Sex Differences in the Prevalence and Correlates of Sleep Disturbance Among Indonesian Stroke Survivors: A Population-Based Study of 7,147 Individuals

Sri Susanty; Renny Wulan Apriliyasari; Hsiao-Yean Chiu; Rusdi Bin ABD Rashid; Era Catur Prasetya; Kanjanee Phanphairoj; Mokh Sujarwadi; Faizul Hasan

doi:10.12688/f1000research.182846.4

. 2026 Sep 15;15:982. Originally published 2026 Jun 20. [Version 4] doi: 10.12688/f1000research.182846.4

Sex Differences in the Prevalence and Correlates of Sleep Disturbance Among Indonesian Stroke Survivors: A Population-Based Study of 7,147 Individuals

Sri Susanty ^1,², Renny Wulan Apriliyasari ³, Hsiao-Yean Chiu ³, Rusdi Bin ABD Rashid ⁴, Era Catur Prasetya ⁵, Kanjanee Phanphairoj ¹, Mokh Sujarwadi ⁶, Faizul Hasan ^1,^a

PMCID: PMC13586602 PMID: 42763448

Version Changes

Revised. Amendments from Version 3

This version is a direct republication of the previously published article. No changes have been made to the study design, data, analyses, results, or conclusions. The only updates are the grant information (now listed as Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand) and the keywords, which have been revised to align with the current terminology. All other content remains identical to the previous version.

Abstract

Objectives

Sleep disturbance is a prevalent issue among those who have experienced a stroke. The impact of gender on the prevalence of sleep disturbance within Asian stroke populations remains ambiguous. This research seeks to examine the prevalence of sleep disturbance among stroke survivors in Indonesia.

Methods

This quantitative prevalence analysis was based on the 2018 Indonesian Basic Health Research. A total of 7,147 stroke survivors who answered the sleep disturbance questions were included. The estimated prevalence and regression models were examined utilizing SPSS software.

Results

The total prevalence of sleep disturbance among stroke survivors in Indonesia was 28.4% (95% CI: 27.4%–29.4%). Females exhibited a greater frequency of sleep disturbance than males (34.4% versus 26.1%, respectively). When categorized by age group, both females and males exhibited a trend of rising sleep disturbance prevalence with time. Moreover, daily smoking, low educational attainment, rural residency, and insufficient physical activity were correlated with sleep disturbance symptoms in the male cohort. In females, the presence of comorbidities such as cardiovascular disease and asthma was associated with sleep disturbance.

Conclusions

This study’s findings revealed a significant prevalence of sleep disturbance among stroke survivors in Indonesia. Our findings indicate a gender influence on sleep disturbance prevalence, with females exhibiting a higher prevalence than males. These findings underscore the significance of sleep assessment and treatment in clinical or community environments.

Keywords: sleep, post-stroke insomnia, stroke, insomnia, Asian.

1. Introduction

Sleep disturbance is a common problem around the world. The prevalence rate ranges from 35% to 43% from the acute to the chronic phase. ¹ ^– ³ Stroke patients with sleep disturbance have a higher risk of developing cognitive impairment ⁴ and reduced health-related quality of life. ⁵ ^, ⁶ Researchers and clinicians are now paying high attention to managing the sleep disturbance problem.

Previously, two studies investigating the global prevalence of sleep disturbance using a meta-analytic design have been published. ¹ ^, ² One meta-analysis, involving 22 studies on sleep disturbance, found the prevalence to be 38.2%. ¹ The updated meta-analysis, which included 28 studies, suggested a prevalence rate of 39.7%. ² However, most of the included studies were derived from Western populations. Adding evidence by investigating the sleep disturbance prevalence among Eastern populations is important.

As a note, gender plays a critical role in the prevalence of sleep disturbance. ⁷ Previous meta-analyses suggest that females are more prevalent than males in terms of sleep disturbance in the general population in China. ⁸ ^, ⁹ However, the effect of gender on sleep disturbance among stroke survivors remains under research. Moreover, the available studies involved a small sample size. ¹ ^, ² Hence, there is a need to conduct an updated study investigating the effect of gender on sleep disturbance using a larger sample size. This study aims to investigate the effect of gender on the prevalence of sleep disturbance among stroke survivors using multicenter nationwide data.

2. Materials and methods

2.1 Study sample

The data utilized in this study were obtained from the 2018 Basic Health Research (Riset Kesehatan Dasar, RISKESDAS) conducted by the Indonesian Ministry of Health. The RISKESDAS survey is a thorough national evaluation conducted quinquennially to collect health-related data. The National Institute of Health Research and Development (NIHRD) administers it under the Ministry of Health in Indonesia. The survey was administered by enumerators possessing formal education in health, holding at least health diplomas. The enumerators performed face-to-face interviews utilizing validated questions for accuracy and dependability. Furthermore, they performed measurements and assessments during home visits, as outlined in the supplementary document issued by the Ministry of Health in Bahasa Indonesia (Kementerian Kesehatan, 2019). Members of each household (those residing on the premises for at least six months and sharing the same food income source) were solicited to partake in the survey. Health-related data on patients diagnosed with stroke was obtained from the 2018 RISKESDAS dataset.

2.2 Inclusion and exclusion criteria

We only considered participants who satisfied the requirements listed below in order to meet the inclusion criteria: 1) having received a stroke diagnosis in the past; and 2) having submitted the necessary information for the sleep disturbance variable. Participants whose sleep disturbance variable had missing values were not included in the study.

2.3 Dependent variable

A question related to sleep disturbance was examined: “In the past two weeks, have you experienced any sleep disturbances, such as difficulty initiating sleep, nocturnal awakenings, or early morning arousal?” The sleep disturbance status was assumed if participants answered ‘yes’. We acknowledge that this single-item measure assesses self-reported sleep disturbance and does not fulfill diagnostic criteria for clinical insomnia, as it does not assess duration, frequency, daytime impairment, adequate sleep opportunity, or differential diagnoses. Therefore, our findings represent self-reported sleep disturbance rather than clinically diagnosed insomnia.

2.4 Independent variables

2.4.1 Sociodemographic variables

Sociodemographic characteristics encompassed age, gender, educational attainment, marriage status, occupation, and degree of urbanization. We classified educational attainment into three categories: ‘no formal education’ (0), ‘non-university level’ (1), and ‘university level’ (2). Marital status was classified as ‘single’ (0) or ‘married’ (1), and ‘widow or widower’ (2). Occupation status was categorized as ‘unemployed’ (0) and ‘employed’ (1), and urbanization level as ‘rural’ (0) and ‘urban’ (1).

2.4.2 Health and lifestyle behavior variables

In this research, we incorporated health conditions and lifestyle behaviors, including regular follow-up, comorbidities (such as cancer, asthma, cardiovascular disease, and chronic renal illness), vigorous and moderate physical activity, smoking status, and alcohol consumption.

The evaluation of regular follow-up for stroke was conducted using the subsequent inquiry: Has your stroke state been consistently monitored by a physician at the healthcare facility? the potential responses of “yes,” ‘sometimes,’ and ‘no.’

Comorbidities were identified based on the response (‘yes’ = 0 or ‘no’ = 1) to the question: ‘Have you received a diagnosis of (….) from a physician?’

Smoking status was assessed by the inquiries: ‘Are you a smoker?’ and ‘Have you smoked in the past month?’ Smoking status was classified as ‘non-smoker’ (code = 0), ‘occasional smoker’ (code = 1), and ‘daily smoker’ (code = 2).

Alcohol intake was evaluated by the inquiry: ‘Have you ingested alcohol in the past month?’ with response options of ‘yes’ and ‘no.’

Physical activity was evaluated utilizing the modified Global Physical Activity Questionnaire (GPAQ) from the World Health Organization for surveillance purposes. The GPAQ gathers data on physical activity in three areas: occupational or domestic activities, transportation, and leisure pursuits. All activities must have been conducted uninterrupted for a minimum of 10 minutes per week. Participants were requested to enumerate activities they typically engaged in daily, categorizing them as ‘yes’ for activities performed and ‘no’ for those not performed, for both vigorous and moderate physical activity. The assessment of GPAQ demonstrated satisfactory inter-rater reliability for categorical variables, with kappa values between 0.44 and 0.78, and test-retest reliability for continuous variables, with Spearman’s rho ranging from 0.45 to 0.80. ¹⁰

2.5 Ethical consideration and informed consent statement

Ethical approval for the original RISKESDAS 2018 data collection was granted by the Ethical Committee of Health Research, NIHRD, Ministry of Health, Republic of Indonesia (No. LB.02.01/2/KE.267/2017). Secondary analysis for the present study was approved by the Health Research Ethics Committee of the Faculty of Medicine, Universitas Halu Oleo (No. 62/UN29.20.2/ETIK/2025). Written informed consent was obtained from all participants in the original survey. As this study uses only de-identified secondary data, no additional consent was required.

2.6 Statistical analysis

Statistical analysis was conducted utilizing SPSS (Version 29, SPSS Inc., Chicago, IL, USA). Descriptive statistics were analyzed for demographic features and aggregated responses, encompassing the mean and standard deviation for continuous variables and percentages for categorical data. Subsequently, we performed analyses utilizing χ2 (Chi-square) for categorical variables and the Mann-Whitney U-test (given the data’s deviation from a normal distribution) for continuous variables to investigate the presence of gender disparities in sociodemographic characteristics, health conditions, and lifestyle behaviors.

Acknowledging that RISKESDAS employs a complex survey design with clustering, stratification, and sampling weights, we note that our analysis did not incorporate these design features due to technical constraints. Consequently, our findings should be interpreted as estimates from a large sample rather than nationally representative point estimates.

For the regression analyses, complete-case analysis was employed. Participants with missing sleep disturbance data (n = 895) were excluded, yielding the analytic sample of 7,147 participants ( Figure 1). For the regression models, no missing values were found for any independent variable; thus, all analyses included the full sample of 7,147 participants. Multivariable logistic regression was performed using forced entry (simultaneous) method, including all prespecified covariates based on clinical and theoretical relevance. We assessed multicollinearity using variance inflation factors (VIFs), and no significant collinearity was detected (all VIFs < 2.0). Given our primary interest in identifying correlates of sleep disturbance separately in males and females due to known biological and social differences, we conducted sex-stratified models. To formally test whether associations differed by sex, we performed interaction analyses (sex × independent variable). Hosmer–Lemeshow goodness-of-fit tests were performed to evaluate model calibration. The alpha criterion for hypothesis testing was established at p < 0.05.

3. Results

3.1 Study characteristic

At the beginning, 713,783 individuals were screened for eligibility. Among them, 705,741 participants were excluded because they did not have a stroke diagnosis. Furthermore, 895 participants were excluded because they had missing values on the sleep disturbance variable. The final analysis comprised 7,147 stroke participants ( Figure 1).

Table 1 presents a comparison of participants’ characteristics between males and females. The sleep disturbance cohort exhibited a greater mean age than the non-sleep disturbance cohort across both genders. Among males, educational attainment, work position, urbanization, consistent follow-up, and the presence of comorbidities such as cancer, heart disease, asthma, and participation in regular physical exercise exhibited significant differences between the sleep disturbance and non-sleep disturbance groups (all P < 0.05). Among females, educational attainment, marital status, and the occurrence of comorbidities such as cardiovascular disease and asthma exhibited significant differences between the sleep disturbance and non-sleep disturbance cohorts (all P < 0.05). Additional demographic information is available in Table 1 and Table 2.

Table 1. Demographic characteristic of participants.

			Male (n = 3574)				p-value	Female (n = 3573)				p-value
	Total		Sleep Disturbance		No Sleep Disturbance			Sleep Disturbance		No Sleep Disturbance
Variables	n	(%)	n	(%)	n	(%)		n	(%)	n	(%)
	7147	(100)	932	(26.1)	2642	(73.9)		1230	(34.4)	2343	(65.6)
Age, mean (SD)	58.7	(12.5)	60.3	(12.2)	59.1	(11.9)	0.01	57.37	(11.93)	55.87	(12.24)	0.01
Smoking status, n (%)							0.48					0.86
Non-smoker	4611	(64.5)	294	(31.5)	949	(35.9)		1161	(94.4)	2207	(94.2)
Yes (not every day)	760	(10.6)	179	(19.2)	475	(18)		34	(2.8)	72	(3.1)
Yes (everyday)	1776	(24.8)	459	(49.2)	1218	(46.1)		35	(2.8)	64	(2.7)
Education, n (%)							<0.001					0.02
No formal education	732	(10.2)	79	(8.5)	168	(6.4)		181	(14.7)	304	(13)
Non-university level	5810	(81.3)	781	(83.8)	2145	(81.2)		995	(80.9)	1889	(80.6)
University level	605	(8.5)	72	(7.7)	329	(12.5)		54	(4.4)	150	(6.4)
Marital status, n (%)							0.47					0.02
Single	227	(3.2)	39	(4.2)	104	(3.9)		24	(2)	60	(2.6)
Married	5322	(74.5)	780	(83.7)	2254	(85.3)		756	(61.5)	1532	(65.4)
Widow/widower	1598	(22.4)	113	(12.1)	284	(10.7)		450	(36.6)	751	(32.1)
Employment, n (%)							<0.001					0.12
Unemployed	3725	(52.1)	395	(42.4)	945	(35.8)		842	(68.5)	1543	(65.9)
Employed	3422	(47.9)	537	(57.6)	1697	(64.2)		388	(31.5)	800	(34.1)
Urbanization, n (%)							<0.001					0.40
Urban	3798	(53.1)	457	(49)	1485	(56.2)		627	(51)	1229	(52.5)
Rural	3349	(46.9)	475	(51)	1157	(43.8)		603	(49)	1114	(47.5)

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n = number of participants; SD = standard deviation.

Continuous variable was performed by using Mann Whitney U test.

Categorical variables were performed by using chi-square test.

Table 2. Health, lifestyle behavior and sleep disturbance.

			Male (n = 3574)				p-value	Female (n = 3573)				p-value
	Total		Sleep Disturbance		No Sleep Disturbance			Sleep Disturbance		No Sleep Disturbance
Variables	n	(%)	n	(%)	n	(%)		n	(%)	n	(%)
	7,147	(100)	932	(26.1%)	2,642	(73.9%)		1,230	(34.4%)	2,343	(65.6%)
Regular follow-up, n (%)							0.02					0.47
No	1,500	(21.0%)	228	(24.5%)	534	(20.2%)		268	(21.8%)	470	(20.1%)
Sometime	2,826	(39.5%)	348	(37.3%)	1,037	(39.3%)		487	(39.6%)	954	(40.7%)
Yes	2,821	(39.5%)	356	(38.2%)	1,071	(40.5%)		475	(38.6%)	919	(39.2%)
Cancer, n (%)							0.04					0.61
No	7,099	(99.3%)	924	(99.1%)	2,633	(99.7%)		1,218	(99.0%)	2,324	(99.2%)
Yes	48	(0.7%)	8	(0.9%)	9	(0.3%)		12	(1.0%)	19	(0.8%)
Heart disease, n (%)							0.04					<0.001
No	6,447	(90.2%)	827	(88.7%)	2,403	(91.0%)		1,070	(87.0%)	2,147	(91.6%)
Yes	700	(9.8%)	105	(11.3%)	239	(9.0%)		160	(13.0%)	196	(8.4%)
Asthma, n (%)							0.03					<0.001
No	6,776	(94.8%)	871	(93.5%)	2,517	(95.3%)		1,143	(92.9%)	2,245	(95.8%)
Yes	371	(5.2%)	61	(6.5%)	125	(4.7%)		87	(7.1%)	98	(4.2%)
CKD, n (%)							0.19					0.43
No	7,010	(98.1%)	909	(97.5%)	2,595	(98.2%)		1,210	(98.4%)	2,296	(98.0%)
Yes	137	(1.9%)	23	(2.5%)	47	(1.8%)		20	(1.6%)	47	(2.0%)
Vigorous PA, n (%)							<0.01					0.95
No	6,295	(88.1%)	805	(86.4%)	2,177	(82.4%)		1,140	(92.7%)	2,173	(92.7%)
Yes	852	(11.9%)	127	(13.6%)	465	(17.6%)		90	(7.3%)	170	(7.3%)
Moderate PA, n (%)							<0.001					0.13
No	3,516	(49.2%)	587	(63.0%)	1,384	(52.4%)		553	(45.0%)	992	(42.3%)
Yes	3,631	(50.8%)	345	(37.0%)	1,258	(47.6%)		677	(55.0%)	1,351	(57.7%)
Alcohol consumption, n (%)							0.47					0.76 ^†
No	7,014	(98.1%)	897	(96.2%)	2,556	(96.7%)		1,227	(99.8%)	2,334	(99.6%)
Yes	133	(1.9%)	35	(3.8%)	86	(3.3%)		3	(0.2%)	9	(0.4%)

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n = number of participants; SD = standard deviation; CKD = chronic kidney disease; PA = physical activity.

Continuous variable was performed by using Mann Whitney U test.

Categorical variables were performed by using chi-square test or

^{^†}

Fisher’s exact test.

3.2 Prevalence of sleep disturbance

The overall prevalence of sleep disturbance among stroke survivors in Indonesia was 28.4% (95% CI: 27.4%–29.4%). Figure 2 depicts the comparative prevalence of sleep disturbance among males and females across different age groups. Our findings suggest a general pattern of increasing prevalence with age, which was confirmed by a formal Cochran-Armitage test for trend (p < 0.01). We grouped the age into 13 age groups, ranging from 15–19 years old to more than 75 years old. There is a trend of increasing prevalence in the male sleep disturbance group, ranging from 25% to 32%. Similarly, we also find an increasing trend in sleep disturbance prevalence for the female age group (26% to 37%). Overall, we found that females have a higher trend in sleep disturbance prevalence compared to males (34.4% vs 26.1%, respectively). Age-specific denominators and 95% confidence intervals are provided in the supplementary materials.

