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Showing content from https://pubmed.ncbi.nlm.nih.gov/27771844/ below:

Accuracy of a smartphone application in estimating sleep in children

Comparative Study

Affiliations

• ¹ University of Pennsylvania, Philadelphia, PA, 19104, USA.
• ² Division of Pediatric Pulmonary and Sleep Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.
• ³ University of Pennsylvania, Philadelphia, PA, 19104, USA. brooksl@email.chop.edu.
• ⁴ Division of Pediatric Pulmonary and Sleep Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA. brooksl@email.chop.edu.

• PMID: 27771844
• DOI: 10.1007/s11325-016-1425-x

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Comparative Study

Pious Patel et al. Sleep Breath. 2017 May.

• ¹ University of Pennsylvania, Philadelphia, PA, 19104, USA.
• ² Division of Pediatric Pulmonary and Sleep Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.
• ³ University of Pennsylvania, Philadelphia, PA, 19104, USA. brooksl@email.chop.edu.
• ⁴ Division of Pediatric Pulmonary and Sleep Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA. brooksl@email.chop.edu.

• PMID: 27771844
• DOI: 10.1007/s11325-016-1425-x

Item in Clipboard

Purpose: Chronic sleep problems can lead to difficulties for both the individual and society at large, making it important to effectively measure sleep. This study assessed the accuracy of an iPhone application (app) that could potentially be used as a simple, inexpensive means to measure sleep over an extended period of time in the home.

Methods: Twenty-five subjects from the ages of 2-14 who were undergoing overnight polysomnography (PSG) were recruited. The phone was placed on the mattress, near their pillow, and recorded data simultaneously with the PSG. The data were then downloaded and certain parameters were compared between the app and PSG, including total sleep time, sleep latency, and time spent in various defined "stages."

Results: Although there seemed to be a visual relationship between the graphs generated by the app and PSG, this was not confirmed on numerical analysis. There was no correlation between total sleep time or sleep latency between the app and PSG. Sleep latency from the PSG and latency to "deep sleep" from the app had a significant relationship (p = 0.03). No combination of PSG sleep stages corresponded with app "stages" in a meaningful way.

Conclusions: The Sleep Cycle App may have value in increasing the user's awareness of sleep issues, but it is not yet accurate enough to be used as a clinical tool.

Keywords: Application; Children; Polysomnography; Sleep measurement; Sleep staging; Smartphone.

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