Cognitive function and its determinants in elderly Indonesians residing in long-term care: Insights from a cross-sectional study

Etty Rekawati; Winda Eriska; Utami Rachmawati; Dwi Nurviyandari Kusuma Wati; Junaiti Sahar; Arief Andriyanto; Jing-Jy Wang; Sri Susanty; Faizul Hasan

doi:10.12688/f1000research.158490.2

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Research Article

Revised

Cognitive function and its determinants in elderly Indonesians residing in long-term care: Insights from a cross-sectional study

[version 2; peer review: 3 approved, 1 approved with reservations]

Etty Rekawati ¹, Winda Eriska¹, Utami Rachmawati¹, [...] Dwi Nurviyandari Kusuma Wati¹, Junaiti Sahar¹, Arief Andriyanto², Jing-Jy Wang^3,4, Sri Susanty⁵, Faizul Hasan ⁶

Etty Rekawati ¹, Winda Eriska¹, [...] Utami Rachmawati¹, Dwi Nurviyandari Kusuma Wati¹, Junaiti Sahar¹, Arief Andriyanto², Jing-Jy Wang^3,4, Sri Susanty⁵, Faizul Hasan ⁶

PUBLISHED 22 Aug 2025

Author details Author details

¹ Department of Community Nursing, Faculty of Nursing, Universitas Indonesia, Depok, West Java, Indonesia
² Department of Community Nursing, Universitas Bina Sehat PPNI Mojokerto, Mojokerto, Indonesia
³ Department of Nursing, College of Medicine, National Cheng Kung University, Tainan City, Tainan City, Taiwan
⁴ Alzheimer’s Disease Research Center, National Cheng Kung University Hospital, Tainan, Taiwan
⁵ Nurse Professional Education Study Program, Faculty of Medicine, Universitas Halu Oleo, Kendari, South East Sulawesi, Indonesia
⁶ Faculty of Nursing, Chulalongkorn University, Bangkok, Bangkok, Thailand

Etty Rekawati
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Winda Eriska
Roles: Data Curation, Formal Analysis, Visualization

Utami Rachmawati
Roles: Data Curation, Formal Analysis, Visualization

Dwi Nurviyandari Kusuma Wati
Roles: Validation, Writing – Review & Editing

Junaiti Sahar
Roles: Validation, Writing – Review & Editing

Arief Andriyanto
Roles: Conceptualization, Writing – Original Draft Preparation

Jing-Jy Wang
Roles: Supervision, Validation, Writing – Review & Editing

Sri Susanty
Roles: Validation, Writing – Review & Editing

Faizul Hasan
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

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

Corresponding authors: Etty Rekawati, Faizul Hasan

Competing interests: No competing interests were disclosed.

Grant information: 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.

Copyright: © 2025 Rekawati E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Rekawati E, Eriska W, Rachmawati U et al. Cognitive function and its determinants in elderly Indonesians residing in long-term care: Insights from a cross-sectional study [version 2; peer review: 3 approved, 1 approved with reservations]. F1000Research 2025, 13:1384 (https://doi.org/10.12688/f1000research.158490.2) First published: 18 Nov 2024, 13:1384 (https://doi.org/10.12688/f1000research.158490.1) Latest published: 22 Aug 2025, 13:1384 (https://doi.org/10.12688/f1000research.158490.2)

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.

See the authors' detailed response to the review by Yuni Asri
See the authors' detailed response to the review by Made Satya Nugraha Gautama
See the authors' detailed response to the review by Rian Adi Pamungkas

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.

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%).

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

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
Variable	(n= 350)	High cognitive function (%)	Low cognitive function (%)	P-Value
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)	0.117
Gender
Male	157 (44.9)	104 (29.7)	53 (15.1)	0.001
Female	193 (55.1)	95 (27.1)	98 (28.0)	0.001
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)	0.013
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)	0.053
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)	0.207

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

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 accumulation⁵⁴ 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

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.14048299⁶²

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgments

The authors thank to the Directorate of Research and Development, Universitas Indonesia under Cluster/Group/Research Centre Grant program in year 2022.

