Background
Television (TV) viewing represents one of the most common sedentary behaviors among the US population, with adults watching approximately 34 h per week [
1]. Watching two or more hours of television per day has been associated with adverse health behaviors [
2], numerous chronic diseases [
3‐
5], cancer-related mortality and early mortality generally [
3,
6]. Television viewing is also a significant predictor for diabetes diagnosis, abnormal glucose metabolism, hypertension, heart disease, high cholesterol, all-cause mortality, cardiovascular mortality, and cancer mortality [
2]. Moreover, a graded association has been found between television consumption and health outcomes: adults watching television between 2.5 and 4 h per day were twice as likely to be overweight, and those watching television for more than 4 h per day were four times more likely to be overweight, compared to those watching less than 1 h per day [
4].
In part, these outcomes might occur as a result of impacts of screen time on a variety of health-related behaviors, in particular dietary behaviors. Long hours of screen time have been associated with poor dietary patterns among adults, including higher consumption of sugar, especially from soft drinks; higher intake of foods with low nutritional quality like French fries, refined grain products, snacks and deserts; and lower intake of fiber, fish, vegetables, fruits, and whole grains [
1‐
5]. Poor dietary choices may lead to overweight and obesity; for example, excess consumption of sugar-sweetened beverages has been found to be significantly associated with an approximate weight gain of 0.12–0.22 kg per year [
6]. Conversely, healthier dietary patterns have been shown to be protective against a number of problematic conditions and diseases. Diets high in cereal fiber, for example, have been associated with a lower risk of developing type 2 diabetes independent of age, sex, and lifestyle factors [
7], and a diet high in fruits and vegetables has been associated with a lower risk of cardiovascular disease and mortality [
8,
9]. Hence, a dietary pattern marked by limited consumption of nutrient-dense foods may fail to confer optimal protection against chronic diseases, which could partially explain the association between prolonged television viewing and conditions such as type 2 diabetes and cardiovascular disease among adults [
4,
10,
11].
Although associations between television time and health-related behaviors and outcomes is now well-established, the issue of screen time remains a complex and evolving problem. For example, even as Americans continue to consume multiple hours of TV per day, patterns of consumption are changing. Binge-watching, the continuous consumption of screen-based entertainment facilitated in part by media streaming services and television-connected devices, is growing as a phenomenon, potentially contributing to more evening-time screen use with the potential to crowd out other healthful behavioral goals and pursuits [
12]. Simultaneously, the use of other sophisticated screen-based devices has grown considerably in recent years. In 2017, the use of ‘apps’ and internet services on a smartphone averaged more than 2 hours per day among American adults, contributing to a total average screen time use (television/television-connected devices/smartphone/tablet) of 8 hours daily for media consumption alone [
13]. These changes could be important for dietary behaviors as well as other health-related behaviors and characteristics, including physical activity, sleep quality and duration, and stress [
14].
Only recently has research begun to explore patterns of modern screen use across devices, times of day, and days of the week [
15]. This approach to examining screen use represents an important next step in screen time research related to health. The combined used of multiple screens during a day can potentially impact health-related behavior in ways that are different than what has been seen in studies exploring TV use as the primary screen-based behavior. Further, it is not yet clear if and how the prolonged use of newer portable technologies, either alone or in combination, may be associated with poor dietary patterns that may further contribute to overweight, obesity, and other health-related outcomes. To the authors’ knowledge, no research exists exploring whether or how a variety of modern screen-based devices might be related to health behaviors differentially and collectively. The purpose of this study was to explore whether extended use of a variety of screen-based devices, in addition to television, was associated with dietary habits, physical activity, stress, sleep, and sleep quality among US adults.
Results
Nine hundred and seventy-eight responses were accepted from MTurk. Fifty-two participants were excluded for reporting greater number of screen time hours possible in a 24-h period for any given screen-based device. Hence, a total of 926 responses were included in the final analyses. Kruskal-Wallis tests indicated that participants were successfully assigned to significantly different total screen time categories;
X2 (2) = 822.68,
p < 0.000. Total screen time for light, moderate, and heavy users had a median of 7, 11.25, and 17.5 h per day, respectively. The only significant demographic differences between total screen time categories were age and race/ethnicity;
X2 (2) = 10.32,
p = .006 and
X2 (8) = 28.26,
p = .000, respectively. Heavy users tended to be slightly younger and have a greater proportion of African-Americans and Asians/Pacific Islander. See Table
1. Participants were also successfully assigned to significantly different screen time categories by type of screen (e.g. television, smartphone); all
p < 0.000 (Table
2).
