Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity

Sleep Health xxx (xxxx) 1e9

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Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity*,** B.C. Galland, PhD a, T. de Wilde, BSc a, R.W. Taylor, PhD b, C. Smith, PhD a,* a b

Department of Women’s & Children’s Health, University of Otago, PO Box 56, Dunedin, New Zealand Department of Medicine, University of Otago, PO Box 56, Dunedin, New Zealand

a r t i c l e

i n f o

Article history: Received 7 February 2019 Received in revised form 27 August 2019 Accepted 5 September 2019 Keywords: Adolescent sleep Chronotype Ethnicity Pre-bedtime Screen use Sleep quality

a b s t r a c t Aim: To describe the screen and nonscreen activities adolescents engage in one hour before bedtime and associations with sleep quantity and quality, including differences by ethnic group. Design: Cross-sectional survey. Participants: 4,192 adolescents aged 13-17 years (52% boys); 71% NZ European, 13% Maori, 8% Asian, 6% Pacific, and 2% other ethnic groups. Measures: Participants completed questions about sleep timing, quality (Pittsburgh Sleep Quality Index), and chronotype (Morningness-Eveningness Scale for Children). Seventeen questions captured pre-bedtime activities. Results: Overall, 39% slept less than the recommended hour of sleep (<8 h) and 57% reported poor sleep quality. Asian teenagers reported shorter sleep duration than New Zealand (NZ) Europeans (-45 min [95% CI: -58 to -32]) primarily from later bedtimes (1 hour), with higher odds of long sleep latency, but less disori and Pacific adolescents turbed sleep and a more “eveningness” chronotype. Bedtimes were later in Ma (15 and 41 min, respectively) than NZ Europeans. Most screen activities were negatively associated with sleep quantity and quality. For nonscreen activities, snacking and drinking caffeinated beverages and alcohol were significantly associated with shorter sleep (-8, -28, and -20 min, respectively), whereas interacting with family and friends and exercise/sports before bed were associated with longer sleep (P < 0.001). Time with family, exercise, schoolwork, and household chores were all associated with better sleep quality (P < 0.001). Ethnic differences were apparent for several pre-bedtime activities. Discussion: Ethnic differences related to subjective sleep parameters exist in NZ adolescents. Observed variations in sleep patterns and presleep activities suggest that sleep health messages should be tailored for different ethnic groups. © 2019 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Introduction In recent years, awareness of sleep as vital to public health follows accumulating evidence that sleep is linked to physical and mental wellbeing.1 Adolescence is a particularly vulnerable period where the epidemic of sleep deprivation is such that every day has been described as “providing a small experiment on the adverse consequences of sleep”. 2 Adverse consequences are widespread, affecting weight, school performance, emotional and behavioral problems,

* Financial Disclosure: The authors have no financial relationships relevant to this article to disclose. ** Conflict of Interest: The other authors have no conflicts of interest to disclose. * Corresponding author at: Department of Women’s & Children’s Health, University of Otago, PO Box 56, Dunedin, New Zealand. Tel.: þ64 3 470 9478. E-mail address: [email protected] (C. Smith).

risk-taking, physical activity, and dietary intake.3,4 A consensus statement on sleep time recommendations based on predominantly subjective data issued by the National Sleep Foundation recommends that teenagers should get 8 to 10 hours of sleep every night. 5 However, many are getting far less when sleep is measured subjectively by their parents or self-report6 or objectively via actigraphy.7 During adolescence, a number of factors can lead to sleep deprivation. These include changes in sleep biology that reduce the homeostatic drive to sleep. This is in concert with delayed circadian rhythms, leading to a delay in sleep onset. If adolescents are free to sleep without restriction, the length of sleep is not affected by the bedtime delay. However, because school start times put the brakes on sleep length, many adolescents become sleep deprived during the school week. 8 Screen use in adolescents has been shown to change sleep timing by delaying sleep onset, 9,10 contributing to poorer sleep quality,10,11 and shorter sleep duration. 9,10 There are

https://doi.org/10.1016/j.sleh.2019.09.002 2352-7218/© 2019 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

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BC. Galland et al. / Sleep Health xxx (xxxx) 1e9

likely multiple reasons why screen and other technology use impacts sleep, including time displacement, psychophysiological arousal, and bright light exposure, leading to increased arousal, disruption in sleep architecture, and/or delaying the circadian rhythm.12 Furthermore, people with later chronotypes (those who prefer later bedtimes) may be hypersensitive to evening light.13 Screen and technology use involves several different types of media, some of which have more impact on sleep than others, 4 and proximity of use to bedtime may be an important factor. A recent intervention study enhanced sleep duration by targeting the one-hour period before bed to restrict adolescents' mobile phone use.14 Electronic media devices are used extensively by adolescents during this one-hour period before bedtime 15 with adverse outcomes linked to longer sleep latency, later bedtimes, and shorter sleep duration.10,15,16 The recent US data illustrate a changing landscape in the time teenagers spend on various screen and nonscreen activities. Shifts to more screen-based activities from 2013 to 2015 were concomitant with a population surge in short sleep duration. 6 The association of nonscreen activities before bed (e.g. sports or interacting with family and peers) and sleep have been studied far less, and predominantly in European adolescents.17 In New Zealand (NZ), we know little about the sleep patterns of adolescents from different ethnic groups (mainly NZ European, ori, Pacific, and Asian), although it is likely that sleep health Ma ori (indigenous people inequities exist based on work in younger Ma of NZ) and Pacific children,18,19 which highlights the significant socioeconomic and health disparities experienced by Maori and Pacific in NZ. 20 We know little about pre-bedtime practices in different cultures, although some research suggests that a stronger homework ethic may play a role in understanding why adolescents from Asian countries have significantly later bedtimes and shorter sleep compared with American and European youth. 21 The purpose of this study was to investigate sleep outcomes by ethnic group, sex, age, and socioeconomic status among NZ adolescents; describe the types of pre-bedtime activities they engage in, with the potential to influence sleep duration and quality (positively or negatively); and to investigate pre-bedtime activities by ethnicity to better understand how these might differ.

