Access to this full-text is provided by Springer Nature.
Content available from Sleep Science and Practice
This content is subject to copyright. Terms and conditions apply.
RESEARCH Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included
in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Muhammad et al. Sleep Science and Practice (2024) 8:6
https://doi.org/10.1186/s41606-024-00100-z Sleep Science and Practice
*Correspondence:
T. Muhammad
muhammad.iips@gmail.com
Full list of author information is available at the end of the article
Abstract
Background Sleep is an essential component of human health and well-being, playing a crucial role in several
cognitive processes, including attention, memory, and executive function. In this study, we aimed to examine the
association between sleep quality, sleep duration and cognitive functioning among older men and women in India.
Methods Data come from the World Health Organization’s Study on global AGEing and adult health (WHO-SAGE),
India wave-2, which was conducted in 2015 in six selected states of India, representing dierent country regions.
The sample included 6,396 older adults aged 50 years and above. We used multivariable linear regression models to
examine the associations between sleep quality, sleep duration and cognitive function, separately among older men
and women.
Results Older men and women with poor sleep and short duration sleep had lower mean scores of cognition than
their peers with good sleep and age-appropriate sleep duration. Poor sleep (aCoef: -5.09, CI: -8.66, -1.51) and short
duration sleep (aCoef: -5.43, CI: -7.77, -3.10) were negatively associated with cognitive functioning among older men
and the associations remained signicant among older men with poor sleep (aCoef: -2.39, CI: -3.78, -1.00) and short
duration sleep (aCoef: -4.39, CI: -6.46, -2.31) after adjusting for a large number of socio-demographic, health and
behavioral factors. Similarly, poor sleep (aCoef: -3.15, CI: -5.79, -0.52) and short duration sleep (aCoef: -2.72, CI: -4.64,
-0.81) were associated with cognitive functioning among older women, however, the associations were insignicant
when the potential confounders were adjusted.
Conclusions This study provides evidence for the signicant association between sleep health and cognitive
functioning in older Indian adults, especially older men, with poor sleep quality and insucient sleep duration
being detrimental to their cognitive health. Healthcare providers should routinely screen for sleep quality and age-
appropriate sleep duration in their older adult patients and consider sex/gender-tailored sleep interventions as part of
cognitive health management strategies.
Keywords Sleep quality, Sleep duration, Cognition, Gender, Older adults
Gender-specic associations between
sleep quality, sleep duration and cognitive
functioning among older Indians: ndings
from WHO-SAGE study
T.Muhammad1* , A. H. SruthiAnil Kumar2 and T. V.Sekher3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 2 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Background
Sleep is an essential component of human health and
well-being, playing a crucial role in several cognitive pro-
cesses, including attention, memory, and executive func-
tion. Changes in sleep patterns, such as decreased sleep
quality and altered sleep duration, are common as people
age (Pace-Schott and Spencer 2011). ese alterations
have been linked to cognitive decline and an increased
risk of neurodegenerative diseases in older adults. Sleep
quality refers to the subjective experience of sleep, which
includes factors such as sleep latency, sleep ecacy, and
the presence of sleep disturbances (Krystal and Edinger
2008). Numerous studies have demonstrated a strong
correlation between poor sleep quality and cognitive
impairment in older individuals (Keage et al. 2012; Beh-
rens et al. 2023; Zhang et al. 2023; Miyata et al. 2013).
Inadequate sleep quality has also been associated
with an increased risk of mild cognitive impairment
(MCI) and dementia, including Alzheimer’s disease
(Rothman and Mattson 2012). Cognitive impairment
is further exacerbated by sleep disorders such as sleep
apnea. (Findley et al. 1986; Gagnon et al. 2014; Vanek et
al. 2020). Further, the duration of sleep that is the total
quantity of time an individual spends asleep also plays a
role in aecting cognition (Fortier-Brochu et al. 2012).
Individual sleep requirements may vary, but most adults
require seven to nine hours of sleep per night for optimal
cognitive function (Hirshkowitz et al. 2015). However,
due to age-related changes in circadian rhythms, medical
conditions, and medication use, senior adults frequently
experience shorter sleep durations (Bombois et al. 2010;
Mattis and Sehgal 2016). Insucient sleep duration has
been linked to decits in cognitive domains such as con-
solidation of memories, attention, and executive function
(Cohen-Zion et al. 2004).
Sleep is necessary for memory consolidation, the pro-
cess by which newly acquired information is transformed
into stable memories. Adequate sleep, specically pro-
found sleep or slow-wave sleep, facilitates the consolida-
tion of memories and improves cognitive performance
(Van Cauter et al. 2000; Nebes et al. 2009). Reduced sleep
duration and quality impede memory retrieval and cogni-
tive performance. Maintaining cognitive health and well-
being in older individuals requires optimal sleep quality
and duration. A meta-analysis of 45 studies on sleep
quality and duration in low and middle income countries
(LMICs) suggests that though sleep health parameters
in LMICs are similar to those in high income countries,
there is huge variability potentially due to specic socio-
cultural and demographic settings (Simonelli et al. 2018).
Other studies also found that individuals in LMICs with
sucient sleep exhibited higher cognitive scores and
those with sleep problems reported higher cognitive
complaints (Gildner et al. 2014; Smith et al. 2022).
The role of gender in sleep and cognition
Understanding the complex connection between sleep
and cognition is essential for promoting healthy aging.
In older individuals, sleep quality, sleep duration, and
cognitive function are intricately linked. However, it is
important to consider how gender may moderate this
relationship. By examining the inuence of gender, we
can gain a deeper understanding of how specic factors
interact and impact cognitive health in dierent popu-
lations. Research indicates that sleep patterns vary by
gender (Quan et al. 2016; Rani et al. 2022). Insomnia and
sleep disturbances are more prevalent among women
than among men, resulting in poorer quality of sleep
(Guidozzi 2015). is disparity in sleep quality between
men and women may contribute to dierences in cogni-
tive performance.
Typically, older women report shorter sleep duration
than older men (Rani et al. 2022). Cognitive impairments
have been associated with sleep deprivation resulting
from insucient sleep duration (Lo et al. 2016). Besides,
gender dierences in sleep duration may contribute to
cognitive function dierences among older adults. In
multiple ways, gender may moderate the relationship
between sleep quality, sleep duration, and cognition.
Importantly, hormonal uctuations in women, such as
those that occur during menopause, can aect the qual-
ity of sleep and cognitive function (Eichling and Sahni
2005). Changes in estrogen levels have been associated
with sleep disorders and cognitive decline (Guidozzi
2013). ese hormonal inuences may partially explain
why women experience poorer sleep quality and cogni-
tive impairments than men. Secondly, gender roles and
social expectations may aect sleep patterns and cogni-
tive performance. Multiple duties and responsibilities,
such as caregiving and housework, are frequently borne
by women, which can result in elevated levels of stress
and sleep disturbances (Cha and Eun 2014).
Additionally, cultural norms may inuence men and
women’s sleeping habits dierently (Maume et al. 2010).
ese factors can contribute to variations in sleep qual-
ity and duration, which in turn impact cognitive func-
tion in older individuals. Also, presence of other health
conditions may impact sleep (Muhammad et al. 2023).
Understanding the role of gender as a moderator in the
association between sleep quality, sleep duration, and
cognition has signicant implications for healthcare
interventions. Adapting sleep interventions to the gen-
der-specic requirements of older adults may improve
sleep quality and cognitive function. us, it is neces-
sary to examine the complex gender-specic relation-
ship between sleep quality, sleep duration, and cognition
in older adults in India, emphasizing the role of various
factors that may inuence and aect optimal sleep pat-
terns for healthy aging. erefore, in this study, we aimed
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 3 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
to examine the association between sleep quality, sleep
duration and cognitive functioning among older men and
women in India. We also examined the role of several
socio-demographic and health-related variables in these
associations (Fig.1).
Methods
Data
Data come from the World Health Organization’s Study
on global AGEing and adult health (WHO-SAGE), India
wave-2, which was conducted in 2015 in six selected
states of India, representing dierent country regions:
Assam (Northeast), Karnataka (South), Maharash-
tra (West), Rajasthan (North), Uttar Pradesh (Central)
and West Bengal (East), covering a broadly illustrative
aggregate sample of 9,116 respondents aged 18 years
and above. SAGE wave-2 India was a follow-up study of
SAGE wave-1 and covered the same states with the same
primary sampling units (PSU) and the sample house-
holds which were covered in the WHO-World Health
Survey (WHS), 2003. From all the states in India, a sys-
tematic random sample selection procedure was fol-
lowed to select the states in WHS. Two-stage sampling
in rural areas was used where the villages were the PSUs
and households as secondary stage unit (SSU). ree-
stage sampling in urban areas with the selection of wards,
census enumeration blocks, and households in a specic
order was followed. e number of households selected
was in proportion to the respective state population and
was distributed in urban and rural population. More
detailed Information about weights and survey design
is available at https://apps.who.int/healthinfo/systems/
surveydata/index.php/catalog/117. SAGE Wave-2 had
two target populations: a large sample of persons aged
50 years and older, which is the focus of the study, and a
smaller sample of persons aged 18–49 years. e survey
had a response rate of 77% for the individual question-
naire (Arokiasamy et al. 2020).e number of respon-
dents in WHO-SAGE, waive-2 included 1,998 aged
18–49 years and 7,118 older adults aged 50 and above.
is study considered respondents aged 50 years and
above. After removing the sample with missing informa-
tion on cognition, our analytical sample reduced to 6,396
older adults age 50 years and above.
