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] = 7.5) without dementia who were in good physical health and enrolled in a longitudinal aging study completed several EF and clinical self-report measures at baseline. Participants subsequently had a sensor installed in their vehicle for a mean of 208 (SD = 38, range = 127-257) days. RESULTS: Participants drove for an average of 54 min per day. Mixed-effects models indicated that after controlling for EF, older age was associated with less time driving per day, decreased number of trips, and less nighttime driving. Age was not associated with hard brakes or hard accelerations. DISCUSSION: After accounting for EF, greater age is associated with higher driving self-regulation but not dangerous driving behaviors in healthy older adults. Future studies should recruit larger samples and collect sensor-measured driving data over a more extended time frame to better determine how and why these self-regulation changes take place.

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. RESULTS: Compared to those with MCI, cognitively healthy participants spent more time using the computer, had a greater number of computer sessions, and had an earlier mean time of first daily computer session. They also spent more time using email and word processing applications, and used email, search, and word processing applications on a greater number of days. Better performance in several cognitive domains, but in particular memory and language, was associated with greater frequency of browser, word processing, search, and game application use. CONCLUSION: Computer and application use are useful in identifying older adults with MCI. Longitudinal studies are needed to determine whether decreases in overall computer use and specific computer application use are predictors of incident cognitive decline.

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. RESEARCH DESIGN AND METHODS: Forty-six heterosexual older adult couples (age = 70.61 ± 6.56) from the Veterans Integrated Service Network 20 cohort of the Collaborative Aging Research using Technology (CART) initiative were included. The average number per day of prolonged inactive periods (no step counts or sleep activity for ≥30 min) was estimated using actigraphy data collected over a month. RESULTS: Multilevel modeling revealed that, within couples, there was no significant difference between partners in the average amount of inactive periods (p = .28). On average across couples, males and females had an average of 6.90 ± 2.02 and 6.56 ± 1.93 inactive periods per day, respectively. For males, older age was the only variable associated with more inactive periods (ß = 0.15, p = .002). For females, having more depressive symptoms in both dyad members was associated with fewer inactive periods (female: ß = -0.30, p = .03; male: ß = -0.41, p < .001), and more dependence in completing their own instrumental activities of daily living predicted more inactive periods (ß = 2.58, p < .001). DISCUSSION AND IMPLICATIONS: Viewing couples' activity as an interdependent phenomenon, rather than individual, provides a novel approach to identifying pathways to reduce inactivity in older adults, especially when focusing on the mental health and level of independence within the couple.

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=85.5 y) followed for a mean of 2.3 years; 9 transitioned to MCI during study follow-up. RESULTS: In the time up to and after MCI diagnosis, incident MCI participants opened their pillbox later in the day (by 19 min/mo; ß=0.46, P<0.001) and had increased day-to-day variability in the first pillbox opening over time (by 4 min/mo) as compared with stable cognitively intact participants (ß=4.0, P=0.003). DISCUSSION: Individuals who transitioned to MCI opened their pillboxes later in the day and were more variable in their medication-taking habits. These differences increased in the time up to and after diagnosis of MCI. Unobtrusive medication-taking monitoring is an ecologically valid approach for identifying early activity of daily living changes that signal transition to MCI.

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.e. load cells placed under bed supports) to detect sleep apnea. Multi-harmonic artifacts generated either biologically or as a result of the impulse response of the bed have made it challenging to track respiration rate and amplitude with high resolution in time. In this paper, we present an algorithm that can accurately track respiration on a second-by-second basis while removing noise harmonics. The algorithm is tested using data collected from 5 patients during overnight sleep studies. Respiration rate is compared with polysomnography estimations of respiration rate estimated by a technician following clinical standards. Results indicate that certain subjects exhibit a large harmonic component of their breathing signal that can be removed by our algorithm. When compared with technician transcribed respiration rates using polysomnography signals, we demonstrate improved accuracy of respiration rate tracking using harmonic artifact rejection (mean error: 0.18 breaths/minute) over tracking not using harmonic artifact rejection (mean error: -2.74 breaths/minute).

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.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral-nasal thermistor, chest belt and abdominal belt). The apnea-hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44  = 0.37, P = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra-class correlation coefficient of 0.97 for the apnea-hypopnea index and an intra-class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea-hypopnea index.

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. The system is based on load cells installed at the supports of a bed. Since the load cell signal varies the most during movement, the approach uses a weighted combination of the short-term mean-square differences of each load cell signal to capture the variations in the signal caused by movement. We use a single univariate Gaussian model to represent each class: movement versus non-movement. We assess the performance of the method against manual annotation performed by a sleep clinic technician from seventeen patients. The proposed detection method achieved an overall sensitivity of 97.9% and specificity of 98.7%.

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. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the "gold-standard" sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

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. apnea and hypopnea) in patients with positional sleep apnea. It has been suggested that individuals with Gastroesophageal Reflux Disease sleep on their left sides in order to help minimize symptoms. In this paper, we present a method of predicting the position of an individual lying on the bed using load cells placed under each of the bed supports. Our results suggest that load cells utilized in this manner could be successfully implemented into a system that tracks or helps train individuals to sleep in a particular lying position.

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. This paper describes an approach for determining two states, in-bed and out-of-bed, using load cells under the bed. These states are important because they can help characterize rest-activity patterns at nighttime or detect bed exits in hospitals or nursing homes. The information derived from the load cells is valuable as an objective and continuous measure of daily patterns, and it is particularly valuable in sleep studies in populations who would not be able to remember specific hours to complete sleep diaries. The approach is evaluated on data collected in a laboratory experiment, in a sleep clinic, and also on data collected from residents of an assisted-living facility.

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. We are investigating an alternative to PSG in which unobtrusive load cells fitted under the bed are used to monitor movement, heart rate, and respiration. In this paper we describe how load cell data can be used to distinguish between clinically relevant disordered breathing (apneas and hypopneas) and normal respiration. The method correctly classified disordered breathing segments with a sensitivity of 0.77 and a specificity of 0.91.

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. When compared to the expert annotation of several signals consisting of over 42,500 pressure beats, the algorithm detected pressure peaks with an average sensitivity of 99.6% +/- 0.27 and an average positive predictivity of 98.6% +/- 1.1.