Assessing Sleep Disturbance in Low Back Pain: The Validity of Portable Instruments

Although portable instruments have been used in the assessment of sleep disturbance for patients with low back pain (LBP), the accuracy of the instruments in detecting sleep/wake episodes for this population is unknown. This study investigated the criterion validity of two portable instruments (Armband and Actiwatch) for assessing sleep disturbance in patients with LBP. 50 patients with LBP performed simultaneous overnight sleep recordings in a university sleep laboratory. All 50 participants were assessed by Polysomnography (PSG) and the Armband and a subgroup of 33 participants wore an Actiwatch. Criterion validity was determined by calculating epoch-by-epoch agreement, sensitivity, specificity and prevalence and bias- adjusted kappa (PABAK) for sleep versus wake between each instrument and PSG. The relationship between PSG and the two instruments was assessed using intraclass correlation coefficients (ICC 2, 1). The study participants showed symptoms of sub-threshold insomnia (mean ISI = 13.2, 95% CI = 6.36) and poor sleep quality (mean PSQI = 9.20, 95% CI = 4.27). Observed agreement with PSG was 85% and 88% for the Armband and Actiwatch. Sensitivity was 0.90 for both instruments and specificity was 0.54 and 0.67 and PABAK of 0.69 and 0.77 for the Armband and Actiwatch respectively. The ICC (95%CI) was 0.76 (0.61 to 0.86) and 0.80 (0.46 to 0.92) for total sleep time, 0.52 (0.29 to 0.70) and 0.55 (0.14 to 0.77) for sleep efficiency, 0.64 (0.45 to 0.78) and 0.52 (0.23 to 0.73) for wake after sleep onset and 0.13 (−0.15 to 0.39) and 0.33 (−0.05 to 0.63) for sleep onset latency, for the Armband and Actiwatch, respectively. The findings showed that both instruments have varied criterion validity across the sleep parameters from excellent validity for measures of total sleep time, good validity for measures of sleep efficiency and wake after onset to poor validity for sleep onset latency.     

Newly developed waist actigraphy and its sleep/wake scoring algorithm

The purpose of this study was to formulate an algorithm for assessing sleep/waking from activity intensities measured with a waist-worn actigraphy, the Lifecorder PLUS (LC; Suzuken Co. Ltd., Nagoya, Japan), and to test the validity of the algorithm. The study consisted of 31 healthy subjects (M/F = 20/11, mean age 31.7 years) who underwent one night of simultaneous measurement of activity intensity by LC and polysomnography (PSG). A sleep(S)/wake(W) scoring algorithm based on a linear model was determined through discriminant analysis of activity intensities measured by LC over a total of 235 h and 56 min and the corresponding PSG-based S/W data. The formulated S/W scoring algorithm was then used to score S/W during the monitoring epochs (2 min each, 7078 epochs in total) for each subject. The mean agreement rate with the corresponding PSG-based S/W data was 86.9%, with a mean sensitivity (sleep detection) of 89.4% and mean specificity (wakefulness detection) of 58.2%. The agreement rates for the individual stages of sleep were 60.6% for Stage 1, 89.3% for Stage 2, 99.2% for Stage 3 + 4, and 90.1% for Stage REM. These results demonstrate that sleep/wake activity in young to middle-aged healthy subjects can be assessed with a reliability comparable to that of conventional actigraphy through LC waist actigraphy and the optimal S/W scoring algorithm.

     

Sleep/wake estimation using only anterior tibialis electromyography data

ABSTRACT: BACKGROUND: In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data. METHODS: Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted. RESULTS: Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients. CONCLUSIONS: The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.     

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.     

The effect of consecutive transmeridian flights on alertness,sleep–wake cycles and sleepiness: A case study

