Journal of Affective Disorders 165 (2014) 126–130
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Research report
Concurrent occurrence of multiple positive lifestyle behaviors and depression among adults in the United States Paul D. Loprinzi n, Sara Mahoney Bellarmine University, Department of Exercise Science, Donna & Allan Lansing School of Nursing & Health Sciences, Louisville, KY 40205, United States
art ic l e i nf o
a b s t r a c t
Article history: Received 14 December 2013 Received in revised form 26 April 2014 Accepted 26 April 2014 Available online 5 May 2014
Background: To our knowledge, no studies have examined the dose-response association between concurrent occurrence of multiple positive health behaviors and depression. As a result, the purpose of this study was to examine the dose-response association between concurrent occurrence of lifestyle behaviors (i.e., diet, physical activity, and smoking) on depression symptoms among a national sample of U.S. adults (20–85 yr). Methods: Using data from the 2005–2006 NHANES (n ¼ 2574), diet was assessed from the healthy eating index variable; physical activity was assessed via accelerometry; smoking was assessed from cotinine levels; and depression was assessed from the Patient Health Questionnaire 9 (PHQ-9). Results: Each lifestyle behavior was independently associated with depression in the expected direction, and there was also evidence of a dose–response relationship. Compared to those having 0 positive lifestyle factors, those with 1, 2, and 3 positive lifestyle factors, respectively, were 15% (p¼ 0.38), 67% (p¼0.001), and 82% (p ¼0.01) less likely to be classifed as having moderate or greater depression symptoms (PHQ-9 Z 10). Limitations: The main limitation of this study was the crosssectional design. Conclusion: there is a dose-response relationship between concurrent occurrence of positive lifestyle behaviors and depression symptoms. & 2014 Elsevier B.V. All rights reserved.
Keywords: Depression Diet Physical activity Smoking
1. Introduction Approximately 20% of U.S. adults report depressive symptoms, with the majority of these individuals receiving no treatment from a mental health professional or with antidepressant medication (Shim et al., 2011). In addition to counseling therapy and antidepresant pharmacotherapy, evidence-based strategies to help prevent and treat depression include making modifications to lifestyle behaviors, such as diet, physical activity, and smoking (Dunn et al., 2001; Lai et al., 2013; Callaghan et al., 2014). Although these modifiable lifestyle factors, including diet, physical activity, and smoking, are associated with reduced depression symptoms, to our knowledge, the combined effect of these lifestyle factors on depression is unknown. As a result, the two objectives of this study were to (1) examine the independent association of each lifestyle behavior on depression symptoms, and (2) examine the dose-response association between concurrent occurrence of these positive lifestyle behaviors on depression symptoms. We hypothesize that each behavior will be independently associated with depression and that individuals engaging in all three modifiable lifestyle behaviors (i.e., healthy diet, adequate physical activity, and
n
Corresponding author. Tel.: þ 502 272 8008; fax: þ 502 272 8389. E-mail address: [email protected] (P.D. Loprinzi).
http://dx.doi.org/10.1016/j.jad.2014.04.073 0165-0327/& 2014 Elsevier B.V. All rights reserved.
non-smoker) will have the lowest depression symptoms compared to those adopting fewer lifestyle behaviors. To increase generalizability, data from the present study comes from the National Health and Nutrition Examination Survey (NHANES), which is a representative sample of non-institutionalized U.S. civilians.
2. Methods 2.1. Study design Data from the present study were obtained from the 2005– 2006 NHANES. NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage, stratified, clustered probability design. The multistage design consists of 4 stages, including the identification of counties, segments (city blocks), random selection of households within the segments, and random selection of individuals within the households. In the 2005–2006 cycle, participants were sampled across 15 different geographic areas across the U.S. Participants were interviewed in their homes and then subsequently examined in a mobile examination center (MEC) by NHANES personnel. Further details about NHANES can be found
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elsewhere (CDC, 2014). NHANES study procedures were approved by the National Center for Health Statistics ethics review board, with informed consent obtained from all participants prior to data collection. 2.2. Measurement of depression At the MEC, participants completed the Patient Health Questionnaire-9 (PHQ-9) during the computer-assisted personal interview (Kroenke et al., 2001). The PHQ-9 is the 9-item depression module from the full Patient Health Questionnaire. The PHQ-9 depression scale consists of the actual 9 criteria upon which the diagnosis of DSM-IV depressive disorders is based. Sample items include, “over the last two weeks, how often have you been bothered by”: “feeling down, depressed or hopeless”, “feeling tired or having little energy”, and “trouble concentrating on things, such as reading the newspaper or watching television.” For each question, participants responded using a 4-point Likert scale, with responses including not at all (0), several days (1), more than half the days (2), and nearly every day (3). Items were summed, with higher scores indicating greater severity of depression. As a measure of severity, the PHQ-9 can range from 0 to 27, since each of the 9 items can be scored from 0 (not at all) to 3 (nearly every day). We used the PHQ-9 cut-point of Z10 to define depression as moderate or greater (Kroenke et al., 2001; Shim et al., 2011). The PHQ-9 has demonstrated evidence of reliability and validity, with Cronbach's alpha ranging from 0.86–0.89 and a 48-h test-retest correlation coefficient of 0.84 (Kroenke et al., 2001). In the present sample, internal consistency of this questionnaire, as measured by Cronbach's alpha, was 0.81. 2.3. Measurement of health behaviors 2.3.1. Dietary behavior Two 24-h recalls were collected during the visit to the MEC. To capture intake on all days of the week, the 24 h recalls were collected on every day of the week. The dietary interviewers used the Dietary Data Collection (DDC) system, which is an automated standardized interactive dietary interview and coding system. The Healthy Eating Index (HEI) 2005 was developed by the USDA as an indicator of dietary quality (USDA, 2013a). The HEI is comprised of 12 components (total fruit; whole fruit; total vegetable; dark green, orange vegetable and legumes; total grain; whole grain; milk; meat and beans; oil; saturated fats; sodium; and calories from solid fats, alcoholic beverages, and added sugars) with a maximum score of 100 and with a higher score reflecting more closely adhering to the dietary guidelines for Americans. The Healthy Eating Index was derived for each of the 24 h recall days using the MyPyramid Equivalents Database and following the methods and SAS code established by the USDA Center for Nutrition Policy and Promotion (NCI, 2005; USDA, 2013b). Using the average of the twoday HEI scores, participants at or above the 60th percentile (i.e., top 40%) of HEI scores in the population were categorized as adhering to the dietary guidelines or consuming a healthy diet (Ford et al., 2012). 2.3.2. Physical activity Participants who were able to walk were asked to wear an ActiGraph 7164 accelerometer on their right hip for 7 days. Accelerometers were affixed to an elastic belt that was worn around the participant's waist near the iliac crest. Participants were asked to wear the accelerometer during all activities except waterbased activities and sleeping. The accelerometer measured the frequency, intensity, and duration of physical activity by generating an activity count proportional to the measured acceleration.
