Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
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Problematic Internet Use, Related Psychosocial
Behaviors, Healthy Lifestyle, and Subjective
Health Complaints in Adolescents
Aija Klavina, PhD
Viktors Veliks, PhD
Anna Zusa, PhD
Juris Porozovs, PhD
Aleksandrs Aniscenko, MSc
Luize Bebrisa-Fedotova, MSc
Objective: In this study, we explored Internet use-associated psychosocial behavior problems in
relationship to adolescents’ subjective health complaints and healthy lifestyle habits. Methods: A
cross-sectional sample of Latvian adolescents (N = 570, age range 11-19 years) completed a survey.
Problematic Internet use (PIU) was assessed by the Problematic and Risky Internet Use Screening
Scale (PRIUSS) that measures social impairment, emotional impairment, and risky/impulsive
Internet use. Subjective health complaints assessed were somatic complaints and psychological
complaints. Healthy lifestyle behaviors assessed were daily physical activity, time spent using
information technologies (IT), eating habits, and sleep duration. Results: We found that 27.02
% (N = 154) of the participants scored at risk for PIU with significantly higher PIU mean scores
in 15-16-year-old girls (pProblematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
plaints are associated with physical and emotional Internet Addiction.26 The Problematic and Risky
well-being and, if overseen, may develop mental Internet Use Screening Scale (PRIUSS) was de-
or musculoskeletal disorders in adulthood.6,11 Na- veloped by Jelenchick et al27 to identify psychoso-
tional survey outcomes from 2018 on school-aged cial health risks related to PIU in adolescents and
children’s health in Latvia show that 11-, 13-, and young adults. It consists of an 18-item scale with
15-year-old students at least once a week experi- 3 subscales: (1) social impairment, (2) emotional
ence mood disturbance (52.9%), sleep problems impairment, and (3) risky/impulsive internet use.
(43.6%), anxiety (39.1%), depression (31.2%), fol- Based on its strong theoretical foundation and re-
lowed by complaints of regular headaches (30.3%), search established psychometric performance, the
back pain (22.6%), abdominal pain (20.0%) and PRIUSS is a valuable instrument for screening and
dizziness (17.9%).10 Earlier study in Latvia on prevention efforts in this population. This scale has
2012 revealed that 25.5%, 14.4% and 8.7% ado- been validated in English and in Dutch.28 Study
lescents aged 14, 15 and 16 years, respectively, had outcomes revealed that 11% of 474 adolescents
thoughts about suicide and/or made plans for sui- who responded to the PRIUSS were at risk for PIU.
cide.12 Research on adolescents’ health complaints However, screen time related to PIU in relation to
associated with psychosocial variables and screen subjective health complaints and health behaviors
time is important, as adolescents increase indepen- has not been analyzed in adolescents.
dence from adults’ control.13 Although much is Obtaining a quantitative estimate of healthy life-
known about the association between health com- style, such as, daily physical activity, eating habits,
plaints related to sedentary screen-based behaviors subjective health complaints, and Internet use-re-
and physical activity in adolescents, the correlates lated psychosocial behavior problems among youth
of psychosocial behavior complaints related to the is important for several reasons. First, knowing the
excessive Internet use have not been extensively association among these variables will clarify hy-
studied. Social interactions with friends influence a potheses regarding intervention strategies for pre-
variety of mental health outcomes affecting cogni- vention programs to improve adolescents’ health
tive, psychosocial, and emotional development.14 behaviors. In addition, knowing the conditions un-
Behavioral measures relating to Internet use are der which psychosocial health behaviors are most
associated with a measure of antisocial behaviors15 strongly linked to healthy functioning will con-
and also with increased aggression depression16 and tribute knowledge to education and health profes-
psychological well-being.17 Several researchers have sionals, as well as to parents of adolescents. In the
described a relationship among Internet use, psy- present study, we explore the associations among
chosocial health, and negative outcomes at home Internet use-related psychosocial behavior prob-
or at education setting.18-21 Davis20 introduced a lems, adolescents’ subjective health complaints,
cognitive-behavioral theory of PIU that attempts and healthy lifestyle habits.
to model the etiology, development, and outcomes
associated with PIU. He describes specific and gen- METHODS
eralized PIU, where specific PIU includes possible Participants
manifestations of a broader behavioral disorder,
and generalized PIU refers to a pathology associ- We conducted this survey between March and
ated with the unique social context available on November 2020. It included 615 students (ages
Internet. 11-19 years) from 34 state schools in different dis-
tricts of Latvia. Schools were selected randomly for
Several instruments have been developed to ex- participation in the study to be representative of
plore impairments of health behaviors associated adolescents in terms of school type (middle schools,
with excessive and problematic Internet usage. secondary schools, grammar schools) and residence
Also, various terms have been created to describe (urban, rural).
diagnostic concepts and measurement criterion
of the screen use-related behavior outcomes. Cur- We obtained consent forms from each school ad-
rently prevalent definitions include Pathological ministration prior to asking consent from students’
Internet Use,22 Problematic Internet Use,21,23 Ex- parents. We provided a link to an online survey to
cessive Internet Use,24 Internet Dependency25 and school personnel or directly to students after receiv-
452Klavina et al
Table 1
Participants’ Characteristics (N = 570)
Total (%)
Age 11-12 147 (25.79)
Boys 68 (11.93)
Girls 79 (13.86)
Age 13-14 167 (29.30)
Boys 79 (13.86)
Girls 88 (15.44)
Age 15-16 136 (23.86)
Boys 54 (9.47)
Girls 82 (14.39)
Age 17-19 120 (21.05)
Boys 51 (8.95)
Girls 69 (12.11)
PRIUSS
≥ 26 Total 154 (27.02)
< 26 Total 416 (72.98)
Somatic Health complaints (almost every week or more)
Headache 4 (0.7)
Stomach ache 6 (1.05)
Backache 6 (1.05)
Dizziness 4 (0.7)
Psychological Health Complaints
Feeling sad 16 (2.81)
Anxiety 24 (4.21)
Nervousness 16 (2.81)
Sleep difficulties 10 (1.75)
Multiple health complaints more than 2 times per week 182 (31.93)
Physical activity
≥ 7 hours/ week 37 (6.49)
< 7 hours/ week 515 (90.35)
Information technology use during weekdays
≥ 3 hours/ day 374 (65.61)
< 3 hours/ day 153 (26.84)
Information technology use during weekends
≥ 3 hours/ day 405 (71.05%)
< 3 hours/ day 95 (16.67%)
Eat breakfast
≥ 5 times/ week 418 (73.33)
< 5 times/ week 152 (26.67)
Eat fruits and vegetables almost daily 441 (77.37)
Eat with family almost daily 375 (65.79)
Eat while being at the screen almost daily 302 (52.98)
Drink sweetened soft drinks almost daily 149 (26.14)
Health Behav Policy Rev.TM 2021;8(5):451-464 DOI: https://doi.org/10.14485/HBPR.8.5.6 453454
Table 2
Comparative Outcomes between Boys and Girls across Age Groups (M, SD, 95% CI): (a) Mean Results for PRIUSS
and Subjective Health Complaints, and (b) Healthy Lifestyle Behaviors and Multiple Health Complaints (N, %)
Boys (N = 252, 44.21%) Girls (N = 318, 55.79%)
11-12 years 13-14 yearsB2 15-16 years 17-19 years 11-12 years 13-14 years 15-16 years 17-19 years
Mean Results for PRIUSS and Subjective Health Complaints
19.98±8.17 19.83±9.29G3 19.26±9.72G3 18.75±9.61G3 16.32±10.73G2,G3 22.22±11.24G3 24.7±10.46 20.56±9.87
PRIUSS total*
(18 - 21.95) (17.75 - 21.91) (16.61 - 21.92) (16.05 - 21.45) (13.92 - 18.72) (19.84 - 24.6) (22.4 - 27.0) (18.18 - 22.93)
7.17±3.15G3 7.27±3.68 7.41±3.21 6.48±2.93 5.71±3.54 7.35±3.79 8.36±4.37 6.56±3.26G3
Social impairment*
(6.41 - 7.93) (6.45 - 8.09) (6.54 - 8.29) (5.65 - 7.3) (4.92 - 6.51) (6.54 - 8.15) (7.4 - 9.32) (5.77 - 7.34)
Emotional 5.47±3.16 4.13±3.33 3.3±3.73B1,G3 3.41±3.63G3 5.02±4.52 5.08±4.15 5.8±3.97 4.3±3.63
impairment* (4.7 - 6.24) (3.38 - 4.87) (2.28 - 4.32) (2.39 - 4.43) (4.01 - 6.03) (4.2 - 5.96) (4.93 - 6.68) (3.43 - 5.18)
Risky, impulsive 7.34±3.76G3 8.43±4.21 8.56±4.45 8.86±4.9 5.6±3.81G2,G4,G3 9.8±5.19 10.54±5.02 9.7±5.07
Internet use* (6.43 - 8.25) (7.49 - 9.37) (7.34 - 9.77) (7.48 - 10.24) (4.75 - 6.45) (8.7 - 10.9) (9.43 - 11.64) (8.48- 10.91)
Physical activity 4.43±1.42 4.08±1.47 4.33±1.45 4.08±1.28 4.2±1.1 3.86±1.56 3.93±1.23 3.81±1.26B1
days* (4.09 - 4.77) (3.75 - 4.41) (3.94 - 4.73) (3.72 - 4.44) (3.96 - 4.45) (3.53 - 4.19) (3.66 - 4.2) (3.51 - 4.12)
Physical activity 3.34±2.45 1.99±1.9B1 1.53±1.64B1 2.26±2.22 2.46±2.14 1.84±1.89B1 2.28±1.93B1 1.66±1.61B1
hours/week* (2.75 - 3.93) (1.56 - 2.42) (1.06 - 2) (1.59 - 2.94) (1.98 - 2.94) (1.43 - 2.24) (1.85 - 2.71) (1.27 - 2.05)
Somatic 0.8±1.21G3 0.66±1.11G3 0.56±1.12G3 0.53±1.09G3 0.87±1.18G3 1.04±1.21 1.48±1.35 0.95±1.06
symptoms* (0.51 - 1.09) (0.42 - 0.91) (0.26 - 0.86) (0.23 - 0.84) (0.6 - 1.13) (0.78 - 1.29) (1.19 - 1.78) (0.69 - 1.2)
Psychological 1.86±1.53 1.31±1.32G2,G3 1.19±1.47G2,G3 1.4±1.54G3 1.6±1.36G3 2.0 ±1.38 2.44±1.23 1.79±1.42
symptoms* (1.49 - 2.23) (1.01 - 1.6) (0.79 - 1.59) (0.96 - 1.83) (1.3 - 1.9) (1.71 - 2.3) (2.17 - 2.71) (1.45 - 2.13)
Sleep hours 8.42±0.99 8.29±1.48 8.54±0.97B1 7.64±1.01B1 8.47±0.87B1 8.38±1.33B1 8.09±1.24 7.96±0.92
working days* (8.18 - 8.66) (7.95 - 8.63) (8.23 - 8.84) (7.34 - 7.94) (8.27 - 8.66) (8.08 - 8.68) (7.81 - 8.37) (7.71 - 8.20)
Sleep hours 9.31±1.7 9.61±1.84 9.66±1.51 9.28±1.11 9.8±1.39 9.93±1.59 9.36±1.33 9.48±1.28
weekend days (8.88 - 9.74) (9.18 - 10.05) (9.2 - 10.11) (8.96 - 9.59) (9.49 - 10.11) (9.58 - 10.28) (9.06 - 9.66) (9.17 - 9.80)
Cont on next page
Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in AdolescentsTable 2 (cont)
Comparative Outcomes between Boys and Girls across Age Groups (M, SD, 95% CI): (a) Mean Results for PRIUSS
and Subjective Health Complaints, and (b) Healthy Lifestyle Behaviors and Multiple Health Complaints (N, %)
Boys (N = 252, 44.21%) Girls (N = 318, 55.79%)
11-12 years 13-14 years 15-16 years 17-19 years 11-12 years 13-14 years 15-16 years 17-19 years
Healthy Lifestyle Behaviors and Multiple Health Complaints (N, %)
Screen time (working
31 (45.6%) 48 (60.8%) 39 (72.2%) 38 (74.5%) 32 (40.5%) 63 (71.6%) 66 (80.5%) 52 (75.4%)
days) ≥ 3 hours/ day
Health Behav Policy Rev.TM 2021;8(5):451-464
Screen time (weekend
44 (64.7%) 62 (78.5%) 41 (75.9%) 36 (70.6%) 41 (51.9%) 66 (75.0%) 62 (75.6%) 53 (76.8%)
days) ≥ 3 hours/ day
Multiple health
complaints more 22 (32.4%) 16 (20.3%) 11 (20.4%) 10 (19.6%) 20 (25.3%) 37 (42.1%) 43 (52.4%) 23 (33.3%)
than 4 variables
Fruit and vegetables
52 (76.5%) 49 (62.0%) 43 (79.6%) 35 (68.6%) 63 (79.8%) 67 (76.1%) 72 (87.8%) 54 (78.3%)
(almost daily)
Breakfast ≥ 5 times/
53 (77.9%) 62 (78.5%) 43 (79.6%) 42 (82.4%) 58 (73.4%) 56 (63.6%) 55 (67.1%) 49 (71.0%)
week
Eat with family
55 (80.9%) 42 (53.2%) 35 (64.8%) 29 (56.9%) 65 (82.3%) 58 (65.9%) 44 (53.7%) 33 (47.8%)
(almost daily)
Eating while being at
the screen 27 (39.7%) 30 (38.0%) 27 (50.0%) 31 (60.8%) 25 (31.7%) 46 (52.3%) 57 (69.5%) 50 (72.5%)
(almost daily)
Drink sweetened soft
19 (27.9%) 26 (4.6%) 17 (31.5%) 11 (21.6%) 20 (25.3%) 22 (25.0%) 19 (23.2%) 16 (23.2%)
drinks (almost daily)
DOI: https://doi.org/10.14485/HBPR.8.5.6
Note.