residence was used to calculate the NZ Index of Deprivation (NZDep2013).22 This is an area-based indicator of socioeconomic status where a score of 10 (scale 1-10) indicates a high level of neighborhood deprivation. For analysis, scores were divided into three groups: low (1 to 3), medium (4 to 6), and high (7 to 10) deprivation. Selfreported height and weight (participants indicated whether height and weight were measured or estimated) were used to calculate body mass index (BMI) z-scores, in accordance with World Health Organization standards. 23 These age- and sex-specific norms were used to classify obese as >95th BMI percentile, overweight as 85th to < 95th percentile, normal weight as 5th to < 85th percentile, and underweight as <5th percentile. Ethnicity was coded as NZ European, Maori, Pacific, Asian, and other ethnic groups (except NZ European). If participants self-identified with more than one ethnic group, they ori over Pacific over were assigned to one group prioritizing Ma Asian over NZ European.

Methods

To determine chronotype, participants completed the validated Morningness-Eveningness Scale for Children (MESC) 25 The MESC has 10 questions, five questions refer to behavior and mood at different times of day, three questions refer to the preferred timing of various activities, and two questions refer to functioning in hypothetical situations. The total score is calculated as the sum of points for each scale item (range 10 to 43), with lower scores, indicating more evening preference and higher scores more pronounced morning preference. In this study, the Cronbach a value was 0.71.

Recruitment Participants (aged 13-17 years) were recruited by advertisements on Facebook placed in news feeds inviting them to complete a questionnaire about their sleep and electronic screen use. Teenagers older than 15 years could directly access the information sheet, provide consent, and complete the survey, whereas those younger than 15 years required consent from their parents before taking part. Parents were emailed a link to the information sheet and completed an online checkbox to provide consent. Once this was ticked, the adolescent could access the survey. A 67-item questionnaire was delivered via the Qualtrics survey platform and took approximately 20 minutes to complete. The inclusion criterion was adolescents aged 13-17 years living in NZ. The survey was open between November 2016 and March 2017. Ethical approval was granted by the University of Otago Human Ethics Committee (Ref: F16/005). Questionnaire Demographics and participant characteristics Demographic information was collected using standard methods from the NZ census including date of birth, sex, ethnic group, school level, and occupation (if applicable). The participant's suburb of

Sleep quality, duration, and timing Participants completed the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-report questionnaire that assesses sleep quality during the previous month. The PSQI provides a global score and seven domain scores for subjective sleep quality, sleep onset latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction. 24 Of the original 21-item score, two items related to sleeping with a bed partner were not included. The algorithm for sleep duration was adjusted to reflect sleep recommendations for adolescents. In this study, the Cronbach a value was 0.74. Sleep duration and sleep-wake timing variables were collected as part of the PSQI. Usual bedtime and wake time to the nearest ten minutes, for both school week nights (Sunday to Thursday) and weekend nights (Friday and Saturday), were self-reported. Sleep duration was calculated from the time between bedtime and wake time minus sleep latency. Sleep latency was the usual time taken to get to sleep once in bed and attempting to go to sleep. Morningness-Eveningness Scale for Children

Pre-bedtime activities Seventeen questions gathered details about the frequency of screen- and nonscreen-based activities over the past week in the hour before bedtime, with responses rated on a 4-point Likert scale (never, twice a week, three or more times a week, and most nights) (Appendix 1). Questions for screen-based devices were based on Harbard’s Pre-Bedtime Behaviour Questionnaire for adolescents,26 and a question about remixing (downloading/editing music) was added. Questions for nonescreen-based activities were adopted to be suitable for NZ adolescents based on consensus of the authors and informed by the literature. These included activities adolescents were likely to engage in, some of which have been demonstrated to positively or negatively influence sleep, or findings are mixed.4 The full questionnaire was piloted on a sample of ten adolescents for face validity, language, comprehension, and clarity.

