Screen time is a marker of sedentary behavior that threatens the health of women. The harmful effects of extended screen time were emphasized in a study showing that sedentary behavior is associated with increased all-cause mortality.1 Sedentary behavior is defined as sitting, lying down, and engaging in behaviors, including television viewing, video game playing, computer use, driving automobiles, and reading, that are associated with very little energy expenditure.2 Screen time is the term for the behaviors associated with television viewing, playing video games, and using the computer.
Screen time affects the health of adults and is associated with obesity, cardiovascular disease (CVD), diabetes, and premature death.3,4 Overall screen time has been systematically evaluated and found to be 3.61 h/d among adults; obese adults had more screen time than their normal weight counterparts did.5 In addition, screen time is associated with sleep disturbances and unhealthy eating behaviors, such as fast-food consumption, suggesting that screen time is likely associated with other health behaviors.6,7 Amount of screen time also differs according to socioeconomic status, and socioeconomically marginalized groups were more likely to engage in sedentary behavior.5,8
Middle-aged women are a vulnerable group who may experience a range of health problems, such as depression, insomnia, and cardiovascular disease. In addition, they have a lower quality of life than men do. Hormonal alterations during the menopausal transition, unhealthy lifestyle behaviors, and multiple stressors are some of the causes of adverse health conditions among middle-aged women.6,9 Among these factors, large amounts of screen time was reported to be associated with a lower quality of life among adult women,3 whereas the associations between screen time and cardiometabolic biomarkers were stronger for women than for men.10 On the other hand, most former studies on screen time have been performed targeting children and youths, whereas few studies have targeted middle-aged women.
Recent research posits that socioeconomic, biological, and behavioral parameters play important roles in promoting and regulating health, whereas these factors independently and collectively influence premature death and health-related quality of life.11,12 The premise of the psycho-socioeconomic biobehavioral model of health is that psychological (eg, depression), socioeconomic (eg, income), biological (eg, menopause), and behavioral (eg, screen time) factors interact with each other and have cumulative effects on health, including cardiovascular health.11,12 Therefore, in the analysis of factors associated with health conditions, simultaneous consideration of multiple variables is needed to gain a comprehensive understanding of the multiple influences on health.
Accordingly, based on the psycho-socioeconomic biobehavioral perspectives,10 we examined factors associated with screen time and whether screen time is associated with the physiological and psychosocial health of middle-aged women, including cardiometabolic biomarkers, insomnia, and quality of life, after controlling for the combined effects of other socioeconomic, biological, and behavioral variables. We also compared the effects of screen time and regular exercise on the physiological and psychosocial health of middle-aged women.
This was a secondary data analysis performed using data from a cross-sectional study on women's health. The aim of the original study was to explore the personal, behavioral, and socioenvironmental factors that are associated with sleep characteristics among middle-aged women in a community setting. Detailed information on the original study can be found elsewhere.6
This study was approved by the Institutional Review Board of Inha University, Incheon, South Korea (150401-1). Data were collected after obtaining written informed consent from the participants. The informed consent forms explained that participants could withdraw from the study at any time without penalty. This study conformed to the ethical principles of the Declaration of Helsinki.
The study participants included 423 middle-aged women aged between 40 and 65 years residing in 1 metropolitan city in South Korea. They were recruited using local newspapers, flyers, and banners with the help of the branch office of the Planned Population Federation of Korea (PPFK), which is a nonprofit organization dedicated to the health of women, youth, and older people. The inclusion criteria were female gender, aged between 40 and 65 years, and able to read and understand the Korean language and understand the purpose of the study. Women diagnosed with mental health problems by a physician and who underwent cancer therapy at the time of data collection were excluded. Participant recruitment was performed between June and August 2015.