References

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Version 2

VERSION 2 PUBLISHED 18 Nov 2024

Author details Author details

Winda Eriska
Roles: Data Curation, Formal Analysis, Visualization

Utami Rachmawati
Roles: Data Curation, Formal Analysis, Visualization

Dwi Nurviyandari Kusuma Wati
Roles: Validation, Writing – Review & Editing

Junaiti Sahar
Roles: Validation, Writing – Review & Editing

Arief Andriyanto
Roles: Conceptualization, Writing – Original Draft Preparation

Jing-Jy Wang
Roles: Supervision, Validation, Writing – Review & Editing

Sri Susanty
Roles: Validation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

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.

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Published: 22 Aug 2025, 13:1384

https://doi.org/10.12688/f1000research.158490.2

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https://doi.org/10.12688/f1000research.158490.1

© 2025 Rekawati E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Rekawati E, Eriska W, Rachmawati U et al. Cognitive function and its determinants in elderly Indonesians residing in long-term care: Insights from a cross-sectional study [version 2; peer review: 3 approved, 1 approved with reservations]. F1000Research 2025, 13:1384 (https://doi.org/10.12688/f1000research.158490.2)

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Key to Reviewer Statuses VIEW HIDE

ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

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Reviewer Report 19 Aug 2025

Saranya Pimolkatekul, Department of Nursing Administration and Professional Fundamention, Navamindradhiraj University, Bangkok, Thailand

Approved with Reservations

https://doi.org/10.5256/f1000research.174085.r395149

This is a well-written cross-sectional study that investigates the relationship between cognitive function and associated risk factors among elderly Indonesians residing in long-term care settings. The study addresses a relevant public health concern and offers useful descriptive data. However, there are a few minor issues that should be considered to improve the clarity and impact of the paper.

Firstly, the background would benefit from a clearer articulation of the knowledge gap that this study aims to address. Including a brief summary of what previous studies have not covered particularly in the context of elderly residents in long-term care would better highlight the importance and novelty of this research. Secondly, the manuscript uses various terms interchangeably, such as elderly, older people, and long-term care, long-term care facilities, long-term care institutions. This inconsistency could cause confusion for readers, especially as these terms may have different definitions. To enhance clarity, it is recommended to use consistent terminology throughout the paper. Lastly, while the introduction effectively provides prevalence data and outlines the factors related to cognitive function, it lacks essential details about the study setting and a more explicit justification for focusing on the long-term care population. Clarifying why this particular population and setting were selected would strengthen the rationale for the study.

Major Comments:
Results (Tables): Please clarify and recheck all the numerical values presented in the tables. For example, there are inconsistencies between Table 1 and Table 2 regarding variables such as education level, subjective memory complaints, and living arrangements. In particular, the data on disease history raises concern. Although the total sample size is reported as 350, the frequencies listed appear to account for only 308 participants. It is also unclear how the 148 participants (42.3%) reporting good health are included in this count. Additionally, some p-values appear to be missing in Table 2. Please ensure that all relevant statistical results are reported and that values are consistent across tables.

Minor Comments:
Study Participants: Please provide more context to clarify the inclusion and exclusion criteria, as well as whether a sample size calculation was conducted and how the final sample size was determined.
Tables: Table 1 appears to present similar information to Table 2, which already provides the detailed results. To avoid redundancy and improve clarity, consider removing Table 1 and retaining only Table 2.