Measurements
Outcome variable
Cognitive functioning was assessed using variables such
as verbal uency, verbal recall, digit span forward and
digit span backward. For assessing verbal recall, inter-
viewer read out a list of ten commonly used words to
the respondents and asked them to repeat again in some
time. For assessing verbal uency, respondents were
asked to produce as many animal names as possible in
one minute time span. Finally for assessing the digit span,
which was utilized to measure working memory, inter-
viewer read a series of digits and asked to immediately
repeat them back. In the backward test, the person must
repeat the numbers in reverse order. A series of number
sequences was presented and the respondent was asked
to reproduce the exact same sequence. Following a cor-
rect recall, longer sequences were given until failure. e
Fig. 1 Conceptual framework
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 4 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
maximum score for the forward digit count was 9 with
a range of 0–9; the score for the backward digit count
ranged from 0 to 8 and a summary score, created by add-
ing forward and backward counting scores, ranged from
0 to 17. e verbal uency test measured respondents’
ability to retrieve information from semantic memory.
is was a one minute assessment in which respondents
were asked to name as many animals as they could. e
verbal uency score was dened by the number of cor-
rectly named animals. Repeated names were not counted.
e overall cognitive score was obtained by adding
scores of verbal recall, digit counting, forward and back-
ward, and verbal uency tests. e scores on these four
tests, which were in dierent scales, were standardised
by rescaling them to have a mean of zero and a stan-
dard deviation of one, and z-score was generated for
each measures. Further, a composite cognitive score was
created using a principal components analysis (PCA).
Finally, the generated index was converted into a 0 (worst
cognition) to 100 (best cognition) scale which facilitates
easier interpretation of the data.
Key explanatory variables
Main predictor variables in this study were sleep quality
and sleep duration. Sleep quality was assessed using the
question, “Please rate the quality of your sleep last night.
Was it very good, good, moderate, poor or very poor?”
Sleep duration was assessed using the question, “How
many hours did you sleep last night?” e responses in
the format of minutes were converted into hours and the
duration was classied as per the total number of hours
the respondent slept in the last night. Less than seven
hours was classied as short duration sleep, 7–8 h was
considered as normal sleep and 9 and more hours was
considered as long duration sleep among older adults in
this study, as recommended by the National Sleep Foun-
dation (Hirshkowitz et al. 2015).
Covariates
Health and behavioral factors included nutritional intake,
assessed by the total number of fruit and vegetable serv-
ings per day (recoded as no for four or less servings, and
yes for more than 4 times per day) (Patel et al. 2019),
body mass index (measured based on height and weight,
and classied as per WHO criteria; less than 18.5 kg/
m2 as underweight, 18.5–24.9 kg/m2 as normal, 25.0–
29.9kg/m2 as overweight, and ≥ 30.0kg/m2 as obese),
and physical activity (vigorous, moderate, light and no
activity). For physical activity, the questions assessed the
duration of activity (minutes and/or hours) on a typi-
cal day. e duration of activity included: (i) activities at
the workplace, (ii) activities done as part of travel to and
from places, and (iii) leisure time or recreational physi-
cal activities. We followed the WHO global guidelines
on physical activity for adult health, categorized as vig-
orous activity, moderate activity and light activity and
physical inactivity (Organization 2020). Vigorous activity
includes individuals spending at least 75min on a vigor-
ously intensive activity on a typical day. Moderate activ-
ity includes individuals spending time at least 150min on
moderately intensive activity on a typical day. Light activ-
ity include any activity that does not fall in the above two
categories.
Self-rated health was assessed using a single overall
self-rated general health question used in SAGE: “In gen-
eral, how would you rate your health today?” with a ve-
point response scale from very good to very bad. Number
of chronic conditions was classied into none, single, two
and three and more. Chronic conditions in this study
included having diagnosed with any of the following con-
ditions; hypertension, diabetes, stroke, arthritis, angina,
asthma and chronic lung disease. e question format
used was, “Have you ever been diagnosed with the con-
dition?” for each health condition. Smoking status was
recoded into never smoked, currently not smoking and
currently smoking.
Socio-demographic variables in this study included
age (grouped as 50–59, 60–69, 70–79, 80 + years), sex
(male and female), educational level (no education, less
than primary, primary, secondary and higher), and cur-
rent marital status (married, widowed and others which
include never married/ separated/ divorced). Household
related variables included household wealth index (com-
puted based on a detailed list of items of household assets
and was available on ve quintiles and lowest represents
the quintile with the poorest households and high-
est represents the quintile with the richest households),
religious groups (Hindus, Muslims and others), social
groups (scheduled castes and scheduled tribes [both are
socioeconomically most disadvantaged] and others),
place of residence (urban and rural), and states (Assam,
Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and
West Bengal).
Statistical analysis
We conducted the descriptive statistics to present the
characteristics of the study sample. Further, we presented
the mean scores of cognitive functioning among older
adults by explanatory variables, including sleep quality
and duration, along with 95% condence intervals (CIs).
Finally, we used multivariable linear regression models
to examine the association between sleep quality, dura-
tion and cognitive function. We employed four models
to examine the unadjusted and adjusted linear regression
estimates of cognitive functioning by sleep quality and
duration. First model provides the unadjusted estimates,
second model is adjusted for the selected socio-demo-
graphic variables, third model is additionally adjusted
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 5 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
for the selected household-related variables and fourth
or nal model is a full model adjusted for all the selected
covariates including the health and behavioral factors.
Survey weights were applied to account for the com-
plex survey design and to provide the estimates at pop-
ulation level. Regression diagnostics such as variance
ination factor (VIF) for multicollinearity (Table S1) and
tests for linearity and normality of residuals (Figures S1
& S2) were carried out and found no violation of basis
assumptions of regression (see Supplemental material).
We report the results in the form of weighted means, and
unadjusted and adjusted coecients (aCoef) with 95%
CIs. All the analyses were carried out in Stata version
15.1 (StataCorp 2017).
Results
Table1 presents the sample characteristics. Around 5%
of the sample age 80 + years, 47.98% of the participants
had no formal education and a total of 23.39% of the
participants were widowed in this study. Around 19% of
older participants reported 5 + intake of fruit and veg-
etables per day, 27.3% were underweight, 15.54% were
overweight and 3.61% were obese in this study. A large
proportion of the sample (43.98%) reported that they
were engaged in none of the physical activities whereas,
21.08% of males and 10.38% of females reported engag-
ing in vigorous physical activity. Around 18% of the par-
ticipants had a bad or very bad self-rated health whereas,
around 42% of the participants had at least one chronic
condition.
Table2 provides the mean scores of cognitive function-
ing (on a scale of 0-100) among older adults by selected
background variables. Older men and women with a
poor sleep and short sleep duration had lower mean
scores of cognition than their peers with good sleep and
age-appropriate sleep duration. Older men consistently
exhibited higher mean scores for cognitive functioning
across all age groups. For instance, men in the 50–59
years age group had a mean score of 62.83 (CI: 61.55,
64.11), while women had a mean score of 56.41 (CI:
55.41, 57.42). is is consistent across all age categories.
Men had higher mean scores across dierent education
levels as compared to women except in the high school
level, where men scored 62.53 (CI: 61.24, 63.83) com-
pared to women’s score of 67.62 (CI: 63.71, 71.53), and
college level, where men scored 67.34 (CI: 64.20, 70.47)
compared to women’s score of 69.86 (CI: 66.29, 73.42).
Table 3 presents the multivariable linear regression
estimates of cognitive functioning among older men.
Poor sleep (aCoef: -5.09, CI: -8.66, -1.51) and short
sleep (aCoef: -5.43, CI: -7.77, 3.10) was negatively asso-
ciated with cognitive functioning among older men.
e associations remained signicant among older men
with poor sleep (aCoef: -2.39, CI: -3.78, -1.00) and short
sleep (aCoef: -4.39, CI: -6.46, -2.31) after adjusting for a
large number of socio-demographic and health-related
variables. In comparison to the reference group (50–59
years), there is a decline in cognitive functioning in older
men belonging to 60–69 age group (aCoef: -3.09, CI:
-4.46, -1.72), 70–79 age group (aCoef: -4.49, CI: -6.30,
-2.68), and 80 + age group (aCoef: -9.22, CI: -12.2, -6.19).
Further, there was a positive association between cog-
nitive functioning and education across all levels, with
the highest likelihood observed among those who have
attended college (aCoef: 11.5, CI: 8.49, 14.4). Widowhood
is linked to a notable decrease in cognitive functioning
(aCoef: -2.52, CI: -4.34, -0.69), whereas no signicant
association was found for other marital statuses. Over-
weight (aCoef: 2.18, CI: 0.23, 4.12) and obesity (aCoef:
3.67, CI: 0.52, 6.81) were signicantly associated with
improved cognition whereas, physical inactivity was
signicantly associated with poor cognitive function
among older men (aCoef: -1.34, CI: -2.77, -0.088). Self-
rated health demonstrates a signicant association with
cognitive functioning, particularly in the moderate and
bad health categories (aCoef: -3.93, CI: -6.98, -0.87 and
aCoef: -4.44, CI: -7.83, -1.04, respectively). e cogni-
tive functioning was much lower for older men in rural
areas (aCoef: -2.24, CI: -4.03, -0.46) compared to those
in urban areas. Nutritional intake, chronic conditions,
smoking status, wealth quintile, religion, social group,
and states did not exhibit signicant associations with
cognitive functioning.
Table 4 presents the multivariable linear regression
estimates of cognitive functioning among older women.