Potential participants were prescreened via telephone, and eligible participants presented voluntarily at the PPFK branch office to enroll in the study. A total of 430 women were prescreened and 7 women were excluded because of the age limit. Managers and staff members of PPFK assisted in recruiting study participants and data collection. Those who agreed to enroll were given survey questionnaires. After completion of the survey, anthropometric and blood pressure measurements were taken by trained nurses. Blood samples were drawn by medical technicians in the clinical laboratory within the PPFK and sent for testing. Research assistants made telephone calls to participating women who were instructed to fast for blood testing after 9 PM on the day before undergoing the blood tests. After the blood samples were drawn, each participant received a $20.00 gift card.
Socioeconomic characteristics collected included the subjects' age, income, education, marital status, and employment status. Age and monthly family income were measured as continuous variables. Women provided their monthly family income in Korean won (1000 won = US$1.00). Categorical variables were used to assess education (≤elementary, middle, high school, or ≥ college), marital status (single, married/cohabiting, or widowed/divorced), and employment status (yes or no).
Psychosocial characteristics that were measured included insomnia, depressive symptoms, and menopause-specific quality of life (MENQOL). The Insomnia Severity Index (ISI) was used to examine the severity of insomnia during the last 2 weeks.13 The ISI is a 7-item scale and is scored using a 5-point Likert scale (0 = not at all, 4 = extremely); higher scores indicated higher insomnia. The Korean version of the ISI translated by the Korean Sleep Research Society was used in this study.14 Previous researchers verified the validity of the Korean version of the ISI and showed good convergent/discriminant validity.15 The Cronbach's α was .90 in the former study13 and .92 in the present study.
Depressive symptoms over a 1-week period were measured using the Center for Epidemiologic Studies Depression Scale (CES-D).15 The CES-D is a 20-item scale that is measured with a 4-point Likert-type scale, ranging from 0 to 3; higher scores indicated more depressive symptoms.16 We used the Korean version of the CES-D, which was translated and back-translated, and good construct and concurrent validity were verified compared with the diagnostic tests for clinical depression by Korean researchers.17 The Cronbach's α was .89 in the former study17 and .87 in the present study.
The MENQOL was used to measure the menopausal quality of life. The MENQOL was developed by Hilditch et al and translated into Korean by Korean researchers.18,19 The Korean version consisted of 30 items in 4 domains (vasomotor, psychosocial, physical, and sexual domains), measured using a 7-point Likert-type scale from 1 to 7 points (1 = hardly ever and 7 = very uncomfortable). Lower scores indicated a better quality of life. The Cronbach's α was .81 in the former study18 and .97 in the present study.
The physiological characteristics included body mass index (BMI), blood glucose, lipid profile, blood pressure, and menopausal status. To obtain BMI (kg/m2), height (m) and weight (kg) were measured using an electronic scale (G-TECH [GL-150], 2006; Uijongbu, Kyunggi-do, Korea) with subjects wearing light clothes and shoes off. Using an electronic sphygmomanometer, trained nurses measured the blood pressure twice at 20-minute intervals on the right arm in the sitting position using a guide provided by the Korea National Health and Nutrition Survey (KNHANES).20 To examine fasting blood sugar and lipid profile (ie, total cholesterol, triglyceride, and high-density lipoprotein and low-density lipoprotein [LDL] cholesterol), clinical laboratory technologists drew 5 mL of blood samples from the forearm vein of the participants who were instructed to not eat anything after 9 PM on the day before the blood test. Menopausal status was measured with 6 questions including regularity of menstruation, initiation date of previous menstruation, amount of menstruation (maintained or decreased), regularity of menstruation, types of menopause (natural or surgical), and last date of menstruation. Based on the menopausal status questions, the participants were divided into 3 groups, including premenopausal (regular cycle and amount of menstruation in the previous 3 months), perimenopausal (experienced irregularity in the cycle and amount of menstruation in the previous 12 months), and postmenopausal (no menstruation in the previous 12 months) groups as recommended by the World Health Organization.21 We recoded menopausal status as postmenopausal (yes) and premenopausal/perimenopausal (no) for the analysis.
