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. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: Behav Sleep Med. 2021 May 13;20(2):269–289. doi: 10.1080/15402002.2021.1916497

Sleep difficulties among Mexican adolescents: Subjective and objective assessments of sleep

Astrid N Zamora a, Laura Arboleda-Merino a, Martha Maria Téllez-Rojo b, Louise M O’Brien c,d, Libni A Torres-Olascoaga b, Karen E Peterson a,e, Margaret Banker f, Erica Fossee a, Peter X Song f, Kirstyn Taylor a, Alejandra Cantoral g, Elizabeth FS Roberts h, Erica C Jansen a,c
PMCID: PMC8589870  NIHMSID: NIHMS1701277  PMID: 33983860

Abstract

Objective/Background:

Self-reported sleep difficulties, such as insomnia symptoms, have been reported among adolescents; yet, studies of their prevalence and correlates are scarce among Latin Americans. This study sought (1) to describe associations between sociodemographic and lifestyle factors with self-reported sleep and (2) to examine associations between self-reported sleep and actigraphy-based sleep.

Participants:

Participants included 477 Mexican adolescents from the ELEMENT cohort.

Methods:

Over 7 days, self-reported sleep measures (hard time falling asleep, overall sleep difficulties, and specific types of sleep difficulties) were obtained from daily sleep diaries. Actigraphy-based sleep measures (duration i.e. sleep onset to morning wake, midpoint, and fragmentation) were concurrently assessed using a wrist actigraph.

Results:

Mean (SD) age was 15.9 (2.2) years, and 53.5% were females. Mean (SD) sleep duration was 8.5 (1.2) h/night. Half reported a hard time falling asleep (at least 3 days), and 25% had sleep difficulties at least 3 days over 7 days. The 3 types of sleep difficulties commonly reported among the entire cohort were insomnia/restlessness (29%), environmental (27%), and mental/emotional (19%). Female sex, smoking behavior, and socioeconomic indicators were among the most consistent factors associated with sleep difficulties. Subjective sleep difficulties were associated with shorter sleep duration (β= −20.8 [−35.3, −6.2] min), while subjective hard time falling asleep was associated with longer sleep duration (β= 11.3 [4.6, 27.2] min).

Conclusion:

A high proportion of Mexican adolescents in the sample reported sleep difficulties. Findings demonstrate the importance of obtaining subjective and objective sleep measures for a more comprehensive assessment of adolescent sleep.

Keywords: sleep difficulties, subjective sleep, objective sleep, sleep diaries, adolescents, insomnia

Introduction

Sleep deprivation among adolescents is a global public health issue (Do et al., 2013; Garaulet et al., 2011; Hysing et al., 2013; Meldrum & Restivo, 2014), although studies in Latin American populations are lacking. Insufficient sleep during adolescence has been associated with adverse effects on daytime cognitive and behavioral functioning (Sadeh et al., 2003), depression, anxiety, immune deficiency, and diabetes (Knutson & Lauderdale, 2007; Mueller et al., 2011) and lower academic performance (Taras & Potts-Datema, 2005).

Technology use, early school start times, academic-related time pressures, and lack of regulated bedtimes can cause insufficient sleep in adolescents (Calamaro et al., 2009; J. Owens et al., 2014). Although most of these factors are related to restricting sleep by choice or time constraints, adolescents may also suffer from insufficient sleep due to difficulties with falling or staying asleep. Research suggests that somewhere between 7% and 40% of adolescents struggle with insomnia (Dohnt, 2012; Hysing et al., 2013; Johnson, 2006). While the causes of insomnia have been documented among adults, less is known about factors contributing to sleep difficulties among adolescents. Further, the experience of sleep difficulties among Latin American adolescents could vary from previously studied populations due to differences in norms and expectations surrounding school, socioeconomic stability, neighborhood environments, and types of sleeping arrangements (Halperin, 2014; Jansen et al., 2020; Roberts, 2021; Simonelli et al., 2017). In particular, room and bed-sharing are more common in Mexico City and other Latin American populations (J. A. Owens, 2004). Another factor specific to Mexico City and other urban Latin American populations is proximity to noisy areas, such as industrial sites and metro stations. Previous research has shown that transportation noise may be a critical anthropogenic (human-generated) environmental factor that influences sleep health (Nassur et al., 2019).

How adolescents’ self-reported sleep difficulties correlate with objectively-measured sleep duration, timing, and efficiency is essential from a clinical and public health perspective. Studies among European and US populations have documented inconsistent findings concerning congruency between subjective and objective assessments of sleep duration among adolescents (Currie et al., 2003; Matthews et al., 2018; O’Donnell et al., 2009; Palermo et al., 2007). However, mixed-methods research has demonstrated that though measures may not be highly correlated, obtaining both subjective and objective sleep measures provides a more comprehensive assessment of sleep health, which relates to other health outcomes (Zavecz et al., 2020).

The purpose of this study was (1) to describe associations between sociodemographic and lifestyle factors with subjective self-reported sleep measures (i.e., hard time falling asleep, sleep difficulties, and specific types of sleep difficulties) and (2) to examine associations between subjective self-reported sleep measures (i.e., hard time falling asleep, sleep difficulties, and types of sleep difficulties) with objective actigraphy-based sleep measures (i.e., sleep duration, timing, and fragmentation) within a cohort of Mexican adolescents. We hypothesized that (1) female sex and older age would be significant factors associated with worse subjective self-reported sleep measures and that (2) subjective self-reported hard time falling asleep and sleep difficulties would be associated with shorter sleep duration and later sleep timing.

Methods

Participants and Design

The study population included adolescents from two of three sequentially-enrolled cohorts of the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study (Perng et al., 2019). From 1997 to 2004, 1012 mother-offspring dyads were recruited from the Mexican Social Security Institute’s public maternity clinics, which served a low-to-moderate income population in Mexico City. In 2017, a follow-up study was conducted among 519 adolescent offspring from the original birth cohorts 2 and 3, who were undergoing the pubertal transition (ages 11 to 20 years). During the follow-up study, information was gathered on sociodemographic and lifestyle factors, pubertal measurements, and subjective and objective sleep data. Objective sleep information was obtained using a wrist actigraphy device and subjective sleep via a self-administered sleep diary over 7 consecutive days. Of the 519 participants who completed the study visit, we obtained objective (wrist-actigraphy) and subjective (sleep diary) data for 477 participants. The Mexico National Institute of Public Health (INSP) and the University of Michigan Human Subjects Committee approved all research protocols and procedures, and all participants provided informed consent.

Protocol and Data Collection

Sleep Diaries

Within the 7-day sleep diaries, participants responded to two questions each night. The first was a close-ended (Likert type) question that asked: “Did you have a hard time falling or going to sleep last night?” Response options included, 1 = No, or very little; 2 = A little; 3 = A lot; 4 = More than a lot. Participant responses were dichotomized such that a score of ≥ 2 represented a hard time falling asleep. We then created a binary variable, representing ≥ 3 nights of a hard time falling asleep over the 7 days. The threshold of ≥ 3 nights was chosen because the accepted criteria for chronic insomnia (chronic difficulty falling asleep or staying asleep) is defined as occurring ≥ 3 nights a week for ≥ 3 months) (Hill & Everitt, 2018).

The second question regarding sleep difficulties was open-ended and asked: “If you slept poorly, what was the problem (e.g., were you restless, was there noise, stress)?” Example responses included “Insomnia,” “Temperature,” and “Noise.” Adolescents could provide multiple responses for experiencing sleep difficulties. Thus, responses were not mutually exclusive, and all sleep difficulties were counted separately for each adolescent per night.

Actigraphy

While completing the 7-day sleep diaries, actigraphy data were obtained using an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL) worn on the non-dominant wrist. Nightly sleep measures were estimated from the actigraphy data using a fused LASSO (least absolute shrinkage and selection operator)-based calculator package (package not currently accessible within the public domain, although available upon request) developed in R (R Foundation for Statistical Computing, Vienna, Austria). We found that the package had an excellent agreement with manual detection of sleep period (r>0.90 for a subsample of 50 participants), as well as with the estimates derived by GGIR (Hees et al., 2015; Migueles et al., 2019) and Tudor-Locke (Tudor-Locke et al., 2014) algorithms (r=0.76 and r=0.69, respectively). This fused Lasso approach also incorporated the self-reported bedtime and wake time as part of its algorithm. For analysis, we obtained weekday and weekend sleep duration (from sleep onset to morning wake and excluding wake after sleep onset, in minutes), weekday and weekend sleep midpoint (the median of sleep onset and wake time; reported in decimal hours), and average sleep fragmentation index. The sleep fragmentation index was calculated as the percentage of one-minute periods of sleep out of all periods of sleep during the sleep period (the algorithm used by the ActiLife® software (What Is Sleep Fragmentation and How Is It Calculated?, 2020), with higher values representing more fragmented sleep (Chung et al., 2016). Wake versus sleep time was determined using the Sadeh algorithm (Sadeh et al., 1994).

Covariates

Covariates included sex, age, familial socioeconomic status (SES), pubertal status, smoking behavior, alcohol behavior, physical activity, maternal education, presence of household food insecurity, shared bedroom status, caffeine intake, and technology use < 1 hour before sleep, and screen time. Age was operationalized according to the American Academy of Pediatrics stages of adolescent development: early (ages 11–14), middle (ages 15–17) and late (ages 18–21) (Hardin et al., 2017). However, to describe the percentage of the population that met age-specific sleep duration recommendations, we operationalized age according to the American Academy of Sleep Medicine (Paruthi et al., 2016).

Familial socioeconomic status (SES) was self-reported and assessed using a 10-item region-specific household-based survey that was developed and index standardized (i.e., AMAI 8 × 7) by the Mexican Association of Marketing Research and Public Opinion Agencies (AMAI) to classify the SES of the Mexican population (Asociación Mexicana de Agencias de Inteligencia de Mercado y Opinión, 2018; López Romo, 2009) (Mexican Association of Marketing Research and Public Opinion Agencies, 2018). The survey included household-based measures for the following items: computer and colored television ownership, type of floor, number of rooms, functioning shower, exclusive bathroom, number of lights, type of stove, number of automobiles, and the education level of the highest income earner in the household. Points were assigned to each item that was found in the home and summed to create an overall measure of SES. The overall measure was then categorized into seven categories ranging from A to E based on the sum of the total points. We further categorized the variable into two groups: higher or middle SES (A/B, C+, C and C−) and lower SES (D+ and D), no participants in the cohort were in the E category.

Trained physicians implemented standard methods for Tanner staging during the in-person visit to assess sexual maturation status (Chavarro et al., 2018). The questionnaire encompassed Tanner stages for pubic hair and breast or genital development. Participants were divided into two pubertal groups: transitioning (Tanner Stage 1 through Tanner Stage 4 in both pubic hair and gonadal development) and mature (Tanner Stage 5 in both pubic hair and gonadal development). We also obtained information on smoking and alcohol behavior. We classified smoking and alcohol behavior into dichotomous variables; for smoking, whether they self-reported having ever tried smoking (yes or no) and for alcohol, whether they had ever consumed an alcoholic drink (yes or no). Physical activity status was assessed using a self-reported questionnaire adapted for and validated among Mexican adolescents (Hernandez et al., 2000). We estimated average hours of physical activity per week by summing the self-reported hours spent participating in all potential physical activities, such as soccer, volleyball, running, etc. We then categorized the average number of weekly hours spent doing physical activity into tertiles.

Maternal education level was self-reported by the mother and was categorized as follows: did not complete secondary (< 9 years), completed some high school (9 – < 12 years), completed high school (12 years), higher education (≥ 12 years). Household food insecurity (HFI) was self-reported and assessed via a reduced version of the Latin American and Caribbean Food Security Scale (ELCSA), which consisted of a 15-question food insecurity questionnaire validated for Spanish speakers. The questionnaire defines food security levels as high food security, marginal food security, low food security, and very low food security (Jones et al., 2017). We further dichotomized HFI into whether there was the presence of any HFI (i.e., marginal, low and very low food security) or no HFI (i.e., having high food security in the household).