Table 1
Demographic characteristics of study participants
Total screen time (minutes per day)* | 690 (480–916.25) | 420 (328.50–480) | 675 (615–750) | 1050 (900–1290) |
Age* | 34 (28–45) | 34 (28–45) | 35.5 (28–49) | 32 (27–41.25) |
Sex |
Male | 44.9 | 42 | 45.8 | 46.5 |
Female | 55.1 | 58 | 54.2 | 53.5 |
Race/ethnicity* |
Non-Hispanic White | 72.7 | 78.2 | 76.8 | 63.7 |
Hispanic | 7.7 | 7.1 | 7.7 | 8.2 |
African-American | 8.6 | 4.8 | 6.5 | 14.2 |
Asian or Pacific Islander | 8.4 | 7.8 | 6.5 | 10.7 |
Native American | 2.6 | 2.0 | 2.6 | 3.2 |
Employment |
Full-time (≥ 35 h/wk) | 63.5 | 60.0 | 61.9 | 68.2 |
Part-time (< 35 h/wk) | 17.0 | 16.6 | 18.7 | 15.7 |
Not currently employed/retired | 19.5 | 23.4 | 19.4 | 16.0 |
Income |
Less than 30,000 | 21.8 | 17.6 | 20.6 | 26.7 |
30,000 – 59,999 | 42.1 | 45.8 | 41.6 | 39.3 |
More than 59,999 | 36.1 | 36.6 | 37.7 | 34.0 |
Education |
Less than high school | 0.5 | 0.3 | 0.3 | 0.9 |
High school/ GED/some college | 36.4 | 35.9 | 37.2 | 36.0 |
Associate’s degree | 12.1 | 12.5 | 12.3 | 11.4 |
Bachelor’s degree | 37.4 | 34.2 | 38.2 | 39.4 |
Graduate or professional degree | 13.7 | 16.9 | 12.0 | 12.3 |
Marital status |
Single (never married) | 31.3 | 27.8 | 31.1 | 34.7 |
Married or in a committed relationship | 60.5 | 66.1 | 60.2 | 55.5 |
Separated or divorced | 6.4 | 4.4 | 6.8 | 7.9 |
Widowed | 1.8 | 1.7 | 1.9 | 1.9 |
Table 2
Screen time by type of screen (minutes per day) reported by study participants
TV* | 60 (0–60) | 222 | 120 (120–135) | 372 | 240 (180–300) | 327 |
TV-connected devices* | 0 (0–0) | 288 | 60 (60–60) | 232 | 180 (120–240) | 406 |
Laptops/computers* | 60 (30–120) | 292 | 240 (192.5–300) | 274 | 480 (405–551.25) | 360 |
Smartphone* | 60 (22.50–60) | 308 | 120 (120–120) | 252 | 240 (180–360) | 366 |
Tablet* | 0 (0–15) | 774 | _ | _ | 120.50 (120–240) | 152 |
Descriptive data for dietary habits and health-related variables can be found in Table
3. Our results indicated that the dietary habits of heavy screen users significantly differed from the other two groups when analyzing by total screen time during the day. Dietary pattern scores were significantly higher in heavy users compared to moderate and light users indicating the least healthful dietary pattern;
X2 (2) = 18.96,
p = .000. Heavy users also reported the greatest number of days eating a meal together as a family while watching television (
X2 (2) = 9.49,
p = .009), and conversely, the least number of days eating a meal together as a family without any screens on (
X2 (2) = 9.31,
p = .009) as compared to moderate and light users. In addition, heavy users reported the highest frequency of fast food consumption compared to both moderate and light users;
X2 (2) = 10.58,
p = 005.