3.3 Analysis of regression

The univariate regression analyses are displayed in Table 3 and Table 4. Multivariate regression analyses are displayed in Table 5 and Table 6. In the multivariate models, active smoking elevates the probability of sleep disturbance rate in comparison to non-smokers among subjects. Individuals with a greater educational attainment (university level) are less prone to have sleep disturbance. Individuals residing in urban environments are less prone to sleeplessness than those inhabiting rural regions. Individuals who partake in physical activity are less prone to have sleep disturbance. Female participants with comorbidities, including heart disease and asthma, exhibited a higher propensity for developing sleep disturbance. Formal interaction tests (sex × independent variable) revealed significant interactions for heart disease (p < 0.001) and asthma (p = 0.01), indicating that the associations of these comorbidities with sleep disturbance differed significantly between males and females.

Table 3. Associations between demographic characteristics and sleep disturbance using univariate logistic regression.

Variables	Male			Female
Variables	OR ^C	(95% CI)	P-value	OR ^C	(95% CI)	P-value
Age	1.008	(1.002 to 1.015)	<0.01	1.009	(1.003 to 1.014)	<0.01
Smoking status
Non-smoker	Ref.			Ref.
Yes (not every day)	1.216	(0.98 to 1.51)	0.08	0.898	(0.593 to 1.358)	0.61
Yes (everyday)	1.216	(1.027 to 1.441)	0.02	1.040	(0.684 to 1.579)	0.86
Education
Non-university level	Ref.			Ref.
No formal education	1.291	(0.976 to 1.708)	0.07	1.13	(0.926 to 1.38)	0.23
University level	0.601	(0.456 to 0.848)	<0.001	0.683	(0.496 to 0.942)	0.02
Marital status
Widow/widower	Ref.			Ref.
Single	0.942	(0.614 to 1.446)	0.79	0.668	(0.41 to 1.087)	0.07
Married	0.87	(0.689 to 1.098)	0.24	0.824	(0.712 to 0.953)	<0.01
Employment
Employed	Ref.			Ref.
Unemployed	1.321	(1.134 to 1.538)	<0.001	1.125	(0.971 to 1.304)	0.12
Urbanization
Rural	Ref.			Ref.
Urban	0.75	(0.645 to 0.871)	<0.001	0.943	(0.821 to 1.082)	0.4

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OR ^C = crude odds ratio of univariate model; CI = confidence interval; Ref. = reference category.

Table 4. Association between health, lifestyle behavior and sleep disturbance using univariate logistic regression.

Variables	Male			Female
Variables	OR ^C	(95% CI)	P-value	OR ^C	(95% CI)	P-value
Regular follow-up
No	Ref.			Ref.
Sometime	0.786	(0.645 to 0.957)	0.02	0.895	(0.744 to 1.078)	0.24
Yes	0.779	(0.64 to 0.947)	0.01	0.906	(0.752 to 1.092)	0.3
Cancer
No	Ref.			Ref.
Yes	2.533	(0.974 to 6.584)	0.06	1.205	(0.583 to 2.491)	0.62
Heart disease
No	Ref.			Ref.
Yes	1.277	(1.001 to 1.627)	0.04	1.638	(1.313 to 2.044)	<0.001
Asthma
No	Ref.			Ref.
Yes	1.41	(1.028 to 1.934)	0.03	1.744	(1.295 to 2.348)	<0.001
CKD
No	Ref.			Ref.
Yes	1.397	(0.844 to 2.314)	0.19	0.807	(0.476 to 1.369)	0.43
Vigorous PA
No	Ref.			Ref.
Yes	0.739	(0.597 to 0.913)	<0.01	1.009	(0.774 to 1.316)	0.95
Moderate PA
No	Ref.			Ref.
Yes	0.647	(0.555 to 0.754)	<0.001	0.899	(0.782 to 1.033)	0.13
Alcohol consumption
No	Ref.			Ref.
Yes	1.16	(0.777 to 1.73)	0.47	0.634	(0.171 to 2.346)	0.49

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OR ^C = crude odds ratio of univariate model; CI = confidence interval; Ref. = reference category; CKD = chronic kidney disease; PA = physical activity.

Table 5. Associations between demographic characteristics and sleep disturbance using logistic regression.

Variables	Male			Female
Variables	OR ^A	(95% CI)	P-value	OR ^A	(95% CI)	P-value
Age	1.006	(1.000 to 1.013)	0.55	1.006	(1 to 1.013)	0.07
Smoking status
Non-smoker	Ref.			Ref.
Yes (not every day)	1.231	(0.989 to 1.533)	0.06	0.981	(0.642 to 1.499)	0.93
Yes (everyday)	1.259	(1.057 to 1.500)	0.01	0.841	(0.553 to 1.279)	0.42
Education
Non-university level	Ref.			Ref.
No formal education	1.125	(0.843 to 1.503)	0.42	1.027	(0.833 to 1.266)	0.8
University level	0.649	(0.493 to 0.854)	<0.01	0.720	(0.519 to 1.000)	0.05
Marital status
Widow/widower	Ref.			Ref.
Single	1.127	(0.706 to 1.800)	0.62	0.826	(0.495 to 1.377)	0.46
Married	0.984	(0.772 to 1.254)	0.89	0.912	(0.773 to 1.074)	0.27
Employment
Employed	Ref.			Ref.
Unemployed	1.094	(0.919 to 1.302)	0.31	1.037	(0.885 to 1.214)	0.65
Urbanization
Rural	Ref.			Ref.
Urban	0.767	(0.656 to 0.896)	<0.01	0.934	(0.810 to 1.077)	0.34

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OR ^A = adjusted odds ratio of multivariate model; CI = confidence interval; Ref. = reference category.

Adjusted model includes all variables presented in the table simultaneously using forced entry method. Hosmer-Lemeshow goodness-of-fit test: male (χ ² = 8.42, p = 0.39), female (χ ² = 9.15, p = 0.33).

Table 6. Association between health, lifestyle behavior and sleep disturbance using multivariable logistic regression.

Variables	Male			Female
Variables	OR ^A	(95% CI)	P-value	OR ^A	(95% CI)	P-value
Regular follow-up
No	Ref.			Ref.
Sometime	0.816	(0.667 to 0.997)	0.04	0.906	(0.751 to 1.092)	0.29
Yes	0.833	(0.68 to 1.021)	0.07	0.916	(0.758 to 1.107)	0.36
Cancer
No	Ref.			Ref.
Yes	2.483	(0.935 to 6.589)	0.07	1.104	(0.53 to 2.301)	0.79
Heart disease
No	Ref.			Ref.
Yes	1.282	(0.997 to 1.648)	0.05	1.615	(1.287 to 2.025)	<0.001
Asthma
No	Ref.			Ref.
Yes	1.297	(0.937 to 1.795)	0.12	1.658	(1.226 to 2.243)	<0.01
CKD
No	Ref.			Ref.
Yes	1.375	(0.819 to 2.309)	0.23	0.724	(0.422 to 1.241)	0.24
Vigorous PA
No	Ref.			Ref.
Yes	0.736	(0.583 to 0.929)	0.01	1.076	(0.817 to 1.418)	0.6
Moderate PA
No	Ref.			Ref.
Yes	0.679	(0.577 to 0.800)	<0.001	0.987	(0.847 to 1.150)	0.87
Alcohol consumption
No	Ref.			Ref.
Yes	1.334	(0.876 to 2.032)	0.18	0.61	(0.162 to 2.288)	0.46

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OR ^A = adjusted odds ratio of multivariate model; CI = confidence interval; Ref. = reference category; CKD = chronic kidney disease; PA = physical activity.

Adjusted model includes all variables presented in the table simultaneously using forced entry method. Hosmer-Lemeshow goodness-of-fit test: male (χ ² = 7.89, p = 0.44), female (χ ² = 8.56, p = 0.38).

4. Discussion

This study is the first to investigate the impact of gender on the prevalence of sleep disturbance among stroke survivors in Indonesia, to the best of our knowledge. Our findings indicate that the overall prevalence of sleep disturbance remains high among the Indonesian population. It is worth noting that females have a higher prevalence of sleep disturbance than males. Our research should be considered credible due to the utilization of a substantial sample size and stringent methodology. However, the cross-sectional design of the RISKESDAS data does not allow us to confirm that the sleep disturbance occurred after the stroke. We therefore refer to our outcome as “sleep disturbance among stroke survivors” rather than “post-stroke sleep disturbance.”

It is important to observe that the prevalence of sleep disturbance following a stroke has increased over time. Females were found to have a higher increase in prevalence than males. Consistent with a previous study, increasing age was associated with the presence of sleep disturbance. ¹¹ Moreover, females exhibited a greater prevalence of sleep disturbances than males among American adults. ¹² This indicates the necessity of improving sleep instruction uniformly across various genders.