Shared bedroom status was assessed via a general questionnaire completed by the adolescent participant. We classified shared bedroom status as either the presence of someone else (i.e., sibling, mother, father, mother’s partner, father’s partner or other) sleeping in the same room as the participant or the participant sleeping alone. We obtained caffeine intake from a semi-quantitative food frequency questionnaire (FFQ) validated in a Mexican population (Denova-Gutiérrez et al., 2016), which asked adolescents to recall how often they had consumed 11 types of beverages in the previous 7 days, including caffeinated beverages. Caffeine intake was estimated using a food composition database and categorized into tertiles. Technology use <1 hour before bed was assessed nightly via a multiple-choice question posed in the sleep diary. The question asked participants to answer the following: “In the hour before you went to sleep last night, which of the listed electronic device(s) were used by you or by someone else around you: 1) Computer, 2) Cell phone, 3) Music player, 4) Tablet, 5) Videogame console, 6) Television, 7) Other or 8) None of the above. Based on the selected response, we created a binary variable that represented technology use in the hour before bed; these include options 1–7 (yes) and option 8 (no). We created a final binary variable that represented whether they self-reported having used technology in the hour before bed for ≥ 4 days (high use) or < 4 days (low use) per week. Screen time was assessed using self-reported information on the total number of hours per day that the participant reported watching TV (not counting time playing video games or watching movies on the VCR), hours per day watching movies or videos on a VCR or DVD player, and the hours per day playing video games and using the internet for entertainment purposes only. We summed the total number of screen time hours reported and categorized screen time into tertiles of the average screen time per week.

Statistical Analysis

We first ran descriptive statistics to report the proportion of adolescents meeting age-specific recommendations (according to the American Academy of Sleep Medicine) across 7 days, on weekdays and weekends. To describe the study population and assess potential confounders, we reported the proportion of adolescents experiencing ≥ 3 days (out of the 7 days) for each subjective sleep measure (hard time falling asleep and sleep difficulties) stratified by sociodemographic and lifestyle factors.

Qualitative data obtained from sleep diaries regarding self-reported sleep difficulties were analyzed based on an inductive thematic analysis approach (Green & Thorogood, 2004). We implemented a data-led approach whereby emerging themes were determined a posteriori rather than preconceived themes (Boeije, 2002). Briefly, two independent research assistants (RAs) translated responses from Spanish to English. RAs regularly consulted with a native Spanish speaker when questions arose regarding the translation of responses. RAs independently segmented data into codes (i.e., a single word or phrase that reflected an opinion, thought, or feeling in response to the reflective questions posed). Pattern coding was then used to collapse open codes into similar themes (Green & Thorogood, 2004). After independent analysis by each researcher, discrepancies were identified and discussed amongst the coding research team (AZ, EJ, EF, and KT) to reach a consensus. One set of unified themes (i.e., category types) were agreed upon, resulting in identifying 5 types of sleep difficulties. Adolescents could report multiple types per night. For each type, sleep difficulties were counted if adolescents self-reported at least one difficulty over 7 days.

To evaluate aim 1, we reported the proportion of all 5 types of self-perceived sleep difficulties according to sociodemographic and lifestyle factors. We further conducted sensitivity analyses using multivariable logistic regression analysis. Each type of sleep difficulty was modeled as a binary dependent variable, and sociodemographic and lifestyle were modeled as independent variables that were mutually adjusted for one another to determine if associations persisted after adjustment. In a post-hoc analysis, we also examined mutually-adjusted associations between sociodemographic and lifestyle factors with objective sleep measures. To evaluate aim 2, multivariable-adjusted linear regression models were used to compare objective sleep measures according to each subjective sleep measure and according to the 5 types of sleep difficulties. We included sex, age, SES, bedroom sharing, physical activity, household food insecurity, caffeine intake, technology use < 1 hour before sleep, and screen time in the models to control for potential confounders. Statistical analyses were conducted using SAS 9.4 software (Cary, NC, USA).

Results

Among the sample of 477 adolescents, the mean (SD) age was 15.9 (2.2) years. Mean (SD) actigraphy-based sleep duration was 8.5 (1.2) h/night and significantly differed from weekdays to weekends, with 8.4 (1.5) h/night versus 8.8 (1.5) h/night, respectively (p < 0.01). Over the 7 days, young adults (18–20 years) had a slightly shorter sleep duration than younger age groups (Table 1). Overall, 68% of the sample met the American Academy of Sleep Medicine (AASM) age-specific sleep recommendations; 20% of those ≤ 12 years of age slept 9–12 h/night, 59.0% of 13 to 17.9-year-olds slept 8–10 h/night, and 89.8% of 18–20 years slept ≥ 7 h/night (Table 1) (Paruthi et al., 2016).

Table 1.

Average sleep duration (h/night) by age group and percentage of study population meeting AASM sleep duration recommendations

Age group N AASM Recommendation Average Week (Mean ± SD, h/night) Met recommendations over weeka Weekday (Mean ± SD, h/night) Met recommendations, Weekdaya Weekend (Mean ± SD, h/night) Met recommendations, Weekenda
≤ 12 years 15 9–12 hours per 24 hours 8.6 ± 0.60 20% 8.1 ± 1.1 20% 9.5 ± 0.91 66.7%
13–17.9 years 305 8–10 hours per 24 hours 8.6 ± 1.2 59.0% 8.4 ± 1.4 52.8% 8.9 ± 1.5 52.1%
18–20 years 157 7–9 hours per night 8.4 ± 1.1 89.8% 8.4 ± 1.5 82.8% 8.5 ± 1.5 80.9%
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ABR: SD: Standard Deviation; AASM: American Academy of Sleep Medicine

a

Percentage of sample that met recommendations for sleep duration based on AASM

Aim 1: Associations between sociodemographic and lifestyle factors with subjective sleep measures

Self-reported hard time falling asleep

An estimated 49% of the sample had a hard time falling asleep ≥ 3 nights during the 7 days. Sex was the only sociodemographic correlate of a hard time falling asleep, with 53% of females compared to about 45% of males reporting a hard time falling asleep (Table 2).

Table 2.