The conclusion section should more clearly emphasize the main findings of the study, particularly those that are statistically and/or clinically significant. Highlighting the key results will help reinforce the contributions of the study. Additionally, the conclusion should briefly acknowledge the study’s limitations such as the cross-sectional design, potential selection bias, or limited generalizability to provide context for interpreting the results. Including these limitations can also guide future research by suggesting areas that need further exploration or different methodological approaches.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Diabetes Mellitus, Frailty, Older adults

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 14 Aug 2025

Rian Adi Pamungkas, Universitas Esa Unggul, Jakarta Barat, Indonesia

Approved

https://doi.org/10.5256/f1000research.174085.r395145

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Gerontology nursing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and ... Continue reading The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and validated instrument, is appropriate and strengthens internal validity. However, several areas require further clarification, elaboration, and strengthening to enhance the manuscript’s scientific rigor and impact
1. While the study highlights a specific population (elderly in LTCIs), the novelty could be more explicitly framed in the Introduction and Discussion. How does this study differ significantly from prior regional studies? How does it inform global policy/practice?
Response:
We thank the reviewer for emphasizing the importance of clarifying the novelty of our study. We have revised the Introduction to explicitly highlight how this research differs from previous regional studies. Most prior studies in Indonesia and Southeast Asia have focused on community-dwelling elderly, while cognitive function among institutionalized elderly has been scarcely investigated. We now emphasize that our study fills this gap by examining elderly residents in LTCIs using the MoCA-Ina, a culturally adapted and validated instrument. Furthermore, we clarify that the findings not only provide locally relevant evidence but also contribute to the global discourse on aging and long-term care.
We appreciate the reviewer’s suggestion regarding the novelty framing in the Discussion. We have revised the section to emphasize how our findings differ from prior research and their broader implications. Specifically, we highlight that while most studies in the region have investigated community-dwelling older adults, our study provides unique evidence on institutionalized elderly in Indonesia. We also explain how the findings can inform global policy and practice on elderly care, particularly in low- and middle-income countries where LTCIs are increasingly important.

2. The authors used univariate logistic regression followed by multivariate logistic regression. However, a rationale for the model-building strategy is missing. Were interaction effects or multicollinearity assessed?
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

3. The explained variance in the logistic regression model (R² = 32.1%) could benefit from more contextual interpretation.
Response:
We thank the reviewer for this valuable comment. We have revised the Results section to provide a clearer contextual interpretation of the explained variance (R² = 32.1%). Specifically, we now note that the model accounted for nearly one-third of the determinants of cognitive function among institutionalized elderly, which is a meaningful proportion but also indicates that additional unmeasured factors may contribute to the outcome. This clarification helps readers better understand the significance and limitations of the model.
The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and validated instrument, is appropriate and strengthens internal validity. However, several areas require further clarification, elaboration, and strengthening to enhance the manuscript’s scientific rigor and impact
1. While the study highlights a specific population (elderly in LTCIs), the novelty could be more explicitly framed in the Introduction and Discussion. How does this study differ significantly from prior regional studies? How does it inform global policy/practice?
Response:
We thank the reviewer for emphasizing the importance of clarifying the novelty of our study. We have revised the Introduction to explicitly highlight how this research differs from previous regional studies. Most prior studies in Indonesia and Southeast Asia have focused on community-dwelling elderly, while cognitive function among institutionalized elderly has been scarcely investigated. We now emphasize that our study fills this gap by examining elderly residents in LTCIs using the MoCA-Ina, a culturally adapted and validated instrument. Furthermore, we clarify that the findings not only provide locally relevant evidence but also contribute to the global discourse on aging and long-term care.
We appreciate the reviewer’s suggestion regarding the novelty framing in the Discussion. We have revised the section to emphasize how our findings differ from prior research and their broader implications. Specifically, we highlight that while most studies in the region have investigated community-dwelling older adults, our study provides unique evidence on institutionalized elderly in Indonesia. We also explain how the findings can inform global policy and practice on elderly care, particularly in low- and middle-income countries where LTCIs are increasingly important.

