Obesity and Computer Use

Hence, it is important for longitudinal studies to examine the potential impact the vast use of computers in this age group may have on overweight development. A cohort of Swedish young adults aged 20—24 years was recruited in The questionnaire could be returned by post or completed online, if desired.

As an incentive to respond, a lottery ticket value approx. Only those with data on self-reported height and weight, i. An additional four individuals were excluded due to implausible values in height, leaving men and women at baseline Fig. The data collection process was similar to that at baseline, including the initial lottery ticket, but with the addition of a third reminder offering a paper version of the questionnaire and two cinema tickets for participating.

In , a 5-year follow-up was conducted with an almost identical web-questionnaire.

1. Leisure time computer use and overweight development in young adults – a prospective study.
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After excluding three individuals with implausible values for weight and 18 with missing data on BMI, men and women remained in the study Fig. Of these, were missing at the 1-year follow-up.

Two aspects of leisure time computer use were examined: Self-reported data was collected from the cohort study questionnaire, through the items: There were four response categories: The questions were identical at baseline and at the 1-year follow-up. In the 5-year follow-up questionnaire, the questions were rephrased to also include the use of mobile phones and tablets. In regression analysis of computer gaming, response category 1 was used as the reference. Demographic information was collected from the baseline questionnaire, including age, highest completed educational level elementary school, upper secondary school, or college or university studies , and occupation: Age and occupation were treated as potential confounders, while educational level was considered to be too closely associated with age in this age group to be included in the models.

Self-reported leisure time physical activity, sleep duration, social support, and total daily computer use were collected from the baseline questionnaire. Level of leisure time physical activity was measured with a slightly modernized version [ 23 ] of the Grimby-Saltin Physical Activity Scale [ 24 , 25 ]: How much do you move and exert yourself physically during leisure time? If your activity varies greatly between, for example summer and winter, try to estimate an average. The question concerns the past 12 months.

In analysis, response categories 3 and 4 were merged into one regular or vigorous physical activity , and thus three categories were used in analyses; physically inactive, light physical activity and regular or vigorous physical activity. The instrument has been validated in relation to serum cholesterol, blood pressure and BMI [ 23 , 25 ]. Sleep duration was measured by a question, constructed for the cohort study: How many hours do you usually sleep per night on… a weekdays work or study days?

The variable social support was based on the item: When I have problems in my private life I have access to support and help. The item had been constructed for the cohort study as a one-item adaptation of the social support dimension in the Karasek-Theorell model of demands-control-social support [ 26 ], here relating to private life rather than working life.

The responses were categorized as low response categories 1—2 , medium response category 3 , and high response category 4. The high category was used as the reference category.

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One questionnaire item was constructed to concern total daily computer use: On average, how much time per day, have you used a computer? The question concerns total time, i. A test-retest reliability study and a validating interview study were done in the process of developing the questionnaire. The variables showed moderate to high correlations at test-retest, and the validity was considered acceptable [ 22 ]. Height was included only in the baseline questionnaire, while weight was reported at all three time points.

Four implausible heights at baseline were excluded, as well as three unreasonable values for weight 0, 1, 10 kg at the 5-year follow-up. All analyses were performed using the SAS statistical package, version 9. Respondents that remained at 5-year follow-up. The number of subjects varied due to partially missing data. The same tests were used to compare the baseline variables of those who remained in the study at 5-year follow-up and those who did not. Due to a low number of cases, no prospective analyses were done with obesity as a separate outcome.



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Model I included the demographic variables and in Model II lifestyle factors were added. An additional exploratory model was tested by adding total computer time. Only baseline values of the explanatory variables were used. Supplementary analysis was performed to check the possible influence of partially missing data in the crude and Model I analyses, using the complete cases of Model II.

Furthermore, change in BMI , i. The same model building as for the logistic regressions was used, with the exception that BMI at baseline was adjusted for in Model Ib. Sex differences were observed in all descriptive variables but age. Mean BMI was Mean reported sleep duration over the week was 7. While the proportion of high gaming males seemed to be steady over the 5-year follow-up period, there was an increase in computer gaming among the women. A decrease of non-gamers can be seen in both men and women. However, the questionnaire item was changed at 5-year follow-up to include also the use of smartphones and tablets, which makes the comparison formally inaccurate.

