The Relationship Between Perceived Barriers for Physical Activity and Depression in Low Active Community-Dwelling Women

Methods

Study design and sample

In this cross-sectional analysis of 318 women in the mPED trial, we analyzed the sociodemographic, clinical, and self-reported questionnaire data collected at the screening/baseline study visit. Detailed descriptions of the study design and outcomes have been previously published(19)^,(20)^,(21)(20, 22, 23). In short, eligibility criteria were female sex, age from 25 to 65 years, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 18.5 to 43.0, physically inactive at work and/or during leisure time based on the Stanford Brief Activity Survey, intent to be physically active, access to a home telephone or mobile phone, ability to speak and read English, no medical conditions or physical problems that required special attention in an exercise program, no current participation in other lifestyle modification programs, and no mild cognitive impairment as determined by the Mini-Cog test. During the screening/baseline visit, sociodemographics, medical and lifestyle history, the Center for Epidemiological Studies Depression Scale (CES-D)(24), body mass index (BMI) and the Barriers to Being Physically Active Quiz (BBAQ) were assessed by a trained research staff. All methods were conducted in accordance with the Declaration of Helsinki.

Measures

Depressive symptoms

The Center for Epidemiological Studies Depression Scale (CES-D)(24) was used to assess self-reported depressive symptoms. The CES-D is a valid and reliable instrument that is widely used to assess depressive symptoms in a research context. The CESD ranges from (0–60), with a cutoff score of 16 indicating risk for clinical depression.

Physical activity barriers scale: The Barriers to Being Physically Active Quiz developed by the Centers for Disease Control and Prevention (CDC)(25) is a 21-item measure assessing the following barriers to physical activity: 1) lack of time, 2) social influence, 3) lack of energy, 4) lack of willpower, 5) fear of injury, 6) lack of skill, and 7) lack of resources (e.g., recreational facilities, exercise equipment). Each domain contains 3 items, with a total score range of 0 to 63. Respondents rate the degree of activity interference on a 4-point scale, ranging from 0 = “very unlikely” to 3 = “very likely.”

Other measures

A research staff asked the participants to fill out the sociodemographic and medical questionnaires immediately after obtaining the written consent form. The emotional support question, “How many people can you count on to provide you with emotional support?” was developed by the research team. To calculate body mass index (BMI), weight was measured with a Tanita WB-110 digital electronic scale, and height was measured at baseline with a standard stadiometer.

Statistical analysis

We assessed the relationship between depressive symptoms and the total Barriers to Being Physically Active Quiz scale score correcting for demographics, clinical variables and social support. We additionally conducted a posthoc analysis to examine the relationship between depression and the BBAQ subscales.

We used descriptive statistics to summarize sample characteristics and linear and logistic regression to examine the association between the total barriers and subscale scores with depression scores. For subscale scores with a non-normal distribution we computed a binary variable based on the median scores. We included self-reported age, BMI, employment (paid work, yes or no), whether participants have children at home (yes/no), whether they have driven a car in the past week (yes/no) and marital status as covariates in the model, based on previous evidence(19)^,(20)^,(21) (26). We examined non-linear effects of depression by including the quadratic effect of depression in the models and used Likelihood Ratio (LR) tests to assess the need for including these non-linear effects. We removed influential observations using Cook’s d (see results; we also conduct a sensitivity test when the influential observations are not removed) and checked further model assumptions by visual inspections of residual plots. Tables are presented in the supplementary analysis. Analyses were carried out in R studio V. 1.1.423.

Results

Baseline characteristics of the 318 participants and the mean or median scores of the total barriers and the subscales are shown in Table 1. For the overall sample, based on the mean and median scores, the greatest barriers were lack of time, lack of energy and lack of willpower. Fear of injury was the least common reported barrier. In the supplementary material (Supplementary Table 1), we also show differences in the total scale and subscales between groups with high depression (CES-D ≥ 16) for illustrative purposes (in analyses we used the continuous scores).