2. The authors used univariate logistic regression followed by multivariate logistic regression. However, a rationale for the model-building strategy is missing. Were interaction effects or multicollinearity assessed?
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

3. The explained variance in the logistic regression model (R² = 32.1%) could benefit from more contextual interpretation.
Response:
We thank the reviewer for this valuable comment. We have revised the Results section to provide a clearer contextual interpretation of the explained variance (R² = 32.1%). Specifically, we now note that the model accounted for nearly one-third of the determinants of cognitive function among institutionalized elderly, which is a meaningful proportion but also indicates that additional unmeasured factors may contribute to the outcome. This clarification helps readers better understand the significance and limitations of the model.
Competing Interests: none Close
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COMMENTS ON THIS REPORT

Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and ... Continue reading The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and validated instrument, is appropriate and strengthens internal validity. However, several areas require further clarification, elaboration, and strengthening to enhance the manuscript’s scientific rigor and impact
1. While the study highlights a specific population (elderly in LTCIs), the novelty could be more explicitly framed in the Introduction and Discussion. How does this study differ significantly from prior regional studies? How does it inform global policy/practice?
Response:
We thank the reviewer for emphasizing the importance of clarifying the novelty of our study. We have revised the Introduction to explicitly highlight how this research differs from previous regional studies. Most prior studies in Indonesia and Southeast Asia have focused on community-dwelling elderly, while cognitive function among institutionalized elderly has been scarcely investigated. We now emphasize that our study fills this gap by examining elderly residents in LTCIs using the MoCA-Ina, a culturally adapted and validated instrument. Furthermore, we clarify that the findings not only provide locally relevant evidence but also contribute to the global discourse on aging and long-term care.
We appreciate the reviewer’s suggestion regarding the novelty framing in the Discussion. We have revised the section to emphasize how our findings differ from prior research and their broader implications. Specifically, we highlight that while most studies in the region have investigated community-dwelling older adults, our study provides unique evidence on institutionalized elderly in Indonesia. We also explain how the findings can inform global policy and practice on elderly care, particularly in low- and middle-income countries where LTCIs are increasingly important.

2. The authors used univariate logistic regression followed by multivariate logistic regression. However, a rationale for the model-building strategy is missing. Were interaction effects or multicollinearity assessed?
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

3. The explained variance in the logistic regression model (R² = 32.1%) could benefit from more contextual interpretation.
Response:
We thank the reviewer for this valuable comment. We have revised the Results section to provide a clearer contextual interpretation of the explained variance (R² = 32.1%). Specifically, we now note that the model accounted for nearly one-third of the determinants of cognitive function among institutionalized elderly, which is a meaningful proportion but also indicates that additional unmeasured factors may contribute to the outcome. This clarification helps readers better understand the significance and limitations of the model.
The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and validated instrument, is appropriate and strengthens internal validity. However, several areas require further clarification, elaboration, and strengthening to enhance the manuscript’s scientific rigor and impact
1. While the study highlights a specific population (elderly in LTCIs), the novelty could be more explicitly framed in the Introduction and Discussion. How does this study differ significantly from prior regional studies? How does it inform global policy/practice?
Response:
We thank the reviewer for emphasizing the importance of clarifying the novelty of our study. We have revised the Introduction to explicitly highlight how this research differs from previous regional studies. Most prior studies in Indonesia and Southeast Asia have focused on community-dwelling elderly, while cognitive function among institutionalized elderly has been scarcely investigated. We now emphasize that our study fills this gap by examining elderly residents in LTCIs using the MoCA-Ina, a culturally adapted and validated instrument. Furthermore, we clarify that the findings not only provide locally relevant evidence but also contribute to the global discourse on aging and long-term care.
We appreciate the reviewer’s suggestion regarding the novelty framing in the Discussion. We have revised the section to emphasize how our findings differ from prior research and their broader implications. Specifically, we highlight that while most studies in the region have investigated community-dwelling older adults, our study provides unique evidence on institutionalized elderly in Indonesia. We also explain how the findings can inform global policy and practice on elderly care, particularly in low- and middle-income countries where LTCIs are increasingly important.

