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  • Oxford University Press (OUP)  (5)
  • Li, Chin-Shang  (5)
  • 2020-2024  (5)
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  • Oxford University Press (OUP)  (5)
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  • 2020-2024  (5)
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  • 1
    Online Resource
    Online Resource
    160 Underlying Factors Contributing to Sleep Health Among Middle-aged and Older Adults
    Oxford University Press (OUP) ; 2021
    In:  Sleep Vol. 44, No. Supplement_2 ( 2021-05-03), p. A65-A65
    Details
    In: Sleep, Oxford University Press (OUP), Vol. 44, No. Supplement_2 ( 2021-05-03), p. A65-A65
    Abstract: Although poor sleep is not inherent with aging, an estimated 50-70 million adults in the US have insufficient sleep. Sleep duration is increasingly recognized as incomplete and insufficient. Instead, sleep health (SH), a multidimensional concept describing sleep/wake patterns that promote well-being has been shown to better reflect how sleep impacts the individual. Therefore, focusing on the underlying factors contributing to sleep health may provide the opportunity to develop interventions to improve sleep health in middle-age and older adults. Methods Data from the 2014 wave of the Health and Retirement Study (HRS) were used. Sample size was restricted to those who completed an additional questionnaire containing sleep variables. A derivation of the SH composite was constructed using eight selected sleep variables from the HRS data based on the five dimensions of sleep: Satisfaction, Alertness, Timing, Efficiency, and Duration. Total score ranged from 0-100, with higher scores indicating better SH. Weighting variables were based on complex sampling procedures and provided by HRS. Machine learning-based framework was used to identify determinants for predicting SH using twenty-six variables representing individual health and socio-demographics. Penalized linear regression with elastic net penalty was used to study the impact of individual predictors on SH. Results Our sample included 5,163 adults with a mean age of 67.8 years (SD=9.9; range 50-98 years). The majority were female (59%), white (78%), and married (61%). SH score ranged from 27-61 (mean=50; SD=6.7). Loneliness (coefficient=-1.92), depressive symptoms (coefficient=-1.28), and physical activity (coefficient=1.31) were identified as the strongest predictors of SH. Self-reported health status (coefficient=-1.11), daily pain (coefficient=-0.65), being middle-aged (coefficient=-0.26), and discrimination (coefficient=-0.23) were also significant predictors in this model. Conclusion Our study identified key predictors of SH among middle-aged and older adults using a novel approach of Machine Learning. Improving SH is a concrete target for health promotion through clinical interventions tailored towards increasing physical activity and reducing loneliness and depressive symptoms among middle-aged adults. Support (if any) This study was supported by National Heart, Lung, and Blood Institute (NHLBI) UB Clinical Scholar Program in Implementation Science to Achieve Triple Aims-NIH K12 Faculty Scholar Program in Implementation Science
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    URL: Article
    DOI: 10.1093/sleep/zsab072.159
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2056761-3
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  • 2
    Online Resource
    Online Resource
    THE IMPACT OF SOCIAL DETERMINANTS ON SLEEP HEALTH AMONG MIDDLE-AGED AND OLDER ADULTS
    Oxford University Press (OUP) ; 2022
    In:  Innovation in Aging Vol. 6, No. Supplement_1 ( 2022-12-20), p. 626-626
    Details
    In: Innovation in Aging, Oxford University Press (OUP), Vol. 6, No. Supplement_1 ( 2022-12-20), p. 626-626
    Abstract: Poor sleep health, including short or long duration and/or irregular timing may lead to a variety of chronic health conditions including diabetes and heart disease. An estimated 50-70 million adults in the United States have poor sleep health and this burden is disproportionately felt among systematically disadvantaged groups. While social and behavioral determinants of sleep duration and quality have been examined, sleep health, a multidimensional concept, has been less explored. The study aims to examine the impact of social determinants on sleep health among middle-aged and older adults. Data from the 2014 wave of the Health and Retirement Study were weighted and restricted to respondents of “Leave-Behind” questionnaire (n=5334). Sleep Health score was derived from sleep variables (range 0-100). Structural equation modeling was conducted using the R package lavaan. Sample mean age was 68.2 years (SD=10.1). Majority were female (60%) and white (76%) with mean Sleep Health score of 50 (SD=5.2). Black (p & lt; 0.0001) and Latinx respondents (p & lt; 0.0001) had worse sleep health than white respondents. Depression, financial strain, and neighborhood characteristics of socioeconomic status, social cohesion, and physical disorder mediated the relationship between race and sleep health. Ongoing chronic stress and everyday discrimination also mediated the relationship between race and Sleep Health among Black vs. white respondents. These findings suggest multiple individual and neighborhood-level determinants may negatively influence sleep health among a nationally representative sample of middle-aged and older Black and Latinx adults. Neighborhood-level characteristics may be modifiable factors that can be targeted to improve sleep and related health outcomes.