Behavioral characteristics included regular exercise, alcohol consumption frequency, eating behavior, and screen time. Regular exercise was measured with 6 questions. Three questions asked the number of days of vigorous, moderate, and walking exercises performed for at least 10 min/d during the past week. The other 3 questions asked the average minutes of vigorous, moderate, and walking exercises per day performed over the past week. Examples of vigorous exercise included jogging, climbing, bicycling, fast swimming, tennis, and carrying heavy loads, whereas examples of moderate exercise included slow swimming, badminton, table tennis, tennis doubles, and carrying light loads. Regular exercisers were defined as those who performed vigorous exercise at least 20 min/d and 3 days per week or those who performed moderate or walking exercise at least 30 min/d and 5 days per week.22
Alcohol consumption frequency was measured using a question “how often do you drink alcohol?” and measured with an ordinal scale (never, less than once a month, once a month, 2–4 times per month, 2–3 times per week, or at least 4 times per week). Questions on eating behavior included the frequency of fast-food consumption and eating a late dinner after 8 PM during the past week. An ordinal scale (zero to everyday) was used to measure eating behavior. Night eating behavior is associated with metabolic syndrome and obesity and is associated with screen time as well.7,23 Therefore, eating a late dinner after 8 PM was included as one of the eating behavior questions.
Screen time behavior was measured using the question “How many hours per day in your leisure time did you spend sitting while watching television or videos, using computers, and playing Internet games during the past week?” Two identical questions were used to measure screen time. One question asked screen time during the weekdays (Monday through Friday), whereas the other question asked screen time during the weekend. The data were scored on an ordinal scale (<1 hour, 1–2 hours, 2–3 hours, 3–4 hours, or ≥4 hours). Screen time was recoded in 3 categories (<1, 1–2.9, or ≥3 h/d) for data analysis, considering former study results. Previous investigators reported that at least 3 h/d of screen time was associated with an increased BMI, whereas 1 to 3 h/d of screen time was associated with increased fast-food consumption compared with those spending less than 1 h/d screen time.7
Questions on regular exercise and alcohol consumption were taken from the KNHANES,22 whereas those on the eating behavior and screen time were obtained from a former study.7 The KNHANES is a nationwide cross-sectional survey conducted every year in Korea by the Korea Centers for Disease Control and Prevention, and the external and internal quality of the national survey and instruments are managed by the Korea Centers for Disease Control and Prevention.24
Analysis of Data
SPSS (version 23.0, SPSS Inc, Chicago, Illinois) was used for the analyses. To identify the relationships between screen time and the general and behavioral characteristics, χ2 analysis of variance tests were performed. Multivariate analysis of covariance (MANCOVA) was performed to test for differences in the physiological and psychosocial characteristics according to the amount of screen time. If MANCOVA exhibited significant results, it was followed up by separate analysis of covariance. Binary and multinomial logistic regression analyses were performed to examine the combined effects of screen time and regular exercise on the physiological and psychosocial health indicators. Differences with a probability of less than .05 were considered significant.
General and Behavioral Characteristics of Women According to Screen Time
The mean (SD) age of women enrolled was 55 (6.01) years, and 67.6% had an education level of at least high school level. A total of 51.7% had a monthly family income above 2.4 million won (equivalent to US$2400.00) and 81.0% had a spouse or a partner. Forty-seven percent of the participants were employed. A total of 73.0% were postmenopausal. The distribution of screen time differed significantly according to age, employment status, and menopausal status (P < .05). Older women, unemployed women, and postmenopausal women were more likely to have higher screen times than their counterparts.