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Estimates for physical activity were summarized in 1-min time intervals. Minutes with activity counts Z2020 per minute were classified as moderate-to-vigorous physical activity (MVPA) (Troiano et al., 2008). A binary variable was created dichotomizing participants as engaging in o or Z150 min/week of moderate-tovigorous physical activity, which is consistent with current physical activity recommendations (USDHHS, 2008). Only those participants with at least 4 days of 10 or more hours/day of accelerometer wear time were included in the analyses in order to make sure that data adequately captured habitual physical activity patterns (Troiano et al., 2008). To monitor the amount of time the device was worn, nonwear was defined by a period of a minimum of 60 consecutive minutes of zero activity counts, with the allowance of 1–2 min of activity counts between 0 and 100 (Troiano et al., 2008).
2.3.3. Smoking Current smoking status was assessed from serum cotinine levels using established gender-specific cut-points (4 1.78 ng/mL and 4.47 ng/mL for males and females, respectively) (Benowitz et al., 2009). Serum cotinine was measured by an isotope dilutionhigh performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. 2.4. Measurement of covariates To control for potential confounding variables, covariates included in the analytic models were: age, gender, race-ethnicity, body mass index (BMI), poverty-to-income ratio (PIR), and comorbidity index. BMI was calculated from measured weight and height (weight in kilograms divided by the square of height in meters). As a measure of socioeconomic status, PIR was assessed, with a PIR value below 1 considered below the poverty threshold. The PIR is calculated by dividing the family income by the poverty guidelines, which is specific to the family size, year assessed, and state of residence (CDC, 2009). Participants were classified as having 0 or 1 þ comorbidities based on self-report of the following chronic diseases/events: arthritis, coronary heart disease, stroke, cancer, diabetes, kidney disease, and hypertension. 2.5. Data analysis Statistical analyses (Stata, version 12.0, College Station, TX) accounted for the complex survey design used in NHANES. To account for oversampling, non-response, non-coverage, and to provide nationally representative estimates, all analyses included the use of survey sample weights, stratum and primary sampling units. In an effort to maintain nationally representative estimates, the sample weights for those with 4 or more days of valid accelerometry data were ratio-adjusted to maintain the age, sex, and race-ethnicity distribution of the full sample. Further details on the sample weights and how to provide weighted estimates in Stata is described elsewhere (Loprinzi and Ramulu, 2013). Two multivariable logistic regression models were computed. As shown in Table 2, a multivariable logisitic regression was computed to examine the independent associations between each of the health behaviors and depression (outcome variable). Next, a variable was created that indicated the number of positive lifestyle factors each participated had (range 0–3; healthy diet, Z150 min/week of physical activity, and non-smoker), and a multivariable logisitic regression was then computed to examine the dose-response association between the number of positive health behaviors and depression (Table 3). Both models controlled for age, gender, race-ethnicity, BMI, PIR, and presence of comorbidities. Statistical significance was established as po 0.05.
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3. Results In the 2005–2006 NHANES cycles, 2701 participants were at least 20 years of age (comorbidity data was only available on those 20 and older) and had data for all study variables excluding depression. After excluding those with missing depression data, 2574 remained, which comprised the analytic sample. With regard to those included and excluded because of missing depression data, there were no differences (p 40.05) in any of the study variables or covariates with the exception of poverty level and race-ethnicity; those excluded had a lower PIR (2.4 vs. 2.8, p ¼0.002), and those excluded were less likely to be nonHispanic white (35% vs. 53%, po 0.001) (these estimates are unweighted). Weighted characteristics of the analyzed sample are shown in Table 1. Approximately 5% (n ¼134) of the sample reported moderate or greater depression symptoms. The weighted prevalence estimates of those with moderate or greater depression symptoms among those with 0, 1, 2, and 3 positive health behaviors, respectively, was 8.9%, 6.9%, 2.6%, and 1.1%. The multivariable logistic regression results examining the independent associations between lifestyle factors and depression are shown in Table 2. All three lifestyle behaviors were independently associated with depression. After adjustments, those consuming a healthy diet were 49% (p ¼0.03), those who enaged in Table 1 Weighted characteristics of the analyzed sample, NHANES 2005–2006 (n¼2574). Variable
Weighted mean/proportion (95% CI)
Age (yr) % Female % non-Hispanic white Body mass index Poverty-to-income ratio % Presence of comorbidities % With depression symptoms Depression score % Healthy diet Healthy eating index score % Engaged in Z 150 min/week of MVPA MVPA (min/day) % Non-smoker
46.3 (44.5–48.0) 51.3 (49.8–52.8) 73.7 (67.9–79.5) 28.4 (27.8–29.0) 3.2 (3.0–3.3) 45.4 (41.8–48.9) 4.6 (3.4–5.8) 2.3 (2.1–2.5) 37.7 (34.8–40.6) 52.6 (51.6–53.5) 44.5 (40.7–48.3) 24.1 (22.6–25.5) 73.8 (71.2–76.5)
MVPA ¼Moderate-to-vigorous physical activity.
