (F(9, 306) = 15.875, p < 0.001, R2 = 0.318) indicating that walking self-efficacy, cardiorespiratory fitness, comorbidity score, and race are independently associated with BMI (p < 0.05). Table 5 provides the analyses results.

4.6. Logistic Regression Models

Because preferring a facility was associated with BMI and walking self-efficacy, a post-hoc binomial logistic regression was performed to determine the relationship between location preference (dependent variable) and BMI independent of walking self-efficacy. The adjusted logistic regression model including BMI, walking self-efficacy, and location was statistically significant, χ2(2) = 10.292, p = 0.006. For every 1-unit increase in BMI, there is a 1.04 greater odds of preferring to exercise at a facility independent of walking self-efficacy score with a 95% confidence interval of (1.001–1.073).

5. Discussion

In this study, we sought to examine the associations between BMI and factors influencing program content and delivery (preferences), current PA (accelerometer-measured), cardiorespiratory fitness, and related SCT constructs (i.e., self-efficacy (barriers and task), exercise barriers interference, social support, outcome expectations (negative outcome expectations and positive outcome expectations)) among breast cancer survivors. We found that BMI was significantly correlated with exercise barriers interference. Further, we found significant associations between higher BMI and preference to exercise at a facility, lower cardiorespiratory fitness, and lower walking self-efficacy, independent of covariates (comorbidity score, Western Ontario and McMaster Universities osteoarthritis index score, income, race, education). These findings suggest that BMI influences PA program preferences, multiple psychosocial factors, and fitness which could be useful for informing future PA interventions for this population.
Overall, our sample of breast cancer survivors preferred to exercise at home or had no preference, which is consistent with the existing literature among diverse cancer survivors [36]. A recent systematic review reported a similar finding that adults with obesity prefer to engage in exercise “close to home”, while Hussien et al. found that adults with severe obesity prefer exercising outdoors [37]. However, when assessing preference by BMI category, we found that as obesity increased (i.e., obese class I/II to class III), there was an increased preference for exercising at a facility. This is similar to findings from another study among rural breast cancer survivors with overweight and obesity [38]. Potential hypotheses are that survivors with obesity may appreciate the support/supervision of facility staff (e.g., personal trainers, health coaches, health club exercise specialist), facility-provided equipment that limits weight bearing, and social context (i.e., seeing others exercise). Further, breast cancer survivors were recruited to participate in the current study, which required in-person visits to a facility.
The current study did not find a statistically significant association between BMI and current PA, similar to a past study among adults with obesity and multiple sclerosis [39]. Our sample consisted of breast cancer survivors who self-reported that they were not currently active (i.e., engaging in no more than 60 min of moderate intensity PA or no more than 30 min of vigorous intensity activity per week), as noted earlier in the inclusion criteria, which may have impacted our ability to test this relationship. However, a majority of the sample met PA recommendations based on free-living PA measured by accelerometer. We did find that overall, breast cancer survivors with and without obesity had low cardiorespiratory fitness levels compared to healthy women without cancer [40]. However, those with higher BMI had significantly lower cardiorespiratory fitness as in past studies among adults with obesity and no history of cancer [41,42]. Engaging cancer survivors, especially those with obesity, in cardiorespiratory fitness enhancing activities (i.e., PA, weight loss) is critical [43].
We found a statistically significant relationship between BMI and several SCT constructs. Specifically, breast cancer survivors with higher BMI had significantly lower walking self-efficacy and higher exercise barriers interference scores. Past studies have found associations between BMI and exercise barriers interference [44], exercise self-efficacy [45], and family social support [46] among adults with obesity, no cancer history [46], and presence of chronic illness (i.e., multiple sclerosis) [39]. The lower levels of self-reported walking self-efficacy among cancer survivors with higher BMI in the current study could possibly be due in part to limited mobility caused by excess body weight and reduced fitness levels, which makes it more difficult to walk at a moderately fast pace without stopping. With regard to exercise barriers interference, participants with increased weight reported more barriers to PA which has been previously described [15]. Similar to walking self-efficacy, bearing more body weight increases the likelihood of barriers (i.e., lack of energy, lack of confidence, lack of enjoyment, lack of interest, pain/discomfort).

