Sleep latency versus shuteye latency: Prevalence, predictors and relation to insomnia symptoms in a representative sample of adults

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use

GOAL AND AIMS

To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement.

FOCUS TECHNOLOGY

Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X.

REFERENCE TECHNOLOGY

Polysomnography.

SAMPLE

Sixty-three participants (36 female) aged 20-68.

DESIGN

Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message.

CORE ANALYTICS

Detection and timing accuracy for the sleep onset times and wake times.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods.

CORE OUTCOMES

Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency.

IMPORTANT ADDITIONAL OUTCOMES

With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit.

CORE CONCLUSIONS

Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

A prospective interventional study investigating sleep disorders prior to and during adjuvant radiotherapy for breast cancer

BACKGROUND

Most breast cancer patients with non-metastatic disease receive adjuvant local or loco-regional radiotherapy. To be scheduled for irradiation may cause distress and fears that can lead to sleep disorders. Few reports focused on sleep problems in patients assigned to radiotherapy. This study evaluates the course of sleep disorders during adjuvant radiotherapy for primary breast cancer and potential risk factors including the use of smartphones or tablets at bedtime.

METHODS

The main goal is the evaluation of sleep disorders prior to radiotherapy and after 15 fractions of radiotherapy. A potential effect of habituation to the procedure of radiotherapy can be assumed that will likely lead to improvement (decrease) of sleep disorders. Improvement of sleep disorders (compared to baseline before radiotherapy) is defined as decrease of the severity of sleep disorders by ≥2 points on a patient self-rating scale (0 = no problems; 10 = maximum problems) or decrease of distress caused by sleep disorders by ≥2 points on a self-rating scale (0 = no distress; 10 = maximum distress) or reduction of the dose of sleeping drugs by ≥25%. Additional endpoints include sleep disorders after 5 fractions and at the end of radiotherapy. Moreover, potential risk factors including the use of smartphones or tablets at bedtime are evaluated. Fifty-one patients (48 plus potential drop-outs) are required. With this sample size, a one-sample binomial test with a one-sided significance level of 2.5% has a power of 80% to yield statistical significance, if the rate of patients with improvement of sleep disorders is 25% (rate under the alternative hypothesis) and assuming that a decrease of ≤10% has to be judged as a random, non-causal change in this uncontrolled study setting (null hypothesis).

DISCUSSION

If a decrease of sleep disorders during the course of radiotherapy is shown, this aspect should be included in the pre-radiotherapy consent discussion with the patients. Moreover, identification of additional risk factors will likely lead to earlier psychological support. If the use of smartphones or tablets at bedtime is a risk factor, patients should be advised to change this behavior.

TRIAL REGISTRATION

clinicaltrials.gov (NCT04879264; URL: https://clinicaltrials.gov/show/NCT04879264 ); registered on 7th of May, 2021.

Lifestyle Modification Using a Wearable Biometric Ring and Guided Feedback Improve Sleep and Exercise Behaviors: A 12-Month Randomized, Placebo-Controlled Study