* Statistically significant differences between mean scores found across different age and sex groups by Tukey post hoc test (p < .05).
Superscript notations indicated statistically significant differences compared to the following categories: B1 - 11-12 years old boys; B2 - 13-14 years old boys;
B3 - 14-15 years old boys; B4 - 17-19 years old boys; G1 - 11-12 years old girls; G2 - 13-14 years old girls; G3 - 14-15 years old girls; G4 - 17-19 years old girls.
455
Klavina et alProblematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
Figure 1
PRIUSS Total Mean Scores for Boys and Girls across Age Groups
ing signed consents. Students completed the survey 26 indicates high risk for PIU; a score 15-25 indi-
using via computer or smartphone. Completion re- cates intermediate risk.29 The Cronbach’s alphas for
quired about 15-20 minutes. There were 45 invalid the 3 subscales in the current study were .89 (social
surveys, resulting in 570 responses available for PIU), .82 (emotional impairment) and .79 (risky/
analysis – including 147 participants 11-12 years impulsive Internet use).
old (25.79 %), 167 participants 13-14 years old Healthy lifestyle behaviors. The survey included
(29.30 %), 136 participants 15-16 years old (23.86 8 questions on subjective health complaints during
%), and 120 participants 17-19 years old (21.05 the last 6 months. The 5-point scale ranged from 1
%). The mean age of participants was 14.0 ± 2.25 = “rarely or never” to 5 = “about every day.” Subjec-
years. More than half of the participants (N = 318, tive health complaints were categorized as somatic
55.79 %) were girls. complaints (headache, stomachache, backache,
dizziness) and psychological complaints (feeling
Instruments sad, having anxiety, being nervous, having sleep
Problematic Internet use. Adolescents’ PIU was difficulties). The Cronbach’s alphas for the 2 sets
assessed by the Problematic and Risky Internet of complaints were .81 (somatic complaints) and
Use Screening Scale (PRIUSS), a validated screen- .75 (psychological complaints). A multiple-health
ing instrument.27 The PRIUSS is an 18-item risk- complaints variable was identified if the participant
based screening scale for problematic Internet use reported more than 4 subjective health complaints
with questions organized into 3 subscales: (1) so- at least 2 times per week
cial impairment (6 items), (2) emotional impair- To assess physical activity, we asked adolescents
ment (5 items), and (3) risky/impulsive internet to indicate the number of days and hours over the
use (7 items). The PRIUSS response selections use past week during which they were doing free time
a Likert scale with scores of 0 through 4, including MVPA outside of school physical education classes.
answers “never” = 0, “rarely” = 1, “sometimes” = 2, Responses were dichotomized into 7 times/hours
“often” = 3, and “very often” = 4. A PRIUSS score ≥ per week and daily, according to the physical activ-
456Klavina et al
Figure 2
Social Impairments: Mean Score from the Corresponding PRIUSS Subscale
ity guidelines.1 The Cronbach’s alphas for the daily Data Analysis
physical activity subscale was .65. We calculated descriptive statistics (means, stan-
We asked participants to report how many hours dard deviation, and percentages) for all variables.
per day they spent using information technologies Bivariate correlations were calculated to identify re-
(IT) (eg, watching TV, playing games, chatting, lations between variables. Sex and age groups were
emailing, messaging on Internet etc) during the compared using 2-way ANOVA followed by Tukey
weekday and weekend, according to the guidelines post hoc tests for unequal samples when a difference
of the scoring system used in the Health Behav- was found in the analysis of variance. Partial eta
iour in School-Aged Children (HBSC) survey.30 A squared (ηp2) was calculated to determine the effect
mean of the responses was used as the measure of size and interpreted as small .0099, medium .0588,
screen time. A cut-off of 3 hours per day was used and large .1379 effect sizes.32 Statistical analyses
to allow for time spent reporting various IT, and were performed using MATHWORKS MATLAB
to keep the results comparable to a recent interna- version 2019b (Natick, MA) and IBM SPSS ver-
tional comparison study.31 sion 22 (Armonk, NY). Statistical significance was
Furthermore, we asked to respond to 5 questions set at p < .05.
about their eating habits. The frequency of the eat-
ing habits was assessed by questions: “How often do RESULTS
you usually have breakfast (in school, or at home)?” Problematic Internet Use Outcomes
“How many times a week do you consume fruits/ Table 1 presents the characteristics of the study
vegetables/sweetened soft drinks/sweets?” “How sample. Table 2 compares risky and problematic
often do you eat with your family?” “How often do Internet use survey outcomes, subjective health
you eat while being at the screen?” and “How often complaints, and healthy lifestyle across sex and age
do you drink sweetened soft drinks such as Coca- groups. A total of 27.02 % (N = 154) of the par-
Cola, Fanta, etc?” We assessed sleep quality using a ticipants scored at risk for problematic Internet use
single-item measure regarding the time when ado- (≥ 26 points).
