Table 1.
Results of validations studies of sleep apps which claim to detect sleep parameters. All comparisons are made with polysomnography8-10.
| Sleep App | Study Population | Exclusion Criteria | Study Findings |
|---|---|---|---|
| Sleep Cycle8 | - n=25 (22 suspected OSA, 3 healthy volunteers); - Age (years) = 8.0 ± 3.6; - % male = 56. |
- Complex genetic or craniofacial disorders. | - No correlation with polysomnography in the measurement of total sleep time (CCC 0.22, p=0.36); - No correlation in the measurement of sleep latency (CCC 0.05, p=0.16); - No correlation in the detection of sleep cycle stages (data not provided). |
| MotionX 24/79 | - n=78 (all suspected OSA); - Age (years) = 8.4 ± 4.0; - % male = 65. |
- Conditions affecting motor control or limb movement. | - Over-estimated total sleep time by 106 minutes (p<0.0001); - Over-estimated sleep efficiency by 17% (p<0.0001); - Over-estimated sleep period time by 16 minutes (p<0.0001). |
| Sleep Time10 | - n=20 (all healthy volunteers); - Age (years) = 39.5 ± 12.4; - % male = 60. |
- Diagnosed sleep disorder. | - No correlation with polysomnography in the measurement of sleep efficiency (p=0.59) or sleep latency (p=0.09); - Under-estimated light sleep by 27.9% (p<0.0001); - Over-estimated deep sleep by 11.1% (p<0.0001). |
Age expressed as mean ± standard deviation; OSA: Obstructive sleep apnoea; CCC: Concordance correlation co-efficient.