scholarly journals The First-Night Effect in Elite Sports: An Initial Glance on Polysomnography in Home-Based Settings

2021 ◽  
Vol 12 ◽  
Author(s):  
Annika Hof zum Berge ◽  
Michael Kellmann ◽  
Sarah Jakowski
Keyword(s):  
Effect Size ◽  
Elite Athletes ◽  
Sleep Onset ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Sleep Monitoring ◽  
Time In Bed ◽  
Home Based ◽  
Small Effect Size ◽  
First Night Effect

Self-applied portable polysomnography is considered a promising tool to assess sleep architecture in field studies. However, no findings have been published regarding the appearance of a first-night effect within a sport-specific setting. Its absence, however, would allow for a single night sleep monitoring and hence minimize the burden on athletes while still obtaining the most important variables. For this reason, the aim of the study was to assess whether the effect appears in home-based sleep monitoring of elite athletes.The study sample included eight male and 12 female German elite athletes from five different sports. Participants slept with a portable polysomnography for two nights, which they self-applied at night before going to bed. Time in bed and wake-up time in the morning were freely chosen by each individual athlete without any restrictions regarding time or sleeping environment. Participants were asked to keep the same location and time frame during the two days of monitoring and stick to their usual sleeping schedules. Sleep stages were manually scored using 30-s epochs. Sleep parameters and stages were later compared with the help of linear mixed models to investigate the factor time.Significant differences between the two nights were found for percentage of Non-REM sleep [T(19) = −2,10, p < 0.05, d = −0.47, 95%-CI (−7.23, −0.01)] with small effect size, Total Wake Time [T(19) = 2.30, p = 0.03, d = 0.51, 95%-CI (1.66, 35.17)], Sleep Efficiency [T(19) = −2.48, p = 0.02, d = −0.55, 95%-CI (−7.43, −0.63)], and Wake percentage [T(19) = 2.47, p = 0.02, d = 0.55, 95%-CI (0.61, 7.43)] with moderate effect sizes, and N3 Sleep Onset Latency [T(19) = 3.37, p < 0.01, d = 0.75, 95%-CI (7.15, 30.54)] with large effect size. Confidence Intervals for all other indices range from negative to positive values and hence specify, that parameters were not systematically negatively affected in the first night.Findings suggest that some individuals are more affected by the first-night effect than others. Yet, in order to keep the measurement uncertainties to a minimum, a more conservative approach with at least two monitoring nights should be used whenever possible, if no other supporting information on the athletes says otherwise.

scholarly journals P062 Sleep parameter validation of a home-based ballistocardiograph sleep tracker

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A41-A42
Author(s):  
M Kholghi ◽  
I Szollosi ◽  
M Hollamby ◽  
D Bradford ◽  
Q Zhang
Keyword(s):  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Daily Routine ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Sleep Monitoring ◽  
Significant Discrepancy ◽  
Home Based ◽  
Wake Detection

Abstract Introduction Consumer home sleep trackers are gaining popularity for objective sleep monitoring. Amongst them, non-wearable devices have little disruption in daily routine and need little maintenance. However, the validity of their sleep outcomes needs further investigation. In this study, the accuracy of the sleep outcomes of EMFIT Quantified Sleep (QS), an unobtrusive and non-wearable ballistocardiograph sleep tracker, was evaluated by comparing it with polysomnography (PSG). Methods 62 sleep lab patients underwent a single clinical PSG and their sleep measures were simultaneously collected through PSG and EMFIT QS. Total Sleep Time (TST), Wake After Sleep Onset (WASO), Sleep Onset Latency (SOL) and average Heart Rate (HR) were compared using paired t-tests and agreement analysed using Bland-Altman plots. Results EMFIT QS data loss occurred in 47% of participants. In the remaining 33 participants (15 females, with mean age of 53.7±16.5), EMFIT QS overestimated TST by 177.5±119.4 minutes (p<0.001) and underestimated WASO by 44.74±68.81 minutes (p<0.001). It accurately measured average resting HR and was able to distinguish SOL with some accuracy. However, the agreement between EMFIT QS and PSG on sleep-wake detection was very low (kappa=0.13, p<0.001). Discussion A consensus between PSG and EMFIT QS was found in SOL and average HR. There was a significant discrepancy and lack of consensus between the two devices in other sleep outcomes. These findings indicate that while EMFIT QS is not a credible alternative to PSG for sleep monitoring in clinical and research settings, consumers may find some benefit from longitudinal monitoring of SOL and HR.

scholarly journals Variations in Elite Female Soccer Players' Sleep, and Associations With Perceived Fatigue and Soccer Games