Table 2 Multivariable logistic regression results examining independent associations between lifestyle factors and depression, NHANES 2005–2006 (n¼2574). Variable
Lifestyle factors Healthy diet vs. unhealthy diet Z150 min/week of MVPA vs. o 150 min/week Non-smoker vs. smoker Covariates Age, 1 year older Female vs. male non-Hispanic white vs. other Body mass index, 1 kg/m2 higher Presence of comorbidities vs. no presence Poverty-to-income ratio, 1 unit higher
Odds ratio (95% CI) for having moderate or greater depression symptoms
P-value
0.51 (0.27–0.93) 0.46 (0.22–0.93)
0.03 0.03
0.68 (0.49–0.95)
0.02
0.98 (0.97–1.00) 0.98 (0.65–1.47) 0.97 (0.46–2.05) 1.01 (0.97–1.06) 1.98 (1.31–2.99)
0.14 0.93 0.95 0.40 0.003
0.73 (0.62–0.86)
0.01
MVPA ¼Moderate-to-vigorous physical activity.
Table 3 Multivariable regression results examining the dose-response association between concurrent health behaviors and depression, NHANES 2005–2006 (n¼2574). Variable
Number of positive lifestyle factors† 1 vs. 0 2 vs. 0 3 vs. 0 Covariates Age, 1 year older Female vs. male non-Hispanic white vs. other Body mass index, 1 kg/m2 higher Presence of comorbidities vs. no presence Poverty-to-income ratio, 1 unit higher
Odds ratio (95% CI) for having moderate or greater depression symptoms
P-value
0.85 (0.57–1.25) 0.33 (0.18–0.60) 0.18 (0.05–0.63)
0.38 0.001 0.01
0.98 (0.97–1.00) 1.03 (0.68–1.56) 0.97 (0.46–2.04) 1.01 (0.97–1.06) 1.98 (1.31–2.99)
0.11 0.85 0.93 0.38 0.003
0.73 (0.62–0.85)
0.001
† The three positive lifestyle factors included: consuming healthy diet, engaging in Z 150 min/week of physical activity, and not smoking.
Z150 min/week of moderate-to-vigorous physical activity were 54% (p ¼0.03), and non-smokers were 32% (p ¼0.02) less likely to report having moderate or greater depression symptoms. A clear dose–response relationship was observed, with those with 0, 1, 2, and 3 lifestyle factors, respectively, having depression scores of 3.4, 2.6, 2.0, and 1.5 (ptrend o0.001). The multivariable logistic regression results examining the dose–response association between concurrent lifestyle behaviors and depression are shown in Table 3. Compared to those with 0 positive lifestyle behaviors, those with 1, 2, and 3 positive lifestyle behaviors, respectively, were 15% (p¼ 0.38), 67% (p ¼0.001), and 82% (p¼ 0.01) less likely to be classifed as having moderate or greater depression symptoms. Given that a relatively small proportion (5%) of the sample had moderate or greater depression (PHQ-9 Z 10), we examined the dose response association between the number of lifestyle behaviors and a continuous depression variable. When depression was treated as a continuous variable, results were similar when compared to the logistic regression model where depression was dichotomized into moderate or greater depression and less than moderate depression (PHQ-9 o10). After adjustments, and compared to having 0 positive health behaviors, participants with 1 (β¼ 0.79, p ¼0.02), 2 (β¼ 1.24, p o0.001), and 3 (β¼ 1.56, po 0.001) positive health behaviors had less depression symptoms (data not shown in tabular format). Additional comparisons were made by changing the referent group. Compared to those with 1 positive lifestyle behavior, odds ratios for those with 0, 2, and 3 positive lifestyle behaviors, respectively, were 1.17 (p ¼0.38), 0.39 (p ¼0.002), and 0.21 (p ¼0.01). Compared to those with 2 positive lifestyle behaviors, odds ratios for those with 0, 1, and 3 positive lifestyle behaviors, respectively, were 2.96 (p ¼ 0.001), 2.52 (p ¼0.002), and 0.55 (p ¼0.28). These results show that the odds of moderate or greater depression are not different between those with 0 and 1 positive lifestyle behaviors. Similarly, there were no differences between those with 2 and 3 positive lifestyle behaviors. Consequently, it appears that adopting two positive lifestyle behaviors may be optimal for reducing the odds of developing depression.