lescents went to sleep and woke up on weekdays
and weekends. As Figure 1 illustrates, the mean results between
Health Behav Policy Rev.TM 2021;8(5):451-464 DOI: https://doi.org/10.14485/HBPR.8.5.6 457Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
Figure 3
Emotional Impairments: Mean Score from the Corresponding PRIUSS Subscale
sex groups indicated that PRIUSS total mean higher regarding psychological health complaints
scores significantly differed in 11-12 and 15-16 age (range 10%-24 %) compared to somatic health
groups where 11-12 years old boys had significant- symptoms (range 4%-6%), as Table 1 shows. Re-
ly higher scores than girls (p = .041), and 15-16 garding PRIUSS total scores, there was also a sta-
years old girls had significantly higher scores than tistically significant difference in subjective health
boys (p = .006). complaints between girls and boys, with girls re-
Among girls, the significant increase of the total porting more different health symptoms more fre-
problematic Internet use mean scores was more ob- quently than boys (p < .05). A significantly higher
servable across the 3 age groups from 11-12 years proportion of 15-16-year-old girls more frequently
mean score 16.32 (95% CI: 13.92, 18.72) to 13- indicated psychological symptoms, particularly
14 years mean score 22.22 (95% CI: 19.84, 24.60), anxiety and nervousness (about 2.44 days per week
and 15-16 years mean score 24.70 (95% CI: 22.40, 95% CI: 2.17, 2.71, p < .05) than girls in other
27.00). In contrast, there were no significant differ- age groups (Table 2). Among boys, there was an in-
ences across the age groups in boys in total PRIUSS creased prevalence of psychological symptoms such
scores (p > .05). Figures 2-4 report analyses of the as anxiety (about 1.62 days per week, 95% CI:
PRIUSS means in the 3 subscales across the 4 age 1.09, 2.14); however, we found no statistically sig-
groups. For girls, we found a statistically significant nificant difference across the 4 age groups (p > .05).
difference in mean scores for the social impairment Regarding somatic health complaints, girls report-
and risky/ impulsive Internet subscales (p = .003 ed higher prevalence of backache than boys (1.26
and p = .002, respectively); there was no differ- days/week, 95% CI: 1.11, 1.42 and .65 days/week,
ence with respect to the emotional subscale results. 95% CI: .51, .79, p = .011). Overall, 31.93% (56
Among boys, the statistically significant difference boys and 123 girls) of adolescents indicated more
was found only with the emotional impairment than 4 subjective health complaints at least 2 times
subscale with 11-12 years old boys scoring signifi- per week with a significantly higher proportion of
cantly higher than boys 15-16 years old (p = .003). girls than boys making this report (p < .05).
Subjective Health Complaints Health Behaviors
Prevalence of subjective health complaints was According obtained responses only 6.49% of
458Klavina et al
Figure 4
Risky / Impulsive Internet Use: Mean Score from the Corresponding PRIUSS Subscale
adolescents engaged in physical activities 7 or more ticipants ate breakfast 5 or more times per week.
hours per week according to the World Health Or- About 77.37% reported that almost daily they had
ganization guidelines for 5-17-year-old children fruits or vegetables, 65.79% had a meal with their
and adolescents (at least average 60 minutes daily family, 52.98% ate while being at the screen, and
of physical activities across the week).8 As Table 2 26.14% drank sweetened soft drinks. A significant-
illustrates, the highest score in weekly physical ac- ly higher proportion of girls than boys responded
tivity time was for boys and girls of 11-12 years that almost daily they are eating while being at the
of age (3.34 hours/week, 95% CI: 2.75, 3.93, and screen (p = .004). Also, there was a significant dif-
2.46 hours/week, 95% CI: 1.98, 2.24, accordingly) ference across the 4 age groups for girls (p = .000)
indicating a statistically significant difference with indicating highest prevalence in 17-19-year-old
other age groups (p < .05). On the other hand, as girls and boys (72.46% and 60.78%, respectively).
age increases, the number of hours spent in physi- Sleep duration mean scores during the week were
cal activity decreases (Table 2). Overall, boys re- > 8 hours, but among adolescents 17-19 years old
ported significantly more physical activity time (7.80 hours, 95% CI: 7.34, 8.20), while > 9 hours
than girls across the 4 age groups (p < .05). Preva- during weekend for all age groups (9.57 hours,
lence of information technology (IT) use was high- 95% CI: 9.44, 9.70). Overall, there is clear trend
er at the weekend indicating that about 71.05% of for increase of problematic Internet use behaviors,
adolescents spent 3 hours or more by screens dur- health complaints, and unhealthy habits between
ing weekend, and 65.61% on weekdays. Among ages 11 and 16 among girls, while most of symp-
boys, excessive use of IT was significantly greater toms slightly decrease among 17-19-year-old girls.
for 17-19-year-olds during a week (74.51%), while Furthermore, our ANOVA revealed a statisti-
for 13-14-year-olds on the weekend (78.48 %). cally significant effect for sex (F = 5.223, p < .001,
Among girls, excessive IT use during the week partial η2 = .053, medium), age (F = 4.533, p <
was greater for 15-16-year-olds (80.49 %) and for .001, partial η2 = .049, small), and sex x age (F =
17-19-year-olds (76.81%) during weekend. Over- 2.045, p = .006, partial η2 = .023, small) on the
all, our data indicated that no significant differ- study variables.
ences in IT use between girls and boys (p > .05). Table 3 presents the correlation coefficients of
Regarding eating habits, about 73.33% of par- the 3 PRIUSS subscales with the subjective health
Health Behav Policy Rev.TM 2021;8(5):451-464 DOI: https://doi.org/10.14485/HBPR.8.5.6 459Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
Table 3
Bivariate Correlations of PRIUSS Subscales with the Subjective Health Complaints
(Somatic and Psychological) and Healthy Lifestyle Behaviors (Physical Activity)
Healthy lifestyle behaviors
Somatic complaints Psychological complaints
(physical activity)
Social impairment .24** .34** .20**
Emotional impairment .30** .42** .21**
Risky/impulsive internet use .26** .45** .31**
Note.
