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

doi:10.1109/EMBC.2015.7319954

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

39 Citations (Scopus)

Abstract

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 daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

Original language	English
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)
Editors	Sergio Cerutti, Paolo Bonato
Place of Publication	Piscataway, NJ
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	6796-6799
Number of pages	4
ISBN (Electronic)	9781424492718
ISBN (Print)	9781424492695
DOIs	https://doi.org/10.1109/EMBC.2015.7319954
Publication status	Published - 2015
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015 Conference number: 37th https://embc.embs.org/2015/ https://ieeexplore.ieee.org/xpl/conhome/7302811/proceeding (Proceedings)

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2015
Abbreviated title	EMBC 2015
Country/Territory	Italy
City	Milan
Period	25/08/15 → 29/08/15
Internet address	https://embc.embs.org/2015/ https://ieeexplore.ieee.org/xpl/conhome/7302811/proceeding (Proceedings)

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10.1109/EMBC.2015.7319954

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Sano, A., Yu, A. Z., McHill, A. W., Phillips, A. J. K., Taylor, S., Jaques, N., Klerman, E. B., & Picard, R. W. (2015). Prediction of Happy-Sad mood from daily behaviors and previous sleep history. In S. Cerutti, & P. Bonato (Eds.), 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015) (pp. 6796-6799). Article 7319954 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2015.7319954

Sano, Akane ; Yu, Amy Z. ; McHill, Andrew W. et al. / Prediction of Happy-Sad mood from daily behaviors and previous sleep history. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015). editor / Sergio Cerutti ; Paolo Bonato. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 6796-6799

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title = "Prediction of Happy-Sad mood from daily behaviors and previous sleep history",

abstract = "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 daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.",

author = "Akane Sano and Yu, {Amy Z.} and McHill, {Andrew W.} and Phillips, {Andrew J. K.} and Sara Taylor and Natasha Jaques and Klerman, {Elizabeth B.} and Picard, {Rosalind W.}",

year = "2015",

doi = "10.1109/EMBC.2015.7319954",

language = "English",

isbn = "9781424492695",

pages = "6796--6799",

editor = "Sergio Cerutti and Paolo Bonato",

booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Conference of the IEEE Engineering in Medicine and Biology Society 2015, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",

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Sano, A, Yu, AZ, McHill, AW, Phillips, AJK, Taylor, S, Jaques, N, Klerman, EB & Picard, RW 2015, Prediction of Happy-Sad mood from daily behaviors and previous sleep history. in S Cerutti & P Bonato (eds), 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)., 7319954, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 6796-6799, International Conference of the IEEE Engineering in Medicine and Biology Society 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7319954

Prediction of Happy-Sad mood from daily behaviors and previous sleep history. / Sano, Akane; Yu, Amy Z.; McHill, Andrew W. et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015). ed. / Sergio Cerutti; Paolo Bonato. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 6796-6799 7319954.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Sano, Akane

AU - Yu, Amy Z.

AU - McHill, Andrew W.

AU - Phillips, Andrew J. K.

AU - Taylor, Sara

AU - Jaques, Natasha

AU - Klerman, Elizabeth B.

AU - Picard, Rosalind W.

N1 - Conference code: 37th

PY - 2015

Y1 - 2015

N2 - 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 daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

AB - 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 daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

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BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway, NJ

T2 - International Conference of the IEEE Engineering in Medicine and Biology Society 2015

Y2 - 25 August 2015 through 29 August 2015

ER -

Sano A, Yu AZ, McHill AW, Phillips AJK, Taylor S, Jaques N et al. Prediction of Happy-Sad mood from daily behaviors and previous sleep history. In Cerutti S, Bonato P, editors, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015). Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 6796-6799. 7319954 doi: 10.1109/EMBC.2015.7319954

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

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