4. Discussion As reports of depression in the United States increase, prevention is increasingly important. The precise etiology of depression is not
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known; however, it may arise from environmental, psychological, and genetic factors (Saveanu and Nemeroff, 2012). We acknowledge that lifestyle behaviors may, in part, be influenced by genetics (Loprinzi et al., 2013); however, there is also substantial evidence demonstrating that these behaviors may be modified through environmental and psychological-based strategies (Matson-Koffman et al., 2005). These data indicate that modifiable lifestyle behaviors including physical activity, healthy diet, and smoking status are independent predictors of depression. Additionally, to our knowledge, we are the first to show a dose-response relationship between the concurrent occurrence of these health behaviors and depression. Although a dose response relationship was observed, the magnitude of this effect was relatively small, as 8.9% and 1.1% of those with 0 and 3 health behaviors, respectively, had moderate or greater depression symptoms. Our first aim was to determine whether each health behavior was independently associated with depression. Regarding diet and depression, several individual nutrients or foods have been associated with depression incidence; however, no comprehensive model of healthy eating and its effect on depression has, to our knowledge, been established. Depressive symptoms have been associated with increased consumption of sweets (Jeffery et al., 2009), high fat foods and processed foods (Sanchez-Villegas et al., 2012). Other nutrients may be associated with a decreased risk of depression including polyunsaturated fatty acids (Sublette et al., 2011), folate (Farah, 2009), and vitamin B12 (Moorthy et al., 2012). Rather than examining each nutrient individually, the Healthy Eating Index utilized in this study accounts for entire dietary patterns and therefore the relationship we have demonstrated may be applied in a broader scale. Regarding the smoking-depression link, we found that nonsmokers had a lower odds of having moderate or greater depression, which is in support of other epidemiological data (Farrell et al., 2003; Grant et al., 2004). Although chronic smoking may facilitate the development of depression, likely through several neurochemical systems and oxidative damage from smoking (Flensborg-Madsen et al., 2011), it is important to recognize that acute smoking may actually help to relieve depression symptoms among smokers. For example, smokers report increased attention, concentration and feelings of well-being after smoking (Hart et al., 2001), and nicotine may increase dopamine release, adding to the effect (Fuxe et al., 1986). Not only are efforts needed to increase smoking cessation to help prevent depression, but adoption of strategies to help cope with smoking withdrawl is needed. Although various strategies are available (e.g., nicotine replacement therapy), encouraging research demonstrates that adoption of some lifestyle behaviors, such as physical activity, may help to reduce cravings and prevent negative withdrawl effects (Haasova et al., 2013). Physical activity has also been associated with reduced risk of depression (Loprinzi and Cardinal, 2012; Loprinzi, 2013), and may be an effective treatment for mild to moderate depression (Carek et al., 2011). The mechanisms through which physical activity may be associated with depression are likely to occur through physiological/neurological (e.g., thermogenic, endorphin, and monamine) and/or psychological (e.g., self-efficacy) changes (Craft and Perna, 2004). The primary aim of this study was to determine if the concurrent occurrence of multiple health behaviors was associated with depression symptoms. While these health behaviors have been studied independently, to our knowledge, we are the first to show a dose– response relationship among these lifestyle behaviors and depression. The largest reduction in depression was found with the occurrence of all three positive health behaviors (82% lower odds); however, those having two positive healthy lifestyle behaviors also had a substantive reduction in the odds of having moderate or greater depression (67%); notably, secondary analyses showed no significant difference in the odds of depression among those adopting 2 vs. 3 positive lifestyle
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behaviors, suggesting that adopting at least 2 behaviors may be reasonable. Physical activity has been shown to increase levels of executive function (Colcombe and Kramer, 2003), which is responsible for self-regulation and goal oriented behavior. In addition to physical activity-induced changes in executive functioning, changing one behavior, such as physical activity, may increase self-efficacy to change other lifestyle behaviors. Therefore, physical actvitiy behavior change may aid those in the adoption of additional health behaviors and may serve as a gateway to overall healthy lifestyle change. In support of this assertion, studies have shown that physical activity is favorably associated with smoking (Haasova et al., 2013) and dietary behavior (Joseph et al., 2011). Indeed, recent population-based interventions show promising results for changing multiple risk-behaviors (Prochaska et al., 2004; Emmons et al., 2005; Prochaska et al., 2005; Johnson et al., 2006; Toobert et al., 2007), which is in contrast to earlier work showing mostly null outcomes (Elder et al., 1986; Farquhar et al., 1990; Luepker et al., 1994). However, few studies have directly evaluated the effectiveness of multifactor vs. single factor interventions within individuals (Prochaska et al., 2008). Also, the strongest evidence for interventions aimed at multiple health behavior change have focused on secondary compared to primary prevention (e.g., individuals already diagnosed with diabetes or cardiovascular disease) (Ketola et al., 2000; Norris et al., 2001). Moreover, we know less about the effects of multiple health behavior change among individuals with mental illness because mental illness is often an exclusionary criteria among clinical trials. However, there is some encouraging intervention work demonstrating that behavior change strategies (e.g., smoking cessation treatment) used in the general population may be effective in eliciting behavior change among those with depression (Hall et al., 2006). The findings of the present study suggest that occurrence of multiple health-enhancing behaviors may play an important role in preventing depression; however, we clearly need intervention trials focusing on depression as a primary intervention, along with trials examining the efficacy of multiple behavior change in reducing depression among those with diagnosed depression. With regard to the latter, there are several design issues that need careful evaluation and exploration, including whether behaviors should be targeted concurrently or consecutively, and if consecutively, research needs to be conducted to determine how the order is determined (Prochaska et al., 2008). Due to the design of the study, directionality is not possible to determine. We must acknowledge the potential bi-directionality of depression and health behaviors; those suffering from depression may be less likely to eat well and exercise, and more likely to self-medicate with smoking, with these unhealthy choices potentially exacerbating existing depressive symptoms (Lopresti et al., 2013). Future work is needed to better understand the direction of this association. In conclusion, we confirm that diet, physical activity, and smoking status are independent predictors of depression, and, to our knowledge, we are the first to demonstrate a dose–response relationship between the concurrent occurrence of these behaviors and depression symptoms. Given that our study was limited by a cross-sectional design, future prospective studies may confirm that concurrent adoption of multiple health-enhancing behaviors may have an additive effect in preventing and reducing depression symptoms.
Role of funding source No funding was used to prepare this manuscript.
Conflict of interest The authors declare no conflicts of interest.
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Acknowledgments The authors wish to acknowledge the participants and the NHANES personnel for all their hard work, as without them, this study would not be possible.