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

BC. Galland et al. / Sleep Health xxx (xxxx) 1e9

Statistical analyses Statistical analyses were completed using Stata 15, version 1 (StataCorp LLC, TX, USA). The associations between continuous sleep outcomes (sleep duration and the MESC score) and each demographic variable were examined using multiple linear regression with other demographic variables (gender, age, ethnic group, NZDep2013, and BMI z-scores) as covariates to produce adjusted means and 95% confidence intervals (CIs). The distribution for both wake time and bedtime were skewed to the right, and the variation in distribution was not similar across demographic variables; therefore, median regression (or quantile regression) was used with the same covariates as the linear regression models. The coefficients from median regression can be interpreted in the same way as linear regression, but outliers and skew have less of an influence on the coefficients and 95% CIs. The associations between poor sleep quality and demographic variables were each examined using multivariate logistic regression with gender, age, ethnic groups, NZDep2013, and BMI z-scores as covariates to produce adjusted odds ratios and 95% CIs. Reference groups for all comparisons were boys, 13 years, NZ Europeans and low neighborhood deprivation. To examine the association between each pre-bedtime behavior with sleep duration and poor sleep quality, pre-bedtime behaviors were dichotomized into i) never or once or twice a week and, ii) three or more times a week. Multiple linear regression was used to examine the association between each pre-bedtime behavior and sleep duration with adjusted coefficients, representing the difference between those reporting never or 1 to 2 times a week and those reporting each activity more than three times a week. Finally, comparisons between ethnic groups and pre-bedtime activities (three or more times a week) were explored using logistic regression and results presented as adjusted percentages (using the post hoc margins command in Stata) with comparisons made to NZ Europeans as the reference group. In all adjusted models only participants with complete data for all variables were included. For all analyses, a pvalue <0.05 was considered statistically significant. Results Participant characteristics Table 1 gives the demographic characteristics of the 4,192 particiori, and pants; 52% were male, 71% were NZ European, 13% were Ma 8% were Asian (of which almost two-thirds were of Chinese ethnicity). Height and weight were reported as ‘measured’ in 66% of participants. Sixty-nine percent of the participants were classified as having a normal BMI for their age and 19% overweight, 9% obese, and 2% underweight. The majority of participants were from areas of low or medium deprivation with approximately one-third residing in areas of high deprivation, indicative of poorer socioeconomic status. Most (85%) participants completed all questions from the PSQI. Sleep patterns For all participants, the median weekday bedtime was 10:30 pm and wake time was 7:05 am. Median sleep latency was 20 minutes, with 27% reporting a long sleep onset latency clinically defined as >30 minutes. The average sleep duration was close to 8 hours, approximately half achieving between 7 and 9 hours of sleep with the percentage distribution shown in Table 1. Table 2 shows the results of adjusted models for sleep duration, wake time, bedtime, and the MESC score. Boys and girls achieved a similar amount of sleep (8 ¼ hours) with 37-41% reporting less than 8 hours a night. Bedtimes were significantly earlier (10 min) for girls than boys with no differences in chronotype.

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Table 1 Demographic characteristics of the participants in the study

Alla Boys Girls Ethnicity NZ European ori Ma Asian Other Pacific BMI categoryb Under weight Normal weight Overweight Obese NZDep2013 Low Med High Age group (years) 13 14 15 16 17 Sleep duration (hrs) 5 >5 and  6 >6 and 7 >7 and 8 >8 and  9 >9 and 10 >10 a b

N

(%)

4192 2145 2015

(52) (48)

2991 535 336 238 92

(71) (13) (8) (6) (2)

89 2818 775 378

(2) (69) (19) (9)

1447 1186 1254

(37) (31) (32)

377 731 966 1175 943

(9) (17) (23) (28) (23)

174 182 426 895 1127 811 484

(4) (4) (10) (22) (27) (20) (11)

32 participants were not identified as a boy or girl. Missing data: height or weight (n 132); NZ Index of Deprivation (n 273).

In both unadjusted (data not shown) and full models, there was evidence of significant differences for all sleep variables apart from wake times by age and by the ethnic group (P < 0.001). The sleep duration of adolescents 13 and 14 years old were comparable (8 h 43 min and 8 h 35 min, respectively; 12 and 14% below 7 h), whereas those 15 to 17 years old slept significantly less mainly because of later bedtimes. On average, 43% of adolescents 15 to 17 years slept less than the recommended 8 hours. As expected, older children (16-17 years) tended towards a more eveningness chronotype compared with 13-year-old children (all P < 0.001). In the full model, the sleep duration of Asian teenagers was significantly shorter than that of the reference group, NZ European (-45 min, 95% CI: -58 to -32), with 29% sleeping below recommended hours compared with 17% of NZ European teenagers (P < 0.001). Similarly, the ‘other’ group (including Middle Eastern and African) slept less than NZ Europeans (-24 min, 95% CI: -38 to -9). Asian (60 min, 95% CI: 49 to 71) and ‘other’ (28 min, 95% CI: 14 to 41) groups had later bedtimes than NZ Europeans, but there was no difference in wake times. Both Maori (13 min, 95% CI: 4 to 22) and Pacific (43 min, 95% CI: 21 to 61) adolescents had significantly later bedtimes than NZ Europeans. Compared to NZ European, the MESC score of Asians was significantly lower, indicating more “eveningness” type. No differences were in observed for any sleep outcome in accordance with neighborhood deprivation. Sleep quality Poor sleep quality was more likely in girls in respect of both the total score and subscores; girls had higher odds of reporting longer sleep latency, lower sleep efficiency, more sleep disturbance, and greater daytime dysfunction (Table 3). Poor sleep quality

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

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BC. Galland et al. / Sleep Health xxx (xxxx) 1e9