ABSTRACT

Travel across time zones disrupts circadian rhythms causing increased daytime sleepiness, impaired alertness and sleep disturbance. However, the effect of repeated consecutive transmeridian travel on sleep–wake cycles and circadian dynamics is unknown. The aim of this study was to investigate changes in alertness, sleep–wake schedule and sleepiness and predict circadian and sleep dynamics of an individual undergoing demanding transmeridian travel. A 47-year-old healthy male flew 16 international flights over 12 consecutive days. He maintained a sleep–wake schedule based on Sydney, Australia time (GMT + 10?h). The participant completed a sleep diary and wore an Actiwatch before, during and after the flights. Subjective alertness, fatigue and sleepiness were rated 4 hourly (08:00–00:00), if awake during the flights. A validated physiologically based mathematical model of arousal dynamics was used to further explore the dynamics and compare sleep time predictions with observational data and to estimate circadian phase changes. The participant completed 191?h and 159 736?km of flying and traversed a total of 144 time-zones. Total sleep time during the flights decreased (357.5?min actigraphy; 292.4?min diary) compared to baseline (430.8?min actigraphy; 472.1?min diary), predominately due to restricted sleep opportunities. The daily range of alertness, sleepiness and fatigue increased compared to baseline, with heightened fatigue towards the end of the flight schedule. The arousal dynamics model predicted sleep/wake states during and post travel with 88% and 95% agreement with sleep diary data. The circadian phase predicted a delay of only 34?min over the 16 transmeridian flights. Despite repeated changes in transmeridian travel direction and flight duration, the participant was able to maintain a stable sleep schedule aligned with the Sydney night. Modelling revealed only minor circadian misalignment during the flying period. This was likely due to the transitory time spent in the overseas airports that did not allow for resynchronisation to the new time zone. The robustness of the arousal model in the real-world was demonstrated for the first time using unique transmeridian travel.     

Direct comparison of two actigraphy devices with polysomnographically recorded naps in healthy young adults

The last 20 yrs have seen a marked increase in studies utilizing actigraphy in free-living environments. The aim of the present study is to directly compare two commercially available actigraph devices with concurrent polysomnography (PSG) during a daytime nap in healthy young adults. Thirty healthy young adults, ages 18–31 (mean 20.77 yrs, SD 3.14 yrs) simultaneously wore AW-64 and GT3X+ devices during a polysomnographically recorded nap. Mann-Whitney U (M-U) test, intraclass correlation coefficients, and Bland-Altman statistic were used to compare total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) between the two actigraphs and PSG. Epoch-by-epoch (EBE) agreement was calculated to determine accuracy, sensitivity, specificity, predictive values for sleep (PVS) and wake (PVW), and kappa and prevalence- and bias-adjusted kappa (PABAK) coefficients. All frequency settings provided by the devices were examined. For both actigraphs, EBE analysis found accuracy, sensitivity, specificity, PVS, and PVW comparable to previous reports of other similar devices. Kappa and PABAK coefficients showed moderate to high agreement with PSG depending on device settings. The GT3X+ overestimated TST and SE, and underestimated SOL and WASO, whereas no significant difference was found between AW-64 and PSG. However, GT3X+ showed overall better EBE agreements to PSG than AW-64. We conclude that both actigraphs are valid and reliable devices for detecting sleep/wake diurnal patterns. The choice between devices should be based on several parameters as reliability, cost of the device, scoring algorithm, target population, experimental condition, and aims of the study (e.g., sleep and/or physical activity). (Author correspondence: smednick@ucr.edu)     

Automatic sleep/wake scoring from body motion in bed: validation of a newly developed sensor placed under a mattress

The purpose of this study was to formulate a "sleep/wake" scoring algorithm for processing activity measurements obtained using a newly developed nonwear actigraphy (NWA) device, and to test its validity. The NWA device has a highly sensitive pressure sensor and is placed under a mattress. It can continuously record the activity of a person lying on the mattress and identify an "in-bed/out-of-bed" state from the vibrations of the mattress. We formulated the sleep/wake scoring algorithm by using data obtained simultaneously by wrist actigraphy (Act) and the NWA device in 33 healthy participants. Agreement rate, sensitivity, and specificity with Act were 95.7%, 97.6%, and 75.8% (33 healthy people); the corresponding values were 85.9%, 89.1%, and 79.8% for 12 nursing home residents and 93.7%, 97.2%, and 60.8% for 60 nights for 6 healthy persons who slept 10 nights on their futons. Agreement rate, sensitivity, and specificity with polysomnography were in almost perfect agreement with Act (12 nights; 6 healthy persons who slept 2 nights). All our validation results indicate that the NWA device, placed under a mattress or a futon, can produce almost identical sleep/wake scores to Act. It is expected that the NWA device, a nonwear device for scoring sleep/wake and in-bed/out-of-bed, enables convenient long-term sleep-related evaluation in various fields, including hospital settings, home-care settings, and care facility settings such as nursing homes.     