2. The authors used univariate logistic regression followed by multivariate logistic regression. However, a rationale for the model-building strategy is missing. Were interaction effects or multicollinearity assessed?
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

3. The explained variance in the logistic regression model (R² = 32.1%) could benefit from more contextual interpretation.
Response:
We thank the reviewer for this valuable comment. We have revised the Results section to provide a clearer contextual interpretation of the explained variance (R² = 32.1%). Specifically, we now note that the model accounted for nearly one-third of the determinants of cognitive function among institutionalized elderly, which is a meaningful proportion but also indicates that additional unmeasured factors may contribute to the outcome. This clarification helps readers better understand the significance and limitations of the model.
Competing Interests: none Close
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Reviewer Report 14 Aug 2025

Made Satya Nugraha Gautama, Department of Nursing, Universitas Pendidikan Ganesha, Bali, Indonesia

Approved

https://doi.org/10.5256/f1000research.174085.r395151

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Adult Nursing, Palliative, Quantitative Research

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The ... Continue reading The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The findings have potential implications for healthcare practices in long-term care settings.
However I have several comments/questions should be addressed below:
- Provide more detail about the "major Long-Term Care Institutions in Jakarta." How many institutions were included?
Response:
We thank the reviewer for this important comment. We have clarified in the Methods section that data were collected from two major Long-Term Care Institutions (LTCIs) in Jakarta. This additional detail has now been specified to improve transparency.

- "Comprehensive sampling method" is vague. Was it a census of all eligible residents in the selected facilities? Or a stratified random sample? If not random sampling, explain potential biases.
Response:
We appreciate the reviewer’s comment and agree that the sampling method needed clarification. We have revised the Methods section to specify that purposive sampling was used. All residents who met the eligibility criteria were screened and included in the study. We have added this explanation in the manuscript.

- "Older people (≥60 years) devoid of eyesight or hearing impairments" – Why were individuals with eyesight or hearing impairments excluded?
Response:
We thank the reviewer for pointing out the need for justification of our exclusion criteria. We excluded older adults with visual or hearing impairments because the MoCA-Ina instrument requires adequate vision and hearing to follow instructions, read items, and respond accurately. Including individuals with such impairments could compromise the validity of test results, as low scores might reflect sensory limitations rather than true cognitive decline. We have now added this justification to the Methods section under “Study setting and participants.”

I believe that addressing these points, particularly in the Methods section, will strengthen the manuscript and enhance its overall impact.
Response:
Thank you
The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The findings have potential implications for healthcare practices in long-term care settings.
However I have several comments/questions should be addressed below:
- Provide more detail about the "major Long-Term Care Institutions in Jakarta." How many institutions were included?
Response:
We thank the reviewer for this important comment. We have clarified in the Methods section that data were collected from two major Long-Term Care Institutions (LTCIs) in Jakarta. This additional detail has now been specified to improve transparency.

- "Comprehensive sampling method" is vague. Was it a census of all eligible residents in the selected facilities? Or a stratified random sample? If not random sampling, explain potential biases.
Response:
We appreciate the reviewer’s comment and agree that the sampling method needed clarification. We have revised the Methods section to specify that purposive sampling was used. All residents who met the eligibility criteria were screened and included in the study. We have added this explanation in the manuscript.

- "Older people (≥60 years) devoid of eyesight or hearing impairments" – Why were individuals with eyesight or hearing impairments excluded?
Response:
We thank the reviewer for pointing out the need for justification of our exclusion criteria. We excluded older adults with visual or hearing impairments because the MoCA-Ina instrument requires adequate vision and hearing to follow instructions, read items, and respond accurately. Including individuals with such impairments could compromise the validity of test results, as low scores might reflect sensory limitations rather than true cognitive decline. We have now added this justification to the Methods section under “Study setting and participants.”

I believe that addressing these points, particularly in the Methods section, will strengthen the manuscript and enhance its overall impact.
Response:
Thank you
Competing Interests: none Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The ... Continue reading The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The findings have potential implications for healthcare practices in long-term care settings.
However I have several comments/questions should be addressed below:
- Provide more detail about the "major Long-Term Care Institutions in Jakarta." How many institutions were included?
Response:
We thank the reviewer for this important comment. We have clarified in the Methods section that data were collected from two major Long-Term Care Institutions (LTCIs) in Jakarta. This additional detail has now been specified to improve transparency.

- "Comprehensive sampling method" is vague. Was it a census of all eligible residents in the selected facilities? Or a stratified random sample? If not random sampling, explain potential biases.
Response:
We appreciate the reviewer’s comment and agree that the sampling method needed clarification. We have revised the Methods section to specify that purposive sampling was used. All residents who met the eligibility criteria were screened and included in the study. We have added this explanation in the manuscript.

- "Older people (≥60 years) devoid of eyesight or hearing impairments" – Why were individuals with eyesight or hearing impairments excluded?
Response:
We thank the reviewer for pointing out the need for justification of our exclusion criteria. We excluded older adults with visual or hearing impairments because the MoCA-Ina instrument requires adequate vision and hearing to follow instructions, read items, and respond accurately. Including individuals with such impairments could compromise the validity of test results, as low scores might reflect sensory limitations rather than true cognitive decline. We have now added this justification to the Methods section under “Study setting and participants.”

I believe that addressing these points, particularly in the Methods section, will strengthen the manuscript and enhance its overall impact.
Response:
Thank you
The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The findings have potential implications for healthcare practices in long-term care settings.
However I have several comments/questions should be addressed below:
- Provide more detail about the "major Long-Term Care Institutions in Jakarta." How many institutions were included?
Response:
We thank the reviewer for this important comment. We have clarified in the Methods section that data were collected from two major Long-Term Care Institutions (LTCIs) in Jakarta. This additional detail has now been specified to improve transparency.

- "Comprehensive sampling method" is vague. Was it a census of all eligible residents in the selected facilities? Or a stratified random sample? If not random sampling, explain potential biases.
Response:
We appreciate the reviewer’s comment and agree that the sampling method needed clarification. We have revised the Methods section to specify that purposive sampling was used. All residents who met the eligibility criteria were screened and included in the study. We have added this explanation in the manuscript.

- "Older people (≥60 years) devoid of eyesight or hearing impairments" – Why were individuals with eyesight or hearing impairments excluded?
Response:
We thank the reviewer for pointing out the need for justification of our exclusion criteria. We excluded older adults with visual or hearing impairments because the MoCA-Ina instrument requires adequate vision and hearing to follow instructions, read items, and respond accurately. Including individuals with such impairments could compromise the validity of test results, as low scores might reflect sensory limitations rather than true cognitive decline. We have now added this justification to the Methods section under “Study setting and participants.”

I believe that addressing these points, particularly in the Methods section, will strengthen the manuscript and enhance its overall impact.
Response:
Thank you
Competing Interests: none Close
Report a concern

Views

Reviewer Report 24 Jul 2025

Yuni Asri, Department of Nursing, Faculty of Health and Science, Institut Teknologi, Sains dan Kesehatan RS dr Soepraoen Kesdam V/Brawijaya, Malang, Indonesia

Approved

https://doi.org/10.5256/f1000research.174085.r390862

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education), justify their inclusion in regression models, and detail data distribution and missing data handling in tables or text. ... Continue reading

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education), justify their inclusion in regression models, and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

References

1. Feigin V, Abate M, Abate Y, Abd ElHafeez S, et al.: Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Neurology. 2024; 23 (10): 973-1003 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: public health, prevalence study, epidiomilogy.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this ... Continue reading This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with mean, median, and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
We confirm that education level has been categorized in our analysis, ranging from “no schooling” to “diploma/other higher education.” This categorization has been presented in Table 1 of the Results section to provide greater clarity.

justify their inclusion in regression models,
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

..and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with median and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with mean, median, and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
We confirm that education level has been categorized in our analysis, ranging from “no schooling” to “diploma/other higher education.” This categorization has been presented in Table 1 of the Results section to provide greater clarity.

justify their inclusion in regression models,
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

..and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with median and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
Competing Interests: none Close
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Respond or Comment

COMMENTS ON THIS REPORT

Author Response 10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

10 Sep 2025

Author Response

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this ... Continue reading This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with mean, median, and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
We confirm that education level has been categorized in our analysis, ranging from “no schooling” to “diploma/other higher education.” This categorization has been presented in Table 1 of the Results section to provide greater clarity.

justify their inclusion in regression models,
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