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research report
Concurrent occurrence of multiple positive lifestyle behaviors and depression among adults in the United States Paul D. Loprinzi n, Sara Mahoney Bellarmine University, Department of Exercise Science, Donna & Allan Lansing School of Nursing & Health Sciences, Louisville, KY 40205, United States
art ic l e i nf o
a b s t r a c t
Article history: Received 14 December 2013 Received in revised form 26 April 2014 Accepted 26 April 2014 Available online 5 May 2014
Background: To our knowledge, no studies have examined the dose-response association between concurrent occurrence of multiple positive health behaviors and depression. As a result, the purpose of this study was to examine the dose-response association between concurrent occurrence of lifestyle behaviors (i.e., diet, physical activity, and smoking) on depression symptoms among a national sample of U.S. adults (20–85 yr). Methods: Using data from the 2005–2006 NHANES (n ¼ 2574), diet was assessed from the healthy eating index variable; physical activity was assessed via accelerometry; smoking was assessed from cotinine levels; and depression was assessed from the Patient Health Questionnaire 9 (PHQ-9). Results: Each lifestyle behavior was independently associated with depression in the expected direction, and there was also evidence of a dose–response relationship. Compared to those having 0 positive lifestyle factors, those with 1, 2, and 3 positive lifestyle factors, respectively, were 15% (p¼ 0.38), 67% (p¼0.001), and 82% (p ¼0.01) less likely to be classifed as having moderate or greater depression symptoms (PHQ-9 Z 10). Limitations: The main limitation of this study was the crosssectional design. Conclusion: there is a dose-response relationship between concurrent occurrence of positive lifestyle behaviors and depression symptoms. & 2014 Elsevier B.V. All rights reserved.
Keywords: Depression Diet Physical activity Smoking
1. Introduction Approximately 20% of U.S. adults report depressive symptoms, with the majority of these individuals receiving no treatment from a mental health professional or with antidepressant medication (Shim et al., 2011). In addition to counseling therapy and antidepresant pharmacotherapy, evidence-based strategies to help prevent and treat depression include making modifications to lifestyle behaviors, such as diet, physical activity, and smoking (Dunn et al., 2001; Lai et al., 2013; Callaghan et al., 2014). Although these modifiable lifestyle factors, including diet, physical activity, and smoking, are associated with reduced depression symptoms, to our knowledge, the combined effect of these lifestyle factors on depression is unknown. As a result, the two objectives of this study were to (1) examine the independent association of each lifestyle behavior on depression symptoms, and (2) examine the dose-response association between concurrent occurrence of these positive lifestyle behaviors on depression symptoms. We hypothesize that each behavior will be independently associated with depression and that individuals engaging in all three modifiable lifestyle behaviors (i.e., healthy diet, adequate physical activity, and
n
Corresponding author. Tel.: þ 502 272 8008; fax: þ 502 272 8389. E-mail address: [email protected] (P.D. Loprinzi).
http://dx.doi.org/10.1016/j.jad.2014.04.073 0165-0327/& 2014 Elsevier B.V. All rights reserved.
non-smoker) will have the lowest depression symptoms compared to those adopting fewer lifestyle behaviors. To increase generalizability, data from the present study comes from the National Health and Nutrition Examination Survey (NHANES), which is a representative sample of non-institutionalized U.S. civilians.
2. Methods 2.1. Study design Data from the present study were obtained from the 2005– 2006 NHANES. NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage, stratified, clustered probability design. The multistage design consists of 4 stages, including the identification of counties, segments (city blocks), random selection of households within the segments, and random selection of individuals within the households. In the 2005–2006 cycle, participants were sampled across 15 different geographic areas across the U.S. Participants were interviewed in their homes and then subsequently examined in a mobile examination center (MEC) by NHANES personnel. Further details about NHANES can be found
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elsewhere (CDC, 2014). NHANES study procedures were approved by the National Center for Health Statistics ethics review board, with informed consent obtained from all participants prior to data collection. 2.2. Measurement of depression At the MEC, participants completed the Patient Health Questionnaire-9 (PHQ-9) during the computer-assisted personal interview (Kroenke et al., 2001). The PHQ-9 is the 9-item depression module from the full Patient Health Questionnaire. The PHQ-9 depression scale consists of the actual 9 criteria upon which the diagnosis of DSM-IV depressive disorders is based. Sample items include, “over the last two weeks, how often have you been bothered by”: “feeling down, depressed or hopeless”, “feeling tired or having little energy”, and “trouble concentrating on things, such as reading the newspaper or watching television.” For each question, participants responded using a 4-point Likert scale, with responses including not at all (0), several days (1), more than half the days (2), and nearly every day (3). Items were summed, with higher scores indicating greater severity of depression. As a measure of severity, the PHQ-9 can range from 0 to 27, since each of the 9 items can be scored from 0 (not at all) to 3 (nearly every day). We used the PHQ-9 cut-point of Z10 to define depression as moderate or greater (Kroenke et al., 2001; Shim et al., 2011). The PHQ-9 has demonstrated evidence of reliability and validity, with Cronbach's alpha ranging from 0.86–0.89 and a 48-h test-retest correlation coefficient of 0.84 (Kroenke et al., 2001). In the present sample, internal consistency of this questionnaire, as measured by Cronbach's alpha, was 0.81. 2.3. Measurement of health behaviors 2.3.1. Dietary behavior Two 24-h recalls were collected during the visit to the MEC. To capture intake on all days of the week, the 24 h recalls were collected on every day of the week. The dietary interviewers used the Dietary Data Collection (DDC) system, which is an automated standardized interactive dietary interview and coding system. The Healthy Eating Index (HEI) 2005 was developed by the USDA as an indicator of dietary quality (USDA, 2013a). The HEI is comprised of 12 components (total fruit; whole fruit; total vegetable; dark green, orange vegetable and legumes; total grain; whole grain; milk; meat and beans; oil; saturated fats; sodium; and calories from solid fats, alcoholic beverages, and added sugars) with a maximum score of 100 and with a higher score reflecting more closely adhering to the dietary guidelines for Americans. The Healthy Eating Index was derived for each of the 24 h recall days using the MyPyramid Equivalents Database and following the methods and SAS code established by the USDA Center for Nutrition Policy and Promotion (NCI, 2005; USDA, 2013b). Using the average of the twoday HEI scores, participants at or above the 60th percentile (i.e., top 40%) of HEI scores in the population were categorized as adhering to the dietary guidelines or consuming a healthy diet (Ford et al., 2012). 2.3.2. Physical activity Participants who were able to walk were asked to wear an ActiGraph 7164 accelerometer on their right hip for 7 days. Accelerometers were affixed to an elastic belt that was worn around the participant's waist near the iliac crest. Participants were asked to wear the accelerometer during all activities except waterbased activities and sleeping. The accelerometer measured the frequency, intensity, and duration of physical activity by generating an activity count proportional to the measured acceleration.
