Self-Efficacy, Social-Support, and Physical Activity Measures Among Hospital Employees: A Multisite Cross-Sectional Study

in Journal of Physical Activity and Health

Introduction: Associations across self-efficacy, social support, and multiple measures of physical activity (PA) have not been thoroughly explored in hospital employees. Methods: Validated surveys assessed psychosocial factors; the IPAQ-long assessed PA, and mixed-effects analyses examined relations between psychosocial variables and PA in 920 employees from 6 Texas hospitals. Results: At P <.05, self-efficacy was significantly associated with light (β = 1.67), moderate (β = 1.63), and vigorous (β = 2.78) leisure PA; with domestic PA (β = 1.64); and with moderate commute PA (β = 0.03). At P < .05, family social-support was significantly associated with light (β = 0.94), moderate (β = 0.63), and vigorous (β = .74) leisure PA; with moderate (β = 0.46) and vigorous (β = 1.24) occupation PA; with light (β = 0.58) and moderate (β = 0.20) commute PA; and with domestic PA (β = 1.18). At P < .05, social support from friends was significantly associated with light (β = 0.74), moderate (β = 0.58), and vigorous (β = .91) leisure PA; with moderate commute (β = 0.21); and with domestic PA (β = 0.82). Conclusion: Interventions must emphasize self-efficacy–building strategies and the role of family support to meaningfully impact PA behaviors in uniquethis unique population.

Physical activity (PA) provides numerous health benefits, such as risk reduction for various chronic diseases, improved cardiovascular and metabolic function, and weight regulation.15 PA engagement has also been linked to sound mental health and improved daily functioning69; individuals who do so regularly are more likely to be productive and less likely to be depressed, anxious, or irritable on the job.6,8,9 Yet, despite widespread public awareness of its benefits, less than a quarter of the United States (US) adult population meets national guidelines for time spent in leisure-time aerobic and muscle-strengthening activities.10

One of the strongest associations with physical inactivity is obesity.3,1113 An examination of the distribution of obesity rates by the employment industry shows that the health care industry has the highest rate of obesity at 32%,3 followed by the public administration and social service industries.3,1416 The data behind this 32% obesity rate implicate irregular work schedules, long hours, shift work, and an inadequate number of breaks as significant risk factors for weight gain and disruption in metabolic and circadian function.15,16

US health care employees are just as likely to engage in health-compromising behaviors as the rest of the US population.17,18 A study by Albert et al18 found that only 30% of its study population was sufficiently active, and the nurses who were more physically active were less likely to be smokers (P < .05) and had a lower body mass index (P = .0003).18 In the outcomes paper for the Shape Up Houston (SUH) study, participants did not meet the national guidelines for fruit and vegetable consumption, and obesity was positively linked to the consumption of fatty, sugary, and starchy foods; sedentary behaviors; blood pressure; blood glucose; and low-density lipoprotein and was inversely associated with high-density lipoprotein.17 Collectively, these findings suggest that, despite knowing the benefits of engaging in healthy behaviors and the need to model these behaviors to patients, health care workers were not putting such knowledge and behaviors into active practice. This is particularly worrisome because health care providers are less likely to recommend and motivate patients toward the adoption of healthy behaviors if they themselves struggle with embracing a healthy lifestyle.1921 As such, it is possible that the level of the quality of health care that these patients receive is undermined, particularly at a time when they are most likely to be vulnerable and impressionable. In recognizing the potential effects of this problem, it is important that behavioral interventions target this population, which is uniquely positioned to help patients engage in behaviors that positively influence their quality of life.

