Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography

Study Objectives: Sleep disturbance is a hallmark feature of cannabis withdrawal. In this study we explored the effects of lithium treatment supplemented with nitrazepam on objective and subjective measures of sleep quality during inpatient cannabis withdrawal. Methods: Treatment-seeking cannabis-dependent adults (n = 38) were admitted for 8 days to an inpatient withdrawal unit and randomized to either oral lithium (500 mg) or placebo, twice daily in a double-blind RCT. Restricted nitrazepam (10 mg) was available on demand (in response to poor sleep) on any 3 of the 7 nights. Dependent outcome measures for analysis included repeated daily objective actigraphy and subjective sleep measures throughout the 8 day detox, subjective cannabis withdrawal ratings, and detoxifi cation completion rates. S leep diffi culties and cannabis use appears to be a bi-directional relationship. Individuals often start using cannabis when they experience sleep diffi culties during stressful times, often for coping reasons 1 ; and this is a risk factor for cannabis lapse (within the fi rst 2 days out of 7) following a self-guided quit attempt.2,3 The most prevalent sleep disturbances with cannabis withdrawal include diffi culty getting to sleep, staying asleep, nightmares and strange dreams, and night sweats.4 During the withdrawal process, sleep diffi culties predict relapse to cannabis use. [5][6][7][8] There is some evidence that pharmacotherapy during cannabis abstinence attenuates withdrawal related sleep disturbances, 9 including improvements in polysomnographic measures of sleep effi ciency (but not latency) with the nonbenzodiazepine GABA(A) receptor agonist zolpidem.10 While there is a plethora of studies exploring self-reported sleep disturbances associated with cannabis withdrawal, there are very S C I E N T I F I C I N V E S T I G AT I O N Sfew that quantify sleep patterns using objective means, with the exception of the paper by Vandrey et al. 10 Poor sleep is often the last symptom to remit, and recent trials have started to specifi cally target sleep disturbances during withdrawal. Haney and colleagues conducted a small BRIEF SUMMARY Current Knowledge/Study Rationale: Sleep disturbance is the most consistent, prevalent, and severe symptom of cannabis withdrawal. However, there are currently no evidence-based medicines to assist with cannabis dependence or cannabis withdrawal. Study Impact: This formal RCT demonstrates that, contrary to positive open label clinical fi ndings, lithium carbonate does not improve cannabis withdrawal related sleep disturbances. However, the hypnotic benzodiazepine rescue medication nitrazepam signifi cantly improved multiple objective measures of sleep disturbance during cannabis withdrawal.

Section: Discussionmentioning

confidence: 99%

The Effects of Lithium Carbonate Supplemented with Nitrazepam on Sleep Disturbance during Cannabis Abstinence

Bartlett

Johnston

Helliwell³

et al. 2015

“…19,20,21 These findings indicate that the contact-free ES sensor may be used as an objective sleep diary. Compared to actigraphy, the ES device has the advantage of being conveniently installed under the mattress, enabling seamless monitoring without the burden of wearing or charging a device.…”

mentioning

confidence: 93%

Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography

Tal

Shinar

Shaki

et al. 2017

121

Study Objectives: To validate a contact-free system designed to achieve maximal comfort during long-term sleep monitoring, together with high monitoring accuracy. Methods: We used a contact-free monitoring system (EarlySense, Ltd., Israel), comprising an under-the-mattress piezoelectric sensor and a smartphone application, to collect vital signs and analyze sleep. Heart rate (HR), respiratory rate (RR), body movement, and calculated sleep-related parameters from the EarlySense (ES) sensor were compared to data simultaneously generated by the gold standard, polysomnography (PSG). Subjects in the sleep laboratory underwent overnight technician-attended full PSG, whereas subjects at home were recorded for 1 to 3 nights with portable partial PSG devices. Data were compared epoch by epoch. Results: A total of 63 subjects (85 nights) were recorded under a variety of sleep conditions. Compared to PSG, the contact-free system showed similar values for average total sleep time (TST), % wake, % rapid eye movement, and % non-rapid eye movement sleep, with 96.1% and 93.3% accuracy of continuous measurement of HR and RR, respectively. We found a linear correlation between TST measured by the sensor and TST determined by PSG, with a coefficient of 0.98 (R = 0.87). Epoch-by-epoch comparison with PSG in the sleep laboratory setting revealed that the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively. Conclusions: TST estimates with the contact-free sleep monitoring system were closely correlated with the gold-standard reference. This system shows good sleep staging capability with improved performance over accelerometer-based apps, and collects additional physiological information on heart rate and respiratory rate.

“…Long-term monitoring of sleep requires more practical methods. Use of actigraphy, while a viable alternative, [7][8][9] is limited by the need for costly, specialized equipment and may be considered cumbersome by some patients. A reliable, affordable and user-friendly smartphone-based app that can monitor sleep-wake cycles would be an invaluable addition to the arsenal at the disposal of practitioners of sleep medicine.…”

Section: S C I E N T I F I C I N V E S T I G At I O N Smentioning

confidence: 99%

Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

Bhat

Ferraris

Gupta

et al. 2015

113

Study Objectives: Several inexpensive, readily available smartphone apps that claim to monitor sleep are popular among patients. However, their accuracy is unknown, which limits their widespread clinical use. We therefore conducted this study to evaluate the validity of parameters reported by one such app, the Sleep Time app (Azumio, Inc., Palo Alto, CA, USA) for iPhones. Methods: Twenty volunteers with no previously diagnosed sleep disorders underwent in-laboratory polysomnography (PSG) while simultaneously using the app. Parameters reported by the app were then compared to those obtained by PSG. In addition, an epoch-by-epoch analysis was performed by dividing the PSG and app graph into 15-min epochs. S martphones are now ubiquitous. As their technological capabilities continue to improve, both consumers and healthcare personnel are constantly fi nding new and innovative uses for smartphone apps in the fi eld of health and medicine.1 Apps have been incorporated into the practice of medical specialties as diverse as diabetology and neurosurgery, and have found a role in activities ranging from interpretation of radiology imaging to smoking cessation counseling. Sleep related concerns are commonplace as well, and given the burgeoning popularity and easy availability of inexpensive apps that purport to monitor multiple physiological parameters, it is not surprising that several apps have been designed to evaluate sleep quality.2 The inexorable permeation of smartphones into the fi eld of sleep medicine has resulted in the development of promising apps that screen for obstructive sleep apnea (OSA) 3,4 and periodic limb movements in sleep (PLMS). S C I E N T I F I C I N V E S T I G AT I O N Sinsomnia, circadian rhythm disorders, and hypersomnolence to have objective data about sleep patterns in order to make diagnoses and treatment recommendations. There are several apps that allow users to report and analyze their sleep quality and duration, 6 but such data are often intrinsically subjective. In-laboratory polysomnography (PSG) is the gold standard for BRIEF SUMMARY Current Knowledge/Study Rationale: There are several preexisting, widely available, inexpensive smartphone apps designed to monitor sleep, but it is unclear whether they have clinical utility. Our goal was to systematically compare the results obtained by using one such app, the Sleep Time app (Azumio, Inc.) to the gold standard, polysomnography (PSG). Study Impact: Our study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG. Further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility.