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Cross-national survey of winter and summer patterns of mood seasonality: a comparison between Italy and India
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Comprehensive Psychiatry 53 (2012) 837 – 842 www.elsevier.com/locate/comppsych
Cross-national survey of winter and summer patterns of mood seasonality: a comparison between Italy and India Lorenzo Tonetti a,⁎, Subhashis Sahu b , Vincenzo Natale a a
Department of Psychology, University of Bologna, 40127 Bologna, Italy Department of Physiology, University of Kalyani, Kalyani, 741235, India
b
Abstract The aim of this study was to compare winter and summer patterns of mood seasonality in university students living at different latitudes: Bologna, 44° N (Italy), and Kalyani, 22° N (India). To assess the mood seasonality, the Seasonal Pattern Assessment Questionnaire was administered to 1370 university students (808 females, 562 males; 862 Italians, 508 Indians), ranging in age between 18 and 28 years. A significantly higher Global Seasonality Score was observed in females than males as well as in Italians than Indians. The estimated rates of summer seasonal affective disorder (SAD) and summer subsyndromal SAD were higher in Indians, whereas Italians reported higher percentage of winter SAD and winter subsyndromal SAD. The present findings are discussed in relation to the different environmental features between the 2 countries: high summer temperature in India and short winter photoperiod along with its great excursion over the year in Italy. © 2012 Elsevier Inc. All rights reserved.
1. Introduction Seasonality of mood seems to be regularly distributed throughout the population [1,2]. Individuals with seasonal affective disorder (SAD) lie to one end of the continuum, whereas individuals whose moods are not susceptible to seasonal variations lie at the other. Individuals experiencing mild vegetative symptoms during winter or summer similar to those of SAD, subsyndromal SAD (sub-SAD), fall in the middle. To evaluate seasonal mood variations, Rosenthal et al [3] developed the Seasonal Pattern Assessment Questionnaire (SPAQ), which is the most widely used self-reporting questionnaire to assess normal and clinical seasonal mood variations. Seasonal Pattern Assessment Questionnaire was not developed as a diagnostic instrument but principally as a screening tool [4], and its psychometric properties have been shown to be good [5,6]. Several studies have been carried out in Western cultures, exploring mood seasonality. On the whole, they have highlighted higher prevalence of winter SAD than summer SAD [7]. On the contrary, few works have been carried out
⁎ Corresponding author. Tel.: +39 051 2091877; fax: +39 051 243086. E-mail address: [email protected] (L. Tonetti). 0010-440X/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.comppsych.2011.11.010
in Eastern cultures [7], and they have usually disclosed higher frequency of summer SAD than winter SAD [8,9]. A possible explanation of the higher frequency of winter SAD is related to the short winter photoperiod [1], whereas the higher prevalence of summer SAD could be related to the hotter temperatures of summer [10]. Thus, it seems plausible that the photoperiod length does not always play a primary role in the development of SAD, but from time to time, other environmental parameters (eg, temperature) can have the major relevance. The aim of the present study was to compare the frequency of winter and summer patterns of mood seasonality in 2 samples of university students living in different countries: Italy (city of Bologna, latitude 44° N) and India (town of Kalyani, latitude 22° N). The city of Bologna is characterized by a shorter winter photoperiod (months of December, January, and February for both countries) than Kalyani (see Fig. 1), with a greater excursion of this parameter over the year (ie, N6 hours of difference in Italy, whereas in India, b3 hours). The summer temperatures in India (months of March, April, and May) are higher than the corresponding temperatures in Italy (months of June, July, and August) (Fig. 2). If the short winter photoperiod along with its great excursion and hottest summer temperatures leads to higher frequency of winter SAD and summer SAD,
838
L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
16
Photoperiod length (hours)
15 14 13 12 India
11
Italy
10 9 8 7
be r O ct ob er N ov em be r D ec em be r
t
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pt
ly
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Ju
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Ja n
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ry
6
Fig. 1. Monthly values of the photoperiod length in India (Kalyani) and Italy (Bologna). The hypothetic minimum threshold is also shown. The photoperiod length has been computed using the calculator available at this Web link: http://www.sci.fi/~benefon/sol.html.
