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Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

We then calculated an anomaly score s(i)∈R for error vectors between NCV, N’CV, and NT, N’T using the Mahalanobis distance, which calculates the distance of a point to a distribution as follows: s(i)=((e(i)-μ)T Σ-1(e(i)-μ))1/2 [47]. The average anomaly score for a single day was calculated from the hourly scores, sd. The Mahalanobis distance from data in NCV was used to optimize an anomaly threshold, τ, for each participant over all sd for that participant.

Daniel A Adler, Dror Ben-Zeev, Vincent W-S Tseng, John M Kane, Rachel Brian, Andrew T Campbell, Marta Hauser, Emily A Scherer, Tanzeem Choudhury

JMIR Mhealth Uhealth 2020;8(8):e19962

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Article has an altmetric score of 93

Causal Factors of Anxiety and Depression in College Students: Longitudinal Ecological Momentary Assessment and Causal Analysis Using Peter and Clark Momentary Conditional Independence

Causal Factors of Anxiety and Depression in College Students: Longitudinal Ecological Momentary Assessment and Causal Analysis Using Peter and Clark Momentary Conditional Independence

All displayed connections were significant at P The PCMCI partial correlation (rp) analysis of contemporaneous relationships revealed significant contemporaneous influences (P Causality graph observing previous week (t-1) connections on mental health metrics collected through ecological momentary assessments weekly for cohorts 1 (top left) and 2 (top right) at a threshold of P Weekly EMAs of self-esteem, depression, stress, and anxiety were collected in two separate cohorts of over 200 college students across

Jeremy F Huckins, Alex W DaSilva, Elin L Hedlund, Eilis I Murphy, Courtney Rogers, Weichen Wang, Mikio Obuchi, Paul E Holtzheimer, Dylan D Wagner, Andrew T Campbell

JMIR Ment Health 2020;7(6):e16684

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Article has an altmetric score of 3

Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

The estimates in the figure represent the t values from a series of pairwise mixed-effect analyses regressing stress with each of the variables in the dataset. From Figure 3, one can see a few themes emerging. For example, it appears that the majority of sensing features seem to be inversely related to stress. Generally, it also appears that a variety of sensing features see a decrease in usage/occurrence during the evening epoch when stress is high. Average daily stress over the course of the term.

Alex W DaSilva, Jeremy F Huckins, Rui Wang, Weichen Wang, Dylan D Wagner, Andrew T Campbell

JMIR Mhealth Uhealth 2019;7(3):e12084

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Article has an altmetric score of 22