Adolescent sociodemographic and lifestyle factors according to subjective sleep measures (n = 477)

N Self-reported hard time falling asleep ≥ 3 nights, Percentage1 Self-reported sleep difficulties ≥ 3 nights, Percentage2
Sex
 Male 222 44.6* 19.4*
 Female 255 52.6* 29.0*
Age groups, years (y)
 11–14 y 159 50.9 23.9
 15–17 y 161 44.1 22.4
 ≥ 18 y 157 51.6 27.4
Familial SES
 Lower 244 46.7 25.4
 Middle/Higher 233 51.1 23.3
Pubertal Status a
 Transitioning 200 47.9 23.4
 Mature 277 52.3 28.5
Smoking Behavior
 Ever tried smoking 249 51.0 27.7*
 Never tried smoking 228 46.5 21.1*
Alcohol Behavior
 Ever Drinker 438 49.3 24.2
 Never Drinker 39 43.6 28.2
Physical activity, tertiles (h/wk)
 0.5 – 5.0 153 46.1 23.5
 6.0 – 10.5 166 52.7 25.6
 11.0 – 38.5 158 47.9 24.6
Maternal Education
 Did not complete secondary (<9) 54 50.0 22.2*
 Completed some high school (9 to <12) 68 50.0 16.2*
 Completed high school (12) 159 50.3 28.3*
 Higher Education (>12) 196 46.9 25.0*
Household Food Insecurity
 None 276 47.8 21.4*
 Any 201 50.3 28.9*
Bedroom Sharing
 No 205 51.2 24.9
 Yes 272 47.1 24.3
Caffeinated beverage consumption, tertiles (mg/day)
 0 – 0.22 162 50.0 24.1
 0.24 – 1.96 154 50.0 23.4
 2.13 – 568.53 161 46.6 26.1
Technology use <1 hour before bed
 < 4 days 85 48.2 21.2
 ≥ 4 days 392 49.0 25.3
Screen time, tertiles (h/wk)
 0.0 – 3.0 166 50.6 21.8
 3.5 – 10.0 152 47.5 25.6
 10.5 – 49.0 159 48.6 26.2
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1

Indicates the percentage of adolescents with ≥ 3 nights of self-reported hard time falling asleep during the 7-consecutive days of assessment

2

Indicates the percentage of adolescents with ≥ 3 nights of self-reported sleep difficulties during the 7-consecutive days of assessment

a

Pubertal Status: Transitioning (Tanner Stage 1 through Tanner Stage 4 in both pubic hair and gonadal development) and Mature (Tanner Stage 5 in both pubic hair and gonadal development) ABR: SES: socioeconomic status, y: years; mg/day: milligrams per day; h/wk: hours per week

**

p-value < 0.0001 (2-tailed),

*

p <0.05 (2-tailed)

Self-reported perceived sleep difficulties

Nearly one-quarter (24.5%) of the sample reported sleep difficulties ≥ 3 nights, with 19% of males and 29% of females reporting ≥ 3 nights of sleep difficulties per week (p < 0.05) (Table 2). Other bivariate correlates of sleep difficulties included having ever tried smoking, being born to mothers with higher maternal education, and household food insecurity (Table 2).

Associations between sociodemographic and lifestyle factors with 5 types of sleep difficulties

From the thematic analysis of sleep dairies, 5 types of sleep difficulties emerged; these included: restlessness/insomnia sleep difficulties (IRSD), environmental sleep difficulties (ESD), mental/emotional sleep difficulties (MESD), illness/injury sleep difficulties (IISD) and technology sleep difficulties (TSD). The most common types of sleep difficulties self-reported by adolescents between 1–7 nights over the recorded week included insomnia/restlessness sleep difficulties (IRSD) (29%), environmental sleep difficulties (ESD) (27%) and, mental/emotional sleep difficulties (MESD) (19%). See Table 3 for examples of open-ended sleep difficulty responses and frequencies.

Table 3.

Adolescent open-ended responses to the perceived reason for sleep difficulties

Sleep Difficulty
Type		 Frequency of difficulty Example participant responsesa
Insomnia/Restlessness Sleep Difficulties (IRSD) 28.7%
(137/477)		 “INSOMNIA”
“RESTLESS”
“I WAS RESTLESS”
Environmental Sleep Difficulties (ESD) 26.6%
(127/477)		 “MY BROTHERS SNORE A LOT AND MAKE NOISE”
“MY CATS WAKE ME UP”
“BECAUSE OF THE HEAT”
Mental/Emotional Sleep Difficulties (MESD) 18.5%
(88/477)		 “STRESS”
“NERVES DUE TO THE EARTHQUAKE THAT HAPPENED TODAY”
“I COULD NOT STOP THINKING”
Injury/Illness Sleep Difficulties (IISD) 10.3%
(49/477)		 “STOMACH ACHE”
“HEADACHE”
“SICK WITH THE FLU”
Technology Sleep Difficulties (TSD) 2.5%
(12/477		 “I WAS ON THE PHONE”
“BECAUSE I WANTED TO KEEP WATCHING VIDEOS ON MY CELL PHONE”
“FOR BEING ON THE CELL PHONE LISTENING TO MUSIC”
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a

Indicates responses to the open-ended question: “If you slept poorly, what was the problem (e.g., were you restless, was there noise, stress)?”

Insomnia/Restlessness Sleep Difficulties (IRSD)

More females (39%) than males (19%) reported ≥ 1 IRSD during the assessment period (Table 4). Among those never having smoked, IRSD was lower (23%) than those ever having smoked (34%) (Table 4).

Table 4.

Baseline household and adolescent characteristics according to types of sleep difficulties (n = 477)

