Version Changes
Revised. Amendments from Version 1
The revisions enhance transparency by clarifying sampling, exclusions, and model-building. Novelty is highlighted by emphasizing the study's focus on institutionalized elderly—an understudied population—and its global relevance. Reproducibility is strengthened with details on data distribution and missingness. These changes address all reviewer feedback while maintaining the manuscript's core strengths.
Abstract
Background
Multiple medical conditions arising from reduced physical and physiological functioning, including cognitive decline, manifest in older persons. This study aims to examine the relationship between cognitive function and associated risk factors in older persons living in long-term care facilities in Indonesia.
Methods
This study involved 350 elderly individuals residing in long-term care institutions. A cross-sectional design utilizing an analytical survey methodology was implemented. Data were gathered via interviews employing a demographic questionnaire and the Montreal Cognitive Assessment (MoCA). Statistical analysis was conducted using SPSS (version 23).
Results
Univariate analysis demonstrated significant correlations between cognitive performance and gender, ethnicity, level of education, medical history, subjective memory issues, smoking habits, alcohol consumption, dietary intake of fruits and vegetables, and employment history (p < 0.05). Higher education (OR = 0.69, 95% CI: 0.56–0.84) and reduced subjective memory complaints (OR = 0.29, 95% CI: 0.20–0.44) correlated positively with enhanced cognitive function, but alcohol intake (OR = 6.79, 95% CI: 2.42–19.1) correlated with impaired cognitive function.
Conclusions
the level of education, subjective memory complaints, and alcohol intake are substantially correlated with cognitive performance in older persons residing in long-term care facilities. Evaluating demographic characteristics in elderly individuals can assist healthcare professionals in the early detection of cognitive impairment, facilitating prompt interventions in long-term care environments.
Keywords: Cognitive function; elderly individuals; risk factors
Introduction
By 2030, one in six individuals worldwide will be senior citizens. In Indonesia, life expectancy improved from 68.6 years in 2018 to 71.8 years in 2022, with an anticipated increase of 72.2 years for the period 2030–2035. 1 The 2022 Indonesia National Health Survey indicated that 10.5% of the population comprises elderly persons. 2 The aging population has transitioned the illness burden from infectious diseases and malnutrition to chronic ailments such as diabetes, hypertension, neoplasms, and coronary heart disease. 3 These alterations hinder daily functioning and augment economic dependency. 4 Moreover, physical, mental, and emotional deterioration intensifies reliance, impairing social interactions, self-care, and health management. 5
Mental changes in older persons encompass transformations in personality, memory, and cognitive ability, shaped by socio-demographic, physical, and psychological factors, 6– 9 with loneliness, social isolation, 7, 10, 11 and late-life mental diseases. 6 With the expansion of the older adult demographic, cognitive impairment has become increasingly common. 8 Cognitive function denotes the capacity to uphold responsibilities and social interactions, and its deterioration impedes engagement with family and community, imposing a burden on caregivers and communities. 12
Numerous individual factors affect cognitive decline, including age, 13 gender, 14– 16 level of education, 17– 19 genetics, and medical history. Chronic disorders include hypertension, 20, 21 diabetes, 22– 24 cardiovascular diseases, 25, 26 gastritis, 27– 29 and depression 30, 31 exacerbate cognitive impairment. Environmental factors, including social engagement and physical activity, significantly influence outcomes. 32– 34
Research has revealed multiple indicators strongly linked to motor-cognitive risk, including extremity functional limits, activities of daily living (ADL) impairment, fatigue, and hypertension. 35 Age, medical history, depression, and resilience are determinants of cognitive function. 36 Timely recognition and intervention of these factors are crucial to avert or alleviate cognitive impairment in elderly individuals. 37 Neuropsychological evaluations, like the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), are essential instruments for identifying cognitive deficits, with MoCA demonstrating heightened sensitivity. 5
Long-term care in Indonesia offers help for anyone requiring extended assistance, especially the elderly or individuals with disabilities. Nonetheless, there is a lack of particular data regarding the population of older adults residing in Long-Term Care Institutions (LTCI). The swift expansion of the elderly demographic has heightened the demand for long-term care insurance, influenced by evolving social dynamics and diminished familial capacity to offer care. Nevertheless, no previous research has investigated the correlation between cognitive function, medical history, and related risk factors in elderly individuals, impeding healthcare practitioners’ capacity to execute preventive measures and inform families.