Table 3
Descriptive data for dietary habits and health-related variables between light-, moderate-, and heavy- users of screens by total screen time per day
Dietary habits variables |
Dietary patterns |
Light | 5.45 | 2.26 |
Moderate | 5.96 | 2.62 |
Heavy | 6.33 | 2.58 |
Frequency of family meals without any screens on |
Light | 2.80 | 1.52 |
Moderate | 2.54 | 1.61 |
Heavy | 2.51 | 1.52 |
Frequency of family meals while watching television |
Light | 2.79 | 1.48 |
Moderate | 2.85 | 1.61 |
Heavy | 3.14 | 1.57 |
Frequency of fast food consumption |
Light | 1.81 | 0.82 |
Moderate | 1.86 | 0.80 |
Heavy | 2.10 | 1.08 |
Health-related variables |
Body mass index (kg/m2) |
Light | 25.44 | 6.23 |
Moderate | 27.06 | 6.33 |
Heavy | 27.00 | 7.95 |
Physical activity |
Light | 2.21 | 1.41 |
Moderate | 2.16 | 1.42 |
Heavy | 1.85 | 1.38 |
Self-rated health |
Light | 3.84 | 0.72 |
Moderate | 3.79 | 0.66 |
Heavy | 3.60 | 0.82 |
Hours of sleep |
Light | 2.10 | 0.80 |
Moderate | 2.05 | 0.84 |
Heavy | 1.87 | 0.88 |
Sleep quality |
Light | 2.86 | 0.70 |
Moderate | 2.79 | 0.71 |
Heavy | 2.70 | 0.81 |
Perceived stress |
Light | 16.19 | 6.69 |
Moderate | 16.11 | 7.08 |
Heavy | 18.73 | 7.23 |
Interestingly, unique dietary habits emerged when examining dietary patterns by type of screen separately. For example, only heavy users of TV and smartphones reported the least healthful dietary patterns as compared to the other groups (
X2 (2) = 13.02,
p = .001 and
X2 (2) = 20.23,
p < .000, respectively); however, no significant differences in dietary patterns were observed between groups across TV-connected devices, laptop/computer, or tablet (
X2 (2) = 2.63,
p = .268;
X2 (2) = .09,
p = .954;
X2 (2) = 1.66,
p = .435). Consumption of fast food was consistently higher among heavy users of all screen devices (all
p < .02), except for laptop/computer (
p = .75). Please see Table
4.
Table 4
Mean ranks in dietary habits between light-, moderate-, and heavy- users of screens by total screen time per day and individual screen-based device
Dietary patterns |
Light users | 415.16 | 296 | 423.84 | 222 | 443.80 | 288 | 465.38 | 292 | 408.64 | 308 | 456.83 | 774 |
Moderate users | 463.37 | 311 | 447.62 | 372 | 464.38 | 232 | 465.91 | 274 | 481.38† | 252 | – | – |
Heavy users | 508.48* | 319 | 501.46ˆ | 327 | 476.97 | 406 | 460.14 | 360 | 497.36ˆ | 366 | 497.47 | 152 |
Frequency of family meals without any screens on |
Light users | 501.56 | 296 | 472.22 | 222 | 472.30 | 288 | 478.75 | 292 | 484.98 | 308 | 456.99 | 774 |
Moderate users | 445.46† | 311 | 475.55 | 372 | 488.11 | 232 | 474.92 | 274 | 453.85 | 252 | – | – |
Heavy users | 445.77* | 319 | 436.83 | 327 | 443.19 | 406 | 442.44 | 360 | 452.07 | 366 | 496.66 | 152 |
Frequency of family meals while watching television |
Light users | 442.04 | 296 | 410.49 | 222 | 466.03 | 288 | 461.31 | 292 | 447.27 | 308 | 463.87 | 774 |
Moderate users | 446.49 | 311 | 420.70 | 372 | 426.17 | 232 | 468.52 | 274 | 454.10 | 252 | – | – |
Heavy users | 500.00ˆ | 319 | 541.14ˆ | 327 | 483.04** | 406 | 461.45 | 360 | 483.63 | 366 | 461.60 | 152 |
Frequency of fast food consumption |
Light users | 435.51 | 296 | 430.11 | 222 | 404.77 | 288 | 472.35 | 292 | 420.16 | 308 | 453.65 | 774 |
Moderate users | 454.33 | 311 | 455.55 | 372 | 496.05† | 232 | 457.30 | 274 | 479.52† | 252 | – | – |
Heavy users | 498.41* | 319 | 488.17* | 327 | 486.56* | 406 | 461.05 | 360 | 488.94 ˆ | 366 | 513.67* | 152 |
Heavy screen users also reported the lowest self-rated health (
X2 (2) = 17.67,
p = .000), the highest perceived stress scores (
X2 (2) = 25.63,
p = .000), and the least healthful behavioral patterns including the lowest number of hours of sleep (
X2 (2) = 12.21,
p = .002), poorest sleep quality (
X2 (2) = 6.82,
p = .033) and lowest amount of physical activity (
X2 (2) = 13.56,
p = .001). In addition, both heavy screen users and moderate screen users reported a significantly higher BMI as compared to light users (
X2 (2) = 14.96,
p = .001). Similarly, unique patterns emerged when examining by type of screen separately. For instance, physical activity was significantly lower only for heavy users of TV (
X2 (2) = 11.39,
p = .003), whereas self-rated health was significantly lower for heavy users of TV, TV-connected devices, and smartphones (
X2 (2) = 6.84,
p = .033;
X2 (2) = 7.54,
p = .023;
X2 (2) = 10.52,
p = .005; respectively) (please see Table
5).
