Stage Validity of the Health Action Process Approach in African American Breast Cancer Survivors

J Immigrant Minority Health DOI 10.1007/s10903-016-0520-1 ORIGINAL PAPER Stage Validity of the Health Action Process Approach in African American Breast Cancer Survivors Rachel Meadows1 · Raheem J. Paxton2 © Springer Science+Business Media New York 2016 Abstract The Health Action Process Approach (HAPA) has been applied in a number of populations because it proposes to overcome limitations from previous health behavior theories. However, it has yet to be applied to cancer survivors or racial/ethnic minorities. In this study, we examined the construct validity of the HAPA phase and stage algorithms in a sample of African American breast cancer survivors. A total of 259 African American breast cancer survivors (mean age = 54 years) participated in a Web-based survey that assessed sociodemographic and medical characteristics, physical activity, and HAPA constructs. Analysis of covariance was used to compare mean differences between HAPA phase/stage. Statistical significance was determined at p < 0.017 due to multiple comparisons. Phase and stage inconsistencies were observed for most constructs. However, adequate distinctions were made for motivational self-efficacy and intentions (i.e., P = I < A) by phase, and both action and coping planning (i.e., P < I < A) by stage. Our data indicate partial support of the HAPA algorithm to classify African American breast cancer survivors according to stage or phase. Modifying the staging algorithm or constructs are needed if * Rachel Meadows meadows.262@osu.edu Raheem J. Paxton Raheem.Paxton@unthsc.edu 1 Comprehensive Cancer Center, Center for Population Health and Health Disparities and the College of Public Health, The Ohio State University, 1590 N. High Street, Suite 525, Columbus, OH 43201, USA 2 Institute of Healthy Aging and the School of Public Health, The University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX 76107, USA stage- or phase-based interventions can be designed for this population. Keywords Breast cancer · African American · Health behavior · Physical activity · Cancer survivorship · Validity · Stage of change Introduction Physical activity (PA) is associated with several benefits for cancer survivors including improvements in cancerrelated fatigue, emotional well-being, sleep disturbances, pain, physical functioning, cardio-respiratory fitness, and reductions in body mass index [1–6]. Despite the benefits, many cancer survivors do not meet current PA guidelines and African Americans report the lowest compliance rates among cancer survivors [7–9]. Inactivity among African American cancer survivors contributes significantly to the cancer-related disparities that exist among African American and non-Hispanic White survivor populations [10]. Literature documenting the correlates of PA in breast cancer survivors has focused almost exclusively on nonHispanic White survivor samples, while fewer studies have addressed the correlates of PA in minority cancer survivors [5, 11–15]. African American breast cancer survivors are in desperate need of studies that document the correlates of PA as well as effective behavior change strategies. The limited data that exist on effective behavior change strategies in minority cancer survivors suggests a need for more theory research in this population [16, 17]. Prior studies in cancer survivors have focused on frameworks such as the Social Cognitive Theory, Health Belief Model, and Theory of Planned Behavior [18–20]. Recently, the Health Action Process Approach (HAPA) was shown to 13 J Immigrant Minority Health be a relevant model to study PA in African American breast cancer survivors [13]. The HAPA includes correlates that mediate the intention-behavior gap (i.e., action and coping planning) and stage-specific self-efficacy (i.e., motivational, coping, and relapse) correlates. Of importance, the HAPA proposes that behavior change occurs in a pre-intentional motivational and a post-intentional volitional phase. The phases relate directly to a pre-intentional stage (those who have not yet developed intentions) and two volitional stages, which include an intentional-(those have developed