Behavioral research recognizes that various psychosocial factors within an individual’s interpersonal and intrapersonal sphere help influence and explain one’s attitudes and behaviors.5,22 In this paper, we focused on self-efficacy and social-support (family and friends) associations with PA, as research shows that these 2 psychosocial factors are facilitators of positive health behaviors.5,2335 Numerous studies highlight Bandura’s construct of self-efficacy as a driving force behind one’s ability to engage successfully in positive behaviors like PA, smoking cessation, and healthy eating.34,3638 The relationship between self-efficacy and PA has been previously explored in health care workers, specifically hospital employees. In the Albert et al18 study, the examination of overall self-efficacy scores for PA showed that nurses exhibited weak self-efficacy toward achieving PA 5 times per week and attempting a new PA routine. However, a significant association was found, such that nurses who were engaged in PA had higher self-efficacy for PA (P < .0001).18 Additionally, a 2017 study among nursing and medical students found that models of PA as an outcome were best predicted by self-efficacy and social support.39 These studies affirm that the uptake of leisure PA can be attributed to a strong sense of confidence in one’s ability to engage in PA, yet to our knowledge, no study has sought to examine how self-efficacy is associated with the other domains of PA (occupation, domestic, and commute) and corresponding levels of intensity (light, moderate, and vigorous) and to what extent these relations differ. As such, this study is uniquely positioned to contribute new findings to the field of behavioral research.

Similarly, the relationship between PA and social support, defined as the physical or emotional support an individual receives from network structures, like family or friends,5,24,26,27,40,41 has seldom been assessed in hospital employees. Data highlight the positive impact that social support (family and friends) has on healthy behavior outcomes, like PA engagement, healthy eating, and smoking cessation.5,25,26,4244 Collectively, these studies confirm that interpersonal relationships heavily influence personal attitudes, beliefs, and health behaviors. As such, it is incumbent upon future behavioral interventions to recognize and intervene on these social structures so that positive behavioral habits are encouraged and sustained at the individual level. To that end, the purpose of our study was 2-fold. We explored potential differences in self-efficacy, social-support, and PA scores across our population. We then conducted inferential analyses to examine the associations between self-efficacy and social support (family and friends) and PA among hospital employees, and the extent by which these relations varied across the 4 domains (commute, leisure, domestic, and occupation).

Methods

Sampling Protocol

We conducted a cross-sectional analysis on the baseline data, which were collected as part of the SUH Texas Medical Center evaluation study in fall 2012. The SUH, a 501(c)(3) nonprofit organization, contacted the hospital leadership of 6 hospitals from 5 hospital systems to implement a 6-month obesity prevention program. These 6 hospitals employ over 40,000 employees.

Research approval was requested from the University of Texas Health Science Center Institutional Review Board and from the institutional review boards of each participating hospital. The human resources department from each participating hospital sent e-mails to employees, which contained study information and an invitation to participate. If a participant was interested, he or she was then screened for eligibility by a trained study staff. The inclusion criteria were individuals who had a fourth-grade reading level of the English language, worked a minimum of 40 hours per week at any of the participating hospitals, and provided written and verbal consent. Individuals were excluded from participating if they were pregnant, had suspected anemia, were on anticoagulation therapy, had bleeding disorders like hemophilia or low platelet count, were prone to seizures, were HIV positive, or had an immune deficiency. In total, 924 participants were enrolled and consented at the baseline, and each participant was given a US$25 gift card incentive after completion of the baseline requirements.

Physical Activity

The participants recorded their engagement in the PA domains—leisure, commute, domestic, and occupation PA—and on these domains’ related levels of intensity (light, moderate, vigorous) via the International PA Questionnaire long version.17 The participants were asked about the PA that they did on a given day within the last 7 days and to record the amount of time spent on each domain-specific prompt.45

Self-Efficacy and Social-Support Scales

We assessed self-efficacy for PA using a previously validated 12-item scale.32 On a 3-point scale of “I know I cannot” to “I know I can,” the respondents rated how confident they were to consistently engage in various activities. A summative score was created to derive the self-efficacy scale, with possible scores ranging from 12 to 36; higher scores indicated higher self-efficacy to participate in exercise. We measured social support for PA using the 13-item validated social-support (family and friends) questionnaire developed by Sallis et al.27 On a 5-point scale of “none” to “very often,” the participants were asked to think of the past 3 months and then rate the extent to which their family and friends either supported or failed to support them in exercise activities. A total of 2 summative scales were created to derive the family social-support and friends social-support scales; the scores ranged from 13 to 65 for each subscale, with higher scores indicating higher social support for PA.