..and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with median and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with mean, median, and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
We confirm that education level has been categorized in our analysis, ranging from “no schooling” to “diploma/other higher education.” This categorization has been presented in Table 1 of the Results section to provide greater clarity.

justify their inclusion in regression models,
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

..and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with median and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
Competing Interests: none Close
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Version 2

VERSION 2 PUBLISHED 18 Nov 2024

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Reviewer Reports

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Version 2 (revision) 22 Aug 25
Version 1 18 Nov 24	read	read	read	read

Yuni Asri, Institut Teknologi, Sains dan Kesehatan RS dr Soepraoen Kesdam V/Brawijaya, Malang, Indonesia
Made Satya Nugraha Gautama, Universitas Pendidikan Ganesha, Bali, Indonesia
Rian Adi Pamungkas, Universitas Esa Unggul, Jakarta Barat, Indonesia
Saranya Pimolkatekul, Navamindradhiraj University, Bangkok, Thailand

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Reviewer Report

10 Views

19 Aug 2025 | for Version 1

Saranya Pimolkatekul, Department of Nursing Administration and Professional Fundamention, Navamindradhiraj University, Bangkok, Thailand

10 Views Cite this report Responses(0)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Diabetes Mellitus, Frailty, Older adults

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Reviewer Report

12 Views

14 Aug 2025 | for Version 1

Rian Adi Pamungkas, Universitas Esa Unggul, Jakarta Barat, Indonesia

12 Views Cite this report Responses(1)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Gerontology nursing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

The topic is highly relevant in light of global population aging and the increasing burden of cognitive decline in older adults. The use of the MoCA-Ina, a culturally adapted and validated instrument, is appropriate and strengthens internal validity. However, several areas require further clarification, elaboration, and strengthening to enhance the manuscript’s scientific rigor and impact
1. While the study highlights a specific population (elderly in LTCIs), the novelty could be more explicitly framed in the Introduction and Discussion. How does this study differ significantly from prior regional studies? How does it inform global policy/practice?
Response:
We thank the reviewer for emphasizing the importance of clarifying the novelty of our study. We have revised the Introduction to explicitly highlight how this research differs from previous regional studies. Most prior studies in Indonesia and Southeast Asia have focused on community-dwelling elderly, while cognitive function among institutionalized elderly has been scarcely investigated. We now emphasize that our study fills this gap by examining elderly residents in LTCIs using the MoCA-Ina, a culturally adapted and validated instrument. Furthermore, we clarify that the findings not only provide locally relevant evidence but also contribute to the global discourse on aging and long-term care.
We appreciate the reviewer’s suggestion regarding the novelty framing in the Discussion. We have revised the section to emphasize how our findings differ from prior research and their broader implications. Specifically, we highlight that while most studies in the region have investigated community-dwelling older adults, our study provides unique evidence on institutionalized elderly in Indonesia. We also explain how the findings can inform global policy and practice on elderly care, particularly in low- and middle-income countries where LTCIs are increasingly important.

2. The authors used univariate logistic regression followed by multivariate logistic regression. However, a rationale for the model-building strategy is missing. Were interaction effects or multicollinearity assessed?
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

3. The explained variance in the logistic regression model (R² = 32.1%) could benefit from more contextual interpretation.
Response:
We thank the reviewer for this valuable comment. We have revised the Results section to provide a clearer contextual interpretation of the explained variance (R² = 32.1%). Specifically, we now note that the model accounted for nearly one-third of the determinants of cognitive function among institutionalized elderly, which is a meaningful proportion but also indicates that additional unmeasured factors may contribute to the outcome. This clarification helps readers better understand the significance and limitations of the model.