5.1. Clinical and Research Implications

The findings from the current study have important implications for clinicians, healthcare professionals, and researchers who prescribe exercise and develop PA interventions for breast cancer survivors, especially with obesity. When prescribing exercise/PA for this vulnerable population, clinicians need to individually assess and consider medical (i.e., BMI), preference (i.e., location), SCT (i.e., walking self-efficacy), and fitness-related (i.e., cardiorespiratory fitness) factors as this could influence participation and the likelihood of meeting PA recommendations.
Although breast cancer survivors with obesity were more likely to prefer to engage in exercise at a facility (versus at home or no preference), potential barriers (e.g., financial, lack of accessibility) may arise for those without facility access. Therefore, distance-delivered interventions (e.g., mHealth, telephone, virtual) could serve as alternative options for delivering such programs. As for walking self-efficacy, exercise/PA programs can target and improve this component through incorporating individual behavior change techniques (e.g., goal setting, self-monitoring, and implementing solutions) [47,48]. The low levels of fitness found in the current study are more indicative of a greater need for PA and a potential intervention target. Thus, this suggests that structured exercise progression focused on improving cardiorespiratory fitness may be needed for this group. Lastly, although our study does not assess strength training, future interventions could leverage a preference for facility-based exercise to increase this type of exercise. In turn, individuals with obesity may be better able to do strength training at first, with a progression to greater aerobic exercise as muscle strength improves.
Taken as a whole, future research efforts need to be aimed at gaining a nuanced understanding regarding specific preferences (e.g., Why is facility more preferred by survivors with obesity?) through a qualitative approach. Moreover, future research directions should include assessing whether tailoring interventions and programs to individual health-related factors and preferences increases active living among this population.

5.2. Strengths and Limitations

The findings of the current study provide insight on a population that might need tailored or targeted PA programing. Specifically, our data related to location preference and health-related factors (i.e., walking self-efficacy and cardiorespiratory fitness) that can affect participation in programs can be useful for future intervention design. However, there are limitations. First, the entire sample consisted of post-treatment cancer survivors, mainly educated, affluent Caucasian women. Hence, findings may not be generalizable to the larger population of cancer survivors (e.g., those who are not White, have not undergone treatment, or are currently receiving treatment, less educated, less affluent, or male). Furthermore, although we used accelerometers to objectively measure minutes of PA, there are limitations (i.e., estimation of minutes of sedentary behaviors and PA). For example, it is unclear whether the device (waist-worn) accurately distinguishes the difference between sitting and standing idle, and upper body movement [49,50]. Moreover, accelerometers assess free-living PA rather than volitional, which results in a higher prevalence of meeting PA recommendations which are often based on leisure-time exercise alone. Lastly, our sample consisted of fewer participants with class III obesity (than with class I/II), which warrants future research with more representation from this group.

6. Conclusions

Cancer survivors are living longer due to advances in the diagnosis and control of cancer. Healthy lifestyle habits (physical activity, weight control) can enhance the quality of these years by reducing risk of cancer recurrence and other chronic diseases. Effective lifestyle programs are needed and should take into consideration the physical activity preferences and potential influences specific to this population, particularly among those with obesity who are most in need. Location, walking self-efficacy, and fitness are factors that should be considered for future PA programs among breast cancer survivors who have obesity. Tailoring for such individuals should involve a theoretically driven program which targets walking self-efficacy and involves activities appropriate for various levels of cardiorespiratory fitness.

Author Contributions

Conceptualization, N.I.B., L.Q.R. and D.W.P.; methodology, L.Q.R., D.W.P., R.A.O., K.S.C., P.A. and E.M.; software, L.Q.R. and R.A.O.; validation, L.Q.R.; formal analysis, N.I.B., L.Q.R., D.W.P. and R.A.O.; investigation, N.I.B., L.Q.R., K.S.C., P.A., E.M., D.K.E. and S.M.P.; resources, L.Q.R.; data curation, L.Q.R., E.M., D.K.E., P.A. and S.M.P.; writing—original draft preparation, N.I.B. and L.Q.R.; writing—review and editing, L.Q.R., D.W.P., R.A.O., K.S.C., P.A., E.M., D.K.E. and S.M.P.; writing—final version, N.I.B., L.Q.R., D.W.P., R.A.O., K.S.C., P.A., E.M., D.K.E. and S.M.P.; visualization, N.I.B., L.Q.R., D.W.P. and R.A.O.; supervision, L.Q.R. and D.W.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Institutes of Health T32, grant number T32HL105349; National Institutes of Health, grant numbers R01CA13685, U01CA13685 and the National Institutes of Health National Cancer Institute, grant number P30CA13148.