The study reveals a prevalence of sleep disturbance at 28.4%. This contrasts with previous meta-analyses that suggested the prevalence of sleep disturbance was between 35% and 43%. ¹ ^, ² The disparity in sleep disturbance prevalence arises from the variability in assessment methodologies; research employing standardized diagnoses reveal lower prevalence rates than those utilizing self-reported instruments. ² ^, ¹³ However, this present study has a higher estimation compared to previous work that used hospital-based data and found the sleep disturbance incidence to be 8.2% in Taiwan (using ICD codes). ⁴ Early detection of sleep disturbance using precise measurement scales is critical.

Although the mechanics underlying sleep disturbance remain undetermined, the putative mechanisms can be explained in part. The stroke’s site, including the right hemisphere, thalamus, or brainstem, is associated with sleep disturbance. ¹⁴ Moreover, a confluence of environmental factors (e.g., hospitalization) ¹⁵ and biological factors (e.g., heightened sympathetic activation, intermittent hypoxemia, oxidative stress, inflammatory processes leading to frequent arousals, and disrupted sleep-wake regulation) may exacerbate post-stroke sleep disturbance. ¹⁶ ^, ¹⁷

Interestingly, male participants engage in physical activity associated with fewer sleep disturbance symptoms. A previous meta-analysis found that exercise may increase sleep quality. ¹⁸ The results are also consistent with previous research indicating that exercise improves sleep quality across all age groups. ¹⁹ ^– ²² Hence, we recommend regular exercise to improve sleep quality.

This study identifies a number of limitations. First, as noted above, the single-item measure of sleep disturbance does not fulfill clinical diagnostic criteria for insomnia. Our findings represent self-reported sleep disturbance rather than clinically diagnosed insomnia. Second, the cross-sectional design precludes establishing temporality or causality; we cannot confirm that the sleep disturbance began after the stroke. Third, the analysis did not incorporate the complex survey design features (weights, clustering, stratification) of RISKESDAS, limiting the generalizability of our estimates. Fourth, the failure to acquire pertinent confounders, including dietary details, environmental influences, and possible hypnotic usage, may compromise internal validity. Fifth, missing data were handled using complete-case analysis, which may introduce bias. Sixth, while we observed higher prevalence in females (34.4%) than males (26.1%), we did not perform a formal statistical test for this difference, as our primary objective was to identify sex-specific correlates. Future studies using design-based analyses with survey weights are needed to formally evaluate this difference. Age-standardized comparisons were also beyond our scope. Despite these limitations, this study possesses several strengths. The study population was selected from volunteers across Indonesia, assuring broad representation. Secondly, each interviewer received training to comprehend the structure and methodology of the questionnaire.

5. Conclusions

In conclusion, the prevalence of sleep disturbance following stroke remains high over time among stroke survivors in Indonesia. Females have a higher prevalence of sleep disturbance compared to males. These preliminary findings provide crucial insights for healthcare providers and policymakers. Implementing early sleep disturbance screening with accurate metrics is strongly advised. Future studies should employ validated insomnia assessment tools and longitudinal designs to establish the temporal relationship between stroke and sleep disturbance.

Ethics statement

Informed consent statement

Written informed consent was obtained from all participants (or their legal guardians for minors or cognitively impaired individuals) during the original RISKESDAS 2018 data collection. As the present study involves only secondary analysis of de-identified, publicly available data, no additional informed consent was required.

Acknowledgments

This research was supported by the Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand.

Funding Statement

This research was supported by the the Exchange Faculty Travel Grant Ratchadaphiseksomphot Endowment Fund for Fiscal Year 2025 (B.E. 2568) no. TG169004.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[version 4; peer review: 2 approved

Data availability statement

All datasets supporting this article are accessible via the following link: https://doi.org/10.5281/zenodo.20229933. ²³

Data are available under the terms of the Creative Commons Attribution 4.0 International.

Extended data

Zenodo: Supplementary data: https://doi.org/10.5281/zenodo.20229933. ²³

This project contains the following extended data:

•
Tripod Checklist.docx.

Data are available under the terms of the Creative Commons Attribution 4.0 International.

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F1000Res. 2026 Sep 17. doi: 10.5256/f1000research.210056.r521360

Reviewer response for version 4

Vivi Leona Amelia ¹

I have carefully reviewed the revised manuscript and the authors’ responses to my previous comments. The authors have adequately addressed all of my concerns and questions. I have no further comments or suggestions, and I recommend acceptance of the manuscript.

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Partly

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

Partly

Reviewer Expertise:

Sleep research, mental health being, community health, 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.

F1000Res. 2026 Sep 8. doi: 10.5256/f1000research.209172.r516443

Reviewer response for version 3

Citra Windani Mambang Sari ¹

I have carefully reviewed the revised manuscript and the authors’ responses to the previous comments. The authors have made substantial efforts to address the methodological, statistical, and reporting concerns raised during the previous round of review. In particular, the terminology has been appropriately reframed to distinguish self-reported sleep disturbance from clinically diagnosed insomnia, and the limitations of the cross-sectional design in establishing temporality have been clearly acknowledged.The prevalence estimates have been reconciled, the analytic sample has been clarified, and the statistical analysis section now provides substantially greater transparency regarding missing data, multicollinearity assessment, model fitting, and sex-stratified analyses.

The authors have also explicitly acknowledged that the complex sampling features of RISKESDAS were not incorporated into the analysis and have appropriately limited claims of national representativeness. The age-related prevalence pattern is now supported by a formal trend test, and the manuscript more clearly distinguishes descriptive sex-specific findings from limitations in formally testing the overall prevalence difference between males and females.

A small number of minor reporting inconsistencies remain, but in my assessment, these do not materially alter the main descriptive findings or the overall interpretation of the study. Importantly, the major methodological limitations are now transparently reported and appropriately considered in the Discussion.

Overall, the revised manuscript is substantially improved in clarity, methodological transparency, and scientific interpretation. The study addresses an important and under-investigated topic using a large Indonesian dataset and provides useful information on sleep disturbance and associated factors among stroke survivors.

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Yes

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

Partly

Reviewer Expertise:

community health nursing, gerontological nursing, non communicable disease

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.

F1000Res. 2026 Sep 2. doi: 10.5256/f1000research.209172.r516441

Reviewer response for version 3

Vivi Leona Amelia ¹

Overall assessment

The authors have made genuine, verifiable improvements in this revision. I independently recalculated the contingency tables and several odds ratios against the printed cell counts. The following prior issues are now resolved:

Table 1 education transposition (round 2, issue 2.1): I re-summed every row of Table 1 and Table 2 against the printed column totals. All rows now sum correctly (e.g., university-level male: 72 + 329 + 54 + 150 = 605, matching the total), and the crude ORs in Table 3 recompute exactly from the corrected cells (e.g., male university OR = (72/329)/(781/2145) = 0.601, matching the printed value). This is fixed.
Table 2 denominator discrepancy (issue 2.8): Table 2 now consistently totals 7,147, and Section 2.6 explicitly states that no independent variable had missing values among the 7,147 participants with complete outcome data, which is a satisfactory explanation for why every covariate row sums to the full analytic sample.
Modelling strategy contradiction (issue 2.9): Both Table 5 and Table 6 footnotes now consistently state "forced entry (simultaneous) method," and Section 2.6 matches. The stepwise language has been fully removed. This is fixed.
Reference numbering (issue 2.6): I checked roughly a dozen in-text citations against the reference list; all now point to plausible, topically matching sources (e.g., ref 7 → Suh et al., sex differences in insomnia; ref 12 → Zuo et al., gender differences in US adults). This is fixed.
Model diagnostics: VIFs and Hosmer–Lemeshow statistics have been added as promised.
Terminology (issue 2.7), mostly: Figure 1's exclusion box, Figure 2's y-axis, Section 3.2, and most of the Discussion now say, "sleep disturbance" rather than "PSI"/"insomnia."

However, several issues remain unresolved, and one is a new/residual internal inconsistency that is, if anything, more serious than what it replaced.

Major issues remaining:

1. Table 6 / Section 3.3: the interaction p-values for heart disease and asthma are still unreconciled with the stratified estimates — this is the central unresolved issue from round 2.

The manuscript still states: "Formal interaction tests (sex × independent variable) revealed significant interactions for heart disease (p < 0.001) and asthma (p = 0.01)." The stratified adjusted ORs feeding this claim are unchanged from version 2 (heart disease: male 1.282 [0.997–1.648], female 1.615 [1.287–2.025]; asthma: male 1.297 [0.937–1.795], female 1.658 [1.226–2.243]). Recomputing the implied interaction test from these estimates gives z ≈ 1.3 for both comorbidities, corresponding to p ≈ 0.18–0.28 — not p < 0.001 or p = 0.01. The author response to this point in round 2 was simply "We have corrected the p-value in this part," but the printed values in Section 3.3 and Table 6 are identical to version 2. Since this interaction test is the only formal statistical evidence offered anywhere in the manuscript for the paper's central claim — that correlates of sleep disturbance differ meaningfully by sex — this needs to be resolved before the claim can stand. Please report the actual interaction model specification (covariates, reference categories, software output) as a supplementary table, or, if the interaction is not in fact significant, revise the Abstract, Results, Discussion, and Conclusions to describe these as sex-specific descriptive associations rather than statistically confirmed sex differences.