** Significant at p < .01
complaints and healthy lifestyle behaviors. The technologies than boys.10 In addition to sex and
three subscales of the PRIUSS were significantly age, psychosocial behavior problems were studied as
correlated with subjective somatic complaints (r PIU-related health risks. Risky, impulsive Internet
= .24 - .30, p < .01), psychological complaints (r use had significantly higher prevalence across the
= .34 - .45, p < .01) and daily physical activity (r 3 subscales, with considerably higher rates in girls,
=.20-.31, p < .01). However, none of the PRIUSS particularly in the 15-16-year age group. Also, sig-
subscales was significantly correlated with eating nificantly higher proportion of the same age girls
behaviors. more frequently indicated psychological symptoms
(eg, anxiety, nervousness). Following these findings,
DISCUSSION the tendency for anxiety and nervousness combined
with spending prolonged time periods using the In-
Many adolescents are exposed to daily computer
ternet has been suggested to explain their higher
use for increased time periods. However, research
PIU levels.35 Also, it might be that PIU and most
regarding the influence on the emotional, psychoso-
online risky practices can be linked to the deficit of
cial, and psychological health factors of adolescents
personal and social skills. The sex-related outcomes
is still lacking. To our knowledge this is the first
in this study were not consistent with findings in
time that the prevalence of PIU has been explored
other studies reporting boys being at higher risk for
in adolescents in Latvia. Our results show that PIU
behaviors were presented in more than one-fourth PIU.36,37
(27.02%) of adolescents, particularly in girls 15-16 Furthermore, studies exploring the harmful ef-
years of age. These outcomes present higher rates fects of screen-based sedentary behaviors on health
than other previous studies conducted in other Eu- have shown a relationship with complaints such as
ropean countries.27,33,34 Also, different prevalence irritability, nervousness, and somatic health prob-
rates might result from various assessment tools, lems.11,38 Our results aligned with literature, spe-
cut-offs used in studies, and sample sizes. Contrary cifically girls having greater prevalence of subjective
to previous reports, in our study, girls had signifi- health complaints.2,39 Additionally, social phobia
cantly higher prevalence of risk for PIU compared and depression have been found to be a significant
to boys. Specifically, there was significant increase in indicator for PIU in adolescent girls.40 The possible
PIU risks with each year from age 11 to 16 years in explanation for this might be normal growth and
girls, while in boys there was tendency to decrease development changes during puberty that can dif-
PIU scores from 11 to 19 years of age. However, our fer between the sexes, as well as certain sociocultur-
results corroborate the national survey outcomes of al phenomena (eg, girls being more active in social
the HBSC from 2018 reporting girls spending sig- networking; using various information technolo-
nificantly more time by using different information gies for interaction and socialization).
460Klavina et al Our findings also were consistent with literature cating < 8 hours of sleep duration during weekdays. about the limited amount of daily physical activ- These results support national school-age survey ity among adolescents.41 Numerous studies have data from 2018 reporting sleep time in 11-15-year- showed that physical activity and sedentary behav- old adolescents of about 7.78 hours during week- iors, including excessive use of IT, are associated days and 9.80 hours during weekends.10 The with increased negative health conditions, which in international literature recommends 8 to 10 hours turn, relate to subjective health complains.5,38 With of sleep per night for adolescents aged 13 to 19 the increasing prevalence of IT in adolescents’ lives, years.48 However, additional studies should explore especially during the pandemic situation of 2020 any association between sleep patterns and screen and 2021 where online education is common, use time (eg, using screens at nighttime) with health of computers plays an increasingly greater role in risks of adolescents. education and extra-curricular activities. Therefore, Our findings aligned with other authors indi- it may be challenging to increase the proportion cating that problematic Internet users were at in- of young adolescents who adhere to the World creased risk for somatic health symptoms49 and Health Organization recommendations on physi- psychological problems.29,50 Previous studies also cal activity.8 have reported an association between PIU and Eating fruits and vegetables daily is related with physical activity, indicating that exercise time is re- fewer health risks and provides protection from a duced due to PIU.51,52 variety of chronic diseases, such as cardiovascular Overall, our results presented a significant me- diseases, diabetes, and some types of cancers.42 Our dium effect of sex on the study variables described results present relatively high rates of daily vegeta- above that align with findings from other studies.36,53 ble and fruit consumption. In Latvia, many schools The effect of the age and age x sex was small. Also, are part of the national program “Milk and Fruit earlier studies have reported a small effect of age on for Schools” that ensures that students from grades PIU.37,54 Cuenca-García et al55 reported that young- 1-9 receive fruits or vegetables at school during er adolescents were more physically active and fol- their meal at least 3 times per week.43 lowed healthy lifestyle habits in Europe that could The practice of eating breakfast regularly among be related to influence of parents on decisions of adolescents ranges from 51% to 95% worldwide.44 younger adolescents. For example, about two-thirds Breakfast frequency has been related to several of children and adolescents are actively engaged in health benefits and healthier lifestyle habits.45 Our organized sport participation in Europe;56 however, findings revealed that majority of participants there is a decrease in participation with age.57 (73.33%) had regular breakfast ≥ 5 times per week. This study has several limitations to consider. These results present higher prevalence of hav- Our convenience sample does not necessarily rep- ing daily breakfast than reported in the National resent the entire population of that age. Moreover, HBSC report from 2019.10 Overall eating habits participants who did not complete all items of the presented in this study represented healthier be- survey were excluded (45 adolescents). Also, data havior than findings by other authors.5,6 However, were self-reported, thereby creating a potential about half of our participants reported that they source of bias. This study did not consider possible are eating while being at the screen. Links between relationships between PIU and subjective health screen time and unhealthy eating habits (eg, hav- complaints and health behaviors. Furthermore, ing energy dense foods and sweetened drinks while data collection was implemented during the COV- watching TV or using computers) have not had ID-19 pandemic when some schools and students in-depth study. This evidence has tended to focus over 13 years of age had distance education. There- on these unhealthy daily habits independently but fore, screen time measures can be increased because emerging evidence suggests that they might actu- of online education mode. However, observations ally co-exist in adolescents.46,47 and anecdotal notes during this study indicated Sleep duration for participants in this study was that leisure screen time also was prolonged. Physi- appropriate (> 8-9 hours during weekdays and cal activity time has been reported as being signifi- weekends), but adolescents 17-19 year of age indi- cantly lower during the COVID-19 pandemic.58 Health Behav Policy Rev.TM 2021;8(5):451-464 DOI: https://doi.org/10.14485/HBPR.8.5.6 461
Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
Conclusions early in adolescence. Finally, it is important to edu-
PIU rates and subjective health complaints were cate adolescents to evaluate their health by promot-
common in adolescents. The increased preva- ing adequate active and sedentary (ie, screen time)
lence of PIU and subjective psychological health lifestyle evaluation. Increasing positive attitude to-
symptoms were reported by 15-16-year-old girls. ward physical activities also might be promising in
As Internet use and physical activities are modifi- health promotion programs aimed at reducing PIU
able behaviors, effective interventions are urgently in adolescents that already present health risks.