Unlike previous regional studies that predominantly examined community-dwelling elderly, this study focuses on older persons residing in LTCIs, a population with distinct vulnerabilities that has rarely been explored in Indonesia. By addressing this gap, the study provides novel insights that not only contextualize cognitive health in institutional care within Indonesia but also contribute to the broader global discourse on aging, long-term care, and cognitive decline. This cross-sectional study aims to examine the relationship between cognitive function and associated risk factors among older persons in long-term care institutions in Indonesia.
Methods
Study setting and participants
This analytical cross-sectional study was performed in the major Long-Term Care Institutions in Jakarta, Indonesia, from February to April 2023. It comprised older people (≥60 years) devoid of eyesight or hearing impairments. Participants with visual or hearing impairments were excluded because the MoCA-Ina requires intact sensory abilities to ensure valid measurement of cognitive function. Deficits in vision or hearing could interfere with item completion and lead to misinterpretation of test scores as cognitive impairment. Participants were selected using purposive sampling. All residents in the two LTCIs who met the eligibility criteria were screened and included in the study. This approach allowed the inclusion of the entire eligible population. During the preliminary phase, health records were examined to ascertain eligible participants, contingent upon the nursing home’s consent. A total of 350 elderly people participated in the study. Specifically, two major long-term care institutions in Jakarta participated in this study. In addition, the study follow the STROBE guideline ( https://www.equator-network.org/).
Variables and measures
Demographic characteristics
Data were obtained via in-person interviews. Demographic variables encompassed age, duration of residence in long-term care institutions and nursing homes, gender, religion, ethnicity, relationship status, level of education, medical history, subjective memory issues, tobacco use, alcohol intake, daily consumption of fruits and vegetables, employment background, utilization of mobility aids, and living situation.
The montreal cognitive assessment (MoCA-Ina)
The Indonesian adaptation of the Montreal Cognitive Assessment (MoCA-Ina) was utilized, with all procedures executed in the native language, Bahasa Indonesia, to guarantee participant comfort. MoCA-Ina has exhibited robust reliability and validity, evidenced by a Cronbach’s alpha of 0.976, signifying exceptional internal consistency. 38 Cognitive function, the principal variable, was evaluated using the 30-point MoCA-Ina, with scores ≥11 signifying strong cognitive function and scores <11 denoting low cognitive function. MoCA-Ina is a reliable and sensitive instrument for identifying moderate cognitive impairment in elderly individuals in Indonesia. 39
Data collection procedure
Ten trained enumerators aided senior citizens in completing the questionnaire. Authorization was secured from the facility director, and the study protocols were comprehensively elucidated. Subsequent to approval, enumerators obtained consent from participants, who signed consent forms upon their agreement to participate. Eligible participants were apprised of the study’s objectives, advantages, and methodologies prior to receiving the questionnaire. Participation was optional and confidential, with each session lasting 15 to 20 minutes.
Ethical consideration
Prior to the investigation, this study has approval from the institutional review board (IRB) Committee of Universitas Indonesia with approval number of KET-168/UN2.F12.D1.2.1/PPM.00.02/2022 on June 21, 2022. This study adhered to the Declaration of Helsinki ( https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/). Written informed consent was obtained from participants prior joining the study and were apprised of the study’s objectives, benefits, and methods.
Data analysis
All the information collected were input into Microsoft Excel and analyzed utilizing SPSS version 23 (IBM SPSS Statistics Version 29, 2023). Descriptive data were displayed as numerical values and percentages. Chi-square tests were performed to evaluate the relationships between independent factors and cognitive function. Univariate analysis utilizing logistic regression calculated unadjusted odds ratios and their respective 95% confidence intervals (CIs) for all relevant risk factors, with significance established at p < 0.05. Variables were included in the multivariate logistic regression model if they were theoretically relevant based on prior research on cognitive decline and/or demonstrated statistical significance in univariate analysis at a threshold of p < 0.25. This approach ensured that the model captured both conceptually important and empirically supported factors.
