Cognitive Status and Quality of Life Among Older Adults: Implications for the Development of an Interdisciplinary Home Visit Model
DOI:
https://doi.org/10.19166/ncjk.v14i1.10471Keywords:
Dementia, Elderly, home visit, interdisciplinary, Quality Of LifeAbstract
Dementia and cognitive impairment are rapidly increasing worldwide, with more than 57 million cases globally and a projected rise in low- and middle-income countries. Indonesia ranks among the countries with the highest number of dementia cases in Southeast Asia, with Yogyakarta reporting a high proportion of older adults at risk. Despite this growing burden, community-based dementia care models remain limited, and most older adults rely primarily on family caregivers. This situation highlights an urgent need for approaches that not only address cognitive decline but also improve the quality of life (QoL) of older adults living in the community. This study aimed to determine the relationship between the severity of cognitive impairment and QoL among community-dwelling older adults and to provide preliminary evidence for the development of an interdisciplinary home visit model tailored to the Indonesian context. A correlational study was conducted involving 30 older adults aged ≥60 years living with their families in Yogyakarta. Cognitive status was assessed using the Mini-Mental State Examination (MMSE), and QoL was measured using the WHOQOL-BREF. Data were analyzed using Spearman’s rank correlation. There was a significant relationship between the severity of dementia and QoL in the physical domain (rₛ = 0.428; p = 0.018) and the environmental domain (rₛ = 0.440; p = 0.015). These findings indicate that greater dementia severity was associated with lower QoL in the physical and environmental domains. The findings provide preliminary evidence supporting the development of an interdisciplinary home visit model in Indonesia, particularly one that integrates physical mobility support, environmental safety optimization, and family-centered care. Larger studies are recommended to strengthen this foundation before full model implementation.
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