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1.
Sleep ; 46(1)2023 01 11.
Artículo en Inglés | MEDLINE | ID: mdl-35767600

RESUMEN

STUDY OBJECTIVES: Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and at-home electroencephalography (EEG)-derived sleep monitoring device, the Dreem 2 Headband. METHODS: Thirty-six healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep patterns were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and two nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband. RESULTS: Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures. CONCLUSION: The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.


Asunto(s)
Actigrafía , Sueño , Adulto , Humanos , Reproducibilidad de los Resultados , Sueño/fisiología , Polisomnografía , Algoritmos
2.
Chronic Stress (Thousand Oaks) ; 6: 24705470211069904, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35128293

RESUMEN

BACKGROUND: Personality traits are important factors with regard to the tendency to experience and response to stress. This study introduces and tests a new stress-related personality scale called the Virtual Inventory of Behavior and Emotions (VIBE). METHODS: Two samples totaling 5512 individuals (with 66% between the ages of 18 and 34) completed the VIBE along with other measures of personality, stress, mood, and well-being. RESULTS: Exploratory factor analyses revealed a four-factor structure for the instrument with dimensions labeled: 1) stressed; 2) energetic; 3) social; and 4) disciplined. Confirmatory factor analytic procedures on the final 23-item version showed good psychometric properties and data fit while machine learning analyses demonstrated the VIBE's ability to distinguish between groups with similar patterns of response. Strong convergent validity was suggested through robust correlations between the dimensions of the VIBE and other established rating scales. CONCLUSION: Overall, the data suggest that the VIBE is a promising tool to help advance understanding of the relations between stress, personality, and related constructs.

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