Sixty-eight percent of women performed regular exercise and 54.2% consumed alcohol at least once a month. Moreover, 47% and 75.4% consumed fast-food and a late dinner after 8 PM for at least once a week, respectively. Screen time was negatively associated with regular exercise. Those who performed regular exercise were less likely to have high screen time than those who did not (P = .009). Twenty-four percent (n = 101) spent at least 3 h/d screen time during the weekdays, whereas 30.7% (n = 130) spent an identical amount of screen time during the weekends. The weekday screen time was associated significantly with that during weekends (P < .001). The data showed a 73.2% (308/421) agreement rate between weekday and weekend screen time (Table 1).
Multivariate Analysis of Covariance Tests for Group Differences in the Physiological and Psychosocial Characteristics
A MANCOVA test was performed to test for group differences regarding the physiological and psychosocial characteristics (ie, BMI, glucose, lipid profile, blood pressure, insomnia, depressive symptoms, and quality of life) according to screen time; age was included as a covariate (Pillai Trace of 0.092 [F = 2.178, P = .003] and Wilks λ of 0.910 [F = 2.186, P = .003]). The MANCOVA was followed up by individual analyses of covariance with age and BMI as covariates. Total cholesterol and LDL cholesterol levels, ISI, and MENQOL were associated significantly with screen time (P < .05). Post hoc analysis showed that women with at least 3 h/d screen time were more likely to have higher total cholesterol and LDL cholesterol levels, ISI, and MENQOL scores than those with less than 1 h/d and 1 to 3 h/d screen time (P < .05). Higher ISI and MENQOL scores indicated higher levels of insomnia and lower quality of life (Table 2).
Combined Effects of Screen Time and Regular Exercise on the Physiological and Psychosocial Characteristics
Binary and multinomial logistic regression analyses were performed to examine the combined influence of screen time and regular exercise on the physiological and psychosocial characteristics of women after controlling for the socioeconomic and biobehavioral variables. Compared with women who spent less than 3 h/d screen time, those who spent 3 h/d or more of screen time during the weekdays were 2.27 times (95% confidence interval [CI], 1.26–4.08) more likely to have high LDL cholesterol levels (≥160 mg/dL) and 1.84 times (95% CI, 1.03–3.30) and 2.19 times (95% CI, 1.12–4.28) more likely to have subthreshold and clinical insomnia, respectively (P < .05).
Compared with those who spent less than 3 h/d screen time, those who spent 3 h/d or more of screen time during the weekend were 1.56 times (95% CI, 1.004–2.41) more likely to have high total cholesterol levels (≥200 mg/dL), 2.18 times (95% CI, 1.24–3.82) more likely to have high LDL cholesterol levels (≥160 mg/dL), and 2.98 times (95% CI, 1.58–5.65) more likely to have clinical insomnia (P < .05). The women were divided into 2 groups based on the mean MENQOL score (mean [SD], 59.77 [43.43]) for logistic regression analysis. Those who spent 3 h/d or more of screen time during the weekend were 1.76 times (95% CI, 1.13–2.73) more likely to belong to the group who scored above the mean (P < .05), which indicates that these women are more likely to have a lower quality of life. Regular exercise was not associated with any of the physiological and psychosocial variables (P > .05). The Hosmer and Lemeshow tests indicated that the models fit the data (P > .05). The Nagelkerke R2 ranged from 0.037 to 0.096 (Table 3).
This study is unique because a psycho-socioeconomic biobehavioral framework was used to illustrate the combined effects of screen time and regular exercise on physiological and psychosocial health of middle-aged women, including cardiometabolic biomarkers, insomnia, and quality of life. Former studies on screen time were conducted mostly among children and adolescents, whereas studies on screen time targeting middle-aged women are scarce.25,26 Age is one of the factors that are associated with screen time, but multiple factors interact with each other and independently and collectively affect health. Thus, taking multiple factors into account simultaneously provides valuable information for achieving a comprehensive understanding of the physiological and psychosocial health of women and its associated factors.25,26
Based on the psycho-socioeconomic biobehavioral models of health,11,12 we found that multiple variables were associated with screen time, including sociodemographic (age and employment status), behavioral (regular exercise), and biological (menopausal status) variables. We demonstrated that after controlling for socioeconomic and biobehavioral variables, screen time was associated with multiple health indicators, including total cholesterol and LDL cholesterol levels, insomnia, and MENQOL in this sample of middle-aged women.