Poor sleep (aCoef: -3.15, CI: -5.79, -0.52) and short sleep
(aCoef: -2.72, CI: -4.64, -0.81) was negatively associated
with cognitive functioning among older women, how-
ever, the associations were insignicant when the poten-
tial health and behavioral confounders were adjusted.
Compared to the reference group (50–59 years), cogni-
tive functioning decreased in 60–69 age group (aCoef:
-1.32, CI: -2.58, -0.065), 70–79 age group (aCoef: -4.20,
CI: -5.82, -2.57) and 80 + age group (aCoef: -3.34, CI:
-6.89, -0.22). A positive association of cognitive function-
ing with all levels of education was observed, with the
highest levels observed for the college-educated group
(aCoef: 13.2, CI: 9.31, 17.1). Being widowed had a sig-
nicant eect on declining cognitive functioning among
older women (aCoef: -1.54, CI: -2.83, -0.26). Having
a higher nutritional intake had a signicantly positive
eect on the levels of cognitive functioning (aCoef: 2.83,
CI: 1.27, 4.40). Compared to being underweight, nor-
mal, overweight, and obese categories had signicantly
increased levels of cognitive functioning. Moderate phys-
ical activity had a signicant positive eect on cognitive
functioning (aCoef: 2.56, CI: 0.32, 4.81). Self-rated health,
chronic conditions, smoking status, wealth quintile,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 6 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Variables Men Women Total
Age (in years)
50–59 1046 (35.37) 1568 (46.07) 2614 (40.95)
60–69 1178 (38.87) 1157 (34.2) 2335 (36.43)
70–79 609 (19.82) 536 (15.54) 1145 (17.59)
80+ 181 (5.94) 121 (4.2) 302 (5.03)
Level of education
No formal education 887 (26.48) 2295 (67.7) 3182 (47.98)
Less than primary 480 (15.72) 361 (10.99) 841 (13.26)
Primary 545 (17.91) 360 (10.18) 905 (13.88)
Secondary 424 (14.83) 189 (5.25) 613 (9.83)
High school 400 (13.96) 98 (3.29) 498 (8.39)
College 278 (11.09) 79 (2.59) 357 (6.66)
Current marital status
Married 2663 (87.96) 2122 (63.09) 4785 (74.98)
Widowed 293 (9.81) 1211 (35.84) 1504 (23.39)
Others 58 (2.23) 49 (1.07) 107 (1.62)
Nutritional intake (> 4 times per day)
No 2412 (79.52) 2798 (82.24) 5210 (80.94)
Yes 602 (20.48) 584 (17.76) 1186 (19.06)
Body mass index
Underweight 791 (27.17) 856 (27.43) 1647 (27.3)
Normal 1700 (57.41) 1705 (50) 3405 (53.54)
Overweight 377 (13.38) 551 (17.53) 928 (15.54)
Obese 68 (2.04) 170 (5.05) 238 (3.61)
Physical activity
Vigorous 654 (21.08) 374 (10.38) 1028 (15.51)
Moderate 536 (17.82) 1079 (31.74) 1615 (25.07)
Light 534 (20.43) 330 (11.43) 864 (15.74)
None 1278 (40.67) 1576 (46.45) 2854 (43.68)
Self-rated health
Very good 136 (4.45) 102 (2.83) 238 (3.61)
Good 986 (33.73) 988 (29.3) 1974 (31.42)
Moderate 1437 (46.33) 1649 (48.01) 3086 (47.21)
Bad 416 (13.93) 603 (18.46) 1019 (16.29)
Very bad 38 (1.55) 40 (1.39) 78 (1.47)
Chronic conditions
Zero 1698 (59.98) 1772 (56.37) 3470 (58.1)
Single 837 (25.25) 1019 (27.91) 1856 (26.64)
Two 319 (9.67) 442 (11.74) 761 (10.75)
Three and more 160 (5.1) 149 (3.98) 309 (4.52)
Smoking
Never 1625 (48.79) 3085 (81.83) 4710 (66.10)
Currently not 489 (14.48) 335 (8.26) 824 (11.22)
Currently smoking 1217 (36.72) 352 (9.91) 1569 (22.68)
Wealth quintile
Poorest 560 (18.62) 672 (21.1) 1232 (19.91)
Poor 543 (18.14) 628 (18.05) 1171 (18.09)
Middle 571 (18.2) 622 (18.2) 1193 (18.2)
Rich 606 (20.49) 703 (21.54) 1309 (21.04)
Richest 734 (24.55) 757 (21.12) 1491 (22.76)
Religion
Hindu 2512 (84.5) 2834 (84.96) 5346 (84.74)
Muslim 378 (12.3) 415 (11.99) 793 (12.14)
Table 1 Sample characteristics
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 7 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
religion, social group, place of residence, and states did
not show signicant eects on cognitive functioning.
Discussion
e ndings of this study provide substantial evidence
for the association between sleep and cognitive func-
tioning in older individuals. Bivariate results suggest that
both poor sleep quality and reduced sleep duration were
associated with reduced cognitive performance in both
men and women. ese results are consistent with previ-
ous research and emphasize the importance of sleep for
maintaining optimal cognitive function (Engleman et al.
2000; Saint Martin et al. 2012). Sleep disturbances can
impair cognitive processes such as memory consolidation
and neural repair, resulting in cognitive decline (Aly and
Moscovitch 2010).
It is noteworthy that, across all age categories, men
consistently demonstrated higher mean cognitive func-
tioning scores. is suggests that men may be more resis-
tant than women to the cognitive eects of increasing
age. is nding is in line with other studies that found
that older women often scored less in cognitive scores as
compared to older men (Zhang 2006; Wang et al. 2020).
e ndings also reveal gender-specic dierences in
the association between sleep and cognitive function-
ing. Unlike older men, cognitive eects of poor sleep
quality and short sleep were insignicant among older
women after adjusting for health and behavioural fac-
tors. is suggests an independent association between
sleep and cognition among men but not women. Addi-
tional research is required to comprehend these gender
dierences and their underlying mechanisms. Hormonal
variations, caregiving responsibilities, and societal
expectations may contribute to men’s and women’s dis-
tinctive sleep patterns and cognitive outcomes (Roepke
and Ancoli-Israel 2010; Mallampalli and Carter 2014).
While the association between sleep and cognitive
functioning is well-established, this study extends the
understanding by examining the role of various back-
ground factors. Age was found to signicantly moderate
the relationship between sleep and cognitive functioning,
with higher cognitive decline observed among women as
age increased which is supported by previous research
(Dzierzewski et al. 2018). is nding underscores the
need for targeted interventions to support cognitive
health in older women, particularly as they age. Educa-
tion level also emerged as a signicant factor, indicating
that higher educational attainment acts as a protective
factor against cognitive decline in older adults, and in
women in particular, cohesive with the extensive body
of literature (van Hooren et al. 2007; Tripathi et al. 2014;
Muhammad et al., 2022a). Education provides individu-
als with cognitive reserve, enhances access to healthcare,
promotes engagement in mentally stimulating activities,
and encourages the adoption of healthier lifestyles. ese
factors collectively contribute to better cognitive func-
tioning and may mitigate the negative impact of poor
sleep on cognitive health, especially among women due
to gender-specic behavior and societal roles.
Marital status demonstrated signicant relationship
with cognitive functioning. Specically, widowhood was
signicantly associated with decreased levels of cognitive
functioning, though observed in both men and women.
