In recent years, numerous smartphone applications (apps) have been developed that purport to diagnose and monitor sleep apnea. Physicians are often approached by patients self-diagnosing themselves with obstructive sleep apnea (OSA) based on a smartphone app recordings. With numerous sleep-related apps available between the App Store (Apple platform; Apple Inc., Cupertino, CA) and Google Play (Android platform; Google LLC, Menlo Park, CA), the array of data can become bewildering to navigate. Physicians must recognize that the validity and accuracy of the smartphone apps are unknown. We set out to examine five representative and sufficiently unique studies to determine the role of smartphone apps in the evaluation of sleep and sleep disorders. In 2016, Ong and Gillespie1 published the largest review to date focusing on the numerous smartphone applications available for sleep analysis. The study analyzed the apps available in the Apple (Apple Inc.) and Google Play (Google LLC) store that analyze and monitor sleep. The search terms used were sleep tracker, sleep apnea, and sleep cycle. There were 60 sleep apps found in the various app stores (33 from the Apple Store, Apple Inc.; 27 from the Google Play store, Google LLC), measuring a combination of sleep duration, time awake, time in light and deep sleep, and time in rapid eye movement. Each app provided data on sleep structure; however, the algorithms used were not validated by scientific studies, regulatory bodies, or against established sleep study methodology (polysomnogram (PSG) or ambulatory sleep studies). The lack of validation metrics were cited as a significant limitation of all existing apps marketed as relating to sleep apnea. The study did not show any one app as being superior or sufficiently accurate to merit being included as part of routine medical sleep analysis. In a study by Bhat et al.,2 the researchers examined a specific smartphone (Apple and Android) app (Sleep Time) marketed as the most popular and widely downloaded sleep assessment app. Twenty subjects with ages ranging from 22 to 57 with no previously diagnosed sleep disorders were recruited. After enrollment, subjects completed an anonymous questionnaire pertaining to sleep-related complaints. The subjects were asked to download the Sleep Time app and use it for five consecutive nights, after which all subjects underwent a PSG study while simultaneously using the app. The results showed that there was no correlation between PSG and the app ability to rate sleep efficiency. In comparison to the PSG, the app had high sensitivity for detecting sleep and was accurate in sleep–wake detection, but overall it performed poorly with low specificity for the diagnosis of OSA and had a poor correlation to PSG results (r = 0.127, P = .592). These results suggested that contrary to claims, this widely used app was unreliable in both diagnosing and monitoring OSA. In a study by Nakanoo et al.,3 the idea of monitoring sound to quantify snoring and sleep apnea using a smartphone was explored. The study consisted of 50 subjects (42 males; 8 females), with a mean age of 47.9 years old and a mean apnea hypopnea index (AHI) of 27.3, who underwent a PSG. Ten of the patients were delegated to develop the program, whereas the other 40 were used for validation. A smartphone was attached to the subjects' sternums, where it acquired snoring sound from the built-in microphone. In this instance, the snoring time measured by the smartphone highly correlated with the snoring time measured by the PSG. In addition, the snoring sounds recorded were able to be correlated with the AHI using proprietary software (r = 0.92). The study concluded that the use of a smartphone to monitor snoring sounds could be a valid way to quantify snoring and OSA, but the positive correlation diminished for subjects with an AHI less than 30. This study was performed in a quiet sleep lab, and no generalizations were made to whether it could apply in a noisier, home environment. Tal et al.4 performed a study in an attempt to validate a contact-free system to monitor sleep with high accuracy while still providing maximum comfort in comparison to the gold standard PSG. The EarlySense, Ramat Gan, Israel (ES) contact-free sensor is placed under the mattress where the patient's chest is estimated to lie and is connected through Bluetooth to a smartphone application. There were 63 subjects of varying ages who participated in the study. The variables tested include heart rate, respiratory rate, and sleep stage. There was a linear correlation between the total sleep time measured by the sensor and the PSG (r = 0.87), with a sleep detection accuracy of approximately 90%. The authors concluded that this system was highly accurate in detecting sleep and wake states in relation to the PSG. Furthermore, the ES contact-free sensor measured heart rate and respiratory rate throughout the night, with the values being highly correlated to those of the ES sensor currently used in hospitals. The authors intend to further explore whether it can record disturbed sleep as a measure of OSA. A unique study was conducted by Al-Mardini et al.5 by pairing smartphone sensor technology with a portable oximetry device. This is the only study conducted with equipment with ability to compare the two parameters in one system. The developed app consisted of an oximeter to measure the oxygen saturation, a microphone to record the respiratory effect, and an accelerometer to detect the body's movement. The results showed that the AHI values obtained from the app were close to the PSG. Further, the results demonstrated that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified with not having the disease. These results supported the concept that in principle a properly configured smartphone app can mimic findings of PSG. A wide variety of smartphone applications exist that are either free or low-priced and purport to aid in the diagnosis of OSA or sleep-disordered breathing. Although for some sleep smartphone apps there is a linear correlation between a PSG and a smartphone application, this is far from replacing the gold standard data provided by hospital or ambulatory PSG testing. No current apps have been rigorously tested against PSG; most do not take oximetry into account; and some may obscure the clinical picture of OSA. Current smartphone apps therefore provide a weak indication of what patients are experiencing while they are sleeping, and the current landscape of sleep apps are not yet a substitute for either level 1 PSG or ambulatory. The technology landscape would seem to be ripe for development of a formally developed smartphone app designed with clinical sleep medicine in mind. One study was a level 2A review article; three studies were level 2C outcomes studies compared against existing standards; and one study was a level 5 basic science experiment.