Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography
Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography
复制标题
DOI：
10.5664/jcsm.4840
复制
发表时间：
2015-01-01
影响因子：
4.3
通讯作者：
Chokroverty, Sudhansu
中科院分区：
文献类型：
--
作者：
Bhat, Sushanth;Ferraris, Ambra;Chokroverty, Sudhansu
研究目的：一些便宜的，现成的智能手机应用程序，声称监测睡眠是受欢迎的患者。然而，它们的准确性是未知的，这限制了它们的广泛临床应用。因此，我们进行了这项研究，以评估由一个这样的应用程序，睡眠时间应用程序（Azumio，Inc.，方法：20名先前未诊断出睡眠障碍的志愿者在使用应用程序的同时接受了实验室多导睡眠图（PSG）。然后将应用程序报告的参数与PSG获得的参数进行比较。此外，通过将PSG和app图划分为15分钟时段来进行逐时段分析。PSG与app睡眠效率之间无相关性（r =-0.127，p = 0.592），轻度睡眠百分比（r = 0.024，p = 0.921）、深睡眠百分比（r = 0.181，p = 0.444）或睡眠潜伏期（rs = 0.384，p = 0.094）。该应用程序略微高估了睡眠效率0.12%（95%置信区间[CI] -4.9至5.1%，p = 0.962），显著低估轻度睡眠27.9%（95% CI 19.4- 36.4%，p < 0.0001），显著高估深度睡眠11.1%（CI 4.7- 17.4%，p = 0.008）和显著高估睡眠潜伏期15.6分钟（CI 9.7-21.6，p < 0.0001）。逐时比较显示，由于阶段间区分较差，总体准确性较低（45.9%），但睡眠-觉醒检测的准确性较高（85.9%）。该应用程序在检测睡眠方面具有高灵敏度，但特异性较差（分别为89.9%和50%）。结论：我们的研究表明，睡眠时间应用程序（Azumio，Inc.）iPhone与PSG的相关性很差。进一步的研究比较应用程序的睡眠-觉醒检测和体动记录可能有助于阐明其潜在的临床实用性。评论：关于这篇文章的评论出现在本期的第695页。
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.Results: There was no correlation between PSG and app sleep efficiency (r = -0.127, p = 0.592), light sleep percentage (r = 0.024, p = 0.921), deep sleep percentage (r = 0.181, p = 0.444) or sleep latency (rs = 0.384, p = 0.094). The app slightly and nonsignificantly overestimated sleep efficiency by 0.12% (95% confidence interval [CI] -4.9 to 5.1%, p = 0.962), significantly underestimated light sleep by 27.9% (95% CI 19.4-36.4%, p < 0.0001), significantly overestimated deep sleep by 11.1% (CI 4.7-17.4%, p = 0.008) and significantly overestimated sleep latency by 15.6 min (CI 9.7-21.6, p < 0.0001). Epochwise comparison showed low overall accuracy (45.9%) due to poor interstage discrimination, but high accuracy in sleep-wake detection (85.9%). The app had high sensitivity but poor specificity in detecting sleep (89.9% and 50%, respectively).Conclusions: 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.Commentary: A commentary on this article appears in this issue on page 695.