is nding has been supported by existing research (Xu
et al. 2021). e negative impact of widowhood on cog-
nitive functioning may be attributed to factors such as
Variables Men Women Total
Others 124 (3.2) 133 (3.05) 257 (3.12)
Social group
Scheduled castes 217 (5.93) 265 (6.99) 482 (6.48)
Scheduled tribes 478 (14.4) 571 (15.18) 1049 (14.81)
Other backward classes 1397 (49.4) 1558 (49.71) 2955 (49.56)
Others 922 (30.26) 988 (28.12) 1910 (29.15)
Place of residence
Urban 580 (26.96) 711 (27.85) 1291 (27.42)
Rural 2434 (73.04) 2671 (72.15) 5105 (72.58)
States
Assam 326 (5.27) 351 (5.3) 677 (5.29)
Karnataka 274 (8.45) 340 (9.18) 614 (8.83)
Maharashtra 514 (23.07) 569 (21.52) 1083 (22.26)
Rajasthan 623 (12.26) 736 (13.1) 1359 (12.7)
Uttar Pradesh 690 (32.63) 680 (31.01) 1370 (31.78)
West Bengal 587 (18.33) 706 (19.9) 1293 (19.15)
Total 3014 (100) 3382 (100) 6396 (100)
Percentages a re weighted to account for comp lex survey design and to acc ount for population es timates
Table 1 (continued)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 8 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Variables Men Women Total
Mean 95% Condence intervals Mean 95% Condence intervals Mean 95% Condence intervals
Sleep quality
Good 57.48 56.57, 58.38 52.18 51.38, 52.97 54.81 54.19, 55.43
Moderate 53.9 52.37, 55.43 50.1 48.95, 51.24 51.69 50.74, 52.64
Poor 52.95 49.59, 56.30 49.79 47.45, 52.14 51.42 49.30, 53.55
Sleep duration
Normal 57.28 56.06, 58.50 51.68 50.77, 52.59 54.38 53.59, 55.16
Short 51.91 50.01, 53.80 48.95 47.36, 50.54 50.34 49.09, 51.59
Long 57.01 56.05, 57.97 52.2 51.12, 53.28 54.5 53.75, 55.24
Age (in years)
50–59 62.83 61.55, 64.11 56.41 55.41, 57.42 59.07 58.23, 59.90
60–69 58.21 56.98, 59.44 53.28 52.15, 54.41 55.8 54.94, 56.65
70–79 55.83 54.05, 57.61 48.68 47.40, 49.96 52.53 51.31, 53.75
80+ 49.6 46.82,52.38 46.43 43.22, 49.64 48.22 46.13, 50.31
Level of education
No formal education 51.32 50.33, 52.31 50.4 49.69, 51.10 50.64 50.06, 51.22
Less than primary 57.18 55.07, 59.28 56.46 54.33, 58.58 56.87 55.35, 58.38
Primary 59.15 57.62, 60.67 59.75 58.06, 61.45 59.38 58.23, 60.52
Secondary 63.97 61.77, 66.17 62.49 60.14, 64.83 63.56 61.82, 65.29
High school 62.53 61.24, 63.83 67.62 63.71, 71.53 63.57 62.20, 64.95
College 67.34 64.20, 70.47 69.86 66.29, 73.42 67.85 65.26, 70.44
Current marital status
Married 59.55 58.68, 60.41 55.57 54.74, 56.40 57.8 57.18, 58.42
Widowed 53.07 51.31, 54.83 50.44 49.36, 51.52 50.97 50.03, 51.90
Others 57.22 52.84, 61.61 54.61 49.96, 59.26 56.33 52.95, 59.71
Nutritional intake (> 4 times per day)
No 58.52 57.57, 59.48 53.06 52.31, 53.81 55.63 55.01, 56.25
Yes 60.17 58.87, 61.48 56.78 55.28, 58.28 58.52 57.52, 59.53
Body mass index
Underweight 54.55 53.51, 55.60 49.6 48.47, 50.74 51.96 51.16, 52.76
Normal 60.06 58.93, 61.19 54.18 53.31, 55.05 57.2 56.45, 57.95
Overweight 63.27 61.05, 65.50 58.3 56.35, 60.24 60.35 58.83, 61.87
Obese 63.46 59.21, 67.71 59.4 56.94, 61.86 60.5 58.38, 62.62
Physical activity
Vigorous 59.44 58.25, 60.64 52.3 50.14, 54.45 56.95 55.80, 58.10
Moderate 59.67 57.55, 61.79 56.1 54.94, 57.26 57.32 56.24, 58.39
Light 60.68 58.96, 62.41 54.07 51.88, 56.26 58.19 56.80, 59.57
None 57.31 55.94, 58.68 52.41 51.49, 53.34 54.6 53.77, 55.43
Self-rated health
Very good 66.57 62.16, 70.97 60.87 57.33, 64.40 64.23 61.15, 67.32
Good 61.74 60.16, 63.32 56.12 54.86, 57.38 59 57.94, 60.06
Moderate 57.56 56.62, 58.49 53.33 52.43, 54.22 55.31 54.65, 55.97
Bad 54.08 52.42, 55.73 50.47 49.02, 51.92 51.94 50.83, 53.05
Very bad 56.2 51.92, 60.48 45.53 39.37, 51.69 50.93 46.59, 55.27
Chronic conditions
Zero 59.33 58.19, 60.48 53.26 52.36, 54.16 56.26 55.50, 57.02
Single 58.46 57.27, 59.65 53.58 52.31, 54.84 55.79 54.88, 56.70
Two 58.38 56.39, 60.38 55.62 53.98, 57.27 56.81 55.53, 58.09
Three and more 56.23 52.52, 59.93 55.67 51.77, 59.57 55.97 53.29, 58.65
Smoking
Never 58.71 57.89, 59.53 54.65 53.83, 55.47 56.05 55.33, 56.76
Currently not 58.13 56.65, 59.62 53.37 51.34, 55.41 56.24 54.79, 57.68
Currently smoking 57.85 56.68, 59.01 51.59 49.49, 53.69 56.49 55.24, 57.74
Table 2 Weighted mean scores of cognitive functioning (on a scale of 0-100) among older adults by their background characteristics
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
social isolation, loss of support networks, and emotional
stress (Förster et al. 2021). ese factors may interact
with poor sleep quality or shorter sleep duration, exacer-
bating cognitive decline in older men. On the other hand,
no signicant associations were found for other mari-
tal statuses, indicating that the inuence of marital sta-
tus on cognitive functioning may be more nuanced and
multifaceted.
Nutritional intake was signicantly associated with
cognitive functioning in this study, and should not be
overlooked as an important factor for overall health and
well-being. Adequate nutrition is essential for optimal
cognitive function (Selvamani and Singh 2018; Khan
2022), and it may interact with sleep patterns and qual-
ity to inuence cognitive outcomes indirectly. Future
research should explore the complex interplay between
sleep, nutritional intake, and cognitive functioning to
gain a more comprehensive understanding of their com-
bined eects. Further, physically inactive older men had
poor cognition and older women who engaged in mod-
erate physical activity had improved cognition in this
study which corresponds to previous studies (Barha et al.
2017; Castells-Sanchez et al. 2021; Sekher and Muham-
mad 2023). Self-rated health demonstrated a signicant
eect among older men but not women, particularly in
the moderate and bad health categories. Older men who
rated their health as moderate or bad exhibited a lower
level of cognitive functioning which suggest dierential
eects of one’s perceived health on their cognitive func-
tioning among men and women which require further
investigation. Poor sleep quality or shorter sleep duration
may contribute to worse self-rated health (Frange et al.
2014; Simoes Maria et al. 2020), leading to a vicious cycle
of deteriorating cognitive health. Interventions aimed at
improving sleep quality and duration may have indirect
benets by enhancing overall health and well-being, thus
positively impacting cognitive functioning.
Our results also revealed that place of residence played
a moderating role in the association between sleep and
cognitive functioning. Notably, older men residing in
urban areas consistently displayed higher mean scores for
cognitive functioning compared to those in rural areas
which is consistent with ndings from previous research
(Xu et al. 2018; Srivastava and Muhammad 2022) ese
ndings suggest that the urban environment may provide
more favorable conditions for cognitive health, poten-
tially due to better access to healthcare facilities, social
engagement opportunities, and a more stimulating liv-
ing environment. Further research is needed to examine
the specic mechanisms underlying this association and
Variables Men Women Total
Mean 95% Condence intervals Mean 95% Condence intervals Mean 95% Condence intervals
Wealth quintile
Poorest 53.37 52.08, 54.66 50.1 48.88, 51.31 51.56 50.68, 52.45
Poor 57.4 55.54, 59.27 51.07 49.90, 52.25 54.11 52.93, 55.29
Middle 58.43 56.74, 60.13 53.09 51.29, 54.88 55.64 54.37, 56.92
Rich 59.37 57.81, 60.92 54.1 52.75, 55.45 56.55 55.49, 57.62
Richest 64 62.16, 65.84 59.77 58.27, 61.26 61.95 60.73, 63.17
Religion
Hindu 59.11 58.22, 60.00 53.57 52.83, 54.32 56.21 55.61, 56.81
Muslim 58.22 56.45, 59.99 54.58 52.88, 56.28 56.35 55.12, 57.57
Others 54.81 50.40, 59.21 54.42 52.03, 56.81 54.61 52.13, 57.09
Social group
Scheduled castes 54.42 52.42, 56.41 51.29 49.65, 52.94 52.66 51.38, 53.94
Scheduled tribes 55.74 54.40, 57.09 50.43 49.17, 51.68 52.9 51.96, 53.84
Other backward classes 59.09 57.83, 60.35 53.85 52.76, 54.94 56.35 55.49, 57.20
Others 60.85 59.43, 62.26 55.88 54.74, 57.01 58.34 57.41, 59.28
Place of residence
Urban 63.49 61.23, 65.75 56.87 54.98, 58.76 59.98 58.44, 61.52
Rural 57.15 56.60, 57.71 52.51 51.97, 53.05 54.74 54.35, 55.14
States
Assam 55.93 54.74, 57.12 52.74 51.61, 53.87 54.26 53.43, 55.09
Karnataka 55.08 53.36, 56.80 54.8 52.95, 56.65 54.93 53.65, 56.20
Maharashtra 62.62 60.17, 65.06 54.61 52.53, 56.70 58.58 56.86, 60.30
Rajasthan 58.71 57.86, 59.57 52.07 51.24, 52.90 55.14 54.51, 55.77
Uttar Pradesh 57.97 56.88, 59.06 52.23 51.02, 53.43 55.05 54.21, 55.88
West Bengal 58.41 57.02, 59.80 55.94 54.80, 57.07 57.07 56.18, 57.96
Total 58.86 58.06, 59.66 53.72 53.05, 54.39 56.18 55.64, 56.72
Table 2 (continued)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 10 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Variables Unadjusted coecients
(95% CI)
Model 1 Model 2 Model 3
Adjusted coecients
(95% CI)
Adjusted coecients
(95% CI)
Adjusted coe-
cients (95% CI)
Sleep quality
Good Ref. Ref. Ref. Ref.
Moderate -3.85*** (-5.69 - -2.02) -2.56*** (-4.11 - -1.01) -2.82*** (-4.24 - -1.39) -2.39*** (-3.78 - -1.00)
Poor -5.09*** (-8.66 - -1.51) -3.24* (-6.48–0.0037) -3.75** (-6.63 - -0.87) -2.34 (-5.41–0.74)
Sleep duration
Normal Ref. Ref. Ref. Ref.
