0795 IMPORTANCE OF SLEEP DATA IN PREDICTING NEXT-DAY STRESS, HAPPINESS, AND HEALTH IN COLLEGE STUDENTS
Notice bibliographique
Résumé
Perceived wellbeing, as measured by self-reported health, stress, and happiness, has a number of important clinical health consequences. The ability to model and predict these measures could therefore be immensely beneficial in the treatment and prevention of mental illness. However, predicting self-reported health, stress, and happiness is a difficult problem often requiring large, multi-modal datasets. We show that the accuracy for predicting next-day wellbeing is improved when including simple sleep features. Data from 144 college students were collected during a 30-day study. Participants wore two sensors to collect actigraphy and physiology data, installed a data logger on their smartphone, and filled out online surveys. Participants self-reported daily on three wellbeing measures (stress - calm; sad - happy; sick - healthy) using a visual analog scale (later scored 0 to 100). The top and bottom 40% of scores were assigned positive and negative labels, respectively. A hierarchical bayes machine learning algorithm was trained to predict each next-day wellbeing label on two data sets: (1) including self-reported sleep features (e.g., self-reported sleep latency, bedtime, and wake time), and (2) discarding sleep features. Both data sets include approximately 20 features computed from wearable sensors, phone, and online surveys. In total, 2,769 days of data were used. Without including the sleep features, hold-out test accuracies for stress, happiness, and healthy were 79.62%, 78.24%, 83.55%, respectively. When including sleep features, the accuracies were improved for the stress and happy predictions to 80.67%, 80.40%, respectively; however the healthy prediction accuracy worsened slightly to 83.12%. Using McNemar’s test we find that including sleep features does not significantly improve the classifiers for the stress or healthy prediction, but does significantly improve the classifier for the happy prediction (p<0.15). The inclusion of sleep features improved the prediction of next-data self-reported stress/calm and happy/sad metric of individuals above a classifier using features from smartphones and wearables. Future studies of personalized prediction of happy/sad and stress/calm ought to consider including self-reported sleep features in order to improve prediction. MIT Media Lab Consortium, NIH (R01GM105018, K24HL105664), Samsung Electronics, and Canada’s NSERC program.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».