respectively, we should expect higher prevalence of winter SAD in Italy and higher incidence of summer SAD in India.
of the overall sample was 23.79 ± 1.95 (median, 22; mode, 20), with age ranging between 18 and 28 years. There was a significant age difference between Indian males (22.73 ± 1.67 years) and females (22.26 ± 1.48 years) (t506 = 3.35; P b .001) but no age difference between males (21.35 ± 2.44 years) and females (21.41 ± 1.74 years) for the Italian sample. The Indian participants (22.48 ± 1.59; median, 23; mode, 23) were significantly older than the Italian participants (21.39 ± 2.04; median, 21; mode, 20) (t1368 = 10.35; P b .001).
2. Methods 2.1. Participants and procedure
40 35 30 25 India
20
Italy
15 10 5
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be r ec
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O
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0 nu ar y
Mean maximum environmental temperature (°C)
The final sample consisted of 1370 participants (808 females, 562 males; 862 Italians, 508 Indians). The mean age
Fig. 2. Monthly values of the mean maximum environmental temperatures in India (Kalyani) and Italy (Bologna). The assumed maximum cutoff value is also shown. Kalyani temperatures have been found at this Web link: http://www.meoweather.com/history/India/na/22.983333/88.483333/Kalyani.html. Bologna temperatures can be found at this web link: http://www.meteo-net.it/statistiche/tempmedie.aspx.
L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
All participants were tested and recruited at the University of Bologna (latitude and longitude: 44° 30′ N; 11° 21′ E) and at University of Kalyani (latitude and longitude: 22° 59′ N; 88° 29′ E). The questionnaires were administered during classes by a research assistant or investigator, with groups ranging in size from 10 to 30 individuals, and participation was voluntary and unpaid. University students provided informed consent before their participation in the research project. The ethics committee of both universities approved the protocol, and the study complied with the tenets of the Declaration of Helsinki. 2.2. Measure The SPAQ was used to measure mood seasonality. The SPAQ is the most widely used self-reporting questionnaire for normal and clinical seasonal mood variations [3,6,11]. Participants indicated on a 5-point Likert scale (0-4) seasonal variations experienced in 6 areas (sleep length, mood, social activity, weight, energy, and appetite). The combined score leads to the so-called Global Seasonality Score (GSS), ranging between 0 and 24, with higher values indicating higher mood seasonality. Another scale of the SPAQ evaluates the extent to which seasonal changes are seen as a problem (none, mild, moderate, severe, or disabling). Another part of the SPAQ includes some questions (eg, “at what time of year do you feel best?” and “at what time of year do you feel worst?”) that help to discriminate the winter or summer type of SAD. We used the SAD criteria for self-administered version of SPAQ [1,2]. Aiming to define winter and summer pattern of SAD, we referred to the following classification of seasons: in both countries, winter includes December, January, and February, whereas summer is defined by March, April, and
18
Global Seasonality Score
16
May in India (state of West Bengal) [12] and June, July, and August in Italy. Winter SAD was so defined: GSS of 11 or more, felt worst in December and/or January and/or February, with acknowledgment that these seasonal changes represent at least a moderate problem. Winter sub-SAD: GSS of 9 or 10 with acknowledgment that seasonal changes represent at least a moderate problem and felt worst in December and/or January and/or February or a GSS of more than 10 with acknowledgment that seasonal changes constitute just a mild problem and felt worst in December and/or January and/or February. Summer SAD and summer sub-SAD are defined as winter SAD and winter sub-SAD, respectively, except for the feeling in worst months: June and/or July and/or August for Italian and March and/or April and/or May for Indian. We excluded participants who met criteria for both winter and summer SAD or winter and summer sub-SAD because their depression was not seasonal. 2.3. Data analysis We performed an analysis of covariance with sex and country as independent variables, age as a covariate, and the GSS as a dependent variable. Where analysis of covariance was significant, Tukey post hoc test for unequal samples was performed. Moreover, we carried out χ 2 tests to analyze the frequency of no SAD, winter SAD, winter sub-SAD, summer SAD, and summer sub-SAD, by sex and nationality. Aiming to understand which of the independent variables considered in this study could be the best predictor of mood seasonality and SAD, we performed a multiple regression analysis (sex, nationality, and age as predictive factors and GSS as dependent
P < .001 P < .001
P < .001
14
12
10
8
6 Italian
Indian Males
839
Females
Total
Fig. 3. Graphic representation of the mean GSS for males, females, and total sample by nationality. SDs and significant differences are shown.