Table 5
Mean ranks in health variables between light-, moderate-, and heavy- users of screens by total screen time per day and individual screen-based device
Body mass index (kg/m2) |
Light users | 411.99 | 295 | 441.37 | 220 | 439.48 | 287 | 447.14 | 289 | 430.93 | 307 | 459.90 | 769 |
Moderate users | 489.27† | 310 | 453.48 | 372 | 452.94 | 231 | 444.63 | 273 | 489.23† | 252 | _ | _ |
Heavy users | 479.02* | 316 | 475.89 | 324 | 480.95 | 403 | 484.61 | 359 | 466.85 | 362 | 466.58 | 152 |
Physical activity |
Light users | 490.16 | 295 | 506.63 | 222 | 459.36 | 288 | 458.87 | 292 | 478.94 | 308 | 467.25 | 774 |
Moderate users | 480.44 | 311 | 457.84 | 371 | 484.85 | 231 | 462.54 | 272 | 462.09 | 251 | _ | _ |
Heavy users | 419.29ˆ | 318 | 430.70* | 326 | 451.99 | 405 | 465.41 | 360 | 448.91 | 365 | 438.01 | 150 |
Self-rated health |
Light users | 495.95 | 296 | 475.05 | 222 | 487.69 | 288 | 471.16 | 292 | 498.91 | 308 | 464.68 | 774 |
Moderate users | 478.67 | 311 | 477.04 | 372 | 475.61 | 232 | 484.83 | 274 | 454.59 | 252 | _ | _ |
Heavy users | 418.60ˆ | 319 | 433.21** | 327 | 439.42* | 406 | 441.81 | 360 | 439.83* | 366 | 457.48 | 152 |
Hours of sleep |
Light users | 486.60 | 295 | 471.67 | 222 | 491.22 | 288 | 470.21 | 292 | 486.33 | 308 | 471.85 | 774 |
Moderate users | 479.86 | 311 | 475.60 | 371 | 463.80 | 231 | 465.47 | 272 | 465.43 | 251 | _ | _ |
Heavy users | 423.17ˆ | 318 | 434.30 | 326 | 441.34* | 405 | 454.00 | 360 | 440.38 | 365 | 414.28* | 150 |
Sleep quality |
Light users | 487.59 | 295 | 451.80 | 222 | 474.48 | 288 | 463.27 | 292 | 497.47 | 308 | 463.06 | 774 |
Moderate users | 464.60 | 311 | 473.43 | 371 | 485.91 | 231 | 477.34 | 272 | 458.18 | 251 | _ | _ |
Heavy users | 437.17* | 318 | 450.31 | 326 | 440.63 | 405 | 450.66 | 360 | 435.97* | 365 | 459.59 | 150 |
Perceived stress |
Light users | 434.01 | 296 | 458.40 | 222 | 420.70 | 288 | 467.40 | 292 | 411.18 | 308 | 464.14 | 774 |
Moderate users | 428.74 | 311 | 436.89 | 372 | 443.80 | 232 | 436.72 | 274 | 436.56 | 252 | _ | _ |
Heavy users | 524.75ˆ | 319 | 490.19** | 327 | 505.12ˆ | 406 | 480.72 | 360 | 526.08ˆ | 366 | 460.25 | 152 |
Lastly, binge watching was significantly associated with least healthful dietary habits (
r = .08,
p = .02), frequency of fast food consumption (
r = .13,
p < .000), eating family meals in front of the television (
r = .09,
p = .008), and perceived stress (
r = .18,
p < .000). Please see Table
6.