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Estimates for physical activity were summarized in 1-min time intervals. Minutes with activity counts Z2020 per minute were classified as moderate-to-vigorous physical activity (MVPA) (Troiano et al., 2008). A binary variable was created dichotomizing participants as engaging in o or Z150 min/week of moderate-tovigorous physical activity, which is consistent with current physical activity recommendations (USDHHS, 2008). Only those participants with at least 4 days of 10 or more hours/day of accelerometer wear time were included in the analyses in order to make sure that data adequately captured habitual physical activity patterns (Troiano et al., 2008). To monitor the amount of time the device was worn, nonwear was defined by a period of a minimum of 60 consecutive minutes of zero activity counts, with the allowance of 1–2 min of activity counts between 0 and 100 (Troiano et al., 2008).
2.3.3. Smoking Current smoking status was assessed from serum cotinine levels using established gender-specific cut-points (4 1.78 ng/mL and 4.47 ng/mL for males and females, respectively) (Benowitz et al., 2009). Serum cotinine was measured by an isotope dilutionhigh performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. 2.4. Measurement of covariates To control for potential confounding variables, covariates included in the analytic models were: age, gender, race-ethnicity, body mass index (BMI), poverty-to-income ratio (PIR), and comorbidity index. BMI was calculated from measured weight and height (weight in kilograms divided by the square of height in meters). As a measure of socioeconomic status, PIR was assessed, with a PIR value below 1 considered below the poverty threshold. The PIR is calculated by dividing the family income by the poverty guidelines, which is specific to the family size, year assessed, and state of residence (CDC, 2009). Participants were classified as having 0 or 1 þ comorbidities based on self-report of the following chronic diseases/events: arthritis, coronary heart disease, stroke, cancer, diabetes, kidney disease, and hypertension. 2.5. Data analysis Statistical analyses (Stata, version 12.0, College Station, TX) accounted for the complex survey design used in NHANES. To account for oversampling, non-response, non-coverage, and to provide nationally representative estimates, all analyses included the use of survey sample weights, stratum and primary sampling units. In an effort to maintain nationally representative estimates, the sample weights for those with 4 or more days of valid accelerometry data were ratio-adjusted to maintain the age, sex, and race-ethnicity distribution of the full sample. Further details on the sample weights and how to provide weighted estimates in Stata is described elsewhere (Loprinzi and Ramulu, 2013). Two multivariable logistic regression models were computed. As shown in Table 2, a multivariable logisitic regression was computed to examine the independent associations between each of the health behaviors and depression (outcome variable). Next, a variable was created that indicated the number of positive lifestyle factors each participated had (range 0–3; healthy diet, Z150 min/week of physical activity, and non-smoker), and a multivariable logisitic regression was then computed to examine the dose-response association between the number of positive health behaviors and depression (Table 3). Both models controlled for age, gender, race-ethnicity, BMI, PIR, and presence of comorbidities. Statistical significance was established as po 0.05.
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3. Results In the 2005–2006 NHANES cycles, 2701 participants were at least 20 years of age (comorbidity data was only available on those 20 and older) and had data for all study variables excluding depression. After excluding those with missing depression data, 2574 remained, which comprised the analytic sample. With regard to those included and excluded because of missing depression data, there were no differences (p 40.05) in any of the study variables or covariates with the exception of poverty level and race-ethnicity; those excluded had a lower PIR (2.4 vs. 2.8, p ¼0.002), and those excluded were less likely to be nonHispanic white (35% vs. 53%, po 0.001) (these estimates are unweighted). Weighted characteristics of the analyzed sample are shown in Table 1. Approximately 5% (n ¼134) of the sample reported moderate or greater depression symptoms. The weighted prevalence estimates of those with moderate or greater depression symptoms among those with 0, 1, 2, and 3 positive health behaviors, respectively, was 8.9%, 6.9%, 2.6%, and 1.1%. The multivariable logistic regression results examining the independent associations between lifestyle factors and depression are shown in Table 2. All three lifestyle behaviors were independently associated with depression. After adjustments, those consuming a healthy diet were 49% (p ¼0.03), those who enaged in Table 1 Weighted characteristics of the analyzed sample, NHANES 2005–2006 (n¼2574). Variable
Weighted mean/proportion (95% CI)
Age (yr) % Female % non-Hispanic white Body mass index Poverty-to-income ratio % Presence of comorbidities % With depression symptoms Depression score % Healthy diet Healthy eating index score % Engaged in Z 150 min/week of MVPA MVPA (min/day) % Non-smoker
46.3 (44.5–48.0) 51.3 (49.8–52.8) 73.7 (67.9–79.5) 28.4 (27.8–29.0) 3.2 (3.0–3.3) 45.4 (41.8–48.9) 4.6 (3.4–5.8) 2.3 (2.1–2.5) 37.7 (34.8–40.6) 52.6 (51.6–53.5) 44.5 (40.7–48.3) 24.1 (22.6–25.5) 73.8 (71.2–76.5)
MVPA ¼Moderate-to-vigorous physical activity.
