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.

A chinstrap alone is not an effective treatment for OSA. It does not improve sleep disordered breathing, even in mild OSA, nor does it improve the AHI in REM sleep or supine sleep. It is also ineffective in improving snoring.

Background. Computerized electrocardiogram (ECG) analysis has been of tremendous help for noncardiologists, but can we rely on it? The importance of ST depression and T wave inversions in lead aVL has not been emphasized and not well recognized across all specialties. Objective. This study's goal was to analyze if there is a discrepancy of interpretation by physicians from different specialties and a computer-generated ECG reading in regard to a TWI in lead aVL. Methods. In this multidisciplinary prospective study, a single ECG with isolated TWI in lead aVL that was interpreted by the computer as normal was given to all participants to interpret in writing. The readings by all physicians were compared by level of education and by specialty to one another and to the computer interpretation. Results. A total of 191 physicians participated in the study. Of the 191 physicians 48 (25.1%) identified and 143 (74.9%) did not identify the isolated TWI in lead aVL. Conclusion. Our study demonstrated that 74.9% did not recognize the abnormality. New and subtle ECG findings should be emphasized in their training so as not to miss significant findings that could cause morbidity and mortality.

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