A validation study of Fitbit Charge 2™ compared with polysomnography in adults

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2?), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults.

In-lab PSG and Fitbit Charge 2? data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2? sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis.

In the main group, Fitbit Charge 2? showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2? significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2? outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2? correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group.

Fitbit Charge 2? shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2? accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).     

R-wave synchronised atrial pacing in pediatric patients with postoperative junctional ectopic tachycardia: the atrioventricular interval investigated by computational analysis and clinical evaluation

Background

R-wave synchronised atrial pacing is an effective temporary pacing therapy in infants with postoperative junctional ectopic tachycardia. In the technique currently used, adverse short or long intervals between atrial pacing and ventricular sensing (AP–VS) may be observed during routine clinical practice.

Objectives

The aim of the study was to analyse outcomes of R-wave synchronised atrial pacing and the relationship between maximum tracking rates and AP–VS intervals.

Methods

Calculated AP–VS intervals were compared with those predicted by experienced pediatric cardiologist.

Results

A maximum tracking rate (MTR) set 10 bpm higher than the heart rate (HR) may result in undesirable short AP–VS intervals (minimum 83 ms). A MTR set 20 bpm above the HR is the hemodynamically better choice (minimum 96 ms). Effects of either setting on the AP–VS interval could not be predicted by experienced observers. In our newly proposed technique the AP–VS interval approaches 95 ms for HR > 210 bpm and 130 ms for HR < 130 bpm. The progression is linear and decreases strictly (? 0.4 ms/bpm) between the two extreme levels.

Conclusions

Adjusting the AP–VS interval in the currently used technique is complex and may imply unfavorable pacemaker settings. A new pacemaker design is advisable to allow direct control of the AP–VS interval.

     

Sleep patterns and nocturnal cardiac autonomic activity in female athletes are affected by the timing of exercise and match location

The aim of the current study was to analyze the influence of the timing of training and matches, and match location, on sleep pattern and nocturnal cardiac autonomic activity in female soccer players. A total of 17 female soccer players (age: 21.6 ± 2.3 years; mean ± SD) wore wrist actigraph units and heart rate (HR) monitors during night-sleep throughout 18 night-training days (NTD), 18 resting days (RD), and 6 match-days (MD; 3 away-matches [AM] and 3 home-matches [HM]) of the competitive period. Training load was quantified by session rating of perceived exertion (sRPE), heart rate (HR), training impulse (TRIMP), and subjective well-being. In NTD, sleep onset time and total sleep time were significantly impaired compared to MD (+ 1:47 [0:48; 1:55] hours and ?1:35 [?1:42; ?0:57] hours, respectively; p< 0.001; mean difference ± 95% confidence interval [CI]) and RD (+ 1:31 [0:52; 1:49] hours and ?1:26 [?1:28; ?0:55] hours, respectively; p< 0.001). In AM, sleep onset time was delayed compared to HM (+ 0:36 [0:12; 0:44] hours; p< 0.001). Sleep latency was higher in NTD compared to RD (+ 4 [2; 5] min; p< 0.001), as well as after AM compared to HM (+ 5 [1; 7] min; p< 0.001). HR during sleep was significantly increased after NTD and MD compared to RD (+ 4 [1; 5] b.p.m and + 3 [1; 4] b.p.m, respectively; p< 0.001). Furthermore, HR variability decreased after NTD and MD compared to RD (e.g., lnRMSSD, ?0.09 [?0.18; ?0.08] ln[ms] and ?0.17 [?0.27; ?0.11] ln[ms], respectively; p< 0.001). Overall, the time of day for soccer practice (i.e., training at night) and match location (i.e., away matches) may cause disruption in sleep patterns and/or in autonomic cardiac activity in female soccer players. Additionally, playing official matches during the day and training at night may affect nocturnal cardiac autonomic activity by decreasing vagal-related modulation and increasing HR during sleep.     