5.13% (12) 7.30% (20) 6.30% (32) 3.85% (9) 4.01% (11) 3.94% (20) 1.28% (3) 0.73% (2) 0.98% (5) 0% (0) 0.36% (1) 0.20% (1) 88.41% (290) 78.65% (420) 82.37% (710) 0.91% (3) 2.06% (11) 1.62% (14) 2.44% (8) 2.43% (13) 2.44% (21) 1.52% (5) 5.99% (32) 4.29% (37) 6.71% (22) 10.86% (58) 9.28% (80) 88.97% (500) 81.68% (660) 84.67% (1160) Percentages and absolute numbers are shown.
2.67% (15) 3.84% (31) 3.36% (46) 3.02% (17) 2.97% (24) 2.99% (41) 1.42% (8) 4.21% (34) 3.07% (42)
Winter sub-SAD Winter SAD Winter SAD Winter sub-SAD
Summer SAD
Summer sub-SAD
No SAD Winter SAD
Winter sub-SAD
Summer SAD
Summer sub-SAD
No SAD
Indian Italian
Our study cross-sectionally compared, for the first time, the winter and summer patterns of mood seasonality in Italian and Indian university students. First, with reference to mood seasonality, we found in the whole sample a significant sex effect, with females more susceptible to seasonal sensitivity than males, confirming results of previous studies [13,14]. Regarding nationality differences, Italians had higher mood seasonality than Indians. Because Bologna (44° N) is located at higher latitude than Kalyani (22° N), it is possible to assume that, in
Total sample
4. Discussion
Table 1 Distribution of males and females across the SAD and sub-SAD categories, in the total sample and separately for nationality
Analysis of covariance showed a significant effect of sex on GSS (F1,1365 = 69.11; P b .001), with females (9.16 ± 3.74) scoring higher than males (7.47 ± 3.70). A significant effect was also observed for nationality (F1,1365 = 64.60; P b .001), with Italians (9.20 ± 4.17) having higher scores than Indians (7.44 ± 2.84) (see Fig. 3). The interaction between the 2 factors resulted significant as well (Fig. 3); performing post hoc tests, Italian females (10.66 ± 3.77) gained significantly higher scores than Italian males (7.73 ± 4.15) (P b .001) as well as Indian females (7.66 ± 2.75) (P b .001). The sex distribution across no SAD, winter SAD, winter sub-SAD, summer SAD, and summer sub-SAD categories was significantly different (χ 42 = 18.24; P b .01) (see Table 1), with higher percentages of females in all categories, except for summer SAD and no SAD, where males were prevalent. Aiming to analyze the sex distribution separately by nationality, we performed another χ 2 test (Table 1), observing a still significantly different sex distribution in Italians (χ 42 = 17.22; P b .01) but not in Indians (χ 42 = 2.26; P = .69). We analyzed the distribution of Italians and Indians across the aforementioned categories, carrying out an additional χ 2 test. We observed a still significantly different distribution (χ 24 = 80.69; P b .001), with higher frequency of winter SAD and winter sub-SAD in Italians and higher prevalence of summer SAD and summer sub-SAD in Indians (see Table 1). Moreover, Indians reported higher occurrence of no SAD than Italians (Table 1). Performing a multiple regression analysis, sex resulted to be the best predictor of GSS (β = .25; t1366 = 9.93; P b .001), followed by nationality (β = .24; t1366 = 8.97; P b .001). Age was not a significant predictor. Finally, at the logistic regression analysis, the entire model resulted significant (χ 32 = 28.77; P b .001); nationality was associated with the highest odds ratio (2.91), followed by sex (1.44) and age (.97).
Summer SAD
3. Results
3.91% (22) 7.30% (59) 5.91% (81)
Summer sub-SAD
No SAD
variable) and a maximum likelihood estimation of the logistic regression analysis, using the quasi-Newton estimate methods, (sex, age, and nationality as independent variables and SAD presence or absence as a dichotomous dependent variable), respectively.
89.74% (210) 87.59% (240) 88.58% (375)
L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
Males Females Total
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L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
this case, latitude plays a modulating role on mood seasonality, adding indirect support to the latitude gradient hypothesis [15]. The significant interaction between sex and nationality on GSS highlighted that Italian males and females differed significantly, whereas this was not true for Indians; the lack of significant sex effect in Indians is discussed below. These results have been confirmed performing a multiple regression analysis, which showed that sex in the whole sample was the best predictor of GSS, followed by nationality. When we analyzed the extremes of the mood seasonality continuum, we found, in the whole sample, a significantly higher prevalence of females in all SAD and sub-SAD categories, except for summer SAD and no SAD. These data confirm the higher prevalence of SAD in females than males [1,2,16]. Because previous studies carried out in Asian populations (specifically in Chinese and Japanese populations) [8,9] failed to detect a preponderance of females in seasonal affected individuals, we analyzed the distribution of sexes in SAD categories separately for Indians and Italians. We observed a still significantly different sex distribution in Italians that was lacking in Indians. These results could be caused by cultural differences between Western and Eastern countries because a previous study [17] showed that the stereotype of females as more emotional is more valid in individualistic (such as Italy) than collectivistic (such as India) cultures. An alternative explanation of this differential sex effect between Italians and Indians could be related to the potentially different effects of photoperiod and temperature on males and females. Specifically, photoperiod could act as a synchronizer for human body (particularly for the sleep-wake cycle) especially when its excursion is great and its length is lower than a hypothetic minimum threshold value. Assuming that females may be more dependent on external stimuli, this could be the reason why they are more sensitive to the variation of the photoperiod length in Italy, which is more extreme than in India. On the other hand, we did not observe any significant sex difference in India, probably because the highest summer temperature could stress the overall homeostasis of human body, which is similar in males and females. The observed higher prevalence of winter SAD and winter sub-SAD in Italians and summer SAD and summer sub-SAD in Indians confirms our expectations based on the role played by the short winter photoperiod with great excursion [1] and hottest summer temperature [10] in the development of winter SAD and summer SAD, respectively. In Kalyani (India), the summer is followed by rainy season, so the last part of summer is very humid and uncomfortable, which could hypothetically contribute to the summer depression. The results of the χ 2 tests have been confirmed by the logistic regression analysis, which showed that nationality was the highest risk factor for the developing of SAD. As an alternative explanation to the
841
short winter photoperiod (with great excursion) and hottest summer temperature, the different prevalence of winter and summer SAD in India and Italy could be related to cultural factors. Kasof [7] has shown that 2 dimensions of nation culture (ie, individualism and power distance) are related to winter SAD and summer SAD, with countries high in individualism and low in power distance (such as Italy) showing higher prevalence of winter SAD. On the contrary, countries characterized by low individualism and high power distance (this is the case of India) seem to be more affected by summer SAD. However, Kasof [7] himself acknowledged that his data can be caused by environmental factors such