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Adolescent Technology-use Rules and Sleep in a Large Representative Sample

Open AccessPublished:January 03, 2022DOI:https://doi.org/10.1016/j.jadohealth.2021.10.025

      Abstract

      Purpose

      This study investigated the prevalence of technology-use rules, typical sleep habits, and associations between rules and sleep using the representative 2017–2018 California Health Interview Survey adolescent sample.

      Methods

      Adolescents aged 12–17 years completed the California Health Interview Survey, including queries of (1) rules at home regarding times to turn off or put away electronics and (2) school-night bedtime and rise time. Rates of rules and associations between rules and sleep were investigated using descriptive statistics and bivariate and multivariable analyses.

      Results

      Seventy-two percent reported technology-use rules. Rates were comparable across subgroups. Rules and sleep were not significantly associated after adjusting for covariates. Reported time in bed fell below National Sleep Foundation guidelines for 38% of participants.

      Conclusions

      Most adolescents reported technology-use rules at home. Associations between rules and bedtime were mixed, suggesting that further exploration of contextual and developmental factors is needed. Many reported inadequate sleep duration, supporting sleep as a key topic in adolescent health.

      Keywords

      Implications and Contribution
      This study investigated rates of technology-use rules and associations between rules and sleep in a representative sample of California adolescents. Technology-use rules were comparable across subgroups and provide a pre-COVID-19 baseline. Results call for both nuanced research and provider assessment of contextual and developmental factors relating to technology use and sleep.
      Nearly half of adolescents report being online “almost constantly” [
      • Anderson M.
      • Teens Jiang J.
      Social Media, & technology 2018.
      ]. Health consequences of digital device use are of interest to adolescent health researchers and practitioners. Of particular concern are impacts on sleep such as delayed bedtime and reduced sleep duration [
      • Hoyt L.T.
      • Maslowsky J.
      • Olson J.S.
      • et al.
      Adolescent sleep barriers: Profiles within a diverse sample of urban youth.
      ,
      • Hale L.
      • Guan S.
      Screen time and sleep among school-aged children and adolescents: A systematic literature review.
      ,
      • Carter B.
      • Rees P.
      • Hale L.
      • et al.
      Association between portable screen-based media device access or use and sleep outcomes: A systematic review and meta-analysis.
      ]. Sleep is crucial for many physiological and cognitive functions [
      • Orzech K.M.
      • Acebo C.
      • Seifer R.
      • Barker D.
      • Carskadon M.A.
      Sleep patterns are associated with common illness in adolescents.
      ,
      • Chaput J.P.
      • Dutil C.
      Lack of sleep as a contributor to obesity in adolescents: Impacts on eating and activity behaviors.
      ,
      • Wild C.J.
      • Nichols E.S.
      • Battista M.E.
      • et al.
      Dissociable effects of self-reported daily sleep duration on high-level cognitive abilities.
      ,
      • Owens J.A.
      • Dearth-Wesley T.
      • Lewin D.
      • et al.
      Self-regulation and sleep duration, sleepiness, and chronotype in adolescents.
      ,
      • Brand S.
      • Kirov R.
      Sleep and its importance in adolescence and in common adolescent somatic and psychiatric conditions.
      ], and the adolescent brain is especially susceptible to inadequate sleep [
      • Galván A.
      The unrested adolescent brain.
      ,
      • Robinson J.L.
      • Erath S.A.
      • Kana R.K.
      • El-Sheikh M.
      Neurophysiological differences in the adolescent brain following a single night of restricted sleep–A 7T fMRI study.
      ].
      Given these risks, in concert with evidence suggesting that limiting technology use can improve youth sleep quality and duration [
      • Carter B.
      • Rees P.
      • Hale L.
      • et al.
      Association between portable screen-based media device access or use and sleep outcomes: A systematic review and meta-analysis.
      ,
      National Sleep Foundation
      2014 sleep in America poll: Sleep in the modern family.
      ], the 2017 American Academy of Pediatrics' Bright Futures guidelines recommend that providers counsel parents to establish rules about device use [
      • Hagan J.F.
      • Shaw J.S.
      • Duncan P.M.
      Bright futures: Guidelines for health supervision of infants, children, and adolescents.
      ]. Prior research on youth technology use suggested that technology-use rules were common but inconsistently enforced [
      National Sleep Foundation
      2014 sleep in America poll: Sleep in the modern family.
      ,
      • Anderson M.
      Parents, teens and digital monitoring.
      ], and more recent rates are unclear. Moreover, approaches to technology limits may vary by both caregiver and youth demographic factors [
      • Anderson M.
      Parents, teens and digital monitoring.
      ,
      • Wang X.
      • Xing W.
      Exploring the influence of parental involvement and socioeconomic status on teen digital citizenship: A path modeling approach.
      ].
      In 2017–2018, the California Health Interview Survey (CHIS) [
      California Health Interview Survey
      CHIS 2017-2018 methodology series: Report 2 - data collection methods.
      ], the nation's largest state health survey, surveyed adolescents about technology-use rules and sleep. A study using 2017 CHIS data alone found that technology-use rules predicted earlier bedtime [
      • Bowers J.M.
      • Moyer A.
      Adolescent sleep and technology-use rules: Results from the California health interview survey.
      ]. Pooling 2017 (used in previous study [
      • Bowers J.M.
      • Moyer A.
      Adolescent sleep and technology-use rules: Results from the California health interview survey.
      ]) and 2018 CHIS data nearly doubles the sample size, allowing for more detailed investigation of technology-use rules and sleep, including exploration of subgroups (e.g., race/ethnicity, income), and patterns by sex and age.
      This study investigates the following in California adolescents who completed the CHIS in 2017 and 2018: (1) technology-use rule rates overall and by demographic subgroup, (2) average bedtime and time in bed, and (3) associations between technology-use rules and sleep, overall and by select subgroups.

