Screen Time and Body Image in Icelandic Adolescents : Sex-Specific Cross-Sectional and Longitudinal Associations

Funding Information: Funding: This research was funded by The University of Iceland Research Fund, grant number. HI16120043, and the Icelandic Centre for research (RANNIS), grant number 152509-051. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Studies of adolescent body image and screen use are mostly limited to girls, and longitudinal data are scarce. We examined cross-sectional and longitudinal associations between these variables in mid-adolescent boys and girls. Data was collected when participants were at age 15 and 17, by questionnaire and objective measurements (n = 152 had complete data). Sex-specific linear regression was used to explore cross-sectional and longitudinal associations of self-reported screen use (total use, and time spent in gaming, TV/DVD/internet-based watching and internet use for communication) and body image, adjusting for vigorous physical activity, symptoms of depression, and body composition. Screen time was negatively associated with body image at both time points, although more strongly at age 15, and for girls only. Gaming and TV/DVD/internet watching was more strongly associated with body image than internet use for communication. Girls with above median screen time at both ages had 14% lower body image score at age 17 than girls with below median screen time at both time points. Our results suggest that screen use is likely to play a role in the development of body dissatisfaction among adolescent females. Limiting screen time may, therefore, help to mitigate body dissatisfaction in adolescent girls.Peer reviewe

Screen Time and Body Image in Icelandic Adolescents: Sex-Specific Cross-Sectional and Longitudinal Associations

Studies of adolescent body image and screen use are mostly limited to girls, and longitudinal data are scarce. We examined cross-sectional and longitudinal associations between these variables in mid-adolescent boys and girls. Data was collected when participants were at age 15 and 17, by questionnaire and objective measurements (n = 152 had complete data). Sex-specific linear regression was used to explore cross-sectional and longitudinal associations of self-reported screen use (total use, and time spent in gaming, TV/DVD/internet-based watching and internet use for communication) and body image, adjusting for vigorous physical activity, symptoms of depression, and body composition. Screen time was negatively associated with body image at both time points, although more strongly at age 15, and for girls only. Gaming and TV/DVD/internet watching was more strongly associated with body image than internet use for communication. Girls with above median screen time at both ages had 14% lower body image score at age 17 than girls with below median screen time at both time points. Our results suggest that screen use is likely to play a role in the development of body dissatisfaction among adolescent females. Limiting screen time may, therefore, help to mitigate body dissatisfaction in adolescent girls

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

Background Sleep and physical activity are modifiable behaviors that play an important role in preventing overweight, obesity, and metabolic health problems. Studies of the association between concurrent objective measures of sleep, physical activity, and metabolic risk factors among adolescents are limited. Objective The aim of the study was to examine the association between metabolic risk factors and objectively measured school day physical activity and sleep duration, quality, onset, and variability in adolescents. Materials and methods We measured one school week of free-living sleep and physical activity with wrist actigraphy in 252 adolescents (146 girls), aged 15.8±0.3 years. Metabolic risk factors included body mass index, waist circumference, total body and trunk fat percentage, resting blood pressure, and fasting glucose and insulin levels. Multiple linear regression adjusted for sex, parental education, and day length was used to assess associations between metabolic risk factors and sleep and activity parameters. Results On average, participants went to bed at 00:22±0.88 hours and slept 6.2±0.7 hours/night, with 0.83±0.36 hours of awakenings/night. However, night-to-night variability in sleep duration was considerable (mean ± interquartile range) 0.75±0.55 hours) and bedtime (0.64±0.53 hours) respectively. Neither average sleep duration nor mean bedtime was associated with any metabolic risk factors. However, greater night-to-night variability in sleep duration and bedtime was associated with higher total body and trunk fat percentage, and less physical activity was associated with higher trunk fat percentage and insulin levels. Conclusion Greater nightly variation in sleep duration and in bedtime and less physical activity were associated with a less favorable metabolic profile in adolescents. These findings support the idea that, along with an adequate amount of physical activity, a regular sleep schedule is important for the metabolic health of adolescents

Associations of screen time and physical activity with symptoms of depression, anxiety and somatic complaints.

<p>Associations of screen time and physical activity with symptoms of depression, anxiety and somatic complaints.</p

Interactive effects of screen time and vigorous PA on mental health.

<p>Interactive effects of screen time and vigorous PA on mental health.</p

Characteristics of the study subjects.

<p>Characteristics of the study subjects.</p

Associations of screen time and physical activity with low self-esteem and life dissatisfaction.

<p>Associations of screen time and physical activity with low self-esteem and life dissatisfaction.</p

Localization of a Susceptibility Gene for Type 2 Diabetes to Chromosome 5q34–q35.2

We report a genomewide linkage study of type 2 diabetes (T2D [MIM 125853]) in the Icelandic population. A list of type 2 diabetics was cross-matched with a computerized genealogical database clustering 763 type 2 diabetics into 227 families. The diabetic patients and their relatives were genotyped with 906 microsatellite markers. A nonparametric multipoint linkage analysis yielded linkage to 5q34–q35.2 (LOD = 2.90, P=1.29×10(-4)) in all diabetics. Since obesity, here defined as body mass index (BMI) ⩾30 kg/m(2), is a key risk factor for the development of T2D, we studied the data either independently of BMI or by stratifying the patient group as obese (BMI ⩾30) or nonobese (BMI <30). A nonparametric multipoint linkage analysis yielded linkage to 5q34–q35.2 (LOD = 3.64, P=2.12×10(-5)) in the nonobese diabetics. No linkage was observed in this region for the obese diabetics. Linkage analysis conditioning on maternal transmission to the nonobese diabetics resulted in a LOD score of 3.48 (P=3.12×10(-5)) in the same region, whereas conditioning on paternal transmission led to a substantial drop in the LOD score. Finally, we observed potential interactions between the 5q locus and two T2D susceptibility loci, previously mapped in other populations