Socio-Demographics

A self-reported survey with standard survey items was administered in English to collect data on characteristics such as age, gender, race, education, smoking, weight, and marital status. These sociodemographic data were used as covariates in all analyses.

Statistical Analysis

Analysis for this study was conducted in Stata statistical software (version 14.0; StataCorp, College Station, TX). Cronbach alpha (α range: 0–1) was conducted to test the internal reliability of the summative scales for self-efficacy and social support (family and friends). We then examined the mean and SD of self-efficacy and social-support (family and friends) scores across levels of demographic variables. We also conducted analysis of co-variance to examine the significance of mean differences in self-efficacy and social-support scores across observed demographic variables. Finally, we used mixed-effects regression analysis techniques to evaluate the associations between psychosocial factors and PA measures and to account for within and between cluster variations. The primary independent variable in each model was either self-efficacy for PA, family social support for PA, or friend social support for PA, while the dependent variable in each model was PA (domain- and intensity-specific). A total of 9 separate models were run for each dependent PA measure (light occupation PA, moderate occupation PA, vigorous occupation PA, light leisure PA, moderate leisure PA, vigorous leisure PA, light commute PA, moderate commute PA, and moderate domestic PA), wherein the hospital site was treated as the grouping variable. We utilized purposeful variable selection to build and select the most parsimonious models (domain and intensity specific).46 Covariates—age, gender, marital status, race, education, weight status, and smoking status— were considered during model building, and bivariate analysis based on the P value <.25 exclusion criteria was used to select variables for inclusion into each model. Q–Q and probability plots and tests for normality, linearity, and independence were conducted to test linear model assumptions. Subjects with missing data were excluded from all analyses, and the final models were adjusted for covariates of interest.

Results

Participant Demographics, Self-Efficacy, and Social-Support Profiles

As shown in Table 1, in our sample, 49.5% were white, 34.7% were black, 9% were Asian, and 7% were “other.” The majority of the participants were women (85.5%), over 70% were 35 years and older, with approximately 33% being over 50 years old. Most participants (78%) were overweight or obese, and the results showed that the mean body mass index of the sample was 30.8 kg/m2. Over half of all participants (54%) completed 4 years or more in college, 78.5% were nonsmokers, and 59% were married.

Table 1

Differences in Self-Efficacy and Social-Support (Family and Friend) Scores by Demographic Characteristics of (SUH) Hospital Employees

CharacteristicsTotal Pop = 920Self-efficacy for PASocial support from family (PA)Social support from friends (PA)
  N = 487N = 640N = 642
  Range = 12–36Range = 13–65Range = 13–65
Overall100%29.93 (4.8)25.79 (10.7)24.76 (10.9)
 Cronbach alphaα range: 0–1.88.90.92
Age, y  P = .03 
 18–3425.7%29.87 (5.1)27.15 (10.8)25.09 (10.4)
 35–5041.4%29.72 (4.6)26.06 (10.6)25.75 (11.6)
 >5032.8%30.23 (4.8)24.12 (10.4)23.42 (10.1)
Gender P = .04  
 Male14.5%30.99 (4.8)26.24 (10.9)24.19 (11.5)
 Female85.5%29.72 (4.8)25.70 (10.7)24.87 (10.8)
Marital status  P < .01P < .01
 Married58.7%29.88 (4.9)27.36 (10.9)23.75 (10.7)
 Divorced/separated17.8%30.11 (4.7)22.76 (9.4)25.64 (10.6)
 Single23.5%29.97 (4.7)23.67 (10.0)26.45 (11.2)
Race
 White49.5%30.00 (4.7)25.29 (10.6)23.57 (10.5)
 Black34.7%29.87 (4.9)26.22 (11.0)26.27 (11.2)
 Asian8.6%29.29 (5.4)29.15 (10.5)27.86 (11.1)
 Other7.2%29.82 (4.4)24.28 (10.1)25.15 (10.8)
Education P = .04  
 ≤ High school12.6%28.59 (5.5)23.51 (11.3)22.64 (10.6)
 College, 1–3 y33.2%30.62 (4.3)25.53 (10.9)24.96 (11.1)
 College, 4+ y54.2%29.74 (4.9)26.28 (10.4)24.87 (10.7)
Weight status
 Normal21.8%30.80 (4.9)26.28 (11.0)25.72 (11.4)
 Overweight28.5%29.94 (4.5)25.35 (9.9)24.53 (10.3)
 Obese49.7%29.53 (4.9)25.80 (10.9)24.44 (11.0)
Smoking status P = .03 P = .02
 Nonsmoker78.5%29.98 (4.7)26.34 (10.8)25.46 (11.1)
 Former smoker18.0%30.18 (4.9)24.66 (10.7)22.94 (10.3)
 Current smoker3.5%26.47 (5.5)21.41 (9.0)19.00 (6.3)