N Insomnia/Restlessness, Percentage1 Environmental, Percentage1 Mental/Emotional, Percentage1 Illness/Injury, Percentage1 Technology, Percentage1
Sex
 Male 222 19.4** 23.0* 13.1* 7.7* 1.8
 Female 255 38.8** 29.8* 23.1* 12.6* 3.1
Age groups, years (y)
 11–14 y 159 28.3 22.0* 13.2* 11.3 3.8
 15–17 y 161 29.8 26.7* 17.4* 10.6 1.9
 ≥ 18 y 157 28.0 31.2* 24.8* 8.9 1.9
Familial SES
 Lower 244 27.1 25.4 14.3* 12.7 2.5
 Middle/Higher 233 30.5 27.9 22.8* 7.7 2.6
Pubertal Status a
 Transitioning 200 29.8 18.1* 19.1 10.6 2.1
 Mature 277 31.5 29.8* 22.6 9.8 2.6
Smoking Behavior
 Ever tried smoking 249 34.1* 29.7* 22.5* 10.8 2.4
 Never tried smoking 228 22.8* 23.3* 14.0* 9.7 2.6
Alcohol Behavior
 Ever Drinker 438 28.8 26.7 19.0 10.1 2.5
 Never Drinker 39 28.2 25.6 12.8 12.8 2.6
Physical activity, tertiles (h/wk)
 0.5 – 5.0 153 29.6 23.0 19.7 9.9 4.0
 6.0 – 10.5 166 32.5 28.3 17.5 11.5 1.8
 11.0 – 38.5 158 24.0 28.5 18.4 9.5 1.9
Maternal Education
 Did not complete secondary (<9) 54 29.6* 20.4 24.1 9.3 3.7
 Completed some high school (9 to <12) 68 20.6* 26.5 16.2 10.3 1.5
 Completed high school (12) 159 35.2* 27.0 21.4 10.1 2.5
 Higher Education (>12) 196 26.0* 28.1 15.3 10.7 2.6
Household Food Insecurity
 None 276 26.5 23.2* 20.7* 12.0* 2.5
 Any 201 31.8 31.3* 15.4* 8.0* 2.5
Bedroom Sharing
 No 205 28.8 27.3 22.9* 6.3* 2.9
 Yes 272 28.7 26.1 15.1* 13.2* 2.2
Caffeinated beverage consumption, tertiles (mg/day)
 0 – 0.22 162 27.8 21.0* 19.8 13.0 1.9*
 0.24 – 1.96 154 27.9 24.7* 17.5 9.7 1.3*
 2.13 – 568.53 161 30.4 34.2* 18.0 8.1 4.4*
Technology use <1 hour before bed
 < 4 days 85 29.4 29.4 18.8 10.6 3.5
 ≥ 4 days 392 28.6 26.0 18.4 10.2 2.3
Screen time, tertiles (h/wk)
 0.0 – 3.0 166 26.1 27.9 20.0 11.5 3.6
 3.5 – 10.0 152 31.6 27.0 15.8 8.6 3.3
 10.5 – 49.0 159 28.9 25.2 19.5 10.7 0.6
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1

Indicates the percentage of adolescents with ≥ 1 night of self-reported sleep difficulties per sleep difficulty type during the 7-consecutive days of assessment

a

Pubertal Status: Transitioning (Tanner Stage 1 through Tanner Stage 4 in both pubic hair and gonadal development) and Mature (Tanner Stage 5 in both pubic hair and gonadal development); ABR: SES: socioeconomic status, y: years; mg/day: milligrams per day; h/wk: hours per week

**

p-value < 0.0001 (2-tailed),

*

p <0.05 (2-tailed)

Environmental Sleep Difficulties (ESD)

The prevalence of ESD was higher in females than males (30% versus 23%). There were significant differences by age group, with ESD increasing with older age. Differences were also observed by pubertal status, with 30% of those in a mature puberty group reporting ESD versus 18% of those in transitioning puberty group reporting ESD. Smoking and living in a food-insecure household were both associated with higher ESD. Further, ESD varied by caffeine intake, with adolescents in the highest tertile of caffeine intake having higher ESD (34.2%) than those in the middle (25%) and lowest tertile (21%) (Table 4).

Mental/Emotional Sleep Difficulties (MESD)

We observed sex-differences in MESD, with more females (23%) than males (13%) reporting ≥ 1 MESD during the 7 days (Table 4). Adolescents ≥ 18 years experienced more MESD than younger age groups (11–14 years and 15–17 years). MESD was also associated with higher SES, with nearly a 3-fold prevalence among the higher SES group (40%) versus the lower SES group (14%). Never smoking was associated with lower MESD (14%) than those that reported having tried smoking (23%). Moreover, bedroom sharing was associated with lower MESD (15%) than adolescents that did not share a bedroom (23%).

Illness and Injury Sleep Difficulties (IISD)

The prevalence of IISD differed by sex, with 13% of females versus 8% of males reporting ≥ 1 IISD (Table 4). A higher prevalence of IISD was reported among those living in food-secure households (12%) than those that lived in food-insecure households (8%). Bedroom sharing was also significantly associated with higher IISD (13%) than adolescents that did not share a bedroom (6%) (p < 0.05).

Technology Sleep Difficulties (TSD)

TSD was only significantly associated with higher caffeinated beverage consumption (p < 0.05) (Table 4).

In sensitivity analyses, bivariate associations between sociodemographic and lifestyle factors with sleep difficulties persisted in mutually-adjusted logistic regression models (Supplementary Table 1). To illustrate, adolescent females had a 2.9 (95% CI: 1.7, 4.7) higher odds of experiencing IRSD than male counterparts. Younger adolescents had a higher odds of IRSD, such that 11–14-year-olds had a 1.3 (95% CI: 0.57, 3.0) higher odds, while 15–17-year-olds had a 1.4 (95% CI: 0.78, 2.6) higher odds compared to adolescents ≥ 18 years. However, adolescents ≥ 18 years had a higher odds of ESD compared to younger adolescents. Adolescents of lower SES had a 30% (95% CI: 0.43, 1.2), 17% (95% CI: 0.50, 1.4) and 42% (95% CI: 0.32, 1.0) lower odds of experiencing IRSD, ESD and MESD, respectively. Albeit, lower SES adolescents had an 80% (95% CI: 0.82, 3.7) and 50% (95% CI: 0.26, 8.6) higher odds of IISD and TSD, respectively.

Aim 2: Associations between subjective sleep measures and objective sleep measures

Associations between sociodemographic and lifestyle factors with objective sleep measures

In post-hoc analysis, baseline household and adolescent characteristics according to objective sleep measures were assessed using adjusted linear regression models (Supplementary Table 2). Findings demonstrated significant differences in sleep duration and sleep fragmentation by sex, with female adolescents having a longer mean (SD) sleep duration than male adolescents [516.1 (70.2) versus 504.9 (68.1), p < 0.05]. Further, females had less fragmented sleep than males [11.2 (3.5) vs 12.7 (4.1), p < 0.05]. Adolescents with a later sleep midpoint were older, of mature pubertal status, had tried smoking, were ever drinkers, and more likely to report ≥ 4 days of technology use < 1 hour before bed than their adolescent counterparts (Supplementary Table 2). No other significant associations were observed.