Purpose: Wearable biometric monitoring devices (WBMD) show promise as a cutting edge means to improve health and prevent disease through increasing accountability. By regularly providing real-time quantitative data regarding activity, sleep quality, and recovery, users may become more aware of the impact that their lifestyle has on their health. The purpose of this study was to examine the efficacy of a biometric tracking ring on improving sleep quality and increasing physical fitness over a one-year period. Methods: Fifty-six participants received a biometric tracking ring and were placed in one of two groups. One group received a 3-month interactive behavioral modification intervention (INT) that was delivered virtually via a smartphone app with guided text message feedback (GTF). The other received a 3-month non-directive wellness education control (CON). After three months, the INT group was divided into a long-term feedback group (LT-GTF) that continued to receive GTF for another nine months or short-term feedback group (ST-GTF) that stopped receiving GTF. Weight, body composition, and VO2max were assessed at baseline, 3months, and 12months for all participants and additionally at 6 and 9months for the ST-GTF and LT-GTF groups. To establish baseline measurements, sleep and physical activity data were collected daily over a 30-day period. Daily measurements were also conducted throughout the 12-month duration of the study. Results: Over the first 3months, the INT group had significant (p<0.001) improvements in sleep onset latency, daily step count, % time jogging, VO2max, body fat percentage, and heart rate variability (rMSSD HRV) compared to the CON group. Over the next 9months, the LT-GTF group continued to improve significantly (p<0.001) in sleep onset latency, daily step count, % time jogging, VO2max, and rMSSD HRV. The ST-GTF group neither improved nor regressed over the latter 9months except for a small increase in sleep latency. Conclusion: Using a WBMD concomitantly with personalized education, encouragement, and feedback, elicits greater change than using a WBMD alone. Additionally, the improvements achieved from a short duration of personalized coaching are largely maintained with the continued use of a WBMD.

Nodding off but can't disconnect: development and validation of the iNOD index of Nighttime Offline Distress

BACKGROUND

There is a pressing need to update sleep models, education and treatment to better reflect the realities of sleep in a 24/7 connected social world. Progress has been limited to date by available measurement tools, which have largely focused on the frequency or duration of individuals' social media use, without capturing crucial sleep-relevant aspects of this inherently social and interactive experience.

METHODS

Survey data from 3008 adolescents (aged 10-18 years) was used to rigorously develop and validate a new self-report measure that quantifies difficulty disengaging from social media interactions at night: the index of Nighttime Offline Distress (iNOD). Exploratory and Confirmatory Factor analyses in a random split sample produced a ten-item two-factor solution, with subscales capturing concerns about Staying Connected and Following Etiquette (Cronbach's alphas = 0.91 and 0.92 respectively).

RESULTS

Those with higher scores on these subscales tended to report using social media for longer after they felt they should be asleep (r_s = 0.41 and 0.26, respectively), shorter sleep duration (r_s = -0.24 and -0.17, respectively) and poorer sleep quality (r_s = -0.33 and -0.31, respectively). Results also pointed towards a potentially fragmented process of sleep displacement for those who may struggle to disconnect - and to stay disconnected - from social interactions in order to allow sufficient uninterrupted sleep opportunity.

CONCLUSIONS

These findings can inform current models for understanding normal and disordered sleep during adolescence, whilst highlighting specific social concerns as important potential targets for sleep education efforts.

Sleep in older adolescents. Results from a large cross-sectional, population-based study

The aim of the present study was to describe sleep patterns in a large and representative sample of Norwegian adolescents. The sample included 4,010 first-year high school students, aged 16-17 years (54% female), who completed a web-based survey on sleep patterns. The process of going to sleep was addressed as a two-step sequence of (a) shuteye latency (interval from bedtime to shuteye time) and (b) sleep onset latency (interval from shuteye time to sleep onset). Results showed that 84.8% of the adolescents failed to obtain the recommended amount of sleep (8+ h) on schooldays, and 49.4% obtained less than 7 h. Mean bedtime on schooldays was 10:33 PM, with rise time 8:19 h later (time in bed). The adolescents reported long school-day shuteye latency (43 min), limiting sleep opportunity to 7:36 h. Sleep onset latency was 32 min and mean school-day sleep duration was only 6:43 h. On free days, 26.3% of the adolescents obtained less than 8 h of sleep, and 11.7% obtained less than 7 h. Mean bedtime was 00:33 AM, time in bed was 10:35 h, shuteye latency was 39 min and sleep onset latency was 24 min. Mean free-day sleep duration was 8:38 h. There were sex differences in several sleep parameters, including shuteye latency. The results indicate that the majority of Norwegian adolescents fail to obtain the recommended amount of sleep (8+ h) on schooldays. Long shuteye latency appears to be a main driver for short school-day sleep duration in adolescents.