Table 2 Sleep duration, timing, and morningness-eveningness preference score by demographic variables Sleep duration (hh:min) n ¼ 3836 Mean (95% CI) All Boys Girls Age group 13 14 15 16 17 Ethnic group NZ European Maori Pacific Asian Other NZ Dep 13b Low Medium High

Bedtime (hh:min) n ¼ 3836 P

8:14 (8:09, 8:19) ref 8:19 (8:14, 8:24)

Median (95% CI)

.623

ref

8:43 (8:32, 8:54) 8:35 (8:27, 8:43) 8:05 (7:58, 8:12)** 8:10 (8:03, 8:16)** 8:13 (8:06, 8:20)**

8:23 (8:19, 8:27) ref 8:15 (8:06, 8:25) 8:11 (7:48, 8:34) 7:39 (7:27, 7:51)*** 7:56 (7:42, 8:10)*** 8:15 (8:10, 8:21) 8:17 (8:11, 8:23) 8:17 (8:11, 8:23)

P

22:38 (22:34, 22:42) ref 22:28 (22:24, 22:32)**

.002

ref

<.001

22:03 (21:53, 22:13) 22:16 (22:08, 22:23)* 22:22 (22:16, 22:29)*** 22:45 (22:39, 22:51)*** 22:54 (22:48, 23:01)***

<.001

22:24 (22:21, 22:28) ref 22:37 (22:2, 22:46)** 23:07 (22:46, 23:27)*** 23:24 (23:14, 23:35)*** 22:52 (22:39, 23:05)***

.987

MESC scorea n ¼ 3712

Wake time (hh:min) n ¼ 3836

22:30 (22:25, 22:35) 22:35 (22:30, 22:41) 22:35 (22:30, 22:40)

Pb

Median (95% CI)

P

Mean (95% CI)

7:06 (7:01, 7:11) ref 7:05 (7:00, 7:11)

0.804

26.3 (26.1, 26.5) ref 26.9 (26.7, 27.1)

ref

<.001

7:12 (6:59, 7:24) 7:12 (7:03, 7:20) 7:01 (6:53, 7:08) 7:04 (6:57, 7:11) 7:06 (6:58, 7:13)

<.001

7:05 (7:01, 7:09) ref 7:07 (6:56, 7:17) 7:13 (6:48, 7:38) 7:08 (6:55, 7:20) 7:08 (6:53, 7:23)

0.211

7:05 (6:59, 7:11) 7:06 (6:59, 7:12) 7:06 (7:00, 7:12)

.863

ref

0.352

27.0 (26.6, 27.5) 27.1 (26.7, 27.4) 26.8 (26.5, 27.2) 26.3 (26.0, 26.5)** 26.2 (25.8, 26.5)**

<.001

0.950

26.7 (26.5, 26.9) ref 26.8 (26.4, 27.2) 25.9 (24.9, 26.9) 25.3 (24.8, 25.8)** 26.5 (25.9, 27.1)

<.001

0.966

26.7 (26.5, 27.0) 26.5 (26.2, 26.8) 26.5 (26.2, 26.7)

0.405

Only participants with complete data for all covariates and sleep outcomes were included in the models. *P <0.05; **P<0.01, ***P < 0.001 compared to reference group (ref). Bolded P values indicate significant values from Wald tests. a MESC score; Morningness-Eveningness Scale for Children e lower values represent more evening preference regression model including gender, ethnic groups, age, and zBMI. b NZ Index of Deprivation 2013.

(total score) was also more likely in older children, although subscale findings were mixed. Older children (15-17 years) were more likely to report better sleep efficiency and less sleep disturbance, but much higher daytime dysfunction. However, few significant ethnic differences were apparent in the quality of sleep, however Asian adolescents were significantly less likely to report long sleep latencies (OR ¼ 0.77) and having less sleep disturbance (OR ¼ 0.60) compared with NZ European adolescents. The level of neighborhood deprivation was not associated with sleep quality scores. Pre-bedtime activities and sleep Table 4 describes the proportion of teenagers engaged in specific pre-bedtime activities (nonscreen and screen) in the hour before bedtime on three or more week nights, and their relation with sleep duration and total score for sleep quality (adjusted models). Nonscreen activities Interacting with family members and engaging in sport or exercise were significantly associated with longer sleep duration and better sleep quality. In contrast, snacking and drinking caffeinated beverages were associated with significantly shorter sleep (-8, and -28 minutes, respectively) and much higher odds of poorer sleep quality (1.49 and 1.83, respectively). Completing written schoolwork in the hour before bed was associated with significantly shorter sleep duration (-7 minutes), but better sleep quality (OR ¼ 0.83). Alcohol consumption was also associated with poor sleep quality (OR ¼ 3.13). Completing household chores in the hour before bed was not related to sleep duration, but was associated with lower odds of poor sleep quality (OR ¼ 0.85). Screen activities Engaging in social media was the most common screen-based activity (88% on three or more week nights) with remixing the least common (12%). All screen-based activities except texting were significantly associated with reduced sleep duration and most were

