Evening electronic device use: The effects on alertness, sleep and next-day physical performance in athletes

The impact of screen use on sleep health across the lifespan: A National Sleep Foundation consensus statement

OBJECTIVE

To achieve consensus on whether screen-based digital media (1) in general, (2) via prebedtime content, and (3) via prebedtime light impairs sleep health in (a) childhood, (b) adolescence, and (c) adulthood. Furthermore, to address whether employing behavioral strategies and interventions may reduce the potential negative effects of screens on sleep health.

METHODS

The National Sleep Foundation convened a 16-person multidisciplinary expert panel ("Panel"). Panelists met virtually 5 times throughout 2023, during which they followed a modified Delphi RAND/UCLA Appropriateness Method to reach consensus.

RESULTS

The Panel conducted a literature review starting with 2209 articles, narrowed down to 522 relevant empirical articles and 52 relevant review articles. The search was refined to include 35 experimental/intervention studies that examined whether there was a causal link between screen-based digital media and sleep. In addition, panelists reviewed 5 recent relevant systematic review articles. After reviewing the summarized current literature, panelists voted on 10 candidate statements about whether screen use impairs sleep health. The Panel met virtually to discuss the results of the first round of votes, which was then followed by a second round of voting, ultimately achieving consensus on 5 out of the 10 statements.

CONCLUSIONS

The Panel achieved consensus that (1) in general, screen use impairs sleep health among children and adolescents, (2) the content of screen use before sleep impairs sleep health of children and adolescents, and (3) behavioral strategies and interventions may attenuate the negative effects of screen use on sleep health.

Nocturnal Smartphone Use Affects Sleep Quality and Cognitive and Physical Performance in Tunisian School-Age Children

Nocturnal smartphone use emits blue light, which can adversely affect sleep, leading to a variety of negative effects, particularly in children. Therefore, the present study aimed to determine the effect of acute (AC) (one night) and repeated (RC) (five nights) nocturnal smartphone exposure on sleep, cortisol, and next-day performance in Tunisian children. Thirteen participants (seven girls and six boys, age 9 ± 0.6, height 1.32 ± 0.06, weight 34.47 ± 4.41) attended six experimental nights. The experiment started with a baseline night (BL) with no smartphone exposure, followed by repeated sessions of nocturnal smartphone exposure lasting 90 minutes (08:00 pm-09:30 pm). Actigraphy; salivary cortisol; the Stroop test (selective attention); choice reaction time (CRT); N-back (working memory); counter-movement jump (CMJ), composed of flight time (time spent in the CMJ flight phase) and jump height; and a 30 m sprint were assessed the morning after each condition. Both AC and RC shortened total sleep time (TST) (p < 0.01), with a greater decrease with RC (-46.7 min, ∆% = -9.46) than AC (-28.8 min, ∆% = -5.8) compared to BL. AC and RC significantly increased waking after sleep onset (3.5 min, ∆% = 15.05, to 9.9 min, ∆% = 43.11%) and number of errors made on the Stroop test (1.8 error, ∆% = 74.23, to 3.07 error, ∆% = 97.56%). Children made 0.15 and 0.8 more errors (∆% = 6.2 to 57.61%) and spent 46.9 s and 71.6 s more time on CRT tasks (∆% = 7.22 to 11.11%) with AC and RC, respectively, compared to BL. The high-interference index of the Stroop task, CMJ performance, and 30 m sprint speed were only altered (p < 0.01) following RC (0.36, Δ% = 41.52%; -34 s, Δ% = -9.29%, for flight time and -1.23 m, -8.72%, for jump height; 0.49 s, Δ% = 6.48, respectively) when compared to BL. In conclusion, one- or five-night exposure to smartphones disturbed the children's sleep quality and their performance, with more pronounced effects following RC.