2021 ◽  
Vol 3 ◽  
Author(s):  
Frode Moen ◽  
Maja Olsen ◽  
Gunvor Halmøy ◽  
Maria Hrozanova
Keyword(s):  
Respiration Rate ◽  
Rem Sleep ◽  
Sleep Onset ◽  
Sleep Time ◽  
Self Report ◽  
Ultra Wideband ◽  
Soccer Players ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Time In Bed

The current study investigated the associations between female perceived fatigue of elite soccer players and their sleep, and the associations between the sleep of players and soccer games. The sample included 29 female elite soccer players from the Norwegian national soccer team with a mean age of ~26 years. Perceived fatigue and sleep were monitored over a period of 124 consecutive days. In this period, 12.8 ± 3.9 soccer games per player took place. Sleep was monitored with an unobtrusive impulse radio ultra-wideband Doppler radar (Somnofy). Perceived fatigue was based on a self-report mobile phone application that detected daily experienced fatigue. Multilevel analyses of day-to-day associations showed that, first, increased perceived fatigue was associated with increased time in bed (3.6 ± 1.8 min, p = 0.037) and deep sleep (1.2 ± 0.6 min, p = 0.007). Increased rapid eye movement (REM) sleep was associated with subsequently decreased perceived fatigue (−0.21 ± 0.08 arbitrary units [AU], p = 0.008), and increased respiration rate in non-REM sleep was associated with subsequently increased fatigue (0.27 ± 0.09 AU, p = 0.002). Second, game night was associated with reduced time in bed (−1.0 h ± 8.4 min, p = <0.001), total sleep time (−55.2 ± 6.6 min, p = <0.001), time in sleep stages (light: −27.0 ± 5.4 min, p = <0.001; deep: −3.6 ± 1.2 min, p = 0.001; REM: −21.0 ± 3.0 min, p = <0.001), longer sleep-onset latency (3.0 ± 1.2 min, p = 0.013), and increased respiration rate in non-REM sleep (0.32 ± 0.08 respirations per min, p = <0.001), compared to the night before the game. The present findings show that deep and REM sleep and respiration rate in non-REM sleep are the key indicators of perceived fatigue in female elite soccer players. Moreover, sleep is disrupted during game night, likely due to the high physical and mental loads experienced during soccer games. Sleep normalizes during the first and second night after soccer games, likely preventing further negative performance-related consequences.

507 Clinical Impression of Excessive Daytime Sleepiness (EDS) at initial Sleep Medicine (SM) consultation and its clinical correlates

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A199-A200
Author(s):  
Leon Rosenthal ◽  
Raúl Aguilar Roblero
Keyword(s):  
Lower Energy ◽  
Energy Levels ◽  
Sleep Onset ◽  
Sleep Time ◽  
Clinical Impression ◽  
Sleep Onset Latency ◽  
Convenience Sample ◽  
Time In Bed ◽  
Number Of Patients ◽  
Cardinal Symptom

Abstract Introduction EDS represents a cardinal symptom in SM. Use of subjective scales are prevalent, which have a modest correlation with the MSLT. While the Clinical Global Impression has been used in research, reports of clinical impression (CI) in medical practice are lacking. We report on the CI of EDS in a convenience sample of patients undergoing initial consultation. Methods Patients reported primary, secondary symptoms and completed the Sleep Wake Activity Inventory (SWAI) prior to Tele-Medicine consultation. A SM physician completed the assessment which included ascertainment of CI of EDS (presence S+ / absence S-). Results There were 39 ♂and 13 ♀. The CI identified 26 patients in each group (S+/S-). Age (52 [14]), BMI (33 [7]), reported time in bed, sleep time, sleep onset latency and # of awakenings did not differ. All identified a primary symptom (S-: 21, S+: 19 reported snoring or a previous Dx of OSA). Sleepiness as a 1ry or 2ry symptom was identified by 0 in the S- and by 13 in the S+ groups. Refreshing quality of sleep differed (χ2 <0.05): un-refreshing sleep was reported by 7 (S-) and by 13 (S+). Naps/week: 0.7 [1.5] and 1.57 [1.5] for the S-, S+ groups respectively (p<0.05). A main effect (p<0.01) was documented on the SWAI. We report on the Sleepiness [SS] and Energy Level [EL] scales (lower scores on the SS reflect higher sleepiness while lower scores on EL denote higher energy). Higher sleepiness (p<0.01) 43 [12] and lower energy levels 24 [6] (p<0.05) were documented on the S+ group (S- 61 [17], and 18 [6] respectively). Available spouse’s Epworth score on 29 patients: S- patients 5.8 [4] and S+ 10.2 [6] (p<0.05). Dx of OSA was identified among all but 1 in the S+ group. Also, Insomnia was diagnosed among 11 (S-) and 19 (S+) patients (p<0.05) despite only 3 and 7 (respectively) identifying it as a presenting symptom. Conclusion While snoring or previous Dx of OSA were prevalent motivations for consultation, sleepiness and insomnia were clinically relevant among a substantial number of patients. Unrefreshing sleep, daytime naps, lower energy, and higher sleepiness were ubiquitous among S+ patients. Support (if any):