      Methods

       Participants

      Adolescents (N = 880), aged 12–17 years, were recruited from participating households to complete the CHIS in 2017–2018 via computer-assisted landline and cellular telephone interviews [
      California Health Interview Survey
      CHIS 2017-2018 methodology series: Report 2 - data collection methods.
      ]. Oversampling was used to augment participation of under-represented groups [
      California Health Interview Survey
      CHIS 2017-2018 methodology series: Report 2 - data collection methods.
      ]. The CHIS study, conducted by the University of California, Los Angeles Center for Health Policy Research, was approved by the University of California, Los Angeles Institutional Review Board and data has been made publicly available.

       Outcome measures

      The outcome measures were as follows: (1) technology-use rules: coded as yes or no in response to “Do you have rules in your home about when you are supposed to turn off or put away computers, phones, or other electronics, such as during meal times or a specific time at night?” and (2) two sleep outcomes, bedtime and time in bed. Time in bed was calculated from adolescent reports of school-week bedtime and rise time and compared with percent of participants meeting National Sleep Foundation (NSF) minimum sleep recommendations of 8 hours for 14- to 17-year-olds and 9 hours for 12- to 13-year-olds [
      • Hirshkowitz M.
      • Whiton K.
      • Albert S.M.
      • et al.
      National Sleep Foundation's sleep time duration recommendations: Methodology and results summary.
      ].

       Sociodemographic variables

      The sociodemographic variables were as follows: adolescent-reported (1) age: dichotomized to younger (12–13 years) and older (14–17 years), (2) sex, and (3) race/ethnicity, and caregiver-reported (4) income and (5) family composition (one- vs. two-caregiver home).

       Study outcomes

      The study outcomes were as follows: (1) rates of technology-use rules overall and by sociodemographic subgroups, (2) bedtime and time in bed (including percent meeting NSF sleep guidelines), overall and by age and sex, and (3) associations between rules and sleep outcomes, overall and by age and sex.

       Analyses

      Technology-use rule estimates (outcome 1) were established for the full sample and subgroups. Group differences were assessed via bivariate and multivariable logistic regression adjusting for the sociodemographic covariates. Average bedtime and total time in bed (outcome 2) were calculated for the overall sample and sex and age subgroups. Unadjusted and adjusted associations between each sleep outcome and technology-use rules (outcome 3) were assessed for the overall sample and by age and sex using linear regression. Analyses, based on CHIS protocols [
      California Health Interview Survey
      CHIS 2017-2018 methodology series: Report 2 - data collection methods.
      ], used Stata 16.1 survey procedures. Replicate weighting was used to provide adjustment for the complex survey design. This method reduces bias and more accurately estimates the variance of data when generalizing to a large population.

      Results

      Seventy-two percent of adolescents reported having technology-use rules at home (Table 1). Rates were similar across socioeconomic, racial/ethnic, and family composition subgroups. Differences by sex and age were not statistically significant.
      Table 1Participant demographic characteristics and differences in rates of technology-use rules: weighted percentages, odds ratios, adjusted odds ratios, and 95% confidence intervals
      Participant demographic characteristicsRates of technology-use rules
      Demographic variableWeighted % of overall sample% reporting technology-use rulesOdds of having rules

      OR (95% CI)