Table 2 Multivariable logistic regression results examining independent associations between lifestyle factors and depression, NHANES 2005–2006 (n¼2574). Variable
Lifestyle factors Healthy diet vs. unhealthy diet Z150 min/week of MVPA vs. o 150 min/week Non-smoker vs. smoker Covariates Age, 1 year older Female vs. male non-Hispanic white vs. other Body mass index, 1 kg/m2 higher Presence of comorbidities vs. no presence Poverty-to-income ratio, 1 unit higher
Odds ratio (95% CI) for having moderate or greater depression symptoms
P-value
0.51 (0.27–0.93) 0.46 (0.22–0.93)
0.03 0.03
0.68 (0.49–0.95)
0.02
0.98 (0.97–1.00) 0.98 (0.65–1.47) 0.97 (0.46–2.05) 1.01 (0.97–1.06) 1.98 (1.31–2.99)
0.14 0.93 0.95 0.40 0.003
0.73 (0.62–0.86)
0.01
MVPA ¼Moderate-to-vigorous physical activity.
Table 3 Multivariable regression results examining the dose-response association between concurrent health behaviors and depression, NHANES 2005–2006 (n¼2574). Variable
Number of positive lifestyle factors† 1 vs. 0 2 vs. 0 3 vs. 0 Covariates Age, 1 year older Female vs. male non-Hispanic white vs. other Body mass index, 1 kg/m2 higher Presence of comorbidities vs. no presence Poverty-to-income ratio, 1 unit higher
Odds ratio (95% CI) for having moderate or greater depression symptoms
P-value
0.85 (0.57–1.25) 0.33 (0.18–0.60) 0.18 (0.05–0.63)
0.38 0.001 0.01
0.98 (0.97–1.00) 1.03 (0.68–1.56) 0.97 (0.46–2.04) 1.01 (0.97–1.06) 1.98 (1.31–2.99)
0.11 0.85 0.93 0.38 0.003
0.73 (0.62–0.85)
0.001
† The three positive lifestyle factors included: consuming healthy diet, engaging in Z 150 min/week of physical activity, and not smoking.
Z150 min/week of moderate-to-vigorous physical activity were 54% (p ¼0.03), and non-smokers were 32% (p ¼0.02) less likely to report having moderate or greater depression symptoms. A clear dose–response relationship was observed, with those with 0, 1, 2, and 3 lifestyle factors, respectively, having depression scores of 3.4, 2.6, 2.0, and 1.5 (ptrend o0.001). The multivariable logistic regression results examining the dose–response association between concurrent lifestyle behaviors and depression are shown in Table 3. Compared to those with 0 positive lifestyle behaviors, those with 1, 2, and 3 positive lifestyle behaviors, respectively, were 15% (p¼ 0.38), 67% (p ¼0.001), and 82% (p¼ 0.01) less likely to be classifed as having moderate or greater depression symptoms. Given that a relatively small proportion (5%) of the sample had moderate or greater depression (PHQ-9 Z 10), we examined the dose response association between the number of lifestyle behaviors and a continuous depression variable. When depression was treated as a continuous variable, results were similar when compared to the logistic regression model where depression was dichotomized into moderate or greater depression and less than moderate depression (PHQ-9 o10). After adjustments, and compared to having 0 positive health behaviors, participants with 1 (β¼ 0.79, p ¼0.02), 2 (β¼ 1.24, p o0.001), and 3 (β¼ 1.56, po 0.001) positive health behaviors had less depression symptoms (data not shown in tabular format). Additional comparisons were made by changing the referent group. Compared to those with 1 positive lifestyle behavior, odds ratios for those with 0, 2, and 3 positive lifestyle behaviors, respectively, were 1.17 (p ¼0.38), 0.39 (p ¼0.002), and 0.21 (p ¼0.01). Compared to those with 2 positive lifestyle behaviors, odds ratios for those with 0, 1, and 3 positive lifestyle behaviors, respectively, were 2.96 (p ¼ 0.001), 2.52 (p ¼0.002), and 0.55 (p ¼0.28). These results show that the odds of moderate or greater depression are not different between those with 0 and 1 positive lifestyle behaviors. Similarly, there were no differences between those with 2 and 3 positive lifestyle behaviors. Consequently, it appears that adopting two positive lifestyle behaviors may be optimal for reducing the odds of developing depression.
