Application effect of a non-contact sleep monitoring mattress based on body movement characteristics during sleep
10.16016/j.2097-0927.202410073
- VernacularTitle:基于睡眠期体动特征的无感睡眠监测床垫的应用效果评价
- Author:
Yanchun ZHANG
1
;
Yan LIU
;
Rui WANG
;
Feilong WANG
;
Yue ZHAO
;
Fei LI
;
Tunan CHEN
;
Jishu XIAN
Author Information
1. 陆军军医大学(第三军医大学)第一附属医院神经外科
- Keywords:
sleep disorder;
wearable devices;
inpatients;
nursing assessment
- From:
Journal of Army Medical University
2025;47(4):326-334
- CountryChina
- Language:Chinese
-
Abstract:
Objective To verify the accuracy of a Non-Contact Sleep Monitoring Mattress(NCSMM)based on body movement during sleep in assessing sleep quality of patients before neurosurgery in order to provide a more portable and efficient assessment tool for clinical staff.Methods A single-arm trial was conducted on 114 inpatients admitted in our department selected with convenience sampling.Sleep quality data of 1 night were collected through 5 sleep quality assessment tools,including NCSMM,polysomnography(PSG),Patient-Reported Outcome Measurement Information System(PROMIS)Sleep Disturbance scale,Richards-Campbell Sleep Scale(RCSQ),and a wearable device(smart watch for body movements and sleep quality monitoring).The sleep efficiency(≤85%)obtained by PSG was used as the diagnostic standard for sleep disorders.The area under the receiver operating characteristic curve(AUC),sensitivity,specificity,positive predictive value,negative predictive value,and Youden index were calculated for the other 4 tools to evaluate and compare their diagnostic effectiveness.Results The AUC value for NCSMM,PROMIS,RCSQ and smart watch was 0.788(95%CI:0.687～0.888,P<0.001),0.664(95%CI:0.543～0.784,P=0.02),0.723(95%CI:0.600～0.846,P=0.001)and 0.750(95%CI:0.654～0.846,P<0.001),respectively.The diagnostic accuracy rate was 0.774,0.559,0.742 and 0.602,with corresponding Youden index value of 0.488,0.321,0.456,and 0.459.NCSMM demonstrated the best AUC value,sensitivity and Youden index when compared with the other 3 tools.Conclusion NCSMM shows high accuracy in assessing sleep quality in pre-neurosurgery inpatients,and it is a viable portable and efficient assessment tool in clinical practice.