Abbreviations: PA, physical activity; SUH, Shape Up Houston. Note: Significant analysis of covariance tests at P < .05 are in bold. All values are represented as mean (SD).

We observed significant differences (P < .05) in self-efficacy for the PA scores for gender, education, and smoking status and significant differences in social support for PA (family and friends) scores for age, marital status, and smoking status. The men had significantly higher self-efficacy for PA than the women (30.99 [4.8] vs 29.72 [4.8]), P = .04. For the education variable, the participants with 1–3 years of college (30.62 [4.3]) and 4 or more years of college (29.74 [4.9]) had significantly higher self-efficacy for PA than the participants who only received a high school education (28.59 [5.5]), P = .04. For the smoking variable, the never smokers (29.98 [4.7]) and former smokers (30.18 [4.9]) exhibited greater self-efficacy for PA engagement compared with the current smokers (26.47 [5.5]), P = .03.

For social support, the participants who were between 18–34 years (27.15 [10.8]) and 35–50 years (26.06 [10.6]) reported higher family social support for PA than the participants who were over 50 years old (24.12 [10.4]), P = .03. The married participants had higher family social support for PA (27.36 [10.9]) versus the divorced/separated (22.76 [9.4]) and single participants (23.67 [10.0]), P < .01. However, social support from friends was significantly higher in those who were single (26.45 [11.2]) compared with those who were divorced/separated (25.64 [10.6]) and those who were married (23.75 [10.7]), P < .01. Lastly, social support from friends was highest in nonsmokers (25.46 [11.1]) compared with former smokers (22.94 [10.3]) and current smokers (19.00 [6.3]), P = .02.

PA Characteristics

The results of the study participants’ PA minutes by domain and level of intensity have been described in great detail in an earlier cross-sectional paper that explored the associations between weight status (body mass index) and PA.47 The normal-weight participants self-reported more minutes in total light PA compared with the overweight and obese study participants (P = .02). The younger adults had significantly more minutes of total vigorous PA (P = .02) and leisure vigorous PA (P = .03) compared with the older participants, and the participants with only a high school education reported higher levels of vigorous occupation PA compared with those who completed college (P < .002).47 Along racial ethnic lines, the black participants reported more PA minutes in light commute (P = .02) and light leisure (P = .01) than all other racial groups, and the white participants self-reported the most minutes in domestic PA versus other racial categories (P = .03).47

Mixed-Effect Models

In this study assessing the relationships between self-efficacy, social support, and various measures of PA, we found significant associations between self-efficacy and commute, domestic, and leisure PA measures. Secondly, social support from family members was positively associated with all measures of PA, while social support from friends was associated with all measures of PA except occupation PA. These results are presented in Table 2.