Short -5.43*** (-7.77 - -3.10) -3.92*** (-6.11 - -1.72) -4.35*** (-6.39 - -2.32) -4.39*** (-6.46 - -2.31)
Long 0.47 (-1.13–2.06) -0.0016 (-1.38–1.38) 0.14 (-1.10–1.38) 0.10 (-1.11–1.31)
Age (in years)
50–59 Ref. Ref. Ref. Ref.
60–69 -4.41*** (-6.11 - -2.71) -3.97*** (-5.50 - -2.43) -3.53*** (-4.88 - -2.17) -3.09*** (-4.46 - -1.72)
70–79 -6.71*** (-8.81 - -4.61) -5.16*** (-7.20 - -3.13) -5.53*** (-7.30 - -3.76) -4.49*** (-6.30 - -2.68)
80+ -12.7*** (-15.6 - -9.76) -10.4*** (-13.7 - -7.04) -10.5*** (-13.6 - -7.38) -9.22*** (-12.2 - -6.19)
Level of education
No formal education Ref. Ref. Ref. Ref.
Less than primary 5.65*** (3.43–7.88) 5.50*** (3.26–7.74) 4.59*** (2.68–6.49) 4.50*** (2.56–6.45)
Primary 7.56*** (5.82–9.30) 6.88*** (5.10–8.65) 5.78*** (4.15–7.41) 5.67*** (4.03–7.30)
Secondary 12.2*** (9.89–14.5) 11.7*** (9.46–13.9) 9.75*** (7.87–11.6) 9.77*** (7.96–11.6)
High school 10.8*** (9.27–12.4) 10.0*** (8.34–11.7) 8.71*** (6.94–10.5) 8.26*** (6.51–10.0)
College 15.5*** (12.3–18.6) 14.6*** (11.5–17.7) 12.1*** (9.25–15.0) 11.5*** (8.49–14.4)
Current marital status
Married Ref. Ref. Ref. Ref.
Widowed -6.22*** (-8.10 - -4.33) -2.89*** (-4.74 - -1.03) -2.56*** (-4.39 - -0.73) -2.52*** (-4.34 - -0.69)
Others -2.26 (-6.54–2.02) -2.29 (-6.55–1.97) -1.08 (-5.24–3.08) -0.92 (-5.16–3.32)
Nutritional intake (> 4 times per
day)
No Ref. Ref.
Yes 1.59** (0.046–3.14) -0.0016 (-1.37–1.37)
Body mass index
Underweight Ref. Ref.
Normal 5.30*** (3.83–6.77) 1.80*** (0.56–3.03)
Overweight 8.39*** (6.04–10.7) 2.18** (0.23–4.12)
Obese 8.56*** (4.38–12.7) 3.67** (0.52–6.81)
Physical activity
Vigorous Ref. Ref.
Moderate 0.23 (-2.10–2.56) -0.20 (-1.89–1.50)
Light 1.20 (-0.81–3.20) 0.39 (-1.29–2.07)
None -2.05** (-3.79 - -0.31) -1.34* (-2.77–0.088)
Self-rated health
Very good Ref. Ref.
Good -4.63** (-9.11 - -0.15) -2.63 (-5.90–0.64)
Moderate -8.64*** (-12.9 - -4.33) -3.93** (-6.98 - -0.87)
Bad -12.0*** (-16.5 - -7.49) -4.44** (-7.83 - -1.04)
Very bad -9.98*** (-15.9 - -4.09) -3.84 (-9.57–1.89)
Chronic conditions
Zero Ref. Ref.
Single -0.81 (-2.39–0.78) -0.48 (-1.67–0.71)
Two -0.88 (-3.09–1.32) -1.06 (-3.03–0.90)
Three and more -2.93 (-6.64–0.78) -2.56 (-5.66–0.54)
Smoking
Never Ref. Ref.
Currently not -1.37 (-3.24–0.50) -0.91 (-2.42–0.60)
Table 3 Estimates from general linear models of cognitive functioning by background variables among older men SAGE- 2015
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 11 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
explore potential interventions to address cognitive dis-
parities between urban and rural populations. e role
of other background factors, such as chronic conditions,
smoking status, wealth quintile, religion, social group,
and states, did not show consistent signicant eects
on cognitive functioning. ese ndings suggest that
the relationship between sleep and cognitive function-
ing may be less inuenced by these background factors.
However, it is important to consider that these factors
may still indirectly impact cognitive health through their
interactions with one’s health and wellbeing.
Limitations and direction for future research
It is important to acknowledge the limitations of this
study. First, the data relied on self-reported measures,
which may be subject to recall and social desirability
biases. Future studies should consider objective mea-
sures of sleep to provide more accurate assessments.
Second, there was a higher proportion of women in our
sample without any formal education or primary educa-
tion that would aect their cognitive function and inu-
ence our ndings. ird, this study focused on a specic
set of background characteristics, and there may be other
factors, such as genetics, lifestyle factors, or comorbidi-
ties, that could inuence the relationship between sleep
and cognitive functioning. Additionally, the detrimental
impact of poor sleep quality on cognitive functioning dif-
ferently among men and women can be attributed to sev-
eral contextual and environmental factors such as indoor
air quality (Hunter et al. 2018; C.-C. Lo et al. 2022; Saenz
et al. 2021), which needs further investigation. Simi-
larly, sleep disturbances, such as frequent awakenings
and fragmented sleep were not considered in this study,
which could disrupt the restorative processes necessary
for memory consolidation and neural repair (Cellini
2017; Zisapel 2007). As documented, inadequate sleep
Variables Unadjusted coecients
(95% CI)
Model 1 Model 2 Model 3
Adjusted coecients
(95% CI)
Adjusted coecients
(95% CI)
Adjusted coe-
cients (95% CI)
Currently smoking -1.08 (-2.81–0.65) 0.35 (-0.98–1.68)
Wealth quintile
Poorest Ref. Ref. Ref.
Poor 3.90*** (1.72–6.08) 1.33 (-0.35–3.00) 1.21 (-0.50–2.92)
Middle 4.90*** (2.86–6.94) 2.16** (0.40–3.92) 1.95** (0.20–3.70)
Rich 5.81*** (3.88–7.75) 2.14** (0.31–3.98) 1.85** (0.020–3.68)
Richest 10.2*** (8.09–12.4) 3.76*** (1.75–5.77) 3.47*** (1.45–5.49)
Religion
Hindu Ref. Ref. Ref.
Muslim -0.85 (-2.75–1.04) 2.18** (0.50–3.85) 2.37*** (0.69–4.06)
Others -4.13* (-8.44–0.19) -3.49 (-8.06–1.08) -3.89* (-8.39–0.60)
Social group
Scheduled castes Ref. Ref. Ref.
Scheduled tribes 1.28 (-1.03–3.60) 1.57 (-0.50–3.64) 1.33 (-0.77–3.43)
Other backward classes 4.50*** (2.23–6.76) 1.89* (-0.011–3.79) 1.75* (-0.20–3.70)
Others 6.19*** (3.84–8.54) 2.27** (0.21–4.32) 2.20** (0.086–4.30)
Place of residence
Urban Ref. Ref. Ref.
Rural -6.10*** (-8.32 - -3.88) -2.25** (-4.07 - -0.43) -2.24** (-4.03 - -0.46)
States
Assam Ref. Ref. Ref.
Karnataka -0.78 (-2.79–1.22) -3.53*** (-5.73 - -1.33) -4.30*** (-6.50 - -2.10)
Maharashtra 6.38*** (3.77–8.98) 4.02*** (1.99–6.05) 2.77*** (0.79–4.76)
Rajasthan 2.67*** (1.26–4.07) 1.67* (-0.0037–3.35) 1.12 (-0.61–2.85)
Uttar Pradesh 1.96** (0.42–3.51) -0.59 (-2.37–1.19) -1.13 (-3.02–0.76)
West Bengal 2.43*** (0.68–4.19) -0.20 (-2.17–1.77) -0.49 (-2.57–1.59)
Constant 56.7*** (55.1–58.3) 54.8*** (51.7–57.8) 58.2*** (53.6–62.8)
Observations 3,000 3,000 2,909
R-squared 0.224 0.273 0.296
Notes: Model 1 is adjusted f or age, education and mar ital status; Model 2 is ad ditionally adjusted fo r other socio-de mographics such as hous ehold wealth quintile,
religion, s ocial group, place of re sidence and states; M odel 3 is fully adjus ted model, i.e., a dditionally adjus ted for the health va riables such as nutri tional intake, bod y
mass index , physical activit y, self-rate d health, chronic condit ions and smoking stat us; CI: Condence interva l; *** p < 0.001, ** p < 0.01, * p < 0.05
Table 3 (continued)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 12 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Variables Unadjusted coecients
(95% CI)
Model 1 Model 2 Model 3
Adjusted coecients
(95% CI)
Adjusted coecients
(95% CI)
Adjusted coe-
cients (95% CI)
Sleep quality
Good Ref. Ref. Ref. Ref.
Moderate -2.36*** (-3.79 - -0.92) -1.30** (-2.59 - -0.017) -1.33** (-2.64 - -0.028) -0.99 (-2.27–0.29)
Poor -3.15** (-5.79 - -0.52) -1.98* (-4.30–0.34) -2.29* (-4.60–0.020) -1.74 (-4.16–0.67)
Sleep duration
Normal Ref. Ref. Ref. Ref.
Short -2.72*** (-4.64 - -0.81) -1.42 (-3.21–0.36) -1.43 (-3.31–0.44) -1.27 (-3.16–0.63)
Long 1.04 (-0.42–2.50) 0.20 (-1.05–1.45) 0.16 (-1.11–1.42) 0.53 (-0.71–1.76)
Age (in years)
50–59 Ref. Ref. Ref. Ref.