needed.
Acknowledgement
IMPLICATIONS FOR HEALTH BEHAVIOR This work was supported by the Latvian Council
OR POLICY of Science under Fundamental and Applied Re-
The World Health Organization (WHO) has search grant Nr. lzp-2019/1-0152.
identified sedentary behavior by youngsters as
a risk factor in global mortality and a contribu- Human Subjects Approval Statement
tor to the rise in obesity.59 Moreover, engaging in The study was approved by the Ethical Commit-
prolonged screen time is contrary to WHO guide- tee of the Latvian Academy of Sport Education
lines that call for children and adolescents to aver- (Nr. 3 from 28.02.2020).
age 60 minutes per day of MVPA across the week,
as well as vigorous-intensity aerobic activities that
strengthen muscle and bone incorporated at least 3 Conflicts of Interest Disclosure Statement
days a week.8 The authors declare no conflict of interest.
Our findings have important implications for
the practice of public health and health promo- References
tion in children and adolescents. First, our study 1. World Health Organization (WHO). Global Recommen-
adds to the knowledge about the prevalence of dations on Physical Activity for Health. Geneva, Switzer-
land: WHO; 2010:17-21.
PIU, subjective health complaints, and participa- 2. World Health Organization (WHO). Growing Up Un-
tion in daily physical activity, which are important equal: Gender and Socioeconomic Differences in Young Peo-
variables related to psychosocial health of adoles- ple’s Health and Well-Being. Health Behaviour in School-
cents. Furthermore, it is necessary not only to in- Aged Children (HBSC) Study: International Report from
the 2013/2014 Survey. Copenhagen, Denmark: WHO;
crease daily physical activity level, but to promote 2016.
healthy lifestyle habits in general, addressing ef- 3. Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The
ficiency of participants’ attitude towards healthy health indicators associated with screen-based sedentary
behaviors across different age, socioeconomic sta- behavior among adolescent girls: a systematic review.
J Adolesc Health. 2013;52(4):382-392. doi: 10.1016/j.
tus, ethnicity, and other specific groups. To de- jadohealth.2012.07.018
crease childhood diseases such as psychological, 4. Janssen I, Leblanc AG. Systematic review of the health
somatic, and other aspects of health, many policy benefits of physical activity and fitness in school-aged
documents and health promotion guidelines have children and youth. Int J Behav Nutr Phys Act. 2010;7:40.
doi: https://doi.org/10.1186/1479-5868-7-40
been ratified for the development of the effective 5. Marques A, Demetriou Y, Tesler R, Gouveia ÉR, Peralta
strategies among children’s and adolescents. How- M, Matos MG de. Healthy lifestyle in children and adoles-
ever, limited evidence has been found on interven- cents and its association with subjective health complaints:
tions primarily targeting PIU prevention. Thus, to findings from 37 countries and regions from the HBSC
implement these evidence-based recommendations Study. Int J Environ Res Public Health. 2019;16(18):3292.
doi: http://doi.org/10.3390/ijerph16183292.
to policymakers factors such as sustainability of in- 6. Bianco A, Napoli G, Di Pasquale M, Filippi AR, Gómez-
terventions, cultural contexts, and environmental López M, Messina G, et al. Factors associated with the
factors (ie, socioeconomic status) should be consid- subjective health complaints among adolescents: results
ered. Additionally, policymakers should ensure that from the ASSO Project. Journal Human Sport & Exer-
cise. 2019;14(2):443-455. doi: https://doi.org/10.14198/
the environment supports healthy lifestyle. Future jhse.2019.142.16
research should be targeting effective measures and 7. Rayner M, Wickramasinghe K, Williams J, McColl K,
interventions for health, education, and lifestyle Mendis S. An Introduction to Prevention of Non-Commu-
462Klavina et al
nicable Diseases. Oxford, UK: Oxford University Press; 2002;18(5):553-575. doi: 10.1016/j.chb.2009.06.006
2017. 22. Morahan-Martin J, Schumacher P. Incidence and corre-
8. World Health Organization (WHO). WHO Guide- lates of pathological Internet use among college students.
lines on Physical Activity and Sedentary Behaviour. Ge- Comput Human Behav. 2000;16(1):13-29. doi: http://
neva, Switzerland: WHO; 2020. https://www.who.int/ doi.org/10.1016/S0747-5632(99)00049-7
publications/i/item/9789240015128. Published Novem- 23. Shapira NA, Goldsmith TD, Keck Jr PE, Khosla UM,
ber 25, 2020. Accessed September 26, 2021. McElroy MD. Psychiatric features of individuals with
9. Alemán-Díaz AY, Backhaus S, Siebers LL, Chukwu- problematic internet use. J Affect Disord. 2000;57(1-
jama O, Fenski F, Henking CN, et al. Child and ado- 3):267-272. doi: http://doi.org/10.1016/S0165-
lescent health in Europe: monitoring implementation 0327(99)00107-X
of policies and provision of services. Lancet Child Ado- 24. Hansen S. Excessive Internet usage or ‘internet addic-
lesc Health. 2018;2(12):891-904. doi: 10.1016/S2352- tion’? The implications of diagnostic categories for stu-
4642(18)30286-4 dent users. J Comput Assist Learn. 2002;18:232-236.