Table 2

Regression Analyses for Self-Efficacy, Social-Support, and PA Domains and Levels of Intensity Among (SUH) Hospital Employees

OccupationCommuteDomesticLeisure
LightModerateVigorousLightModerateModerateLightModerateVigorous
Self-efficacy
β coefficient0.800.900.730.800.341.641.671.632.78
 95% CI−0.05 to 2.06−0.19 to 1.99−0.26 to 1.73−0.07 to 1.660.03–0.660.34–2.940.94–2.400.95–2.302.02–3.59
Social support
 Family
  β coefficient0.270.461.240.580.201.180.940.640.74
  95% CI−0.23 to 0.770.02–0.890.74–1.750.21–0.940.05–0.340.67–1.690.63–1.210.36–0.910.42–1.06
 Friends
  β coefficient0.050.3410.260.260.210.820.740.580.91
  95% CI−0.44 to 0.54−0.09 to 0.77−0.15 to 0.67−0.11 to 0.620.07–0.350.31–1.320.43–1.050.31–0.860.61–1.22
Abbreviations: CI, confidence interval; SUH, Shape Up Houston. Note: Grouping variable, hospital; significant models noted below: P value <.05.
Self-efficacy and commute moderate PA: adjusted for gender and education (n = 476)Family support and domestic PA: adjusted for age, gender, race, and education (n = 574)
Self-efficacy and domestic PA: adjusted for age, gender, education, and race (n = 438)Family support and leisure light PA: adjusted for gender, race, education smoker, and marital status (n = 552)
Self-efficacy and leisure light PA: adjusted for gender, race, education, smoking, and marital status (n = 419)Family support and leisure moderate PA: adjusted for age, gender, and education (n = 624)
Self-efficacy and leisure moderate PA: adjusted for age, gender, and education (n = 476)Family support and leisure vigorous PA: adjusted for age, gender, education, and smoker (n = 609)
Self-efficacy and leisure vigorous PA: adjusted for age, gender, education, and smoking (n = 460)Friends support and commute moderate PA: adjusted for gender and education (n = 626)
Family support and occupation moderate PA: adjusted for age, gender, smoker, marital status, and education (n = 600)Friends support and domestic PA: adjusted for age, gender, education, and race (n = 573)
Family support and occupation vigorous PA: adjusted for age, gender, education, and race, smoking (n = 560)Friends support and leisure light PA: adjusted for gender, race, education, smoking, and marital status (n = 554)
Family support and commute light PA: adjusted for age, gender, race, and education (n = 574)Friends support and leisure moderate PA: adjusted for age, gender, and education (n = 625)
Family support and commute moderate PA: adjusted for gender and education (n = 625)Friends support and leisure vigorous PA: adjusted for age, gender, education, and smoker (n = 611)

Association Between Self-Efficacy and PA

There was no statistically significant association between self-efficacy and any of the occupation-related PA measures. However, there was a significant positive association between self-efficacy and all measures of leisure PA. Self-efficacy was associated with all measures of leisure activity after controlling for covariates. Increased efficacy was positively linked to light exercise, after adjusting for  gender, race education, marital status, and smoking status (β adjusted = 1.67; P = .000), with moderate leisure exercise after adjusting for gender, age, and education status (β adjusted = 1.63; P = .000), and lastly with vigorous leisure activity after controlling for gender, age, education, and smoking status (β adjusted = 2.78; P = .000). There was also a significant positive association between both self-efficacy and domestic PA (β adjusted = 1.64; P = .013) and with the moderate commute model (β adjusted = 0.34; P = .03) when we adjusted for gender and education status.

Association Between Social-Support (Family and Friends) and PA

We observed positive associations between social support from family and many of the PA measures. Family social support was positively associated with moderate occupation PA (β adjusted = 0.46; P = .04) after controlling for age, gender, smoker, marital status, and education, and was positively associated with vigorous occupation PA (β adjusted = 1.24; P = .000) upon controlling for age, gender, education, race, and smoking. We also observed positive associations between family social support and the commute PA models, light commute (β adjusted = 0.58; P = .002) and moderate commute (β adjusted = 0.20; P = .009), after adjusting for covariates. Family-provided social support was also positively linked to domestic PA (β adjusted = 1.18; P = .000) after we adjusted for age, gender, race, and education in the model, and with all measures of leisure PA, light leisure (β adjusted = 0.94; P = .000), moderate leisure (β adjusted = 0.64; P = .00), and vigorous leisure (β adjusted = 0.74; P = .000), after we adjusted for covariates of interest in each model.