Results
Demographic characteristics
The frequency distribution of all demographic and clinical variables is presented in Table 1. No missing data were identified in the dataset, as all 350 participants provided complete information across the included variables.
Table 1. Demographic characteristics of older adults in nursing homes (n = 350).
| Frequency | (%) | |
|---|---|---|
| Length of stay in nursing home (years) | ||
| ≤ 3 | 227 | 64.9 |
| > 3 | 123 | 35.1 |
| Gender | ||
| Male | 157 | 44.9 |
| Female | 193 | 55.1 |
| Religion | ||
| Islam | 303 | 86.6 |
| Christianity/Protestantism | 35 | 10 |
| Catholicism | 4 | 1.1 |
| Buddhism | 5 | 1.4 |
| Hinduism | 2 | 0.6 |
| Other | 1 | 0.3 |
| Ethnic group | ||
| Javanese | 135 | 38.6 |
| Sundanese | 51 | 14.6 |
| Betawi | 97 | 27.7 |
| Sumatran | 44 | 12.6 |
| Chinese | 9 | 2.6 |
| Other | 14 | 4.0 |
| Marital status | ||
| Married | 138 | 39.4 |
| Divorced dead | 79 | 22.6 |
| Divorced alive | 38 | 10.9 |
| Single | 95 | 27.1 |
| Education level | ||
| No schooling | 52 | 14.9 |
| Did not graduate from elementary school | 93 | 26.6 |
| Graduated from elementary school | 107 | 30.6 |
| Graduated from junior high school | 44 | 12.6 |
| Graduated from high school | 47 | 13.4 |
| Diploma/Other higher education school | 7 | 2.0 |
| Disease history | ||
| Hypertension (HT) | 50 | 14.3 |
| Diabetes mellitus (DM) | 6 | 1.7 |
| Cholesterol | 2 | 0.6 |
| Heart failure | 2 | 0.6 |
| Depression | 5 | 1.4 |
| Mental disorders | 65 | 18.6 |
| Strokes | 8 | 2.3 |
| Arthritis | 14 | 4.0 |
| Cataracts | 5 | 1.4 |
| Gastritis | 3 | 0.9 |
| Subjective memory complaint | ||
| Very bad | 3 | 0.9 |
| Bad | 77 | 22.0 |
| Medium | 183 | 52.3 |
| Very good | 87 | 24.9 |
| Smoking | ||
| Yes (active) | 79 | 22.6 |
| Yes (passive) | 8 | 2.3 |
| Stopped | 47 | 13.4 |
| Does not smoke | 216 | 61.7 |
| Consumption of alcoholic beverages | ||
| Which, now | 3 | 0.9 |
| Which, once was | 38 | 10.9 |
| Never | 309 | 88.3 |
| Consumption of fruits and vegetables every day | ||
| Yes | 317 | 90.6 |
| No | 33 | 9.4 |
| Employment history | ||
| Formal/professional work | 10 | 2.9 |
| Informal work | 335 | 95.7 |
| Retired | 5 | 1.4 |
| Use of walking aids | ||
| Yes | 44 | 12.6 |
| No | 306 | 87.4 |
| Living arrangement | ||
| Living with family | 142 | 40.6 |
| Alone | 208 | 59.4 |
| MoCA | ||
| High cognitive function | 199 | 56.9 |
| Low cognitive function | 151 | 43.1 |
MoCA: Montreal Cognitive Assessment.
Overall, the mean age of participants was 68.9 years (SD 7.01), with a median of 68. The mean duration of residence in the long-term care institution (LTCI) was almost 3 years (SD 3.6), with a median of 3 years. The average cognitive function score was 12.8 (SD 7.25), with a median of 11.
