Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey.

Introduction: Estimating physical activity, sedentary behavior, and sleep from wrist-worn accelerometer data requires reliable detection of sensor nonwear and sensor wear during both sleep and wake.

Purpose: This study aimed to develop an algorithm that simultaneously identifies sensor wake-wear, sleep-wear, and nonwear in 24-h wrist accelerometer data collected with or without filtering.

Methods: Using sensor data labeled with polysomnography ( n = 21) and directly observed wake-wear data ( n = 31) from healthy adults, and nonwear data from sensors left at various locations in a home ( n = 20), we developed an algorithm to detect nonwear, sleep-wear, and wake-wear for "idle sleep mode" (ISM) filtered data collected in the 2011-2014 National Health and Nutrition Examination Survey.

Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data.

Human activity recognition using wearable accelerometers can enable detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data.

Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

Background: Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement.

Background: Ecological momentary assessment (EMA) is an in situ method of gathering self-report on behaviors using mobile devices. In typical phone-based EMAs, participants are prompted repeatedly with multiple-choice questions, often causing participation burden. Alternatively, microinteraction EMA (micro-EMA or μEMA) is a type of EMA where all the self-report prompts are single-question surveys that can be answered using a 1-tap glanceable microinteraction conveniently on a smartwatch.

Relationship between pain, fatigue, and physical activity levels during a technology-based physical activity intervention.

The majority of individuals with spinal cord injury (SCI) experience chronic pain. Chronic pain can be difficult to manage because of variability in the underlying pain mechanisms. More insight regarding the relationship between pain and physical activity (PA) is necessary to understand pain responses during PA.

Posture and Physical Activity Detection: Impact of Number of Sensors and Feature Type.

Unlabelled: Studies using wearable sensors to measure posture, physical activity (PA), and sedentary behavior typically use a single sensor worn on the ankle, thigh, wrist, or hip. Although the use of single sensors may be convenient, using multiple sensors is becoming more practical as sensors miniaturize.

Purpose: We evaluated the effect of single-site versus multisite motion sensing at seven body locations (both ankles, wrists, hips, and dominant thigh) on the detection of physical behavior recognition using a machine learning algorithm.

Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research.

Human activity recognition using wearable accelerometers can enable detection of physical activities to support novel human-computer interfaces and interventions. However, developing valid algorithms that use accelerometer data to detect everyday activities often requires large amounts of training datasets, precisely labeled with the start and end times of the activities of interest. Acquiring annotated data is challenging and time-consuming.

Correction: Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

[This corrects the article DOI: 10.1371/journal.pone.

Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels.

In vivo relationship between serotonin 1A receptor binding and gray matter volume in the healthy brain and in major depressive disorder.

Serotonin 1A (5-HT) receptors mediate serotonin trophic role in brain neurogenesis. Gray matter volume (GMV) loss and 5-HT receptor binding alterations have been identified in major depressive disorder (MDD). Here we investigated the relationship between 5-HT receptor binding and GMV in 40 healthy controls (HCs) and, for the first time, 47 antidepressant-free MDD patients using Voxel-Based Morphometry and [C]WAY100635 Positron Emission Tomography.

Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder.

Background: Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD.

Transcranial Magnetic Stimulation of Left Dorsolateral Prefrontal Cortex Induces Brain Morphological Changes in Regions Associated with a Treatment Resistant Major Depressive Episode: An Exploratory Analysis.

Background: Repetitive transcranial magnetic stimulation (TMS) is an FDA-approved antidepressant treatment but little is known of its mechanism of action. Specifically, downstream effects of TMS remain to be elucidated.

Objective/hypothesis: This study aims to identify brain structural changes from TMS treatment of a treatment resistant depressive episode through an exploratory analysis.

Omega-3 polyunsaturated fatty acid supplementation and white matter changes in major depression.

White matter abnormalities are implicated in major depressive disorder (MDD). As omega-3 polyunsaturated fatty acids (PUFAs) are low in MDD and affect myelination, we hypothesized that PUFA supplementation may alleviate depression through improving white matter integrity. Acutely depressed MDD patients (n = 16) and healthy volunteers (HV, n = 12) had 25-direction diffusion tensor imaging before and after 6 weeks of fish oil supplementation.

Cortical thickness differences between bipolar depression and major depressive disorder.

Objectives: Bipolar disorder (BD) is a psychiatric disorder with high morbidity and mortality that cannot be distinguished from major depressive disorder (MDD) until the first manic episode. A biomarker able to differentiate BD and MDD could help clinicians avoid risks of treating BD with antidepressants without mood stabilizers.

Methods: Cortical thickness differences were assessed using magnetic resonance imaging in BD depressed patients (n = 18), MDD depressed patients (n = 56), and healthy volunteers (HVs) (n = 54).

Prediction of selective serotonin reuptake inhibitor response using diffusion-weighted MRI.

Pre-treatment differences in serotonergic binding between those who remit to antidepressant treatment and those who do not have been found using Positron Emission Tomography (PET). To investigate these differences, an exploratory study was performed using a second imaging modality, diffusion-weighted MRI (DW-MRI). Eighteen antidepressant-free subjects with Major Depressive Disorder received a 25-direction DW-MRI scan prior to 8 weeks of selective serotonin reuptake inhibitor treatment.