Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Smartphones as Sleep Duration Sensors : Validation of the iSenseSleep Algorithm. / Ciman, Matteo; Wac, Katarzyna.

In: JMIR mHealth and uHealth, Vol. 7, No. 5, e11930, 21.05.2019.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ciman, M & Wac, K 2019, 'Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm', JMIR mHealth and uHealth, vol. 7, no. 5, e11930. https://doi.org/10.2196/11930

APA

Ciman, M., & Wac, K. (2019). Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm. JMIR mHealth and uHealth, 7(5), [e11930]. https://doi.org/10.2196/11930

Vancouver

Ciman M, Wac K. Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm. JMIR mHealth and uHealth. 2019 May 21;7(5). e11930. https://doi.org/10.2196/11930

Author

Ciman, Matteo ; Wac, Katarzyna. / Smartphones as Sleep Duration Sensors : Validation of the iSenseSleep Algorithm. In: JMIR mHealth and uHealth. 2019 ; Vol. 7, No. 5.

Bibtex

@article{283b053304ec4dab81c0ce4ada5a7b37,
title = "Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm",
abstract = "BACKGROUND: Smartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up. This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals' lifestyle and sleep patterns.OBJECTIVES: The objective of this study was to estimate sleep duration based on the analysis of the users' ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm.METHODS: We used smartwatch sleep assessment data as the ground truth. Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each.RESULTS: Results showed that based on the smartphone ON-OFF patterns, individual's sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns.CONCLUSIONS: It is possible to estimate sleep duration patterns using only data related to smartphone screen interaction.",
author = "Matteo Ciman and Katarzyna Wac",
note = "{\textcopyright}Matteo Ciman, Katarzyna Wac. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 21.05.2019.",
year = "2019",
month = may,
day = "21",
doi = "10.2196/11930",
language = "English",
volume = "7",
journal = "J M I R mHealth and uHealth",
issn = "2291-5222",
publisher = "J M I R Publications, Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Smartphones as Sleep Duration Sensors

T2 - Validation of the iSenseSleep Algorithm

AU - Ciman, Matteo

AU - Wac, Katarzyna

N1 - ©Matteo Ciman, Katarzyna Wac. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 21.05.2019.

PY - 2019/5/21

Y1 - 2019/5/21

N2 - BACKGROUND: Smartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up. This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals' lifestyle and sleep patterns.OBJECTIVES: The objective of this study was to estimate sleep duration based on the analysis of the users' ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm.METHODS: We used smartwatch sleep assessment data as the ground truth. Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each.RESULTS: Results showed that based on the smartphone ON-OFF patterns, individual's sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns.CONCLUSIONS: It is possible to estimate sleep duration patterns using only data related to smartphone screen interaction.

AB - BACKGROUND: Smartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up. This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals' lifestyle and sleep patterns.OBJECTIVES: The objective of this study was to estimate sleep duration based on the analysis of the users' ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm.METHODS: We used smartwatch sleep assessment data as the ground truth. Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each.RESULTS: Results showed that based on the smartphone ON-OFF patterns, individual's sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns.CONCLUSIONS: It is possible to estimate sleep duration patterns using only data related to smartphone screen interaction.

U2 - 10.2196/11930

DO - 10.2196/11930

M3 - Journal article

C2 - 31115341

VL - 7

JO - J M I R mHealth and uHealth

JF - J M I R mHealth and uHealth

SN - 2291-5222

IS - 5

M1 - e11930

ER -

ID: 225419002