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Evaluating the Role of Stress, Self-Perceptions of Health, and Self-Efficacy on Physical Activity Over Time: Results from Project DRIVE

Ashlynn Riley B.A., Allison Sweeney, Ph.D., and Dawn K. Wilson, Ph.D., Dept of Psych





Most Americans are not meeting the recommended levels of physical activity. In underserved communities, these inequities are even greater, with African American women being less likely to engage in physical activity and being at greater risk for chronic disease relative to non-minority women. This study seeks to further understand the factors that support or undermine engagement in physical activity among African American women. Specifically, the present study evaluates the impact of stress, self-perceptions of health, and self-efficacy on physical activity engagement in a 10-week physical activity intervention program for African American women. Participants (N = 33) completed measures of stress, self-efficacy, and self-perceptions of health at a baseline assessment as part the Developing Real Incentives and Volition for Exercise (DRIVE) study. As part of a group-based intervention, participants received FitBits to track their daily steps and were guided each week in setting a physical activity goal. A linear mixed effects model was used to test whether high stress, low self-efficacy, and low health-related quality of life was associated with lower daily steps across the 10-week intervention. There was a significant interaction between stress and time (B = -1440.250, SE = 644.210, p = .029), such that there was an increase in daily steps across time for those with low stress, but not for those with high stress. There was also a marginal interaction between self-efficacy and time (B= -985.230, SE = 498.090, p = .052), such that there was an increase in daily steps across time for those with relatively low self-efficacy, but not for those with high self-efficacy. There was no significant interaction between self-perceptions of health and time when predicting daily steps. The present study suggests that stress may be important correlates to consider for understanding engagement in behavioral interventions. Continuing to evaluate the best practices for helping underserved communities participate in behavioral interventions holds promise for reducing major health disparities.



There is substantial evidence supporting the positive effects physical activity has on one’s health (Lee et al., 2012). The Centers for Disease Control and Prevention recommends that adults complete at least 150 minutes of moderate to vigorous intensity aerobic physical activity a week in order to receive substantial health benefits. (U.S. Department of Health and Human Services, 2018) However, most American adults are not meeting this guideline (U.S. Department of Health and Human Services, 2018). Among African American communities, engagement in physical activity ranks lowest with only 35% of African American women meeting the national physical activity guideline relative to 50.9% of White women (Benjamin et al., 2018). Prevalence rates for physical activity tend to be self-reported and overestimated, thus these inequities may be even greater. Low levels of physical activity place African American women at a higher risk for many chronic diseases, including type 2 diabetes, hypertension and stroke (Benjamin et al., 2018). This has led to an almost 3 times higher risk of overall mortality for African American women compared to Caucasian women. (Nies, Vollman, & Cook, 2002)

These findings show the considerable need for physical activity interventions in African American women, yet this group continues to be highly underrepresented in clinical trials (Haughton et al., 2018). This may be, in part, due to enhanced barriers and lack of cultural competency when recruiting and engaging underserved communities. (Otado et al., 2015) Past research has found that lack of time, low motivation, tiredness/fatigue, caregiving responsibilities and lack of social support are major barriers to physical activity among African American women. (Joseph, Ainsworth, Keller, & Dodgson, 2015) Current reviews of behavioral lifestyle interventions involving African American women show that regular attendance and sustainability of outcomes remain critical issues (Lemacks, Wells, Ilich, & Ralston, 2013). These findings suggest that it may be important to further identify barriers at the onset of a study in order to adapt and improve behavioral interventions to meet the needs of African America women. We propose that more research is needed with African American women in order to understand the barriers that interfere with engagement in behavioral lifestyle interventions and propose evaluating three potential correlates of engagement in physical activity: perceived stress, health-related quality of life, and self-efficacy.