Association between subjective sleep measures and objective sleep duration

Linear regression analysis showed that adolescents that had a hard time falling asleep ≥ 3 nights had longer weekday sleep duration compared to their counterparts (β= 11.3 [4.6, 27.2], p = 0.0498; Table 5). In contrast, adolescents with ≥ 3 nights of sleep difficulties had shorter weekly average sleep duration (β= −20.8 [−35.3, −6.2], p = 0.0043; Table 5) and shorter weekday sleep duration (β= −30.2 [−48.5, −11.9], p = 0.0013; Table 5) compared to their counterparts. The overall association between sleep difficulties and shorter sleep duration was driven by IRSD and MESD. To illustrate, MESD was associated with lower weekly average sleep duration (β = −18.8 [−35.4, -−2.4], p = 0.0234; Table 5) and lower weekday sleep duration (β = −28.5 [−49.1, −7.8], p = 0.0070; Table 5). We found similar associations between IRSD and lower weekly average sleep duration (β = −13.2 [−27.3, −0.89], p = 0.0518; Table 5) and weekday sleep duration (β = −19.2 [−36.9, −1.4], p = 0.0343; Table 5), but no significant associations with other sleep difficulty types and sleep duration.

Table 5.

Adjusted Linear Regression analysis of subjective sleep and objective sleep measures

Sleep Duration, Minutes
β (95% CI)a 		Midpoint, Decimal hours
β (95% CI)a 		Sleep Fragmentation, Percentage
β (95% CI)a
Total Weekday Weekend Total Weekday Weekend Total Weekday Weekend
Self-reported hard time falling asleep, ≥ 3 nights 8.7 (−3.9, 21.3) 11.3 (−4.6, 27.2)* −2.2 (−18.5, 13.3) 0.32 (0.08, 0.55)* 0.34 (0.08, 0.60)* 0.26 (0.02, 0.51)* 0.45 (−0.24, 1.1) 0.20 (−0.59, 1.0) 0.88 (−0.14, 1.9)
Self-reported sleep difficulties, ≥ 3 nights −20.8 (−35.3, −6.2)* −30.2 (−48.5, −11.9)* −13.2 (−31.8, 5.4) −0.11 (−0.38, 0.16) −0.09 (−0.40, 0.21) −0.08 (−0.36, 0.21) 0.28 (−0.53, 1.1) −0.05 (−0.98, 0.88) 1.2 (−0.01 2.4)*
Insomnia/Restlessness Sleep Difficulties (IRSD) −13.2 (−27.3, −0.89)* −19.2 (−36.9, −1.4)* −12.5 (−30.3, 5.3) 0.13 (−0.13, 0.40) 0.14 (−0.15, 0.43) 0.17 (−0.10, 0.44) 0.18 (−0.59, 0.96) −0.05 (−0.95, 0.84) 0.77 (−0.37, 1.9)
Environmental Sleep Difficulties (ESD) −4.6 (−19.1, 9.9) 1.3 (−17.0, 19.6) −14.3 (−32.5, 4.0) −0.05 (−0.32, 0.22) −0.06 (−0.36, 0.24) 0.02 (−0.26, 0.30) 0.87 (0.08, 1.7)* 0.70 (−0.21, 1.6) 1.1 (0.11, 2.2)*
Mental/Emotional Sleep Difficulties (MESD) −18.8 (−35.4, −2.4)* −28.5 (−49.1, −7.8)* −0.26 (−21.1, 20.5) −0.21 (−0.52, 0.10) −0.26 (−0.60, 0.07) −0.02 (−0.34, 0.30) 0.69 (−0.22, 1.6) 0.31 (−0.74, 1.4) 1.7 (0.4, 3.0)*
Injury/Illness Sleep Difficulties (IISD) 12.6 (−8.2. 33.4) 13.9 (−12.3, 40.1) 9.9 (−16.3, 36.1) 0.25 (−0.13, 0.64) 0.31 (−0.12, 0.74) −0.10 (−0.30, 0.50) −0.65 (−1.8, 0.50) −0.86 (−2.2, 0.46) −0.36 (−2.0, 1.3)
Technology Sleep Difficulties (TSD) 2.6 (−37.5, 42.7) −0.11 (−50.6, 50.4) 8.6 (−41.3, 58.5) 0.93 (0.20, 1.7)* 0.99 (0.17, 1.8)* 0.78 (0.02, 1.5)* 2.3 (0.11, 4.5)* 3.2 (0.73, 5.8)* −0.29 (−3.5, 2.9)
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a

Includes adjustment for sex, age, SES, bedroom sharing, physical activity (tertiles, h/wk), food insecurity, caffeinated beverage consumption (tertiles, mg/day), technology use <1 before bed, screen time (tertiles, h/wk)

*

p <0.05 (2-tailed),

**

p-value < 0.0001 (2-tailed)

ABR: SD: Sleep Difficulties; CI: Confidence Interval; SES: socioeconomic status

Association between subjective sleep measures and objective midpoint sleep

Results from adjusted linear regression demonstrated that adolescents with ≥ 3 nights of hard time falling asleep had a later weekly average sleep midpoint (β = 0.32 [0.08, 0.55], p = 0.0094; Table 5), during weekdays (β = 0.34 [0.08, 0.60], p = 0.0108; Table 5), and weekends (β = 0.26 [0.02, 0.51], p = 0.0345; Table 5). TSD was also associated with a later weekly average (β = 0.93 [0.20, 1.7], p = 0.0135; Table 5), weekday (β = 0.99 [0.17, 1.8], p = 0.0196; Table 5) and weekend midpoint (β = 0.78 [0.02, 1.5], p = 0.0444; Table 5).

Association between subjective sleep measures and sleep fragmentation

Adolescents with ≥ 3 nights of sleep difficulties had higher weekend sleep fragmentation (β= 1.2 [−0.01, 2.4], p = 0.0551; Table 5) compared to counterparts. The association with weekend sleep fragmentation was primarily driven by self-reported MESD (β= 1.7 [0.4, 3.0], p = 0.0117; Table 5). Regression analysis also demonstrated higher weekly average sleep fragmentation (β = 0.87 [0.08, 1.7], p = 0.0306; Table 4) among adolescents that self-reported ESD. Further, higher weekly average (β = 2.3 [0.11, 4.5], p = 0.0109, Table 5) and weekday sleep fragmentation (β = 3.2 [0.73, 5.8], p = 0.0003, Table 5) were associated with TSD.

