Identifying the Effect of Living Alone on Life in Later Adulthood: Comparison between Living Alone and Those Living with Others with a Propensity Score Matching Analysis

Kang, Eun-Na; Hong, Lee Min

doi:10.15709/hswr.2018.38.4.196

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…Comparing with the findings of previous studies [12,54], this discrepancy should be interpreted with caution because the previous studies measured sleep components once through self-report, whereas our study assessed daily sleep efficiency for 2 weeks through repeated measures using actigraphy. Older adults living alone may have more difficulty in the early detection of sleep disturbances than do those living with others because of the absence of a bedroom partner [55]. Thus, further research using both subjective and objective measures is needed to confirm the predictive value of sleep efficiency with the larger sample.…”

Section: Discussionmentioning

confidence: 99%

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Kim¹,

Lee²,

Lee³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundAlthough geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups.ObjectiveThe objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression.MethodsThe prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models).ResultsOn the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960).ConclusionsThis study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

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Section: Discussionmentioning

confidence: 99%

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Kim¹,

Lee²,

Lee³

et al. 2019

JMIR Mhealth Uhealth

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“…노인이 자신의 거주지에서 안정된 노후를 영위하는 것은 노인의 사회통합을 위해 노인복지가 지향해야 하는 과제이다 [6]. 그러나 독거노인은 가족과 거주하는 노인에 비해서 우울감과 자살 생각의 수준이 높을 뿐 아니라 정신건강과 영양 관리가 열악하며 사회적 지지체계가 취약하여 고독사의 위험에 노출된다 [7] [8]. 따라서…”

Section: 서론unclassified

Measures to Improve the Role and Function of Elderly Residential Facilities: Focusing on Free Elderly Residential Facilities in Seoul

Yoon¹,

Choi²

2023

APJCRI

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The purpose of this study is to examine the function and role of elderly residential facilities to ensure the quality of life for elderly without dependents and find ways to improve elderly residential facilities as alternative welfare services for the elderly in the blind spot of community care. To this end, this study investigated seven elderly residential facilities in Seoul to identify the difficulties faced in the operation and operation status. The results of the study are as follows: First, the average age of the residents was 84.9 years old due to the aging of the elderly, and 20% of the elderly needed care for more than 7 hours a day. Second, there was a problem of the adequacy of residential space due to the deterioration and spatial composition of facilities. Most of the facilities consisted of shared rooms, and the space was too small to ensure privacy. Third, there were facilities without dedicated social workers, and service quality could not be guaranteed by appropriate work force. Finally, programs linked to local communities or programs for lifelong education were found to be weak. Based on the research results, this study suggested the need for residential space composition for privacy protection of the elderly, specialized programs centered on the needs of the elderly, strengthened connection with the local community, expanded case management functions and placement of case management experts, and the necessity of strengthening the function of elderly residential facilities as an intermediate facility of homestay and nursing homes.

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Longitudinal Relationship Between Baseline Social Frailty and Cognitive Impairment in Older Adults: 14-Year Follow-Up Results From the Korean Longitudinal Study of Ageing

Lee,

Park,

Kim

et al. 2024

Journal of the American Medical Directors Association

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Identifying the Effect of Living Alone on Life in Later Adulthood: Comparison between Living Alone and Those Living with Others with a Propensity Score Matching Analysis

Cited by 10 publications

References 11 publications

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Measures to Improve the Role and Function of Elderly Residential Facilities: Focusing on Free Elderly Residential Facilities in Seoul

Longitudinal Relationship Between Baseline Social Frailty and Cognitive Impairment in Older Adults: 14-Year Follow-Up Results From the Korean Longitudinal Study of Ageing

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