Contributing Factors Associated With Impulsivity-Related Falls in Hospitalized, Older Adults

BACKGROUND Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors—circadian rhythm–related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states—on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures. OBJECTIVE The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model. METHODS A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing. RESULTS Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day’s sleep and nighttime behaviors (odds ratio [OR] 0.9, 95% CI 0.8-1.0; <i>P</i>=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; <i>P</i>=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes. CONCLUSIONS BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care.

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DEVELOPMENT AND TESTING OF AN ACCELEROMETER-BASED POSITIONAL MONITORING SYSTEM

Innovation in Aging ◽

10.1093/geroni/igz038.1222 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S336-S337

Author(s):

Leighanne Jarvis ◽

Sarah Moninger ◽

Chandra Throckmorton ◽

Juliessa M Pavon ◽

Kevin Caves

Keyword(s):

Older Adults ◽

Functional Disability ◽

Learning Algorithm ◽

Functional Decline ◽

Accelerometer Data ◽

Contributing Factors ◽

Postural Changes ◽

Patient Reported ◽

The Subject ◽

Hospitalized Older Adults

Abstract Health and fitness are contributing factors to physical resilience, or the ability to resist or recover from functional decline following health stressors. Accelerometer based activity monitors have been used in both the in-patient and outpatient setting to monitor mobility. While using sensors to track mobility is increasing, most clinical settings rely on patient reported outcomes. These measures often under or overestimate movement. The lack of a clinically meaningful way to measure mobility in the in-patient setting is a barrier to improving the mobility of hospitalized individuals. This is especially important when considering that over one-third of hospitalized older adults are discharged with a major new functional disability in performing activities of daily living. Our goal was to automatically determine if the subject is laying, reclining, sitting, standing, and walking to better reflect actual activity. Other platforms and studies indicate the ability to determine a difference in activity vs. inactivity or laying and reclining vs. standing and walking, but not all five phases of movement defined here. The aim of this study was to use accelerometer data to train a machine learning algorithm to automatically classify the postural changes (i.e. laying, reclining, sitting, standing, and walking). Preliminary results demonstrate that our trained algorithm is overall 95% accurate in determining each position from unlabeled data from the subject population. Additionally, this algorithm will be applied to in-patient hospitalized older adults for tracking of positions throughout the day.

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Risk factors associated with lower defecation frequency in hospitalized older adults: a case control study

BMC Geriatrics ◽

10.1186/s12877-015-0041-0 ◽

2015 ◽

Vol 15 (1) ◽

Cited By ~ 4

Author(s):

Jen-Tzer Gau ◽

Utkarsh H Acharya ◽

M Salman Khan ◽

Tzu-Cheg Kao

Keyword(s):

Risk Factors ◽

Older Adults ◽

Case Control Study ◽

Case Control ◽

Factors Associated ◽

Defecation Frequency ◽

Hospitalized Older Adults ◽

Control Study

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Factors associated with the level of oral intake in hospitalized older adults with dysphagia: The importance of mental activity

Clinical Nutrition ESPEN ◽

10.1016/j.clnesp.2016.03.001 ◽

2016 ◽

Vol 13 ◽

pp. e52-e54 ◽

Cited By ~ 2

Author(s):

Yoshitoshi Kuroda

Keyword(s):

Older Adults ◽

Oral Intake ◽

Mental Activity ◽

Factors Associated ◽

Hospitalized Older Adults

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Factors Associated With Behavioral and Psychological Symptoms of Dementia: Prospective Observational Study Using Actigraphy

Journal of Medical Internet Research ◽

10.2196/29001 ◽

2021 ◽

Vol 23 (10) ◽

pp. e29001

Author(s):

Eunhee Cho ◽

Sujin Kim ◽

Sinwoo Hwang ◽

Eunji Kwon ◽

Seok-Jae Heo ◽

...

Keyword(s):

Physical Activity ◽

Older Adults ◽

Prospective Observational Study ◽

Psychological Symptoms ◽

Information Criterion ◽

Activity Levels ◽

Contributing Factors ◽

Factors Associated ◽

Physical Activity Levels ◽

Behavioral And Psychological Symptoms

Background Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors—circadian rhythm–related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states—on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures. Objective The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model. Methods A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing. Results Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day’s sleep and nighttime behaviors (odds ratio [OR] 0.9, 95% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes. Conclusions BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care.

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Factors Associated with Mortality in Hospitalized Older Adults with COVID-19: A Large Retrospective Cohort Study

10.21203/rs.3.rs-148659/v1 ◽

2021 ◽

Author(s):

Liron Sinvani ◽

Allison Marziliano ◽

Alex Makhnevich ◽

Yan Liu ◽

Michael Qiu ◽

...

Keyword(s):

Older Adults ◽

New York ◽

Cohort Study ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Factors Associated ◽

York Metropolitan Area ◽

Included Patient ◽

Hospitalized Older Adults ◽

Oxygen Requirements

Abstract Background: Age has been implicated as the main risk factor for COVID-19-related mortality. Our objective was to determine patient factors associated with mortality in hospitalized older adults with COVID-19. Methods: Retrospective cohort study of adults age 65+ (N=4,949) hospitalized with COVID-19 in the greater New York metropolitan area between 3/1/20-4/20/20. Data included patient demographics and clinical presentation. Multivariate logistic regression was used to evaluate associations. Results: Average age 77.3 (SD=8.4), 56.0% male, 20.8% African American, 15.1% Hispanic. In a multivariate analysis, male gender (OR=1.47), higher comorbidity index (OR=1.10), admission from a facility (lower baseline function; OR=1.71), early DNR (declining life-sustaining treatments, OR=2.45), and higher illness severity (higher MEWS, OR=6.26, and higher oxygen requirements, OR=15.00) were associated with mortality, while age was not (p = 0.22). Conclusion: Our findings highlight the need to look beyond age in hospitalized older adults with COVID-19 when considering prognosis and treatment decisions.

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