behavioral intentions) and action (those who are active) stage. Our prior research using the HAPA in African American breast cancer survivors indicated that several correlates were related to PA in bivariate analyses and that both action and coping planning mediated the intention-behavior gap [13]. The next obvious step would be to test the validity of the staging algorithm. Although the HAPA has been studied extensively [21–26], limited data exist on the validity of the staging algorithm [27]. Studies addressing this question may help to determine whether the HAPA is an appropriate model for African American breast cancer survivors or whether adaptations to the staging algorithm or constructs are warranted. Such steps are essential before proposing any revisions or adaptations to a theoretical framework that has been effective in various populations. The goals of this study are two-fold. In particular, we propose to (1) determine whether constructs of the HAPA differ between phases and stages, and (2) determine whether specific PA intensities differ between phases and stages. Data supporting the validity of the staging algorithm would be a fundamental step in determining whether the staging algorithm is relevant for ethnic and underserved cancer survivors. This study will provide a true test of falsification and help to improve the significance of theorybased research in ethnic minority cancer survivors. Method Study Population Female breast cancer survivors were recruited between May 2012 and July 2012 from Sisters Network Inc., which is the largest African American breast cancer survivorship organization in the United States. We recruited survivors using multiple email blasts and anonymous survey links posted on social media sites (i.e., Twitter, Facebook, and blog sites) affiliated with Sisters Network. Each email blast was one round of emails sent to all members in the Sisters Network’s mailing list. Multiple blasts were sent during a 5-week period until we reached a sufficient sample size. The email blasts reached approximately 16,000 members 13 in their database, which consisted of approximately 3800 breast cancer survivors as well as about 12,200 healthy African American women. All surveys were completed using Survey Monkey®, a web-based platform that allows investigators to create surveys and manage responses. Inclusion criteria included (a) being African American female (b) having a prior breast cancer diagnosis, (c) 18–80 years old at the time of survey administration, (d) being diagnosed with stage I to IIIc breast cancer, and (e) willing to consent to a web-based survey. Participants were eliminated from the final analyses if they were not breast cancer survivors (n = 235), were not African American (n = 7), reported being diagnosed before the age of 18 years (n = 9), and had substantial missing data or questionable data (n = 250). Examples of questionable responses included: (a) diagnosed at age 0 and (b) reporting strongly agree to every Likert-type item in the survey. The study describing the recruitment methods was reported elsewhere [28]. This study refers to data from a total of 259 respondents. A $10 incentive was provided to all women who completed the survey. Institutional Review Board approval was obtained before data collection, and all subjects were treated in compliance with ethical standards. Measures Staging and Phase Algorithm One variable was used to classify participants into HAPA stages and phases. The item asked, “Did you engage in physical activity (exercise) at least 3-days per week for 30 min or more per day?” Responses were (a) no, and I do not intend to start, (b) no, but I’m considering it, (c) no, but I seriously intend to start, (d) yes, but only for a brief period of time, and (e) yes, and for a long period of time. The description of the staging algorithm was published elsewhere [21]. Persons indicating (a) or (b) were classified as Pre-intenders. Those indicating (c) were classified as Intenders. Finally, those indicating (d) or (e) were classified as Actors. The staging algorithm was designed as a rating scale with pre-intenders on the very left, intenders in the middle, and actors on the very right. Prior studies have indicated that the