Women who spent 3 h/d or more of screen time were more likely to have insomnia, high total cholesterol and LDL cholesterol levels, and a poorer quality of life than those who spent less time. The results also showed that older women, unemployed women, and those who did not perform regular exercise were more likely to have longer screen times (≥3 h/d) than their counterparts.
As the use of smartphones and computers has become an important part of daily life, the influence of screen time on the health and quality of life is greater than before. Health professionals should be aware that among middle-aged women, screen time is associated with physiological and psychosocial health independent of regular exercise. Hence, interventions should aim to decrease screen time and promote regular exercise.
Screen time has detrimental health effects and demonstrates distinct biological processes due to a lack of physical activity.27 Tremblay et al introduced the movement continuum in that individual behavior moves along the continuum from sedentary to intense exercise. They contended that one behavior does not substitute for another and individuals may actively engage in physical activity sometime during the day, while they could still be highly sedentary at other times.27
The association between screen time and dyslipidemia found in the present study was consistent with a former study.28 Others reported that the associations between screen time and cardiometabolic biomarkers were stronger for women than men.10 Women have less skeletal muscle mass and more fat mass than men do, which makes women more vulnerable to the adverse effects of prolonged screen time10 because skeletal muscle is involved in the lipid metabolism during exercise and rest.29 Researchers observed reduced lipoprotein lipase (LPL) activity in sedentary individuals and argued that the LPL activity mediated the association between sedentary behavior and cardiometabolic health.27 The LPL activity contributed to uptake of free fatty acid and lipids (such as total cholesterol and LDL cholesterol) into the skeletal muscle and adipose tissue.30 Accordingly, reduced LPL activity due to extended screen time might increase circulating free fatty acid and lipids and in turn increase the cardiometabolic risk.
In the present study, we found that 23.9% and 30.7% of women had at least 3 h/d screen time during the week and weekend, respectively. These are lower than seen in a previous study, in which 37.3% had at least 4 h/d screen time during their day off.28 Others reported an average screen time of 90 minutes among American adults.31 In that study, younger adults had more screen time than their older counterparts did. In contrast, we found that older women spent more screen time than their younger counterparts.31 Those who spent large amounts of screen time were less likely to be employed. Hence, unemployed older women may have more free time that can be used for screen time.
We also found that screen time was associated with insomnia in these women. The blue light produced from televisions, computers, and smartphones interferes with the production of melatonin, resulting in disturbed sleep and insomnia.31 In addition, former researchers reported that increased time in sedentary behaviors was associated with decreased melatonin levels among nurses.32 Others have suggested that physical activity is beneficial for obstructive sleep apnea, depressive symptoms, and restless leg syndrome, all of which affect insomnia and sleep quality.33 In our study, women with high screen time were less likely to perform regular exercise, suggesting that screen time may have displaced physical activity time in the present sample. Extended screen time at evening and night may decrease sleep duration and quality, whereas some media content, such as video games or violent media, may increase psychophysiological arousal at bedtime that may cause disturbed sleep.34 These potential mechanisms may explain the association between screen time and insomnia in the present study.
In line with a psycho-socioeconomic biobehavioral framework, we found that extended screen time is associated with a lower MENQOL after controlling for the combined effects of other socioeconomic and biobehavioral variables. Consistent with the present study, others have reported that high screen time along with no physical activity had a negative impact on health-related quality of life in adults, particularly in women.3 Individuals with high screen time were also more likely to report a lower quality of life, even if they performed sufficient physical activities. More time spent in screen-based activity is independently associated with multiple chronic diseases risk, including metabolic syndrome, type 2 diabetes, and cardiovascular disease,3,10 and these associations may account for the negative effects of screen time on the physical domain of the quality of life. Therefore, those with high screen time may have lower scores on the psychosocial and physical domains of quality of life, leading to a significant association between screen time and MENQOL in the present study.