Stress is defined here as ongoing demands that threaten to exceed the resources of an individual in many areas of life, such as, family and work (Dunkel Schetter & Dolbier, 2011). It becomes a negative response when a person faces continuous challenges that threaten their well-being. Numerous studies have shown that stress can worsen certain symptoms of disease and is linked to 6 of the leading causes of death such as heart disease, cancer and diabetes (Cleveland Clinic, 2015). Previous research has shown that African American women have higher levels of stress compared to their peers (Williams, Mohammed, Leavell, & Collins, 2010). Studies show higher allostatic load scores in African Americans compared to White women, suggesting greater prolonged activation of the sympathetic nervous system. Additionally, African American women were found to have consistently higher levels of stress than African American men, possibly due to the double jeopardy of racial and gender discrimination (Williams et al., 2010). High levels of stress are associated with poor health behavior patterns such as low levels of physical activity (Allison, Adlaf, Ialomiteanu, & Rehm, 1999; Stults-Kolehmainen & Sinha, 2014) and depression (Dunkel Schetter & Dolbier, 2011), but engaging in physical activity can also be an important coping strategy for reducing stress (Gauvin, Rejeski, & Norris, 1996). Given this bi-directional relationship between stress and physical activity, we propose that it is important to further evaluate how stress relates to engagement in physical activity among African American women.

Health-Related Quality of Life (HRQOL) encompasses aspects of overall quality of life that can be clearly shown to affect health, either mental or physical. HRQOL has been shown to correlate with physical activity, as well as health risks, functional status, social support and socioeconomic status. (U.S. Department of Health & Human Services, n.d.; Kruger, Bowles, Jones, Ainsworth, & Kohl, 2007).  Consistent with findings that chronic disease is a major concern among African American women (Benjamin et al., 2018), African Americans also tend to report worse HRQOL than their peers. (Assari, Smith, & Bazargan, 2019). Relatedly, many qualitative studies have found that health limitations are viewed as a barrier to physical activity among African American women (Joseph, Ainsworth, Keller, & Dodgson, 2015). Despite the importance of HRQOL, surprisingly little research has examined whether perceiving oneself as healthy or unhealthy impacts engagement in physical activity. Thus, the present study focuses on self-perceptions of physical health as a potential a barrier to engagement among African American women.

Self-efficacy refers to an individual’s belief in his or her capacity to execute behaviors and reflects confidence in one’s ability to have control over their motivation, behavior and the social environment (Bandura, 2010). Social Cognitive Theory proposes that self-efficacy plays an essential role in how people set goals and how they respond to setbacks (Bandura, 2010). Individuals with higher levels of self-efficacy tend to engage in greater physical activity (Elavsky et al., 2005; Sharma, Sargent, & Stacy, 2005; Shieh, Weaver, Newsome, Hanna, & Mogos, 2015). Previous research shows that when assessing self-efficacy for exercise, African Americans first show higher rates but show a significant decline in self-efficacy over time compared to their White peers. (Martin, Dutton, & Brantley, 2004) Approaches for enhancing self-efficacy include developing volition for exercise and engaging in self-monitoring of physical activity (Sniehotta, Scholz, & Schwarzer, 2005). Given the importance of self-efficacy for sustained self-regulation, we propose that more research is needed to evaluate how initial levels of self-efficacy among African American women impact engagement in physical activity over the course of an intervention program.

The present study uses data collected as part of the Developing Real Incentives and Volition for Exercise (DRIVE) program: a randomized pilot study evaluating two group-based physical activity intervention programs for African American women. The 10-week intervention program evaluates different approaches for engaging inactive African American women in greater physical activity. The aim of the present study is to identify factors that interfere with engagement during the intervention program in order to further understand how to improve behavioral intervention programs for African American women. Specifically, the present study tests the hypothesis that participants who start the intervention with high levels of stress, low self-perceptions of health, and low self-efficacy will engage in lower levels of physical activity across time.




Thirty-three African American women were recruited to participate in the ongoing DRIVE pilot intervention study (see Table 1 for demographics). Participant eligibility criteria included: 1) > 21 years of age; and 2) engaging in < 150 minutes of moderate to vigorous physical activity per week for the last three months. Exclusion criteria included: 1) having a condition that would limit participating in physical activity as assessed with the Physical Activity Readiness Questionnaire; or 2) uncontrolled blood pressure (systolic >180 mmHg/diastolic  >110 mmHg). Participants were recruited from a church-based community center in a suburban southeastern community.

Study design

This study used data collected as part of a randomized pilot study, which aimed to provide proof-of-concept for two group-based motivationally targeted intervention programs. This program of research was developed from qualitative data collected from focus groups with inactive African American women about physical activity barriers and facilitators (Sweeney, Wilson, & Brown, 2019) and a pilot study evaluating the feasibility and acceptability of the two intervention programs (Sweeney et al., 2020). Both interventions included a baseline week, 8 weeks of intervention, and a post-intervention measurement week, for a total of 10 weeks. Participants were randomized to a challenge-focused program (targeted toward high autonomous motivation), which focused on enjoyment, excitement, and valuation of PA through competitive intergroup PA games and the use of weekly team-based goals; or a rewards-focused program (targeted toward low autonomous motivation), which focused on partner-based support and competency for PA through non-competitive group walking, individual-based goals, and performance-contingent financial incentives.