associated with higher odds of poorer sleep quality (not significant for texting or remixing). Web browsing and remixing had the strongest impacts on sleep duration (-22 and -24 minutes, respectively), and the highest odds of poor sleep quality was associated with web browsing (OR ¼ 1.51). Pre-bedtime activities: differences by ethnicity Figures 1a and 1b illustrate pre-bedtime activities (nonscreen and screen activities reported as occurring on three or more nights) in accordance with the ethnic group. In multivariate models, significant differences were found for engaging in 8 of the 9 nonscreen activities (alcohol consumption was the only exception) and 4 (phone/texting, web browsing, listening to music and remixing) of the 8 screen activities by the ethnic group . Compared with NZ Europeans, significantly fewer Asian teenagers interacted with friends/family (53% compared with 67%), ate snacks before bed (41% vs 48%), undertook sports or exercise (23% vs 33%), or paid work (5% vs 12%) in the hour before bed (Figure 1a) but were more engaged in written schoolwork (59% vs 35%). A higher perori teenagers did schoolwork than NZ Europeans (40% centage of Ma vs 35%), participated in sports (41% vs 33%), did household chores (36% vs 31%), and drank caffeinated beverages (19% vs 13%), whereas ori teenagers read books (13% vs 20%). More Pacific teenfewer Ma agers engaged in written schoolwork (52% vs 35%), participated in sports (46% vs 33%), did household chores (43% vs 31%), snacked (72% vs 48%), and drank caffeinated drinks (23% vs 13%) before bed than NZ Europeans. More of the “other” ethnicities drank caffeinated beverages (19% vs 13%) and did schoolwork (46% vs 35%) in the hour before bed than NZ Europeans. There were fewer differences by the ethnic group for screen activities, but notably Web browsing was significantly more prevalent in Asian teenagers (89%) than in NZ Europeans (77%), as was listening to music (67% vs 14%, respectively) and remixing (14% vs 10%), whereas texting/phone use by Asian teenagers was significantly less common (69% vs 77%). Listening to music and remixing were activities that were considori (65% and 14%, respectively), erably more prevalent among Ma

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

Sleep quality (Total) n ¼ 3250

All Boys Girls Age group (yrs) 13 14 15 16 17 Ethnic group NZ European ori Ma Pacific Asian Other NZDep13 Low Medium High

Sleep latency n ¼ 3776

Sleep efficiency n ¼ 3474

%

Adjusted OR (95% CI)

P

%

Adjusted OR (95% CI)

P

%

Adjusted OR (95% CI)

50 64

ref 1.85 (1.60, 2.13)

<.001

49 61

ref 1.60 (1.47, 1.91)

<.001

13 16

ref 1.23 (1.01, 1.50)

52 48 54 60 59

ref 0.88 (0.62, 1.23) 1.25 (0.92, 1.71) 1.55 (1.14, 2.10) ** 1.45 (1.06, 1.98) *

<.001

58 52 54 57 55

ref 0.8 (0.65, 1.11) 1.0 (0.78, 1.30) 1.11 (0.86, 1.43) 1.05 (0.81, 1.36)

.126

20 20 11 13 14

ref 1.11 (0.75, 1.63) 0.53 (0.36, 0.78) *** 0.68 (0.47, 0.99) * 0.74 (0.51, 1.08)

56 57 66 58 55

ref 0.98 (0.79, 1.23) 1.25 (0.73, 2.12) 1.04 (0.80, 1.35) 1.05 (0.77, 1.42)

.930

56 55 51 50 47

ref 0.85 (0.70, 1.04) 0.68 (0.43, 1.09) 0.77 (0.60, 0.98) ** 0.68 (0.51, 0.90) **

.007

14 17 17 13 14

ref 1.14 (0.85, 1.52) 1.28 (0.67, 2.45) 0.99 (0.68, 1.44) 1.04 (0.69, 1.57)

56 56 58

ref 0.98 (0.83, 1.17) 1.08 (0.91, 1.28)

.570

57 53 56

ref 0.88 (0.75, 1.03) 1.00 (0.86, 1.17)

.173

14 13 16

ref 0.88 (0.69, 1.13) 1.06 (0.84, 1.33)

Sleep disturbance n ¼ 3524 P

Daytime dysfunction n ¼ 3746

%

Adjusted OR (95% CI)

P

%

Adjusted OR (95% CI)

P

.036

13 22

ref 1.84 (1.53, 2.22)

<.001

28 42

ref 2.13 (1.85, 2.45)

<.001

<.001

24 20 16 16 15

ref 0.80 (0.56, 1.15) 0.62 (0.44, 0.87)** 0.65 (0.47, 0.91)* 0.61 (0.43, 0.87)**

.024

24 26 34 39 43

ref 1.13 (0.83, 1.54) 2.02 (1.50, 2.72)*** 2.33 (1.75, 3.11)*** 2.79 (2.08, 3.75)***

<.001

.866

17 22 22 11 14

ref 1.14 (0.88, 1.49) 1.29 (0.72, 2.29) 0.60 (0.41, 0.89) * 0.72 (0.47, 1.11)

.024

34 37 47 39 33

ref 1.13 (0.91, 1.40) 1.82 (1.13, 2.91) 1.11 (0.86, 1.43) 1.01 (0.75, 1.36)

.117

.358

16 17 18

ref 1.00 (0.80, 1.25) 1.09 (0.88, 1.36)

.655

34 35 36

ref 1.02 (0.87, 1.21) 1.07 (0.90, 1.26)

.774

BC. Galland et al. / Sleep Health xxx (xxxx) 1e9

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

Table 3 Sleep quality: demographic logistic regression models for the possibility of reporting a “poor” score on the PSQI (scores >5) and 4 of the sleep factor components (scores >1)

% - Unadjusted. OR models adjusted for other covariates on table and BMI z-scores for age. Only participants with complete data for all covariates and sleep outcome were included in the models. *P < 0.05; **P < 0.01; ***P < 0.001 compared to reference group (ref).

