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Effect of Sunlight Exposure on Cognitive Function Among Depressed and Non-Depressed Participants: A REGARDS Cross-Sectional Study

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Possible physiological causes for the effect of sunlight on mood are through the suprachiasmatic nuclei and evidenced by serotonin and melatonin regulation and its associations with depression. Cognitive function involved in these same pathways may potentially be affected by sunlight exposure. We evaluated whether the amount of sunlight exposure (i.e. insolation) affects cognitive function and examined the effect of season on this relationship. We obtained insolation data for residential regions of 16,800 participants from a national cohort study of blacks and whites, aged 45+. Cognitive impairment was assessed using a validated six-item screener questionnaire and depression status was assessed using the Center for Epidemiologic Studies Depression Scale. Logistic regression was used to find whether same-day or two-week average sunlight exposure was related to cognitive function and whether this relationship differed by depression status. Among depressed participants, a dose-response relationship was found between sunlight exposure and cognitive function, with lower levels of sunlight associated with impaired cognitive status (odds ratio = 2.58; 95% CI 1.43-6.69). While both season and sunlight were correlated with cognitive function, a significant relation remained between each of them and cognitive impairment after controlling for their joint effects. The study found an association between decreased exposure to sunlight and increased probability of cognitive impairment using a novel data source. We are the first to examine the effects of two-week exposure to sunlight on cognition, as well as the first to look at sunlight's effects on cognition in a large cohort study.
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Environmental Health
Open Access
Research
Effect of sunlight exposure on cognitive function among depressed
and non-depressed participants: a REGARDS cross-sectional study
Shia T Kent*1, Leslie A McClure2, William L Crosson3, Donna K Arnett1,
Virginia G Wadley4 and Nalini Sathiakumar1
Address: 1Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, Birmingham,
Alabama, USA, 2Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, Birmingham,
Alabama, USA, 3National Space Science and Technology Center, NASA Marshall Space Flight Center, 320 Sparkman Drive, Huntsville, Alabama,
USA and 4Department of Medicine, University of Alabama at Birmingham, 1808 7th Avenue South Birmingham, Alabama, USA
Email: Shia T Kent* - shia@uab.edu; Leslie A McClure - lmcclure@uab.edu; William L Crosson - bill.crosson@nasa.gov;
Donna K Arnett - Arnett@ms.soph.uab.edu; Virginia G Wadley - vwadley@uab.edu; Nalini Sathiakumar - NSathiakumar@ms.soph.uab.edu
* Corresponding author
Abstract
Background: Possible physiological causes for the effect of sunlight on mood are through the
suprachiasmatic nuclei and evidenced by serotonin and melatonin regulation and its associations
with depression. Cognitive function involved in these same pathways may potentially be affected by
sunlight exposure. We evaluated whether the amount of sunlight exposure (i.e. insolation) affects
cognitive function and examined the effect of season on this relationship.
Methods: We obtained insolation data for residential regions of 16,800 participants from a
national cohort study of blacks and whites, aged 45+. Cognitive impairment was assessed using a
validated six-item screener questionnaire and depression status was assessed using the Center for
Epidemiologic Studies Depression Scale. Logistic regression was used to find whether same-day or
two-week average sunlight exposure was related to cognitive function and whether this
relationship differed by depression status.
Results: Among depressed participants, a dose-response relationship was found between sunlight
exposure and cognitive function, with lower levels of sunlight associated with impaired cognitive
status (odds ratio = 2.58; 95% CI 1.43–6.69). While both season and sunlight were correlated with
cognitive function, a significant relation remained between each of them and cognitive impairment
after controlling for their joint effects.
Conclusion: The study found an association between decreased exposure to sunlight and
increased probability of cognitive impairment using a novel data source. We are the first to examine
the effects of two-week exposure to sunlight on cognition, as well as the first to look at sunlight's
effects on cognition in a large cohort study.
Published: 28 July 2009
Environmental Health 2009, 8:34 doi:10.1186/1476-069X-8-34
Received: 8 January 2009
Accepted: 28 July 2009
This article is available from: http://www.ehjournal.net/content/8/1/34
© 2009 Kent et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Environmental Health 2009, 8:34 http://www.ehjournal.net/content/8/1/34
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Introduction
It is widely accepted that climate and season affect psycho-
logical characteristics [1,2]. Recent research has shown
that serotonin and melatonin regulation, mechanisms
that are involved in the relationship between sunlight and
light therapy on mood, are also involved in cognition,
which suggests that cognitive function may also be influ-
enced by light [3-5]. Melatonin, serotonin and other
mechanisms involved in circadian rhythms are associated
with cognitive functioning, and are regulated by the
suprachiasmatic nuclei (SCN), which are susceptible to
the effects of differing intensities and patterns of environ-
mental illumination [6]. However, the effect of sunlight
and light therapy on cognitive function has not been ade-
quately studied. This study aimed to explore if sunlight
exposure, measured by insolation (the rate of solar radia-
tion received in an area), is associated with cognitive
impairment. In addition, examined the role of season in
this relationship. This study was conducted using baseline
data from a large prospective study, the REasons for Geo-
graphic And Racial Differences in Stroke (REGARDS)
Study, and National Aeronautics and Space Administra-
tion (NASA) satellite and ground data. We hypothesized
that lower levels of sunlight exposure at participants' resi-
dences would be associated with increased rates of cogni-
tive impairment. This study was the first to examine the
effects of two-week exposure to natural sunlight on cogni-
tion, as well as the first to look at solar effects on cognition
in a large cohort study.
Methods
Participants
The present study consisted of participants from the
REGARDS study, which has been described in detail else-
where [7]. In brief, REGARDS is a longitudinal study
being conducted to determine relationships between var-
ious risk factors and the incidence of stroke. The partici-
pants are aged 45 and older and sampled from the 48
conterminous United States. Study participants were over-
sampled from the "Stroke Belt", a high stroke mortality
region consisting of the 8 southeastern states of Arkansas,
Louisiana, Tennessee, Mississippi, Alabama, Georgia,
North Carolina, and South Carolina. The sample popula-
tion was particularly oversampled from the "Stroke
Buckle", a region with even higher stroke mortality along
the coastal plains of Georgia, North Carolina, and South
Carolina. Within each region the planned recruitment
included half white and half African-Americans (actual:
41% African-American, 59% white). Planned recruitment
within each race-region strata was half male and half
female (actual total recruitment: 45% male, 55% female).
At baseline, a telephone interview was conducted that
recorded the patient's medical history, demographic data,
socioeconomic status, stroke-free status, depression, and
cognitive screening. An in-home exam was conducted
recording height, weight, and blood pressure. All partici-
pants provided written informed consent, and the study
was approved by the Institutional Review Board for
Human Subjects at the University of Alabama at Birming-
ham, as well as all other participating institutions.
Sunlight Exposure (Insolation) Assessment
Sunlight exposure was measured using data values pre-
pared and provided by NASA's Marshall Space Flight
Center. Solar radiation values were obtained for 2003 to
2006 from the North American Regional Reanalysis
(NARR), an assimilated data product produced by the
National Center for Environmental Prediction (NCEP), a
division of the U.S. National Weather Service. The prod-
uct, including information from satellites and ground
observations, was compiled on a grid with a 32 km reso-
lution over North America and matched to each partici-
pant by the geocoded residence obtained from the
REGARDS database. Solar radiation in Watts/meters2 (W/
m2), a measure of the instantaneous solar energy reaching
the Earth's surface, was assessed 8 times a day at 3-hour
intervals for each residence starting at 1:00 AM Pacific
Standard Time (PST). A daily integral of solar radiation
was calculated; this is referred to as insolation and has
units of kilojoules per meters2 per day (KJ/m2/day). As a
point of reference, under clear skies in late spring or early
summer, a typical daily insolation value in the central U.S.
is approximately 25,000–30,000 KJ/m2. In late fall or
early winter, a typical daily value is approximately 8,000–
10,000 KJ/m2.