algorithm had high sensitivity (i.e., 70 % agreement between classification as an Actor by the staging algorithm and mean minutes of PA per week) and high specificity (80 % agreement between classification as being a nonactive Pre-intender or Intender and mean minutes of PA per week) [21]. The phase algorithm according to the HAPA separates the pre-intentional motivational and postintentional volitional phase. Pre-intenders (P) were classified in the motivational phase, and Intenders (I) and Actors (A) were classified in the volitional phase. J Immigrant Minority Health Physical Activity Data was gathered using a self-report instrument designed for the Women’s Health Initiative [29]. The survey consisted of nine-items that assessed recreational walking as well as light, moderate, and vigorous PA using a frequency and duration item format. In a sample of breast cancer survivors, the instrument was highly correlated with accelerometer counts and had high sensitivity [29]. For the purpose of this study, we estimated mean minutes of walking, moderate, and vigorous PA per week for each stage and phase. Risk Perception Risk perception was assessed using conceptually distinct subscales for Relative Vulnerability (i.e., compared to an average person my age, my chances of acquiring a specific health condition are …) and Absolute Risk Perception (e.g., how likely is it that I will have any of these conditions in my lifetime) [30]. The health conditions included common comorbidities following cancer diagnoses such as high blood pressure, high cholesterol, diabetes, and cancer recurrence. Each subscale contained the five original items and one author-derived item that pertained to the risk of cancer recurrence. The response scale of Relative Vulnerability ranged from one (much below average) to five (much above average). The response scale of Absolute Risk Perception ranged from one (very unlikely) to five (very likely). The internal consistency reliability of relative vulnerability and absolute risk perception were α = 0.88 and 0.83, respectively. These items were reverse scored so that higher levels would equate to lower levels of risk. Self-Efficacy Three distinct subscales were used including motivational self-efficacy (e.g., I’m sure I can change to a physically active lifestyle; three-items), coping self-efficacy (e.g., I’m sure I can keep being active even if I’m tired; six-items), and recovery self-efficacy (e.g., I’m sure I can be active again regularly, even if I postpone my plans several times; three-items) [30]. Each item was rated on a four-point Likert-type response scale ranging from one (not at all true) to four (exactly true). The instruments have been applied and validated in previous studies [15, 21, 22]. The internal consistency reliabilities for motivational, coping, and recovery self-efficacy were α = 0.88, 0.96, and 0.87, respectively. ‘What do you think will be the consequences if you exercise regularly?’ The stem was ‘If I exercise regularly,’ was followed by examples such as ‘my quality of life would improve (pro),’ and ‘I would spend a lot of time trying to do it (con).’ The answers were rated on a four-point Likert scale ranging from (1) not at all true to (4) exactly true. The instruments have been applied and validated in previous studies [15, 21, 22]. The internal consistency reliabilities for pros and cons were α = 0.87 and 0.80, respectively. Intention Intention was assessed with the original two items developed for the HAPA and a new author created item [30]. Participants were asked, whether or not they intended to: (1) be active regularly over the next month, (2) be active at least three-times per week, or (3) be active at least fivetimes per week over the next month. Intentions were rated on a five-point Likert-type response scale ranging from one (strongly disagree) to five (strongly agree). The construct validity of intentions was validated previously [31]. The internal consistency reliability for intentions was α = 0.70. Planning We used the Action and Coping Planning subscales created by Sniehotta and colleagues [32]. Action planning was measured with five items. The item stem ‘I already have concrete plans… was followed by when, where, how, how often, and with whom to exercise. Coping planning utilized the same stem followed by items such as ‘what to do if something intervenes.’ All items were rated on a four-point Likert-type response scale that ranged from (one) not at all true to (three) exactly true. The internal consistency reliabilities for action and coping planning were α = 0.96 and 0.97, respectively. Socio-Demographic and Medical Data All socio-demographic and medical data were self-reported by participants. We collected data on the following variables: current age, education, years out from cancer diagnosis, disease stage at diagnosis, and chronic health conditions. We summed the number of chronic diseases (e.g., cardiovascular disease, blood sugar/diabetes, digestive disorders, arthritis, and osteoporosis) that were self-reported to create a variable representing number of comorbidities. Statistical Analysis Outcome Expectancies Twelve items were used to assess positive (pros) and negative (cons) attributes of PA [30]. Participants were asked, Descriptive statistics were used to characterize the study population. Next, a series of analyses (Analysis of Covariance) were used to determine whether mean differences 13 J Immigrant Minority Health existed between the stages and phases. In these models, HAPA constructs and PA intensities were our dependent variables and stages or phases were our independent variables. Models were adjusted for years out from cancer diagnosis, disease stage at diagnosis, and number of comorbid conditions. Mean values and standard deviations were reported. Effect sizes (η²) were calculated to estimate the magnitude of difference across phase/stage, whereby η² ≤ 0.05 was considered small, 0.06 ≤ η² ≤ 0.13 was moderate, and η² ≥0.14 is large [33]. To adjust for multiple comparisons the nominal alpha (0.05) was divided by the number of contrasts (n = 3), resulting in a significance level of p ≤ 0.017. All analyses were conducted using STATA 14.0. Results Validity of the Staging Algorithm Differences by Phase Motivational self-efficacy and intentions were operating according to the hypothesized motivational and volitional phases. Survivors classified in the motivational phase had significantly lower mean motivational self-efficacy scores than those classified in the volitional phase. The effect size estimating the mean differences in motivational self-efficacy between phases was moderate (η² = 0.085). Likewise, mean intention scores were significantly lower for survivors in the motivational phase compared to those in the volitional phase. The effect size estimating the mean differences in intentions to be physically active between phases was moderate (η² = 0.091). Table 2 reports means, comparisons between stages and phases, and p-values. Sample Characteristics Differences by Stage On average survivors were 55 years old and 7 years out from diagnosis. Approximately 47 % of the sample was obese with average BMI of 30 kg/m2. Less than half (49 %) of the survivors were meeting current guidelines for PA. Most survivors presented with stage II breast cancer and were college educated. The sociodemographic and medical characteristics are reported in Table 1. Table 1 Sample characteristics of African American breast cancer survivors Characteristic Age, M (SD) Age group <50 50–59 60+ Years out from diagnosis, M (SD) Stage I II III+ Married Education High school Some college College graduate Professional school BMI, M (SD) % Obese Physical activity metabolic equivalents, M (SD) % Meeting guidelines 13 Action and coping planning were operating according to the hypothesized pre-intentional, intentional, and acting stages. Mean levels of both action and coping planning increased significantly with progression in stage. The effect size estimating the mean differences in action planning between stages was large (η² = 0.21). Likewise, the effect Total N = 259 (%) Preintenders N = 50 (%) Intenders N = 62 (%) Actors N = 147 (%) 54 (0.61) 92 (35 %) 87 (34 %) 80 (31 %) 6.9 (6.3) 7 (8 %) 19 (22 %) 24 (30 %) 9.52 (1.1) 27 (29 %) 20 (23 %) 15 (19 %) 7.77 (0.9) 58 (63 %) 48 (55 %) 41 (51 %) 5.65 (0.4) 91 (35 %) 116 (45 %) 52 (20 %) 129 (50 %) 22 (24 %) 22 (19 %) 6 (11 %) 24 (19 %) 21 (23 %) 26 (22 %) 15 (29 %) 24 (19 %) 48 (53 %) 68 (59 %) 31 (60 %) 81 (62 %) 17 (7 %) 103 (40 %) 69 (27 %) 67 (26 %) 30.6 (0.4) 131 (51) 783 (898) 6 (35 %) 24 (23 %) 15 (22 %) 5 (7 %) 32.9 (1.0) 32 (25 %) 300 (67) 4 (24 %) 20 (20 %) 18 (26 %) 18 (27 %) 31.4 (0.8) 33 (25 %) 227 (30) 7 (41 %) 59 (57 %) 36 (52 %) 44 (66 %) 29.55 (0.4) 66 (50 %) 1181 (81) 128 (49 %) 8 (6 %) 8 (6 %) 112 (88 %) J Immigrant Minority Health Table 2 Analysis of covariance results Walking Moderate physical activity Strenuous physical activity Absolute risk Coping self-efficacy Relapse self-efficacy Motivational self-efficacy Relative vulnerability Positive outcomes expectations Negative outcomes expectations Intentions Action planning Coping planning Preintenders (P) Intenders (I) Actors (A) Difference Phase differential Stage differential 45 (12.12) 19 (8.47) 12.4 (8.45) 3.09 (0.14) 2.65 (0.13) 2.69 (0.12) 3.01 (0.09) 2.90 (0.13) 2.56 (0.11) 3.23 (0.14) 3.27 (0.14) 2.27 (0.12) 1.82 (0.14) 48.71 (10.89) 9.677 (7.61) 6.94 (7.59) 2.77 (0.13) 2.93 (0.11) 3.14 (0.11) 3.44 (0.08) 2.76 (0.12) 2.81 (0.10) 3.17 (0.12) 3.90 (0.12) 2.98 (0.11) 2.27 (0.12) 124.97 (7.07) 64.69 (4.94) 63.33 (4.93) 2.47 (0.08) 2.94 (0.07) 2.94 (0.07) 3.60 (0.05) 2.57 (0.07) 2.80 (0.06) 2.96 (0.08) 4.14 (0.08) 3.47 (0.07) 2.95 (0.08) (P = I) < A (P = I) < A (P = I) < A P=I=A P=I=A P = I, P < A, I = A P<(I = A) P=I=A P=I=A P=I=A P<(I = A) P<I<A P<I<A ✗ ✗ ✗ ✗ ✗ ✗ ✓ ✗ ✗ ✗ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✓ ✓ Means and comparisons are adjusted for age, years since diagnosis, number of comorbidities, stage at cancer diagnosis. Statistical significance was determined at p < 0.017 due to multiple comparisons. ✗ denotes lack of accordance to hypothesized phase or stage differential, and ✓ denotes accordance to hypothesized phases or stage differential size estimating the mean differences in coping planning between stages was large (η² = 0.19). Discussion In this study, we observed partial support for the staging algorithm of the HAPA in our sample of African American breast cancer survivors. In particular, we observed that action and coping planning, intentions, and motivational self-efficacy were operating according to the hypothesized phase or stage structure. Unfortunately, the staging algorithm did not perform well when examining mean differences in physical activity intensities or the remaining HAPA constructs. Overall, our data suggest that revisions may be warranted to the staging algorithm or the constructs to ensure that model is operating as hypothesized. Consistent with previous studies, our results show that action and coping planning were operating as theorized based on the large effect sizes observed. We observed a significant linear increase in action and coping planning as survivors move from the pre-intentional phase to acting on the behavior. The results observed here were similar to those observed in other studies [34, 35]. Action and coping planning are relevant to the PA habits of African American breast cancer survivors because they are self-regulatory processes, which are essential for initiating and maintaining a positive health behavior [15, 22, 36]. Planning is a critical component in bridging the intention to behavior gap [19, 22, 30, 36], and may be a key mediator for African American breast cancer survivors [13]. Our findings also show that motivational self-efficacy and intentions were operating according to the hypothesized phase structure. The moderate effect size observed here, provides support for the hypothesized separation that exists between the motivational and volitional phases. Lippke and colleagues noted similar phase effects for motivational self-efficacy in a sample of cardiac rehabilitation patients as well as partial support for intentions operating under the phase structure [22]. Both self-efficacy and intentions have been identified as important correlates of PA in non-Hispanic White cancer survivors [37, 38]. Currently, there is not enough literature to determine whether these constructs are relevant correlates for African American breast cancer survivors. More work is needed to identify correlates pertinent to the adoption and maintenance of PA in ethnic and underserved survivor groups. It is important to determine the constructs that were not operating as theorized according to either the phase or stage structure of HAPA. The risk perception variables did not function as theorized. Our findings for these variables are consistent with prior studies examining these constructs [36, 39–41]. This effect of risk perception may be diminished in a sample of breast cancer survivors with substantial comorbidities. The high prevalence of comorbidities may shield any effect that risk would have on PA in this sample. Similarly, constructs of coping and recovery self-efficacy did not operate as theorized, which replicated previous literature showing mixed results [42–44]. It is not clear why these self-efficacy variables did not operate consistently between phase or stage. It could be that the items used were not relevant for our sample or that there may be 13 J Immigrant Minority Health Movaonal Self-efficacy Coping Selfefficacy Relapse Selfefficacy Pros Acon Planning Physical Acvity Iniaon Intenons Maintenanc Coping Planning Cons Recovery Relave Risk/Vulnera bility Pre-intenders Movaonal Phase Intenders Actors Volional Phase Fig. 1 Health action process approach stage- and phase-based theoretical framework [46] other ethnically relevant constructs that outweigh efficacy beliefs. Intrinsic motivation, physical appearance (i.e., hair care maintenance), and lack of enjoyment in PA have been identified as important determinants of meeting PA guidelines among African American women [14, 45]. Thus, ethnically relevant determinants may outweigh efficacy beliefs to navigate barriers to PA. When interpreting these data, several limitations should be recognized. These data are self-report and subject to recall and response biases. Additionally, many of the instruments used were developed for a German population, and may not be directly relatable to African American breast cancer survivors. Furthermore, the survey items used to measure severity were focused on comorbid conditions such as high blood pressure, high cholesterol, diabetes, and cancer recurrence. The prevalence of these conditions was elevated in our sample and may have weakened the effect of risk perception. Lastly, the majority of our sample was educated with most having “some college” or beyond education. Thus, our women may not be generalizable to the overall population of African American breast cancer survivors. Despite these potential limitations, there are several strengths of our study. Our survey measures were validated 13 previously in various populations. Additionally, we had a modest sample size of underrepresented cancer survivors. Our study highlights the applicability of the HAPA staging algorithm in an underrepresented population of African American breast cancer survivors. We identified that constructs of motivational self-efficacy, intentions, action and coping planning are predictive of transitions between stages or phases and should be explored further in this population. However, several constructs may need to be adapted to become relevant in this population. Further studies should explore the motivational aspects and strategies to overcome the ethnically relevant barriers that have been identified in previous literature [7, 14, 45]. Studies refining focal points from theoretical frameworks are important because limited data exists on the correlates of PA in ethnic and underserved cancer survivors. Studies such as these will advance our understanding of the psychosocial mechanisms behind PA in vulnerable populations and help to develop effective lifestyle interventions (Fig. 1). Acknowledgments We wish to thank the women of the Sisters Network Inc. for participating in our study and staff members of the Sisters Network for facilitating all data collection activities. J Immigrant Minority Health Funding This research was supported in part by National Cancer Institute Grants K01CA158000. Compliance with Ethical Standards Conflict of Interest Raheem Paxton received research funding for this study from NCI Grant K01CA158000. Rachel Meadows declares no conflicts of interest. Financial Disclosures The authors have no financial disclosures. Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent Informed consent was obtained from all individual participants included in the study. References 1. Kenzik KM, Morey MC, Cohen HJ, Sloane R, Demark-Wahnefried W. Symptoms, weight loss and physical function in a lifestyle intervention study of older cancer survivors. J Geriatr Oncol. 2015;6(6):424–32. 2. Fong DY, Ho JW, Hui BP, et al. Physical activity for cancer survivors: meta-analysis of randomised controlled trials. BMJ. 2012;344:e70. 3. Demark-Wahnefried W, Morey MC, Sloane R, et al. Reach out to enhance wellness home-based diet-exercise intervention promotes reproducible and sustainable long-term improvements in health behaviors, body weight, and physical functioning in older, overweight/obese cancer survivors. J Clin Oncol. 2012;30(19):2354–61. 4. Mishra SI, Scherer RW, Geigle PM, et al. Exercise interventions on health-related quality of life for cancer survivors. Cochrane Database Syst Rev. 2012;8:CD007566. 5. Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH. An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv. 2010;4(2):87–100. 6. Morey MC, Snyder DC, Sloane R, Cohen HJ, Peterson B, Hartman TJ, et al. Effects of home- based diet and exercise on functional outcomes among older, overweight long-term cancer survivors: RENEW: a randomized controlled trial. JAMA. 2009;301:1883–91. 7. Spector D, Deal AM, Amos KD, et al. A pilot study of a homebased motivational exercise program for African American breast cancers: clinical and quality-of-life outcomes. Integr Cancer. 2014;13(2):121–32. 8. Paxton RJ, Taylor WC, Chang S, Courneya KS, Jones LA. Lifestyle behaviors of African American breast cancer survivors: a Sisters Network, Inc. study. PLoS One. 2013;8(4):e61854. 9. Paxton RJ, Phillips KL, Jones LA, Chang S, Taylor WC, Courneya KS, Pierce JP. Associations among physical activity, body mass index, and health-related quality of life by race/ethnicity in a diverse sample of breast cancer survivors. Cancer. 2012;118:4024–31. 10. Tammemagi CM, Nerenz D, Neslund-Dudas C, Feldkamp C, Nathanson D. Comorbidity and survival disparities among black and white patients with breast cancer. JAMA. 2005;294(14):1765–72. 11. Lemacks J., Wells B., Ilich J., Ralston P. Interventions for improving nutrition and physical activity behaviors in adult African American populations: a systematic review, January 2000 THROUGH December 2011. Prev Chronic Dis. 2013;10:(E99). 12. Stolley MR, Sharp LK, Wells AM, Simon N, Schiffer L. Health behaviors and breast cancer: experiences of urban African American women. Health Educ Behav. 2006;33(5):604–24. 13. Paxton RJ. The health action process approach applied to African American breast cancer survivors. Psychooncology. 2016;25(6):648–55. 14. Oyekanmi G, Paxton R. Barriers to physical activity among African American breast cancer survivors. Psychooncology. 2014;23(11):1314–7. 15. Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the health action process approach (HAPA). Rehabil Psychol. 2011;56(3):161–70. 16. Noar S, Benac C, Harris M. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull. 2007;133(4):673–93. 17. Graves KD. Social cognitive theory and cancer patients’ quality of life: a meta-analysis of psychosocial intervention components. Health Psychol. 2003;22(2):210–9. 18. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. 19. Bandura A. Self-efficacy: toward a unifying theory of behavior change. Psychol Rev. 1997;84(2):191–215. 20. Maiman L, Becker M. The health belief model: origins and correlates in psychological theory. Health Educ Behav. 1974;2(4):336–53. 21. Lippke S, Ziegelmann JP, Schwarzer R, Velicer WF. Validity of stage assessment in the adoption and maintenance of physical activity and fruit and vegetable consumption. Health Psychol. 2009;28(2):183–93. 22. Lippke S, Fleig L, Pomp S, Schwarzer R. Validity of a stage algorithm for physical activity in participants recruited from orthopedic and cardiac rehabilitation clinics. Rehabil Psychol. 2010;55(4):398–408. 23. Schwarzer R, Schuz B, Ziegelmann JP, Lippke S, Luszczynska A, Scholz U. Adoption and maintenance of four health behaviors: theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Ann Behav Med. 2007;33(2):156–66. 24. Parschau L, Barz M, Richert J, Knoll N, Lippke S, Schwarzer R. Physical activity among adults with obesity: testing the health action process approach. Rehab Psych. 