In our social support from friends and PA models, we found that friend social support had a significant positive relation with moderate commute (β = 0.21; P = .004) after controlling for gender and age, and a positive association with domestic PA (β adjusted = 0.82; P = .002) after adjusting for age, gender, education, and race. We also observed that friend-provided social support was positively associated with all measures of leisure PA after adjusting for covariates in each model: light leisure (β adjusted = 0.74; P = .000), moderate leisure (β adjusted = 0.58; P = .000), and vigorous leisure (β adjusted = 0.91; P = .000).

Discussion

Self-Efficacy

Self-efficacy scores were significantly higher in men compared with women. Not surprisingly, in this study, men also reported higher levels of PA engagement than women. Previous research suggests that disproportionate access to opportunities, PA experiences, and possible social biases and expectations for each gender may help explain overall differences in PA output as well as men’s higher self-efficacy and PA levels at every stage in life.4853 Given that self-efficacy is nurtured through experiences,54,55 it is quite possible that women, on average, are less efficacious in PA due to an uneven distribution in PA opportunities between the two genders. Equal access to PA opportunities for both genders can help stem PA and self-efficacy disparities that, unfortunately, persist into adulthood. We also observed differences in self-efficacy scores across education levels. Studies show that people with more years of formal education are financially positioned to afford access to better neighborhoods, resources, and other amenities; these assets collectively engender a greater confidence to engage in healthy lifestyles that include behaviors like PA engagement.5660 Lastly, the smokers in the study had lower self-efficacy for PA compared with the never smokers or former smokers. This finding is reinforced by existing studies, which show that smokers are less likely to exercise, have lower PA endurance, and are less likely to participate in healthy behaviors, like proper nutrition, low alcohol intake, and good sleep (duration and quality).22,6166 Because behaviors (either positive or negative) often coexist, behavioral interventions should be designed with a multifocused approach to collectively address these behaviors and promote healthy lifestyles in this population.

The results from our mixed-effects analysis showed that self-efficacy had positive, significant associations with many of our PA measures. This is consistent with behavioral studies, which have shown positive associations between self-efficacy and PA. In Perkins et al,31 the authors examined self-efficacy and PA participation among older adults in Spain and the United States and found that self-efficacy was positively associated with PA in Spain: (β = 0.46; P < .001) and the United States (β = 0.39; P < .01).31 A separate study, by Castro et al,36 also examined the relation between self-efficacy and PA across 125 inactive, ethnic-minority women and found that high levels of self-efficacy were significantly associated with increased walking from the baseline to a 5-month follow-up.36 And lastly, in Hartman et al,34 the authors found that self-efficacy influenced PA engagement in persons living with chronic obstructive pulmonary disease. Our study uniquely identified that occupational PA was the only domain not significantly associated with self-efficacy for PA. It is conceivable that the workplace setting provides little latitude to gain sustained bouts of exercise, especially if job functions strictly interfere with the ability to achieve activity. As such, study participants may demonstrate limited confidence to obtain PA at work. Given that 83% of US-based hospitals provide wellness initiatives,67 modifications to existing worksite wellness programs such that initiatives build self-efficacy via behavior change approaches, such as goal setting and promoting tailored exercise patterns, should be actively promoted.18 Concurrently, administrative policies must also examine the overall workplace culture so as to isolate barriers that not only impede PA engagement within and around the workplace, but also counteract participation in worksite wellness initiatives. Some of these barriers that may weaken one’s confidence to engage in PA include job-related stress, worker burnout, and absenteeism.68 Effective strategies such as standing work desks, walking meetings, and active recess are feasible strategies that employees can incorporate into their workday to facilitate confidence and the adoption of healthy PA habits.6971 Lastly, given that employees’ activities outside of work have the potential to impact work functioning and productivity, wellness strategies should also underscore the integration and promotion of healthy behavior strategies in other areas so that individuals are more likely to meet personal PA goals and national PA recommendations.18 For example, the participation in PA domains such as leisure and domestic PA, wherein participants have greater autonomy in setting and engaging in preferred exercise routines, should be heavily encouraged.50