      AOR (95% CI)
      Overall100%71.9%
      Age
       12–1333%83.3%
       14–1767%66.1%
      OR (95% CI).39 (.13–1.19)
      AOR
      Adjusted odds ratios were obtained by including all other demographic variables (age, sex, race/ethnicity, income level, and family composition) as covariates.
      (95% CI)
      .36 (.10–1.32)
      Sex
       Male51%76.8%
       Female49%66.6%
      OR (95% CI).60 (.29–1.25)
      AOR (95% CI).59 (.30–1.16)
      Race/ethnicity
       White26%71.7%
       Nonwhite, including Hispanic
      Other, Asian, and two or more were combined with Hispanic into one nonwhite category because of small sample sizes.
      74%71.9%
      OR (95% CI)1.01 (.41–2.51)
      AOR (95% CI).92 (.31–2.74)
       Hispanic only
      Hispanic youth were also examined separately because of significant representation of Hispanic youth in this sample and in the California population.
      52%71.2%
      OR (95% CI).97 (.29–3.25)
      AOR (95% CI).83 (.19–3.63)
      Income level
       <200% FPL39%74.1%
       200%–399% FPL22%73.2%
      OR (95% CI).96 (.50–1.83)
      AOR (95% CI).84 (.40–1.75)
       ≥400% FPL39%68.8%
      OR (95% CI).77 (.40–1.49)
      AOR (95% CI).56 (.15–2.11)
      Family composition
       Two-caregiver home78%72.7%
       One-caregiver home22%68.9%
      OR (95% CI).83 (.44–1.58)
      AOR (95% CI).80 (.40–1.62)
      AOR = adjusted odds ratio; CI = confidence interval; OR = odds ratios.
      Reference groups (coded as 0): Younger participants, males, white participants, and <200% FPL.
      a Adjusted odds ratios were obtained by including all other demographic variables (age, sex, race/ethnicity, income level, and family composition) as covariates.
      b Other, Asian, and two or more were combined with Hispanic into one nonwhite category because of small sample sizes.
      c Hispanic youth were also examined separately because of significant representation of Hispanic youth in this sample and in the California population.
      The mean bedtime in the overall sample = 10:02 p.m., and the mean total time in bed = 8 hours 17 minutes. Nearly 2 of 5 (38.15%) participants and nearly half (45.78%) of females reported overall time in bed below the NSF minimum sleep recommendations (Table 2) [
      • Hirshkowitz M.
      • Whiton K.
      • Albert S.M.
      • et al.
      National Sleep Foundation's sleep time duration recommendations: Methodology and results summary.
      ].
      Table 2Associations between sleep and technology use rules
      OverallOlder (14–17)Younger (12–13)FemalesMales
      N880583297423457
      Weighted % not meeting NSF sleep guidelines38.15%36.96%40.53%45.78%30.85%
      % with technology-use rules71.9%66.1%83.3%66.6%76.8%
      Bedtime
       Overall10:02 p.m. (5.92)10:19 (11.85)9:29 (6.53)10:14 p.m. (5.46)9:51 p.m. (12.00)
       With rules9:53 p.m. (5.89)10:10 (9.88)9:26 (6.18)10:03 p.m. (6.38)9:45 p.m. (9.18)
       Without rules10:26 p.m. (11.28)10:36 (11.17)9:45 (23.93)10:35 p.m. (15.06)10:14 p.m. (41.88)
      Unadjusted difference: rules versus no rules
       Unstandardized ß (95% CI)−.55∗ (−1.01 to −.08)−.43∗ (−.81 to −.05)−.32 (−1.15 to .51)−.53∗ (−1.04 to −.02)−.49 (−2.00 to 1.03)
       Standardized ß (minutes)33 minutes26 minutes19 minutes32 minutes29 minutes
      Adjusted difference
      Adjusted coefficients were obtained by controlling for age, sex, race/ethnicity, income level, and family composition.
      : rules versus no rules
       Unstandardized ß (95% CI)−.26 (−.83 to .30)−.32 (−.75 to .11)−.33 (−1.46 to .80)−.32 (−.70 to .07)−.18 (−1.1 to .79)
       Standardized ß (minutes)16 minutes19 minutes20 minutes19 minutes11 minutes
      Time in bed
       Overall8 hours 17 m (6.6 m)7 hours 58 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (12.6 m)
      8 hours 57 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (7.2 m)
      8 hours 4 m (7.2 m)8 hours 31 m (13.8 m)
       With rules8 hours 25 m (6.0 m)8 hours 2 m (9.0 m)8 hours 59 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (6.0 m)
      8 hours 10 m (9.0 m)8 hours 37 m (7.2 m)
       Without rules8 hours 1 m (18.0 m)7 hours 49 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (18.0 m)
      8 hours 47 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (27.0 m)
      7 hours 54 m
      Does not meet National Sleep Foundation recommended guidelines [18] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      (11.4 m)
      8 hours 10 m (50.4 m)
      Unadjusted difference: rules versus no rules
       Unstandardized ß (95% CI).39 (−.32 to 1.10).21 (−.25 to .68).20 (−.70 to 1.10).25 (−.15 to .65).45 (−1.28 to 2.17)
       Standardized ß (minutes)24 minutes13 minutes12 minutes16 minutes27 minutes
      Adjusted difference: rules versus no rules
       Unstandardized ß (95% CI).07 (−.71 to .85).08 (−.65 to .81).15 (−.83 to 1.14).00 (−.71 to .70).12 (−.77 to 1.01)
       Standardized ß (minutes)4 minutes5 minutes9 minutes0 minutes7 minutes
      NSF = National Sleep Foundation.
      p < .05.
      a Adjusted coefficients were obtained by controlling for age, sex, race/ethnicity, income level, and family composition.
      b Does not meet National Sleep Foundation recommended guidelines [
      • Hirshkowitz M.
      • Whiton K.
      • Albert S.M.
      • et al.
      National Sleep Foundation's sleep time duration recommendations: Methodology and results summary.
      ] (9–11 hours for youth aged 6–13 years; 8–10 hours for youth aged 14–17 years).
      Bivariate linear regression analyses indicated that adolescents with technology-use rules reported earlier bedtimes (33 minutes, p < .05) than those without rules (Table 2), but this was not significant after adjustment in the multivariable analysis. Likewise, bivariate analyses of age and sex subgroups suggested that older participants and females with technology-use rules had earlier bedtimes than those without rules (26 and 32 minutes, respectively, both p < .05), but these were not significant in the multivariable analyses. No associations were found between technology-use rules and total time in bed.