The current residence from the original recruitment file
plus updated information from participant at time of
scheduling in-home exam was used to geocode each par-
ticipant. Geocoding of the participants was performed
using SAS/GIS batch geocoding. Information obtained
from SAS/GIS with 80% accuracy or greater was utilized.
Using a subset of the data, we validated the results from
the SAS/GIS procedure against a commercially available
program http://www.geocode.com using the Haversine
formula, and found there to be high agreement between
the two algorithms [8]. For those with a SAS/GIS accuracy
of 80% or greater, the difference between the latitudes
given between the two programs had a mean of 0.23 kil-
ometers and a maximum of 0.95 kilometers.
Cognitive Assessment
A six-item screener questionnaire was used to evaluate
global cognitive status by assessing short-term recall and
temporal orientation [9-11]. As REGARDS has done in
other studies, the score of this screener was dichotomized
into an outcome of cognitively impaired or intact. A score
of four or fewer correct responses out of the six questions
indicated cognitive impairment. Callahan et al. 2002 val-
idated the screener in both a community-based popula-
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Table 1: Demographic, medical, and lifestyle characteristics by cognitive status
Characteristics All Subjects
(N = 16800)
Missing Intact Cognitive Status
(N = 15421; 92%)
Impaired Cognitive Status
(N = 1379; 8%)
p-value
N (%) N N (%) N (%)
Demographics
Male 6657 (40) 0 6033 (39) 624 (45) <0.0001
Education 19
Less than High School 2081 (12) 1719 (11) 362 (26)
High School 4404 (26) 3978 (26) 426 (31) <0.0001
Some College 4525 (27) 4230 (27) 305 (22)
College Diploma 5761 (34) 5480 (36) 281 (20)
Age 5
Less than 55 years 2077 (12) 1982 (13) 95 (7)
55 to 59 years 2908 (17) 2756 (18) 152 (11)
60 to 64 years 3197 (19) 3009 (20) 188 (14) <0.0001
65 to 69 years 3117 (19) 2870 (19) 247 (18)
70 to 74 years 2418 (14) 2172 (14) 246 (18)
75 to 79 years 1765 (11) 1536 (10) 229 (17)
80 or more years 1313 (8) 1091 (7) 222 (16)
Income 2171
Less than $20,000/year 3097 (21) 2698 (20) 399 (35)
$20,000–35,000/year 4017 (27) 3633 (27) 384 (33) <0.0001
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$35,000-$75,000/year 4968 (34) 4678 (35) 290 (25)
Over $75,000/year 2547 (17) 2467 (18) 80 (7)
Region 0
Non Belt/Buckle 7850 (47) 7211 (47) 639 (46) 0.13
Stroke Belt 6095 (36) 5567 (36) 528 (38)
Stoke Buckle 2855 (17) 2643 (17) 212 (15)
Population Density 0
Urban 13532 (81) 12365 (80) 1167 (85)
Mixed 1678 (10) 1571 (10) 107 (8) 0.0003
Rural 1590 (9) 1485 (10) 105 (8)
Season 0
Spring 3610 (22) 3260 (21) 350 (25) <0.0001
Summer 6439 (38) 5998 (39) 441 (32)
Fall 3410 (20) 3181 (21) 229 (17)
Winter 3341 (20) 2982 (19) 359 (26)
Medical Factors
BMI 238
Underweight 212 (1) 189 (1) 23 (2)
Normal 3973 (24) 3651 (24) 322 (24) 0.26
Overweight 5970 (36) 5462 (36) 508 (38)
Table 1: Demographic, medical, and lifestyle characteristics by cognitive status (Continued)
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Obese 6407 (39) 5906 (39) 501 (37)
Diabetic 3527 (22) 623 3150 (21) 377 (29) <0.0001
Hypertensive 9854 (59) 73 8943 (58) 911 (67) <0.0001
High Cholesterol 1740 (10) 80 1595 (10) 145 (11) 0.85
Depressed 1877 (11) 155 1624 (11) 253 (18) <0.0001
PCS-12 (mean, stddev) 46.1 (10.6) 0 46.3 (10.5) 44.0 (11.0) <.0001
Lifestyle Factors
Never Used Alcohol 5129 (31) 0 4606 (30) 523 (40) <0.0001
No weekly exercise 5948 (36) 234 5359 (35) 589 (44) <0.0001
Smoking 67
Current 2445 (15) 2227 (15) 218 (16) 0.18
Past 6609 (40) 6055 (39) 554 (40)
Never 7679 (46) 7078 (46) 601 (44)
stddev = standard deviation; BMI = Body Mass Index; PCS = Physical Components Summary
P-values for categorical variables provided from a chi squared test statistic and for continuous variables provided from a scatterthwaite t-test statistic calculated for each variable by cognitive
status.
P-values in bold indicate values that are significant at α = 0.05.
Table 1: Demographic, medical, and lifestyle characteristics by cognitive status (Continued)
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tion of 344 black adults aged 65 or older and a population
of 651 subjects who were referred to the Alzheimer's dis-
ease Center (16% black). Results from the community-
based sample found that for a six-item screener score of 4
or fewer, using clinically confirmed cognitive impairment
as the gold standard, the sensitivity was 74% and specifi-
city was 80% [10]. The instrument was based on and val-
idated against the Mini-mental State examination [11]. It
was also validated against other cognitive measures and
diagnoses of both dementia and non-dementia cognitive
impairment [10].
Participant Selection
A total of 19,853 participants without previous stroke
were enrolled in the study at the time of this analysis
(December 1, 2006). Of these, 3,020 patients with poor
geocoding (less than 80% accuracy) and a further 33
patients who were missing cognitive scores were excluded,
leaving 16,800 participants. Due to data missing in any of
the potential confounders, 3,253 of the 16,800 partici-
pants were excluded during model selection. Once the
model selection was completed, only 2,326 of the 16,800
participants were excluded due to missing covariates
selected for the final multivariable model. Chi-squared
and t-tests were used to measure differences of the 5,379
excluded and 14,474 included subjects in the final model.
Statistical Analyses
We analyzed insolation from the day the six-item screener
was administered and the preceding two weeks. Insola-
tion measurements were analyzed as continuous variables
Table 2: Crude logistic univariate relationships of depression
with two-week insolation
Characteristics OR (95% CI)
Primary Variable of Interest
2 week solar radiation
(by 5,000 KJ/m2/day)
<10,000 J/m21.14 (0.91–1.42)
10,000–15,000 J/m20.94 (0.81–1.09)
15,000–20,000 J/m20.87 (0.75–1.01)
20,000–25,000 J/m20.87 (0.76–1.00)
>25,000 J/m21.00 (Referent)
CMH chisq p = 0.6054
OR = odds ratio; CI = confidence interval
CMH chisq = Cohran-Mantel-Haenzel chi-squared
Table 3: Crude logistic univariate relationships of cognitive
impairment with predictors and final covariates
Characteristics OR (95% CI)
Primary Variable of Interest
Same day solar radiation
(by 5,000 KJ/m2/day)
<10,000 J/m21.15 (0.96–1.38)
10,000–15,000 J/m21.02 (0.86–1.21)
15,000–20,000 J/m20.96 (0.82–1.13)
20,000–25,000 J/m21.03 (0.88–1.21)
>25,000 J/m21.00 (Referent)
CMH chisq p = 0.4346
2 week solar radiation
(by 5,000 KJ/m2/day)
<10,000 J/m21.36 (1.08–1.70)
10,000–15,000 J/m21.13 (0.95–1.33)
15,000–20,000 J/m21.09 (0.92–1.30)
20,000–25,000 J/m21.01 (0.86–1.19)
>25,000 J/m21.00 (Referent)
CMH chisq p = 0.0075
Covariates
Male 1.29 (1.15–1.44)
Education
Less than High School 4.56 (3.57–5.83)
High School 3.26 (2.55–4.17)
Some College 1.91 (1.49–2.46)
College Diploma 1.00 (Referent)
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and categorical variables (by 5,000 KJ/m2/day incre-
ments).