Social Support

Our study found that family social-support scores were inversely associated with age. Research shows that aging adults have a strong preference for family support.7275 Yet, societal changes have transformed family dynamics significantly. It is quite probable that middle-aged adults are not as socially engaged due to key factors, which include the absence of adult children in the home and personal obligations to care for aging parents.7678 Our study also revealed that the married couples expressed significantly higher family social-support scores compared with the single and divorced/separated participants; conversely, the single and divorced/separated participants reported higher friend-related social support over the married participants. This is in accordance with previous literature, which shows that married individuals are more likely to turn to their spouses and family who live in the same household for emotional and physical support. On the other hand, single adults are more inclined to turn to friendship-based support systems that reinforce their personal views, behaviors, and socialization habits.75,79 Lastly, PA social support from family and friends was lowest among the smokers; this is especially likely given that negative lifestyle behaviors often co-occur. As such, it is possible that smokers gravitate toward social environments wherein attitudes, intentions, and behaviors toward PA and other healthy activities are seldom prioritized.

Regression analyses revealed stronger associations between family support and PA over friends support and PA. This is consistent with past studies, which show that family-focused interventions have a greater impact on the adoption and maintenance of positive health behaviors.40,43,44 A study by Hooper et al43 found that adults who engaged in exercise were more likely to say that their families supported their decision to engage in said behavior. Additional studies, like the meta-analysis by Carron et al,44 have also found significant differences between the support offered from family and friends, such that small to moderate effects on PA engagement were observed with friend support compared with the larger effect that family support had on PA engagement.44 Because many social interactions occur at home and away from the workplace, family social-support interventions hold significant promise in making a meaningful impact on behavior change. In our study, hospital employees had low- and mid-range scores for social support from both family and friends, often recording scores below the midpoint (range 13–65). This shows that, even though significant associations between social support and PA were identified, hospital employees generally perceived their families and friends to provide less than ideal support for PA engagement. Worksite wellness programs are thus primely positioned to address social-support-related barriers via initiatives such as health coaching and active recess. Additionally, given the fine line between work and personal responsibilities, wellness initiatives must explore novel approaches to facilitate healthy behaviors among employees. For example, integrating effective communication-building strategies into wellness initiatives can help employees properly articulate their wants and needs from family members when considering exercise outside of the workplace.

There are a few limitations to this study that we must mention. Limitations in this study include the utilization of a cross-sectional analysis design, which limits our ability to make causal inferences about the relationships between self-efficacy, social support, and PA. Future research should utilize temporal sequence study designs to evaluate the true causal link between these psychosocial factors and PA measures. Second, the parent study, SUH, utilized convenience sampling to recruit participants, which impairs the study’s external validity. Therefore, researchers must exercise caution in generalizing results to populations that are dissimilar to this hospital-based population. Lastly, the participants’ self-reporting of self-efficacy and social support could have been subject to social desirability and recall bias.

The significant strengths of this study include the diverse sampling of participants from 6 different hospital sites in the Texas Medical Center. Secondly, our use of mixed-effects regression techniques robustly accounted for any potential within and between cluster variations across the 6 hospital sites from which the participants were recruited. Thirdly, we used validated scales to assess psychosocial measures and used the International PA Questionnaire long version to explore PA and psychosocial associations across a broad spectrum of self-reported PA behaviors.80 It is significant to note that very few studies examine associations between self-efficacy and social support and the 4 domains of PA that we addressed in this paper. In that sense, our study is at the fore and is useful to future studies that wish to know more about the self-efficacy and social-support profiles of this unique population and how these psychosocial factors may help to influence PA behaviors.