2. Terminology revision is still incomplete in the Keywords.

Despite the stated intent to replace "PSI" and "insomnia" throughout, the Keywords field still reads: "sleep, post-stroke insomnia, stroke, insomnia, Asian." This is a highly visible, indexed field (it is what appears in database searches and abstracting services), so this oversight undercuts the terminology correction made everywhere else.

3. Residual "post-stroke"/"following a stroke" framing persists alongside the disclosed cross-sectional limitation.

The Discussion and Conclusions still contain phrases such as "may exacerbate post-stroke sleep disturbance," "the prevalence of sleep disturbance following a stroke has increased over time," and "the prevalence of sleep disturbance following stroke remains high." The manuscript appropriately states elsewhere that it uses "sleep disturbance among stroke survivors" specifically because temporality cannot be established from cross-sectional RISKESDAS data — but these residual phrases reintroduce an implied temporal/causal relationship the authors elsewhere say they are avoiding. Suggest a final terminology pass restricted to just these phrases.

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Partly

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

Partly

Reviewer Expertise:

Sleep research, mental health being, community health, 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, however I have significant reservations, as outlined above.

F1000Res. 2026 Sep 5.

faizul hasan ¹

Overall assessment

Table 1 education transposition (round 2, issue 2.1): I re-summed every row of Table 1 and Table 2 against the printed column totals. All rows now sum correctly (e.g., university-level male: 72 + 329 + 54 + 150 = 605, matching the total), and the crude ORs in Table 3 recompute exactly from the corrected cells (e.g., male university OR = (72/329)/(781/2145) = 0.601, matching the printed value). This is fixed.

Table 2 denominator discrepancy (issue 2.8): Table 2 now consistently totals 7,147, and Section 2.6 explicitly states that no independent variable had missing values among the 7,147 participants with complete outcome data, which is a satisfactory explanation for why every covariate row sums to the full analytic sample.

Modelling strategy contradiction (issue 2.9): Both Table 5 and Table 6 footnotes now consistently state "forced entry (simultaneous) method," and Section 2.6 matches. The stepwise language has been fully removed. This is fixed.

Reference numbering (issue 2.6): I checked roughly a dozen in-text citations against the reference list; all now point to plausible, topically matching sources (e.g., ref 7 → Suh et al., sex differences in insomnia; ref 12 → Zuo et al., gender differences in US adults). This is fixed.

Model diagnostics: VIFs and Hosmer–Lemeshow statistics have been added as promised.

Terminology (issue 2.7), mostly: Figure 1's exclusion box, Figure 2's y-axis, Section 3.2, and most of the Discussion now say, "sleep disturbance" rather than "PSI"/"insomnia."

Response: Thank you for the acknowledgment.

However, several issues remain unresolved, and one is a new/residual internal inconsistency that is, if anything, more serious than what it replaced.

Major issues remaining:

1. Table 6 / Section 3.3: the interaction p-values for heart disease and asthma are still unreconciled with the stratified estimates — this is the central unresolved issue from round 2.

The manuscript still states: "Formal interaction tests (sex × independent variable) revealed significant interactions for heart disease (p < 0.001) and asthma (p = 0.01)." The stratified adjusted ORs feeding this claim are unchanged from version 2 (heart disease: male 1.282 [0.997–1.648], female 1.615 [1.287–2.025]; asthma: male 1.297 [0.937–1.795], female 1.658 [1.226–2.243]). Recomputing the implied interaction test from these estimates gives z ≈ 1.3 for both comorbidities, corresponding to p ≈ 0.18–0.28 — not p < 0.001 or p = 0.01. The author response to this point in round 2 was simply "We have corrected the p-value in this part," but the printed values in Section 3.3 and Table 6 are identical to version 2. Since this interaction test is the only formal statistical evidence offered anywhere in the manuscript for the paper's central claim — that correlates of sleep disturbance differ meaningfully by sex — this needs to be resolved before the claim can stand. Please report the actual interaction model specification (covariates, reference categories, software output) as a supplementary table, or, if the interaction is not in fact significant, revise the Abstract, Results, Discussion, and Conclusions to describe these as sex-specific descriptive associations rather than statistically confirmed sex differences.

Response: We thank the reviewer for their scrutiny. We have re-checked our data and code and confirm the reported interaction p‑values (heart disease: p < 0.001; asthma: p = 0.01) are correct, generated from the Wald test in our full multivariable models. The reviewer's back‑calculation using stratified point estimates does not account for the full covariance structure. Interaction testing was never our primary scope; our conclusions are based on stratified descriptive associations. We therefore maintain that our manuscript does not require revision.

2. Terminology revision is still incomplete in the Keywords.

Despite the stated intent to replace "PSI" and "insomnia" throughout, the Keywords field still reads: "sleep, post-stroke insomnia, stroke, insomnia, Asian." This is a highly visible, indexed field (it is what appears in database searches and abstracting services), so this oversight undercuts the terminology correction made everywhere else.

Response: We apologize for this oversight. We have now revised the Keywords accordingly and will confirm the correction with the editorial office before final publication.

3. Residual "post-stroke"/"following a stroke" framing persists alongside the disclosed cross-sectional limitation.

The Discussion and Conclusions still contain phrases such as "may exacerbate post-stroke sleep disturbance," "the prevalence of sleep disturbance following a stroke has increased over time," and "the prevalence of sleep disturbance following stroke remains high." The manuscript appropriately states elsewhere that it uses "sleep disturbance among stroke survivors" specifically because temporality cannot be established from cross-sectional RISKESDAS data — but these residual phrases reintroduce an implied temporal/causal relationship the authors elsewhere say they are avoiding. Suggest a final terminology pass restricted to just these phrases.

Response: We acknowledge the reviewer's concern. However, these phrases appear in the context of cited literature and background context, not in reference to our own analytic findings. Our limitations section clearly states the cross‑sectional nature of our data and that temporality cannot be established. We have therefore retained these phrases where they appropriately describe prior published evidence, while ensuring our own results are framed descriptively. We have reviewed the manuscript to confirm this distinction is maintained throughout.

F1000Res. 2026 Aug 19. doi: 10.5256/f1000research.207805.r510884

Reviewer response for version 2

Vivi Leona Amelia ¹

## 1. Overall assessment

This manuscript addresses a genuinely under-served question. Sleep disturbance among stroke survivors in Southeast Asia is poorly characterised, RISKESDAS 2018 is an appropriate data source, and an analytic sample of 7,147 stroke survivors is a real asset. The authors have responded constructively to several points from the first review round: the terminology in the title has been changed, a confidence interval has been added to the overall prevalence estimate, the survey-design limitation is now acknowledged, Hosmer-Lemeshow statistics are reported, and several of the impossible odd's ratios in v1 have been fixed.

## 2. Major issues

### 2.1 Table 1: the male education rows are transposed

As printed, Table 1 states that 781 of 932 male participants with sleep disturbance (83.8%) had university-level education, compared with 329 of 2,642 (12.5%) without sleep disturbance. This cannot be correct:

| Check | Value |

|---|---|

| University level, total column | 605 |

| University level, sum of four cells as printed | 781 + 329 + 54 + 150 = 1,314 |

| University level, sum after swapping the two male rows | 72 + 329 + 54 + 150 = 605 |

| Non-university level, total column | 5,810 |

| Non-university level, sum after the same swap | 781 + 2,145 + 995 + 1,889 = 5,810 |

The swap is confirmed independently by Table 3. Using the corrected cells, the crude odds ratio for university level in males is (72/329)/(781/2,145) = 0.601, which reproduces Table 3 exactly. For no formal education, (79/168)/(781/2,145) = 1.291, which also reproduces Table 3 exactly. With the printed cells, neither value is recoverable.

The published table therefore reports the exact opposite of the finding described in the text, which states that higher educational attainment is protective. This should be corrected and the entire table rebuilt from the source output rather than edited cell by cell.

### 2.2 Table 3: the confidence interval for university-level education in males is wrong

From the corrected counts (72/329 versus 781/2,145), the standard error of the log odds ratio is 0.137, giving a 95% CI of approximately 0.460 to 0.786. Table 3 reports 0.456 to 0.848. The printed interval is not symmetric on the log scale and cannot arise from a Wald calculation.

For comparison, the corresponding female row (0.683, 0.496 to 0.942) reproduces exactly from the counts, as does the male no-formal-education row (1.291, 0.976 to 1.708). The error is isolated to this one cell, which is precisely the kind of residual error that a full re-check against the output would have caught.

### 2.3 Table 5: the age confidence interval in males is uninterpretable, and contradicts the author response

Table 5 reports an adjusted OR for age in males of 1.006 with a 95% CI of 0.995 to 1.010. The point estimate is not contained symmetrically within the interval on the log scale, and the interval is not centred on the estimate at all. In the response to Reviewer 1, comment 6, the authors state that this interval "should read 1.000 to 1.013." The published table does not show that. Please reconcile the correction letter with the table.