In line with a former study demonstrating that sedentary physiology is distinguished from exercise physiology,27 our study yielded disparate results between screen time and regular exercise. Regular exercise was not associated with physiological and psychosocial characteristics after controlling for the socioeconomic and biobehavioral variables in our study. The reasons for the insignificant association between regular exercise and physiological and psychosocial variables are unknown. The significant association between screen time and multiple health indicators may indicate that screen time is more influential on the health and quality of life of women than regular exercise.
The American Academy of Pediatrics provided screen time recommendations for children and advice to limit their screen use to 1 hour per day.35 There are no screen time guidelines for adults. Some adults may use extended screen time at work, making a single recommendation problematic. However, because screen time is associated with multiple health indicators, there is a need for guidelines for leisure-time use or nonoccupational use of screen time along with efforts to decrease screen time, particularly for women.
Frequent breaks for activity during sedentary periods have beneficial effects on a range of metabolic biomarkers.10 A larger number of breaks during sedentary time has been positively associated with reduced cardiovascular risks, including waist circumference, BMI, triglycerides, and blood glucose.36 An intervention study to reduce screen time was effective in reducing screen time and BMI among adults.37 Therefore, future interventions may include efforts to reduce screen time using multiple strategies, such as counseling and education, and alarm services for the intermittent breaks during screen time. Women should be informed of the harmful effects of extended screen time on the cardiovascular health, even if they perform regular exercise.
The generalizability of the study results may be limited because of the use of a convenience sample. In this study, workplace screen time or other sedentary time, such as driving and other sedentary hobbies (sewing, knitting, reading, board games, etc), was not considered, which may limit the study results. In addition, the use of a self-report method for measuring screen time, physical activity, and other behavioral variables may have generated a response bias as adults generally overestimate the amount of exercise they perform.38 In addition, former studies on the screen time of middle-aged women are scarce. Thus, our study results were compared with former studies conducted with adults (men and women), youths, or children, which may limit the interpretation of the study results. Lastly, although smartphones have become an important part of daily life, screen time behavior was measured without including a statement on smartphone use, which could have resulted in underestimation of screen time behavior.
Summary and Implications
Based on the psycho-socioeconomic biobehavioral framework, we found that screen time is associated with multiple variables, including sociodemographic, behavioral, biological, and psychological variables, and that screen time is associated independently with total cholesterol and LDL cholesterol levels, insomnia, and MENQOL among middle-aged women. Because screen time is associated independently with dyslipidemia and insomnia, interventions to reduce the amount of screen time are required, particularly targeting high-risk groups of women. These interventions may improve cardiometabolic biomarkers, help promote sleep and improve quality of life among middle-aged women.
In future studies, investigators should determine if there are differences in health outcomes between screen time due to work versus leisure time. Comparison of the effects of screen time between men and women will provide baseline data for the development of tailored interventions for each group. These studies may need to consider other types of sedentary behavior, such as driving and reading.
What’s New and Important
- Based on the psycho-socioeconomic biobehavioral framework, we found that screen time is associated with multiple variables, including age, employment status, regular exercise, and menopausal status, among middle-aged women. Older and unemployed women were more likely to use high screen time.
- Increased screen time was associated independently with increased total cholesterol and LDL cholesterol levels and insomnia, as well as lower MENQOL after controlling for the effects of regular exercise.
- Twenty-four percent of the subjects had at least 3 h/d of screen time on weekdays, whereas it was 30.7% on weekends. Therefore, interventions aimed at decreasing the amount of screen time and those promoting intermittent breaks of screen time may help reduce dyslipidemia and help improve the quality of life of middle-aged women.
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