Evaluation of a consumer fitness-tracking device to assess sleep in adults

Wearable fitness-tracker devices are becoming increasingly available. We evaluated the agreement between Jawbone UP and polysomnography (PSG) in assessing sleep in a sample of 28 midlife women. As shown previously, for standard actigraphy, Jawbone UP had high sensitivity in detecting sleep (0.97) and low specificity in detecting wake (0.37). However, it showed good overall agreement with PSG with a maximum of two women falling outside Bland–Altman plot agreement limits. Jawbone UP overestimated PSG total sleep time (26.6?±?35.3?min) and sleep onset latency (5.2?±?9.6?min), and underestimated wake after sleep onset (31.2?±?32.3?min) (p’s?<?0.05), with greater discrepancies in nights with more disrupted sleep. The low-cost and wide-availability of these fitness-tracker devices may make them an attractive alternative to standard actigraphy in monitoring daily sleep–wake rhythms over several days.     

Permanent night workers´ sleep and psychosocial factors in hospital work. A comparison to day and shift work

ABSTRACT

We aimed to study whether permanent night workers sleep and psychosocial factors differ from day workers and shift workers. The participants (n = 9 312, 92% females, average age 45 years, most commonly nurses and departmental secretaries) were day workers (DW, n = 2 672), shift workers (SW, n = 6 486) and permanent night workers (PNW, n = 154). The Finnish Public Sector survey responses from six hospital districts from 2012 were combined to payroll data from 91 days preceding the survey. The data were analyzed using Pearson χ²-test, one-way ANOVA and multinomial logistic regression analysis. The PNWs reported slightly longer average sleep length than the SWs or the DWs (7:27 vs. 7:13 and 7:10 h, p < 0.001). The PNWs reported least often difficulties in maintaining sleep (p < 0.001) compared to the SWs and the DWs. The PNWs reported most often difficulties to fall asleep and fatigue during free-time (p-values <0.001). The DWs and PNWs experienced less often work-life conflict than the SWs (25 and 26 vs. 38%, p < 0.001). The PNWs were more often satisfied with autonomy at work and appreciation and fair treatment by colleagues than the DWs or the SWs (p < 0.001). The SWs and PNWs reported remarkably higher occurrence of verbal (p < 0.001, OR 3.71, 95% CI 3.23–4.27 and OR 7.67, 95% CI 5.35–10.99, respectively) and physical workplace violence (p < 0.001, OR 9.24, 95% CI 7.17–11.90 and OR 28.34, 95% CI 16.64–43.06, respectively) compared to DWs. Conclusively, PNWs reported contradictory differences in sleep quality compared to DWs and SWs. PNWs are more often satisfied with their colleagues and autonomy at work than DWs or SWs but face workplace violence remarkably more often.     

Different times of day do not change heart rate variability recovery after light exercise in sedentary subjects: 24 hours Holter monitoring

Incidence of cardiovascular events follows a circadian rhythm with peak occurrence during morning. Disturbance of autonomic control caused by exercise had raised the question of the safety in morning exercise and its recovery. Furthermore, we sought to investigate whether light aerobic exercise performed at night would increase HR and decrease HRV during sleep. Therefore, the aim of this study was to test the hypothesis that morning exercise would delay HR and HRV recovery after light aerobic exercise, additionally, we tested the impact of late night light aerobic exercise on HR and HRV during sleep in sedentary subjects. Nine sedentary healthy men (age 24 ± 3 yr; height 180 ± 5 cm; weight 79 ± 8 kg; fat 12 ± 3%; mean±SD) performed 35 min of cycling exercise, at an intensity of first anaerobic threshold, at three times of day (7 a.m., 2 p.m. and 11 p.m.). R-R intervals were recorded during exercise and during short-time (60 min) and long-time recovery (24 hours) after cycling exercise. Exercise evoked increase in HR and decrease in HRV, and different times of day did not change the magnitude (p < 0.05 for time). Morning exercise did not delay exercise recovery, HR was similar to rest after 15 minutes recovery and HRV was similar to rest after 30 minutes recovery at morning, afternoon, and night. Low frequency power (LF) in normalized unites (n.u.) decreased during recovery when compared to exercise, but was still above resting values after 60 minutes of recovery. High frequency power (HF-n.u.) increased after exercise cessation (p < 0.05 for time) and was still below resting values after 60 minutes of recovery. The LF/HF ratio decreased after exercise cessation (p < 0.05 for time), but was still different to baseline levels after 60 minutes of recovery. In conclusion, morning exercise did not delay HR and HRV recovery after light aerobic cycling exercise in sedentary subjects. Additionally, exercise performed in the night did change autonomic control during the sleep. So, it seems that sedentary subjects can engage physical activity at any time of day without higher risk.     

Intra-individual variability in the sleep of senior and junior rugby league athletes during the competitive season

This study examined the sleep intra-individual variability (IIV) of rugby league athletes across senior and junior levels during one week of the competitive season. Forty-five rugby league athletes across elite senior, sub-elite senior and elite junior levels each wore actigraphy monitors for seven days during the competitive season, and completed a subjective sleep diary each morning upon waking. Linear mixed models were used to assess differences in sleep measures between playing levels. Intra-individual standard deviations for each sleep measure were calculated for each athlete to determine their respective IIV, allowing differences in IIV between groups to be assessed. Elite junior athletes went to bed later (ES = 0.94 ± 0.50, p < 0.05) and woke later than elite senior athletes (ES = 0.94 ± 0.40, p < 0.05), while also displaying greater IIV when considering time in bed (ES = 1.14 ± 0.60, p < 0.05) and sleep duration (ES = 1.53 ± 0.64, p < 0.05) compared with elite senior athletes. Similarly, IIV was more pronounced in elite junior players for time in bed (ES = 0.88 ± 0.60, p < 0.05) and sleep duration (ES = 1.03 ± 0.64, p < 0.05) compared with sub-elite senior athletes. Despite this, elite junior athletes still obtained sufficient sleep duration, efficiency and quality. The outcomes of this investigation show a distinct difference in the habitual sleep-wake patterns of senior and junior rugby league athletes, and show how sleep IIV differs between playing levels in rugby league.     

Melatonin ingestion after exhaustive late-evening exercise improves sleep quality and quantity,and short-term performances in teenage athletes

ABSTRACT

The present study aimed to explore the effects of a single 10-mg dose of melatonin (MEL) administration after exhaustive late-evening exercise on sleep quality and quantity, and short-term physical and cognitive performances in healthy teenagers. Ten male adolescent athletes (mean ± SD, age = 15.4 ± 0.3 years, body-mass = 60.68 ± 5.7 kg, height = 167.9 ± 6.9 cm and BMI = 21.21 ± 2.5) performed two test sessions separated by at least one week. During each session, participants completed the Yo-Yo intermittent-recovery-test level-1 (YYIRT-1) at ~20:00 h. Then, sleep polysomnography was recorded from 22:15 min to 07:00 h, after a double blind randomized order administration of a single 10-mg tablet of MEL (MEL-10 mg) or Placebo (PLA). The following morning, Hooper wellness index was administered and the participants performed the Choice Reaction Time (CRT) test, the Zazzo test and some short-term physical exercises (YYIRT-1, vertical and horizontal Jumps (VJ; HJ), Hand grip strength (HG), and five-jump test (5-JT)). Evening total distance covered in the YYIRT-1 did not change during the two conditions (p > 0.05). Total sleep time (Δ = 24.55 mn; p < 0.001), sleep efficiency (Δ = 4.47%; p < 0.001), stage-3 sleep (N3 sleep) (Δ = 1.73%; p < 0.05) and rapid-eye-movement sleep (Δ = 2.15%; p < 0.001) were significantly higher with MEL in comparison with PLA. Moreover, sleep-onset-latency (Δ = –8.45mn; p < 0.001), total time of nocturnal awakenings after sleep-onset (NA) (Δ = –11 mn; p < 0.001), stage-1 sleep (N1 sleep) (Δ = –1.7%; p < 0.001) and stage-2 sleep (N2 sleep) (Δ = ?1.9%; p < 0.05) durations were lower with MEL. The Hooper index showed a better subjective sleep quality, a decrease of the subjective perception of fatigue and a reduced level of muscle soreness with MEL. Moreover, MEL improved speed and performance but not inaccuracy during the Zazzo test. CRT was faster with MEL. Morning YYIRT-1 (Δ = 82 m; p < 0.001) and 5-JT (Δ = 0.08 m; p < 0.05) performances were significantly higher with MEL in comparison with PLA. In contrast, HG, VJ and HJ performances did not change during the two conditions (p > 0.05). The administration of a single dose of MEL-10 mg after strenuous late-evening exercise improved sleep quality and quantity, selective attention, subjective assessment of the general wellness state, and some short-term physical performances the following morning in healthy teenagers.     