For women, all the gaming categories were associated with overweight ORs 1. Cross-sectional logistic regressions for leisure time computer use and overweight at baseline. ORs with a CI not including 1. For the men, medium 1—2 h daily emailing or chatting was actually associated with a lower prevalence of overweight in the crude analysis OR 0. However, when adjusting for demographic and lifestyle factors the negative association was no longer statistically significant.

A supplementary complete case analysis was performed to check possible influence of partially missing data in the crude and Model 1 analyses. An additional, exploratory third model, which included total time spent on computer, was tested, and seemed to strengthen the existing associations over all data not shown. There were clear associations between computer gaming and obesity in both sexes data not shown in table.

In the prospective analyses, i. The additional, third model which adjusted for total daily computer use, slightly amplified the associations. For the men, no statistically significant prospective associations were seen between computer gaming and overweight at either follow-up. Complete case crude and Model I analyses did not change results notably. Prospective logistic regressions for leisure time computer use at baseline and new cases of overweight at 1- and 5-year follow-ups. The number of subjects varied in the models due to partially missing data. A dose—response relationship emerged, however, with overlapping CIs in most analyses.

Belonging to the highest category of female gamers implied an additional BMI-increase of an estimated 1. The estimated total increase in BMI from baseline to 5-year follow-up in the female group, i. Linear regressions for leisure time computer use at baseline and change in BMI from baseline to 5-year follow-up. For the young men, only cross-sectional associations could be detected.

The results are partly in line with earlier studies finding a relationship between screen time and overweight or BMI in children and adults e. However, to our knowledge there are only few studies that have specifically examined computer use as a risk factor for overweight in adults, and only cross-sectional associations seem to have been found previously [ 18 — 20 ]. The present study suggests that the content of the computer use can be of importance, as time spent on computer gaming appeared to be more connected to weight gain than time spent on emailing or chatting.

In this regard, Kautiainen et al. However, the Kautiainen study was cross-sectional and in a younger age group 14—18 years. That the increased change in BMI from baseline to 5-year follow-up in relation to computer gaming was seen mainly among the normal weight women and not among the overweight, is contrary to the results of Falbe et al.

The prospective results seen only in the women in the present study possibly indicate gender differences.

Introduction

In the study of Finnish adults, Heinonen et al. Gender differences have also been found in some of the studies on children or adolescents. For example, in the mentioned study by Kautiainen et al. Furthermore, Falbe et al. However, in the longitudinal study by Altenberg et al. Using the internet for communication and social networking has been considered to be a more common activity among females compared to males [ 3 , 29 ]. In our study population, the reported amount of leisure time spent on emailing and chatting was about the same in both sexes, which is in accordance with Swedish internet statistics from [ 3 ].

But, there are apparent gender differences in time spent on computer gaming. We have no information about game content in our study. But how is it that women gamers in particular seemed to be vulnerable to weight gain in our study? One of the potential mechanisms for weight gain in connection to screen time is the sedentary nature of the activity, i. In Vandelanotte et al.

Spending on technology equals more obesity, study shows

Interestingly, on the national public health level in Sweden, the increased use of computers the past decades, and thus the inferred increase of sedentary activities, has been paralleled with an actual increase in reported leisure time physical activity [ 30 ]. In our data, the high gamers reported lower levels of physical activity compared to the others. While this also applied to the men, it was especially pronounced among the women gamers. However, the associations between time spent on gaming and overweight were significant even after adjusting for level of physical activity.

Moreover, an additional stratified regression analysis showed that gaming was associated with increased change in BMI in all levels of physical activity in the women data not shown. It is plausible that regular physical exercise a few times per week does not compensate for physical inactivity the rest of the week. It is also possible that physical inactivity by the computer is especially detrimental to women. In a study by Scheers et al. It is possible that our female gamers are subject to longer bouts of inactivity than the men even if the total time by the computer is the same.

On the other hand, there is evidence that diet is the most important factor for weight gain [ 32 , 33 ]. Screen time has been associated with a less healthy diet including higher consumption of energy dense snacks and drinks and lower consumption of fruits and vegetables, among children, adolescents and adults [ 15 , 34 , 35 ]. Computer gaming has been suggested to entail a lower energy intake in combination with a slightly higher energy expenditure, compared to TV viewing, as both hands may be busy using the controls e.

Also, TV viewing to a larger extent implies exposure to advertisements for fat and sugary foods [ 15 , 16 , 36 ]. But the question is then if there are gender differences in energy intake while at the computer? Unfortunately, we have no data on diet in our study. However, there is some evidence that gender differences may exist in this regard; in the systematic review by Pearson et al. The conclusion was that the associations between sedentary behavior and diet were more consistent for females than for males.