Due to prior evidence regarding relationships with cogni-
tive function, we considered the following as potential
confounders: sex, geographic region (stroke belt, stroke
buckle, or non-stroke belt), population density (urban,
suburban, and rural), income (less than $20,000, $20,000
to $34,999, $35,000 to $74,900, or $75,000 and more),
education (less than high school, high school diploma,
some college, or college diploma), race (black or white),
smoking (current, past, or never), alcohol use (never used
or ever used), Body Mass Index (BMI) (underweight, nor-
mal, overweight, or obese), hypertension status (systolic
blood pressure 140, diastolic blood pressure 90 or self-
reported use of hypertension medications), high choles-
Predicted Probabilities and Odds Ratios of Cognitive Impair-ment by Depression StatusFigure 1
Predicted Probabilities and Odds Ratios of Cognitive
Impairment by Depression Status.
0.00
0.05
0.10
0.15
0.20
0.25
0.30
<10,000 10,000-
15,000
15,000-
20,000
20,000-
25,000
>25,000
Insola tion (J/m
2
)
Proability of Cognitive Impairment
Depress ed
Non-depress ed
OR
d
=2.59
(95% CI:1.43-4. 70)
OR
d
=0.94
(95% CI:0.59-1.51)
OR
d
=0.83
(95% CI:0.51-1. 37)
OR
d
=1.14
(95% CI:0.74-1. 76) OR
d
=1.00
(reference)
OR
n
=1.00
(reference)
OR
n
=1.14
(95% CI:0.85-1. 53)
OR
n
=1.10
(95% CI:0.90-1. 35)
OR
n
=1.13
(95% CI:0.91-1. 40) OR
n
=0.89
(95% CI:0.73-1. 09)
Spring 1.46 (1.26–1.69)
Depressed 1.90 (1.65–2.20)
PCS-12 (by 10 unit increase) 0.82 (0.78–0.86)
Never Used Alcohol 1.44 (1.28–1.61)
OR = odds ratio; CI = confidence interval; PCS = Physical
Components Summary
CMH chisq = Cohran-Mantel-Haenzel chi-squared
ORs in bold have CI's which do not overlap a null value
Table 3: Crude logistic univariate relationships of cognitive
impairment with predictors and final covariates (Continued)
CMH chisq p < .0001
Income
Less than $20,000 per year 4.47 (3.46–5.77)
$20,000 to $35,000 per year 3.21 (2.49–4.15)
$35,000 to $75,000 per year 1.88 (1.45–2.45)
$75,0000 or more per year 1.00 (Referent)
CMH chisq p < .0001
Age
Less than 55 years 1.00 (Referent)
55 to 59 years 1.15 (0.89–1.50)
60 to 64 years 1.30 (1.01–1.68)
65 to 69 years 1.80 (1.41–2.29)
70 to 74 years 2.36 (1.85–3.02)
75 to 79 years 3.11 (2.43–3.99)
80 or more years 4.25 (3.30–5.46)
CMH chisq p < .0001
Region
Non Belt/Buckle 1.00 (Referent)
Stroke Belt 1.07 (0.95–1.21)
Stoke Buckle 0.91 (0.77–1.06)
Season
Summer 1.00 (Referent)
Fall 0.98 (0.83–1.16)
Winter 1.64 (1.42–1.90)
Table 3: Crude logistic univariate relationships of cognitive
impairment with predictors and final covariates (Continued)
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terol (cholesterol >240), diabetes status (fasting glucose
126, non-fasting glucose 200, or self-reported diabetes
medications), exercise (weekly or less than weekly),
depression status based on the Center for Epidemiologic
Studies Depression Scale (CES-D) scale, physical function
as measured by the 100 point scale Physical Components
Summary (PCS) in the 12-item Short Form (SF-12), sea-
son of phone interview (spring, summer, fall, or winter),
and age in years (45–54, 55–59, 60–64, 65–69, 70–74,
75–79, or 80 or more) [12,13].
T-tests, chi-squared tests, and correlation tests were used
to determine preliminary relationships between insola-
tion, cognition, and the covariates. Cochran-Mantel-
Haenzel (CMH) chi-squared tests were used to determine
if ordinally categorized insolation had relationships with
categorical predictors. Logistic regressions were used to
model the association between insolation and cognition.
Backwards elimination was used to build the final multi-
variable model. Covariates whose relationships with cog-
nitive function carried p-values over 0.10 were not
considered for inclusions in the multivariable model. Due
to the REGARDS sampling methods, the variables race,
region, and sex were included in the multivariable model
regardless of statistical significance. All interactions of the
remaining covariates with insolation in the final models
were considered. For any significant interactions the pre-
dicted probabilities and odds ratios (ORs) of cognitive
impairment with their 95% confidence intervals were cal-
culated. Since other factors related to seasonality besides
insolation may be related to cognitive function, such as
temperature, activity level, allergies, and stress, the final
model was run both with and without season as a covari-
ate [1,14-17].
Finally, the cognitive screener was divided into two com-
ponents, the three points that measure short-term recall
and the three points that measure temporal orientation.
Each of these components was used to explore individual
relationships with insolation. Univariate relationships
were analyzed using chi-square tests and multivariable
relationships were evaluated by taking the final logistic
regression model obtained above and replacing the sum-
mary measure of cognitive impairment with each of the
individual components of the six-item screener. For these
analyses, the three point component measures were
dichotomized, with one missing point indicating a deficit
in either orientation or recall. The measures were also ana-
lyzed continuously, so that each component would be
equal to the number of points obtained (0, 1, 2, or 3).
Results
Continuous two-week insolation (p = 0.005) but not
same-day insolation (p = 0.332) differed significantly by
cognitive status (data not shown). Table 1 presents the
baseline characteristics overall and by cognitive status.
Sex, education, age, income, population density, season,
diabetes status, hypertension status, depression status,
PCS, alcohol usage, and weekly exercise all differed signif-
icantly by cognitive status (all p-values < 0.05; Table 1).
CMH chi-squared tests indicated that gender, age, region,
population density, season, BMI, PCS-12, and weekly
exercise had dose-response relationships with ordinally
categorized insolation, but education, diabetes, hyperten-
sion, high cholesterol, smoking (data not shown), and
depression (Table 2) did not. Dose-response relationships
of income (p = 0.0956) and alcohol use (p = 0.0650) with
insolation were marginal (data not shown).
Table 3 shows the univariate analyses of categorized inso-
lation testing for dose-response relationships with cogni-
tive function. Two week insolation categorized by 5,000
KJ/m2/day showed does-response effects (p = 0.0075).
Participants in the lowest category of insolation compared
to those in the highest insolation category had 1.36 times
(95% CI = 1.08–1.70) the odds of cognitive impairment.
When this measure of insolation was modeled as an ordi-
nal variable, each successively lower insolation level com-
pared to the adjacent higher insolation level had 1.06
(95% CI 1.02–1.11) fold odds of cognitive impairment.
This study also confirmed prior study findings that there
is a dose-response relationship between cognitive impair-
ment and age, income, and education status (Table 3).
Depressed participants showed an increased odds of cog-
nitive impairment (OR = 1.90; 95% CI 1.65–2.20). In
addition, univariate analyses showed that the seasons of
winter (OR = 1.64; 95% CI 1.42–1.90) and spring (OR =
1.46; 95% CI 1.26–1.69) compared to fall gave increased
odds of cognitive impairment.
Due to non-significant (p > 0.10) relationships of high
cholesterol, BMI, and smoking with cognitive impairment
in crude analyses, these variables were not considered for
model-building. Further, population density, diabetes,
hypertension, and weekly exercise were dropped from the
model for non-significance (p > 0.10) during backwards
selection. The final multivariable model included sex,
race, education, income, age, region, depression, PCS-12
and alcohol as covariates. Because depression had a signif-
icant interaction with sunlight exposure in this model (p
= 0.008), the final model included this interaction term
and predicted probabilities of cognitive impairment were
calculated according to sunlight exposure category and
depression status. Figure 1 shows that the predicted prob-
abilities of cognitive impairment for depressed partici-
pants are consistently higher than the predicted
probabilities of impairment for non-depressed partici-
pants. Figure 1 also shows that depressed participants
receiving less than 10,000 KJ/m2/day of sunlight exposure
compared to depressed participants in other solar expo-
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Table 4: Final covariates of excluded and modeled participants
Characteristics Participants in the Final Model Excluded Participants p-value
N (%) N (%)
Total 14,474 (73) 5378 (27)
Male 5944 (41) 1887 (35) <.0001
Education
Less than High School 1670 (12) 798 (15)
High School 3751 (26) 1525 (28) <.0001
Some College 3963 (27) 1407 (26)
College Diploma 5090 (35) 1628 (30)
Income
Less than $20,000 per year 3071 (21) 623 (22)
$20,000 to $35,000 per year 3991 (28) 797 (28) 0.42
$35,000 to $75,000 per year 4904 (34) 914 (32)
$75,0000 or more per year 2508 (17) 484 (17)
Age
Less than 55 years 1885 (13) 585 (11)
55 to 59 years 2583 (18) 848 (16)
60 to 64 years 2759 (19) 1052 (20) <.0001
65 to 69 years 2654 (18) 1038 (19)
70 to 74 years 2063 (14) 758 (14)
75 to 79 years 1443 (10) 653 (12)
80 or more years 1087 (8) 436 (8)
Black 6291 (44) 2046 (38) <.0001
Region
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sure categories had a significantly higher predicted proba-
bility of cognitive impairment. When season was added to
the multivariable model, it was significantly related to
cognitive function (p < 0.01). Both spring (OR = 1.20;
95% CI 1.01–1.42) and winter (OR = 1.33; 95% CI 1.07–
1.67) seasons compared to summer season showed
increased odds of cognitive impairment. The relationships
between sunlight exposure, depression, and cognitive
impairment were unchanged when season was added to
the model, giving identical predicted probabilities as in
Figure 1.
Relationships between temporal orientation and short-
term recall components of the six-item screener did not
reveal any significant univariate or multivariable relation-
ships with insolation, indicating that no single compo-
nent was likely responsible for the relationship (data not
shown).
Conclusion
We found that among participants with depression, low
exposure to sunlight was associated with a significantly
higher predicted probability of cognitive impairment.
This relationship remained significant after adjustment
for season. Among participants without depression, inso-
lation did not have a significant effect on cognitive func-
tion.
This study adds to the body of literature that shows that
environment and lifestyle profoundly affect those who are
prone to Seasonal Affective Disorder (SAD) and other
types of depression. Studies based on violent homicides,
suicides, and aggressive behaviors have repeatedly dem-
onstrated seasonal characteristics, typically with peaks in
the spring. These peaks have been associated with sunlight
and other climatic variables [18]. Those with SAD have
mental states that vary with season, with regular depres-
sions occurring in the winter and remissions in the spring
or summer. It is established that these SAD episodes are
associated with the shorter daylight hours occurring in
winter [19].
The fact that sunlight exposure was associated with cogni-
tion in depressed participants supports our hypothesis
that the physiological mechanisms which give rise to sea-
sonal depression may also be involved with sunlight's
effect on cognitive function. Leonard and Myint, 2006
laid out a paradigm showing how lack of environmental
Non Belt/Buckle 6802 (47) 2158 (40)
Stroke Belt 5228 (36) 2074 (39) <.0001
Stoke Buckle 2444 (17) 1145 (21)
Season
Summer 5542 (38) 2080 (39)
Fall 2957 (20) 1116 (21) 0.76
Winter 2891 (20) 1040 (19)
Spring 3084 (21) 1142 (21)
Never Used Alcohol 4291 (30) 1842 (34) <.0001
Depressed 1605 (11) 653 (13) 0.0047
PCS-12 (mean, stddev) 46.2 (10.6) 45.9 (10.5) 0.0449
stddev = standard deviation; PCS = Physical Components Summary
P-values for categorical variables provided from a chi squared test statistic and for continuous variables provided from a pooled t-test statistic
calculated for each variable by inlcusion status.
Table 4: Final covariates of excluded and modeled participants (Continued)
Environmental Health 2009, 8:34 http://www.ehjournal.net/content/8/1/34
Page 11 of 14
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illumination and other stresses might lead to altered sero-
tonin levels, neurodegeneration, depression, cognitive
deficits, and ultimately dementia [20]. Both seasonal and
non-seasonal depression have been shown to have rela-
tionships with environmental illumination [19,21,22].
Theories regarding the body's seasonal cycles, which affect
depression and may also affect cognition, are mostly
based on the regulation of the body's circadian rhythms
by the hypothalamic suprachiasmatic nuclei (SCN)
[6,23]. The SCN are modulated by various factors such as
body temperature and physical activity, but are in particu-
lar modulated by light received by retinal sensors at opti-
mal wavelengths close to sunlight's dominant wavelength
of 477 nanometers [23]. The SCN regulate the body's
sleep cycle, body temperature, blood pressure, digestion,
immune system, and various hormonal systems. Dysfunc-
tional circadian rhythms and sleep disorders, which can
occur from inadequate environmental light, have been
associated with cognitive deficits [24]. One of the SCN's
regulatory functions are their inhibition of the pineal
gland from turning serotonin into melatonin during the
presence of daytime light [19]. Abnormalities and regula-
tion of both the melatonin and serotonin systems have
been found to vary according to sunlight and light therapy
in SAD [25,26], bipolar [5] and schizophrenic [27]
patients, and even among those without psychiatric diag-
noses [28]. Serotonin and melatonin have also been
implicated in many mental and cognitive disorders, such
as Alzheimer's disease, Parkinson's disease, and sleep dis-
orders [25,29].
Light has been shown to also affect brain blood flow. Cer-
ebral blood flow has specifically been found to improve
after phototherapy in pre-term infants [30] and SAD
patients [31], and has repeatedly been found to be associ-
ated with cognitive functions, such as memory. Inade-
quate cerebral blood flow has been found to be a likely
cause or result of decreasing cognitive functions among
those with cardiovascular diseases [32-34], as well as cor-
related with age-related diseases such as Alzheimer's [35]
and non-age related diseases such as Lyme disease [36].
The relationships that serotonin, melatonin, and cerebral
hemodynamics have with sunlight, depression, and cog-
nitive function suggest that persons prone to sunlight-
related mood disturbances may also be prone to sunlight-
related cognitive difficulties.
This study adds to the limited base of knowledge regard-
ing the relationship of weather variables with cognitive
function. Studies that have tested the effects of artificial
light on cognitive abilities have found that increased light
exposure leads to increased alertness and a variety of
changes in regional brain activity [37]. In addition, differ-
ent spectral wavelengths have been found to have differ-
ing effects on memory and other cognitive abilities [38].
However, unlike our study, these studies only dealt with
immediately acute effects and did not directly examine the
effects of natural sunlight. They also have poor generaliz-
ability due to using animals or small numbers of human
subjects from populations with particular occupations,
socio-economic statuses, or ethnicities. We found only
two studies that examined the relationship between cog-
nition and sunlight, both of which only dealt with the
effects of immediate, short-term exposure. Sinclair et al.
(1994) found that increased sunlight exposure was associ-
ated with an increase in heuristic processing, which
requires memory storage and relevant memory retrieval,
but a decrease in systematic processing, a more compli-
cated process requiring analysis and judgment [39]. Keller
et al. (2005) found weak positive correlations between
sunny days and performance on two measures of cogni-
tion, digit span and openness to new information [1]. A
major difference between our study and the previous stud-
ies is our method of obtaining the participant's exposure
to sunlight. The NASA satellite used to obtain the insola-
tion data in this study is able to record data eight times a
day as well as provide an accurate characterization of inso-
lation matched to each participant's geocoded home
address. This gives superior space and time precision com-
pared to ground sensors used by previous studies. Keller et
al. (2005) used barometric pressure as a surrogate for
measuring sunny, clear days. Other studies that have not
found significant associations between mood or cogni-
tion and sunlight in the general population [40] have
directly measured insolation using the nearest available
ground sensors, which are centered on metropolitan
areas. Satellite data allowed us to obtain multiple daily
measurements across urban, suburban, and rural areas.
Exposure misclassification exists as a possible limitation
of the study. Exposure misclassifications may have taken
place if during the two week exposure measurements par-
ticipants spent a large amount of time in a climate differ-
ent than the climate recorded by the satellite. This could
happen if participants spent large amounts of time
indoors or away from their reported home addresses.
Also, the daily insolation values were taken by the satellite
sensors recorded simultaneously throughout the four dif-
ferent time zones in the U.S. Thus, this point represented
different times in the day for different regions of the coun-
try. For example, the insolation values used to calculate
insolation for participants in the Eastern time zone corre-
spond to 3-hour sampling periods of 1:00, 4:00, 7:00 and
10:00 AM/PM standard time, while for the Mountain time
zone the sampling times are 2:00, 5:00, 8:00 and 11:00
AM/PM standard time. However, the relatively short
three-hour intervals at which the measurements were
taken captures the diurnal cycle well, and the misclassifi-
cation due to this issue is quite small. It should also be
noted that while the relationships found in this study may
Environmental Health 2009, 8:34 http://www.ehjournal.net/content/8/1/34
Page 12 of 14
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not apply in younger people (as our study was restricted
to those 45 years or older), the participants of the study
were recruited from all over the country, with differing
demographics, medical factors, and lifestyle factors.
Due to the exclusion of a considerable proportion (27%)
of 19,853 enrolled REGARDS participants from the final
model as a result of missing values and poor geocoding,
we investigated if the excluded participants differed from
those with complete information. While sex, education,
region, alcohol, age, and depression status of the excluded
subjects were statistically different, the proportions of
these variables all differed by eight percentage points or
less (Table 4). Covariates with larger differences (over 2%)
show a disproportionate inclusion into the model of
males, those with college diplomas, blacks, non-belt resi-
dents, and those that have ever used alcohol. These varia-
bles all have known relationships with cognitive
impairment and would be the most likely causes of any
bias, which might have resulted in underestimating or
overestimating the effect of insolation on cognition.
There always remains the possibility of residual con-
founding. In addition to the imprecision or bias that may
be present in any measurement, we could not account for
specific psychiatric diagnoses or medicine consumption.
Also, environmental temperature may be related to cogni-
tive function, although temperature fluctuations are par-
tially controlled for by season, exercise, cardiovascular
factors, and other possible correlates of temperature [41-
44]. Eye function is another possible confounder. Specifi-
cally crystalline lens transmittance and papillary area have
been found affect circadian photoreception, although
controlling for age may reduce confounding from these
factors. [23]. The interview's time of the day may also have
an effect on cognition; however, the sampling method of
REGARDS should result in all participants having an
equal chance of being interviewed during a given time
resulting in similar time distributions at any given varia-
ble level [6].
This new finding that weather may not only affect mood,
but also cognition, has significant implications and needs
to be further elucidated in future studies. That insolation
had a relationship with cognitive function but not depres-
sion, and that the effect of insolation on cognition is
shown among depressed, but not non-depressed partici-
pants indicates that insolation may have a relationship
with cognition that is independent of, but modified by,
depression. It also suggests the possibility that light ther-
apy that is prescribed for SAD may also improve cognitive
function. Future studies involving light and other thera-
pies for SAD should include cognitive function as a varia-
ble in order to determine relationships with insolation,
mood, and cognitive function. Future studies are also
needed to demonstrate particular cognitive deficits. The
six-item screener was designed to test global cognitive sta-
tus for large numbers of participants in an easy and effi-
cient manner. While it has adequate sensitivity and
specificity as a screening procedure to identify those most
likely to have cognitive deficits, it cannot be used to make
any particular diagnosis and is limited in its sensitivity to
cognitive deficits of small magnitude. In the future, more
specific exams and diagnoses can be used to find the spe-
cific effects of sunlight on cognitive processes and dis-
eases. We also show that future research regarding
treatment and lifestyle should in particular focus on eld-
erly people, since the older a participant is, the more likely
the participant is to be cognitively impaired. In addition,
research and possibly programs regarding outreach and
health education might be targeted to depressives in lower
education groups, not only because they are known to
have lower access to healthcare in general, but also
because they are at a particularly high risk of cognitive
impairment. Many of the prior studies have looked at the
effects of weather on mood and cognition as seasonal, but
the results of this study demonstrate that the effect of sea-
son on cognition can be explained by sunlight and other
variables. This study also has an interesting finding
regarding those without an elevated level of depressive
symptoms. We did not find that sunlight meaningfully
affected the cognitive abilities of these individuals. How-
ever, this lack of a significant finding may be found due to
a number of inadequately controlled for indirect behav-
iors acting as confounders, since there is previous environ-
mental evidence for both season's effects on cognition
and environmental illumination's effects on mood and
cognition in general populations. Of particular impor-
tance, it may be true that those who are non-depressed
may spend more time outside, thus receiving a more ade-
quate supply of environmental illumination
[17,19,21,22,45-47].
Because cognitive impairment is also associated with
other psychological and neurological disorders, discover-
ing the environment's impact on cognitive functioning
within the context of these disorders may lead not only to
better understanding of the disorders, but also to the
development of targeted interventions to enhance every-
day functioning and quality of life.
Abbreviations
BMI: Body Mass Index; CESD: Center for Epidemiologic
Studies Depression Scale; CMH: Cochran-Mantel-Haen-
zel; KJ/m2/day: kilojoules per meters2 per day; NASA:
National Aeronautics and Space Administration; NCAP:
National Center for Environmental Prediction (NCEP);
NARR: North American Regional Reanalysis; REGARDS:
REasons for Geographic And Racial Differences in Stroke;
OR: odds ratio; PST: Pacific Standard Time; PCS: Physical
Environmental Health 2009, 8:34 http://www.ehjournal.net/content/8/1/34
Page 13 of 14
(page number not for citation purposes)
Components Summary; SAD: Seasonal Affective Disorder;
SF-12: 12-item Short Form; SCN: the suprachiasmatic
nuclei; W/m2: Watts/meters2.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SK performed the analysis and drafted the manuscript. LM
was a mentor for the methods, statistical analyses, manu-
script editing, and data procurement. WC was a consult-
ant for environmental science, manuscript editing, and
procured and managed data. DA was a mentor for meth-
ods and manuscript editing. VW was a consultant for cog-
nitive function in the REGARDS dataset and manuscript
editing. NS was a mentor for the methods, statistical anal-
yses, and manuscript editing.
Authors' informations
SK is a PhD student in the Department of Epidemiology at
the University of Alabama at Birmingham (UAB) and has
done work with Marshall Space Flight Center in Hunts-
ville. LM is an Assistant Professor in the Department of
Biostatistics at UAB and is also currently working with
Marshall Space Flight Center. BC is a scientist working for
the Universities Space Research Association and the Mar-
shall Space Flight Center and has extensive experience
using satellite data to characterize earth environment var-
iables. VW is an Assistant Professor, works in the Depart-
ment of Psychology in the School of Medicine at UAB, is
the Director of the Dementia Care Research Program,
Assistant Director for Translational Research on Aging and
Mobility, and has previously used the cohort used in this
study for cognitive research. DA is a Professor in and the
chair of the Department of Epidemiology at UAB and has
extensive experience in cardiovascular and genetic
research, an example being the PI of the Genetics of Left
Ventricular Hypertrophy: HyperGEN study. NS is an Asso-
ciate Professor and an environmental and occupational
epidemiologist and pediatrician whose research interests
include cancer and infectious diseases epidemiology. Her
current research activities include epidemiologic studies
relating pesticide exposure and suicide, and of workers in
the rubber industry, plastics industry, and chemical man-
ufacturing facilities.