Discussion

This sample of Mexican adolescents had a high prevalence of self-reported hard time falling asleep and overall self-reported perceived sleep difficulties, with almost half and one-quarter of participants, respectively, reporting such complaints. These estimates appear to be higher than some other studied populations, including the prevalence of insomnia reported among Australian adolescents (10.8%) (Dohnt, 2012) and Norwegian adolescents (13.6% to 23.8%) (Hysing et al., 2013). Of the 5 types of self-reported sleep difficulties within this Mexican cohort, insomnia/restlessness, environmental and mental/emotional factors were the main perceived reasons for sleep difficulties. Overall, adolescent females were more likely to experience a higher prevalence of ≥ 3 nights of a hard time falling and ≥ 3 nights of sleep difficulties than adolescent males. Further, females had a significantly higher prevalence of sleep difficulties for all sleep difficulty types, except for TSD.

The objectively-assessed sleep characteristics of the present sample differed from other adolescent populations in several key ways. In particular, we found sleep duration longer in this sample than in European adolescents (Chattu et al., 2018; Kerkhof, 2017), with about two-thirds meeting AASM age-specific sleep duration recommendations. In contrast, a 2015 national survey of US adolescents found that only 27% met AASM sleep duration recommendations (Wheaton et al., 2018). The sleep midpoint of the present study also differed in that it was later than other comparison populations. To illustrate, among the present cohort, the mean (SD) sleep midpoint across 7 days, during weekdays and on weekends were 4.5 (1.3), 4.3 (1.5) and 5.0 (1.3), respectively. In comparison, a large study conducted among 17,355 adolescents and young people (16–30 years) from 107 countries reported 7 day, weekday and weekend sleep midpoint mean (SD) was 3.54 (1.12), 3.50 (1.14) and 4.03 (1.23) (Kuula et al., 2019). Finally, although there are more limited data available on sleep fragmentation, we found that among the present cohort, mean (SD) sleep fragmentation over 7 days, weekdays and weekends was 11.9 (3.3), 12.0 (4.4) and 11.7 (5.6), whereas a Finnish study showed sleep fragmentation means (SD) of 15.5 (6.3) and 16.4 (4.6) for females and males, respectively (Pesonen et al., 2011).

Aim 1 Findings

Multiple sociodemographic and lifestyle factors were associated with subjective sleep measures. For example, female sex was associated with a hard time falling asleep and sleep difficulties. Though not statistically significant, the proportion of a hard time falling asleep and sleep difficulties was higher among older adolescents. Similarly, a study among Japanese adolescents demonstrated that females experienced more difficulties initiating sleep than males (Ohida et al., 2004) and that difficulties initiating sleep increased with age. We also found that adolescents that ever tried smoking had a significantly higher prevalence of ≥ 3 nights of sleep difficulties, although no difference in difficulty falling asleep. This finding is somewhat similar to findings from a study among Chinese adolescents, which reported that current smokers had a higher odds of reporting difficulty maintaining sleep (DMS), but lower odds of reporting difficulty initiating sleep (DIS) compared to never smokers (Mak et al., 2010).

Results from the thematic analysis demonstrated that the main drivers of sleep difficulties among the present sample included insomnia/restlessness, environment, and mental/emotional factors. It is not necessarily surprising that insomnia/restlessness and mental/emotional factors were commonly reported since the two are often interwoven (Armstrong et al., 2014) and are strongly associated with sleep difficulties in the literature (Lovato & Gradisar, 2014; Short et al., 2020). However, to our knowledge, no other studies have asked adolescents to record sleep difficulties via sleep diary in the same way as the present study. Thus, it is possible that having to record their nightly sleep made adolescents more aware of their difficulties with falling asleep. One qualitative study of Swedish adolescents that asked adolescents to reflect on their sleep perceptions and difficulties found that adolescents perceived their sleep difficulties were due to stress (Jakobsson et al., 2020), but they did not specifically mention insomnia/restlessness. There are physiological mechanisms that help explain the high prevalence of self-reported insomnia or restlessness in this sample. In particular, as puberty progresses, a phase delay in circadian timing occurs, which typically pushes bedtimes progressively later (until the early 20s, when the circadian clock shifts earlier) (Crowley et al., 2007). Therefore, even when sleep-deprived, many adolescents in the cohort may experience alertness or lack of tiredness at night due to circadian biology.

Environmental factors were another major perceived cause of sleep difficulties, with nearly 27% of reported sleep difficulties being related to temperature, noise, and bedroom partners. These factors may be more common in Mexico City than in the settings typically studied due to housing being small, densely populated, and near noisy streets. More studies would be necessary to confirm these findings. We also found that the prevalence of ESD increased with older age. Many of the environmental factors that adolescents perceived as causing their sleep difficulties were related to caring for something or someone else in the household (i.e., children, siblings, pets), and noise-related issues (i.e., hearing siblings snore through the night, outdoor sounds, etc.). Thus, it is possible that older adolescents experience more ESD because they have more responsibilities within their household and a greater awareness of noise and light disturbances. Interestingly, although ESD prevalence was high among the sample, regression analysis findings did not demonstrate ESD to be strongly related to objective sleep measures (Table 4). This could potentially indicate that adolescents in this population are more accustomed to these noises, with minimal impact on their overall sleep duration and fragmentation.

It is also important to point out that exposure to these environmental factors likely differ by socioeconomic status (Loredo et al., 2010), and research points to SES and SES-related factors as social determinants of sleep health (Bixler, 2009). For example, previous literature has noted associations between lower SES, food insecurity and bedroom sharing with poorer sleep outcomes (Hayes et al., 2001; Marco et al., 2018; Nagata et al., 2019). However, in the present sample, we found the opposite, with MESD associated with higher SES, living in a food-secure household and not sharing a bedroom. Further, studies among European, US and Asian school-aged children have demonstrated associations between co-sleeping or bedroom sharing with poorer sleep health, including problems initiating sleep, less nighttime sleep, greater bedtime resistance, and increased sleep anxiety (Hayes et al., 2001; X. Liu, 2005; Xianchen Liu et al., 2003; Lozoff et al., 1996). In contrast, our findings showed that bedroom sharing was associated with fewer mental/emotional sleep difficulties. An explanation for differences could be related to a higher prevalence of co-sleeping/bed-sharing among Latin American populations (Schachter et al., 1989). Furthermore, the value placed on family ties in Latin American populations may provide emotional and mental health benefits (Jenkins, 1991; Keefe et al., 1979; Roberts, 2015; Sabogal et al., 1987; Snowden, 2007) that impact sleep. It is also possible that in lower SES households in Mexico City, where bedroom-sharing is common, access to technology may be more limited, resulting in lower exposure to light at night, which is associated with better sleep (Falbe et al., 2015).