as winter photoperiod and summer temperature. Based on our data, it is possible to posit that the photoperiod plays a primary role on the development of winter SAD only when its excursion is great and when its length is lower than a hypothetic minimum threshold (in our case, 9.6 hours) (Fig. 1). On the other hand, it seems possible that summer temperature leads to a mental distress only when it goes over an assumed maximum cutoff value (in the present article, 33.8°C) (Fig. 2), altering the overall homeostatic process. Aiming to verify the plausibility of these hypotheses, future studies should explore the prevalence of winter and summer SAD in countries with extreme excursion in the temperature and minimum excursion in the photoperiod length and vice versa. Our data do not allow us to exclude an alternative hypothesis explaining the winter and summer SAD, the phase-shift hypothesis [18,19], which postulates abnormally delayed circadian rhythms in seasonal depressed patients. Future studies should try to verify this hypothesis using a biologic marker for internal circadian misalignment (eg, phase angle difference between the dim light melatonin onset and the midpoint of sleep) [18]. References [1] Kasper S, Wehr TA, Bartko JJ, Gaist PA, Rosenthal NE. Epidemiological findings of seasonal changes in mood and behaviour: a telephone survey of Montgomery County, Maryland. Arch Gen Psychiatry 1989;46:823-33. [2] Rosen LN, Targum SD, Terman M, Bryant MJ, Hoffman H, Kasper SF, et al. Prevalence of seasonal affective disorder at four latitudes. Psychiatry Res 1990;31:131-44. [3] Rosenthal NE, Bradt GH, Wehr TA. Seasonal Pattern Assesssment Questionnaire (SPAQ). Bethesda (Md): National Institute of Mental Health; 1984. [4] Rosenthal NE, Genhart M, Sack DA, Skwerer RG, Wher TA. Seasonal affective disorder and its relevance for the understanding and treatment of bulimia. In: Hudson JI, & Pope HG, editors. The psychobiology of bulimia. Washington (DC): American Psychiatric Press; 1987. p. 203-28. [5] Magnusson A, Friis S, Opjordsmoen S. Internal consistency of the Seasonal Pattern Assessment questionnaire (SPAQ). J Affect Disord 1997;42:113-6. [6] Natale V, Danesi E, Scapellato P. An Italian version of Seasonal Pattern Assessment Questionnaire (SPAQ). Test Psicometria Metodol 2003;10:33-42 [n Italian].
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[7] Kasof J. Cultural variation in seasonal depression: cross-national differences in winter versus summer patterns of seasonal affective disorder. J Affect Disord 2009;115:79-86. [8] Han L, Wang K, Cheng Y, Du Z, Rosenthal NE, Primeau F. Summer and winter patterns of seasonality in Chinese college students: a replication. Compr Psychiatry 2000;41:57-62. [9] Ozaki N, Ono Y, Ito A, Rosenthal NE. Prevalence of seasonal difficulties in mood and behavior among Japanese civil servants. Am J Psychiatry 1995;152:1225-7. [10] Wehr TA, Sack DA, Rosenthal NE. Seasonal affective disorder with summer depression and winter hypomania. Am J Psychiatry 1987;144: 1602-3. [11] Muscettola G, Barbato G, Ficca G, Puca M, Aguglia E, Amati A. Seasonality of mood in Italy: role of latitude and sociocultural factors. J Affect Disord 1995;33:135-9. [12] Pal SK. Population ecology of free-ranging urban dogs in West Bengal, India. Acta Theriol 2001;46:69-78.
[13] Parslow RA, Jorn AF, Butterworth P, Jacomb PA, Rodgers B. An examination of seasonality experienced by Australian living in a continental temperate climate zone. J Affect Disord 2004;80: 181-90. [14] Tonetti L, Barbato G, Fabbri M, Adan A, Natale V. Mood seasonality: a cross-sectional study of subjects aged between 10 and 25 years. J Affect Disord 2007;97:155-60. [15] Magnusson A. An overview of epidemiological studies on seasonal affective disorder. Acta Psychiatr Scand 2000;101:176-84. [16] Natale V, Adan A, Scapellato P. Are seasonality of mood and eveningness closely associated? Psychiatry Res 2005;136:51-60. [17] Singh-Manaoux A. Culture and gender issues in adolescence: evidence from studies on emotion. Psicothema 2000;12:93-100. [18] Lewy AJ, Lefler BJ, Emens JS, Bauer VK. The circadian basis of winter depression. Proc Natl Acad Sci USA 2006;103:7414-9. [19] Lewy AJ. Circadian misalignment in mood disturbances. Curr Psychiatry Rep 2009;11:459-65.