Thus, diet may be an underlying issue in our population. Sleep is another possible mediator between computer use and overweight, for example shown by Arora et al. Screen activities may interfere with sleep [ 37 — 39 ] and short sleep is associated with overweight and obesity [ 40 — 42 ]. There were no major differences in reported sleep between the men and women in our study.

However, there may be gender differences in the association between sleep and weight [ 41 ]. Self-reported short sleep duration was associated with increased weight in the men but not in the women in a study of young adults by Meyer et al. Another aspect that needs to be addressed is that computer gaming is much less common among women.

This raises the question if the women gamers possibly are a more select group than the male gamers. Apart from the fact that women play less than men, women seem to take up gaming later in life, and the average female online gamer is older than the male [ 6 ]. Due to the limited age span of our study population we could not investigate age-related gender differences. As mentioned earlier, the female gamers seemed to have lower levels of physical activity. The high gaming women also reported being subject to lower social support in private life, and thirty percent were neither in work nor in school.

In a previous study in the same cohort, we found that the female computer gamers more often reported stress and depressive symptoms [ 43 ], and they had a prospective risk of developing depressive symptoms [ 37 ]. There is a reciprocal relationship between obesity and depression, i. Altogether, these women may be subject to several health-related risk factors. The strengths of this study include the prospective design with follow-ups after one and five years, and a fairly large study group from a population-based sample.

However, there are also several limitations that should be considered. All variables except for age and sex are based on self-reported data. Self-reported BMI is known to be underestimated, mostly because of the underreporting of weight [ 45 , 46 ], and this may bias the results in unknown ways. Another concern in relation to BMI is that height was only asked for in the baseline questionnaire.

BMIs could be overestimated at the follow-ups, due to the fact that men may still be growing at the age of 20—24, although, the proportion of men still growing in this group is probably small. Further, the validity of self-reported computer use may be questioned [ 47 , 48 ], implying recall difficulties and recall bias. There may also be potential misclassifications of the two main exposure variables because of them not being mutually exclusive: Further, we assessed only two types of leisure time computer use; gaming and communicating, and thus fail to examine other potential leisure time computer activities.

These two were chosen because they were the dominating leisure pastimes by the computer that emerged in a qualitative interview study about computer use and potential mental health effects [ 49 ]. It should also be pointed out that the data collection started in , which is prior to the broad use of social media applications such as Facebook, Twitter, etc.

Moreover, we do not examine gaming and communicating on other devices. It can be questioned if it is relevant to single out computers as a specific exposure, as technological development gives us a variety of devices for similar activities. For example, in the computer gaming reference group i. Furthermore, the baseline data was collected before the widespread use of smartphones and the mobile internet, but there are probably participants who handled emails and chatting via a mobile phone at the time of the data collection.

This may also be a source of misclassification. In order to keep up with developments, the questionnaire items were changed from only concerning computer use to also including smartphones and tablets at the 5-year follow-up in Participants with low leisure-time Internet and computer use had the highest levels of educational attainment and employment, and engaged in less other sedentary behaviors when compared to participants with no or high Internet and computer use.

Multinomial logistic regression, adjusted for gender, age, employment, education, other sedentary behaviors and physical activity, determined that participants with a high leisure-time Internet and computer use were 1. Leisure-time physical activity levels were largely independent of Internet and computer use. These findings suggest that, apart from nutritional and physical activity interventions, it may also be necessary to decrease time spent in sedentary behaviors, such as leisure-time Internet and computer use, in order to reduce the prevalence of overweight and obesity.

Future Internet interventions to reduce weight or increase physical activity may need to differentiate between participants with different levels of Internet use in order to increase their effectiveness. Longitudinal studies are required to examine further the potential causal relationships between the development of overweight and specific sedentary behaviors such as Internet and computer use. Many studies have shown that physical inactivity is associated with higher levels of overweight and obesity and that physical activity is essential in the prevention and treatment of overweight and obesity [ 1 , 2 ].

Recently, this evidence has led to the development of specific physical activity guidelines for overweight and obese people [ 3 , 4 ] which state that 60 to 90 minutes of daily moderate to vigorous physical activity are necessary to lose weight or to maintain weight loss. There are strong adverse associations between time spent in sedentary behaviors and different health indicators [ 5 - 7 ], including the increased likelihood of being overweight or obese [ 6 , 8 - 10 ].