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Competing Interests

none

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Reviewer Report

11 Views

14 Aug 2025 | for Version 1

Made Satya Nugraha Gautama, Department of Nursing, Universitas Pendidikan Ganesha, Bali, Indonesia

11 Views Cite this report Responses(1)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Adult Nursing, Palliative, Quantitative Research

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

The study addresses an important topic: cognitive function in elderly Indonesians in long-term care. This is a growing population with specific needs. The manuscript is generally well-written and organized.
The findings have potential implications for healthcare practices in long-term care settings.
However I have several comments/questions should be addressed below:
- Provide more detail about the "major Long-Term Care Institutions in Jakarta." How many institutions were included?
Response:
We thank the reviewer for this important comment. We have clarified in the Methods section that data were collected from two major Long-Term Care Institutions (LTCIs) in Jakarta. This additional detail has now been specified to improve transparency.

- "Comprehensive sampling method" is vague. Was it a census of all eligible residents in the selected facilities? Or a stratified random sample? If not random sampling, explain potential biases.
Response:
We appreciate the reviewer’s comment and agree that the sampling method needed clarification. We have revised the Methods section to specify that purposive sampling was used. All residents who met the eligibility criteria were screened and included in the study. We have added this explanation in the manuscript.

- "Older people (≥60 years) devoid of eyesight or hearing impairments" – Why were individuals with eyesight or hearing impairments excluded?
Response:
We thank the reviewer for pointing out the need for justification of our exclusion criteria. We excluded older adults with visual or hearing impairments because the MoCA-Ina instrument requires adequate vision and hearing to follow instructions, read items, and respond accurately. Including individuals with such impairments could compromise the validity of test results, as low scores might reflect sensory limitations rather than true cognitive decline. We have now added this justification to the Methods section under “Study setting and participants.”

I believe that addressing these points, particularly in the Methods section, will strengthen the manuscript and enhance its overall impact.
Response:
Thank you

View more View less

Competing Interests

none

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Reviewer Report

18 Views

24 Jul 2025 | for Version 1

Yuni Asri, Department of Nursing, Faculty of Health and Science, Institut Teknologi, Sains dan Kesehatan RS dr Soepraoen Kesdam V/Brawijaya, Malang, Indonesia

18 Views Cite this report Responses(1)

Approved

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education), justify their inclusion in regression models, and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

public health, prevalence study, epidiomilogy.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

10 Sep 2025

faizul hasan, Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

This robust study used appropriate tools (MoCA-Ina) and valid analyses. To enhance clarity and reproducibility, authors should categorize variables (e.g., age, education),
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with mean, median, and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.
We confirm that education level has been categorized in our analysis, ranging from “no schooling” to “diploma/other higher education.” This categorization has been presented in Table 1 of the Results section to provide greater clarity.

justify their inclusion in regression models,
Response:
We thank the reviewer for this insightful comment. We have clarified the rationale for including variables in the regression model. Specifically, variables were selected based on their theoretical relevance as supported by prior literature on cognitive function in older adults, as well as statistical significance in univariate analysis (p < 0.25). This dual approach ensured that both evidence-based and empirically relevant factors were considered. We have added this justification in the Methods section under “Data Analysis.”

..and detail data distribution and missing data handling in tables or text. These improvements would boost transparency and strengthen the study’s reliability.
Response:
We thank the reviewer for this insightful suggestion. While categorical groupings were applied to some demographic variables (e.g., education), we presented age as a continuous variable with median and standard deviation. Since most participants in our study were within a relatively narrow older age range, categorization would have been less meaningful. By reporting mean, median, and SD, we were able to capture the variability in age without imposing arbitrary cut-off points that may not reflect significant differences.

View more View less

Competing Interests

none

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Cognitive function and its determinants in elderly Indonesians residing in long-term care: Insights from a cross-sectional study

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 1

Introduction

Methods

Study setting and participants

Variables and measures

Data collection procedure

Ethical consideration

Data analysis

Results

Demographic characteristics

Table 1. Demographic characteristics of older adults in nursing homes (n = 350).

Univariate analysis

Table 2. Association between cognitive levels and risk factors in older adults living in nursing homes.

Multivariate logistic regression

Table 3. Multivariate logistic regression: predictive factors for cognitive levels in older adults living in nursing homes.

Discussion

Conclusions

Ethical declaration

Data availability statement

Reporting guidelines

Acknowledgments

References

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