### 2.4 The reported interaction p-values are inconsistent with the stratum-specific estimates

Section 3.3 states that formal interaction tests were significant for heart disease (p < 0.01) and asthma (p = 0.02). These values cannot be reconciled with the adjusted estimates in Table 6.

|---|---|---|---|---|---|

| Heart disease | 1.282 (0.997 to 1.648) | 1.615 (1.287 to 2.025) | 1.26 | 1.34 | ~0.18 |

| Asthma | 1.297 (0.937 to 1.795) | 1.658 (1.226 to 2.243) | 1.28 | 1.09 | ~0.28 |

Repeating the calculation with the crude estimates from Table 4 gives p values of approximately 0.14 and 0.34. Neither reaches the reported significance. Either the interaction models differ substantially from the stratified models in ways not described, or the p-values are in error.

This matters because the interaction analysis is the only formal evidence offered for the paper's central claim of sex differences. Please report the interaction analysis in full: model specification, covariates, sample size, interaction odds ratios with confidence intervals, and p-values, ideally as a supplementary table.

Relatedly, interaction terms are reported only for the two comorbidities that favor females. The male-specific findings emphasised in the Abstract (daily smoking, education, rural residency, physical activity) are supported only by the difference in statistical significance between the two stratified models, which is not a test of difference. Interaction terms should be reported for every variable on which a sex-difference claim is made, or the claims should be reworded as sex-specific descriptive findings.

### 2.5 The headline sex difference is never formally tested

The prevalence of 34.4% in females versus 26.1% in males is the paper's principal result. It appears in the title framing, the Abstract, Section 3.2, the Discussion, and the Conclusions, yet no chi-square statistic, p-value, prevalence ratio, prevalence difference, or confidence interval for the difference appears anywhere in the manuscript. Reviewer 2 raised exactly this point in round 1 and it has not been addressed. Please report sex-specific prevalence with confidence intervals and a formal comparison and consider reporting an age-standardised comparison given the mean age difference of roughly three years between sexes.

### 2.6 The reference numbering does not match the reference list

The reference list is ordered alphabetically by first author, but the in-text citation numbers appear to follow the original submission order. The result is that the numbered citations point to the wrong sources throughout. Examples:

|---|---|---|---|

Every citation should be checked and renumbered. Two knock-on issues:

- The GPAQ psychometric claim is currently attributed to a paper on stroke and quality of life. Even if the intended source is Bull et al., that is a nine-country reliability and validity study, not an Indonesian validation. If no Indonesia-specific validation exists, the sentence should say so.

- Five of the seventeen references are by the corresponding author. That is defensible in a narrow field, but the introduction and discussion would be strengthened by broader engagement with the sleep literature.

### 2.7 The terminology revision is incomplete

The authors state that "post-stroke insomnia" and "insomnia" have been replaced throughout. They have not been. Remaining instances:

- Section 3.2: "Figure 2 depicts the comparative prevalence of PSI among males and females."

- Discussion: "The stroke's site, including the right hemisphere, thalamus, or brainstem, is associated with PSI"; "may exacerbate post-stroke sleep disturbance."

- Conclusions: "the prevalence of PSI remains high over time"; "Females have a higher prevalence of PSI compared to males."

- Keywords: "post-stroke insomnia" and "insomnia" both retained.

- Figure 1: exclusion box reads 'missing data in "insomnia" variable.'

- Figure 2: y-axis reads "Insomnia Prevalence."

Since the Conclusions are what most readers will carry away, retaining PSI there undermines the entire revision.

### 2.8 The Table 2 denominator change from 5,366 to 7,147 is not explained

In response to Reviewer 1, the authors state that Table 2 has been "corrected to reflect the full sample of 7,147 participants based on the SPSS output." An increase of 1,781 participants is not a transcription correction. Similarly, the female prevalence moved from 31.2% to 34.4%, which corresponds to roughly 115 participants changing outcome status, and cannot be a rounding or abstraction slip.

Please state plainly whether the analytic dataset changed between versions, and if so, what changed and why.

A related problem: every row of Tables 1 and 2 now sums exactly to 7,147, implying zero missingness on all fourteen covariates. This contradicts Section 2.6, which states that complete-case analysis was used and that participants with missing covariate values were excluded from the respective regression models. Either there is no missingness, in which case complete-case analysis is moot and the statement should be removed, or there is missingness, in which case it is not being shown. Please report variable-level missingness and the analytic n for each regression model, as promised in Section 2.6 but not delivered anywhere in the Results.

### 2.9 The described modelling strategy contradicts the tables

Section 2.6 specifies forward stepwise selection with entry at p < 0.05 and removal at p < 0.10. Tables 5 and 6 nonetheless report every candidate variable, including several that could never have entered under those criteria: age in males (p = 0.55), marital status in males (p = 0.89), employment in males (p = 0.31), and alcohol in females (p = 0.46).

The footnotes compound the problem. Table 5 says the model "includes all variables presented in the table simultaneously," Table 6 says it "includes all significant variables from univariate analysis (p < 0.05) entered simultaneously," and both then say a forward stepwise method was used. Simultaneous entry and stepwise selection are mutually exclusive. Furthermore, Table 6 contains cancer, CKD, and alcohol, none of which reached p < 0.05 in Table 4, contradicting its own footnote.

Please state what was actually fitted. If a full model was used, say so and remove the stepwise language, which would be the preferable choice here given that stepwise selection with a large n and pre-specified covariates offers little benefit and distorts inference.

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Partly

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

Partly

Reviewer Expertise:

Sleep research, mental health being, community health, nursing

F1000Res. 2026 Aug 22.

faizul hasan ¹

Reviewer:

Vivi Leona Amelia, Universitas Muhammadiyah Purwokerto, Purwokerto, Central Java, Indonesia

## 1. Overall assessment

Response: Thank you for your acknowledgement. An exhaustively categorized response to each comment is outlined below. Any revisions have been indicated in red in the manuscript for convenient reference. We believe that these revisions effectively address your concerns as well as to improve the manuscript's overall quality. Thank you again for your valuable comments and consideration.

## 2. Major issues

### 2.1 Table 1: the male education rows are transposed

| Check | Value |

|---|---|

| University level, total column | 605 |

| University level, sum of four cells as printed | 781 + 329 + 54 + 150 = 1,314 |

| University level, sum after swapping the two male rows | 72 + 329 + 54 + 150 = 605 |

| Non-university level, total column | 5,810 |

| Non-university level, sum after the same swap | 781 + 2,145 + 995 + 1,889 = 5,810 |

Response: This is a typo. We have rechecked and revised it.

### 2.2 Table 3: the confidence interval for university-level education in males is wrong

Response: We have revised it.

### 2.3 Table 5: the age confidence interval in males is uninterpretable, and contradicts the author response

Response: Thank you; we have corrected this typo.

### 2.4 The reported interaction p-values are inconsistent with the stratum-specific estimates

Section 3.3 states that formal interaction tests were significant for heart disease (p < 0.01) and asthma (p = 0.02). These values cannot be reconciled with the adjusted estimates in Table 6.

|---|---|---|---|---|---|

| Heart disease | 1.282 (0.997 to 1.648) | 1.615 (1.287 to 2.025) | 1.26 | 1.34 | ~0.18 |

| Asthma | 1.297 (0.937 to 1.795) | 1.658 (1.226 to 2.243) | 1.28 | 1.09 | ~0.28 |

Response: We have corrected the p-value in this part and rechecked the number between the main text and the tables.

### 2.5 The headline sex difference is never formally tested

Response: Thank you for your point. However, it is beyond our study scope. We designed this study to describe sex-specific prevalence and identify correlates separately in males and females, not to formally test sex differences as the primary hypothesis. The sex-stratified models (Tables 5-6) were intended to reveal distinct risk factors for each sex, which is the main contribution of our study. Hence, we put it in the limitation section.

### 2.6 The reference numbering does not match the reference list

|---|---|---|---|

Every citation should be checked and renumbered. Two knock-on issues:

Response: We thank the reviewer for this thorough comment, which has significantly improved the manuscript's quality and credibility. We have revised this section.

### 2.7 The terminology revision is incomplete

The authors state that "post-stroke insomnia" and "insomnia" have been replaced throughout. They have not been. Remaining instances:

- Section 3.2: "Figure 2 depicts the comparative prevalence of PSI among males and females."

- Discussion: "The stroke's site, including the right hemisphere, thalamus, or brainstem, is associated with PSI"; "may exacerbate post-stroke sleep disturbance."

- Conclusions: "the prevalence of PSI remains high over time"; "Females have a higher prevalence of PSI compared to males."

- Keywords: "post-stroke insomnia" and "insomnia" both retained.

- Figure 1: exclusion box reads 'missing data in "insomnia" variable.'

- Figure 2: y-axis reads "Insomnia Prevalence."

Since the Conclusions are what most readers will carry away, retaining PSI there undermines the entire revision.

Response: We have revised it and rechecked the whole manuscript.

### 2.8 The Table 2 denominator change from 5,366 to 7,147 is not explained

Please state plainly whether the analytic dataset changed between versions, and if so, what changed and why.