Comprehensive Psychiatry 53 (2012) 837 – 842 www.elsevier.com/locate/comppsych
Cross-national survey of winter and summer patterns of mood seasonality: a comparison between Italy and India Lorenzo Tonetti a,⁎, Subhashis Sahu b , Vincenzo Natale a a
Department of Psychology, University of Bologna, 40127 Bologna, Italy Department of Physiology, University of Kalyani, Kalyani, 741235, India
b
Abstract The aim of this study was to compare winter and summer patterns of mood seasonality in university students living at different latitudes: Bologna, 44° N (Italy), and Kalyani, 22° N (India). To assess the mood seasonality, the Seasonal Pattern Assessment Questionnaire was administered to 1370 university students (808 females, 562 males; 862 Italians, 508 Indians), ranging in age between 18 and 28 years. A significantly higher Global Seasonality Score was observed in females than males as well as in Italians than Indians. The estimated rates of summer seasonal affective disorder (SAD) and summer subsyndromal SAD were higher in Indians, whereas Italians reported higher percentage of winter SAD and winter subsyndromal SAD. The present findings are discussed in relation to the different environmental features between the 2 countries: high summer temperature in India and short winter photoperiod along with its great excursion over the year in Italy. © 2012 Elsevier Inc. All rights reserved.
1. Introduction Seasonality of mood seems to be regularly distributed throughout the population [1,2]. Individuals with seasonal affective disorder (SAD) lie to one end of the continuum, whereas individuals whose moods are not susceptible to seasonal variations lie at the other. Individuals experiencing mild vegetative symptoms during winter or summer similar to those of SAD, subsyndromal SAD (sub-SAD), fall in the middle. To evaluate seasonal mood variations, Rosenthal et al [3] developed the Seasonal Pattern Assessment Questionnaire (SPAQ), which is the most widely used self-reporting questionnaire to assess normal and clinical seasonal mood variations. Seasonal Pattern Assessment Questionnaire was not developed as a diagnostic instrument but principally as a screening tool [4], and its psychometric properties have been shown to be good [5,6]. Several studies have been carried out in Western cultures, exploring mood seasonality. On the whole, they have highlighted higher prevalence of winter SAD than summer SAD [7]. On the contrary, few works have been carried out
⁎ Corresponding author. Tel.: +39 051 2091877; fax: +39 051 243086. E-mail address: [email protected] (L. Tonetti). 0010-440X/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.comppsych.2011.11.010
in Eastern cultures [7], and they have usually disclosed higher frequency of summer SAD than winter SAD [8,9]. A possible explanation of the higher frequency of winter SAD is related to the short winter photoperiod [1], whereas the higher prevalence of summer SAD could be related to the hotter temperatures of summer [10]. Thus, it seems plausible that the photoperiod length does not always play a primary role in the development of SAD, but from time to time, other environmental parameters (eg, temperature) can have the major relevance. The aim of the present study was to compare the frequency of winter and summer patterns of mood seasonality in 2 samples of university students living in different countries: Italy (city of Bologna, latitude 44° N) and India (town of Kalyani, latitude 22° N). The city of Bologna is characterized by a shorter winter photoperiod (months of December, January, and February for both countries) than Kalyani (see Fig. 1), with a greater excursion of this parameter over the year (ie, N6 hours of difference in Italy, whereas in India, b3 hours). The summer temperatures in India (months of March, April, and May) are higher than the corresponding temperatures in Italy (months of June, July, and August) (Fig. 2). If the short winter photoperiod along with its great excursion and hottest summer temperatures leads to higher frequency of winter SAD and summer SAD,
838
L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
16
Photoperiod length (hours)
15 14 13 12 India
11
Italy
10 9 8 7
be r O ct ob er N ov em be r D ec em be r
t
em Se
pt
ly
us Au g
Ju
ne Ju
ay M
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ar
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Fe
Ja n
ua
ry
ry
6
Fig. 1. Monthly values of the photoperiod length in India (Kalyani) and Italy (Bologna). The hypothetic minimum threshold is also shown. The photoperiod length has been computed using the calculator available at this Web link: http://www.sci.fi/~benefon/sol.html.
