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Prediction of Happy-Sad mood from daily behaviors and previous sleep history
2015
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants, for 30 days each, to address questions related to the relationships among ...
sleep duration, sleep irregularity, self-reported Happy-Sad mood and other factors in college students. ...
Acknowledgments This project is supported by the MIT Media Lab Consortium, Samsung and NIH (R01GM105018, T32HL007901).
VII. References ...
doi:10.1109/embc.2015.7319954
pmid:26737854
pmcid:PMC4768795
dblp:conf/embc/SanoYMPTJKP15
fatcat:txfnzqn3drfs7inqumnu4miere
Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring
2021
Frontiers in Digital Health
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skin conductance can be used to detect stress and anxiety disorders. ...
Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and ...
be predictive of depressed mood (61) . ...
doi:10.3389/fdgth.2021.662811
pmid:34713137
pmcid:PMC8521964
fatcat:3f7z7omkq5cspbdn4fie3ehwby
Non-motor correlates of wrist-worn wearable sensor use in Parkinson's disease: an exploratory analysis
2019
npj Parkinson's Disease
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NMS were assessed by the validated NMS Scale, and included, e.g., cognition, mood and sleep, and gastrointestinal, urinary and sexual problems. ...
Wearable sensors are becoming increasingly more available in Parkinson's disease and are used to measure motor function. ...
ACKNOWLEDGEMENTS We acknowledge data collection efforts by all contributors, collaborators and administrative staff of the NILS study. ...
doi:10.1038/s41531-019-0094-4
pmid:31602393
pmcid:PMC6775049
fatcat:d2ijs2zcbnaozkm23agtr5rmca
Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment
2020
Journal of Epidemiology and Community Health
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assessment to track momentary mood and stress and environmental perceptions; and (4) electrodermal activity for the tentative prediction of stress. ...
This article discusses methods for the sensor-based continuous assesment of geographic environments, stress and depressive symptoms in older age. ...
wrist to measure sleep patterns; (3) smartphone-based ecological momentary assessment (EMA) to survey environmental perceptions and anxious and depressive mood through the Eco-Emo Tracker application ...
doi:10.1136/jech-2020-214274
pmid:33148684
fatcat:entwgo2znbecbbpznlqgidpz3e
Depressed Mood Prediction of Elderly People with a Wearable Band
2022
Sensors
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From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. ...
Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. ...
They observed the effects of the acoustic environment on the experimenters' anxiety and stress states. Anxiety or stress might affect the depressed mood. ...
doi:10.3390/s22114174
pmid:35684797
fatcat:l3tel2sydzfvfhfjb76b3npqaa
Remote Monitoring for Understanding Mechanisms and Prediction in Psychiatry
2019
Current Behavioral Neuroscience Reports
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This review provides an overview of how remote monitoring might inform understanding of mood and anxiety disorders, and may be used by clinicians to predict clinical outcomes and treatment response. ...
Remote monitoring has shown promise in phenotyping, predicting symptoms and clinical severity, predicting treatment response and has informed psychological models of anxiety disorders. ...
Wearable technologies such as wrist worn actigraphs and mobile ECGs devices provide additional data which can complement symptom monitoring. ...
doi:10.1007/s40473-019-00176-3
fatcat:kquhyaz2mfb6vebjrn2s46kww4
Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey
2017
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
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Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. ...
We will also provide an insight into some sensors and algorithms gathered through survey in order to provide advantages and disadvantages of these technologies as well as to present any challenges that ...
High-level activities are predicted by time and place, for example sleeping. ...
doi:10.1109/ithings-greencom-cpscom-smartdata.2017.133
dblp:conf/ithings/NewcombeYCH17
fatcat:byl56hej2rd5blev7tgqxz254q
Circadian phase shifts and mood across the perinatal period in women with a history of major depressive disorder: A preliminary communication
2013
Journal of Affective Disorders
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Participants completed sleep diaries, wore wrist actigraphs and light sensors, and had mood assessed with the Hamilton Depression Rating Scale (HAMD-17) during 3 separate weeks of the perinatal period; ...
associations between circadian measures and depressed mood in women with a history of major depressive disorder (MDD). ...
