SleepThermo

Katsumata, Kento; Noda, Yuka; Isokawa, Naohiro; Katayama, Shin; Okoshi, T.; Nakazawa, Jin

doi:10.1145/3341162.3347079

Cited by 7 publications

(8 citation statements)

References 2 publications

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“…The works presented in this section focus on variations in sleep cycles between different groups [50,51], sleep phase monitoring [52,53], stress management [54] and relationships between physiological parameters and stress [55]. In addition, we report on [56], in which the aim was to monitor sleep without wearing sensors, on [57], which investigated the relationship between BT and mental/physical condition, and [58], which focused on integrating and fusing data from inaccessible and accessible sources. Looking at the articles' reporting on participant demographics, which are presented in Table 12, one article provided no such information [53].…”

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

“…Two of the works [55,58] provided no information on age. Five works [50,52,54,57,58] presented information on gender distribution. Four works [51,52,55,57] reported that the participants were healthy, whereas the remaining works lacked information on the participants being healthy or patients.…”

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

“…Five works [50,52,54,57,58] presented information on gender distribution. Four works [51,52,55,57] reported that the participants were healthy, whereas the remaining works lacked information on the participants being healthy or patients. The research design varies: two works [50,51] reported on case-control studies, whereas one work [54] reported on a randomized control study.…”

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

“…The relationship between an individual's BT during sleep and mental/physical condition was explored by Katsumata et al (2019) [57]. BT and environmental temperature were collected using a wearable sensor "Ran's Night".…”

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

“…The authors discussed how the assessment method may affect the sleep, the cost and the impracticality in using these methods in larger studies. In Section 6, we outline seven articles [50][51][52][53][56][57][58] presenting results from sleep assessment studies based on data collected using wearable or contactless sensors. However, these were conducted with young people who are not representative of older people.…”

Section: Toward Reducing the Number Of Ncd Cases Using Wearable Sensorsmentioning

confidence: 99%

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Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

Kristoffersson

Lindén

2020

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Ensuring healthy lives and promoting a healthy well-being for all at all ages are listed as some of the goals in Agenda 2030 for Sustainable Development. Considering that noncommunicable diseases (NCDs) are the leading cause of death worldwide, reducing the mortality of NCDs is an important target. To reach this goal, means for detecting and reacting to warning signals are necessary. Here, remote health monitoring in real time has great potential. This article provides a systematic review of the use of wearable sensors for the monitoring and prevention of NCDs. In addition, this article not only provides in-depth information about the retrieved articles, but also discusses examples of studies assessing warning signals that may result in serious health conditions, such as stroke and cardiac arrest, if left untreated. One finding is that even though many good examples of wearable sensor systems for monitoring and controlling NCDs are presented, many issues also remain to be solved. One major issue is the lack of testing on representative people from a sociodemographic perspective. Even though substantial work remains, the use of wearable sensor systems has a great potential to be used in the battle against NCDs by providing the means to diagnose, monitor and prevent NCDs.

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Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

Section: Use Of Wearable Sensors For Assessing Stress and Sleepmentioning

confidence: 99%

Section: Toward Reducing the Number Of Ncd Cases Using Wearable Sensorsmentioning

confidence: 99%

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Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

Kristoffersson

Lindén

2020

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“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality

Kheirinejad

Visuri

Ferreira

et al. 2022

Pers Ubiquit Comput

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Smartphones have become an integral part of people’s everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one’s sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one’s sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7–456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87–55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.

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NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset

et al. 2021

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SleepThermo

Cited by 7 publications

References 2 publications

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality

NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset

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