      Discussion

      Most participants had technology-use rules. Similar rates of rules across income, race/ethnicity, and family type suggest that limits on technology use are common in diverse California families.
      Observed patterns of association between technology-use rules and sleep were inconclusive but point to potential differences by age and sex. A more nuanced examination of differences in both technology use and caregiver limit-setting by demographic subgroups is warranted.
      Despite high rates of technology-use rules, around 40% of adolescents in the present sample did not meet minimum sleep recommendations of 9 hours for younger adolescents and 8 hours for older adolescents [
      • Hirshkowitz M.
      • Whiton K.
      • Albert S.M.
      • et al.
      National Sleep Foundation's sleep time duration recommendations: Methodology and results summary.
      ]. Technology-use rules did not impact amount of time in bed, suggesting that additional strategies may be necessary to promote adequate sleep duration among youth.
      A strength of this investigation is use of a large representative sample of adolescents. Moreover, these data were collected before the COVID-19 pandemic, which has contributed to increases in youth technology use and as such provides a pre-pandemic baseline [
      • Xiang M.
      • Zhang Z.
      • Kuwahara K.
      Impact of COVID-19 pandemic on children and adolescents' lifestyle behavior larger than expected.
      ]. However, several limitations must be acknowledged. The survey asked about general technology-use rules, not specific to sleep, and did not ask about enforcement or compliance. Further research should gather more detailed contextual and developmental information regarding rules about night-time use of digital devices. Time in bed was based on youth report of bedtime and rise time, not accounting for time to fall asleep. Thus, means are likely an overestimate of time sleeping. Research on technology-related sleep disruptions (e.g., phone-related awakenings) [
      • Foerster M.
      • Henneke A.
      • Chetty-Mhlanga S.
      • Röösli M.
      Impact of adolescents' screen time and nocturnal mobile phone-related awakenings on sleep and general health symptoms: A prospective cohort study.
      ] suggests that bedtime and rise time alone may also underestimate the relationship between rules and sleep. Future investigations may choose to leverage rapidly advancing actigraphic methods for tracking sleep and technology use for a more accurate picture. Finally, more research is needed regarding how technology-use rules may be most effective across demographics and developmental stages.
      A significant proportion of adolescents in this sample report inadequate sleep duration. Females in particular fall short at high rates. This variability, combined with prior evidence linking night-time technology use with adolescent sleep disruption, suggests that providers should continue to thoroughly assess and counsel adolescent patients and families about technology use.

      Acknowledgments

      The authors would also like to acknowledge Ronald Dahl for his contributions to the generation of the focal questions examined in the present study.

      Funding Sources

      This research was primarily supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under cooperative agreement UA6MC27378, Maternal and Child Health Bureau (MCHB) Adolescent and Young Adult Health Research Network (AYAH-RN). Additional support was provided by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under #U45MC27709, Adolescent and Young Adult Health Capacity Building Program , MCHB's Leadership Education in Adolescent Health Training Grant T71MC00003 , and by the National Institutes of Health (NIH): National Institute on Drug Abuse (NIDA) 5 R34 DA035349-03 (co-PIs Emily J. Ozer and Allison G. Harvey).

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