Table 6
Correlation matrix of binge-watching and health-related variables
Binge watching | 1.00 | 0.08* | −0.05 | 0.09** | 0.13** | 0.02 | −0.01 | −0.04 | −0.03 | −0.04 | 0.18** |
Dietary patterns | | 1.00 | −0.03 | 0.09** | 0.45** | 0.12** | −0.26** | − 0.25** | − 0.13** | − 0.11** | 0.20** |
Family meals without screens on | | | 1.00 | −0.27** | 0.08* | − 0.10** | 0.14** | 0.15** | 0.09** | 0.14** | −0.17** |
Family meals watching television | | | | 1.00 | 0.05 | 0.09** | −0.04 | −0.09** | −0.01 | − 0.02 | −0.13** |
Frequency of fast food consumption | | | | | 1.00 | 0.12** | −0.17** | −0.17** | − 0.06 | −0.09** | 0.21** |
Body mass index | | | | | | 1.00 | −0.18** | − 0.36** | −0.10** | − 0.13** | −0.10** |
Physical activity | | | | | | | 1.00 | 0.35** | 0.08* | 0.14** | −0.23** |
Self-rated health | | | | | | | | 1.00 | 0.19** | 0.29** | −0.32** |
Hours of sleep | | | | | | | | | 1.00 | 0.50** | −0.19** |
Sleep quality | | | | | | | | | | 1.00 | −0.36** |
Perceived stress | | | | | | | | | | | 1.00 |
Discussion
The present study found poorer dietary habits among individuals spending a significant portion of their day using a variety of screen-based devices (i.e. total screen time). These “heavy users” reported the least healthy dietary patterns (e.g. they consumed few fruits/vegetables and regularly consumed sodas/sweet tea), the lowest frequency of meals shared with the family without screens on, and the highest frequency of fast food consumption. When analyzed separately by type of screen, only “heavy users” of television and smartphones showed statistically different scores in dietary patterns compared to the other groups. At the same time, both “heavy users” of TV and TV-connected devices reported a statistically higher frequency of family meals while watching TV, and “heavy users” of all devices except laptops showed a statistically higher frequency of fast food consumption. These results highlight the importance of separately exploring the impact of different screen-based devices on dietary habits instead of focusing only on an aggregate measure of total screen time.
Further, while frequency of fast food consumption, poor intake of fruits and vegetables, and excessive intake added sugars have all been connected to risk factors, and incidence of, chronic disease, the role of family meals is also important to health outcomes [
6,
8,
9]. Research has identified frequency of family meals as a predictor of healthier dietary patterns and better weight management among children and adolescents [
25]. Other work has shown that children who watched more television and also consumed fewer meals with the family were at greater risk of overweight [
26]. Frequency of family meals may support improved family cohesion, problem-solving, and emotional coping as mediators of improved health outcomes [
27], but it remains unclear if the potential of family meals to support more healthful outcomes for children and families could be disrupted by watching television or otherwise engaging in screen time during those meals. Given that heavy users of screens in our study reported the greatest number of days sharing a family meal while watching television as well as the highest intake of fast foods, more research is likely necessary to explore how simultaneous engagement in screen time and family meals might relate to the emotional and physical health of families.
More specifically, it may be possible that each screen-based device is associated with distinct factors that influence diet. For instance, TV viewing has been associated with poor dietary choices among children in part because of heavy advertising of candy, chips, sugary beverages, and fast foods [
28,
29], and consequently advertisement may have a similar effect on food intake among adults watching TV [
30]. Even so, while advertisements are embedded in social networks and free mobile apps, advertisement is less frequent in popular video-on-demand streaming services that can be accessed through an internet-enabled device, or TV-connected devices, as defined by this study.