Table 1 reveals that the predominant demographic of respondents consisted of early seniors (60-68 years old), with 186 participants (53.1%), whereas 164 participants (46.9%) were older. Over fifty percent resided in the institution for fewer than three years (227 participants, 64.9%). Female enrollment was greater, with 193 individuals (55.1%), in contrast to male enrollment, which comprised 157 persons (44.9%). The majority of participants identified as Muslim (303 participants, 86.6%), with 148 (42.3%) reporting good health, and hypertension as the most prevalent health concern (50 individuals, 14.3%). The majority consisted of married individuals (138 participants, 39.4%), those of Javanese ethnicity (135 participants, 38.6%), and elementary school graduates (107 participants, 30.6%).
Participants indicated moderate subjective memory problems (183 participants, 52.3%), with the majority abstaining from alcohol consumption (309 participants, 88.3%) and not engaging in smoking (216 participants, 61.7%). A considerable percentage of participants ingested fruits and vegetables daily (317 participants, 90.6%). Nearly all participants were informal workers (335 individuals, 95.7%). Fifty-nine point four percent of participants had previously lived alone (208 participants), whereas forty point six percent lived with family (142 participants). Additionally, fifty-nine point four percent of participants (208 individuals) did not utilize walking assistance. A total of 199 participants (56.9%) achieved higher scores on the MoCA, whilst 151 participants (43.1%) attained lower scores.
Univariate analysis
Table 2 illustrates the association between participant characteristics and MoCA scores, which function as an indicator of cognitive ability. The findings demonstrate that multiple factors significantly correlate with MoCA scores, including gender, ethnicity, level of education, medical history, subjective memory issues, smoking behaviors, alcohol intake, dietary practices (particularly fruit and vegetable consumption), and employment background.
Table 2. Association between cognitive levels and risk factors in older adults living in nursing homes.
| Variable | Total | MoCA Score | P-Value | |
|---|---|---|---|---|
| (n= 350) | High cognitive function (%) | Low cognitive function (%) | ||
| Length of stay in nursing home | ||||
| ≤ 3 years | 227 (64.9) | 136 (38.9) | 91 (26) | 0.117 |
| > 3 years | 123 (35.1) | 63 (18) | 60 (17.1) | |
| Gender | ||||
| Male | 157 (44.9) | 104 (29.7) | 53 (15.1) | 0.001 |
| Female | 193 (55.1) | 95 (27.1) | 98 (28.0) | |
| Religion | ||||
| Islam | 303 (86.6) | 169 (48.3) | 134 (38.3) | 0.522 |
| Christian | 35 (10) | 23 (6.6) | 12 (3.4) | |
| Catholic | 4 (1.1) | 2 (0.6) | 2 (0.6) | |
| Buddhism | 5 (1.4) | 3 (0.9) | 2 (0.6) | |
| Hinduism | 2 (0.6) | 2 (0.6) | 0 (0) | |
| Others | 1 (0.3) | 0 (0) | 1 (0.3) | |
| Ethnic group | ||||
| Javanese | 135 (38.6) | 68 (19.4) | 67 (19.1) | 0.044 |
| Sundanese | 51 (14.6) | 23 (6.6) | 28 (8.0) | |
| Betawi | 97 (27.7) | 63 (18) | 34 (9.7) | |
| Sumatra | 44 (12.6) | 28 (8) | 16 (4.6) | |
| Chinese | 9 (2.6) | 7 (2) | 2 (0.6) | |
| Other | 14 (4) | 10 (2.9) | 4 (1.1) | |
| Marital status | ||||
| Married | 138 (39.4) | 76 (21.7) | 62 (17.7) | 0.851 |
| Divorced dead | 79 (22.6) | 45 (12.9) | 34 (9.7) | |
| Divorced alive | 38 (10.9) | 24 (6.9) | 14 (4.0) | |
| Single | 95 (27.1) | 54 (15.4) | 41 (11.7) | |
