Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data.

Background: Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown.

Methods: We analysed data from 139 patients (76 female, mean age = 23.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study.

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR.

Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study.

Background: Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.

Mechanical Behavior of Fine Recycled Concrete Aggregate Concrete with the Mineral Admixtures.

The paper describes the mechanical behavior of fine recycled concrete aggregate (FRCA) concrete according to the mineral admixtures. Three types of the mineral admixtures, i.e.

Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study.

Background: Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter.

Objective: The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach.