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.
authors.forename: Akane
authors.initials: A
authors.lastname: Sano
authors.forename: Amy Z
authors.initials: AZ
authors.lastname: Yu
authors.forename: Andrew W
authors.initials: AW
authors.lastname: McHill
authors.forename: Andrew J K
authors.initials: AJ
authors.lastname: Phillips
authors.forename: Sara
authors.initials: S
authors.lastname: Taylor
authors.forename: Natasha
authors.initials: N
authors.lastname: Jaques
authors.forename: Elizabeth B
authors.initials: EB
authors.lastname: Klerman
authors.forename: Rosalind W
authors.initials: RW
authors.lastname: Picard
mesh_terms: D000339:Affect; D034742:Electronic Mail; D005260:Female; D006240:Happiness; D006801:Humans; D007398:Interpersonal Relations; D008297:Male; D012890:Sleep / Q000502:physiology*; D013334:Students; D055815:Young Adult