Acknowledgements
This research project is supported by a cooperative agreement U01
NS041588 from the National Institute of Neurological Disorders and
Stroke, National Institutes of Health, Department of Health and Human
Services. The content is solely the responsibility of the authors and does
not necessarily represent the official views of the National Institute of Neu-
rological Disorders and Stroke or the National Institutes of Health. Repre-
sentatives of the funding agency have been involved in the review of the
manuscript but not directly involved in the collection, management, analysis
or interpretation of the data The authors acknowledge the participating
investigators and institutions for their valuable contributions: The Univer-
sity of Alabama at Birmingham, Birmingham, Alabama (Study PI, Statistical
and Data Coordinating Center, Survey Research Unit): George Howard
DrPH, Leslie McClure PhD, Virginia Howard PhD, Libby Wagner MA, Vir-
ginia Wadley PhD, Rodney Go PhD, Monika Safford MD, Ella Temple PhD,
Margaret Stewart MSPH, J. David Rhodes BSN; University of Vermont
(Central Laboratory): Mary Cushman MD; Wake Forest University (ECG
Reading Center): Ron Prineas MD, PhD; Alabama Neurological Institute
(Stroke Validation Center, Medical Monitoring): Camilo Gomez MD,
Susana Bowling MD; University of Arkansas for Medical Sciences (Survey
Methodology): LeaVonne Pulley PhD; University of Cincinnati (Clinical
Neuroepidemiology): Brett Kissela MD, Dawn Kleindorfer MD; Examina-
tion Management Services, Incorporated (In-Person Visits): Andra Graham;
Medical University of South Carolina (Migration Analysis Center): Daniel
Lackland DrPH; Indiana University School of Medicine (Neuropsychology
Center): Frederick Unverzagt PhD; National Institute of Neurological Dis-
orders and Stroke, National Institutes of Health (funding agency): Claudia
Moy PhD.
Additional funding, data, data processing, and consultation were provided
by an investigator-initiated grant-in-aid from NASA. NASA did not have any
role in the design and conduct of the study, the collection, management,
analysis, and interpretation of the data or the preparation or approval of
the manuscript. The manuscript was sent to NASA Marshall Space Flight
Center for review prior to submission for publication.
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... In evolving socio-ecological models of cognitive health, the role of the natural environment -particularly temperature -is not well characterized. Environmental exposures that may affect cognitive function include air pollution, sunlight, and seasonality; although further study of the seasonal effects, fully accounting for temperature, is required [12][13][14][15]. Outside of experimental studies, literature examining heat and cognition demonstrates that participants exposed to hot temperatures have slower reaction times and commit more errors on cognitive tests [16,17]. ...
... In a study of3 000 older adults from Canada, the United States and France, participants evaluated in the winter/spring (January-June) had higher odds of meeting the clinical criteria for dementia or mild cognitive impairment than those examined in the summer or fall [15]. A previous cross-sectional study in older adults from the REGARDS cohort also noted a significant adverse effect of spring and winter on cognition [13]. ...
... However, prior research, though limited geographically, has shown that outdoor temperatures correlate well with indoor temperatures in at least some locations in the U.S., such as Massachusetts (e.g., Nguyen and colleagues (2014) [52]). Second, we lacked data on sunlight, which previous literature has found to be associated with cognitive performance [13]. We did include seasonality within our regression model, and other studies in older adults in the REGARDS cohort have shown that season and sunlight are well correlated [13]. ...
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Background Older adults are particularly vulnerable to the adverse health effects of extreme temperature-related events. A growing body of literature highlights the importance of the natural environment, including air pollution and sunlight, on cognitive health. However, the relationship between exposure to outdoor temperatures and cognitive functioning, and whether there exists any differences across climate region, remains largely unexplored. We address this gap by examining the temperature-cognition association, and whether there exists any variation across climate regions in a national cohort of aging adults. Methods In this cross-sectional study, we obtained data on temperature exposure based on geocoded residential location of participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. For each participant, this information was linked to their cognitive scores from Word List Learning and Recall tests to assess cognitive functioning. We used distributed lag non-linear models (dlnm) to model temperature effects over 2 days. Multivariable linear regression was used to compute temperature-cognitive functioning associations, adjusted for important covariates. Region-specific (“Dry”, “Mediterranean/oceanic”, “Tropical” and “Continental”) associations were examined by including an interaction term between climate region and temperature. Results Amongst 20,687 individuals (mean age = 67.8; standard deviation = 9.2), exposure to region-specific extreme cold temperatures in the “dry” region (e.g., Arizona) over 2 days was associated with lower cognitive scores (Mean Difference [MD]: -0.76, 95% Confidence Interval [CI]: − 1.45, − 0.07). Associations remained significant for cumulative effects of temperature over 2 days. Extremely cold exposure in the “Mediterranean/oceanic” region (e.g., California) over 2 days was also associated with significantly lower cognitive performance (MD: -0.25, 95% CI: − 0.47, − 0.04). No significant associations were observed for exposure to hot temperatures. Cognitive performance was slightly higher in late summer and fall compared to early summer. Conclusion We noted adverse cognitive associations with cold temperatures in traditionally warmer regions of the country and improved cognition in summer and early fall seasons. While we did not observe very large significant associations, this study deepens understanding of the impact of climate change on the cognitive health of aging adults and can inform clinical care and public health preparedness plans.
... The combination of genetic, environmental and lifestyle factors have been recognized to play an important role in AD progression [15]. Interestingly, it was reported that exposure to sunlight was beneficial with a decreased risk for dementia [16,17]. Old people with non-melanoma skin cancer (NMSC) probably caused by overexposure to the sunlight had a markedly reduced risk of developing AD as compared to whom without NMSC [18,19]. ...
... Interestingly, it has been reported that sunlight exposure could decrease the risk of developing AD [16][17][18][19]. Sunlight mainly contains three lights, namely ultraviolet light, visible light and infrared light, as well as their multiple sub-divisions, such as near infrared light and far infrared light [27,29]. ...
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Background Exposure to sunlight may decrease the risk of developing Alzheimer’s disease (AD), and visible and near infrared light have been proposed as a possible therapeutic strategy for AD. Here, we investigated the effects of the visible, near infrared and far infrared (FIR) light on the cognitive ability of AD mice, and found that FIR light also showed potential in the improvement of cognitive dysfunction in AD. However, the related mechanism remains to be elucidated. Methods Morris water maze was used to evaluate the cognitive ability of APPswe/PSEN1dE9 double-transgenic AD mice after light treatment. Western blot was carried out to detect the expression of protein involved in synaptic function and amyloid-β (Aβ) production. The protein amount of interleukin (IL)-1β, IL-6, Aβ1-40 and Aβ1-42 were determined using enzyme-linked immunosorbent assay. The mRNA level of receptors was performed using real-time quantitative polymerase chain reaction. Immunostaining was performed to characterize the Aβ burden and microglial Aβ phagocytosis in the brain of AD mice. The Aβ phagocytosis of primary cultured microglia and BV2 were assessed by flow cytometry. The energy metabolism changes were evaluated using related assay kits, including adenosine triphosphate (ATP), lactate content, mitochondrial respiratory chain complex enzymatic activity and oxidized/reduced nicotinamide adenine dinucleotide assay kits. Results Our results showed that FIR light reduced Aβ burden, a hallmark of AD neuropathology, alleviated neuroinflammation, restored the expression of the presynaptic protein synaptophysin, and ameliorated learning and memory impairment in the AD mice. FIR light enhanced mitochondrial oxidative phosphorylation pathway to increase ATP production. This increased intracellular ATP promoted the extracellular ATP release from microglia stimulated by Aβ, leading to the enhanced Aβ phagocytosis through phosphoinositide 3-kinase/mammalian target of rapamycin pathways for Aβ clearance. Conclusions Our findings have uncovered a previously unappreciated function of FIR light in inducing microglial phagocytosis to clean Aβ, which may be the mechanisms for FIR light to improve cognitive dysfunction in AD mice. These results suggest that FIR light treatment is a potential therapeutic strategy for AD.