Randomization was stratified by autonomous motivation for PA at baseline by dividing participants into terciles based on their autonomous motivation (low, medium or high) and then randomizing half of the participants within each tercile to each of the two interventions. Both intervention programs involved completing weekly 90 minutes group sessions and followed the same structure: 1) goal feedback; 2) health curriculum; 3) physical activity session; and 4) behavioral skills training. Participants received FitBits in week 2. Measures were obtained at baseline and week 10 by trained research assistants, including accelerometry-assessed physical activity, height, weight, and a psychosocial survey. The study was approved by the University of South Carolina’s IRB and participants completed informed consent upon enrollment. Table 2 provides an overview of the health curriculum and Table 3 reviews the essential elements of the two intervention programs.



Demographic and anthropometric measures. Age, marital status, education, annual household income, number of children living at home, and body mass index (BMI) were assessed at baseline. A SCA scale and wall-mounted height board were used to measure weight and height.

Autonomous Motivation for PA. The Behavioral Regulation for Exercise Questionnaire (BREQ-3) was used to measure autonomous motivation for PA prior to randomization. The BREQ-3 consists of 24 items rated on a 6-point Likert scale (α = .84) (Markland & Tobin, 2004). The scale consists of subscales measuring low autonomy (introjected, external motivation; e.g., “I feel under pressure from my family to exercise”) and high autonomy (intrinsic, integrated, identified; e.g., “Exercise is consistent with my life goals”). An autonomy index score was computed by applying a weight to each subscale and then summing the weighted scores, with higher scores indicating greater autonomous motivation (Wilson, Rodgers, Loitz, & Scime, 2007). In the present study, this scale was included as a control variable to account for the anticipated treatment effects. 

Stress. Stress was measured with the 10-item Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983) which measures ongoing stress within the last month (α = .86). An example item is, “In the last month, how often have you been upset because of something that happened unexpectedly?”. Participants used a 5-point Likert scale where 1 = never and 5 = very often. Multiple studies have proven the reliability and internal structural validity of the PSS (Medvedev et al., 2019; Siqueira Reis, Ferreira Hino, & Romélio Rodriguez Añez, 2010).

Self-Perceptions of Health. The HRQOL measure was used for the present study to evaluate participants’ self-perceptions of their physical health. (Atlanta, GA: Centers for Disease Control and Prevention; 2000). This questionnaire has been previously validated in several national studies. (Hennessy, Moriarty, Zack, Scherr, & Brackbill, 1994) The present study used a single item from this scale to evaluate self-perceptions of physical health “Would you say in general your health is,” which was answered using a 5-point scale ranging from 1 = Excellent to 5 = Poor. This scale also includes items about days of poor physical and mental health within the last month, which are scaled from 0-30. Given that days of physical and mental health are on a different scale then self-perceptions of health, these items were not included in the present analyses.

Self-Efficacy for Physical Activity. The 10-item self-efficacy for physical activity scale was used to evaluate self-efficacy (Sallis, Pinski, Grossman, Patterson, & Nader, 1988). This scale includes items about confidence for dealing with different barriers to physical activity, such as “How confident are you that you can stick to being physically active when your family is demanding more time from you.” (α = .90) This questionnaire has demonstrated high levels of reliability and validity. (Sallis et al., 1988)

Physical Activity. Participants received a FitBit Flex 2 during week 2 of the intervention, which provided estimates of participants’ daily steps and active minutes. The FitBit Flex 2 is waterproof, and participants were asked to wear the device on their non-dominant wrist during all waking hours. Profiles were created for participants prior to distributing the Fitbits, including entering participants’ height and weight data (measured at baseline) to yield more accurate calorie estimates. Data from the FitBits was used to create average daily steps during three time points: weeks 2-4 (time 1), weeks 5-7 (time 2), and week 8-9 (time 3). Note that no FitBit data was collected during the first and final week of the program.