Conclusion

We observed that self-efficacy and social support from friends were positively associated with all domains of PA except occupation PA, whereas social support from family was found to be positively associated with all domains of PA. Moving forward, it is recommended that longitudinal studies be used to evaluate whether self-efficacy and social support truly predict PA engagement in this population; the result of such studies can keenly define the role of interpersonal and intrapersonal factors in facilitating or encouraging positive PA habits. At the worksite level, it is important that administrative policies consider the barriers that facilitate workplace sedentarism. Including simple effective strategies such as standing work desks, active recess, and walking meetings can build confidence for PA and may help promote sustained activity at work. Also, given the complex overlap between employees’ personal and work lives, worksite wellness strategies must integrate behavioral skills such that employees are able to effectively petition support from those closest to them in achieving PA. Behavioral research must also underscore the benefits of all types of PA since our results showed that these various PA measures were significantly associated with both our self-efficacy and social-support (family and friends) measures. Understanding these distinct associations moves us closer toward developing and implementing more suitable behavioral interventions that aim to address PA behaviors in hospital employees.

Acknowledgments

This work was supported by funding from The University of Texas Health Science Center School of Public Health and Shape Up Houston, a 501(c)(3) nonprofit organization, and received research approval from the University of Texas Health Science Center Committee for Protection of Human Subjects and the Committee for the Protection of Human Subjects (Shape Up Houston TMC HSC-SPH-12-0098). The author also thanks her primary mentor, Dr. Larkin Strong (and by extension, the Health Disparities Research Department at The University of Texas MD Anderson Cancer Center) who provided Dr. John with the time and resources needed to complete this project.

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If the inline PDF is not rendering correctly, you can download the PDF file here.

John is with the University of Texas MD Anderson Cancer Center, Houston, TX, USA. Sharma and Swartz are with the University of Texas Health Science Center at Houston, Houston, TX, USA. Hoelscher is with the University of Texas School of Public Health, Austin Regional Campus, Austin, TX, USA. Huber is with the Biostatistics, Stata Corp LP, College Station, TX, USA.

John (jcjohn@mdanderson.org) is corresponding author.
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    U.S. Department of Health and Human Services. Physical activity guidelines for Americans. Health.gov. Updated 2008. Accessed July, 2016.

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    Pronk N, Martinson B, Kessler R, Beck A, Simon G, Wang P. The association between work performance and physical activity, cardiorespiratory fitness, and obesity. J Occup Environ Med. 2004;46(1):1925. PubMed ID: 14724474 doi:10.1097/01.jom.0000105910.69449.b7

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    Kannel W, Sorlie P. Some health benefits of physical activity: the Framingham study. Arch Intern Med. 1979;139:857861. PubMed ID: 464698 doi:10.1001/archinte.1979.03630450011006

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  • 5.

    Treiber F, Baranoswki T, Braden D, Strong W, Levy M, Knox W. Social support for exercise: relationship to physical activity in young adults. Prev Med. 1991;20:737750. PubMed ID: 1766945 doi:10.1016/0091-7435(91)90068-F

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    Ohta M, Mizoue T, Mishima N, Ikeda M. Effect of the physical activities in leisure time and commuting to work on mental health. J Occup Health. 2017;49:4652. doi:10.1539/joh.49.46

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    Corbin C, Pangrazi R. Surgeon general’s report on physical activity and health. PCPFS Res Digest. 1996;2(6):416.

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    Centers for Disease Control and Prevalence: National Center for Health Statistics. Health, United States. Participation in leisure-time aerobic and muscle-strengthening activities that meet the federal 2008 physical activity guidelines for Americans among adults aged 18 and over, by selected characteristics: United States, selected years 1998–2016. https://www.cdc.gov/nchs/data/hus/2017/057.pdf. Updated 2017.

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    Chaput JP, Klingenberg L, Rosenkilde M, Gilbert JA, Tremblay A, Sjödin A. Physical activity plays an important role in body weight regulation. J Obes. 2011;2011:111. doi:10.1155/2011/360257

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    Bell J, Hamer M, van Hees V, Singh-Manoux A, Kivimäki M, Sabia S. Healthy obesity and objective physical activity. Am J Clin Nutr. 2015;102(2):268275. PubMed ID: 26156738 doi:10.3945/ajcn.115.110924

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