... Consistent with our findings, Shaohua et al. (2019) reported a significant association between sunlight and hospital admissions for schizophrenia on lag 0-21 days (Gu et al., 2019). Another study led by Shia et al. (2009) compared the same-day and two-week average sunlight exposures on MDs and found that only two-week sunlight exposure was related to cognitive function among depressed participants (Kent et al., 2009). As for RH, Salib et al. (2002) found a delayed association between RH and MD admissions at lag 14 days, which was greater than the same-day association (Salib and Sharp, 2002). ...
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Background Mental disorders (MDs) are behavioral or mental patterns that cause significant distress or impairment of personal functioning. Previously, temperature has been linked to MDs, but most studies suffered from exposure misclassification due to limited monitoring sites. We aimed to assess whether multiple meteorological factors could jointly trigger MD-related emergency department (ED) visits in warm season, using a highly dense weather monitoring system. Methods We conducted a time-stratified, case-crossover study. MDs-related ED visits (primary diagnosis) from May-October 2017-2018 were obtained from New York State (NYS) discharge database. We obtained solar radiation (SR), relative humidity (RH), temperature, heat index (HI), and rainfall from Mesonet, a real-time monitoring system spaced about 17 miles (126 stations) across NYS. We used conditional logistic regression to assess the weather-MD associations. Results For each interquartile range (IQR) increase, both SR (excess risk (ER): 4.9%, 95% CI: 3.2-6.7%) and RH (ER: 4.0%, 95% CI: 2.6-5.4%) showed the largest risk for MD-related ED visits at lag 0-9 days. While temperature presented a short-term risk (highest ER at lag 0-2 days: 3.7%, 95% CI: 2.5-4.9%), HI increased risk over a two-week period (ER range: 3.7-4.5%), and rainfall hours showed an inverse association with MDs (ER: -0.5%, 95% CI: 0.9-(-0.1)%). Additionally, we observed stronger association of SR, RH, temperature, and HI in September and October. Combination of high SR, RH, and temperature displayed the largest increase in MDs (ER: 7.49%, 95% CI: 3.95-11.15%). The weather-MD association was stronger for psychoactive substance usage, mood disorders, adult behavior disorders, males, Hispanics, African Americans, individuals aged 46-65, or Medicare patients. Conclusions Hot and humid weather, especially the joint effect of high sun radiation, temperature and relative humidity showed the highest risk of MD diseases. We found stronger weather-MD associations in summer transitional months, males, and minority groups. These findings also need further confirmation.
... People tend to exercise more and/or experience a better mood when there is more green coverage. The impact of precipitation might be due to the effect on mobility (36) and decreased exposure to sunlight (61). With higher precipitation, people might be less likely to go outside for physical and/or social activities (34,62). ...
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Prior research has shown that environmental hazards, such as limited green space, air pollution, and harmful weather, have the strong adverse impact on older adults' cognitive function; however, most of the studies were conducted in developed countries and limited to cross-sectional analyses. China has the largest aging population in the world so the research evidence from it can offer an insight to the study in other developing countries facing similar issues and inform future public health policy and disease control. This study examined the long-term impact of environmental factors, namely, green space coverage, air pollution, and weather conditions on cognitive function using a nationally representative sample consisting of adults aged 45 years and older selected from the China Health and Retirement Longitudinal Study (CHARLS 2011–2018), the China City Statistical Yearbook, and other sources. Multilevel growth curve models were utilized for analysis and the mediator effects of physical activity and social engagement on the relationship between environmental factors and cognitive function were examined. Findings of this study showed that after controlling for sociodemographic characteristics, annual precipitation of 80 cm or more, living in areas with July temperature of 28°C or higher, urban community, and green space coverage were positively associated with cognition score at the baseline and lower precipitation, urban community, and greater green space coverage were associated with slower cognitive decline over a 7-year period. The impact of gross domestic product (GDP) seemed to take into effect more and more over time. These effects did not substantially change after weekly total hours of physical activities and levels of social engagement were added. More research on the mechanisms of the effect of environmental factors on cognition is needed such as the subgroup analyses and/or with more aspects of environmental measures.
... Patients with depression in a room with eastern light had a mean 3.67-day shorter hospital stay than those in west-facing rooms [8]. It has been proven that less sunlight increases the risk of cognitive decline in patients with depression by 2.58 times [9]. Moreover, bright light treatment (BLT) is considered as a promising non-drug intervention for dementia, since it has positive effects on behavioral and psychological symptoms of dementia, sleep, and circadian rhythms [1]. ...
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Background Data on the association between free-living daytime sunlight exposure and incident dementia are scarce. The objective is to evaluate whether the time spent in outdoor light is related to the dementia risk and to investigate whether the optimal duration varies with clinical parameters. Methods Data were from a prospective cohort of 362,094 UK Biobank participants. A questionnaire survey was conducted to investigate how many hours the participants spent outdoors on typical summer and winter days. A restricted cubic spline (RCS) was performed to explore the potential nonlinear relationship between sunlight exposure and the risk of dementia. We used multivariate Cox proportional hazard regression models to estimate the hazard ratios (HRs) for the associations between sunlight exposure and dementia outcomes, with the change points as a reference. Results After a median follow-up of 9.0 years, 4149 (1.15%) individuals were diagnosed with dementia. RCS showed a J-shaped relationship between time spent in outdoor light and the dementia risk, with the lowest risk at three change points (1.5 h/day on average, 2 h/day in summer, and 1 h/day in winter). Cox hazard regression models showed a marked increase in risk at low exposure (HR=1.287, 95%CI 1.094–1.515) but a relatively slow increase at higher exposure (HR=1.070, 95%CI 1.031–1.10). Results are more pronounced among participants over 60 years old, females, and those with exactly 7 h of sleep every night. Conclusions Sunlight exposure had a J-shaped association with dementia risk. Giving detailed guidance on sunlight exposure can effectively prevent dementia.
... Beside ambient temperature, exposure to sunlight is an important season-dependent factor that may drive seasonality effects. An association of sunlight exposure with cognitive functioning has been 16,35]. While previous studies found associations of low vitamin D levels with poor cognition [36], it remains to be determined whether the seasonal fluctuations of vitamin D also lead to fluctuations in cognitive functioning. ...
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Seasonal variation in cognitive function and underlying cerebral hemodynamics in humans has been suggested, but not consistently shown in previous studies. We assessed cognitive function in 10,276 participants from the population-based Rotterdam Study, aged 45 years and older without dementia, at baseline and at subsequent visits between 1999 and 2016. Seasonality of five cognitive test scores and of a summary measure of global cognition were determined, as well as of brain perfusion. Using linkage with medical records, we also examined whether a seasonal variation was present in clinical diagnoses of dementia. We found a seasonal variation of global cognition (0.05 standard deviations [95% confidence interval: 0.02–0.08]), the Stroop reading task, the Purdue Pegboard test, and of the delayed world learning test, with the best performance in summer months. In line with these findings, there were fewer dementia diagnoses of dementia in spring and summer than in winter and fall. We found no seasonal variation in brain perfusion. These findings support seasonality of cognition, albeit not explained by brain perfusion.
... old) from nine countries to examine the association between time of sunset and children's physical activity revealed that daylight plays a significant role in increasing physical activity in the late afternoon and early evening; a longer daylight exposure, particularly in the evening, was associated with elevated levels of daily physical activity.Kent et al. (2009) looked at the influence of daylight on psychological function such as mood and cognitive functions in a large cohort study of 16,800 black and white participants, aged 45 and above, from North and South Carolina, USA. The study revealed that there is a significant relationship between daylight exposure at participants' residences and co ...
... Previous studies confirmed that decreased exposure to sunlight had positive impact on increased probability of cognitive impairment. Cognitive impairment also had positive impact on other psychological and neurological disorders, like depressive symptoms (Kent et al., 2009;McColl and Veitch, 2001). Thus, reduced sunlight exposure and increased probability of cognitive impairment caused by PM 2.5 may increase the risk of depressive symptoms. ...