Institutional Review Board Statement

Institutional Review Board (IRB) approval was obtained at all three study sites and participants provided informed consent before initiating data collection.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank the BEAT and COMPARE participants and study staff.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Campbell, K.L.; Winters-Stone, K.M.; Wiskemann, J.; May, A.M.; Schwartz, A.L.; Courneya, K.S.; Zucker, D.S.; Matthews, C.E.; Ligibel, J.A.; Gerber, L.H.; et al. Exercise Guidelines for Cancer Survivors: Consensus Statement from International Multidisciplinary Roundtable. Med. Sci. Sport. Exerc. 2019, 51, 2375–2390. [Google Scholar] [CrossRef] [Green Version]
  2. Rock, C.L.; Thomson, C.; Gansler, T.; Gapstur, S.M.; McCullough, M.L.; Patel, A.V.; Bandrews, K.S.; Bandera, E.V.; Spees, C.K.; Robien, K.; et al. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J. Clin. 2020, 70, 245–271. [Google Scholar] [CrossRef]
  3. Arem, H.; Mama, S.; Duan, X.; Rowland, J.; Bellizzi, K.; Ehlers, D. Prevalence of Healthy Behaviors among Cancer Survivors in the United States: How Far Have We Come? Cancer Epidemiol. Biomark. Prev. 2020, 29, 1179–1187. [Google Scholar] [CrossRef]
  4. Greenlee, H.; Shi, Z.; Molmenti, C.L.S.; Rundle, A.; Tsai, W.Y. Trends in Obesity Prevalence in Adults With a History of Cancer: Results From the US National Health Interview Survey, 1997 to 2014. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2016, 34, 3133–3140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Zlotoff, D.A.; Nayor, M. Can We Estimate Cardiorespiratory Fitness in Breast Cancer Survivors Without Exercise? JACC Cardio Oncol. 2021, 3, 692–694. [Google Scholar] [CrossRef]
  6. Gourlan, M.; Bernard, P.; Bortolon, C.; Romain, A.J.; Lareyre, O.; Carayol, M.; Ninot, G.; Boiché, J. Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials. Health Psychol. Rev. 2016, 10, 50–66. [Google Scholar] [CrossRef]
  7. Ma, J.K.; A Floegel, T.; Li, L.C.; Leese, J.; A De Vera, M.; Beauchamp, M.R.; Taunton, J.; Liu-Ambrose, T.; Allen, K.D. Tailored physical activity behavior change interventions: Challenges and opportunities. Transl. Behav. Med. 2021, 11, 2174–2181. [Google Scholar] [CrossRef] [PubMed]
  8. Rimer, B.K.; Glanz, K.; National Cancer Institute (U.S.). Theory at a Glance: A Guide for Health Promotion Practice; U.S. Dept. of Health and Human Services, National Institutes of Health, National Cancer Institute: Bethesda, MD, USA, 2005. [Google Scholar]
  9. Glanz, K.R.B.; Viswanath, K. Health Behavior and Health Education: Theory, Research, and Practice; Jossey-Bass: San Francisco, CA, USA, 2015. [Google Scholar]
  10. Stacey, F.G.; James, E.L.; Chapman, K.; Courneya, K.S.; Lubans, D. A systematic review and meta-analysis of social cognitive theory-based physical activity and/or nutrition behavior change interventions for cancer survivors. J. Cancer Surviv. 2015, 9, 305–338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Maxwell-Smith, C.; Hagger, M.S.; Kane, R.; Cohen, P.A.; Tan, J.; Platell, C.; Makin, G.B.; Saunders, C.; Nightingale, S.; Lynch, C.; et al. Psychological correlates of physical activity and exercise preferences in metropolitan and nonmetropolitan cancer survivors. Psychooncology 2021, 30, 221–230. [Google Scholar] [CrossRef] [PubMed]