Alternatively, associations found in this study between poor dietary habits and TV-connected devices may be related to the long hours of dedicated viewing that adults engage in through binge-watching, given that longer hours of TV-watching are associated with worse dietary patterns [
1‐
5]. Such behavior might provide ample opportunity for the intake of multiple snacks of low nutritional value. Moreover, it has been suggested that the phenomenon of binge-watching differs from TV watching given the greater attentional focus required by complex narrative structures provided by video-on-demand shows [
17], potentially interfering with conscious dietary choices. Our exploratory data supports this hypothesis; binge-watching was significantly associated with the least healthful dietary habits, frequency of fast food consumption, and eating family meals in front of the television.
The present study also found significant differences in other health variables among different screen time categories. Heavy users of all screens reported the lowest physical activity, self-rated health, hours of sleep, sleep quality, and highest perceived stress. Heavy and moderate users of all screens reported higher BMI scores compared to light users. Similar to dietary habits, unique patterns emerged depending on the type of screen analyzed; for example, “heavy users” of TV, TV-connected devices, and smartphones reported the poorest self-rated health compared to the other groups, whereas only “heavy users” of TV reported statistically lower physical activity compared to “intermediate” and “light users.”
Prior studies have found long hours of television viewing associated with premature mortality and cardiovascular disease (CVD) after adjusting for numerous covariates such as diet (e.g. energy intake, consumption of fruits/vegetables), physical activity, and sleep duration [
3,
11]. It has been suggested that sedentary behavior may be primarily responsible for these adverse outcomes by decreasing the activity of lipoprotein lipase and subsequently affecting lipid and glucose metabolism [
31,
32]. Our data indicate other potential explanatory factors that may mediate the relationship between extended television viewing – and other forms of screen time – and poor health. One such factor may be frequency of fast food consumption, which has been associated with poor diet quality [
33,
34], and in turn has been associated with all-cause, CVD, and cancer mortality [
35]. Other potential contributing factors include poor sleep quality, which has been shown to be related to disruptions in circadian rhythmicity [
36,
37] and glucose metabolism [
31]; and perceived stress, which has been shown to be significantly associated with adverse behaviors (e.g. smoking, drinking) related to CVD [
38,
39]. In sum, our results indicate that prolonged screen time may be associated with a constellation of diverse factors that adversely impact health, perhaps differentially by type of screen.
While future research is needed to further characterize screen-time use across multiple screen-based devices, studies should also be conducted to better understand which factors across various devices might be associated with adverse health behaviors and in turn poor health-related outcomes either differentially or in combination. This line of inquiry is of priority especially for TV-connected devices given the increased popularity of video-on-demand services among Americans. A recent report indicated that 66% of the general population pays for a subscription of such services, and the percentage rises to 87% for households that consume more family-oriented movies and programming [
40]. As the cultural phenomenon of binge-watching continues to gain momentum and acceptance, it is possible that increases in prolonged sedentary behavior coupled with poorer dietary choices could occur that could negatively impact overall health, especially among younger generations that are often more attracted to video-on-demand services [
41]. On the other hand, an interesting new line of inquiry would be the potential benefits derived from moderate use of screens such as online learning and discovery, relaxation, and social connection [
42]. One such study, for example, noted that media consumption could provide a boost to well-being, but only as long as other issues, such as a sense of guilt or the possible conflict between media consumption and pursuit of other goals, did not arise [
43].
As with any study, important limitations existed in this work. The sample used to gather data for analyses was a convenience sample derived through MTurk; nonetheless, previous research has found that MTurk workers more closely resemble the US population compared to college samples and other internet samples [
16]. Another important limitation is the nature of self-reporting, which may lead to underestimation of actual screen time use. Self-reporting has been characteristic of screen time cross-sectional research [
44,
45], but with the advent of new technologies such as smartphone apps and screen time manager for TV, future studies would be able to use more objective assessments of screen time use. Further, as a cross-sectional study, it is not possible to verify directionality of the significant relations identified in this research. However, the unique findings of this work are strongly suggestive of the need for more studies that can further differentiate the ways health behaviors and health outcomes could vary by screen type and duration of screen use.
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