| Education level | ||||
| Diplomas/higher education | 7 (2.0) | 6 (1.7) | 1 (0.3) | 0.000 |
| Graduated high school | 47 (13.4) | 33 (9.4) | 14 (4.0) | |
| Graduated middle school | 44 (12.6) | 32 (9.1) | 12 (3.4) | |
| Graduated from elementary school | 107 (30.6) | 65 (18.6) | 42 (12.0) | |
| Did not graduate from elementary school | 93 (26.6) | 50 (14.3) | 43 (12.3) | |
| No schooling | 57 (14.9) | 13 (3.7) | 39 (11.1) | |
| Disease history | ||||
| Hypertension (HT) | 50 (14.3) | 34 (9.7) | 16 (4.6) | |
| Diabetes mellitus (DM) | 6 (1.7) | 6 (1.7) | 0 (0) | |
| Cholesterol | 2 (0.6) | 1 (0.3) | 1 (0.3) | |
| Heart failure | 2 (0.6) | 2 (0.6) | 0 (0) | |
| Depression | 5 (1.4) | 3 (0.9) | 2 (0.6) | |
| Mental disorders | 65 (18.6) | 28 (8.0) | 37 (10.6) | |
| Strokes | 8(2.3) | 6 (1.7) | 2 (0.6) | |
| Arthritis | 14 (4.0) | 11 (3.1) | 3 (0.9) | |
| Cataracts | 5 (1.4) | 4 (1.1) | 1 (0.3) | |
| Gastritis | 3 (0.9) | 3 (0.9) | 0 (0) | |
| Subjective memory complaints | ||||
| Very good | 91 (24.5) | 17 (19.1) | 20 (5.3) | 0.000 |
| Currently | 183 (52.3) | 111 (31.7) | 72 (20.6) | |
| Bad | 77 (22) | 20 (5.7) | 57 (16.3) | |
| Very bad | 3 (0.9) | 0 (0) | 3(0,9) | |
| Smoking | ||||
| Does not smoke | 216 (61.7) | 112 (32) | 104 (29.7) | 0.03 |
| Quit smoking | 47 (13.4) | 28 (8.0) | 19 (5.4) | |
| Passive smoker | 8 (2.3) | 6 (1.7) | 2 (0.6) | |
| Active smoker | 79 (22.6) | 53 (15.1) | 26 (7.4) | |
| Alcohol consumption | ||||
| Never | 309 (88.3) | 164 (46.9) | 145 (41.4) | 0.000 |
| Yes, once | 38 (10.9) | 32 (9.1) | 6 (1.7) | |
| Yes, now | 3 (0.9) | 3 (0.9) | 0 (0) | |
| Consumption of fruits and vegetables | ||||
| Yes | 317 (90.6) | 187 (53.4) | 130 (37.1) | 0.013 |
| No | 33 (9.4) | 12 (3.4) | 21 (6.0) | |
| Employment history | ||||
| Formal/professional | 10 (2.9) | 8 (2.3) | 2 (0.6) | 0.045 |
| Informal | 335 (95.7) | 186 (53.1) | 149 (42.6) | |
| Retired | 5 (1.4) | 5 (1.4) | 0 (0) | |
| Use of walking aids | ||||
| No | 306 (87.4) | 180 (51.4) | 126 (36) | 0.053 |
| Yes | 44 (12.6) | 19 (5.4) | 25 (7.1) | |
| Living arrangement | ||||
| Living with family | 208 (59.4) | 124 (35.4) | 84 (24.0) | 0.207 |
| Alone | 142 (40.6) | 75 (21.4) | 67 (19.1) | |
MoCA: Montreal Cognitive Assessment.
Multivariate logistic regression
Table 3 indicates that the model accounts for 32.1% of the variance in MoCA scores and accurately classifies 72.6% of instances. This proportion suggests that the model was able to capture nearly one-third of the determinants of cognitive function among institutionalized elderly. While this is a meaningful contribution, it also indicates that other factors not included in the model may explain the remaining variability. Upon integrating all notable predictors, three variables were recognized as significantly correlated with cognitive levels: education level (OR = 0.686; p = 0.000), subjective memory complaints (OR = 0.293; p = 0.000), and alcohol intake (OR = 6.786; p = 0.000). Older persons who abstained from drinking were 6.786 times more likely to exhibit a high cognitive level than those who consumed alcohol. Furthermore, individuals with advanced education possessed a 0.686 probability of attaining elevated cognitive capabilities as they aged. Moreover, persons devoid of subjective memory complaints exhibited a 0.293 probability of possessing elevated cognitive levels in contrast to those who indicated bad or very poor memory complaints.