  12. Rogers, L.Q.; Markwell, S.J.; Verhulst, S.; McAuley, E.; Courneya, K.S. Rural breast cancer survivors: Exercise preferences and their determinants. Psychooncology 2009, 18, 412–421. [Google Scholar] [CrossRef] [PubMed]
  13. Elshahat, S.; Treanor, C.; Donnelly, M. Factors influencing physical activity participation among people living with or beyond cancer: A systematic scoping review. Int. J. Behav. Nutr. Phys. Act. 2021, 18, 50. [Google Scholar] [CrossRef]
  14. Rogers, L.Q.; Courneya, K.S.; Verhulst, S.; Markwell, S.J.; McAuley, E. Factors Associated With Exercise Counseling and Program Preferences Among Breast Cancer Survivors. J. Phys. Act. Health 2008, 5, 688–705. [Google Scholar] [CrossRef]
  15. Baillot, A.; Chenail, S.; Polita, N.B.; Simoneau, M.; Libourel, M.; Nazon, E.; Riesco, E.; Bond, D.S.; Romain, A.J. Physical activity motives, barriers, and preferences in people with obesity: A systematic review. PLoS ONE 2021, 16, e0253114. [Google Scholar] [CrossRef] [PubMed]
  16. Short, C.E.; Vandelanotte, C.; Duncan, M. Individual characteristics associated with physical activity intervention delivery mode preferences among adults. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 25. [Google Scholar] [CrossRef] [Green Version]
  17. Rogers, L.Q.; Courneya, K.S.; Oster, R.A.; Anton, P.M.; Phillips, S.; Ehlers, D.K.; McAuley, E. Physical activity intervention benefits persist months post-intervention: Randomized trial in breast cancer survivors. J. Cancer Surviv. 2023, 1–13. [Google Scholar] [CrossRef]
  18. Rogers, L.Q.; Courneya, K.S.; Anton, P.M.; Hopkins-Price, P.; Verhulst, S.; Vicari, S.K.; Robbs, R.S.; Mocharnuk, R.; McAuley, E. Effects of the BEAT Cancer physical activity behavior change intervention on physical activity, aerobic fitness, and quality of life in breast cancer survivors: A multicenter randomized controlled trial. Breast Cancer Res. Treat. 2015, 149, 109–119. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Groll, D.L.; To, T.; Bombardier, C.; Wright, J.G. The development of a comorbidity index with physical function as the outcome. J. Clin. Epidemiol. 2005, 58, 595–602. [Google Scholar] [CrossRef]
  20. Rogers, J.C.; Irrgang, J. Measures of adult lower extremity function: The American Academy of orthopedic surgeons lower limb questionnaire, the activities of daily living scale of the knee outcome survey (ADLS), foot function index (FFI), functional assessment system (FAS), Harris hip score (HHS), index of severity for hip osteoarthritis (ISH), index of severity for knee osteoarthritis (ISK), knee injury and osteoarthritis outcome score (KOOS), and Western Ontario and McMaster universities osteoarthritis index (WOMAC™). Arthritis Care Res. Off. J. Am. Coll. Rheumatol. 2003, 49, S67–S84. [Google Scholar]
  21. Rogers, L.Q.; McAuley, E.; Anton, P.M.; Courneya, K.S.; Vicari, S.; Hopkins-Price, P.; Verhulst, S.; Mocharnuk, R.; Hoelzer, K. Better exercise adherence after treatment for cancer (BEAT Cancer) study: Rationale, design, and methods. Contemp. Clin. Trials 2012, 33, 124–137. [Google Scholar] [CrossRef] [Green Version]
  22. Rogers, L.; Courneya, K.; Shah, P.; Dunnington, G.; Hopkins-Price, P. Exercise stage of change, barriers, expectations, values and preferences among breast cancer patients during treatment: A pilot study. Eur. J. Cancer Care 2007, 16, 55–66. [Google Scholar] [CrossRef] [PubMed]