5

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BC. Galland et al. / Sleep Health xxx (xxxx) 1e9

Table 4 Pre-bedtime activities in the hour before bedtime (and on >3 nights/week): associations with sleep duration and poor sleep quality > 3 nights/week (%) Nonscreen activities Family Snacking School work Sports and exercise Household chores Reading book Caffeinated beverages Employed work Alcohol Screen/technology based Social media Web browsing Texting Email/chat Watching TV/videos Listening to music Gaming Remixing

Poor sleep quality (Total)*

Sleep duration (mins) Adjusted coef (95% CI)

P

Adjusted OR (95% CI)

P

65 49 38 33 32 19 15 11 1

13 (6, 21) -8 (-15, -1) -7 (-14, -.04) 9 (2, 16) 7 (-1, 14) 4 (-5, 13) -28 (-37, -18) -2 (-12, 9) -20 (-53, -13)

<.001 .019 .049 .015 .053 .336 <.001 .759 .235

.68 (.58, .79) 1.49 (1.29, 1.73) .83 (.71, .96) .66 (0.57, .77) .85 (0.73, .99) .93 (.77, 1.12) 1.83 (1.48, 2.26) 1.18 (0.94, 1.47) 3.13 (1.41, 6.99)

<.001 <.001 .012 <.001 .038 .473 <.001 .151 .005

88 78 77 67 64 60 34 12

-12 (-22, -2) -22 (-31, -14) -6 (-13, 3) -11 (-18, -4) -11 (-18, -4) -19 (-26, -12) -19 (-26, -11) -24 (-34, -13)

.020 <.001 .178 .002 .006 <.001 .001 <.001

1.26 (1.02, 1.56) 1.51 (1.27, 1.80) 1.15 (0.97, 1.36) 1.22 (1.05, 1.42) 1.20 (1.16, 1.60) 1.44 (1.25, 1.66) 1.42 (1.21, 1.67) 1.22 (0.98, 1.54)

.030 <.001 .097 .009 <.001 <.001 <.001 .081

Adjusted for gender, age, ethnic group, NZDep13, and BMI z-scores. * From the Pittsburgh Sleep Quality Index (PSQI) with possibility of reporting a “poor” sleep quality as score >5.

Pacific (73% and 20%, respectively), and Asian (67% and 14%, respectively) respondents than among NZ European participants (58% and 10%, respectively). Gaming was an activity that was used significantly less by “other” ethnicities compared with Europeans (26% vs 33%). No other ethnic differences were found for screen/technology activities. Discussion The research provides important information regarding the sleep habits and quality of NZ adolescents in addition to describing prebedtime behaviors. This study shows that screen/technology use by adolescents in the pre-bedtime period was ubiquitous and negatively associated with sleep quantity and quality. NZ has large health inequities by ethnicity and socioeconomic variables across many health outcomes that are related to sleep. For example, the prevaori and Pacific lence of overweight and obesity is higher among Ma children and is higher in areas of low socioeconomic status.27 In adoori report lower levels of general and emotional health lescents, Ma and higher rates of attempted suicide and motor vehicle risk-taking than NZ European adolescents,28 highlighting the importance of NZ specific research on sleep among adolescents. Some nonscreen activities, such as snacking in the hour before bed and drinking caffeinated beverages were associated with shorter sleep and sleep of lower quality. Alcohol consumption was also associated with lower sleep quality. On the positive side, interacting with family and friends and doing exercise/sports were associated with longer sleep and better sleep quality, and doing written schoolwork and household chores were associated with better sleep quality. Several ethnic differences existed in the activities adolescents engaged in before bed. Asian teenagers had the greatest difference in sleep compared with NZ Europeans (later bedtimes, shorter sleep duration, more eveningness chronotype, and less sleep disturbance). Many adolescents slept less than 8 hours (39%), and typical developmental trends were observed between sleep duration and age. Girls went to bed earlier than boys, and sleep quality was rated poorer by girls than by boys, as has been reported previously.29,30 The shorter sleep duration of Asian teenagers compared with NZ Europeans was a consequence of later bedtimes (1 hour later). More Asian teenagers also reported ‘long sleep latencies’, but rated their sleep as less disturbed. Shorter sleep duration in Asian participants