Seasonal Variation in the Responsiveness of the Melanopsin System to Evening Light: Why We Should Report Season When Collecting Data in Human Sleep and Circadian Studies

It is well known that variations in light exposure during the day affect light sensitivity in the evening. More daylight reduces sensitivity, and less daylight increases it. On average days, we spend less time outdoors in winter and receive far less light than in summer. Therefore, it could be relevant when collecting research data on the non-image forming (NIF) effects of light on circadian rhythms and sleep. In fact, studies conducted only in winter may result in more pronounced NIF effects than in summer. Here, we systematically collected information on the extent to which studies on the NIF effects of evening light include information on season and/or light history. We found that more studies were conducted in winter than in summer and that reporting when a study was conducted or measuring individual light history is not currently a standard in sleep and circadian research. In addition, we sought to evaluate seasonal variations in a previously published dataset of 72 participants investigating circadian and sleep effects of evening light exposure in a laboratory protocol where daytime light history was not controlled. In this study, we selectively modulated melanopic irradiance at four different light levels (<90 lx). Here, we aimed to retrospectively evaluate seasonal variations in the responsiveness of the melanopsin system by combining all data sets in an exploratory manner. Our analyses suggest that light sensitivity is indeed reduced in summer compared to winter. Thus, to increase the reproducibility of NIF effects on sleep and circadian measures, we recommend an assessment of the light history and encourage standardization of reporting guidelines on the seasonal distribution of measurements.

Association of Social Media Use on Sleep Quality and Performance Among Collegiate Athletes

OBJECTIVE

(1) To examine the effect of social media (SM) use on sleep quality and (2) to determine the effect of SM on competition performance among college athletes.

DESIGN

Cross-sectional study.

SETTING

Two local Division III colleges, MA, USA.

PARTICIPANTS

National Collegiate Athletic Association Division III swim and track and field athletes.

INTERVENTION

Data were collected onsite through an SM use form, the lifestyle survey, and the Patient-Reported Outcomes Measurement Information System Sleep Disturbance Questionnaire. Participants were divided into 3 groups based on their SM volume: (1) less active SM users (≤2 h/d), (2) moderately active SM users (>2-≤5 h/d), and (3) active SM users (>5 h/d) for analysis.

MAIN OUTCOME MEASURES

Sleep quality and sport performance data collected through publicly available competition results. The independent variable, SM volume (hours of SM use per day over 7 days), was collected using the iPhone screen time function.

RESULTS

The cohort consisted of 40 male track and field athletes and 47 female track and field (37, 79%) and swimming (10, 21%) athletes. Mean SM use was 4.6 ± 3.4 h/d. The sleep quality of active SM users was worse than less active SM users ( P = 0.033). There was an independent association between increased Facebook time and poor performance after adjusting confounders (adjusted odds ratio: 0.990, 95% confidence interval: 0.981-0.999, P = 0.049).

CONCLUSIONS

High SM use has a negative impact on sleep quality and may hinder competition performance among college athletes. These findings may have implications for developing SM use guidelines for college athletes to improve their sleep quality and performance.

Sleep, health, and well-being in elite athletes from different sports, before, during, and after international competition

Objectives: Limited research has been conducted on sleep problems in elite athletes at international competition, and how this relates to their general health and well-being. Methods: Sixty-five elite international athletes (37 males, 28 females, 21.8 ± 2.1 years) from different sports completed validated sleep (Athlete Sleep Screening Questionnaire), health (Subjective Health Complaints Inventory) and well-being (Sports Profile of Mood States) questionnaires; 1 month pre-competition, at the end of international competition, and 1 month post-competition. Results: Twenty-three percent of the elite athletes were identified as having a moderate or severe clinically significant sleep problem during competition, with 82% reporting less than 8 h of sleep per night. Athletes with a moderate or severe clinically significant sleep problem during competition had significantly greater general health complaints (p = 0.002), mood disturbance (p = 0.001) and poorer sleep hygiene (p = 0.002). Swimmers had more sleep difficulty pre and during competition compared to athletics and soccer (p = 0.009). Conclusion: Sleep disturbance during international competition is common and associated with poorer health and lower mood. Swimmers may be more at risk of sleep difficulty pre and during competition compared to those competing in athletics and soccer. Sleep services may be required to support elite athletes at international competition.

Deconstructing athletes' sleep: A systematic review of the influence of age, sex, athletic expertise, sport type, and season on sleep characteristics

PURPOSE

This systematic review aimed to describe objective sleep parameters for athletes under different conditions and address potential sleep issues in this specific population.