  23. Courneya, K.S.; McKenzie, D.C.; Mackey, J.R.; Gelmon, K.; Reid, R.D.; Friedenreich, C.M.; Ladha, A.B.; Proulx, C.; Vallance, J.K.; Lane, K.; et al. Moderators of the effects of exercise training in breast cancer patients receiving chemotherapy: A randomized controlled trial. Cancer 2008, 112, 1845–1853. [Google Scholar] [CrossRef] [PubMed]
  24. Carter, S.J.; Hunter, G.R.; Blackston, J.W.; Liu, N.; Lefkowitz, E.J.; Van Der Pol, W.J.; Morrow, C.D.; Paulsen, J.A.; Rogers, L.Q. Gut microbiota diversity is associated with cardiorespiratory fitness in post-primary treatment breast cancer survivors. Exp. Physiol. 2019, 104, 529–539. [Google Scholar] [CrossRef] [Green Version]
  25. Wasserman, K.; Hansen, J.E.; Sue, D.Y.; Whipp, B.J.; Froelicher, V.F. Principles of exercise testing and interpretation. J. Cardiopulm. Rehabil. Prev. 1987, 7, 189. [Google Scholar] [CrossRef]
  26. Roitman, J. ACSM’s Resource Manual for Guidelines for Exercise Testing and Prescription, 3rd ed.; Williams & Wilkins: Baltimore, MD, USA, 1998. [Google Scholar]
  27. Balady, G.; Berra, K.A.; Golding, L.A.; Gordon, N.F.; Mahler, D.A.; Myers, J.N.; Sheldahl, L.M. ACSM’s Guidelines for Exercise Testing and Prescription; Lippincott, Williams & Wilkins: Baltimore, MD, USA; Philadelphia, PA, USA, 2000. [Google Scholar]
  28. Rogers, L.Q.; Courneya, K.S.; Verhulst, S.; Markwell, S.; Lanzotti, V.; Shah, P. Exercise barrier and task self-efficacy in breast cancer patients during treatment. Support. Care Cancer 2006, 14, 84–90. [Google Scholar] [CrossRef]
  29. McAuley, E.; Mihalko, S. Advances in sport and exercise psychology measurement. Measuring exercise-related self-efficacy. Fit. Inf. Technol. 1998, 1998, 371–389. [Google Scholar]
  30. McAuley, E.; Mailey, E.L.; Mullen, S.P.; Szabo, A.N.; Wójcicki, T.R.; White, S.M.; Gothe, N.; Olson, E.A.; Kramer, A.F. Growth trajectories of exercise self-efficacy in older adults: Influence of measures and initial status. Health Psychol. 2011, 30, 75–83. [Google Scholar] [CrossRef] [Green Version]
  31. Rogers, L.Q.; McAuley, E.; Courneya, K.S.; Verhulst, S.J. Correlates of physical activity self-efficacy among breast cancer survivors. Am. J. Health Behav. 2008, 32, 594–603. [Google Scholar] [CrossRef] [PubMed]
  32. Rogers, L.Q.; Shah, P.; Dunnington, G.; Greive, A.; Shanmugham, A.; Dawson, B.; Courneya, K.S. Social cognitive theory and physical activity during breast cancer treatment. Oncol. Nurs. Forum 2005, 32, 807–815. [Google Scholar] [CrossRef] [Green Version]
  33. Lewis, C.E.; Raczynski, J.M.; Heath, G.W.; Levinson, R.; Hilyer, J.C.; Cutter, G.R. Promoting physical activity in low-income African-American communities: The PARR project. Ethn. Dis. 1993, 3, 106–118. [Google Scholar]
  34. Sallis, J.F.; Grossman, R.M.; Pinski, R.B.; Patterson, T.L.; Nader, P.R. The development of scales to measure social support for diet and exercise behaviors. Prev. Med. 1987, 16, 825–836. [Google Scholar] [CrossRef]
  35. Sallis, J.F.; Hovell, M.F.; Hofstetter, C.R.; Faucher, P.; Elder, J.P.; Blanchard, J.; Caspersen, C.J.; Powell, K.E.; Christenson, G.M. A multivariate study of determinants of vigorous exercise in a community sample. Prev. Med. 1989, 18, 20–34. [Google Scholar] [CrossRef]