Goal-Setting. In both intervention programs, participants set a weekly PA goal that was specific, measurable, attainable, realistic, and time-bound (SMART). In the Rewards-program, participants set an individual-based goal, whereas in the Challenge-focused program participants set a team-based goal (see Table 3). Participants were required to specify quantities, including steps/minutes/days (e.g., “Walk for 20 minutes, 3 days per week.”)  Fitabase (Small Steps Labs LLC) was used to compile participants’ PA data and provide participants with a personalized summary of their weekly PA and feedback about their progress toward meeting their weekly goal.

Data Analytic Plan. A hierarchical linear mixed effects model was conducted to evaluate whether the association between perceived stress, self-perceptions of health, self-efficacy and average daily steps changed across time. Treatment condition (dummy coded (0/1) as 1 = challenge), baseline autonomous motivation, age, education (dummy coded (0/1) as 1 = college or greater), and baseline BMI were included as covariates. All continuous variables were mean centered prior to analysis. First, model 1 evaluated the association between the covariates and average daily steps. Model 2 included the addition of the main effects of stress, self-perceptions of health, and self-efficacy and Model 3 included the addition of the time*stress, time* self-perceptions of health, and time*self-efficacy interactions. The models included a random intercept for subjects.



As shown in Table 4, the final model revealed that being in the challenge program was associated with greater steps,[1] whereas being older was associated with lower steps. Furthermore, there was a significant interaction between stress and time (B = -1440.250, SE = 644.210, p = .029). Although the interaction between self-perceptions of health and time was not significant, the interaction between

self-efficacy and time approached significance (B = -985.230, SE = 498.090, p = .052). Participants with relatively low stress (1 SD below the mean) showed an increase in daily steps across time (B = 1252.910, SE = 518.723, p = 0.019), whereas participants with high stress (1 SD above the mean) did not show a significant change in daily steps across time (B = -460.353, SE = 502.065, p = 0.363). Although the interaction between self-efficacy and time did not meet the a priori criteria of p < .05, we conducted exploratory analyses to evaluate the relationship between self-efficacy and time. Participants with relatively low self-efficacy (1 SD below the mean) showed an increase in steps across time (B = 1166.001, SE = 535.414, p = .033), whereas participants with high self-efficacy (1 SD above the mean) did not show a significant change in daily steps across time (B = -372.810, SE = 533.301, p = .487).



The primary aim of this study was to evaluate whether stress, self-perceptions of health, and self-efficacy at the beginning of an intervention program impacted engagement in physical activity across time. There was a significant interaction between stress and time, such that there was an increase in daily steps across time for African American women with low stress, but not for those with high stress. The results also indicated that there was an increase in daily steps across time for women who started with relatively low self-efficacy, but not for those with high self-efficacy. However, the findings around self-efficacy were relatively small in magnitude and should be considered as preliminary given that the p-value for the interaction was marginal. Lastly, we found that there was no significant interaction between self-perceptions of health and time when predicting daily steps.

The findings of this study add to existing literature on African American women and physical activity engagement in several ways. In this study we found that African American women showed the greatest increase in daily steps when they had low levels of perceived stress. This finding is consistent with previous studies which have found that stress has a negative impact on physical activity. (Allison et al., 1999; Stults-Kolehmainen & Sinha, 2014) Based on our results, future studies may consider addressing stress management more systematically throughout physical activity intervention programs, especially given that stress tends to be elevated in African American women. (Williams et al., 2010).

The present study also adds to our understanding of how self-efficacy relates to physical activity among African American women. We hypothesized that low self-efficacy would be detrimental for physical activity, but instead found that those with low self-efficacy showed a greater increase in daily steps over time. One reason for this finding could be that both intervention programs were designed to increase self-efficacy and incorporated a variety of strategies that targeted competency and volition, including the use of self-monitoring through FitBits. The fact that we found that those with low self-efficacy showed the greatest improvements may provide preliminary evidence that the interventions are meeting needs around building confidence for physical activity. This finding (although it should be interpreted preliminarily as described above) adds to the growing literature demonstrating the importance of self-efficacy for self-regulation. (Elavsky et al., 2005; Sharma, Sargent, & Stacy, 2005; Shieh, Weaver, Newsome, Hanna, & Mogos, 2015) Finally, our findings did not a show a significant association between self-perceptions of health and physical activity. However, given that we used a single-item measure, future studies may consider using more rigorous methods and examining other aspects of HRQOL over the course of more long-term interventions. (Kaplan et al., 2019). 