Table 1

Baseline sample characteristics (N = 318)
	Mean (± SD), median ( IQR; not normally distributed variables) or n (%)
Age
Median	54.0 (50/60/60)
Ethnicity
Native Hawaiian/Pacific Islander	1 (0.3%)
Black/African-American	26 (8.2%)
Hispanic/Latino	20 (6.3%)
Asian	65 (20.4%)
White (non-Hispanic)	179 (56.3%)
More than 1 race	27 (8.5%)
Education
Completed High School & some college	77 (24.2%)
Completed College	128 (40.3%)
Completed Graduate School	113 (35.5%)
Annual household income
Under $40,000	62 (19.5%)
$40,001 to 75,000	77 (24.2%)
Over $75,000	153 (48.1%)
Don't know or Declined to state	26 (8.2%)
Marital status
Never married	99 (31.1%)
Currently married/cohabitating	158 (49.7%)
Divorced/widowed	61 (19.2%)
Employment
No, Full or Part time Job No Shift Work	160 (50.3%)
Yes Full or Part time Job with Shift Work	71 (22.3%)
No paid employment	87 (27.4%)
Antidepressant
No	246 (77.4%)
Yes	72 (22.6%)
Emotional support
Support from ≥ 3 people	257 (80.8%)
Body mass index (kg/m²)
Mean (SD)	29.6 (6.13)
Self-reported high blood pressure (%)
No	232 (73.0%)
Yes	80 (25.2%)
Don't know	6 (1.9%)
Self-reported high cholesterol (%)
No	175 (55.0%)
Yes	97 (30.5%)
Don't know	46 (14.5%)
Barriers scale and subscales
BBAQ Sum score	25 (17/25/31)
Lack of time	4.23 (2.68)
Social influence	3.53 (2.10)
Lack of energy	4.18 (2.64)
Willpower	7.00 (6/7/8)
Fear of injury	0.00 (0/0/2)
Lack of skills	1.00 (0/1/3)
Lack of resources	2.00 (0/2/4)
BBAQ = the Barriers to Being Active Quiz.

The Barriers to Being Physically Active Quiz Total Score

For the total Barriers to Being Physically Active Quiz scale score, a model that included a quadratic term was a better fit than a model without this term (of note a cubic term was not significant, p = 0.09). We removed 4 influential observations (Cook’s d). The total barriers scale showed a positive significant relationship with depression (linear effect of depression, estimate = 0.75, 95% CI: 0.39–1.12, p < 0.001), which tapered off for the higher barrier scores (quadratic effect of depression, estimate: -0.02, 95% CI: -0.03 – -0.01, p = 0.002). In all models we corrected for self-reported age, BMI, employment (paid work, yes or no), whether participants have children at home (yes/no), whether they have driven a car in the past week (yes/no) and marital status. When influential observations were retained in the model in a sensitivity analysis, results were similar for the linear effect (linear effect of depression, estimate = 0.38, 95% CI: 0.05–0.70, p = 0.023), but the quadratic effect was no longer significant (quadratic effect of depression, estimate: -0.005, 95% CI: -0.01–0.00, p = 0.23).

Results of the regression model are shown in Table 2 and a plot of this relationship is shown in Fig. 1. Other factors associated with a higher barriers score were age and employment (Table 3). To assess this relationship using more flexible modeling, we also examined a spline regression model. Spline regression is a non-parametric technique that divides the datasets into intervals with different fits, which may yield better results depending on the nature of the data. Results for spline regression were relatively similar but with a slower taper off at higher depression scores (see supplementary material). Likelihood ratio tests showed that a model including a quadratic term was a better fit than a spline regression model.

Table 2

Multivariate linear regression predicting the Barriers to Being Physically Active Quiz total score
	Total Barriers Scale
Predictors	Estimates	CI	p
(Intercept)	29.48	21.76–37.20	< 0.001
Age (10-year intervals)¹	-0.17	-0.26 – -0.08	< 0.001
Paid full or part-time employment	2.71	0.37–5.04	0.024
Children living at home	-0.01	-2.52–2.50	0.992
Never married²	0.18	-2.17–2.52	0.881
Divorced/widowed²	-0.49	-3.24–2.26	0.725
Driving in past week	-0.54	-3.21–2.13	0.690
Body mass index (kg/m² )	-0.08	-0.24–0.09	0.363
CES-D Score (continuous)	0.75	0.39–1.12	< 0.001
Quadratic effect of depression	-0.02	-0.03 – -0.01	0.002

CES-D: Center for Epidemiological Studies Depression Scale. ¹Age was divided into 10-year intervals to increase interpretability. ²being married was the reference level.