  36. Wong, J.N.; McAuley, E.; Trinh, L. Physical activity programming and counseling preferences among cancer survivors: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2018, 15, 48. [Google Scholar] [CrossRef] [Green Version]
  37. Hussien, J.; Brunet, J.; Romain, A.J.; Lemelin, L.; Baillot, A. Living with severe obesity: Adults’ physical activity preferences, self-efficacy to overcome barriers and motives. Disabil. Rehabil. 2022, 44, 590–599. [Google Scholar] [CrossRef]
  38. Vallance, J.; Lavallee, C.; Culos-Reed, N.; Trudeau, M. Rural and small town breast cancer survivors’ preferences for physical activity. Int. J. Behav. Med. 2013, 20, 522–528. [Google Scholar] [CrossRef]
  39. So, W.-Y.; Kalron, A. The Association between Body Mass Index and Leisure-Time Physical Activity in Adults with Multiple Sclerosis. Int. J. Environ. Res. Public Health 2020, 17, 920. [Google Scholar] [CrossRef] [Green Version]
  40. Imboden, M.T.; Harber, M.P.; Whaley, M.H.; Finch, W.H.; Bishop, D.L.; Kaminsky, L.A. Cardiorespiratory Fitness and Mortality in Healthy Men and Women. J. Am. Coll. Cardiol. 2018, 72, 2283–2292. [Google Scholar] [CrossRef] [PubMed]
  41. Hung, T.-H.; Liao, P.-A.; Chang, H.-H.; Wang, J.-H.; Wu, M.-C. Examining the relationship between cardiorespiratory fitness and body weight status: Empirical evidence from a population-based survey of adults in Taiwan. Sci. World J. 2014, 2014, 463736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Zeiher, J.; Manz, K.; Kuntz, B.; Perumal, N.; Keil, T.; Mensink, G.B.M.; Finger, J.D. Individual and interpersonal correlates of cardiorespiratory fitness in adults—Findings from the German Health Interview and Examination Survey. Sci. Rep. 2020, 10, 445. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Aadland, E.; Robertson, L. Physical activity is associated with weight loss and increased cardiorespiratory fitness in severely obese men and women undergoing lifestyle treatment. J. Obes. 2012, 2012, 810594. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Leone, L.A.; Ward, D. A mixed methods comparison of perceived benefits and barriers to exercise between obese and nonobese women. J. Phys. Act. Health 2013, 10, 461–469. [Google Scholar] [CrossRef] [Green Version]
  45. Brooks, J.M.; Deiches, J.; Xiang, X.; Batsis, J.A.; Chan, F.; DiMilia, P.; Chiu, C.; Thompson, K.; Bartels, S. Differences in Self-Reported Physical Activity, Exercise Self-Efficacy and Outcome Expectancies, and Health Status by Body Mass Index Groups in People with Chronic Pain. J. Rehabil. 2018, 84, 46–52. [Google Scholar]
  46. Hartman, S.J.; Dunsiger, S.I.; Pekmezi, D.W.; Barbera, B.; Neighbors, C.J.; Marquez, B.; Marcus, B.H. Impact of Baseline BMI upon the Success of Latina Participants Enrolled in a 6-Month Physical Activity Intervention. J. Obes. 2011, 2011, 921916. [Google Scholar] [CrossRef] [Green Version]
  47. Bailey, R.R. Goal Setting and Action Planning for Health Behavior Change. Am. J. Lifestyle Med. 2019, 13, 615–618. [Google Scholar] [CrossRef]
  48. Kitagawa, T.; Higuchi, Y.; Todo, E.; Ueda, T.; Ando, S.; Murakami, T. Tailored feedback reduced prolonged sitting time and improved the health of housewives: A single-blind randomized controlled pilot study. Women Health 2020, 60, 212–223. [Google Scholar] [CrossRef] [PubMed]
  49. Kalisch, T.; Theil, C.; Gosheger, G.; Ackmann, T.; Schoenhals, I.; Moellenbeck, B. Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers—Are the results comparable? Ther. Adv. Musculoskelet. Dis. 2022, 14, 1759720x221079256. [Google Scholar] [CrossRef] [PubMed]