It should be noted the limitations that exist in this study. First, this study included a relatively small sample size, which limits the generalizability of these findings. Additionally, although the present included an 8-week intervention period, more research is needed to evaluate how these barriers impact physical activity over a longer time period. Finally, these results are correlational, which means that causal conclusions about the relationship between stress, self-efficacy and physical activity engagement cannot be made.  However, this study also had several strengths, such as the use of an objective measure of physical activity through FitBits. The present study also integrated daily data collection across an 8-week intervention period, which yielded an in-depth evaluation of physical activity over time. Lastly, the focus of this study was on an underserved population that is often underrepresented in research.

In conclusion, the present study suggests that stress and self-efficacy may be important

factors to consider for understanding engagement in physical activity among African American women. Low levels of physical activity remain a major health concern in underserved communities. Continuing to evaluate the best practices for developing and facilitating behavioral interventions for physical activity in underserved communities holds promise for reducing major health inequities and chronic disease.


About the Author

Ashlynn Riley Ashlynn Riley

I am from Irmo, South Carolina, just 25 minutes away from USC Columbia’s beautiful campus. I graduated in December of 2019 with a Bachelor of Arts in Public Health and a minor in Psychology. After achieving this degree, I will be attending graduate school at The Medical University of South Carolina to attain a Doctorate degree in Occupational Therapy. I chose to assist in research with USC’s Behavioral Medicine Research Group because I too was passionate about their ongoing projects related to physical activity interventions in underserved communities. Working with the professionals and other colleagues in this department has given me an abundance of experience in the process of research. I will continue to use the knowledge I have gained in my future academic endeavors. Thank you to my mentors, Dr. Sweeney, Dr. Wilson, the USC Behavioral Medicine Research Group and the M. H. Newton Family Life Community Center for giving me so many opportunities and allowing me to grow my skills in this field.  



Allison, K. R., Adlaf, E. M., Ialomiteanu, A., & Rehm, J. (1999). Predictors of Health Risk Behaviours Among Young Adults: Analysis of the National Population Health Survey. Canadian Journal of Public Health, 90(2), 85–89.

Assari, S., Smith, J., & Bazargan, M. (2019). Health-related quality of life of economically disadvantaged African American older adults: Age and gender differences. International Journal of Environmental Research and Public Health, 16(9), 1522.

Bandura, A. (2010). Self-Efficacy. In The Corsini Encyclopedia of Psychology (pp. 1–3).

Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., … American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. (2018). Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation, 137(12), e67–e492.

CDC – National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health – Homepage. Atlanta, GA. March 6, 2020

Cleveland Clinic. (2015). What Is Stress? Retrieved October 8, 2019, from

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A Global Measure of Perceived Stress. Journal of Health and Social Behavior, 24(4), 385.

Dunkel Schetter, C., & Dolbier, C. (2011). Resilience in the Context of Chronic Stress and Health in Adults. Social and Personality Psychology Compass, 5(9), 634–652.

Elavsky, S., McAuley, E., Motl, R. W., Konopack, J. F., Marquez, D. X., Hu, L., … Diener, E. (2005). Physical activity enhances long-term quality of life in older adults: Efficacy, esteem, and affective influences. Annals of Behavioral Medicine, 30(2), 138–145.

Gauvin, L., Rejeski, W. J., & Norris, J. L. (1996). A naturalistic study of the impact of acute physical activity on feeling states and affect in women. Health Psychology, 15(5), 391–397.

Haughton, C. F., Silfee, V. J., Wang, M. L., Lopez-Cepero, A. C., Estabrook, D. P., Frisard, C., … Lemon, S. C. (2018). Racial/ethnic representation in lifestyle weight loss intervention studies in the United States: A systematic review. Preventive Medicine Reports, 9, 131–137.

Hennessy, C. H., Moriarty, D. G., Zack, M. M., Scherr, P. A., & Brackbill, R. (1994). Measuring health-related quality of life for public health surveillance. Public Health Reports (Washington, D.C. : 1974), 109(5), 665–672. Retrieved from

Joseph, R. P., Ainsworth, B. E., Keller, C., & Dodgson, J. E. (2015). Barriers to Physical Activity Among African American Women: An Integrative Review of the Literature. Women & Health, 55(6), 679–699.