    Type of Medium: Online Resource
    ISSN: 2399-5300
    URL: Article
    DOI: 10.1093/geroni/igac059.2325
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2905697-4
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  • 3
    Online Resource
    Online Resource
    Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
    Oxford University Press (OUP) ; 2022
    In:  Innovation in Aging Vol. 6, No. 6 ( 2022-09-01)
    Details
    In: Innovation in Aging, Oxford University Press (OUP), Vol. 6, No. 6 ( 2022-09-01)
    Abstract: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. Research Design and Methods Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015–2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR] ). Results IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers’ age, and the person living with dementia’s income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). Discussion and Implications Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study—that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care.
    Type of Medium: Online Resource
    ISSN: 2399-5300
    URL: Article
    DOI: 10.1093/geroni/igac051
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2905697-4
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  • 4
    Online Resource
    Online Resource
    QUALITY OF LIFE PREDICTS END-OF-LIFE CARE TRANSITIONS AMONG PERSONS WITH DEMENTIA OVER NINE YEARS
    Oxford University Press (OUP) ; 2022
    In:  Innovation in Aging Vol. 6, No. Supplement_1 ( 2022-12-20), p. 469-469
    Details
    In: Innovation in Aging, Oxford University Press (OUP), Vol. 6, No. Supplement_1 ( 2022-12-20), p. 469-469
    Abstract: Physical, financial, and emotional burdens are profound and affect the quality-of-life of persons with dementia (PWD) and their caregivers, particularly as cognitive decline accelerates and death approaches, which leads to burdensome, non-beneficial transitions in care that can lead to further decline of PWD. Measures of quality-of-life for PWD and their caregivers include important physical, psychological/cognitive, existential, caregiving burden, and socio-demographic factors that may be useful for predicting and preventing these end-of-life transitions. This secondary data analysis of the National Health and Aging and Trends Study (NHATS) linked to the National Study of Caregiving (NSOC) investigates the relationship between quality-of-life factors and end-of-life care transition (overnight hospitalization, hospice use, and place of death) over nine years (2011-2019) among caregiver/PWD dyads using multivariable logistic regression for hospice and place of death and multivariable logistic regression with generalized estimating equations for longitudinal overnight hospitalizations. Overnight hospitalizations are predicted by overall health (p=0.048), pain (p=0.016), and having a regular doctor (p=0.012). Predictors of hospice include health prevents enjoying life (p=0.0002) and receiving food stamps (p=0.008). Place of death is predicted by PWD needing & gt;30 mins to fall asleep (p=0.007), dementia status (p=0.015), health preventing enjoying life (p=0.0005), race (p=0.033), and census division (p=0.012). End-of-life care transitions can be predicted far in advance by quality-of-life and socio-demographic factors of PWD and their caregivers. With this knowledge it may be possible to develop upstream interventions to facilitate appropriate transitions and improve end-of-life quality-of-life far in advance of avoidable care transitions of PWD.
    Type of Medium: Online Resource
    ISSN: 2399-5300
    URL: Article
    DOI: 10.1093/geroni/igac059.1820
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2905697-4
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  • 5
    Online Resource
    Online Resource
    QUALITY OF LIFE AND SOCIAL DETERMINANTS PREDICT HOSPICE IN DEMENTIA CAREGIVING DYADS: A MACHINE LEARNING APPROACH
    Oxford University Press (OUP) ; 2022
    In:  Innovation in Aging Vol. 6, No. Supplement_1 ( 2022-12-20), p. 540-540
    Details
    In: Innovation in Aging, Oxford University Press (OUP), Vol. 6, No. Supplement_1 ( 2022-12-20), p. 540-540
    Abstract: Hospice care is available to assist people with serious illness and their caregivers who wish to age in place, avoid unnecessary hospitalizations, and remain at home through the end-of-life. However, hospice care is under-utilized nationally despite the disproportionate prevalence of end-of-life dementia caregiving burdens among disadvantaged groups. The reasons are unclear, but emerging research suggests that systemic barriers may contribute to underutilization. Commonly used quality-of-life frameworks have long included social determinant of health (SDH) factors such as social, environmental, financial, and healthcare access needs. Investigating the link between quality-of-life and SDH concerns of persons with dementia (PWD) and their caregivers may help identify when a PWD might benefit from hospice care. This study uses machine learning techniques to longitudinally analyze caregiver/care-recipient dyads in the National Health and Aging and Trends Study (NHATS) linked to the National Study of Caregiving (NSOC) (2015-2018) to identify quality-of-life and SDH predictors of hospice use among 117 PWD and their primary caregivers. Results indicate that distinguishing features selected by Information Gain Ratio [IGR] predict that memory rating, receiving food stamps, whether health prevents enjoying life, having trouble chewing or swallowing, diabetes, a regular doctor, and nobody to talk to can predict hospice use well (accuracy=0.6848; sensitivity=0.8244; specificity=0.5371; AUC=0.7425). Quality-of-life/SDH factors are important longitudinal predictors of hospice that can be detected up to three years prior to death. Our study uses inductive, machine learning approaches to provide testable hypotheses for future research to improve the quality of end-of-life care through hospice for PWD and their caregivers.
    Type of Medium: Online Resource
    ISSN: 2399-5300
    URL: Article
    DOI: 10.1093/geroni/igac059.2051
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2905697-4
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