10. Pudule I, Velika B, Grīnberga D, Gobiņa I, Villeruša A, doi: doi: 10.1046/j.1365-2729.2002.t01-2-00230.x
Kļaviņa-Makrecka S, et al. Latvijas skolēnu veselības para- 25. Sherer K. College life on-line: healthy and unhealthy In-
dumu pētījums 2017/2018. Rīga, Latvja: SPKC; 2020. ternet use. J Coll Stud Dev. 1997;38(6):655-665.
11. Hakala PT, Saarni LA, Punamäki RL, Wallenius MA, 26. Young KS. Caught in the Net: How to Recognize the Signs
Nygård CH, Rimpelä AH. Musculoskeletal symptoms of Internet Addiction and a Winning Strategy for Recovery.
and computer use among Finnish adolescents - pain in- New York, NY: John Wiley; 1998.
tensity and inconvenience to everyday life: a cross-sec- 27. Jelenchick LA, Eickhoff J, Christakis DA, Brown RL,
tional study. BMC Musculoskelet Disord. 2012;13:41. doi: Zhang, C, Benson M, et al. The Problematic and Risky
https://doi.org/10.1186/1471-2474-13-41. Internet Use Screening Scale (PRIUSS) for adolescents
12. Pulmanis T, Spriņģe L, Trapencieris M, Taube M. and young adults: scale development and refinement.
Pašnāvnieciskās uzvedības mūža prevalence un tās Comput Human Behav. 2014;35:171-178. doi: 10.1016/j.
izmaiņas dinamikā 15-16 gadus veciem pusaudžiem dzi- chb.2014.01.035
mumu grupās Latvijā. RSU 2012. gada medicīnas nozares 28. Jelenchick LA, Hawk ST, Moreno MA. Problematic in-
pētnieciskā darba publikācijas. 2013;1:167-171. ternet use and social networking site use among Dutch
13. Sirard JR, Bruening M, Wall MM, Eisenberg ME, Kim adolescents. Int J Adolesc Med Health. 2016;28(1):119-
SK, Neumark-Stzainer D. Physical activity and screen 121. doi: 10.1515/ijamh-2014-0068
time in adolescents and their friends. Am J Prev Med. 29. Marino C, Hirst CM, Murray C, Vieno A, Spada MM.
2013;14:48-55. doi: 10.1016/j.amepre.2012.09.054 Positive mental health as a predictor of problematic in-
14. Ali MM, Amialchuk A, Heiland FW. Weight-related be- ternet and Facebook use in adolescents and young adults.
havior among adolescents: the role of peer effects. PLoS J Happiness Stud. 2018;19:2009-2022. doi: 10.1007/
One. 2011;6(6):e21179. doi: http://doi.org/10.1371/ s10902-017-9908-4
journal.pone.0021179 30. Ravens-Sieberer U, Erhart M, Torsheim T, Hetland J,
15. DeLisi M, Vaughn MG, Gentile DA, Anderson CA, Shook Freeman J, Danielson M, et al. HBSC Positive Health
JJ. Violent video games, delinquency, and youth violence Group. An international scoring system for self-reported
new evidence. Youth Violence Juv Justice. 2013;11(2):132- health complaints in adolescents. Eur J Public Health.
142. doi: http://doi.org/10.1177/1541204012460874 2008;18(3):294-299. doi: 10.1093/eurpub/ckn001
16. Gitter SA, Ewell PJ, Guadagno RE, Stillman TF, Baumeis- 31. Hoare E, Milton K, Foster C, Allender S. The associations
ter RF. Virtually justifiable homicide: the effects of pro- between sedentary behaviour and mental health among
social contexts on the link between violent video games, adolescents: a systematic review. Inter J Behav Nutr Phys
aggression, and prosocial and hostile cognition. Aggress Act. 2016;13:108. doi: 10.1186/s12966-016-0432-4
Behav. 2013;39(5):346-354. doi: 10.1002/ab.21487 32. Cohen J. Statistical Power Analysis for the Behavioral Sci-
17. Kim J, LaRose R, Peng W. Loneliness as the cause and ences. 2nd ed. Hillsdale, NJ: Erlbaum; 1988.
the effect of problematic Internet use: the relationship 33. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C,
between Internet use and psychological well-being. Cy- Floderus B, et al. Prevalence of pathological internet
berPsychol Behav. 2009;12(4):451-455. doi: 10.1089/ use among adolescents in Europe: demographic and so-
cpb.2008.0327 cial factors. Addiction. 2012;107(12):2210-2222. doi:
18. Beard KW, Wolf EM. Modification in the proposed diag- 10.1111/j.1360-0443.2012.03946.x
nostic criteria for Internet addiction. CyberPsychol Behav. 34. Salgado PG, Boubeta AR, Tobio TB, Mallou TB, Couto
2001;4:377-383. doi: 10.1089/109493101300210286 CB. Evaluation and early detection of problematic Inter-
19. Brenner V. Psychology of computer use: XLVII: parame- net use in adolescents. Psicothema. 2014;26:21-26. doi:
ters of Internet use, abuse and addiction: the first 90 days 10.7334/psicothema2013.109
of the Internet usage survey. Psychol Rep. 1997;80(3 Pt 35. Jackson LA, Von Eye A, Biocca F, Barbatsis G. Children’s
1):879-882. doi: 10.2466/pr0.1997.80.3.879 home Internet use: antecedents and psychological, social,
20. Davis RA. A cognitive–behavioral model of pathological and academic consequences. In Kraut R, Brinyn M, Kiesel
Internet use. Comput Human Behav. 2001;17:187-195. S, eds. Computers, Phones, and the Internet: Domesticating
doi:10.1016/S0747-5632(00)00041-8 Information Technology. Oxford Scholarship Online; 2012.