respectively, we should expect higher prevalence of winter SAD in Italy and higher incidence of summer SAD in India.
of the overall sample was 23.79 ± 1.95 (median, 22; mode, 20), with age ranging between 18 and 28 years. There was a significant age difference between Indian males (22.73 ± 1.67 years) and females (22.26 ± 1.48 years) (t506 = 3.35; P b .001) but no age difference between males (21.35 ± 2.44 years) and females (21.41 ± 1.74 years) for the Italian sample. The Indian participants (22.48 ± 1.59; median, 23; mode, 23) were significantly older than the Italian participants (21.39 ± 2.04; median, 21; mode, 20) (t1368 = 10.35; P b .001).
2. Methods 2.1. Participants and procedure
40 35 30 25 India
20
Italy
15 10 5
be r em
be r ec
m D
ov e
ob er N
ct
be r m pt e
O
t gu s Se
Au
Ju ly
ne Ju
ay M
ril Ap
ch ar M
ua r
Fe br
Ja
y
0 nu ar y
Mean maximum environmental temperature (°C)
The final sample consisted of 1370 participants (808 females, 562 males; 862 Italians, 508 Indians). The mean age
Fig. 2. Monthly values of the mean maximum environmental temperatures in India (Kalyani) and Italy (Bologna). The assumed maximum cutoff value is also shown. Kalyani temperatures have been found at this Web link: http://www.meoweather.com/history/India/na/22.983333/88.483333/Kalyani.html. Bologna temperatures can be found at this web link: http://www.meteo-net.it/statistiche/tempmedie.aspx.
L. Tonetti et al. / Comprehensive Psychiatry 53 (2012) 837–842
All participants were tested and recruited at the University of Bologna (latitude and longitude: 44° 30′ N; 11° 21′ E) and at University of Kalyani (latitude and longitude: 22° 59′ N; 88° 29′ E). The questionnaires were administered during classes by a research assistant or investigator, with groups ranging in size from 10 to 30 individuals, and participation was voluntary and unpaid. University students provided informed consent before their participation in the research project. The ethics committee of both universities approved the protocol, and the study complied with the tenets of the Declaration of Helsinki. 2.2. Measure The SPAQ was used to measure mood seasonality. The SPAQ is the most widely used self-reporting questionnaire for normal and clinical seasonal mood variations [3,6,11]. Participants indicated on a 5-point Likert scale (0-4) seasonal variations experienced in 6 areas (sleep length, mood, social activity, weight, energy, and appetite). The combined score leads to the so-called Global Seasonality Score (GSS), ranging between 0 and 24, with higher values indicating higher mood seasonality. Another scale of the SPAQ evaluates the extent to which seasonal changes are seen as a problem (none, mild, moderate, severe, or disabling). Another part of the SPAQ includes some questions (eg, “at what time of year do you feel best?” and “at what time of year do you feel worst?”) that help to discriminate the winter or summer type of SAD. We used the SAD criteria for self-administered version of SPAQ [1,2]. Aiming to define winter and summer pattern of SAD, we referred to the following classification of seasons: in both countries, winter includes December, January, and February, whereas summer is defined by March, April, and
18
Global Seasonality Score
16