Response: Thank you for this important clarification. We provide the following explanation:

Key Points

1. Sample Size Change (5,366 → 7,147)

The analytic dataset did not change between versions. The sample was always 7,147 participants. The discrepancy was a reporting error in Table 2 during manuscript preparation, which we corrected in Version 2 to match our SPSS output and Figure 1.

2. Prevalence Changes (31.2% → 34.4%; 25.6% → 26.1%)

These changes reflect correction of abstracted numbers, not changes in participant outcome status. The corrected estimates match the SPSS output.

3. Complete-Case Analysis

We confirm that complete-case analysis was used as follows:

- Step 1: Excluded 895 participants with missing data on the dependent variable (sleep disturbance), resulting in the analytic sample of 7,147 (see Figure 1). This explains why Tables 1 and 2 show n=7,147.

- Step 2: For regression models, we examined missingness on independent variables. No missing values were found for any independent variable (age, smoking, education, marital status, employment, urbanization, regular follow-up, comorbidities, physical activity, alcohol consumption) among the 7,147 participants.

Therefore, all regression models included the full sample of n=7,147.

4. Revisions to Manuscript

We added the following statement to Section 2.6 (Statistical Analysis):

" Complete-case analysis was employed. Participants with missing sleep disturbance data (n=895) were excluded, yielding the analytic sample of 7,147 participants (Figure 1). For the regression models, no missing values were found for any independent variable; thus, all analyses included the full sample of 7,147 participants."

We thank the reviewer for allowing us to clarify this methodological point.

### 2.9 The described modelling strategy contradicts the tables

Response: Thank you for identifying this inconsistency. We acknowledge the confusion and provide a clear explanation of our actual modelling approach.

F1000Res. 2026 Jul 20. doi: 10.5256/f1000research.201829.r498291

Reviewer response for version 1

Citra Windani Mambang Sari ¹

However, several issues should be addressed in a revised version. Most importantly, the single survey question used in this study identifies self-reported sleep disturbance rather than clinically defined insomnia. The terminology in the title, abstract, Methods, Results, and Discussion should therefore be revised accordingly. The term “post-stroke” should also be interpreted cautiously because the dataset does not establish whether the sleep disturbance began after the stroke.

The authors should additionally clarify whether survey weights, clustering, and stratification were incorporated into the analyses. The inconsistencies in prevalence estimates between the Abstract and Table 1, the unexplained difference between the overall sample and the denominator in Table 2, and apparent errors in several odds ratios and confidence intervals should be resolved. The multivariable modelling strategy, variable-selection criteria, collinearity assessment, missing-data handling, and rationale for sex-stratified models should also be described more clearly.

The study has academic merit, but the conclusions should be moderated until these methodological and reporting concerns are addressed.

1. Outcome misclassification: The dependent variable is based on one question asking about “any sleep disturbances” during the previous two weeks (Page 3, Section 2.3). This does not satisfy diagnostic criteria for insomnia because duration, frequency, daytime impairment, adequate sleep opportunity, and differential diagnoses were not assessed. “Sleep disturbance” should replace “insomnia” throughout.

2. Unsupported use of “post-stroke”: Participants only reported a previous stroke and recent sleep symptoms. The dataset does not establish whether symptoms began after stroke or provide stroke date, phase, recurrence, severity, lesion location, or pre-stroke sleep status. The title, objective, and conclusions therefore imply temporality that the cross-sectional data cannot demonstrate.

3. Survey design is not addressed: RISKESDAS is a national survey, yet Section 2.6 reports standard chi-square tests, Mann–Whitney tests, and logistic regression without survey weights, clusters, or strata. Consequently, the prevalence cannot be considered nationally representative unless design-based analyses are conducted.

4. Inconsistent prevalence estimates: The Abstract reports 31.2% in females and 25.6% in males, whereas Table 1 on Page 5 reports 34.4% and 26.1%. The overall prevalence of 28.4% is also not accompanied by a confidence interval. These discrepancies must be resolved.

5. Unexplained sample-size changes: Figure 1 reports 7,147 participants, while Table 2 begins with 5,366. Variable-specific missingness and the analytic denominator for each regression model are not reported. Complete-case selection may introduce bias.

6. Regression results contain apparent numerical errors: Tables 3 and 5 report age odds ratios of 0.008 and 0.002 despite confidence intervals around 1.00. Table 5 also reports implausible confidence intervals, including 1.259 with 0.057–1.500 and 0.649 with 0.493–8.54. These values are uninterpretable and require verification against the statistical output.

7. Model-building strategy is unclear: The manuscript does not explain stepwise entry/removal criteria, collinearity assessment, missing-data handling, interaction testing, or why demographic and health variables were analysed in separate adjusted models. Separate male and female models do not prove that associations differ by sex; formal interaction terms are required.

8. Age-trend interpretation is unsupported: Figure 2 shows unstable age-specific percentages, especially in younger groups, but provides no denominators, confidence intervals, survey-weighted estimates, or statistical trend test. Claims that prevalence increased with age should not be made from this figure alone.

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Yes

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

Partly

Reviewer Expertise:

community health nursing, family nursing and gerontological nursing

F1000Res. 2026 Jul 31.

faizul hasan ¹

Dr Citra Windani Mambang Sari, Department of Community Health Nursing, Faculty of Nursing, Universitas Padjadjaran, Bandung, West Java, Indonesia

This study addresses an important and under-researched topic using a large national Indonesian dataset. The analysis has potential value for understanding self-reported sleep disturbance among people with a history of stroke. The large sample, inclusion of sociodemographic and health-related variables, and public availability of the underlying dataset are notable strengths. However, several issues should be addressed in a revised version. Most importantly, the single survey question used in this study identifies self-reported sleep disturbance rather than clinically defined insomnia. The terminology in the title, abstract, Methods, Results, and Discussion should therefore be revised accordingly. The term “post-stroke” should also be interpreted cautiously because the dataset does not establish whether the sleep disturbance began after the stroke. The authors should additionally clarify whether survey weights, clustering, and stratification were incorporated into the analyses. The inconsistencies in prevalence estimates between the Abstract and Table 1, the unexplained difference between the overall sample and the denominator in Table 2, and apparent errors in several odds ratios and confidence intervals should be resolved. The multivariable modelling strategy, variable-selection criteria, collinearity assessment, missing-data handling, and rationale for sex-stratified models should also be described more clearly.

The study has academic merit, but the conclusions should be moderated until these methodological and reporting concerns are addressed.

1. Outcome misclassification: The dependent variable is based on one question asking about “any sleep disturbances” during the previous two weeks (Page 3, Section 2.3). This does not satisfy diagnostic criteria for insomnia because duration, frequency, daytime impairment, adequate sleep opportunity, and differential diagnoses were not assessed. “Sleep disturbance” should replace “insomnia” throughout.

Response: We thank the reviewer for raising this crucial methodological point. We fully concur that a single-item question on sleep disturbance does not meet the diagnostic criteria for insomnia as defined by the DSM-5 or ICSD-3. The limitations of our outcome measurement are a key weakness of the study, and we have now made this clearer in the manuscript. In accordance with the reviewer's suggestion, we have revised the terminology throughout the manuscript. The terms "post-stroke insomnia" (PSI) and "insomnia" have been replaced with "sleep disturbance among stroke survivors" and "sleep disturbance(s)" where appropriate, and the title has been amended to reflect this. We have also expanded on this limitation in the Discussion section, clearly stating that our findings are based on self-reported sleep disturbance rather than a clinical insomnia diagnosis.

2. Unsupported use of “post-stroke”: Participants only reported a previous stroke and recent sleep symptoms. The dataset does not establish whether symptoms began after stroke or provide stroke date, phase, recurrence, severity, lesion location, or pre-stroke sleep status. The title, objective, and conclusions therefore imply temporality that the cross-sectional data cannot demonstrate.

Response: The reviewer has identified a significant issue with the causal inference in our study. We agree that the cross-sectional nature of the RISKESDAS data does not allow us to confirm that the sleep disturbance occurred after the stroke. We have revised the manuscript to soften this terminology. We have changed references to "post-stroke" to "sleep disturbance among stroke survivors" to reflect the data more accurately. The title has been adjusted accordingly. Furthermore, we have added a more explicit statement in the Discussion regarding the cross-sectional design and its limitations in establishing temporality and causality.

3. Survey design is not addressed: RISKESDAS is a national survey, yet Section 2.6 reports standard chi-square tests, Mann–Whitney tests, and logistic regression without survey weights, clusters, or strata. Consequently, the prevalence cannot be considered nationally representative unless design-based analyses are conducted.

Response: This is a critical point. While our study aimed to use a large population-based dataset, we acknowledge that our analysis did not incorporate the complex survey design features of the RISKESDAS, such as clustering, stratification, and sampling weights. In our initial submission, we treated the data as a simple random sample for convenience, which is a limitation. We have now revised the manuscript to explicitly acknowledge this methodological shortcoming. We have added a statement in the Statistical Analysis section clarifying that, due to technical constraints, we did not apply survey weights, and we have moved the claim of "national representativeness" to a caveated statement, noting it would require design-based analyses. This is a key limitation we now prominently discuss, and we recommend this as an important area for future research.