Associations between chronotype,morbidity and mortality in the UK Biobank cohort

Later chronotype (i.e. evening preference) and later timing of sleep have been associated with greater morbidity, including higher rates of metabolic dysfunction and cardiovascular disease (CVD). However, no one has examined whether chronotype is associated with mortality risk to date. Our objective was to test the hypothesis that being an evening type is associated with increased mortality in a large cohort study, the UK Biobank. Our analysis included 433 268 adults aged 38–73 at the time of enrolment and an average 6.5-year follow-up. The primary exposure was chronotype, as assessed through a single self-reported question-defining participants as definite morning types, moderate morning types, moderate evening types or definite evening types. The primary outcomes were all-cause mortality and mortality due to CVD. Prevalent disease was also compared among the chronotype groups. Analyses were adjusted for age, sex, ethnicity, smoking, body mass index, sleep duration, socioeconomic status and comorbidities. Greater eveningness, particularly being a definite evening type, was significantly associated with a higher prevalence of all comorbidities. Comparing definite evening type to definite morning type, the associations were strongest for psychological disorders (OR 1.94, 95% CI 1.86–2.02, p = < 0.001), followed by diabetes (OR 1.30, 95% CI 1.24–1.36, p = < 0.001), neurological disorders (OR 1.25, 95% CI 1.20–1.30, p = < 0.001), gastrointestinal/abdominal disorders (OR 1.23, 95% CI 1.19–1.27, p = < 0.001) and respiratory disorders (OR 1.22, 95% CI 1.18–1.26, p = < 0.001). The total number of deaths was 10 534, out of which 2127 were due to CVD. Greater eveningness, based on chronotype as an ordinal variable, was associated with a small increased risk of all-cause mortality (HR 1.02, 95% CI 1.004–1.05, p = 0.017) and CVD mortality (HR 1.04, 95% CI 1.00–1.09, p = 0.06). Compared to definite morning types, definite evening types had significantly increased risk of all-cause mortality (HR 1.10, 95% CI 1.02–1.18, p = 0.012). This first report of increased mortality in evening types is consistent with previous reports of increased levels of cardiometabolic risk factors in this group. Mortality risk in evening types may be due to behavioural, psychological and physiological risk factors, many of which may be attributable to chronic misalignment between internal physiological timing and externally imposed timing of work and social activities. These findings suggest the need for researching possible interventions aimed at either modifying circadian rhythms in individuals or at allowing evening types greater working hour flexibility.     