A Comparative Study of Sleep and Mood Between Young Elite Athletes and Age-Matched Controls

2017 ◽  
Vol 14 (6) ◽  
pp. 465-473 ◽  
Author(s):  
Anette Harris ◽  
Hilde Gundersen ◽  
Pia Mørk Andreassen ◽  
Eirunn Thun ◽  
Bjørn Bjorvatn ◽  
...  
Keyword(s):  
Physical Activity ◽  
Total Sleep Time ◽  
Elite Athletes ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Social Jetlag ◽  
Weekly Training

Background:Sleep and mood have seldom been compared between elite athletes and nonelite athletes, although potential differences suggest that physical activity may affect these parameters. This study aims to explore whether adolescent elite athletes differ from controls in terms of sleep, positive affect (PA) and negative affect (NA).Methods:Forty-eight elite athletes and 26 controls participating in organized and nonorganized sport completed a questionnaire, and a 7-day sleep diary.Results:On school days, the athletes and the controls who participated in organized and nonorganized sport differed in bedtime (22:46, 23:14, 23:42, P < .01), sleep onset (23:03, 23:27, 00:12, P < .01), and total sleep time (7:52, 8:00, 6:50, P < 01). During weekend, the athletes, the controls who participated in organized and nonorganized sport differed in bedtime (23:30, 00:04, 00:49, P < .01), sleep onset (23.42, 00:18, 01:13, P < .01), rise time (9:15, 9:47, 10:55, P < .01), sleep efficiency (95.0%, 94.2%, 90.0%, P < 05), and sleep onset latency (11.8, 18.0, 28.0 minutes, P < .01). Furthermore, the athletes reported less social jetlag (0:53) and higher score for PA (34.3) compared with the controls who participated in nonorganized sport (jetlag: 1:25, P < .05, PA: 29.8, P < .05).Conclusions:An almost dose-response association was found between weekly training hours, sleep, social jetlag and mood in adolescents.

Utility of Activity Monitors and Thermometry in Assessing Sleep Stages and Sleep Quality

2018 ◽  
Vol 1 (3) ◽  
pp. 108-121
Author(s):  
Natashia Swalve ◽  
Brianna Harfmann ◽  
John Mitrzyk ◽  
Alexander H. K. Montoye
Keyword(s):  
Sleep Quality ◽  
Skin Temperature ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Activity Monitors ◽  
Research Grade

Activity monitors provide an inexpensive and convenient way to measure sleep, yet relatively few studies have been conducted to validate the use of these devices in examining measures of sleep quality or sleep stages and if other measures, such as thermometry, could inform their accuracy. The purpose of this study was to compare one research-grade and four consumer-grade activity monitors on measures of sleep quality (sleep efficiency, sleep onset latency, and wake after sleep onset) and sleep stages (awake, sleep, light, deep, REM) against an electroencephalography criterion. The use of a skin temperature device was also explored to ascertain whether skin temperature monitoring may provide additional data to increase the accuracy of sleep determination. Twenty adults stayed overnight in a sleep laboratory during which sleep was assessed using electroencephalography and compared to data concurrently collected by five activity monitors (research-grade: ActiGraph GT9X Link; consumer-grade: Fitbit Charge HR, Fitbit Flex, Jawbone UP4, Misfit Flash) and a skin temperature sensor (iButton). The majority of the consumer-grade devices overestimated total sleep time and sleep efficiency while underestimating sleep onset latency, wake after sleep onset, and number of awakenings during the night, with similar results being seen in the research-grade device. The Jawbone UP4 performed better than both the consumer- and research-grade devices, having high levels of agreement overall and in epoch-by-epoch sleep stage data. Changes in temperature were moderately correlated with sleep stages, suggesting that addition of skin temperature could increase the validity of activity monitors in sleep measurement.

scholarly journals Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Isaac Moshe ◽  
Yannik Terhorst ◽  
Kennedy Opoku Asare ◽  
Lasse Bosse Sander ◽  
Denzil Ferreira ◽  
...  
Keyword(s):  
Multilevel Models ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Wearable Devices ◽  
Screening Methods ◽  
Sleep Onset Latency ◽  
Depression And Anxiety ◽  
Symptoms Of Depression ◽  
Time In Bed

