ShutEye

Bauer, Jared S.; Consolvo, Sunny; Greenstein, Benjamin; Schooler, Jonathan W.; Wu, Eric Hsiao-Kuang; Watson, Nathaniel F.; Kientz, Julie A.

doi:10.1145/2207676.2208600

Cited by 102 publications

(26 citation statements)

References 23 publications

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“…We also found features in mobile phone usage, wearable sensor and survey data that were significantly related to perceived stress level using correlation analysis. In the current study, we have increased our sampling period to ~30-days per person and our population to 66 participants to collect more intensive multi-modal data including perceived stress, sleep, personality, physiological, behavioral and social interaction data that are important factors in academic performance, sleep, stress, and mental health in addition to what were monitored on the phone in previous studies [4, 5, 8, 9, 10]. …”

Section: Introductionmentioning

confidence: 99%

“…While previous studies have examined multiple days and nights (from one week to a couple months) of daily behaviors including sleep, activity and social interactions, [4, 5, 8, 9, 10], few studies have examined capturing both internal and external factors to understand which factors are related and how, and fewer have used objective sensor data. Understanding these associations can be used to design tools to reduce stress, improve academic performance, sleep quality, and mental health.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Sano

Phillips

et al. 2015

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

205

129

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What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Sano

Phillips

et al. 2015

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

205

129

View full text Add to dashboard Cite

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“…Conclusion made was that supplementary practical support by adequate profession is needed. There exist mobile phone apps that are developed by researchers aimed to improve sleeping habits [31], also related to intervention programs [32]. The latter pioneer study by [32] presents results from a four-week field study with 12 participants.…”

Section: Discussionmentioning

confidence: 99%

“…There exist mobile phone apps that are developed by researchers aimed to improve sleeping habits [31], also related to intervention programs [32]. The latter pioneer study by [32] presents results from a four-week field study with 12 participants. Their results demonstrate that a very low-effort, recommendationbased peripheral display can be effective method for improving awareness of healthy sleep habits.…”

Section: Discussionmentioning

confidence: 99%

An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study (Preprint)

Rönkkö¹

2017

Preprint

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Background: The amount of mobile self-tracking devices connected to the web has exploded in today's society. With these mobile wearables related to Web 2.0 apps and social media has followed new ways of monitoring, measuring, representing and sharing experiences of the human body. New opportunities related to health, and new areas of implementation for professionals have appeared. One identified area that can benefit from mHealth technologies is social work. Objective: There are still few publications presenting experiences of applying wearables and social apps in social work. To contribute to this area it was started a research project targeting wearables and social apps for youths in need of social care. The project was framed by the overarching questions: What improved physical and sleeping effects did the youths and the professional staff experience as a result of using the wearables and the social apps? Methods: To answer the question a field study was performed were wearables and social apps were tried out and studied in the context of vulnerable youths in need of social care, living at a Swedish municipalities care and accommodation home. Results: The study confirmed earlier reported mHealth research results claiming that instant graphical feedback, sharing information, and being part in social community can have positive impact on life style changes. In addition, the present study identified that the most important factor for positive health related life style changes for youths was the establishment of own long-term goals. The professional social workers identified that the studied wearable and social app could function as a valuable counseling tool opening up for life style talks that otherwise were hard to accomplish.Conclusions: This study demonstrate how wearables and social apps can help vulnerable youths to change bad life style patterns, and also suggests that they can be used as counseling tools for professionals in social work.

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“…Similarly, the ShutEye system lets users specify their desired sleep schedule and then provides real-time information on when activities such as caffeine consumption, exercise, or napping are appropriate. 18 This display lets users see at a glance how to structure their behaviors to best match their desired sleep schedule. Both systems give users a sense that a desirable outcome is within their capabilities, and hence they would be expected to be particularly useful in cases where users lack this sense of control.…”

Section: Suggestions and Perceived Controlmentioning

confidence: 99%

Psychological Frameworks for Persuasive Information and Communications Technologies

Simons

2016

IEEE Pervasive Comput.

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ShutEye

Cited by 102 publications

References 23 publications

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study (Preprint)

Psychological Frameworks for Persuasive Information and Communications Technologies

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