Subjective and objective sleep among air ambulance personnel

ABSTRACT

The present study aimed to investigate the effects of shift work on sleep among pilots and Helicopter Emergency Medical Service crew members (HCM) in the Norwegian Air Ambulance. Sleep was assessed by diaries and actigraphy during a workweek (24 h duty for 7 consecutive days) in the winter season and a workweek during the summer season in pilots and HCM (N = 50). Additionally, differences in sleep were studied between the week before work, the workweek, and the week after work in both seasons. Results indicated that bedtime was later (p <.001) and time spent in bed (p <.05) was shorter during the summer, compared to the winter, season. The workers delayed the sleep period in the workweek, compared to the week before (winter: p <.001, summer: p <.001) and the week after (winter: p <.05-.001, summer: p <.001). They spent more time in bed during the workweek, compared to the week before (winter: p <.001, summer: p <.01) and after (winter: p <.001, summer: p =.37). Further, the workers had longer wake after sleep onset during the workweek, compared to the week before (winter: p <.001, summer: p <.01) and the week after (winter: p <.01, summer: p <.01). Finally, the workers had lower sleep efficiency during the workweek recorded by actigraphy compared to the week before (winter: p <.01, summer: p <.001) and the week after (winter: p <.01, summer: p <.001). According to the sleep diaries the total sleep time was 7:17 h in the winter and 7:03 h in the summer season. Overall, the sleep was somewhat affected during the workweek, with delayed sleep period, longer wake after sleep onset, and lower sleep efficiency compared to when off work. However, the workers spent more time in bed during the workweek compared to the weeks off, and they obtained over 7 h of sleep in both workweeks. Our findings suggest that the pilots and the HCM sleep well during the workweek, although it affected their sleep to some extent.     

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

ABSTRACT

The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here, we performed a retrospective EHR study based on a cohort of 3,652 individuals with self-reported sleep behaviors documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m²) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was 1 hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p < 10^?12). Sleep duration varied considerably as a function of age and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q < 0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n = 555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p < 0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.     

Effects of chronotype and environmental factors upon sleep and mental health in Japanese students aged 18–40 yrs

An integrated questionnaire was administered to a total of 4142 (2137 women, 2005 men; answer rate: 94.4%) university students and medical training schools students aged 18–40 years. The survey was carried out between May and October, 2004–2013. This questionnaire consisted of assessment of diurnal type, questions on sleep habits, mental health (upset emotionally, irritated, angered by small triggers, and suppressed), frequency of watching TV at night, use of mobile phone and playing TV games between 21:00 and 03:00 h, and questions on lighting conditions during the daytime and night. Sleep length in evening-type students (E-type; diurnal-type scores = 7–12) was shorter than in intermediate-types (I-type; diurnal-type scale (DTS) = 13–16) and morning-types (M-type; DTS = 17–28) on weekdays (p < 0.001), whereas sleep length in evening-types was shorter than intermediate- and morning-type students at weekends (p < 0.001). Mental health index scores of the E-type students were significantly lower than those of I-type and M-type students in both sexes (p < 0.001). Seventy-three percent of E-type women students watched TV after 23:00 h, significantly higher than 65.0 and 52.5% of I-type and M-type females, respectively (p < 0.001), and 70.4% of E-type male students watched the TV after 23:00 h, significantly higher than 66.1 and 59.7% of I-type and M-type males, respectively (p = 0.001). With regard to lighting conditions in the room in the afternoon, a slightly lower, but significantly so, percentage of the E-type students used the sunlight coming through the window than did the other types (p < 0.001). The frequency of having three nutritionally rich meals (especially breakfast) – including carbohydrates, proteins, vitamins, and minerals – was significantly higher in M-type than I-types and E-types (p < 0.001). Premenstrual syndrome (PMS) was significantly more severe in E-type than I-type and M-type females (p = 0.002). Lighting conditions throughout the 24 h and at breakfast can act as a strong zeitgeber for students and exert a great influence on their mental and physical health and can also affect PMS in women students.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

Comparison of body adiposity index (BAI) and bmi with estimations of % body fat in clinically severe obese women

Objective:

Body adiposity index (BAI), a new surrogate measure of body fat (hip circumference/(height^1.5 – 18)), has been proposed as an alternative to body mass index (BMI). We compared BAI with BMI, and each of them with laboratory measures of body fat‐derived from bioimpedance analysis (BIA), air displacement plethysmography (ADP), and dual‐energy X‐ray absorptiometry (DXA) in clinically severe obese (CSO) participants.