is well documented across all age groups 31 and in children and adolescents, apparently as a function of later bedtimes with similar wake times.25,32 The results of our study support this. However, fewer studies have reported differences in sleep quality. Our findings of shorter sleep with a perception of less sleep disturbance among Asian adolescents suggest they may have better sleep continuity than NZ Europeans, although this would need to be confirmed through objective measures of sleep. The pre-bedtime activities Asian teenagers engaged in were associated with both better and worse sleep. Compared with NZ Europeans, a higher percentage engaged in written schoolwork in the hour before bed, browsed Web, remixed, and listened to music, whereas interacting with family and friends was less common, as was snacking, sports/exercise, employment, and email/texting. However, the lack of time-use data surrounding these activities prevents us from suggesting which of these, if any, drive later bedtimes. Importantly, Asian teenagers reported themselves to be more evening types, signaling a preference for later sleep. The possibility that Asians have less ‘sleep need’ compared with NZ Europeans, cannot be ruled out, given that a greater proportion reported longer sleep latencies, and the fact that shorter sleep in Asians is apparent across all age groups.31e34 ori and Pacific teenagers, compared with NZ Europeans, Ma reported later bedtimes, indicative of a slightly advanced sleep phase, most pronounced in Pacific teenagers. These findings support ori and Pacific our previous objective data (actigraphy) comparing Ma children's sleep onset and offset with NZ Europeans (aged 4-12 years), although in that study, the sleep duration of Pacific children was also shorter.18 These ethnic differences in sleep mirror many other studies comparing ethnic minority groups with Europeans and may reflect differences in environment, culture, and greater challenges to obtain healthy sleep and knowledge and value placed on the importance of sleep for health. For Pacific youths, their level of acculturation within NZ may also influence this.34 More than 84% of teenagers used at least one form of screen/technology three or more nights, supporting the notion that, in current times, electronic media pervades teenagers' lives.35 More specifically, our findings confirm the widespread use of electronic media use in the hour before bedtime. Most screen/technology activities asked about here have been well probed in other population studies 35,36;

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

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Fig. 1. (a) Proportion of adolescents engaged in each nonscreen activity (on three or more nights/week) in the hour before bed and by ethnicity. *p < 0.05, **p < 0.01, ***p < 0.001 relative to NZ European. (b) Proportion of adolescents engaged in each screen activity (on three or more nights/week) in the hour before bed and by ethnicity. *p < 0.05, **p < 0.01, ***p < 0.001 relative to NZ European.

however, the inclusion of “remixing”, i.e. downloading/editing music, was novel. Twelve percent engaged in remixing, and this was associated with 24 minutes less sleep a night. Remixing requires a certain amount of attentiveness, and therefore may be unfavorable for sleep through physiological arousal and/or the influence of light on the circadian system, or simply by displacing potential sleep time. Gaming was one of the least common activities (34%), but was associated with both sleep quantity and quality and past studies have linked gaming with several health issues, including sleep deprivation (later bedtimes and longer sleep latencies) and poorer daytime functioning.35,37 Nonscreen activities associated with poor sleep quality and/or shorter sleep duration included snacking, drinking caffeinated beverages, and alcohol. Although there have been few studies examining the influence of evening snacking alone on sleep, time spent on screens may contribute, given these behaviors tend to cluster in this age group. 38 Longer screen time has also been linked to greater

portion sizes of evening snacks and poorer diet quality of adolescents.39 In meta-analyses, snack consumption as a regular dietary pattern in adolescents has also been linked to shorter sleep duration. 40 Few studies have related snacking to sleep quality, although habitual sleep variability measured by actigraphy has been linked to increased snack consumption in adolescents, especially snacks consumed after the evening meal. 41 Of note, pre-bedtime snacking was most prevalent among Pacific youth (71%), a finding with important implications for obesity rates; current statistics place Pacific adolescents and adults (aged 15þ) in NZ at 2.3-times higher odds of being obese than their non-Pacific counterparts, and for Pacific children (2-14 years), 3 times higher odds. 42 Caffeine, a central nervous system stimulant, not surprisingly, was associated with poorer sleep quantity and sleep quality, supporting other study findings. 4,29 Only 1% reported drinking alcohol in the hour before bed three or more times per week, but this was associated with higher odds of poor sleep quality. Experimental studies suggest

Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002

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that alcohol aids a person to get to sleep and consolidates sleep in the first half of the night, but at the expense of sleep quality with more disruption in the second half. 43 Adolescents interacting with family members and engaging in sports or exercise exhibited longer sleep duration and better sleep quality. Although these positive effects for sleep duration were small (9 and 13 minutes longer, respectively), these activities had the highest odds of better sleep quality of all nonscreen activities examined. Although not exercising in the few hours before bedtime was a past recommendation for good sleep hygiene, the scientific evidence is mixed,44,45 leading the National Sleep Foundation to amend their sleep hygiene recommendations to encourage exercise at any time of day, but not at the expense of displacing sleep.46 Our survey findings suggest exercising in the hour before bed may benefit adolescent sleep, but we cannot say what type or intensity of sports/exercise adolescents were engaging in. Completing written schoolwork in the hour before bed was associated with shorter sleep, but betterperceived sleep quality as reported within the PSQI. The significance of this is undetermined, but shorter sleep could suggest schoolwork displacing sleep time, although the difference was small (7 minutes less). Although a previous study in younger children found that a more intense homework schedule promoted later bedtimes and wake times and shorter sleep duration, the researchers did not report on sleep quality.47 It is possible that doing written schoolwork in the hour before bed in the absence of the stimulating effects of bright screen lights, together with increased sleep pressure (due to later bedtime), may facilitate a more consolidated sleep and perceived as better quality by our participants. However, this suggestion would need to be confirmed or refuted by objective measures. Few studies have looked at interactions with family and friends in connection with sleep, but a positive family environment with low conflict potentially benefits sleep patterns. 48 In the present study, doing household chores was associated with better sleep quality, although how much of this overlapped with family interactions remains unknown. We did not investigate the strength of relationships with friends or family, but family interactions themselves could signal a positive environment mediating the good association with sleep, for example, by reinforcing positive family factors like daily routines and good sleep hygiene. As this was a cross-sectional survey, we cannot establish causality for any of the associations discussed. There may be bidirectional relations, for example, poorer sleep having a negative impact on social or family interactions. Future research using longitudinal designs or intervention studies will enable a better understanding of the directionality of each relationship. The strengths of this study are the large sample size and nationwide sampling across the full adolescent age range. Limitations include the small number of Pacific adolescents in our sample, inherent biases within any cross-sectional study, self-report of measures, together with the lack of any corroboratory (parental) or objective measures of sleep or pre-bedtime activities. Although the pre-bedtime questionnaire has not been validated for NZ teenagers, we are currently undertaking validation of the questionnaire against objective observation of pre-bedtime behaviors using automated wearable cameras.49 Validated questionnaires were used to measure sleep time including bedtimes and wake times. However, there are some limitations to using questionnaires including a reduced accuracy because the recall time increases. 50 The questionnaire did not measure night-to-night variation in sleep duration known to be common in adolescents. 51 Research in adults has shown greater sleep variability for ethnic minorities, 52 and emerging research suggests that stability in sleep duration and timing may be just as important as duration itself for health outcomes.53 In 2013, the NZ population selected for 15 to 19 years of age was ori, 10% Pacific, 12.5% Asian, and two 65% NZ European, 20% Ma