Alterations of the characteristics of the circadian rest-activity rhythm of cancer in-patients

The aim of the present study was to evaluate the characteristics of the circadian rest-activity rhythm of cancer patients. Thirty-one in-patients, consisting of 19 males and 12 females, were randomly selected from the Regional Cancer Center, Pandit Jawaharlal Nehru Medical College, Raipur, India. The rest-activity rhythm was studied non-invasively by wrist actigraphy, and compared with 35 age-matched apparently healthy subjects (22 males and 13 females). All subjects wore an Actiwatch (AW64, Mini Mitter Co. Inc., USA) for at least 4-7 consecutive days. Fifteen-second epoch length was selected for gathering actigraphy data. In addition, several sleep parameters, such as time in bed, assumed sleep, actual sleep time, actual wake time, sleep efficiency, sleep latency, sleep bouts, wake bouts, and fragmentation index, were also recorded. Data were analyzed using several statistical techniques, such as cosinor rhythmometry, spectral analysis, ANOVA, Duncan's multiple-range test, and t-test. Dichotomy index (I相似文献   

Influence of chronotype, season, and sex of subject on sleep behavior of young adults

The aim of this study was to investigate whether sex, season, and/or chronotype influence the sleep behavior of university students. Detailed data were collected on activity/rest patterns by wrist actigraphy combined with diaries. Thirty-four medical students (19 female and 15 male) were monitored by Actiwatch actometers for 15 consecutive days in May and again in November. The data of a modified Horne and Ostberg chronotype questionnaire, which were collected from 1573 female and 1124 male medical school students surveyed in the spring and autumn over an eight-year period, were evaluated. Actiwatch sleep analysis software was used to process the activity data with statistical analyses performed with ANOVA. We found no significant sex-specific differences in sleep efficiency, sleep onset latency, or actual sleep-time duration. However, we did find a difference in sleep efficiency between morning and evening types, with morning types having a higher sleep efficiency (87.9%, SD=1.3) than evening types (84.3%, SD=0.87%; p=0.007). Seasonal differences were also detected: the actual sleep-time duration in autumn was significantly longer (mean 6.9 h, SD=0.13 h) than in spring (6.6 h, SD=0.1 h; p=0.013). Evaluation of the chronotype questionnaire data showed that individuals with no special preference for morningness or eveningness (i.e., so-called intermediates) were most common. The distribution of chronotypes was related to the sex of subject. Men displayed eveningness significantly more often than women (28.9% males vs. 20.8% females; p<0.001), while females exhibited greater morningness (20.3% females vs.15.6% males; p<0.001). Sex influences chronotype distribution, but not actual sleep time-duration, sleep onset latency, or sleep efficiency. The latter, however, differed among chronotypes, while actual sleep-time duration was affected by season.     

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.     

Validity of an algorithm for determining sleep/wake states using a new actigraph

Background

This study aimed to develop an algorithm for determining sleep/wake states by using chronological data on the amount of physical activity (activity intensity) measured with the FS-750 actigraph, a device that can be worn at the waist, allows for its data to be downloaded at home, and is suitable for use in both sleep research and remote sleep medicine.

Methods

Participants were 34 healthy young adults randomly assigned to two groups, A (n =17) and B (n =17), who underwent an 8-hour polysomnography (PSG) in the laboratory environment. Simultaneous activity data were obtained using the FS-750 attached at the front waist. Sleep/wake state and activity intensity were calculated every 2 minutes (1 epoch). To determine the central epoch of the sleep/wake states (x), a five-variable linear model was developed using the activity intensity of Group A for five epochs (x_-2, x_-1, x, x₊₁, x₊₂; 10 minutes). The optimal coefficients were calculated using discriminant analysis. The agreement rate of the developed algorithm was then retested with Group B, and its validity was examined.

Results

The overall agreement rates for group A and group B calculated using the sleep/wake score algorithm developed were 84.7% and 85.4%, respectively. Mean sensitivity (agreement rate for sleep state) was 88.3% and 90.0% and mean specificity (agreement rate for wakeful state) was 66.0% and 64.9%, respectively. These results confirmed comparable agreement rates between the two groups. Furthermore, when applying an estimation rule developed for the sleep parameters measured by the FS-750, no differences were found in the average values between the calculated scores and PSG results, and we also observed a correlation between the two sets of results. Thus, the validity of these evaluation indices based on measurements from the FS-750 is confirmed.

Conclusions

The developed algorithm could determine sleep/wake states from activity intensity data obtained with the FS-750 with sensitivity and specificity equivalent to that determined with conventional actigraphs. The FS-750, which is smaller, less expensive, and able to take measurements over longer periods than conventional devices, is a promising tool for advancing sleep studies at home and in remote sleep medicine.     

Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data

ABSTRACT

We compared performance of four popular interpretative algorithms (IAs), i.e., Cole–Kripke, Rescored Cole–Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of ‘Rescoring Rules’ improved performance of the Cole–Kripke IA. The Sadeh and Rescored Cole–Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen’s Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen’s kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention.     

Feature selection for sleep/wake stages classification using data driven methods

This paper focuses on the problem of selecting relevant features extracted from human polysomnographic (PSG) signals to perform accurate sleep/wake stages classification. Extraction of various features from the electroencephalogram (EEG), the electro-oculogram (EOG) and the electromyogram (EMG) processed in the frequency and time domains was achieved using a database of 47 night sleep recordings obtained from healthy adults in laboratory settings. Multiple iterative feature selection and supervised classification methods were applied together with a systematic statistical assessment of the classification performances. Our results show that using a simple set of features such as relative EEG powers in five frequency bands yields an agreement of 71% with the whole database classification of two human experts. These performances are within the range of existing classification systems. The addition of features extracted from the EOG and EMG signals makes it possible to reach about 80% of agreement with the expert classification. The most significant improvement on classification accuracy is obtained on NREM sleep stage I, a stage of transition between sleep and wakefulness.     

Longitudinal Study of Mothers' Sleep-Wake Behaviors and Circadian Time Patterns from Late Pregnancy to Postpartum - Monitoring of Wrist Actigraphy and Sleep Logs

This longitudinal study investigated sleep-wake behavior patterns during and after pregnancy, using an actimeter worn on the non-dominant wrist and a sleep log. Records were obtained from ten mothers, from the 34th week of gestation until the 15th week postpartum. Ten non-pregnant women were used as a control group, data being collected from them for 2 weeks. The sleep-wake behavior after delivery, obtained from wrist actigraphy, was greater in the postpartum period. Total sleep time, sleep efficiency, and circadian amplitude decreased in the weeks immediately following parturition, but wake after sleep onset increased. Subsequently, all the sleep and circadian variables improved slightly, but they had not returned to the levels of the non-pregnant control group even by the 15th postpartum week. The length of daytime naps increased, in order to make up for nocturnal sleep deprivation when the number of awakenings during nighttime had increased. There were significant positive correlations between total sleep time, sleep efficiency, wake after sleep onset, and the length of daytime naps, but the numbers of awakenings at night and daytime naps did not show this correlation. The total sleep time indicated by sleep logs tended to be greater than that indicated by actigraphy, but wake after sleep onset tended to be underestimated by the sleep logs. The implications of these results are discussed.     

Longitudinal Study of Mothers’ Sleep-Wake Behaviors and Circadian Time Patterns from Late Pregnancy to Postpartum – Monitoring of Wrist Actigraphy and Sleep Logs

This longitudinal study investigated sleep-wake behavior patterns during and after pregnancy, using an actimeter worn on the non-dominant wrist and a sleep log. Records were obtained from ten mothers, from the 34th week of gestation until the 15th week postpartum. Ten non-pregnant women were used as a control group, data being collected from them for 2 weeks. The sleep-wake behavior after delivery, obtained from wrist actigraphy, was greater in the postpartum period. Total sleep time, sleep efficiency, and circadian amplitude decreased in the weeks immediately following parturition, but wake after sleep onset increased. Subsequently, all the sleep and circadian variables improved slightly, but they had not returned to the levels of the non-pregnant control group even by the 15th postpartum week. The length of daytime naps increased, in order to make up for nocturnal sleep deprivation when the number of awakenings during nighttime had increased. There were significant positive correlations between total sleep time, sleep efficiency, wake after sleep onset, and the length of daytime naps, but the numbers of awakenings at night and daytime naps did not show this correlation. The total sleep time indicated by sleep logs tended to be greater than that indicated by actigraphy, but wake after sleep onset tended to be underestimated by the sleep logs. The implications of these results are discussed.     

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).     