METHODS

PubMed and Scopus were searched from inception to April 2019. Included studies measured sleep only via objective evaluation tools such as polysomnography or actigraphy. The modified version of the Newcastle-Ottawa Scale was used for the quality assessment of the studies.

RESULTS

Eighty-one studies were included, of which 56 were classified as medium quality, 5 studies as low quality, and 20 studies as high quality. A total of 1830 athletes were monitored over 18,958 nights. Average values for sleep-related parameters were calculated for all athletes according to sex, age, athletic expertise level, training season, and type of sport. Athletes slept on average 7.2 ± 1.1 h/night (mean ± SD), with 86.3% ± 6.8% sleep efficiency (SE). In all datasets, the athletes' mean total sleep time was <8 h. SE was low for young athletes (80.3% ± 8.8%). Reduced SE was attributed to high wake after sleep onset rather than sleep onset latency. During heavy training periods, sleep duration and SE were on average 36 min and 0.8% less compared to pre-season and 42 min and 3.0% less compared to in-season training periods, respectively.

CONCLUSION

Athletes' sleep duration was found to be short with low SE, in comparison to the general consensus for non-athlete healthy adults. Notable sleep issues were revealed in young athletes. Sleep quality and architecture tend to change across different training periods.

Complete sleep evaluation of top professional cross-country mountain bikers athletes

BACKGROUND

Despite growing interest in athletes' sleep, few studies have focused on professional athletes, especially in individual sports. Moreover, limited investigations included female athletes. This study aims to evaluate sleep chronotype, as well as objective and subjective sleep characteristics in male and female professional cross-country mountain bikers.

METHODS

Thirteen athletes (7 males and 6 females) of the French national team took part in this study. The Chronotype was assessed by the Horne and Östberg Morningness-Eveningness Questionnaire and sleep by actigraphy for one month, by ambulatory polysomnography (PSG) for one night and by the Pittsburgh Sleep Quality Index.

RESULTS

Most athletes (77 %) are classified as moderately morning type and a minority of athletes (23 %) are intermediate type. Athletes sleep on average 8 hours per night and during the night recorded by PSG, N3 and REM sleep stages represented 21.2 ± 3.4 % and 20.9 ± 3.1 % of the total sleep time, respectively. These good sleep parameters were confirmed by subjective data with 77 % good sleepers. Except the poorer subjective sleep quality in female athletes (5.7 ± 1.6) compared to male athletes (2.6 ± 1.7, p < 0.05), no significant sex difference was found for all characteristics evaluated.

CONCLUSIONS

The professional status of these athletes and the organization of mountain bike calendar may explain their good sleep characteristics.

Influence of Electronic Devices on Sleep and Cognitive Performance During Athlete Training Camps

Jones, MJ, Dawson, B, Eastwood, PR, Halson, SL, Miller, J, Murray, K, Dunican, IC, Landers, GJ, and Peeling, P. Influence of electronic devices on sleep and cognitive performance during athlete training camps. J Strength Cond Res 35(6): 1620-1627, 2021-This study investigated the effects of removing athletes' electronic devices in the evening on sleep and performance during training camps. Water polo athletes (n = 26) attending a 7-night training camp (study 1) and triathletes (n = 23) attending a 4-night training camp (study 2) were randomly allocated to a no-device group (no electronic devices could be used after dinner or overnight; ND) or control group (unrestricted electronic device use; CON). Sleep was monitored through wrist actigraphy. The ND group completed a questionnaire measuring anxiety related to being unable to use electronic devices ("nomophobia"). Triathletes also completed a psychomotor vigilance test (PVT) at the start and end of camp. Water polo ND athletes went to bed earlier and spent longer time in bed than CON on the first night, but not on other nights. In triathletes, sleep quantity was not different between groups on any night. No statistically significant differences were observed for changes in nomophobia from the first to the last night of camp. No differences in PVT performance were observed between ND and CON triathletes. In conclusion, removal of evening electronic devices does not improve sleep quantity or cognitive performance in athletes during short-duration (4-7 nights) training camps.