percent other ethnic groups. Our sample is therefore not a representative of the NZ population with an overrepresentation of NZ European (71%) and other ethnic groups (6%) and underrepresentation ori (13%), Asian (8%) and Pacific (2%) ethnic groups. However, of Ma there were sufficient numbers to allow comparisons between ethnic groups. We also acknowledge that reporting results for Asians as one ethnic category may have masked distinct cultural differences because they represent a heterogeneous group comprising both South Asian and Southeast Asian origins. 54 Furthermore, because recruitment was via the Internet, the sample may be higher users of social media than the general population. Finally, although we covered activities in the hour before bed, there are many other factors that could alter the strength of the associations found with sleep, such as time on each task, multitasking, and in-bed screen use. Conclusions In conclusion, the cross-ethnic variations in sleep patterns and presleep activities observed in this study suggest sleep health messages could be tailored to reach different ethnic groups. Future research in NZ needs to explore, objectively, why the sleep of Asian teenagers is so much later and shorter than that of NZ Europeans, and if there are daytime consequences for this population. Almost all screen activities were adversely associated with sleep-suggesting managing screen time and even “digital detoxification” as the most obvious solution to adolescents' poor sleep, yet extremely hard to implement.14 Finally, our study highlights factors in the pre-bedtime space that may promote better sleep, such as spending time interacting with family and friends. We acknowledge that changing the landscape of adolescents' evening time use requires an enormous cultural change, but one that may eventually happen with continued research efforts and education. Acknowledgments The authors thank the adolescents who participated in this study. Tanja de Wilde was supported by the Department of Women's and Children's Health, University of Otago Summer Scholarship. Rachael Taylor is in receipt of the KPS Fellowship in Early Childhood Obesity. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.sleh.2019.09.002. References 1. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37(1):9e17. https://doi.org/10.5665/sleep.3298. 2. McGlinchey EL. Sleep and Adolescents. In: Babson KA, ed. Sleep and Affect. Feldner MT: Elsevier Inc; 2015:421e439. 3. Shochat T, Cohen-Zion M, Tzischinsky O. Functional consequences of inadequate sleep in adolescents: a systematic review. Sleep Med Rev. 2014;18(1):75e87. https://doi.org/10.1016/j.smrv.2013.03.005. 4. Bartel KA, Gradisar M, Williamson P. Protective and risk factors for adolescent sleep: a meta-analytic review. Sleep Med Rev. 2015;21:72e85. https://doi.org/ 10.1016/j.smrv.2014.08.002. 5. Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation's sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40e43. https://doi.org/10.1016/j.sleh.2014.12.010. 6. Twenge JM, Krizan Z, Hisler G. Decreases in self-reported sleep duration among U.S. adolescents 2009-2015 and association with new media screen time. Sleep Med. 2017;39:47e53. https://doi.org/10.1016/j.sleep.2017.08.013. 7. Galland BC, Short MA, Terrill P, et al. Establishing normal values for pediatric nighttime sleep measured by actigraphy: a systematic review and meta-analysis. Sleep. 2018;41(4):zsy017. https://doi.org/10.1093/sleep/zsy017. 8. Crowley SJ, Wolfson AR, Tarokh L, Carskadon MA. An update on adolescent sleep: New evidence informing the perfect storm model. J Adolesc. 2018;67:55e65. https://doi.org/10.1016/j.adolescence.2018.06.001. 9. Gamble AL, D'Rozario AL, Bartlett DJ, et al. Adolescent sleep patterns and night-time technology use: results of the Australian Broadcasting Corporation's

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Please cite this article as: B.C. Galland, T. de Wilde, R.W. Taylor, et al., Sleep and pre-bedtime activities in New Zealand adolescents: differences by ethnicity, Sleep Health: Journal of the National Sleep Foundation, https://doi.org/10.1016/j.sleh.2019.09.002