393 Hits in 4.1 sec

Prediction of Happy-Sad mood from daily behaviors and previous sleep history

Akane Sano, Amy Z. Yu, Andrew W. McHill, Andrew J. K. Phillips, Sara Taylor, Natasha Jaques, Elizabeth B. Klerman, Rosalind W. Picard
2015 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
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

Mahsa Sheikh, M. Qassem, Panicos A. Kyriacou
2021 Frontiers in Digital Health  
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

Daniel J. van Wamelen, Shweta Hota, Aleksandra Podlewska, Valentina Leta, Dhaval Trivedi, Alexandra Rizos, Miriam Parry, K. Ray Chaudhuri
2019 npj Parkinson's Disease  
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

Amanda Fernandes, Frank J Van Lenthe, Julie Vallée, Cedric Sueur, Basile Chaix
2020 Journal of Epidemiology and Community Health  
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

Jinyoung Choi, Soomin Lee, Seonyoung Kim, Dongil Kim, Hyungshin Kim
2022 Sensors  
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

George Gillett, Kate E A Saunders
2019 Current Behavioral Neuroscience Reports  
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

Lee Newcombe, Po Yang, Chris Carter, Martin Hanneghan
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)  
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

Katherine M. Sharkey, Teri B. Pearlstein, Mary A. Carskadon
2013 Journal of Affective Disorders  
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

Tae-Rim Lee, Geon-Ha Kim, Mun-Taek Choi
2022 Applied Sciences  
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

Isaac Moshe, Yannik Terhorst, Kennedy Opoku Asare, Lasse Bosse Sander, Denzil Ferreira, Harald Baumeister, David C. Mohr, Laura Pulkki-Råback
2021 Frontiers in Psychiatry  
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

Heide Klumpp, Julia Roberts, Mary C. Kapella, Amy E. Kennedy, Anand Kumar, K. Luan Phan
2017 Depression and Anxiety  
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

Nicole K.Y. Tang, Claire E. Goodchild, Adam N. Sanborn, Jonathan Howard, Paul M. Salkovskis
2012 Sleep  
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

David C. Mohr, Mi Zhang, Stephen M. Schueller
2017 Annual Review of Clinical Psychology  
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

Desirée Colombo, Javier Fernández-Álvarez, Andrea Patané, Michelle Semonella, Marta Kwiatkowska, Azucena García-Palacios, Pietro Cipresso, Giuseppe Riva, Cristina Botella
2019 Journal of Clinical Medicine  
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

Thierry Jossou, Daton Medenou, Aziz Et-tahir, Héribert Ahouandjinou, Thierry Edoh, Roland Houessouvo, Leandro Pecchia, M. Sbihi, A. Mounadi, M. Garoum
2022 ITM Web of Conferences  
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
« Previous Showing results 1 — 15 out of 393 results