60–69 -3.01*** (-4.46 - -1.56) -1.70** (-3.02 - -0.37) -1.74*** (-3.06 - -0.42) -1.32** (-2.58
- -0.065)
70–79 -7.42*** (-8.98 - -5.86) -4.99*** (-6.64 - -3.34) -5.13*** (-6.76 - -3.50) -4.20*** (-5.82 - -2.57)
80+ -9.60*** (-12.8 - -6.37) -5.94*** (-9.35 - -2.53) -6.37*** (-9.79 - -2.94) -3.34* (-6.89–0.22)
Level of education
No formal education Ref. Ref. Ref. Ref.
Less than primary 5.83*** (3.70–7.97) 4.89*** (2.70–7.08) 3.72*** (1.55–5.89) 3.32*** (1.19–5.46)
Primary 9.02*** (7.27–10.8) 8.36*** (6.55–10.2) 6.90*** (5.06–8.73) 6.49*** (4.61–8.38)
Secondary 11.7*** (9.34–14.0) 10.6*** (8.17–13.0) 8.64*** (6.07–11.2) 7.75*** (5.08–10.4)
High school 16.6*** (12.8–20.4) 15.8*** (12.0–19.6) 13.2*** (9.49–16.9) 12.3*** (8.76–15.9)
College 18.8*** (15.3–22.2) 18.3*** (14.7–21.9) 14.9*** (11.0–18.9) 13.2*** (9.31–17.1)
Current marital status
Married Ref. Ref. Ref. Ref.
Widowed -4.92*** (-6.23 - -3.61) -1.97*** (-3.30 - -0.63) -1.89*** (-3.24 - -0.55) -1.54** (-2.83 - -0.26)
Others -0.94 (-5.48–3.59) -1.73 (-6.06–2.60) -1.02 (-5.11–3.07) 0.51 (-3.67–4.69)
Nutritional intake (> 4 times per
day)
No Ref. Ref.
Yes 3.59*** (1.97–5.20) 2.83*** (1.27–4.40)
Body mass index
Underweight Ref. Ref.
Normal 4.41*** (3.03–5.78) 2.14*** (0.81–3.48)
Overweight 8.38*** (6.22–10.5) 3.25*** (1.13–5.38)
Obese 9.44*** (6.84–12.0) 4.04*** (1.34–6.73)
Physical activity
Vigorous Ref. Ref.
Moderate 3.66*** (1.31–6.01) 2.56** (0.32–4.81)
Light 1.69 (-1.26–4.64) 2.24 (-0.58–5.06)
None 0.11 (-2.14–2.37) 0.90 (-1.33–3.13)
Self-rated health
Very good Ref. Ref.
Good -4.54** (-8.14 - -0.94) -0.64 (-4.24–2.96)
Moderate -7.23*** (-10.7 - -3.73) -1.89 (-5.44–1.67)
Bad -9.99*** (-13.7 - -6.32) -2.93 (-6.72–0.86)
Very bad -14.8*** (-21.6 - -7.97) -2.62 (-10.5–5.27)
Chronic conditions
Zero Ref. Ref.
Single 0.34 (-1.15–1.83) -0.62 (-1.97–0.73)
Two 2.28** (0.48–4.09) 0.37 (-1.33–2.07)
Three and more 2.36 (-1.46–6.19) -1.62 (-5.05–1.80)
Smoking
Never Ref. Ref.
Table 4 Estimates from general linear models of cognitive functioning by background variables among older women SAGE- 2015
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
duration deprives the brain of sucient time to engage in
these crucial processes, leading to cognitive impairments
over time (Lim and Dinges 2010; Lo et al. 2016), and
these pathways should be considered in future studies.
Furthermore, sleep is closely intertwined with other
physiological and psychological factors that inuence
cognitive health, such as inammation, hormonal regu-
lation, and emotional well-being (Tartar et al. 2015;
ompson et al. 2022). Finally, stressors, such as insuf-
cient or lack of income (Muhammad et al. 2021), child
rearing/caregiving burden (Muhammad and Srivastava
2022), and absence of adult children (Muhammad et al.,
2022b), may induce mental strain, aect sleep and/or lead
to cognitive decits. erefore, future research should
delve deeper into the specic mechanisms through
which sleep disturbances aect cognitive functioning
in older adults, including potential gender dierences.
us, addressing the moderating factors identied in this
study can shed light on direction for future research and
enhancing the eectiveness of interventions targeting
sleep and cognitive functioning.
Conclusions
is study provides evidence for the signicant associa-
tion between sleep health and cognitive functioning in
older Indian adults, especially older men, with poor sleep
quality and shorter sleep duration being detrimental
to cognitive health. e ndings of this study also have
important implications for healthcare providers and
policymakers. Prioritizing the assessment and manage-
ment of sleep disturbances in older adults is crucial for
promoting healthy cognitive aging. Healthcare providers
should routinely screen for sleep quality and age-appro-
priate sleep duration in their older adult patients and
Variables Unadjusted coecients
(95% CI)
Model 1 Model 2 Model 3
Adjusted coecients
(95% CI)
Adjusted coecients
(95% CI)
Adjusted coe-
cients (95% CI)
Currently not -2.51** (-5.01 - -0.0070) -1.09 (-3.20–1.01)
Currently smoking -2.49*** (-4.35 - -0.64) 1.16 (-0.57–2.89)
Wealth quintile
Poorest Ref. Ref. Ref.
Poor 0.96 (-0.67–2.58) 0.35 (-1.37–2.06) 0.28 (-1.40–1.96)
Middle 2.91*** (0.84–4.99) 1.07 (-0.98–3.12) 1.01 (-0.99–3.01)
Rich 3.88*** (2.14–5.62) 1.36 (-0.40–3.12) 0.68 (-1.10–2.45)
Richest 9.33*** (7.48–11.2) 3.60*** (1.74–5.47) 2.94*** (1.04–4.84)
Religion
Hindu Ref. Ref. Ref.
Muslim 0.96 (-0.82–2.75) 1.78* (-0.052–3.61) 1.53 (-0.32–3.38)
Others 0.85 (-1.56–3.26) 1.52 (-0.67–3.70) 1.26 (-0.97–3.49)
Social group
Scheduled castes Ref. Ref. Ref.
Scheduled tribes -0.83 (-2.82–1.15) -0.53 (-2.52–1.45) -0.69 (-2.68–1.30)
Other backward classes 2.48** (0.58–4.37) 0.75 (-1.06–2.57) 0.54 (-1.28–2.37)
Others 4.42*** (2.49–6.34) 1.31 (-0.58–3.20) 1.21 (-0.71–3.12)
Place of residence
Urban Ref. Ref. Ref.
Rural -4.21*** (-6.09 - -2.33) -1.06 (-2.92–0.80) -0.63 (-2.43–1.17)
States
Assam Ref. Ref. Ref.
Karnataka 1.92* (-0.16–4.01) 1.75* (-0.32–3.82) 0.86 (-1.25–2.97)
Maharashtra 1.73 (-0.54–4.00) 1.29 (-0.66–3.24) 0.90 (-1.07–2.86)
Rajasthan -0.70 (-2.04–0.65) 0.69 (-0.89–2.28) 0.31 (-1.34–1.97)
Uttar Pradesh -0.56 (-2.15–1.02) 0.30 (-1.43–2.02) 0.24 (-1.57–2.06)
West Bengal 3.04*** (1.51–4.58) 2.12*** (0.53–3.71) 2.41*** (0.64–4.17)
Constant 53.7*** (52.3–55.0) 51.8*** (48.8–54.7) 49.6*** (44.6–54.6)
Observations 3,364 3,364 3,249
R-squared 0.193 0.210 0.231
Notes: Model 1 is adjusted f or age, education and mar ital status; Model 2 is ad ditionally adjusted fo r other socio-de mographics such as hous ehold wealth quintile,
religion, s ocial group, place of re sidence and states; M odel 3 is fully adjus ted model, i.e., a dditionally adjus ted for the health va riables such as nutri tional intake, bod y
mass index , physical activit y, self-rate d health, chronic condit ions and smoking stat us; CI: Condence interva l; *** p < 0.001, ** p < 0.01, * p < 0.05
Table 4 (continued)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
consider sex/gender-tailored sleep interventions as part
of cognitive health management strategies. Educational
programs aimed at improving cognitive health should be
accessible and tailored to the diverse educational back-
grounds of older adults. Social support interventions,
particularly for widowed older women, can help mitigate
the negative impact of widowhood on cognitive function-
ing. Additionally, comprehensive healthcare approaches
that address overall health, including nutritional intake
and self-rated health, should be integrated into cognitive
health promotion strategies.
Abbreviations
MCI Mild cognitive impairment
WHO-SAGE World Health Organization’s Study on global AGEing and adult
health
PSU Primary Sampling Units
WHS World Health Survey
PCA Principal components analysis
aCoef Adjusted Coecients
CI Condence Interval
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s41606-024-00100-z.
Supplementary Material 1
Author contributions
All authors full the criteria for authorship. Conceived and designed the
research paper: T.M.; analyzed the data: T.M.; Wrote the manuscript: T.M., A.A.
and T.V.S; Rened the manuscript: T.M. and T.V.S. All authors read, reviewed and
approved the manuscript.
Funding
The authors received no funding for this research.