Design and Methods:

Nineteen prebariatric surgery CSO, nondiabetic women were recruited (age = 32.6 ± 7.7 SD; BMI = 46.5 ± 9.0 kg/m²). Anthropometrics and body fat percentage (% fat) were determined from BIA, ADP, and DXA. Scatter plots with lines of equality and Bland–Altman plots were used to compare BAI and BMI with % fat derived from BIA, ADP, and DXA. BAI and BMI correlated highly with each other (r = 0.90, P < 0.001).

Results:

Both BAI and BMI correlated significantly with % fat from BIA and ADP. BAI, however, did not correlate significantly with % fat from DXA (r = 0.42, P = 0.08) whereas BMI did (r = 0.65, P = 0.003). BMI was also the single best predictor of % fat from both BIA (r² = 0.80, P < 0.001) and ADP (r² = 0.65, P < 0.001). The regression analysis showed that the standard error of the estimate (SEE), or residual error around the regression lines, was greater for BAI comparisons than for BMI comparisons with BIA, ADP, and DXA. Consistent with this, the Bland and Altman plots indicated wider 95% confidence intervals for BAI difference comparisons than for BMI difference comparisons for their respective means for BIA, ADP, and DXA.

Conclusions:

Thus, BAI does not appear to be an appropriate proxy for BMI in CSO women.     

Exposure by males to light emitted from media devices at night is linked with decline of sperm quality and correlated with sleep quality measures

ABSTRACT

The last several decades have been characterized by the widespread usage of digital devices, especially smartphones. At the same time, there have been reports of both decline in sleep duration and quality and male fertility decline. The aim of this study was to assess the relationship between evening exposure to the light-emitting screens of digital media devices and measures of both sleep and sperm quality. Semen samples were obtained from 116 men undergoing fertility evaluation for the following sperm variables: volume (mL), pH, sperm concentration (million/mL), motility percentage (progressive% + non-progressive motility%), and total sperm count. Exposure to the screens of electronic devices and sleep habits was obtained by means of a questionnaire. Smartphone and tablet usage in the evening and after bedtime was negatively correlated with sperm motility (?0.392; ?0.369; p < .05), sperm progressive motility (?0.322; ?0.299; p < .05), and sperm concentration (?0.169; p < .05), and positively correlated with the percentage of immotile sperm (0.382; 0.344; p < .05). In addition, sleep duration was positively correlated with sperm total and progressive motility (0.249; 0.233; p < .05) and negatively correlated with semen pH (?0.349; p < .05). A significant negative correlation was observed between subjective sleepiness and total and progressive motility (?0.264; p < .05) as well as total motile sperm number (?0.173; p < .05). The results of this study support a link between evening and post-bedtime exposure to light-emitting digital media screens and sperm quality. Further research is required to establish the proposed causative link and may lead to the future development of relevant therapeutic and lifestyle interventions.     

Biomarker-specific differences between transpulmonary and peripheral arterial–venous blood sampling in patients with pulmonary hypertension

Abstract

Purpose: Transpulmonary biomarkers may provide insight into pulmonary hypertension (PH) pathophysiology, but require cardiac catheterization. We investigated whether the peripheral arterial–venous ratio (PR) could substitute for the transpulmonary ratio (TPR).

Materials and methods: Blood from the pulmonary artery (PA), pulmonary arterial wedge (PAW), peripheral venous, and peripheral arterial positions was analysed for ET-1, NT-pro-BNP and cAMP levels in subjects with no PH (n?=?18) and PH due to left heart disease (PH-LHD), which included combined pre- and post-capillary PH (Cpc-PH; n?=?7) and isolated post-capillary PH (Ipc-PH; n?=?9). Bland–Altman comparisons were made between peripheral venous and PA samples and between peripheral arterial and PAW samples. TPR was defined as [PAW]/[PA].