  50. Lee, I.M.; Shiroma, E. Using accelerometers to measure physical activity in large-scale epidemiological studies: Issues and challenges. Br. J. Sport. Med. 2014, 48, 197–201. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Overall sample characterization (n = 320).
Table 1. Overall sample characterization (n = 320).
CharacteristicOverall (n = 320)
Age (years) (mean ± SD (range))54.8 ± 8.3 (21–70)
Race n (%)
  Caucasian256 (80)
  African American48 (15)
  Other16(5)
Ethnicity n (%)
  Non-Hispanic315 (99)
Education (years) (mean ± SD (range)) 15.5 ± 2.5 (9–21)
Income n (%)
  >$50 K 214 (68)
Employed n (%)216 (68)
Marital Status n (%)
  Married or living with significant other 221 (69)
  Widowed/Divorced/Single99 (31)
BMI (mean ± SD (range))31.1 ± 7.3 (18–58)
  Non-obese n (%)152 (48)
  Obese Classes I/II n (%)129 (40)
  Obese Class III n (%)39 (12)
Weekly minutes of Moderate-to-Vigorous PA (mean ± SD (range)) 173.6 ± 105.5 (30–1016)
Cardiorespiratory Fitness (mL/kg/min) (mean ± SD (range)) 20.7 ± 5.1 (6–39)
Cancer Stage at Diagnosis n (%)
  DCIS40 (13)
  I 125 (39.1)
  II121 (38)
  III33 (10.3)
Months since diagnosis (mean ± SD (range)) 53.4 ± 54.2 (2–276)
History of chemotherapy n (%)197 (62)
History of radiation n (%)210 (66)
Hormonal Therapy (type) n (%)
  Estrogen receptor modulator 75 (23)
  Aromatase inhibitor 85 (27)
  None160 (50)
Number of comorbidities (mean ± SD (range))2.2 ± 1.8 (0–18)
WOMAC score (mean ± SD (range))16.7 ± 15.0 (0–68)
Note: WOMAC score indicates lower extremity joint pain, stiffness, and dysfunction. Abbreviations: BMI, body mass index; DCIS, ductal carcinoma in situ; PA, physical activity; SD, standard deviation; WOMAC, Western Ontario and McMaster Universities osteoarthritis index.
Table
Table 2. Differences in moderate-to-vigorous intensity PA, cardiorespiratory fitness, and SCT variables between the three levels of BMI.
Table 2. Differences in moderate-to-vigorous intensity PA, cardiorespiratory fitness, and SCT variables between the three levels of BMI.
GroupMeanSDF(2, 317)p-Value
Moderate-to-Vigorous Intensity PA
(Weekly minutes)		Non-Obese172.7110.40.3560.701
Obese Class I/II178.397.5
Obese Class III162112.4
Cardiorespiratory
Fitness
(VO2peak; mL/kg/minute)		Non-Obese22.44.535.175<0.001
Obese Class I/II20.24.9
Obese Class III15.73.8
Exercise Barriers Interference
(Score, possible range 0 to 100)		Non-obese56.811.81.8690.156
Obese Class I/II59.413.4
Obese Class III59.913.3
Walking Self-Efficacy
(Score, possible range 0 to 100)		Non-obese77.622.722.587<0.001
Obese Class I/II65.922.2
Obese Class III49.830.7
Barriers Self-Efficacy
(Score, possible range 21 to 105)		Non-obese38.621.40.0420.959
Obese Class I/II39.320.3
Obese Class III39.325.7
Positive Outcome Expectations
(Score, possible range 14 to 70)		Non-obese58.16.20.1290.879
Obese Class I/II57.86.2
Obese Class III57.77.7
Negative Outcome Expectations
(Score, possible range 3 to 15)		Non-obese7.62.63.7560.024
Obese Class I/II8.42.8
Obese Class III7.72.7
Total Social Support
(Score, possible range 0 to 16)		Non-obese4.54.30.8450.430
Obese Class I/II4.03.9
Obese Class III4.93.9
Abbreviations: PA, physical activity; SD, standard deviation.
Table