Christian Gillin Award from the Sleep Research Society Foundation and K23-MH086689 to KMS. ...
doi:10.1016/j.jad.2013.04.046
pmid:23706877
pmcid:PMC3759598
fatcat:d7zasgfr5rbqvmxsri2zqa2h3y
Identification of Geriatric Depression and Anxiety Using Activity Tracking Data and Minimal Geriatric Assessment Scales
2022
Applied Sciences
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The identification of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. ...
The goal of this study was to build a machine learning framework that identifies geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales ...
The ability to screen for geriatric depression and anxiety using a low-cost wrist-worn activity tracker will provide practical benefits to both physicians and patients. ...
doi:10.3390/app12052488
fatcat:6qzbcdhmdvap3e2j6lzkbdb77i
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
2021
Frontiers in Psychiatry
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valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep. ...
, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). ...
From the sleep measures, we found a significant relationship between total sleep time and depression [beta
Combined Predictions Depression could be predicted by EMA and smartphone and wearable data. ...
doi:10.3389/fpsyt.2021.625247
pmid:33584388
pmcid:PMC7876288
fatcat:eqa27bwdlzfrtjaprf5u7eo4zi
Subjective and objective sleep quality modulate emotion regulatory brain function in anxiety and depression
2017
Depression and Anxiety
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Background-Disturbances in emotion regulation and sleep are shared across anxiety and mood disorders. ...
Post-hoc stepwise regression analysis showed these sleep measures predicted DACC activity whereas anxiety and depression symptoms did not. ...
part by NIMH R01MH101497 (KLP) and the Center for Clinical and Translational Research (CCTS) UL1RR029879. ...
doi:10.1002/da.22622
pmid:28419607
pmcid:PMC5503154
fatcat:4cuwazpocfchblpbbleooisbb4
Deciphering the Temporal Link between Pain and Sleep in a Heterogeneous Chronic Pain Patient Sample: A Multilevel Daily Process Study
2012
Sleep
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Instead, higher pain was predicted by longer sleep duration and lengthy awakenings the night before. ...
such as mood and arousal. ...
ACKNOWLEDGMENTS This research was funded by a personal award to Nicole Tang, DPhil from the National Institute for Health Research (Department of Health), UK. ...
doi:10.5665/sleep.1830
pmid:22547894
pmcid:PMC3321427
fatcat:5zn53ew2fvaa7msnaxupnd6gqe
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
2017
Annual Review of Clinical Psychology
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Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. ...
Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. ...
ACKNOWLEDGEMENTS This work was supported by the National Institutes of Mental Health with grants P20MH090318, R01MH100482, and R01MH095753 to Dr. Mohr, and K08MH102336 to Dr. Schueller. ...
doi:10.1146/annurev-clinpsy-032816-044949
pmid:28375728
pmcid:PMC6902121
fatcat:rxu55uc7jvaeplcrzaeotkmv6i
Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review
2019
Journal of Clinical Medicine
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for major depressive disorder (MDD). ...
Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances ...
Similarly, a compact wrist-worn electronic diary was used by Littlewood et al. to collect both self-reports and sleep/wake cycles with an embedded actimetry sensor [33] . ...
doi:10.3390/jcm8040465
pmid:30959828
pmcid:PMC6518287
fatcat:jcu7rhbforhb7eb5ardujmdbua
A review about Technology in mental health sensing and assessment
2022
ITM Web of Conferences
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Various systems based on wearable sensor networks, the Internet of Things and pervasive and ubiquitous computing have been designed and implemented in this sense. ...
Information and communication technologies (ICT) such as smart devices, the Internet of Things and wireless sensor networks are gradually being introduced into the health system for early diagnosis and ...
y, Wrist-worn Cross-sectional Muaremi et al., 2014 Wearable devices Cross-sectional Palmius et al., 2014 Smartphone RCT Applin et al., 2015 IoT, sensors None Osmani et al., 2015 Smartphones Cross-sectional ...
doi:10.1051/itmconf/20224601005
fatcat:dzwvlvs5w5atrmzqma4y7xchja
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