They have independent effects on total energy expenditure, weight, and metabolic variables [ 10 ]. However, most of the evidence on associations between sedentary behavior and health outcomes, such as weight status and levels of physical activity, is specific to time spent watching television [ 5 , 6 , 8 , 10 , 12 ], which is the most commonly studied leisure-time sedentary behavior.

Associations of health outcomes with other sedentary behaviors such as Internet and computer use remain largely unknown. Internet and computer use are increasingly common leisure-time sedentary behaviors [ 13 , 14 ] which have the potential to impact negatively on health, independent of other sedentary behaviors. Extensive use of the Internet and computers may also displace time spent in leisure-time physical activity. Some studies show that high leisure-time Internet and computer use is associated with higher Body Mass Index BMI and lower physical activity levels [ 15 - 18 ]; other studies are not able to confirm this [ 19 - 21 ].

However, to our knowledge no studies have evaluated these relationships in adults. Further, little is known about how sedentary behaviors relate to each other. In relation to health outcomes, it is important to know whether high leisure-time Internet and computer use is a marker for high levels of other sedentary behaviors. It may be that leisure-time Internet and computer use is related to poor health outcomes due to its association with a broader pattern of sedentary behavior.

A study by Sugiyama et al [ 22 ] demonstrated that, in women, time spent watching TV was associated positively with time in other sedentary behaviors. To our knowledge, no studies have evaluated how Internet and computer use relate to other sedentary behaviors.

The aim of this study is to examine associations of Internet and computer use, specifically in leisure time excluding occupational computer use , with overweight and obesity, leisure-time physical activity, and other sedentary behaviors, in a large socially-diverse sample of Australian adults. Physical Activity in Localities and Community Environments conducted in urban areas of Adelaide, Australia during - Detailed methods of the study have been described elsewhere [ 23 , 24 ]. Briefly, a study sample was drawn from residential addresses within 32 neighbourhoods which are known to vary in socio-economic status.

In each neighbourhood, addresses were randomly selected and sent a letter of invitation to participate. Eligible respondents English speaking, aged between 20 and 65, residing in private dwellings, and able to walk without assistance who agreed to participate were mailed a survey that included questions about Internet and computer use, other sedentary behaviors, physical activity, body weight, height, and socio-demographic characteristics.

Participant recruitment and data collection were handled in a series of waves, between July and June , in order to obtain data from respondents across the range of seasons. A total of eligible participants returned the questionnaire. The return rate for those who completed the survey, as a proportion of those known to be contacted was Participants reported leisure-time Internet and computer use as part of a measurement tool assessing total leisure-time sedentary behavior in the last seven days. This instrument has been shown to have acceptable reliability and validity, especially for Internet and computer use [ 25 ].

To evaluate the validity of this measure, three-day sedentary behavior logs were collected from participants. Test-retest reliability was evaluated in a sample of participants. The amount of leisure-time Internet and computer use was split into three categories: BMI was also used as a continuous variable.

Yes, if someone is to sedentary.

Participants reported the number of days per week and the time spent per day on walking, as well as vigorous-intensity and moderate-intensity leisure-time activities, during the last seven days. The amount of leisure-time physical activity was split into three categories: This variable included time spent: The amount of other sedentary behaviors was split into: One-way ANOVA and Chi-square tests were used for analysing differences in socio-demographic factors according to different categories of Internet and computer use. Multinomial logistic regression analyses were conducted to estimate associations of Internet and computer use with overweight and obesity model 1 , leisure-time physical activity model 2 , and other sedentary behaviors model 3.

The models were adjusted for age, gender, employment, level of education, overweight and obesity only in models 2 and 3 , other sedentary behaviors only in models 1 and 2 , and leisure-time physical activity only in models 1 and 3. Binary logistic regression was conducted to estimate the odds ratios of being overweight or obese, comparing levels of Internet and computer use no, low, and high Internet and computer use and physical activity low, medium, and high leisure-time physical activity.

This model was adjusted for age, gender, education, employment, and other sedentary behaviors. Analyses were conducted using SPSS version Significance was accepted at an alpha level of 0. Average leisure-time Internet and computer use was Table 1 shows socio-demographic characteristics for the total sample according to Internet and computer use. Participants with low Internet and computer use had the highest levels of educational attainment and employment, were younger, and participated in less other sedentary behaviors compared to participants with either high Internet and computer use or no use.