Data availability
The dataset analyzed for this study is available at data repository in https://
iipsindia.ac.in/content/SAGE-wave-2
Declarations
Ethics approval and consent to participate
The procedures undertaken in this study and the data collection processes
were conducted ethically per the World Medical Association’s Declaration
of Helsinki. Ethical approvals were obtained for SAGE study from the Ethics
Review Committee of the World Health Organization, the Ethics and Protocol
Review Committee of the Ghana Medical School, Accra, Ghana, the Ethics
Committee of the School of Preventive and Social Medicine, and the Russian
Academy of Medical Sciences, Moscow, Russia. Approval was also obtained
for the SAGE 1 study from the Ethics Committee of the Shanghai Municipal
Centre for Disease Control and Prevention, Shanghai, China, Institutional
Review Board of the International Institute of Population Sciences, Mumbai,
India, and nally from the Research Ethics Committee of the Human Sciences
Research Council, Pretoria, South Africa. These approvals also covered all
procedures through which written informed consent was obtained from each
participant. Condential records of participants’ consent were maintained by
SAGE. Further, written informed consents for participating in the SAGE study
were obtained from each participant.
Consent for publication
All authors consent to publish this research article.
Competing interests
The authors declare that there is no competing interest.
Author details
1Department of Human Development and Family Studies | Center for
Healthy Aging, Pennsylvania State University, University Park, PA
16802, USA
2WHO-SAGE Project, International Institute for Population Sciences,
Mumbai 400088, Maharashtra, India
3Department of Family and Generations | WHO-SAGE Project,
International Institute for Population Sciences, Mumbai 400088,
Maharashtra, India
Received: 16 January 2024 / Accepted: 19 March 2024
References
Aly M, Moscovitch M. The eects of sleep on episodic memory in older
and younger adults. Memory. 2010;18(3):327–34. https://doi.
org/10.1080/09658211003601548.
Arokiasamy P, Sekher TV, Lhungdim H, Dhar M, Roy AK. Study on global AGEing
and adult health (SAGE) Wave 2 India National Report; 2020.
Barha CK, Davis JC, Falck RS, Nagamatsu LS, Liu-Ambrose T. Sex dierences in
exercise ecacy to improve cognition: a systematic review and meta-
analysis of randomized controlled trials in older humans. Front Neuroendocr.
2017;46:71–85.
Behrens A, Anderberg P, Berglund JS. Sleep disturbance predicts worse cognitive
performance in subsequent years: a longitudinal population-based cohort
study. Arch Gerontol Geriatr. 2023;106:104899. https://doi.org/10.1016/j.
archger.2022.104899.
Bombois S, Derambure P, Pasquier F, Monaca C. Sleep disorders in aging and
dementia. J Nutr Health Aging. 2010;14(3):212–7. https://doi.org/10.1007/
s12603-010-0052-7.
Castells-Sanchez A, Roig-Coll F, Lamonja-Vicente N, Toran-Monserrat P, Pera G,
Montero P, Dacosta-Aguayo R, Bermudo-Gallaguet A, Bherer L, Erickson KI.
Sex matters in the Association between Physical Activity and Fitness with
Cognition. Med Sci Sports Exerc. 2021;53(6):1252–9.
Cellini N. Memory consolidation in sleep disorders. Sleep Med Rev. 2017;35:101–12.
Cha S-E, Eun K-S. Gender dierence in sleep problems: focused on Time Use
in Daily Life of Korea. Soc Indic Res. 2014;119(3):1447–65. https://doi.
org/10.1007/s11205-013-0550-1.
Cohen-Zion M, Stepnowsky C, Johnson S, Marler M, Dimsdale JE, Ancoli-Israel S.
Cognitive changes and sleep disordered breathing in elderly: dierences
in race. J Psychosom Res. 2004;56(5):549–53. https://doi.org/10.1016/j.
jpsychores.2004.02.002.
Dzierzewski JM, Dautovich N, Ravyts S. Sleep and cognition in older adults. Sleep
Med Clin. 2018;13(1):93–106. https://doi.org/10.1016/j.jsmc.2017.09.009.
Eichling PS, Sahni J. Menopause related Sleep disorders. J Clin Sleep Med.
2005;01(03):291–300. https://doi.org/10.5664/jcsm.26347.
Engleman HM, Kingshott RN, Martin SE, Douglas NJ. Cognitive function in the
sleep apnea/hypopnea syndrome (SAHS). Sleep. 2000;23(Suppl 4):S102–8.
Findley LJ, Barth JT, Powers DC, Wilhoit SC, Boyd DG, Suratt PM. Cognitive impair-
ment in patients with obstructive sleep apnea and Associated Hypoxemia.
Chest. 1986;90(5):686–90. https://doi.org/10.1378/chest.90.5.686.
Förster F, Luppa M, Pabst A, Heser K, Kleineidam L, Fuchs A, Pentzek M, Kadusz-
kiewicz H, van der Leeden C, Hajek A, König H-H, Oey A, Wiese B, Mösch E,
Weeg D, Weyerer S, Werle J, Maier W, Scherer M, Riedel-Heller SG. The role of
social isolation and the development of Depression. A comparison of the
widowed and married Oldest Old in Germany. Int J Environ Res Public Health.
2021;18(13). https://doi.org/10.3390/ijerph18136986. Article 13.
Fortier-Brochu É, Beaulieu-Bonneau S, Ivers H, Morin CM. Insomnia and daytime
cognitive performance: a meta-analysis. Sleep Med Rev. 2012;16(1):83–94.
https://doi.org/10.1016/j.smrv.2011.03.008.
Frange C, de Queiroz SS, da Silva Prado JM, Tuk S, de Mello MT. The impact of
sleep duration on self-rated health. Sleep Sci. 2014;7(2):107–13. https://doi.
org/10.1016/j.slsci.2014.09.006.
Gagnon K, Baril A-A, Gagnon J-F, Fortin M, Décary A, Lafond C, Desautels A, Mont-
plaisir J, Gosselin N. Cognitive impairment in obstructive sleep apnea. Pathol
Biol (Paris). 2014;62(5):233–40. https://doi.org/10.1016/j.patbio.2014.05.015.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
Gildner TE, Liebert MA, Kowal P, Chatterji S, Snodgrass JJ. Associations between
Sleep Duration, Sleep Quality, and cognitive test performance among older
adults from six Middle Income countries: results from the study on Global
Ageing and Adult Health (SAGE). J Clin Sleep Med. 2014;10(06):613–21.
https://doi.org/10.5664/jcsm.3782.
Guidozzi F. Sleep and sleep disorders in menopausal women. Climacteric.
2013;16(2):214–9. https://doi.org/10.3109/13697137.2012.753873.
Guidozzi F. Gender dierences in sleep in older men and women. Climacteric.
2015;18(5):715–21. https://doi.org/10.3109/13697137.2015.1042451.
Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N,
Herman J, Hillard A, Katz PJ, Kheirandish-Gozal ES, Neubauer L, O’Donnell
DN, Ohayon AE, Peever M, Rawding J, Sachdeva R, Setters RC, Vitiello B, M. V.,
Ware JC. National Sleep Foundation’s updated sleep duration recommenda-
tions: nal report. Sleep Health. 2015;1(4):233–43. https://doi.org/10.1016/j.
sleh.2015.10.004.
Hunter JC, Handing EP, Casanova R, Kuchibhatla M, Lutz MW, Saldana S, Plass-
man BL, Hayden KM. Neighborhoods, sleep quality, and cognitive decline:
does where you live and how well you sleep matter? Alzheimer’s Dement.
2018;14(4):454–61.
Keage HAD, Banks S, Yang KL, Morgan K, Brayne C, Matthews FE. What sleep
characteristics predict cognitive decline in the elderly? Sleep Med.
2012;13(7):886–92. https://doi.org/10.1016/j.sleep.2012.02.003.
Khan J. Nutritional status, alcohol-tobacco consumption behaviour and cogni-
tive decline among older adults in India. Sci Rep. 2022;12(1). https://doi.
org/10.1038/s41598-022-25563-x.
Krystal AD, Edinger JD. Measuring sleep quality. Sleep Med. 2008;9:S10–7. https://
doi.org/10.1016/S1389-9457(08)70011-X.
Lim J, Dinges DF. A meta-analysis of the impact of short-term sleep deprivation on
cognitive variables. Psychol Bull. 2010;136(3):375.
Lo JC, Groeger JA, Cheng GH, Dijk D-J, Chee MWL. Self-reported sleep duration and
cognitive performance in older adults: a systematic review and meta-analysis.
Sleep Med. 2016;17:87–98. https://doi.org/10.1016/j.sleep.2015.08.021.
Lo C-C, Liu W-T, Lu Y-H, Wu D, Wu C-D, Chen T-C, Fang Y-T, Lo Y-C, Chen Y-Y, Kang
L. Air pollution associated with cognitive decline by the mediating eects of
sleep cycle disruption and changes in brain structure in adults. Environ Sci
Pollut Res. 2022;29(35):52355–66.
Mallampalli MP, Carter CL. Exploring sex and Gender Dierences in Sleep
Health: a society for women’s Health Research Report. J Women’s Health.
2014;23(7):553–62. https://doi.org/10.1089/jwh.2014.4816.
Mattis J, Sehgal A. Circadian rhythms, Sleep, and disorders of Aging. Trends
Endocrinol Metabolism. 2016;27(4):192–203. https://doi.org/10.1016/j.
tem.2016.02.003.
Maume DJ, Sebastian RA, Bardo AR. Gender, work-family responsibilities, and Sleep.
Gend Soc. 2010;24(6):746–68. https://doi.org/10.1177/0891243210386949.