A critical review on sleep assessment methodologies in athletic populations: factors to be considered

A growing body of research focus on athletes' sleep in order to investigate the effects of sleep in sports performance and recovery or the prevalence of sleep disorders in athletes. At the same time, several sleep monitoring tools have been developed and used in athletic populations for fulfilling these purposes. This review aimed to provide critical assessment to the most used by athletes' methodological approaches and compared them with the gold standard approach. Advantages and disadvantages of the various sleep monitoring tools were critically discussed. Literature related to aspects of athletes' sleep was reviewed. From the shortlisted studies, several factors that seem to affect sleep in athletes were identified using objective methods such as polysomnography/electroencephalography and actigraphy. These factors were associated to sleep (eg such as sleep environment, familiarization procedures and napping) and daily habits (eg nutrition, fluid consumption, alcohol and caffeine intake, tobacco use). The selected studies that evaluated sleep objectively were screened according the reporting rates of these variables. The majority of the screened studies were found to underreport these variables. Practical issues were addressed and recommendations about reporting sleep-related factors were made in order to improve studies' quality assessment and allow for more robust comparisons between studies.

Sleep and performance in Eathletes: for the win!

Over the last decade, Esports, defined as a form of organized video game competition, has emerged as a global phenomenon. The professional players who compete in Esports, namely, Eathletes, share many similarities with their traditional athlete counterparts. However, in sharp contrast to traditional athletes, there is a paucity of research investigating the factors that influence the performance of Eathletes. This gap in the literature is problematic because Eathletes are unable to make informed and empirically supported decisions about their performance management, unlike traditional athletes. Sleep is an important factor that influences athletic performance in traditional sports, particularly those that require a high level of cognitive demand. Research is yet to examine whether sleep also plays an important function in optimal performance and success of Eathletes in Esports. Accordingly, the aim of this opinion piece is to review the broader sleep and sports medicine literature and provide theoretically grounded suggestions as to how existing findings may apply to Eathletes competing professionally in Esports. Overall, it appears that Eathlete performance may be vulnerable to the deleterious effects of sleep restriction. Furthermore, Eathletes are likely at risk of sleep disturbances due to the unique situations and conditions that characterize Esports.

Night-time screen-based media device use and adolescents' sleep and health-related quality of life

OBJECTIVE

The present study investigates the relationship between night-time screen-based media devices (SBMD) use, which refers to use within 1 h before sleep, in both lit and dark rooms, and sleep outcomes and health-related quality of life (HRQoL) among 11 to 12-year-olds.

METHODS

We analysed baseline data from a large cohort of 6616 adolescents from 39 schools in and around London, United Kingdom, participating in the Study of Cognition Adolescents and Mobile Phone (SCAMP). Adolescents self-reported their use of any SBMD (mobile phone, tablet, laptop, television etc.). Sleep variables were derived from self-reported weekday and/or weekend bedtime, sleep onset latency (SOL) and wake time. Sleep quality was assessed using four standardised dimensions from the Swiss Health Survey. HRQoL was estimated using the KIDSCREEN-10 questionnaire.

RESULTS

Over two-thirds (71.5%) of adolescents reported using at least one SBMD at night-time, and about a third (32.2%) reported using mobile phones at night-time in darkness. Night-time mobile phone and television use was associated with higher odds of insufficient sleep duration on weekdays (Odds Ratio, OR = 1.82, 95% Confidence Interval, CI [1.59, 2.07] and OR = 1.40, 95% CI [1.23, 1.60], respectively). Adolescents who used mobile phones in a room with light were more likely to have insufficient sleep (OR = 1.32, 95% CI [1.10, 1.60]) and later sleep midpoint (OR = 1.64, 95% CI [1.37, 1.95]) on weekends compared to non-users. The magnitude of these associations was even stronger for those who used mobile phones in darkness for insufficient sleep duration on weekdays (OR = 2.13, 95% CI [1.79, 2.54]) and for later sleep midpoint on weekdays (OR = 3.88, 95% CI [3.25, 4.62]) compared to non-users. Night-time use of mobile phones was associated with lower HRQoL and use in a dark room was associated with even lower KIDSCREEN-10 score (β = -1.18, 95% CI [-1.85, -0.52]) compared to no use.

CONCLUSIONS

We found consistent associations between night-time SBMD use and poor sleep outcomes and worse HRQoL in adolescents. The magnitude of these associations was stronger when SBMD use occurred in a dark room versus a lit room.

