Clinical Manifestations.

Background: Agitation is one of the most challenging behaviors exhibited by people with cognitive decline, causing distress in caregivers, earlier placement into long-term care and faster disease progression. In order to better manage agitated behaviors in people with cognitive decline, it is important to identify associated factors. The MODERATE (Monitoring Dementia-Related Agitation Using Technology Evaluation) study aims to characterize agitation using technology and identify precipitants (behavioral or environmental) of agitation.

Clinical Manifestations.

Background: Traditional cognitive and daily functioning measures that utilize episodic assessment schedules are less sensitive to subtle within-person change in those at risk for Alzheimer's disease (AD). This study aimed to evaluate whether longitudinal trajectories of high frequency cognitive assessments (HFA) and passively assessed higher order instrumental activities of daily living (IADLs) differ between those with intact cognition (CN, n = 59) and mild cognitive impairment (MCI, n = 45). An exploratory aim evaluated whether the use of person-specific distributions would detect differences in longitudinal trajectories not captured by traditional between-group analyses.

Corrigendum to: Prolonged Physical Inactivity in Older Adult Couples: A Dyadic Analysis Using Actigraphy.

[This corrects the article DOI: 10.1093/geroni/igaa066.].

Passively-Measured Routine Home Computer Activity and Application Use Can Detect Mild Cognitive Impairment and Correlate with Important Cognitive Functions in Older Adulthood.

Background: Computer use is a cognitively complex instrumental activity of daily living (IADL) that has been linked to cognitive functioning in older adulthood, yet little work has explored its capacity to detect incident mild cognitive impairment (MCI).

Objective: To examine whether routine home computer use (general computer use as well as use of specific applications) could effectively discriminate between older adults with and without MCI, as well as explore associations between use of common computer applications and cognitive domains known to be important for IADL performance.

Methods: A total of 60 community-dwelling older adults (39 cognitively healthy, 21 with MCI) completed a neuropsychological evaluation at study baseline and subsequently had their routine home computer use behaviors passively recorded for three months.

Age Predicts Older Adults' Driving Self-Regulation but Not Dangerous Driving Behaviors after Controlling for Executive Function.

Objectives: The aim of the study was to examine the unique contributions of age to objectively measure driving frequency and dangerous driving behaviors in healthy older adults after adjusting for executive function (EF).

Method: A total of 28 community-dwelling older adults (mean age = 82.0 years, standard deviation [SD] = 7.

Prolonged Physical Inactivity in Older Adult Couples: A Dyadic Analysis Using Actigraphy.

Background And Objectives: Many older adults remain inactive despite the known positive health implications of physical activity (improved mood, reduced mortality risk). Physical inactivity is an interdependent phenomenon in couples, but most research examines physical inactivity at the individual level. We estimated the average amount of prolonged physical inactivity for older adult couples and, using dyadic analysis, identified physical and mental health determinants thereof.

Subtle Changes in Medication-taking Are Associated With Incident Mild Cognitive Impairment.

Introduction: Medication-taking is a routine instrumental activity of daily living affected by mild cognitive impairment (MCI) but difficult to measure with clinical tools. This prospective longitudinal study examined in-home medication-taking and transition from normative aging to MCI.

Methods: Daily, weekly, and monthly medication-taking metrics derived from an instrumented pillbox were examined in 64 healthy cognitively intact older adults (Mage=85.

A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection.

Sleep apnea is a breathing disorder that affects many individuals and has been associated with serious health conditions such as cardiovascular disease. Clinical diagnosis of sleep apnea requires that a patient spend the night in a sleep clinic while being wired up to numerous obtrusive sensors. We are developing a system that utilizes respiration rate and breathing amplitude inferred from non-contact bed sensors (i.

Unobtrusive classification of sleep and wakefulness using load cells under the bed.

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting.

A Gaussian model for movement detection during sleep.

Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of mobility in bed during sleep can be a disease marker or can reflect various abnormal physiological and neurological conditions. This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography.

Accurate scoring of the apnea-hypopnea index using a simple non-contact breathing sensor.

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient's sleep during the overnight testing. We investigated the use of breathing signals derived from non-contact force sensors (i.

Classification of lying position using load cells under the bed.

Individuals who suffer from acid reflux at night, who snore chronically, or who have sleep apnea are frequently encouraged to sleep in a particular lying position. Side sleeping decreases the frequency and severity of obstructive respiratory events (e.g.

A subject state detection approach to determine rest-activity patterns using load cells.

A patient's sleep/wake schedule is an important step underlying clinical evaluation of sleep-related complaints. Aspects related to timing of a person's sleep routine provide important clues regarding diagnosis and treatments. Solutions for sleep complaints may sometimes rely solely on changes in habits and life style, based on what is learned from daily rest-activity patterns.

Classification of breathing events using load cells under the bed.

Sleep disturbances are prevalent, financially taxing, and have a negative effect on health and quality of life. One of the most common sleep disturbances is obstructive sleep apnea-hypopnea syndrome (OSAHS) which frequently goes undiagnosed. The gold standard for diagnosing OSAHS is polysomnography (PSG)-a procedure that is inconvenient, time-consuming, and interferes with normal sleep patterns.

Algorithm for automatic beat detection of cardiovascular pressure signals.

Pressure beat detection is an integral part of most analysis techniques for arterial blood pressure (ABP), intracranial pressure (ICP), and pulse oximetry (SpO(2)) signals. Beat detection has been used to estimate heart rate in the ABP signal, to classify ICP morphologies, and to estimate blood pressure using pulse oximeter waveforms. This paper describes an algorithm that was developed to detect pressure peak beats in ABP, ICP, and SpO(2) signals.