Miyata S, Noda A, Iwamoto K, Kawano N, Okuda M, Ozaki N. Poor sleep quality
impairs cognitive performance in older adults. J Sleep Res. 2013;22(5):535–41.
https://doi.org/10.1111/jsr.12054.
Muhammad T, Srivastava S. Why rotational living is bad for older adults? Evidence
from a cross-sectional study in India. J Popul Ageing. 2022 Mar;15(1):61–78.
Muhammad T, Srivastava S, Sekher TV. Association of self-perceived income status
with psychological distress and subjective well-being: a cross-sectional
study among older adults in India. BMC Psychol. 2021;9(1):82. https://doi.
org/10.1186/s40359-021-00588-5.
Muhammad T, Srivastava S, Sekher TV. Assessing socioeconomic inequalities in
cognitive impairment among older adults: a study based on a cross-sectional
survey in India. BMC Geriatr. 2022a;22(1):389. https://doi.org/10.1186/
s12877-022-03076-6.
Muhammad T, Sulaiman MK, Srivastava S. Migration of adult male children
and associated depression among community-dwelling older parents: a
cross‐sectional gender analysis from Longitudinal Ageing Study in India,
2017–2018. Int J Geriatr Psychiatry. 2022b;37(2):gps5675. https://doi.
org/10.1002/gps.5675.
Muhammad T, Meher T, Siddiqui LA. Mediation of the association between
multi-morbidity and sleep problems by pain and depressive symptoms
among older adults: evidence from the longitudinal aging study in India,
wave- 1. PLoS ONE. 2023;18(2):e0281500. https://doi.org/10.1371/journal.
pone.0281500.
Nebes RD, Buysse DJ, Halligan EM, Houck PR, Monk TH. Self-reported Sleep Quality
predicts poor cognitive performance in healthy older adults. Journals Geron-
tology: Ser B. 2009;64B(2):180–7. https://doi.org/10.1093/geronb/gbn037.
Organization WH. (2020). WHO guidelines on physical activity and
sedentary behaviour. https://apps.who.int/iris/bitstream/han
dle/10665/336656/9789240032170-rus.pdf.
Pace-Schott EF, Spencer RMC. (2011). Chapter 5—Age-related changes in the
cognitive function of sleep. In A. M. Green, C. E. Chapman, J. F. Kalaska, &
F. Lepore, editors, Progress in Brain Research (Vol. 191, pp. 75–89). Elsevier.
https://doi.org/10.1016/B978-0-444-53752-2.00012-6.
Patel S, Ram F, Patel SK, Kumar K. Association of behavioral risk factors with self-
reported and symptom or measured chronic diseases among adult popula-
tion (18–69 years) in India: evidence from SAGE study. BMC Public Health.
2019;19(1):560. https://doi.org/10.1186/s12889-019-6953-4.
Quan S-A, Li Y-C, Li W-J, Li Y, Jeong J-Y, Kim D-H. Gender dierences in Sleep
disturbance among Elderly koreans: Hallym Aging Study. J Korean Med Sci.
2016;31(11):1689–95. https://doi.org/10.3346/jkms.2016.31.11.1689.
Rani R, Arokiasamy P, Selvamani Y, Sikarwar A. Gender dierences in self-reported
sleep problems among older adults in six middle-income countries: a cross-
sectional study. J Women Aging. 2022;34(5):605–20. https://doi.org/10.1080/
08952841.2021.1965425.
Roepke SK, Ancoli-Israel S. Sleep disorders in the elderly. Indian J Med Res.
2010;131(2):302.
Rothman SM, Mattson MP. Sleep disturbances in Alzheimer’s and Parkinson’s
diseases. Neuromol Med. 2012;14(3):194–204. https://doi.org/10.1007/
s12017-012-8181-2.
Saenz JL, Adar SD, Zhang YS, Wilkens J, Chattopadhyay A, Lee J, Wong R.
Household use of polluting cooking fuels and late-life cognitive function:
A harmonized analysis of India, Mexico, and China. Environ Int. 2021 Nov
1;156:106722.
Saint Martin M, Sforza E, Barthélémy JC, Thomas-Anterion C, Roche F. Does
subjective sleep aect cognitive function in healthy elderly subjects? The
Proof cohort. Sleep Med. 2012;13(9):1146–52. https://doi.org/10.1016/j.
sleep.2012.06.021.
Sekher T, Muhammad T. (2023). Yoga/Meditation and Physical Exercises and
Associated Cognitive Function Among Older Adults: Findings from Baseline
Wave of the Longitudinal Aging Study in India. In Handbook of Aging,
Health and Public Policy (pp. 1–23). Springer Nature Singapore. https://doi.
org/10.1007/978-981-16-1914-4_162-1.
Selvamani Y, Singh P. Socioeconomic patterns of underweight and its associa-
tion with self-rated health, cognition and quality of life among older adults
in India. PLoS ONE. 2018;13(3):e0193979. https://doi.org/10.1371/journal.
pone.0193979.
Simoes Maria M, Büla C, Santos-Eggimann B, Krief H, Heinzer R, Seematter-
Bagnoud L. Sleep characteristics and self-rated health in older persons. Eur
Geriatr Med. 2020;11(1):131–8. https://doi.org/10.1007/s41999-019-00262-5.
Simonelli G, Marshall NS, Grillakis A, Miller CB, Hoyos CM, Glozier N. Sleep health
epidemiology in low and middle-income countries: a systematic review and
meta-analysis of the prevalence of poor sleep quality and sleep duration.
Sleep Health. 2018;4(3):239–50.
Smith L, Oh H, Jacob L, López-Sánchez GF, Veronese N, Soysal P, Shin JI, Schuch
F, Tully MA, Butler L. Sleep problems and subjective cognitive complaints
among middle-aged and older adults in 45 low-and middle-income coun-
tries. Aging Clin Exp Res. 2022;34(6):1285–93.
Srivastava S, Muhammad T. Rural-urban dierences in food insecurity and associ-
ated cognitive impairment among older adults: ndings from a nationally
representative survey. BMC Geriatr. 2022;22(1):287. https://doi.org/10.1186/
s12877-022-02984-x.
StataCorp L. (2017). Stata statistical software: Release 15 (2017). College Station, TX:
StataCorp LP.
Tartar JL, Fins AI, Lopez A, Sierra LA, Silverman SA, Thomas SV, Craddock TJ.
Sleep restriction and delayed sleep associate with psychological health
and biomarkers of stress and inammation in women. Sleep Health.
2015;1(4):249–56.
Thompson KI, Chau M, Lorenzetti MS, Hill LD, Fins AI, Tartar JL. Acute sleep depriva-
tion disrupts emotion, cognition, inammation, and cortisol in young healthy
adults. Front Behav Neurosci. 2022;16:945661.
Tripathi R, Kumar K, Bharath S, Marimuthu P, Varghese M. Age, education and
gender eects on neuropsychological functions in healthy Indian older
adults. Dement Neuropsychologia. 2014;8(2):148–54. https://doi.org/10.1590/
s1980-57642014dn82000010.
Van Cauter E, Leproult R, Plat L. Age-related changes in slow Wave sleep and REM
sleep and relationship with growth hormone and cortisol levels in healthy
men. JAMA. 2000;284(7):861–8. https://doi.org/10.1001/jama.284.7.861.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 16 of 16
Muhammad et al. Sleep Science and Practice (2024) 8:6
van Hooren SAH, Valentijn AM, Bosma H, Ponds RWHM, van Boxtel MPJ, Jolles J.
Cognitive functioning in healthy older adults aged 64–81: a Cohort Study
into the eects of Age, Sex, and Education. Aging Neuropsychol Cognition.
2007;14(1):40–54. https://doi.org/10.1080/138255890969483.
Vanek J, Prasko J, Genzor S, Ociskova M, Kantor K, Holubova M, Slepecky M,
Nesnidal V, Kolek A, Sova M. Obstructive sleep apnea, depression and
cognitive impairment. Sleep Med. 2020;72:50–8. https://doi.org/10.1016/j.
sleep.2020.03.017.
Wang J, Xiao LD, Wang K, Luo Y, Li X. Gender dierences in cognitive impairment
among rural Elderly in China. Int J Environ Res Public Health. 2020;17(10).
https://doi.org/10.3390/ijerph17103724.
Xu H, Ostbye T, Vorderstrasse AA, Dupre ME, Wu B. Place of Residence and cogni-
tive function among the Adult Population in India. Neuroepidemiology.
2018;50(3–4):119–27. https://doi.org/10.1159/000486596.
Xu P, Wei R, Cheng B, Wang A, -jun, Li X, Li H, Sun L, Du J, Sheng J, Liu K, Tao F,
Yang L. The association of marital status with cognitive function and the role
of gender in Chinese community-dwelling older adults: a cross-sectional
study. Aging Clin Exp Res. 2021;33(8):2273–81. https://doi.org/10.1007/
s40520-020-01743-5.
Zhang Z. Gender dierentials in cognitive impairment and decline of the Oldest
Old in China. Journals Gerontology: Ser B. 2006;61(2):S107–15. https://doi.
org/10.1093/geronb/61.2.S107.
Zhang L, Chen C, Zhang H, Peng B. Longitudinal associations between daytime
napping and cognitive function in Chinese older adults. Arch Gerontol Geri-
atr. 2023;107:104909. https://doi.org/10.1016/j.archger.2022.104909.
Zisapel N. Sleep and sleep disturbances: Biological basis and clinical implications.
Cell Mol Life Sci. 2007;64:1174–86.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