2014;59(1):42–9. 25. Renner B, Spivak Y, Kwon S, Schwarzer R. Does age make a difference? Predicting physical activity of South Koreans. Psychol Aging. 2007;22(3):482–93. 26. Luszczynska A, Schwarzer R. Planning and self-efficacy in the adoption and maintenance of breast self-examination: a longitudinal study on self-regulatory cognitions. Psychology Health. 2003;18:93–108. 27. Schuz B, Sniehotta F, Mallach N, Wiedemann A, Schwarzer R. Predicting transitions from preintentional, intentional and actional stages of change. Health Educ Res. 2009;24(1):64–75. 28. Paxton RJ, Nayak P, Taylor WC, Chang S, Courneya KS, Schoer, Hodges K, Jones LA. African-American breast cancer survivors’ preferences for various types of physical activity interventions: a Sisters Network Inc. Web-based survey. J Cancer Surviv. 2014;8(1):31–8. 29. Johnson-Kozlow M, Rock CL, Gilpin EA, Hollenbach KA, Pierce JP. Validation of the WHI brief physical activity questionnaire among women diagnosed with breast cancer. Am J Health Behav. 2007;31(2):193–202. 13 J Immigrant Minority Health 30. Schwarzer R. Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. J Applied Psyc. 2008;67:1–29. 31. Paxton RJ, Gao Y, Herrmann SD, Norman GJ. Measurement properties of the sedentary behavior strategy self-management instrument in African American breast cancer survivors. Am J Health Behav. 2015;39(2):175–82. 32. Sniehotta FF, Scholz U, Schwarzer R. Action plans and coping plans for physical exercise: a longitudinal intervention study in cardiac rehabilitation. Br J Health Psychol. 2006;11(1):23–37. 33. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Erlbaum; 1988. 34. Lippke S, Plotnikoff R. Testing two principles of the health action process approach in individuals with type 2 diabetes. Health Psychol. 2014;33(1):77–84. 35. Craciun C, Schuz N, Lippke S, Schwarzer R. Enhancing planning strategies for sunscreen use at different stages of change. Health Educ Research. 2012;27(5):857–67. 36. Ziegelmann JP, Lippke S, Schwarzer R. Adoption and maintenance of physical activity: planning interventions in young, middle-aged, and older adults. Psychol Health. 2006;21(2):145–63. 37. Aparicio-Ting FE, Farris M, Courneya KS, Schiller A, Friedenreich CM. Predictors of physical activity at 12 month followup after a supervised exercise intervention in postmenopausal women. Int J Behav Nutr Phys Act. 2015;12:55. 38. Vallance JK, Courneya KS, Plotnikoff RC, Mackey JR. Analyzing theoretical mechanisms of physical activity behavior change in breast cancer survivors: results from the activity promotion (ACTION) trial. Ann Behav Med. 2008;35(2):150–8. 13 39. Scholz U, Keller R, Perren S. Predicting behavioral intentions and physical exercise: a test of the health action process approach at the intrapersonal level. Health Psychol. 2009;28(6):702–8. 40. Barg CJ, Pomery EA, Rivers SE, Rench TA, Prapavessis H, Salovey P. Examining predictors of physical activity among inactive middle-aged women: an application of the health action process approach. Psychol Health. 2012;27(7):826–45. 41. Tavares LS, Plotnikoff RC. Not enough time? Individual and environmental implications for workplace physical activity programming among women with and without young children. Health Care Women Int. 2008;29(3):244–81. 42. Luszczynska A, Sutton S. Physical activity after cardiac rehabilitation: evidence that different types of self-efficacy are important in maintainers and relapsers. Rehab Psych. 2006;51(4):314–21. 43. Luszczynska A, Mazurkiewicz M, Ziegelmann JP, Schwarzer R. Recovery self-efficacy and intention as predictors of running: a cross-lagged panel analysis over a two-year period. Psychology of Sport Exercise. 2007;8:247–60. 44. Scholz U, Sniehotta FF, Schwarzer R. Predicting exercise in cardiac rehabilitation: the role of phase-specific self-efficacy. J Sport Exer Psychol. 2005;27:135–51. 45. Joseph R, Ainsworth B, Keller C, Dodgson J. Barriers to physical activity among African American women: an integrative review of the literature. Women Health. 2015;55(6):679–99. 46. Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the health action process approach (HAPA). Rehabil Psychol. 2011;56(3):161–70.