The Barriers to Being Physically Active Quiz Subscales

Multivariate linear regression showed a significant relationship with depression scores for the social influence subscales (linear effect of depression, estimate = 0.03, 95% CI: 0.00–0.06, p = 0.027, Table 3A), and the energy subscale (linear effect of depression, estimate = 0.04, 95% CI: 0.01–0.07, p = 0.017, Table 3B). The subscales lack of resources, lack of skill and lack of willpower were all marginally significant and positively associated (p < 0.01; Supplementary materials). Models without quadratic effects were better fits. For the social influence scale and the energy scale, we removed 3 influential observations based on Cook’s d. Results were similar when retaining influential observations in the model. Of note, the subscale results are in the context of an exploratory analysis and would not pass the threshold for multiple comparisons.

Table 3: Multivariate linear regression predicting the Social Influence and Energy subscale scores
3A.	Influence Others
Predictors	Estimates	CI	p
(Intercept)	3.87	2.17–5.57	< 0.001
Age (10 year intervals) ¹	-0.03	-0.05 – -0.01	0.005
Paid full or part-time	-0.17	-0.69–0.34	0.516
Employment	-0.17	-0.69–0.34	0.516
Children living at home	-0.03	-0.58–0.53	0.928
Never married²	-0.26	-0.79–0.26	0.321
Divorced/widowed²	0.28	-0.33–0.88	0.371
Driving in past week	-0.12	-0.72–0.48	0.697
Body Mass Index (kg/m²)	0.04	0.00–0.08	0.03
CES-D scores (continuous)	0.03	0.00–0.06	0.027
3B.	Energy
Predictors	Estimates	CI	p
(Intercept)	6.13	4.13–8.13	< 0.001
Age (10-year intervals)¹	-0.06	-0.09 – -0.04	< 0.001
Paid full or part-time	1.59	0.99–2.18	< 0.001
Employment	1.59	0.99–2.18	< 0.001
Children living at home	0.08	-0.57–0.73	0.81
Never married²	0.47	-0.14–1.08	0.135
Divorced/widowed²	-0.02	-0.73–0.69	0.955
Driving in past week	0.07	-0.62–0.76	0.84
Body Mass Index (kg/m²)	-0.01	-0.05–0.04	0.784
CES-D scores (continuous)	0.04	0.01–0.07	0.017

CES-D: Center for Epidemiological Studies Depression Scale. ¹Age was divided into 10-year intervals to increase interpretability. ²being married was the reference level.

Discussion

We showed that higher depression scores were associated with higher physical activity barriers when adjusting for sociodemographic and clinical variables. There was a quadratic effect of depression on physical activity barriers, so that as CES-D scores increased, the positive relationship with physical activity barriers tapered off. These results indicate that depression level is a particularly important factor to consider when designing physical activity interventions for physically inactive women.

Our study shows that physical activity interventions for inactive women with depression diagnoses or symptoms may benefit from taking into account their greater perceived barriers to engaging in exercise. Many physical activity interventions still use a ‘one size fits all’ approach. For instance, a review paper illustrated that unique barriers to physical activity in mental health are usually not accounted for within behavior change theories used to design physical activity interventions(27). This may be one of the reasons that individuals with depression are less likely to engage in exercise programs, especially when they have a low baseline level of fitness(10). Previous work shows that physical activity interventions are more effective when they are tailored to subgroups like women and ethnic minorities(28). These findings suggest interventions may further increase their potential by tailoring depression levels as well.

Most important barriers for physical activity

For the sample as a whole, we identified that willpower, lack of time and lack of energy were the most significant barriers whereas injury and lack of skill were less often reported. This is consistent with previous research in physically inactive pregnant women and a community sample of Brazilian women, also using the same barriers scale(29, 30).

The Barriers to Being Physically Active Quiz subscales that were most associated with depression in a posthoc analysis were the Social Influence and Lack of Energy subscales. Although we assessed these relationships in an exploratory analysis, these barriers may be the driving factors behind the differences in total subscale scores.

The Social Influence subscale included statements such as: “None of my family members or friends like to do anything active, so I don’t have a chance to exercise.” and “I’m embarrassed about how I will look when I exercise with others”. A lack of social support has been suggested as a risk factor for physical inactivity before(31), and our results show that this may be even more important in women with high depression scores. A review on physical activity barriers in depression (not specific to women) also identified other people’s behaviors and a lack of social encouragement as important barriers, and social support as a facilitator(14). Other research found that having a family member who exercises, or who encourages exercise, motivates engaging in healthy behaviors(32). Our findings also suggest that self-consciousness in social exercise-related situations (e.g. appearance towards others when exercising) may be an important factor discouraging women with higher depressive symptoms from physical activity.