Kaplan, R. M., Gold, M., Duffy, S. Q., Miller, N., Glassman, J. R., Chambers, D. A., … Wilson, D. K. (2019). Economic analysis in behavioral health: Toward application of standardized methodologies. Health Psychology, 38(8), 672–679.

Kruger, J., Bowles, H. R., Jones, D. A., Ainsworth, B. E., & Kohl, H. W. (2007). Health-related quality of life, BMI and physical activity among US adults (⩾18 years): National Physical Activity and Weight Loss Survey, 2002. International Journal of Obesity, 31(2), 321–327.

Lee, I.-M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The Lancet, 380(9838), 219–229.

Lemacks, J., Wells, B. A., Ilich, J. Z., & Ralston, P. A. (2013). Interventions for improving nutrition and physical activity behaviors in adult African American populations: a systematic review, January 2000 through December 2011. Preventing Chronic Disease, 10, E99.

Markland, D., & Tobin, V. (2004). A modification to the behavioural regulation in exercise questionnaire to include an assessment of amotivation. Journal of Sport and Exercise Psychology, 26(2), 191–196.

Martin, P. D., Dutton, G. R., & Brantley, P. J. (2004). Self-efficacy as a predictor of weight change in African-American women. Obesity Research, 12(4), 646–651.

Medvedev, O. N., Krägeloh, C. U., Hill, E. M., Billington, R., Siegert, R. J., Webster, C. S., … Henning, M. A. (2019). Rasch analysis of the Perceived Stress Scale: Transformation from an ordinal to a linear measure. Journal of Health Psychology, 24(8), 1070–1081.

Nies, M. A., Vollman, M., & Cook, T. (2002). African American Women’s Experiences with Physical Activity in their Daily Lives. Public Health Nursing, 16(1), 23–36.

Otado, J., Kwagyan, J., Edwards, D., Ukaegbu, A., Rockcliffe, F., & Osafo, N. (2015). Culturally Competent Strategies for Recruitment and Retention of African American Populations into Clinical Trials. Clinical and Translational Science, 8(5), 460–466.

Sallis, J. F., Pinski, R. B., Grossman, R. M., Patterson, T. L., & Nader, P. R. (1988). The development of self-efficacy scales for healthrelated diet and exercise behaviors. Health Education Research, 3(3), 283–292.

Sharma, M., Sargent, L., & Stacy, R. (2005). Predictors of Leisure-time Physical Activity Among African American Women. American Journal of Health Behavior, 29(4), 352–359.

Shieh, C., Weaver, M., Newsome, K., Hanna, K., & Mogos, M. (2015). How Self-Efficacy and Self-Regulation Influence Nutrition and Exercise Behaviors of a Community Sample of Adults. Retrieved from

Siqueira Reis, R., Ferreira Hino, A. A., & Romélio Rodriguez Añez, C. (2010). Perceived Stress Scale. Journal of Health Psychology, 15(1), 107–114.

Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2005). Bridging the intention–behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychology & Health, 20(2), 143–160.

Stults-Kolehmainen, M. A., & Sinha, R. (2014). The Effects of Stress on Physical Activity and Exercise. Sports Medicine, 44(1), 81–121.

Sweeney, A. M., Wilson, D. K., & Brown, A. (2019). A qualitative study to examine how differences in motivation can inform the development of targeted physical activity interventions for African American women. Evaluation and Program Planning, 77, 101718.

Sweeney A., Wilson D.K., Zarrett N., Van Horn M.L., Resnicow, K. (2020). The Feasibility and Acceptability of the Developing Real Incentives and Volition for Exercise (DRIVE) program for Promoting Physical Activity in African American Women. Under Review.

U.S. Department of Health & Human Services. (n.d.). HRQOL Concepts | CDC. Retrieved March 1, 2020, from

U.S. Department of Health and Human Services. (2018). Physical Activity Guidelines for Americans, 2nd edition. Washington, DC.

Wenger, N. K., Mattson, M. E., Furberg, C. D., & Elinson, J. (1984). Assessment of quality of life in clinical trials of cardiovascular therapies. American Journal of Cardiology, 54(7), 908–913.

Wilson, P. M., Rodgers, W. M., Loitz, C. C., & Scime, G. (2007). “It’s Who I Am … Really!’ The Importance of Integrated Regulation in Exercise Contexts1. Journal of Applied Biobehavioral Research, 11(2), 79–104.

Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186, 69–101. 