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

scholarly journals Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Shahab Haghayegh ◽  
Sepideh Khoshnevis ◽  
Michael H Smolensky ◽  
Kenneth R Diller ◽  
Richard J Castriotta
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Meta Analysis ◽  
Body Movement ◽  
Sleep Onset ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Exclusion Criteria ◽  
Significant Difference ◽  
Sleep Parameters

BACKGROUND Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. OBJECTIVE We conducted a systematic review of publications reporting on the performance of wristband <italic>Fitbit</italic> models in assessing sleep parameters and stages. METHODS In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword <italic>Fitbit</italic> to identify relevant publications meeting predefined inclusion and exclusion criteria. RESULTS The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging <italic>Fitbit</italic> models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=8.8%, <italic>P</italic>=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=24.0%, <italic>P</italic>=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92) and there was no significant difference in sleep onset latency (SOL; <italic>P</italic>=.37; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92). In reference to PSG, nonsleep-staging <italic>Fitbit</italic> models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation <italic>Fitbit</italic> models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging <italic>Fitbit</italic> models, in comparison to PSG, showed no significant difference in measured values of WASO (<italic>P</italic>=.25; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92), TST (<italic>P</italic>=.29; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.98), and SE (<italic>P</italic>=.19) but they underestimated SOL (<italic>P</italic>=.03; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.66). Sleep-staging <italic>Fitbit</italic> models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. CONCLUSIONS Sleep-staging <italic>Fitbit</italic> models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Efficacy of Micronized Progesterone for Sleep: A Systematic Review and Meta-analysis of Randomized Controlled Trial Data

2020 ◽  
Author(s):  
Brendan J Nolan ◽  
Bonnie Liang ◽  
Ada S Cheung
Keyword(s):  
Systematic Review ◽  
Randomized Controlled Trials ◽  
Effect Size ◽  
Meta Analysis ◽  
Sleep Onset ◽  
Sleep Time ◽  
Controlled Trials ◽  
Sleep Onset Latency ◽  
Randomized Controlled ◽  
Micronized Progesterone

Abstract Context Preclinical data has shown progesterone metabolites improve sleep parameters through positive allosteric modulation of the γ-aminobutyric acid type A receptor. We undertook a systematic review and meta-analysis of randomized controlled trials to assess micronized progesterone treatment on sleep outcomes. Evidence Acquisition Using preferred reporting items for systematic review and meta-analysis guidelines, we searched MEDLINE, Embase, PsycInfo, and the Cochrane Central Register of Controlled Trials for randomized controlled trials of micronized progesterone treatment on sleep outcomes up to March 31, 2020. This study is registered with the International Prospective Register of Systematic Reviews, number CRD42020165981. A random effects model was used for quantitative analysis. Evidence Synthesis Our search strategy retrieved 9 randomized controlled trials comprising 388 participants. One additional unpublished trial was found. Eight trials enrolled postmenopausal women. Compared with placebo, micronized progesterone improved various sleep parameters as measured by polysomnography, including total sleep time and sleep onset latency, though studies were inconsistent. Meta-analysis of 4 trials favored micronized progesterone for sleep onset latency (effect size, 7.10; confidence interval [CI] 1.30, 12.91) but not total sleep time (effect size, 20.72; CI -0.16, 41.59) or sleep efficiency (effect size, 1.31; CI -2.09, 4.70). Self-reported sleep outcomes improved in most trials. Concomitant estradiol administration and improvement in vasomotor symptoms limit conclusions in some studies. Conclusions Micronized progesterone improves various sleep outcomes in randomized controlled trials, predominantly in studies enrolling postmenopausal women. Further research could evaluate the efficacy of micronized progesterone monotherapy using polysomnography or validated questionnaires in larger cohorts.

scholarly journals 0402 Impact of Various Actigraphic Editing Approaches on Sleep/Wake Outputs in Adults with Insomnia and Healthy Sleepers

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A154-A154
Author(s):  
C E Kline ◽  
M E Egeler ◽  
A G Kubala ◽  
S R Patel ◽  
H M Lehrer ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Sleep Onset ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Linear Mixed Effects ◽  
Automated Algorithm ◽  
Time In Bed ◽  
Patient Status ◽  
Approach Time