Results: For ET-1, Bland–Altman analysis indicated negative bias (?24%) in peripheral arterial compared to PAW concentration and positive bias (23%) in peripheral venous compared to PA concentration. There was <10% absolute bias for NT-pro-BNP and cAMP. For ET-1, there was no difference in PR between Cpc-PH and Ipc-PH (0.87?±?0.4 vs. 0.94?±?0.6, p?=?0.8), whereas there was a difference in TPR (2.2?±?1.1 vs. 1.1?±?0.2, p?<?0.05).

Conclusions: In PH-LHD, peripheral samples may be inadequate surrogates for transpulmonary samples, particularly when measuring mediators with prominent pulmonary secretion or clearance, such as ET-1.     

Coping with shift work-related circadian disruption: A mixed-methods case study on napping and caffeine use in Australian nurses and midwives

ABSTRACT

Introduction: Two of the most ubiquitous fatigue countermeasures used by shift-working nurses are napping and caffeine. This mixed-methods case study investigated the ways nurses and midwives utilised napping and caffeine countermeasures to cope with shift work, and associated sleep, physical health and psychological health outcomes.

Materials and Methods: N = 130 Australian shift-working nurses and midwives (mean age = 44 years, range = 21–67, 115F, 15M) completed the Standard Shiftwork Index. A sub-set of 22 nurses and midwives completed an in-depth interview.

Results: Nearly 70% of participants reported napping. Those who napped during night shifts had significantly less total sleep time before (F_2,75 = 5.5, p < 0.01) and between days off (F_2,82 = 3.9, p < 0.05). By the end of the night shift, average hours of time awake were significantly less for prophylactic and on-shift nappers compared to non-nappers (F_2,85 = 97.2, p < 0.001). Since starting shift work, the percentage of high caffeine consumers (>400 mg/day) increased from 15% to 33% of the sample and an average of 4 (SD = 2) caffeinated beverages per day was reported. Increased caffeine consumption was associated with greater sleep disturbance (r = 0.26, p < 0.05), psychological distress (r = 0.37, p < 0.001), abdomen pain (r = 0.27, p < 0.05) and weight gain since starting shift work (r = 0.25, p < 0.05). Interviews confirmed these relationships and revealed that caffeine consumption on night shift was common, whereas napping on night shift was dependent on a number of factors including ability to sleep during the day.

Conclusion: This study identified reasons shift workers chose to engage in or abstain from napping and consuming caffeine, and how these strategies related to poor sleep and health outcomes. Further research is required to help develop recommendations for shift workers regarding napping and caffeine consumption as fatigue countermeasures, whilst taking into account the associated hazards of each strategy.     

Changes in growth and sleep across school nights,weekends and a winter holiday period in two Australian schools

Studies suggest that there may be an association between sleep and growth; however, the relationship is not well understood. Changes in biology and external factors such as school schedule heavily impact the sleep of adolescents, during a critical phase for growth. This study assessed the changes in sleep across school days, weekends and school holidays, while also measuring height and weight changes, and self-reported alterations in food intake and physical activity. The impact of morningness–eveningness (M-E) on height change and weight gain was also investigated. In a sample of 63 adolescents (mean age = 13.13, SD = 0.33, 31 males) from two independent schools in South Australia, height and weight were measured weekly for 4 weeks prior to the school holidays and 4 weeks after the school holidays. Participants also completed a Morningness/Eveningness Scale and 7-day sleep, diet and physical activity diaries prior to, during and after the school holidays. Participants at one school had earlier wake times during the weekends than participants attending the other school, leading to a significantly shorter sleep duration on weekends for those participants. Regardless of school, sleep was significantly later and longer during the holidays (p < 0.001) and those with a stronger morning preference fell asleep (F_18,36 = 3.4, p = 0.001) and woke (F_18,44 = 2.0, p = 0.027) earlier than evening types. Growth rate was lower during the holiday weeks. For those attending the school with limited sleep in opportunities, growth after the holidays was lower for those with greater evening preference, whereas for those at the other school, growth was greater for those with greater evening preference. The increase in average weight from pre- to post-holidays was greater for those attending the school with limited opportunities to sleep longer. Participants reported greater food intake during the holidays compared to school days and greater physical activity levels on weekends compared to school days, and school days compared to holidays. Results suggest that time of day preference may impact growth, with evening types who cannot sleep in growing at a slower rate than evening types who can or morning types. This may be related to sleep restriction. Despite sleep being both later and longer during the school holidays, participants’ growth slowed during the holiday period. It is possible that this may be a reflection of other behavioural changes in the holidays (increased food intake and reduced physical activity), as sleep timing during the school period was related to growth.     