Table 3. Multivariate logistic regression: predictive factors for cognitive levels in older adults living in nursing homes.
| Predictor variables | B | SE | Wald | p- value | OR | 95%CI Lower–Upper |
|---|---|---|---|---|---|---|
| Gender | -0.196 | 0.304 | 0.415 | 0.520 | 0.822 | 0.453–1.493 |
| Ethnic group | -0.104 | 0.097 | 1.160 | 0.282 | 0.901 | 0.746–1.089 |
| Education level | -0.377 | 0.106 | 12.716 | 0.000 | 0.686 | 0.558–0.844 |
| Disease history | -0.004 | 0.021 | 0.038 | 0.846 | 0.996 | 0.955–1.039 |
| Subjective memory complaints | -1.228 | 0.205 | 5.950 | 0.000 | 0.293 | 0.196–0.437 |
| Smoking | -0.035 | 0.127 | 0.075 | 0.785 | 0.966 | 0.753–1.239 |
| Alcohol consumption | 1.915 | 0.527 | 13.203 | 0.000 | 6.786 | 2.416–19.062 |
| Consumption of fruits and vegetables | 0.423 | 0.467 | 0.823 | 0.364 | 1.527 | 0.612–3.813 |
| Employment history | -0.126 | 0.736 | 0.029 | 0.864 | 0.864 | 0.208–3.732 |
B: beta coefficients; SE: standard error; OR: odds ratio; CI: confident interval.
Discussion
This study revealed that older persons living in long-term care institutions (LTCIs) in Jakarta, Indonesia, displayed elevated MoCA scores, signifying enhanced cognitive performance. This study contradicts prior studies, which indicated that older persons in long-term care institutions are more prone to cognitive deterioration than those in community settings. 40 A nationally representative longitudinal study in China revealed that living arrangements significantly influence cognitive decline, with solitary living associated with accelerated cognitive deterioration in older men, whereas diverse living arrangements, including cohabitation with spouses and adult children, correlated with cognitive decline in older women. 41 Social isolation, loneliness, and restricted social involvement have been linked to reduced cognitive results in later life. 42 A recent meta-analysis indicated a broad spectrum of moderate cognitive impairment (MCI) prevalence among older persons in long-term care institutions, ranging from 4.0% to 87.4%, with a pooled prevalence of 21.2%. 43 The superior cognitive function noted in older adults in Jakarta may be ascribed to diverse social activities offered by these institutions, including entertainment gatherings, daily exercise, religious activities, and essential services, all of which improve quality of life in accordance with the Republic of Indonesia’s Social Welfare Law of 2012 concerning the care of older adults.
The research revealed educational attainment, self-reported memory issues, and alcohol intake as predictors of cognitive decline in elderly individuals residing in long-term care institutions. A meta-analysis indicated that each additional year of schooling decreases the risk of Alzheimer’s disease by 8% and dementia by 7%. 44 The association is exacerbated by age, as prior research suggests that schooling may alleviate racial and ethnic differences in cognitive performance among older adults. 45 Elevated educational attainment enhances cognitive reserve, postponing the clinical onset of Alzheimer’s disease until brain pathology is further advanced. 46 Thus, evaluating educational attainment in LTCIs can yield significant baseline data for the early identification of cognitive deterioration. 47, 48 Educational background cultivates cultural competency, augments reading proficiency, and develops problem-solving skills. 49
Subjective memory complaints (SMCs) are frequently observed in older persons and may signify possible cognitive impairment. 50 While several studies indicate that subjective memory complaints (SMCs) may not consistently align with objective cognitive impairments, 51 they may nonetheless be associated with anatomical alterations in the brain that facilitate cognitive deterioration. 52 A study in India identified correlations between tobacco use, smoking, alcohol intake, and cognitive impairment in older adults. 53 Alcohol intake has been demonstrated to induce brain damage by processes such as iron accumulation 54 and increased white matter hyperintensity volumes, 55 and is associated with Wernicke-Korsakoff syndrome, which negatively impacts memory and heightens the risk of cognitive impairment.