Abstract Introduction Actigraphy data can be edited using a variety of approaches. However, whether time-intensive manual editing provides different sleep/wake estimates compared to other approaches is unknown. The purpose of this study was to compare sleep/wake data obtained from a standardized editing approach that incorporates multiple inputs versus three other common approaches. Methods 72 adults (33.8±11.1 y, 74% female, 71% white) provided 1022 nights of data for analysis; 45 were healthy sleepers (678 nights) and 27 met DSM-5 criteria for insomnia. Participants wore an Actiwatch Spectrum on their nondominant wrist and completed a sleep diary for 3-24 nights. Each night’s rest interval was set using four different approaches: (1) a standardized process based upon published guidelines (Patel et al., Sleep 2015) that incorporates a hierarchical order of multiple inputs (event marker, light, diary, activity; STANDARD); (2) software-provided automated algorithm (AUTO); (3) automated algorithm with incorporation of event markers (AUTOE); and (4) sleep diary (DIARY). We used linear mixed-effects models to evaluate whether sleep/wake parameters differed between the STANDARD and other editing approaches, accounting for patient status (healthy sleeper, insomnia) and the possibility that differences among editing approaches may be dependent on patient status. Results All results are expressed relative to the STANDARD approach. Bedtime was 36.1±5.1 min earlier (P&lt;.0001) and morning out-of-bed time was 13.6±5.7 min later (P=.02) using the AUTO (P&lt;.0001) approach. Time in bed was 42.3±4.7 min longer with AUTO (P&lt;.0001). Sleep onset latency was 11.7±1.4 min and 2.8±1.4 min longer for AUTO (P&lt;.0001) and DIARY (P=.05), respectively. Sleep duration was 22.5±4.4 min longer with AUTO (P&lt;.0001). Wake after sleep onset was 6.8±1.2 min greater with AUTO (P&lt;.0001). Similar patterns were observed for all sleep/wake measures among healthy sleepers and adults with insomnia. Conclusion A standardized approach to editing actigraphy data leads to different sleep/wake estimates compared to other common approaches, though the differences were often small in magnitude and not dependent upon sleep status. Most notably, reliance upon the automated algorithm yielded longer time in bed, sleep duration, sleep onset latency, and wake after sleep onset compared to the standardized approach. Support NIH K23HL118318

scholarly journals 0519 Mindfulness Based Therapy for Insomnia Improves Objective Markers of Sleep in the Elderly: Preliminary Data from the Mindfulness Sleep Therapy (MIST) Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A198-A199
Author(s):  
K F Wong ◽  
F Perini ◽  
S L Henderson ◽  
J Teng ◽  
Z Hassirim ◽  
...  
Keyword(s):  
Preliminary Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Controlled Trial ◽  
Sleep Onset ◽  
Sleep Diary ◽  
Control Group ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Time In Bed

Abstract Introduction Mindfulness-based treatment for insomnia (MBTI) is a viable intervention for improving poor sleep. We report preliminary data from an ongoing pre-registered, randomized controlled trial which investigates the effect of MBTI on elderly adults. Methods Participants above 50 years old with PSQI ≥ 5 were recruited and randomised into either MBTI or an active control group (Sleep hygiene education and exercise program, SHEEP) in sequential cohorts with about 20 participants per cohort (10 per group). Before and after the intervention, 1 night of portable polysomnography (PSG) and 1 week of actigraphy (ACT) and sleep diary (DIARY) data were collected. We report the ACT and DIARY results of the first 3 cohorts (n = 46, male = 23, mean age = 62.3, std = 6.3) and PSG data of the first 2 cohorts (n = 29, male = 12, mean age = 62.5, std = 5.7). Time in bed (TIB), total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were analysed with mixed-model repeated-measures ANOVA. Results We observed increases in TIBDIARY (F1,44 = 5.151, p &lt; .05) and SEDIARY (F1,44 = 22.633, p &lt; .0001), and significant reductions in SOLDIARY (F1,44 = 7.031, p &lt; .05) and WASODIARY (F1,39 = 7.411, p &lt; .05). In the actigraphy data, we found a significant interaction in SOLACT (F1,39 = 4.273, p &lt; .05) with an increase in SHEEP SOLACT (t18= 2.36, p &lt; .05). Significant reductions were also observed in WASOACT (F1,44 = 16.459, p &lt; .0001) Finally, we observed a reduction in SOLPSG (F1,26 = 5.037, p &lt;. 05). All other tests were non-significant. Conclusion Preliminary results suggest that both interventions lead to improvements in sleep with more pronounced effects in subjective sleep reports. Objective sleep data suggest that improvements in sleep is a result of improved sleep quality and not simply extending sleep opportunity. These preliminary data shows that MBTI may be a promising intervention for elderly individuals with sleep difficulties. Support This study was supported by an award from the 7th grant call of the Singapore Millennium Foundation Research Grant Programme

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