HEART RATE AND ELECTROCARDIOGRAM CHARACTERISTICS OF A YOUNG CALIFORNIA GRAY WHALE (ESCHRICHTIUS ROBUSTUS)1

Electrocardiogram (ECG) analyses of Holter monitor recordings from a young California gray whale were performed to determine ECG waveform characteristics, evaluate the heart rate pattern for sinus arrhythmia, obtain resting heart rates at known body masses as the whale increased in size, and compare those heart rates with predicted heart rates from allometric equations. The PR and QRS intervals (475 ± 35 msec, 208 ± 24 msec, respectively, n= 20) support the concept (Meijler et al. 1992) that atrioventricular transmission and ventricular excitation times do not increase linearly in very large mammals. A sinus arrhythmia pattern at rest (apneic heart rates of 15–25 beats per min [bpm] and eupneic heart rates of 34–40 bpm) is consistent with a relative eupneic tachycardia and apneic bradycardia during diving activity of whales. The heart rate-body mass measurements (35–24 bpm at body masses of 3,591-8,200 kg) in this study (1) extend the range of allometric heart rate and body mass data in mammals a full order of magnitude, to almost 10,000 kg, (2) support the use of allometric equations (based primarily on mammals <1,000 kg in body mass) in estimating resting heart rates in whales, and (3) demonstrate that previously reported heart rates in large whales are not representative of resting heart rate, probably secondary to circumstances during measurement.     

Resilience,sleep quality and morningness as mediators of vulnerability to depression in medical students with sleep pattern alterations

The medical career is considered highly stressful, especially during internships when academic and clinical demands, combined with changes in sleep patterns, increase students’ likelihood to develop depression. Resilience, which is considered as opposite vulnerability to stress and, along with another protective factor, namely morningness, may cause a student to be less reactive to stimuli and, therefore, less prone to depression. The objective of this study was to evaluate the role of resilience and morningness facing to sleep quality and main risk factors, on the development of depression symptoms in a group of students with sleep pattern alterations. To this end, an observational and longitudinal study was performed with 30 undergraduate interns, with an average age of 22.63 years (SE ± 0.13), 33% men and 67% women. A survey was conducted in three different times during the year of internship: at the beginning (T₁), in the middle (T₂) and the end (T₃). The instruments were the Brief Resilience Scale, Composite Scale of Morningness, Pittsburgh Sleep Quality Index and Patient Health Questionnaire. The path analysis examined the roles of morningness, sleep quality and resilience as potential mediators between family history of depression and depression symptoms at different times. The results showed that resilience had a protective effect on depression symptoms at T₂ (β = ?0.18, p < 0.05) and with greater power at T₃ (β = ?0.41, p < 0.05), as did morningness, although less strongly, on the symptoms at T₃ (β = ?0.13, p < 0.05). A relationship between these two mediating variables was also observed (β = 0.30, p < 0.05). The initial sleep quality had an effect on the increase of depression symptoms at T₁ (β = 0.61, p < 0.05) and T₃ (β = 0.21, p < 0.05), while family history of depression had a direct effect on the measures of depression at T₂ (β = 0.49, p < 0.05) and T₃ (β = 0.19, p < 0.05). Aside from personal risk factors, it is possible to conclude that the levels of resilience, morningness and sleep quality manifested by students at the beginning of their internship may explain the decrease in depression symptoms at the end of the course.