• Corpus ID: 152105791

Measuring college students' sleep, stress, mental health and wellbeing with wearable sensors and mobile phones

@inproceedings{Sano2016MeasuringCS,
  title={Measuring college students' sleep, stress, mental health and wellbeing with wearable sensors and mobile phones},
  author={Akane Sano},
  year={2016}
}
This thesis carries out a series of studies and develops a methodology and tools to measure and analyze ambulatory physiological, behavioral and social data from wearable sensors and mobile phones with trait data such as personality, for learning about behaviors and traits that impact human health and wellbeing. This thesis also validates the methodology and tools on a selected subset of the questions that can be answered by the data collected. First, I conducted a study to characterize wrist… 
Mood, Stress and Sleep Sensing with Wearable Sensors and Mobile Phones
This paper highlights lessons learned from a four-year ambulatory study, developed to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques (SNAPSHOT), which was
Passive mobile sensing and psychological traits for large scale mood prediction
TLDR
This paper shows that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population.
Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study
TLDR
iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well- being and revealing the potential of this system for improving sleep disorders among shift workers.
Characterizing electrodermal responses during sleep in a 30-day ambulatory study
TLDR
This thesis describes an EDR event detection algorithm and extracts shape features from these events to discuss the difference in shape between sleep and wake and model the detected EDR events as a point process using a state-space generalized linear model.
Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress
  • Boning Li, A. Sano
  • Computer Science
    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
  • 2020
TLDR
A deep neural network framework is proposed to automatically extract features from passively collected raw sensor data and perform personalized prediction of self-reported mood, health, and stress scores with high precision for developing real-time health and wellbeing monitoring and intervention systems that can benefit various populations.
QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform †
TLDR
This work designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-Experiments in an automated and scientific manner using their smartphones, and evaluates its use with four different kinds of personalized investigations.
Modeling Mental Stress Using a Deep Learning Framework
TLDR
It is found that the neural signals significantly improve the efficiency of the proposed classification model in computing mental stress, and supports the idea that the deep learning framework results in an improved estimate to determine mental stress.
Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network
TLDR
A job-role based multitask and multilabel deep learning model is proposed, where physiological and behavioral data for nurses and doctors simultaneously are modeled to predict participants’ next day’s multidimensional self-reported health and wellbeing status.
Predicting Tomorrow's Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
TLDR
Empirical results show that the new personalized models — a MTL deep neural network, and a Gaussian Process with DA — both significantly outperform their generic counterparts, providing substantial performance enhancements in automatic prediction of continuous levels of tomorrow’s reported mood, stress, and physical health based on data through today.
An Effectiveness Comparison between the Use of Activity State Data and That of Activity Magnitude Data in Chronic Stress Recognition
TLDR
To determine which approach leads to better stress recognition performance, evaluations using a database of 64 subjects and compared results showed that the “Activity State” approach was, to a statistically significant degree, superior to the ‘Activity Magnitude’ approach in the recognition of chronic stress.
...
...

References

SHOWING 1-10 OF 153 REFERENCES
Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
TLDR
This work 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.
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
TLDR
This work uses information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models and presents a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts.
Stress Recognition Using Wearable Sensors and Mobile Phones
  • A. Sano, Rosalind W. Picard
  • Psychology
    2013 Humaine Association Conference on Affective Computing and Intelligent Interaction
  • 2013
TLDR
The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
Impact of lifestyle and technology developments on sleep
  • T. Shochat
  • Psychology
    Nature and science of sleep
  • 2012
TLDR
This review aims to highlight current lifestyle trends that have been shown in scientific investigations to be associated with sleep patterns, sleep duration and sleep quality, as well as some of the reported consequences.
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
TLDR
This paper introduces a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder, and outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.
Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - a prospective cohort study
TLDR
High frequency of mobile phone use at baseline was a risk factor for mental health outcomes at 1-year follow-up among the young adults, and the risk for reporting mental health symptoms at follow- up was greatest among those who had perceived accessibility via mobile phones to be stressful.
Sleep patterns and predictors of disturbed sleep in a large population of college students.
How accurately does wrist actigraphy identify the states of sleep and wakefulness?
TLDR
Actigraphy may be useful for measuring circadian period and sleep-wake consolidation and has face validity as a measure of rest/activity, but low PV's and overestimation of sleep currently disqualify actigraphy as an accurateSleep-wake indicator.
Personality Correlates with Sleep‐Wake Variables
TLDR
In both initial and confirmatory studies, increased sub‐clinical manic‐type symptoms were found to be significantly associated with later bedtimes and wake‐times during the work‐week and lower (more evening‐type) CSM scores, and higher neuroticism was associated with poorer sleep as indicated by higher PSQI scores.
...
...