Variability in medication taking is associated with cognitive performance in nondemented older adults

Austin, Johanna; Klein, Krystal A.; Mattek, Nora; Kaye, Jeffrey

doi:10.1016/j.dadm.2017.02.003

Cited by 36 publications

(37 citation statements)

References 20 publications

(29 reference statements)

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“…The observation holds for duration features, as well as for the entire feature vector. This is consistent with the literature, which indicates that day-to-day variability in behavior is an indicator of a change in cognitive health [ 55 ]. One smart home resident in particular, the resident living in Home 2, exhibited changes in behavior that were statistically significant.…”

Section: Resultssupporting

confidence: 93%

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Lin

Cook

2020

Sensors

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Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual’s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident’s cognitive health diagnosis, with an accuracy of 0.84.

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Section: Resultssupporting

confidence: 93%

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Lin

Cook

2020

Sensors

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“…Austin et al 19 demonstrated that cognitive function is linked closely to the spread in the timing of taking medications and that patients with poorer cognitive function exhibited a larger increase in this spread over time. In addition, Jerant et al 20 in their observational cohort study from 771 patients showed that a lower cognitive function is significantly associated with nonadherence.…”

Section: Introductionmentioning

confidence: 99%

Relation between cognitive impairment and treatment adherence in elderly hypertensive patients

Chudiak¹,

Uchmanowicz²,

Mazur³

2018

CIA

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BackgroundNonadherence to medical treatment and lack of cooperation in hypertensive patients >65 years of age are believed to be caused by a number of age-related problems, such as cognitive impairment. Numerous epidemiological and prospective studies have demonstrated that hypertension that remains untreated for many years or is unsuccessfully treated for reasons such as poor compliance and adherence of the patient may lead to cognitive impairment.ObjectiveThe objective of this study was to investigate the occurrence of cognitive impairment and its effect on treatment compliance and adherence in elderly hypertensive patients.DesignThis study was an analytical cross-sectional study.Patients and methodsThe study was conducted on 300 patients aged 65–91 years (mean age=71.8 years, SD=7.8 years) diagnosed with hypertension. The following research tools were used: 1) Hill-Bone High Blood Pressure Compliance Scale (HBCS) and 2) Mini–Mental State Examination (MMSE). We also analyzed medical documentation to obtain basic sociodemographic and clinical data. The study was approved by the Bioethics Committee of the Medical University of Wrocław (no KB-144/2016).ResultsCognitive impairment occurred in 60% of the patients. A group of 63% patients complied with antihypertensive therapy, with the mean score of 20.8 points. Cognitive impairment was strongly correlated with the total score of the HBCS questionnaire (p<0.001) and two of its subscales: “appointment keeping” (p<0.001) and “medication taking” (p<0.001).ConclusionCompliance and adherence levels are higher in patients with a higher educational level, whereas male sex adversely affects treatment adherence in elderly hypertensive patients.

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“…The sensor system allows unobtrusive and continuous monitoring of the volunteers in their own homes. The system has been used in dozens of studies involving hundreds of volunteers in research examining key domains of health and function such as walking speed and mobility, medication-taking behavior, mood, time in or out of home, sleep, and computer use 8,9,10,11,12,13,14,15,16,17,18 . Representative results are presented here.…”

Section: Representative Resultsmentioning

confidence: 99%

“…. Diminished medication adherence, as followed using the MedTracker pillbox, was also found to be a marker for early cognitive change 12,17 . Computer usage tracking software was used to discover that computer use decreases with MCI and patterns in the use of a computer mouse may also reflect cognitive decline 13,14 .…”

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confidence: 99%

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Kaye

Reynolds

Bowman

et al. 2018

JoVE

Self Cite

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An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

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Variability in medication taking is associated with cognitive performance in nondemented older adults

Cited by 36 publications

References 20 publications

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Relation between cognitive impairment and treatment adherence in elderly hypertensive patients

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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