The Lack of Energy subscale includes statements like: “I’m too tired after work to catch up on exercise”, and “I need the weekend to catch up on sleep”. A lack of energy and increased fatigue are symptoms of depression. Low energy was also identified before as one of the most important barriers in men and women with depression(14). Of note, changes in physical activity may improve energy, and reduce the magnitude of perceived energy as a barrier(29).

Our posthoc results, though they need to be confirmed in future work, suggests the need for an emphasis on social influence and boosting energy to increase the effectiveness of physical activity promotion. Physical activity interventions could, for instance, integrate social support from family or friends, utilize peer-support or use community-based structures. Furthermore, exercise interventions can build-in graded exercise, personalized to a women’s individual fitness levels to help slowly overcome feelings of fatigue(33). Future work should also quantify and integrate facilitators to exercise in women with high depressive symptoms. In mixed gender populations with depression, facilitators included having a reason for exercising, being able to identify the psychological benefits of exercise, having positive social support and integrating cognitive behavior change strategies(34).

Physical activity interventions are increasingly delivered in digital formats and via smartphones, using apps, and text-messaging and conversational agents. There is a growing interest in adaptive interventions, which alter their content based on the day-to-day behavior of individuals(35). We argue that physical activity interventions should both adapt to individuals’ daily changing circumstances, and tailor their content to overcoming barriers of user subgroups.

The quadratic effect of depression

After a depression score (CES-D) of around 20, past the clinical cut-off for identifying individuals at risk for clinical depression (≥ 16), physical activity barriers no longer increased with higher scores. We can’t make finite conclusions about why this effect tapers off but there are multiple possible explanations. First, for participants with higher depression scores, physical activity may not be a priority and therefore they are less aware of their physical activity barriers. Another potential explanation is that the Barriers to Being Physically Active Quiz scale doesn’t capture all barriers that are relevant when women reach more severe levels of depression. For instance, previous research(36) showed that in severe mental illness, low mood and stress are perceived as the most significant barriers for physical activity, followed by social support. In addition, in outpatients with depression, physical exertion was the most common reported PA barrier(37). The Barriers to Being Physically Active Quiz scale used in this study assess lack of energy, but it does not capture whether low mood, high stress or physical exhaustion prevent women from exercising. We recommend these questions be included in future versions.

One caveat here is that our data were sparse for very high depression scores, making these estimates less precise. Further, when we retained influential observations in the model the quadratic effect lost significance. Future work should assess differences in barriers between women with slightly elevated symptoms and clinically diagnosed depression.

Age, employment and physical activity

Another interesting finding was the inverse relationship between age and perceived physical activity barriers. In our posthoc analyses higher age was associated with lower barrier subscale scores, except for the injury and skills scales. These findings complement previous work in community samples showing that younger adults, both men and women, (25–44 years) report most physical activity barriers, and older adults (> 65 years) least(38). In line with our findings, earlier work has also identified lack of time and energy due to family and household responsibilities among the top barriers to physical activity for women(39). Older women may be less impacted by these responsibilities.

Finally, full-time or part-time employment was associated with higher barriers opposed to unemployment. Past work revealed that working women perceived lack of time and energy as most frequently reported barriers to physical activity(26). Future interventions may want to incorporate strategies for promoting physical activity into working hours to overcome these barriers for working women.

Strengths & Limitations

We included a relatively large sample of diverse women. To our knowledge, this is one of the first studies to systematically examine the relationship between physical activity barriers and depressive symptoms in women using the instrument that was developed and validated by the CDC(14).

A limitation is that findings may be specific to our sample of female adults aged 25–65 years with relatively high levels of education from the San Francisco Bay Area. Our sample included fewer women with high depression scores, i.e. CESD > 20, therefore the estimate of the relationship may be less reliable after these higher scores. In addition, our analyses are cross-sectional, thus don’t allow us to understand the causal relationship between perceived barriers and depression. This should be explored in future work. Another limitation may be selection bias. Since we included women who signed up to participate in a physical activity intervention, the study participants might be more motivated to engage in physical activity. Further, women with higher depressive symptoms are less likely to participate in the study. This may in part explain the tapering off effects we observe for high depression scores.

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