The study revealed that disease history was associated with cognitive levels, but it did not serve as a predictive variable. Cognitive performance can be affected by various factors, including age, gender, education, lifestyle choices, and the existence of chronic illnesses such as hypertension and diabetes. 56, 57 Studies have shown that elderly individuals with chronic conditions, especially hypertension or diabetes, may demonstrate diminished cognitive ability. 58 Furthermore, strokes may result in dementia, with severity influenced by factors like stroke site, volume, and pre-existing cognitive deficits. 59 The steady progression of cognitive decline, affected by multiple intricate factors, complicates this relationship. 60 Consequently, SMCs function as significant indicators of cognitive impairments and initial manifestations of Alzheimer’s disease and associated dementias.
To alleviate cognitive decline, it is essential for healthcare practitioners and family to cultivate trusting connections, encourage social engagement, and involve older persons in group activities. 37 Regular engagement in cognitive-stimulating activities, including adequate relaxation and sleep, is crucial, in addition to practices such as reading and media consumption. 61 In addition to these findings, this study provides novelty by focusing on institutionalized elderly in Indonesia, a population that has been rarely examined in prior regional research. While earlier studies have largely addressed community-dwelling older adults, our results demonstrate how the LTCI environment may shape cognitive outcomes. This contribution not only enriches the local evidence base but also offers insights relevant to global policy and practice, particularly in the design and evaluation of elderly care models in low- and middle-income countries.
This study’s limitations encompass variances in the cognitive status of older persons and its cross-sectional methodology, which constrains causal assumptions; thus, additional longitudinal investigations are essential for deeper insights. Moreover, increased sample sizes would improve analytical precision. Healthcare practitioners must prioritize initiatives that enhance cognitive function in older persons, while future research should concentrate on creating customized therapies for cognitive impairments associated with particular health problems.
Conclusions
This research revealed educational level, subjective memory issues, and alcohol use as significant predictors of cognitive performance in older persons residing in long-term care facilities. Moreover, variables like gender, ethnicity, medical history, tobacco use, dietary intake of fruits and vegetables, and occupational background were associated with cognitive performance, underscoring the necessity for customized healthcare interventions. These findings offer significant insights into the responses of older persons to cognitive impairment risk factors, allowing nurses and healthcare professionals to formulate more effective treatment regimens. Moreover, comprehending these links helps guide personalized interventions and promote equitable health policy, ultimately enhancing care for older individuals in Indonesia.
Ethical declaration
Prior to the investigation, this study has approval from the institutional review board (IRB) Committee of Universitas Indonesia with approval number of KET-168/UN2.F12.D1.2.1/PPM.00.02/2022 on June 21, 2022. This study adhered to the Declaration of Helsinki ( https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/). Written informed consent was obtained from participants prior joining the study and were apprised of the study’s objectives, benefits, and methods.
Acknowledgments
The authors thank to the Directorate of Research and Development, Universitas Indonesia under Cluster/Group/Research Centre Grant program in year 2022.
Funding Statement
This research received grant from the Directorate of Research and Development, Universitas Indonesia under Cluster/Group/Research Centre Grant program: NKB-049/UN2.RST/ HKP.05.00/2022.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 2; peer review: 3 approved
Data availability statement
The data behind this work can be obtained from the corresponding author (Ast. Prof. Faizul Hasan, RN, PhD, Email: faizul.h@chula.ac.th or Asc. Prof. Dr. Etty Rekawati, E-mail: rekawati@ui.ac.id) upon a reasonable request. Access to the data is restricted due to ethical issues and standards established by the Institutional Review Board (IRB) to safeguard participant confidentiality. Prospective data users must submit a written request detailing the intended purpose of data utilization and evidence of adherence to ethical norms. Approval will be contingent upon compliance with the stipulations set forth by the IRB, and applicants may be required to furnish institutional endorsement or present supplementary evidence to guarantee the proper use of the data.
Reporting guidelines
Zenodo Repository: STROBE checklist for ‘Cognitive Function and Its Determinants in Elderly Indonesians Residing in Long-Term Care: Insights from a Cross-Sectional Study’. https://doi.org/10.5281/zenodo.14048299 62
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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