Table 1. Baseline characteristics (N = 33)



Age M (SD)

53.48 (14.58)

Married (%)


Employment (N)








Permanently Disabled




Education (N)


< HS degree


HS degree


Some college/technical school


College degree


Graduate/professional degree


Income (N)


 < $10,000








$55,000 or greater





40.02 (9.04)





Table 2. Summary of health curriculum used in both interventions




Behavioral Skills

1  “Getting Started”

Introduce Staff and program overview

Positive communication skills


Why are we here discussion

Communication and ground rules

Baseline Measures

2 “Making Lifestyle Changes through Effective Goals”

Lifestyle changes


National PA guidelines

Overcoming barriers



Distribute FitBits


3 “Finding time for Being Active”

Importance of tracking PA


PA intensities

Making time for PA (lifestyle approach)

Making time for PA


4 “Getting Support”

The role of family/friends in PA

Tangible social support

Types of social support

Lifestyle approaches to support

Strategies for increasing support for PA

Engagement strategies

5 “ Building a Healthy Plate”

Introduce nutrition basics

Hunger and satiety cues

Energy balance

Strategies for balancing hunger

6 “Managing stress”

Introduction to stress management

Problem and emotion-focused coping

Coping strategies and relaxation techniques

Progressive Muscle Relaxation

7 “Creating Healthy Lifestyles Together”

Shopping for healthy food

Planning healthy meals

Communicating with and engaging family members

Engagement strategies


Push/Pull Language

8 “Move More, Sit Less”

Sedentary Behavior and screen time


Risk associated with sedentary time

Overcoming barriers

Strategies for sitting less


9 “Promoting a Lifetime of Health”

Relapse prevention and triggers


Reframing and staying positive

Positive self-talk

Positive body-image


10 “Staying Motivated”

Review progress


Group testimonials


Post-Program measures




Table 3. DRIVE Theoretical Essential Elements


Challenge-Focused Intervention



Essential Elements

Description of Program Elements


Self Determination Theory

Group-Based Social Support

Facilitators model and reinforce a positive social climate. Participants are encouraged to support each other throughout the week through the FitBit app.


Self Determination Theory

Enjoyment, Excitement, and Valuation of PA

Participants complete competitive intergroup activities at each session and set a weekly team-based goal.


Social Cognitive Theory

Group-Based Goal-Setting

Guided by the facilitators, participants select and agree upon a weekly team-based PA goal, which they track individually with their FitBits.


Social Cognitive Theory

Group-Based Problem-Solving

Guided by the facilitators, participants share anticipated or actual PA barriers and brainstorm problem-solving strategies as a group.


Social Cognitive Theory

Group-Based Self-Efficacy

By working in teams, participants have opportunities to practice and master group-based behavioral strategies for engaging in PA




Rewards-Focused Intervention



Essential Elements

Description of Program Elements


Self Determination Theory

Partner-Based Social Support

Participants work with a partner to develop an action-plan for supporting one another in meeting their weekly PA goals.


Self Determination Theory

Competency for PA

Participants complete a 20-minute group-walk at each session.


Social Cognitive Theory

Individual-Based Goal-Setting

Guided by the facilitator, participants develop a weekly personal PA goal, which they track individually with their Fitbits.


Social Cognitive Theory

Individual-Based Problem-Solving

Guided by the facilitator, participants generate anticipated or actual PA barriers and brainstorm problem-solving strategies.


Social Cognitive Theory

Individual-Based Action-planning

Participants develop an action-plan, including specifying when and how they will complete their weekly PA goal, which is integrated into their weekly partner-based action plan.


Social Cognitive Theory

Individual-Based Self-Efficacy

By working with a partner, participants have opportunities to practice and master behavioral strategies for engaging in PA.


General Interest Theory

Interest in PA

Participants receive financial incentives to build interest in engaging in regular PA.




Table 4. Linear mixed model predicting average steps over time


Model 1

Model 2

Model 3































Treatment condition










Autonomous Motivation




















College Educated










Body Mass Index










Self-Perceived Health
























Self-Perceived  Health*Time




















Note. Likelihood ratio tests indicated that the addition of the main effects in Model 2 did not significantly reduce the residual from Model 1 (χ2 (3) =.74, p  = .86). The addition of the interaction terms yielded a marginal improvement in the residual from Model 2 (χ2 (3) =6.53, p = .09)

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