Article
Depressive symptoms have become a serious public health issue worldwide. Several studies showed that air pollution, especially fine particulate matter (PM2.5), may be a risk factor of mental disorders. However, existing studies reported inconsistent results and little evidence is available in developing countries, like China. To fill the gap, in this study, we explored the relationship between ambient PM2.5 exposure and depressive symptoms among middle-aged and elderly Chinese adults in the Chinese Health and Retirement Longitudinal Study (CHARLS). The social and demographic variables and depressive symptoms were obtained from the Wave4 of CHARLS in 2018. PM2.5 concentrations were obtained from the national urban air quality real-time release platform of China Environmental Monitoring Station. We applied generalized linear mixed models to determine the association between PM2.5 exposure and depressive symptoms. A total of 15,105 middle-aged and elderly adults from CHARLS Wave4 were included in the analyses. We found positive impact of ambient PM2.5 on depressive symptoms for the exposure windows of 30-day, 60-day, 120-day, 180-day, 1-year and 2-year. The most significant increase was observed for 180-day moving average. For every 10μg/m³ increment in PM2.5 exposure, the incidence of depressive symptoms increased by 9% (OR = 1.09; 95%CI: 1.05, 1.14) after adjusting for age, sex and residence. In interaction analyses, we found PM2.5 had weaker effect on depressive symptoms among people who used to drink alcohol (OR = 1.05; 95%CI: 1.00, 1.10) and exercise (OR = 1.10; 95%CI: 1.02, 1.18). People living in western China (OR = 1.09; 95%CI: 1.03, 1.16) were more vulnerable than those living in eastern China (OR = 0.99; 95%CI: 0.94, 1.05). In conclusion, exposure to PM2.5 was significantly associated with depressive symptoms in middle-aged and elder Chinese adults, particularly for people who never drink, with lower physical activity levels, or lived in western China.
... Besides, air pollution can decrease physical activities and sports in polluted outdoors and consequently results in depression symptoms (21)(22)(23)(24). Moreover, air pollution biologically may decrease sunlight that may be a stressor for residents' nervous system that results in depression (25)(26)(27). Some studies have shown that air pollution can decrease faceto-face social contact among neighbors, therefore, the danger of depression enhances (28)(29)(30). ...
Article
Background: Exposure to environmental pollutants which have entered the atmosphere due to human and industrial activities results in harmful and unwanted effects on human health. The undesired effects of air pollution on mental disorders, mortality, and psychological disorders have been proven. Depression is a common mental health problem in today’s societies which impacts the quality of life. The objective of this study was to determine the relation between exposure to air pollution and frequency of depression in Shiraz (Southern Iran). Methods: Archived documents of 2658 patients diagnosed with major depressive disorder (MDD) from a psychiatric university hospital were reviewed. The concentrations of five air pollutants including CO, NOX, O3 , PM10, and NO2 were determined daily in two air quality monitory stations in downtown Shiraz (Imam Hossein Square) and Kazeroon Gate. The data relevant to these five pollutants were gathered every day for 83 months, from 2010 to 2017, and for more precision, the mean values were used. The relationship between the concentration of each pollutant in the air and the number of patients referring to hospital was determined using the Spearman’s correlation coefficient. Results: There was a direct and significant relationship between all pollutants and the number of hospital admissions in each month (P<0.05) except for CO which did not show a significant relationship with hospital admission due to depression (P>0.05). Conclusion: The results of this study indicated a positive relationship between air pollution and depression. Of course, to prove this relationship, it is essential to conduct a number of studies with appropriate methodology and design.
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Background: The COVID-19 pandemic has resulted in a large increase in the use of the home office environment. Compared to traditional commercial offices, home offices typically have more variable lighting conditions that can affect the worker's performance and well-being. Objective: To review and discuss various lighting sources and their ergonomic impacts on the population of office employees now working from home. Specifically addressing the impacts of electronic light from screens, daylight, and task lighting's impact on health and well-being in the frame of the COVID-19 pandemic. Methods: Over 75 ergonomic and lighting literature publications covering various work environments were reviewed, with a focus on the health effects of screen light, daylight, and task light. Results: Resulting from the literature review, design recommendations regarding the safety and health of home office lighting conditions are discussed. Conclusion: As COVID-19 continues to encourage remote working, individuals will be exposed to daylight and screen light in different ways compared to a traditional office environment. The impact of these different lighting conditions should be further studied to understand best design and well-being practices.
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Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n = 2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Component Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with the SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week) correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n = 232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery from depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median = 0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 1.07 (median = 0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
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Previous research has shown that respondents in sad moods are likely to elaborate persuasive messages and to be highly persuaded by strong but not weak arguments. In contrast, respondents in happy moods are less likely to elaborate persuasive messages and hence show equal, moderate, persuasion by both strong and weak arguments. To examine how mood-related persuasion effects might be moderated by attributions concerning the source of a person's mood, students were approached on days with either pleasant or unpleasant weather and were asked to participate in a survey relating to higher education. Participants either were or were not cued to the weather as a source of their mood. They listened to either strong or weak arguments supporting the implementation of comprehensive examinations for graduating students. Attitudes toward comprehensive exams were measured. Participants reported more positive moods on pleasant days than on unpleasant days. When the weather was not made salient, weather (i.e., mood) interacted with argument strength: When weather was unpleasant, argument strength had an effect on attitudes, but when weather was pleasant, argument strength had no effect. In contrast, when participants were cued to the weather, there were no weather effects on persuasion; instead, attitudes were affected only by argument strength. Implications, mechanisms, and directions for future research are discussed.
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The CES-D is a well-known index of acute depressive symptoms experienced over a 7-day period that has been used in literally hundreds of studies. This article presents the psychometric derivation of 8- and 4-item screening versions of the CES-D for use in research with community-based samples. These short depressive symptom indices can be used in those instances where a brief assessment is needed for broad screening or research purposes. Using data from a heterogeneous community sample of 411 women, the 8-item CES-D was found to correlate .93 with the full 20-item CES-D while the 4-item CES-D was found to correlate .87 with the full CES-D. In a second sample of 83 women in a residential drug abuse program, the 8- and 4-item measures correlated .54 and .47, respectively, with the BPI Depression scale.
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Evidence has accumulated over the past 15 years that a€ect in humans is cyclical. In winter there is a tendency to depression, with remission in summer, and this e€ect is stronger at higher latitudes. In order to determine whether human cognition is similarly rhythmical, this study investigated the cognitive processes of 100 participants living at 698N. Participants were tested in summer and winter on a range of cognitive tasks, including verbal memory, attention and simple reaction time tasks. The seasonally counterbalanced design and the very northerly latitude of this study provide optimal conditions for detecting impaired cognitive performance in winter, and the conclusion is negative: of ®ve tasks with seasonal e€ects, four had disadvantages in summer. Like the menstrual cycle, the circannual cycle appears to in¯uence mood but not cognition. Copyright # 1999 John Wiley & Sons, Ltd. There are three major astronomical cycles that have a period short enough to be important in the study of behaviour. The ®rst is the 24-hour rotation of the Earth on its axis, the second is the monthly rotation of the moon about the Earth and the third is the 12-month orbit of the Earth around the Sun. While cognitive psychologists have studied the daily cycle using time-of-day as an experimental variable (e.g. Smith, 1989) and the monthly cycle in particular in connection with studies of the menstrual cycle (e.g. Richardson, 1991a), the annual cycles have been seldom studied from a cognitive perspective. An investigation of the e€ect of the seasons upon normal cognitive performance is timely because evidence is accumulating of seasonal e€ects on the incidence of mood-related problems. Rosenthal et al. (1984) named this disorder Seasonal A€ective Disorder (SAD). People who su€er from this disorder have symptoms of depression that only occur during one part of the year, but that recur from year to year. The vast majority of SAD patients su€er in winter, with remission in summer, and frequently reported symptoms include sadness, anxiety, irritation, decreased activity and libido, leading both to social problems and diculty at work.