We also found that more than 10% of adolescents reported that illness or injury caused sleep difficulties. The prevalence of IISD among the sample was notably higher than among Swedish adolescents, among whom the frequency of pain or illness-related sleep difficulties was only 1.7% (Jakobsson et al., 2019). Although speculative, differences may be due to the overall prevalence of acute illnesses being higher in Mexican populations than in other populations. For example, in 2019, the most common illnesses in Mexico included acute respiratory infections (Common Illnesses in Mexico 2019, 2020). Thus, bedroom sharing may also be linked with greater exposure to infectious agents. Within the cohort, adolescents did not perceive technology-related sleep difficulties (TSD) as a reason for experiencing sleep difficulties; only 2.5% of adolescents reported TSD. This finding is contrary to existing literature, which shows that technology use is a common inhibitor of sleep among adolescents (Lemola et al., 2015; J. Owens et al., 2014). One potential reason to explain this discrepancy could be that the adolescents did not perceive the true impact of technology use on their sleep.

Aim 2 Findings

Although our findings revealed that adolescents in the present sample had a longer average sleep duration than other adolescent populations, we still found that adolescents in our cohort with perceived sleep difficulties had objectively worse sleep. In particular, we found that ≥ 3 nights of sleep difficulties was associated with shorter sleep duration. Although European and US studies have documented congruency between subjective and objective assessment of sleep duration among adolescents, there is a lack of studies that have specifically examined the relationship between self-reported sleep difficulties with objective sleep duration. Thus it is difficult to compare our findings. However, Jakobsson and colleagues’ findings demonstrated that 55% of the adolescents slept less than the recommended 8 hours per night, and only 11% had sleeping difficulties (Jakobsson et al., 2019). Among our study, we found the opposite, such that a larger percentage of the population met age-specific sleep recommendations but also experienced sleep difficulties. Our findings may demonstrate the importance of obtaining a combination of subjective and objective measures to assess sleep health more holistically.

Contrary to our previous finding, we found that ≥ 3 nights of a hard time falling asleep was associated with longer sleep duration. Our findings differ from another study conducted among Chinese adolescents, which reported that sleep duration did not differ among adolescents who reported difficulty initiating sleep and maintaining sleep (Xianchen Liu & Zhou, 2002), although this study used self-reported sleep duration. However, it is possible that the adolescents who reported a hard time falling asleep spent a longer time in bed (the actigraph cannot distinguish between sleep and lying still in bed).

When examining types of sleep difficulties, MESD and ESD were linked to more fragmented sleep. These findings align with the adult (Hayashino et al., 2010) and adolescent literature showing that mental health symptoms and sub-optimal sleep conditions are linked to poorer sleep quality (Lund et al., 2010; Van Dyk Tori R. et al., 2019).

Strengths and Limitations

To our knowledge, the present study is among the first that has examined self-reported perceived sleep difficulties and the relationship between subjective and objective sleep measures in Latin American adolescents. The collected subjective sleep measures allowed us to obtain qualitative information that wrist-actigraphy devices would not have provided, thus offering a more complete sleep assessment. Our study included limitations. First, we used self-reported sleep diaries to assess subjective sleep. These measures are subject to recall and response bias. In particular, asking adolescents to report their nightly use of technology may be subject to social desirability bias, given that adolescents may be either aware that they should not be using technology before bed or it is discouraged by their parents. Another limitation of the diaries was that we included examples of factors that may cause sleep difficulty, such as, “Was there noise?” Listing examples could have resulted in biased responses. Although many adolescents self-reported noise as perceived environmental sleep difficulties, we did not measure neighborhood characteristics, such as proximity to metro stations or noise level within and between neighborhoods. Thus, we were unable to evaluate the impact of noise on sleep outcomes. Generalizability may also be limited to Mexico City adolescents of lower to mid-SES.

Conclusion

Compared to European adolescents, this sample of adolescents experienced longer sleep duration and later sleep midpoint as assessed by objective wrist-actigraphy. Though sleep duration was longer, subjective self-reports demonstrated that about one-quarter of the sample experienced sleep difficulties, while nearly half reported a hard time falling asleep. This finding has significant clinical relevance as it underscores the fact that adolescents are aware of their sleep difficulties and may be open to discussing these difficulties and possible solutions with clinicians. These findings may also inform public health interventions that address factors that drive sleep difficulties among adolescents. Further, our findings provide evidence that Mexican adolescents of varying lifestyles and demographic backgrounds may be differentially at risk of experiencing sleep difficulties.

Overall, the present study highlights the importance of collecting subjective information on self-perceived sleep difficulties among adolescents and objective sleep measures to obtain a more comprehensive picture of sleep health among adolescents. Future research should build on these findings to further assess the presence of chronic insomnia and mental health disorders and employ a longitudinal framework, such as a longitudinal cohort study, to demonstrate trends in sleep difficulties over time.

Supplementary Material

Supplementary Tables 1-2

Acknowledgments

We gratefully acknowledge the research staff and the British Cowdray Medical Center (ABC) for their research facilities.

Funding

This work was supported by the US Environmental Protection Agency (US EPA) grant RD83543601, National Institute for Environmental Health Sciences grants P01 ES02284401, P30 ES017885, R24ES02850 and National Heart, Lung, and Blood Institute grant K01 HL151673. This study was also supported and partially funded by the National Institute of Public Health/Ministry of Health of Mexico.

Footnotes

Disclosure Statement

The authors reported no potential conflict of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, EJ, upon reasonable request.

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Supplementary Materials

Supplementary Tables 1-2
NIHMS1701277-supplement-Supplementary_Tables_1-2.docx (27KB, docx)

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