Table 3. Crosstabulations and chi-square tests for three-level BMI and meeting PA recommendations.
Table 3. Crosstabulations and chi-square tests for three-level BMI and meeting PA recommendations.
Moderate-to-Vigorous Intensity PA
GroupTotal (n)Met Recommendations n (Row%)Did Not Meet Recommendations n (Row%)χ2dfp-Value
Non-obese14670 (48)76 (52)2.44620.294
Obese Class I/II12168 (56)53 (44)
Obese Class III3817 (45)21 (55)
Note: Met PA recommendations: >150 min of moderate-to-vigorous PA vs. did not meet PA recommendations: <150 min of moderate-to-vigorous PA.
Table
Table 4. Pearson correlation matrix for BMI, moderate-to-vigorous PA, cardiorespiratory fitness, and SCT variables.
Table 4. Pearson correlation matrix for BMI, moderate-to-vigorous PA, cardiorespiratory fitness, and SCT variables.
BMIMVPACardiorespiratory FitnessExercise
Barriers
Interference		Walking Self-EfficacyBarriers
Self-Efficacy		Positive
Outcome
Expectations		Negative
Outcome
Expectations		Total Social Support
BMI
MVPA−0.043
Cardiorespiratory Fitness−0.431 **0.040
Exercise Barriers 0.131 *−0.117 *−0.137 *
Walking Self-Efficacy−0.391 **0.0880.337 **−0.228 **
Barriers Self-Efficacy0.0190.074−0.026−0.220 **0.179 **
Positive Outcome Expectations0.0120.061−0.012−0.0830.1060.250 **
Negative Outcome Expectations0.073−0.010−0.0990.373 **−0.217 **−0.122 *−0.126 *
Total Social Support0.0140.099−0.043−0.251 **0.0910.0740.178 **−0.035
Note: ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Abbreviations: BMI, body mass index; MVPA, moderate-to-vigorous PA.
Table
Table 5. Multiple variable linear regression analyses using BMI as the dependent variable.
Table 5. Multiple variable linear regression analyses using BMI as the dependent variable.
VariableBβp-Value
Constant36.717 <0.001
Exercise Barriers Interference0.0130.0220.677
Walking Self-Efficacy−0.052−0.1820.001
Negative Outcome Expectations−0.103−0.0380.474
Cardiorespiratory Fitness−0.375−0.259<0.001
Comorbidity Score0.7450.184<0.001
WOMAC0.0420.0860.135
Income0.2470.0160.756
Race2.9470.1410.008
Education−0.167−0.0090.859
Note: B represents the unstandardized regression coefficient; β represents the standardized regression coefficient. Abbreviations: WOMAC, Western Ontario and McMaster Universities osteoarthritis index score.
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Brown, N.I.; Pekmezi, D.W.; Oster, R.A.; Courneya, K.S.; McAuley, E.; Ehlers, D.K.; Phillips, S.M.; Anton, P.; Rogers, L.Q. Relationships between Obesity, Exercise Preferences, and Related Social Cognitive Theory Variables among Breast Cancer Survivors. Nutrients 2023, 15, 1286. https://doi.org/10.3390/nu15051286

AMA Style

Brown NI, Pekmezi DW, Oster RA, Courneya KS, McAuley E, Ehlers DK, Phillips SM, Anton P, Rogers LQ. Relationships between Obesity, Exercise Preferences, and Related Social Cognitive Theory Variables among Breast Cancer Survivors. Nutrients. 2023; 15(5):1286. https://doi.org/10.3390/nu15051286

Chicago/Turabian Style

Brown, Nashira I., Dorothy W. Pekmezi, Robert A. Oster, Kerry S. Courneya, Edward McAuley, Diane K. Ehlers, Siobhan M. Phillips, Philip Anton, and Laura Q. Rogers. 2023. "Relationships between Obesity, Exercise Preferences, and Related Social Cognitive Theory Variables among Breast Cancer Survivors" Nutrients 15, no. 5: 1286. https://doi.org/10.3390/nu15051286

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