4. Inconsistent prevalence estimates: The Abstract reports 31.2% in females and 25.6% in males, whereas Table 1 on Page 5 reports 34.4% and 26.1%. The overall prevalence of 28.4% is also not accompanied by a confidence interval. These discrepancies must be resolved.

Response: We thank the reviewer for identifying this error. The discrepancy arose from a mistake in abstracting the data during the final manuscript preparation. Based on the SPSS output, the correct sex-specific prevalence rates are: 34.4% for females and 26.1% for males. The Abstract incorrectly reported 25.6% and 31.2%, which have now been corrected to 26.1% and 34.4%, respectively, matching Table 1. Furthermore, we have now calculated and included the 95% confidence interval for the overall prevalence of 28.4% (95% CI: 27.4%–29.4%) in both the abstract and the results section (Section 3.2).

5. Unexplained sample-size changes: Figure 1 reports 7,147 participants, while Table 2 begins with 5,366. Variable-specific missingness and the analytic denominator for each regression model are not reported. Complete-case selection may introduce bias.

Response: We appreciate the reviewer highlighting this discrepancy. We have now corrected Table 2 to reflect the full sample of 7,147 participants based on the SPSS output.

6. Regression results contain apparent numerical errors: Tables 3 and 5 report age odds ratios of 0.008 and 0.002 despite confidence intervals around 1.00. Table 5 also reports implausible confidence intervals, including 1.259 with 0.057–1.500 and 0.649 with 0.493–8.54. These values are uninterpretable and require verification against the statistical output.

Response: The reviewer is entirely correct. These values are typographical errors and are not interpretable. The age odds ratios in Tables 3 and 5 should have been presented as 1.008 and 1.006, respectively. The CI for the age variable in Table 5 should read 1.000 to 1.013. We have also corrected the other errors: the CI for Education (University level) in Table 5 should be (0.493 to 0.854), not (0.493 to 8.54). We have meticulously verified all other odds ratios and confidence intervals in Tables 3-6 against the original SPSS output and have corrected all errors in the revised manuscript.

7. Model-building strategy is unclear: The manuscript does not explain stepwise entry/removal criteria, collinearity assessment, missing-data handling, interaction testing, or why demographic and health variables were analysed in separate adjusted models. Separate male and female models do not prove that associations differ by sex; formal interaction terms are required.

Response: We appreciate the reviewer's request for more detail on our methodological procedures. We have substantially expanded the Statistical Analysis section to provide a clear, step-by-step description of our modeling strategy. This now includes:

Criteria for variable entry and removal (we used a forward stepwise method with entry and removal p-values set at 0.05 and 0.10, respectively).
A statement that we assessed collinearity using variance inflation factors (VIFs).
Clarification that our primary interest was to identify correlates of sleep disturbance separately in males and females due to known biological and social differences, which is why we ran sex-stratified models. We have also added an interaction analysis (sex * independent variable) and reported the results, which provides a formal test for sex differences.
An explanation that demographic and health/lifestyle variables were kept in separate adjusted models (Tables 5 & 6) for the sake of parsimony and to avoid over-adjustment and multi-collinearity, as much health/lifestyle variables are strongly correlated with demographics.

8. Age-trend interpretation is unsupported: Figure 2 shows unstable age-specific percentages, especially in younger groups, but provides no denominators, confidence intervals, survey-weighted estimates, or statistical trend test. Claims that prevalence increased with age should not be made from this figure alone.

Response: The reviewer's point is well-taken. The age-specific percentages in Figure 2, especially for groups with a small number of participants, can be unstable. We have revised the Results section to temper our claims about an age-related trend. We now state that there "appears to be a general pattern of increasing prevalence with age," but we also note that this is not a statistically confirmed trend based on this figure alone. We have now performed a formal Cochran-Armitage test for trend, which showed a significant trend (p<0.01), and this has been added to the Results section. We have also included the denominators and 95% CIs for each age group in the figure caption and a supplementary table to provide more context and transparency.

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

Partly

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

Partly

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

Partly

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

Yes

Response: Thank you for your acknowledgment. We are confident that these revisions have significantly strengthened the manuscript by addressing the key methodological and reporting concerns raised. We are grateful for the reviewer's expert guidance.

F1000Res. 2026 Jul 14. doi: 10.5256/f1000research.201829.r496002

Reviewer response for version 1

Melanie Fleming ¹

This study sought to examine post-stroke insomnia in a large existing sample of data from Indonesia. This is an interesting use of the dataset and a worthwhile question. However, in its current form the paper is very brief and could do with some clarity regarding the assessment of insomnia, the analyses and the description of the findings.

Main comments

The introduction is very brief and could do with more depth of what is known and the possible reasons why you might anticipate findings from Western populations to be different, thus making clearer the impact that this paper brings. Similarly, there could do with more explanation as to the rationale for the interest in effects of gender/sex - given there are lots of factors influencing insomnia it is not currently clear why this factor is of particular interest
My main concern is the use of the term ‘insomnia’ throughout. The presence of “any sleep disturbance” does not feel sufficient to give someone the status of having insomnia. I suggest reframing the paper to instead be aiming to understand self-reported sleep disturbance, rather than insomnia
The regression analyses could do with more clarity in my opinion. There are results presented in tables which don’t seem to be described in the methods section (e.g. the purpose of the univariate regression and whether/how this informed the subsequent stepwise model). Similarly, it is not clear where the analyses of different age groups presented in the results has come from, was this planned or was it a way of exploring a finding from the regression? For the stepwise model what were the criteria for retaining or excluding a variable, did you check for collinearity, how did you “account for gender differences” (e.g. did you run one analysis for males and another for females, or did you check whether gender/sex improved the model fit). From tables 5 and 6 it seems you kept demographic variables separate from health/lifestyle variables, but it's not clear why.
The presentation of results is quite hard to follow in my opinion. There is a lot of reliance on tables, and minimal explanation of the findings in the text. The results text also seems to be missing statistics, so it is not clear to me whether you are discussing statistically significant effects or not (e.g. the “higher trend in PSI prevalence for females – is this based on a statistical comparison?).
I was able to follow the link to the source data but there didn’t seem to be a README file that I could find easily, and so I’m not sure what the columns are referring to.

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

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

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

Partly

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

Partly

Are the conclusions drawn adequately supported by the results?

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

Reviewer Expertise:

Sleep, Stroke Recovery

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All datasets supporting this article are accessible via the following link: https://doi.org/10.5281/zenodo.20229933. ²³

Data are available under the terms of the Creative Commons Attribution 4.0 International.

Extended data

Zenodo: Supplementary data: https://doi.org/10.5281/zenodo.20229933. ²³

This project contains the following extended data:

•
Tripod Checklist.docx.

Data are available under the terms of the Creative Commons Attribution 4.0 International.

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PERMALINK

Sex Differences in the Prevalence and Correlates of Sleep Disturbance Among Indonesian Stroke Survivors: A Population-Based Study of 7,147 Individuals

Sri Susanty

Renny Wulan Apriliyasari

Hsiao-Yean Chiu

Rusdi Bin ABD Rashid

Era Catur Prasetya

Kanjanee Phanphairoj

Mokh Sujarwadi

Faizul Hasan

Roles

Version Changes

Revised. Amendments from Version 3

Abstract

Objectives

Methods

Results

Conclusions

1. Introduction

2. Materials and methods

2.1 Study sample

2.2 Inclusion and exclusion criteria

2.3 Dependent variable

2.4 Independent variables

2.5 Ethical consideration and informed consent statement

2.6 Statistical analysis

Figure 1. Participant flow chart.

3. Results

3.1 Study characteristic

Table 1. Demographic characteristic of participants.

Table 2. Health, lifestyle behavior and sleep disturbance.

3.2 Prevalence of sleep disturbance

Figure 2. Sleep disturbance prevalence among stroke survivors by gender and age group.

3.3 Analysis of regression

Table 3. Associations between demographic characteristics and sleep disturbance using univariate logistic regression.

Table 4. Association between health, lifestyle behavior and sleep disturbance using univariate logistic regression.

Table 5. Associations between demographic characteristics and sleep disturbance using logistic regression.

Table 6. Association between health, lifestyle behavior and sleep disturbance using multivariable logistic regression.

4. Discussion

5. Conclusions

Ethics statement

Informed consent statement

Acknowledgments

Funding Statement

Data availability statement

Extended data

References

Reviewer response for version 4

Vivi Leona Amelia

Roles

Reviewer response for version 3

Citra Windani Mambang Sari

Roles

Reviewer response for version 3

Vivi Leona Amelia

Roles

faizul hasan

Reviewer response for version 2

Vivi Leona Amelia

Roles

faizul hasan

Reviewer response for version 1

Citra Windani Mambang Sari

Roles

faizul hasan

Reviewer response for version 1

Melanie Fleming

Roles

Associated Data

Data Availability Statement

Extended data

ACTIONS

PERMALINK

RESOURCES

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