Which parameters to use for sleep quality monitoring in team sport athletes? A systematic review and meta-analysis

Background

Sleep quality is an essential component of athlete's recovery. However, a better understanding of the parameters to adequately quantify sleep quality in team sport athletes is clearly warranted.

Objective

To identify which parameters to use for sleep quality monitoring in team sport athletes.

Methods

Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus, SPORTDiscus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. For the meta-analysis, effect sizes with 95% CI were calculated and heterogeneity was assessed using a random-effects model. The coefficient of variation (CV) with 95% CI was also calculated to assess the level of instability of each parameter.

Results

In general, 30 measuring instruments were used for monitoring sleep quality. A meta-analysis was undertaken on 15 of these parameters. Four objective parameters inferred by actigraphy had significant results (sleep efficiency with small CV and sleep latency, wake episodes and total wake episode duration with large CV). Six subjective parameters obtained from questionnaires and scales also had meaningful results (Pittsburgh Sleep Quality Index (sleep efficiency), Likert scale (Hooper), Likert scale (no reference), Liverpool Jet-Lag Questionnaire, Liverpool Jet-Lag Questionnaire (sleep rating) and RESTQ (sleep quality)).

Conclusions

These data suggest that sleep efficiency using actigraphy, Pittsburgh Sleep Quality Index, Likert scale, Liverpool Jet-Lag Questionnaire and RESTQ are indicated to monitor sleep quality in team sport athletes.

PROSPERO registration number

CRD42018083941.

Evening electronic device use and sleep patterns in athletes

The present study aimed to investigate pre-sleep behaviours (including evening electronic device use) and sleep quantity in well-trained athletes. Seventy well-trained athletes (44 females, 26 males) aged 21 ± 4 y from a range of team and individual sports were asked to complete an online sleep diary for 7 days. The sleep diary included questions about pre-sleep behaviours (e.g. napping, caffeine intake), electronic device use in the 2 h prior to bedtime (e.g. type of device and duration of use) and sleep (e.g. time in bed, sleep onset latency). On average, athletes spent 8:20 ± 1:21 h in bed each night. Associations between age, time in bed and sleepiness suggested that younger athletes spent more time in bed (B = -0.05, p = 0.001) but felt sleepier (r = -0.32, p < 0.01) than older athletes. On average, athletes mostly used electronic devices for 0-30 min prior to sleep. The use of multiple devices in the evening was associated with more perceived difficulty in falling asleep (B = 0.22, p = 0.03), but no associations existed with other sleep variables. In summary, younger athletes may require later start times or improved sleep quality to resolve excessive sleepiness.

Effects of training and competition on the sleep of elite athletes: a systematic review and meta-analysis

Objectives

To characterise the sleep of elite athletes and to identify factors associated with training and competition that negatively affect sleep.

Design

Prognosis systematic review.

Data sources

Three databases (PubMed, SCOPUS and SPORTDiscus) were searched from inception to 26 February 2018.

Eligibility criteria for selecting studies

Included studies objectively reported total sleep time (TST) and/or sleep efficiency (SE) in elite athletes. Studies were required to be observational or to include an observational trial.

Results

Fifty-four studies were included. During training, many studies reported athletes were unable to achieve TST (n=23/41) and/or SE (n=16/37) recommendations. On the night of competition, most studies reported athletes were unable to achieve TST (n=14/18) and/or SE (n=10/16) recommendations. TST was shorter (60 min) the night of competition compared with previous nights. SE was lower (1%) the night of competition compared with the previous night. TST was shorter the night of night competition (start ≥18:00; 80 min) and day competition (20 min) compared with the previous night. SE was lower (3%–4%) the night of night competition but unchanged the night of day competition compared with previous nights. Early morning training (start <07:00), increases in training load (>25%), late night/early morning travel departure times, eastward air travel and altitude ascent impaired sleep.

Conclusion

Athletes were often unable to achieve sleep recommendations during training or competition periods. Sleep was impaired the night of competition compared with previous nights. Early morning training, increases in training load, travel departure times, jet lag and altitude can impair athletes’ sleep.

PROSPERO registration number

CRD42017074367.