Alterations of the Characteristics of the Circadian Rest‐Activity Rhythm of Cancer In‐Patients

The aim of the present study was to evaluate the characteristics of the circadian rest‐activity rhythm of cancer patients. Thirty‐one in‐patients, consisting of 19 males and 12 females, were randomly selected from the Regional Cancer Center, Pandit Jawaharlal Nehru Medical College, Raipur, India. The rest‐activity rhythm was studied non‐invasively by wrist actigraphy, and compared with 35 age‐matched apparently healthy subjects (22 males and 13 females). All subjects wore an Actiwatch (AW64, Mini Mitter Co. Inc., USA) for at least 4–7 consecutive days. Fifteen‐second epoch length was selected for gathering actigraphy data. In addition, several sleep parameters, such as time in bed, assumed sleep, actual sleep time, actual wake time, sleep efficiency, sleep latency, sleep bouts, wake bouts, and fragmentation index, were also recorded. Data were analyzed using several statistical techniques, such as cosinor rhythmometry, spectral analysis, ANOVA, Duncan's multiple‐range test, and t‐test. Dichotomy index (I<O) and autocorrelation coefficient (r24) were also computed. The results validated a statistically significant circadian rhythm in rest‐activity with a prominent period of 24 h for most cancer patients and control subjects. Results of this study further revealed that cancer patients do experience a drastic alteration in the circadian rest‐activity rhythm parameters. Both the dichotomy index and r24 declined in the group of cancer patients. The occurrence of the peak (acrophase, Ø) of the rest‐activity rhythm was earlier (p<0.001) in cancer patients than age‐ and gender‐matched control subjects. Results of sleep parameters revealed that cancer patients spent longer time in bed, had longer assumed and actual sleep durations, and a greater number of sleep and wake bouts compared to control subjects. Further, nap frequency, total nap duration, average nap, and total nap duration per 1 h awake span were statistically significantly higher in cancer patients than control subjects. In conclusion, the results of the present study document the disruption of the circadian rhythm in rest‐activity of cancer in‐patients, with a dampening of amplitude, lowering of mean level of activity, and phase advancement. These alterations of the circadian rhythm characteristics could be attributed to disease, irrespective of variability due to gender, sites of cancer, and timings of therapies. These results might help in designing patient‐specific chronotherapeutic protocols.     

Comparison of Two Different Actigraphs with Polysomnography in Healthy Young Subjects

The present study aimed to compare two commercially available actigraphs, with a concurrent polysomnographic (PSG) recording. Twelve healthy volunteers (six women; age range 19–28 yrs) simultaneously wore the Basic Mini‐Motionlogger® and Actiwatch® for seven overnight polysomnographic recordings. Comparisons of the following sleep measures were focused on: sleep onset latency (SOL), total sleep time, wake after sleep onset, and sleep efficiency. Both devices underestimated SOL in comparison to PSG, but they had similar performance compared to PSG for the other sleep measures. A limit of the study is that the results can be only generalized to healthy young subjects.     

Comparison of two different actigraphs with polysomnography in healthy young subjects

The present study aimed to compare two commercially available actigraphs, with a concurrent polysomnographic (PSG) recording. Twelve healthy volunteers (six women; age range 19-28 yrs) simultaneously wore the Basic Mini-Motionlogger^® and Actiwatch^® for seven overnight polysomnographic recordings. Comparisons of the following sleep measures were focused on: sleep onset latency (SOL), total sleep time, wake after sleep onset, and sleep efficiency. Both devices underestimated SOL in comparison to PSG, but they had similar performance compared to PSG for the other sleep measures. A limit of the study is that the results can be only generalized to healthy young subjects.     

Neurophysiologic measurement of continuity in the sleep of fetuses during the last week of pregnancy and in newborns

Our aim was to measure the correlation between fetal electrocardiographic (FECG) recordings of low-risk pregnancies and polysomnographic (PSG) study parameters in low-risk infants born at term as a measurement of perinatal sleep-development continuity.We designed a short, prospective, observational follow-up of physiologic parameters between fetuses and newborns. We studied 10 fetuses from low-risk pregnant female out-patients and the same subjects as low-risk newborns delivered at term. Fetal state (FS) was defined in FECG recordings reassembling the following: fetal state I (quiet sleep or QS); fetal state II (active sleep or AS); fetal state III (quiet waking), and fetal state IV (active waking). Percentages of AS, QS, and wakefulness in PSG studies of newborns were also determined.Comparisons of FS I with QS showed a significant reduction in QS, while comparison of FS II with AS showed significant reduction in AS. Negative correlations were found between FS I with QS, and FS II with AS. Number of cycles in FECG recordings and PSG sleep cycles also demonstrated significant correlation.In conclusion our data showed partial but significant sleep function continuity from fetal to neonatal period.