21. Caplan SE. Problematic Internet use and psychosocial doi: 10.1093/acprof:oso/9780195312805.001.0001
well-being: development of a theory-based cognitive-be- 36. Hong S, You S, Kim E, No U. A group-based model-
havioral measurement instrument. Comput Human Behav. ing approach to estimating longitudinal trajectories of
Health Behav Policy Rev.TM 2021;8(5):451-464 DOI: https://doi.org/10.14485/HBPR.8.5.6 463Problematic Internet Use, Related Psychosocial Behaviors, Healthy Lifestyle, and Subjective Health Complaints in Adolescents
Korean adolescents’ on-line game time. Pers Individ Diff. results summary. Sleep Health. 2015;1(1):40-43. doi:
2014;59:9-15. doi: 10.1016/j.paid.2013.10.018 10.1016/j.sleh.2014.12.010
37. Yu L, Shek DT. Internet addiction in Hong Kong 49. Cerruti R, Spensieri V, Presaghi F, Valastro C, Fontana A,
adolescents: a three-year longitudinal study. J Pedi- Guidetti V. An exploratory study on Internet addiction,
atr Adolesc Gynecol. 2013;26(3):10-17. doi: 10.1016/j. somatic symptoms and emotional and behavioral func-
jpag.2013.03.010 tioning in school-aged adolescents. Clin Neuropsychiatry.
38. White RL, Parker PD, Lubans DR, MacMillan F, Olson 2017;14(16):374-383. doi: https://psycnet.apa.org/re-
R, Astell-Burt T, et al. Domain-specific physical activ- cord/2018-00251-002
ity and affective wellbeing among adolescents: an obser- 50. Keles B, McCrae N, Grealish A. A systematic review:
vational study of the moderating roles of autonomous the influence of social media on depression, anxiety and
and controlled motivation. Inter J Behav Nutr Phys Act. psychological distress in adolescents. Int J Adolesc Youth.
2018;15:87. doi: 10.1186/s12966-018-0722-0 2020;25:79-93. doi: 10.1080/02673843.2019.1590851
39. Keane E, Kelly C, Molcho M, Gabhainn NS. Physical 51. Khan MA, Shabbir F, Rajput TA. Effect of gender
activity, screen time and the risk of subjective health com- and physical activity on Internet addiction in medi-
plaints in school-aged children. Prev Med. 2017;96:21- cal students. Pak J Med Sci. 2017;33(1):191-194. doi:
27. doi: 10.1016/j.ypmed.2016.12.011 10.12669/pjms.331.11222
40. Ciarrochi J, Parker P, Sahdra B, Marshall S, Jackson C, 52. Lam LT. Risk factors of internet addiction and the health
Gloster AT, et al. The development of compulsive Inter- effect of internet addiction on adolescents: a systematic
net use and mental health: a four-year study of adoles- review of longitudinal and prospective studies. Curr Psy-
cence. Develop Psych. 2016;52:271-283. doi: 10.1037/ chiatry Rep. 2014;16(11):508. doi: 10.1007/s11920-014-
dev0000070 0508-2
41. Janssen I, Leblanc AG. Systematic review of the health 53. Baceviciene M, Jankauskiene R, Emeljanovas A. Self-
benefits of physical activity and fitness in school-aged perception of physical activity and fitness is related
children and youth. Int J Behav Nutr Phys.2010; 7. doi: to lower psychosomatic health symptoms in adoles-
10.1201/b18227-14 cents with unhealthy lifestyles. BMC Public Health.
42. Boeing H, Bechthold A, Bub A, Ellinger S, Haller 2019;19:980. doi:10.1186/s12889-019-7311-2
D, Kroke A, et al. Critical review: vegetables and 54. Shek DTL, Yu L. Internet addiction phenomenon in
fruit in the prevention of chronic diseases. Eur J Nutr. early adolescents in Hong Kong. ScientificWorldJournal.
2012;51(6):637-663. doi: 10.1007/s00394-012-0380-y 2012:104304. doi: 10.1100/2012/104304
43. Lauku atbalsta dienests. Piens un augļi skolai. https:// 55. Cuenca-García M, Huybrechts I, Ruiz JR, Ortega FB, Ot-
www.lad.gov.lv/lv/atbalsta-veidi/tirgus-pasakumi/pi- tevaere C, González-Gross, M, et al. Clustering of mul-
ens-un-augli-skolai/. Published 2009. Accessed Decem- tiple lifestyle behaviors and health-related fitness in Eu-
ber 23, 2020. ropean adolescents. J Nutr Educ Behav. 2013;45(6):549-
44. Keski-Rahkonen A, Kaprio J, Rissanen A, Virkkunen 557. doi:10.1016/j.jneb.2013.02.006
M, Rose RJ. Breakfast skipping and health compromis- 56. Kokko S, Martin L, Geidne S, Van Hoye A, Lane A,
ing behaviors in adolescents and adults. Eur J Clin Nutr. Meganck J, et al. Does sports club participation con-
2003;57:842-853. doi: 10.1038/sj.ejcn.1601618 PMID: tribute to physical activity among children and ado-
12821884 lescents? A comparison across six European coun-
45. Affinita A, Catalani L, Cecchetto G, Lorenzo G, Di- tries. Scand J Public Health. 2019;47(8):851-858. doi:
lillo D, Donegani G, et al. Breakfast: a multidisci- 10.1177/1403494818786110
plinary approach. Ital J Pediatr. 2013;39(44):1-10. doi: 57. Crane J, Temple V. A systematic review of dropout from
10.1186/1824-7288-39-44 organized sport among children and youth. Eur Phys
46. Leech RM, McNaughton SA, Timperio A. The cluster- Educ Review. 2015;21(1):114-131. doi: https://eric.
ing of diet, physical activity and sedentary behavior in ed.gov/?id=EJ1050586
children and adolescents: a review. Inter J Behav Nutr Phys 58. Xiang M, Zhang Z, Kuwahara K. Impact of COV-
Act.2014;11:4. doi: 10.1186/1479-5868-11-4. ID-19 pandemic on children and adolescents’ lifestyle
47. Pearson N, Griffiths P, Biddle SJ, Johnston JP, McGeorge behavior larger than expected. Prog Cardiovasc Dis.
S, Haycraft E. Clustering and correlates of screen-time 2020;63(4):531-532. doi: 10.1016/j.pcad.2020.04.013
and eating behaviours among young adolescents. BMC 59. World Health Organization (WHO). To grow up healthy,
Public Health. 2017;17:533. doi: 10.1186/s12889-017- children need to sit less and play more. https://www.who.
4441-2 int/news/item/24-04-2019-to-grow-up-healthy-chil-
48. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bru- dren-need-to-sit-less-and-play-more. Published April
ni O, DonCarlos L, et al. National Sleep Foundation’s 24, 2019. Accessed September 26, 2021.
sleep time duration recommendations: methodology and
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