4. Discussion As reports of depression in the United States increase, prevention is increasingly important. The precise etiology of depression is not
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known; however, it may arise from environmental, psychological, and genetic factors (Saveanu and Nemeroff, 2012). We acknowledge that lifestyle behaviors may, in part, be influenced by genetics (Loprinzi et al., 2013); however, there is also substantial evidence demonstrating that these behaviors may be modified through environmental and psychological-based strategies (Matson-Koffman et al., 2005). These data indicate that modifiable lifestyle behaviors including physical activity, healthy diet, and smoking status are independent predictors of depression. Additionally, to our knowledge, we are the first to show a dose-response relationship between the concurrent occurrence of these health behaviors and depression. Although a dose response relationship was observed, the magnitude of this effect was relatively small, as 8.9% and 1.1% of those with 0 and 3 health behaviors, respectively, had moderate or greater depression symptoms. Our first aim was to determine whether each health behavior was independently associated with depression. Regarding diet and depression, several individual nutrients or foods have been associated with depression incidence; however, no comprehensive model of healthy eating and its effect on depression has, to our knowledge, been established. Depressive symptoms have been associated with increased consumption of sweets (Jeffery et al., 2009), high fat foods and processed foods (Sanchez-Villegas et al., 2012). Other nutrients may be associated with a decreased risk of depression including polyunsaturated fatty acids (Sublette et al., 2011), folate (Farah, 2009), and vitamin B12 (Moorthy et al., 2012). Rather than examining each nutrient individually, the Healthy Eating Index utilized in this study accounts for entire dietary patterns and therefore the relationship we have demonstrated may be applied in a broader scale. Regarding the smoking-depression link, we found that nonsmokers had a lower odds of having moderate or greater depression, which is in support of other epidemiological data (Farrell et al., 2003; Grant et al., 2004). Although chronic smoking may facilitate the development of depression, likely through several neurochemical systems and oxidative damage from smoking (Flensborg-Madsen et al., 2011), it is important to recognize that acute smoking may actually help to relieve depression symptoms among smokers. For example, smokers report increased attention, concentration and feelings of well-being after smoking (Hart et al., 2001), and nicotine may increase dopamine release, adding to the effect (Fuxe et al., 1986). Not only are efforts needed to increase smoking cessation to help prevent depression, but adoption of strategies to help cope with smoking withdrawl is needed. Although various strategies are available (e.g., nicotine replacement therapy), encouraging research demonstrates that adoption of some lifestyle behaviors, such as physical activity, may help to reduce cravings and prevent negative withdrawl effects (Haasova et al., 2013). Physical activity has also been associated with reduced risk of depression (Loprinzi and Cardinal, 2012; Loprinzi, 2013), and may be an effective treatment for mild to moderate depression (Carek et al., 2011). The mechanisms through which physical activity may be associated with depression are likely to occur through physiological/neurological (e.g., thermogenic, endorphin, and monamine) and/or psychological (e.g., self-efficacy) changes (Craft and Perna, 2004). The primary aim of this study was to determine if the concurrent occurrence of multiple health behaviors was associated with depression symptoms. While these health behaviors have been studied independently, to our knowledge, we are the first to show a dose– response relationship among these lifestyle behaviors and depression. The largest reduction in depression was found with the occurrence of all three positive health behaviors (82% lower odds); however, those having two positive healthy lifestyle behaviors also had a substantive reduction in the odds of having moderate or greater depression (67%); notably, secondary analyses showed no significant difference in the odds of depression among those adopting 2 vs. 3 positive lifestyle
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behaviors, suggesting that adopting at least 2 behaviors may be reasonable. Physical activity has been shown to increase levels of executive function (Colcombe and Kramer, 2003), which is responsible for self-regulation and goal oriented behavior. In addition to physical activity-induced changes in executive functioning, changing one behavior, such as physical activity, may increase self-efficacy to change other lifestyle behaviors. Therefore, physical actvitiy behavior change may aid those in the adoption of additional health behaviors and may serve as a gateway to overall healthy lifestyle change. In support of this assertion, studies have shown that physical activity is favorably associated with smoking (Haasova et al., 2013) and dietary behavior (Joseph et al., 2011). Indeed, recent population-based interventions show promising results for changing multiple risk-behaviors (Prochaska et al., 2004; Emmons et al., 2005; Prochaska et al., 2005; Johnson et al., 2006; Toobert et al., 2007), which is in contrast to earlier work showing mostly null outcomes (Elder et al., 1986; Farquhar et al., 1990; Luepker et al., 1994). However, few studies have directly evaluated the effectiveness of multifactor vs. single factor interventions within individuals (Prochaska et al., 2008). Also, the strongest evidence for interventions aimed at multiple health behavior change have focused on secondary compared to primary prevention (e.g., individuals already diagnosed with diabetes or cardiovascular disease) (Ketola et al., 2000; Norris et al., 2001). Moreover, we know less about the effects of multiple health behavior change among individuals with mental illness because mental illness is often an exclusionary criteria among clinical trials. However, there is some encouraging intervention work demonstrating that behavior change strategies (e.g., smoking cessation treatment) used in the general population may be effective in eliciting behavior change among those with depression (Hall et al., 2006). The findings of the present study suggest that occurrence of multiple health-enhancing behaviors may play an important role in preventing depression; however, we clearly need intervention trials focusing on depression as a primary intervention, along with trials examining the efficacy of multiple behavior change in reducing depression among those with diagnosed depression. With regard to the latter, there are several design issues that need careful evaluation and exploration, including whether behaviors should be targeted concurrently or consecutively, and if consecutively, research needs to be conducted to determine how the order is determined (Prochaska et al., 2008). Due to the design of the study, directionality is not possible to determine. We must acknowledge the potential bi-directionality of depression and health behaviors; those suffering from depression may be less likely to eat well and exercise, and more likely to self-medicate with smoking, with these unhealthy choices potentially exacerbating existing depressive symptoms (Lopresti et al., 2013). Future work is needed to better understand the direction of this association. In conclusion, we confirm that diet, physical activity, and smoking status are independent predictors of depression, and, to our knowledge, we are the first to demonstrate a dose–response relationship between the concurrent occurrence of these behaviors and depression symptoms. Given that our study was limited by a cross-sectional design, future prospective studies may confirm that concurrent adoption of multiple health-enhancing behaviors may have an additive effect in preventing and reducing depression symptoms.
Role of funding source No funding was used to prepare this manuscript.
Conflict of interest The authors declare no conflicts of interest.
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Acknowledgments The authors wish to acknowledge the participants and the NHANES personnel for all their hard work, as without them, this study would not be possible.
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