Participants with high Internet and computer use had the highest BMI compared to the other groups and were more likely to be male. As shown in Table 2 , leisure-time Internet and computer use was significantly associated with overweight and obesity. Compared to participants that reported no Internet and computer use, participants with low Internet and computer use were 1. Multinomial logistic regression models predicting overweight or obesity, leisure-time physical activity, and other sedentary behaviors by computer and Internet use a.

Leisure-time physical activity was largely independent of leisure-time Internet and computer use. However, participants with low Internet and computer use were 1. Participants with low and high leisure-time Internet and computer use were respectively 1. For example, participants with low leisure-time physical activity and high Internet and computer use were 2. The reference category is having high leisure-time physical activity and not using the Internet and computer, for which the odds ratios are equal to 1.

The significance levels on top of the figure bars are differences in relation to the reference category: The main finding of this study is that leisure-time Internet and computer use is strongly related to being overweight or obese, whereas it is largely independent of leisure-time physical activity. After adjusting for socio-demographic variables, leisure-time physical activity and other sedentary behaviors, participants who used the Internet and computer for three hours or more in the last seven days were 1. Although there are no direct comparisons with other studies for these outcomes in adults, they are in line with studies that report that higher amounts of time in sedentary behavior and television viewing are strongly associated with overweight and obesity [ 5 - 10 , 12 , 27 ].

The strong associations of leisure-time Internet and computer use with overweight and obesity may in part be explained by the association of leisure-time Internet and computer use with other leisure-time sedentary behaviors. Participants who had high Internet and computer use in their leisure time were 2. This is consistent with a study by Sugiyama et al [ 22 ] which showed that time spent watching TV was positively associated with other leisure-time sedentary behaviors; however, this was only the case for women.

Our results showed that leisure-time Internet and computer use was not strongly associated with leisure-time physical activity. Contrary to what might be expected, participants with low leisure-time Internet and computer use were slightly more likely to be in a higher leisure time physical activity category.

While it is difficult to explain this outcome, it might be argued that it could be due to the higher socio-economic profile observed in participants with low Internet use. It is generally the case that those of higher socio-economic status are more physically active [ 28 ]. However, the analyses controlled for educational attainment and employment status, so such an interpretation would not apply to our findings. Other than this particular relationship, no associations between leisure-time Internet and computer use and physical activity were observed. This finding is, thus, for the major part consistent with studies that showed non-significant associations between Internet use and physical activity in children and adolescents [ 19 - 21 ] and those between TV viewing time and physical activity in adults [ 5 , 6 , 8 - 10 , 12 ].

From this perspective, the apparent paradox of increasing physical activity using an intervention delivery mode that promotes sedentary behavior Internet and computer use appears to be invalid. Our results suggest that the time that spent taking part in Internet interventions is not likely to displace leisure-time physical activity; hence, Internet interventions should be considered as an acceptable method to increase physical activity.

As may be seen in Figure 1 , a combination of high Internet and computer use and low leisure-time physical activity was associated with a higher odds ratio of being overweight or obese. This finding is consistent with those of a study by Salmon et al [ 12 ] in which higher levels of TV viewing in combination with lower levels of physical activity participation were found to be associated with being overweight or obese.

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This figure also shows that adults who use the Internet and computer for more than three hours in their leisure time are significantly more likely to be overweight, even if they are highly active in their leisure time. Consistent with what was reported by Salmon et al [ 12 ], these findings suggest that, in order to reduce the prevalence of overweight and obesity, it may be important not only to increase participation in physical activity, but also to reduce time spent in sedentary behaviors, such as leisure-time Internet and computer use.

As the level of Internet penetration increases, its users become more representative of the general population; thus, gender, age, and socio-economic differences are diminishing [ 13 ]. Nevertheless, interesting socio-demographic profiles emerged when leisure-time Internet and computer use were categorized into different levels of usage. Our findings indicate that participants with low leisure-time Internet and computer use had the highest socio-economic profile, engaged in less time in other sedentary behaviors and were slightly more likely to do more leisure-time physical activity.

On the other hand, participants with high leisure-time Internet and computer use had lower socio-economic profiles, engaged in more time in other sedentary behaviors, had a higher BMI, and were more likely to be male. This suggests that different levels of leisure-time Internet and computer use are related to different socio-demographic profiles and health behaviors. Given the high prevalence of Internet use, and its potential impact on health, it is important to address health issues for Internet users.

Internet interventions to reduce weight or increase physical activity are likely to be more effective if they take differences among Internet users into account.