May in India (state of West Bengal) [12] and June, July, and August in Italy. Winter SAD was so defined: GSS of 11 or more, felt worst in December and/or January and/or February, with acknowledgment that these seasonal changes represent at least a moderate problem. Winter sub-SAD: GSS of 9 or 10 with acknowledgment that seasonal changes represent at least a moderate problem and felt worst in December and/or January and/or February or a GSS of more than 10 with acknowledgment that seasonal changes constitute just a mild problem and felt worst in December and/or January and/or February. Summer SAD and summer sub-SAD are defined as winter SAD and winter sub-SAD, respectively, except for the feeling in worst months: June and/or July and/or August for Italian and March and/or April and/or May for Indian. We excluded participants who met criteria for both winter and summer SAD or winter and summer sub-SAD because their depression was not seasonal. 2.3. Data analysis We performed an analysis of covariance with sex and country as independent variables, age as a covariate, and the GSS as a dependent variable. Where analysis of covariance was significant, Tukey post hoc test for unequal samples was performed. Moreover, we carried out χ 2 tests to analyze the frequency of no SAD, winter SAD, winter sub-SAD, summer SAD, and summer sub-SAD, by sex and nationality. Aiming to understand which of the independent variables considered in this study could be the best predictor of mood seasonality and SAD, we performed a multiple regression analysis (sex, nationality, and age as predictive factors and GSS as dependent
P < .001 P < .001
P < .001
14
12
10
8
6 Italian
Indian Males
839
Females
Total
Fig. 3. Graphic representation of the mean GSS for males, females, and total sample by nationality. SDs and significant differences are shown.
5.13% (12) 7.30% (20) 6.30% (32) 3.85% (9) 4.01% (11) 3.94% (20) 1.28% (3) 0.73% (2) 0.98% (5) 0% (0) 0.36% (1) 0.20% (1) 88.41% (290) 78.65% (420) 82.37% (710) 0.91% (3) 2.06% (11) 1.62% (14) 2.44% (8) 2.43% (13) 2.44% (21) 1.52% (5) 5.99% (32) 4.29% (37) 6.71% (22) 10.86% (58) 9.28% (80) 88.97% (500) 81.68% (660) 84.67% (1160) Percentages and absolute numbers are shown.
2.67% (15) 3.84% (31) 3.36% (46) 3.02% (17) 2.97% (24) 2.99% (41) 1.42% (8) 4.21% (34) 3.07% (42)
Winter sub-SAD Winter SAD Winter SAD Winter sub-SAD
Summer SAD
Summer sub-SAD
No SAD Winter SAD
Winter sub-SAD
Summer SAD
Summer sub-SAD
No SAD
Indian Italian
Our study cross-sectionally compared, for the first time, the winter and summer patterns of mood seasonality in Italian and Indian university students. First, with reference to mood seasonality, we found in the whole sample a significant sex effect, with females more susceptible to seasonal sensitivity than males, confirming results of previous studies [13,14]. Regarding nationality differences, Italians had higher mood seasonality than Indians. Because Bologna (44° N) is located at higher latitude than Kalyani (22° N), it is possible to assume that, in
Total sample
4. Discussion
Table 1 Distribution of males and females across the SAD and sub-SAD categories, in the total sample and separately for nationality
Analysis of covariance showed a significant effect of sex on GSS (F1,1365 = 69.11; P b .001), with females (9.16 ± 3.74) scoring higher than males (7.47 ± 3.70). A significant effect was also observed for nationality (F1,1365 = 64.60; P b .001), with Italians (9.20 ± 4.17) having higher scores than Indians (7.44 ± 2.84) (see Fig. 3). The interaction between the 2 factors resulted significant as well (Fig. 3); performing post hoc tests, Italian females (10.66 ± 3.77) gained significantly higher scores than Italian males (7.73 ± 4.15) (P b .001) as well as Indian females (7.66 ± 2.75) (P b .001). The sex distribution across no SAD, winter SAD, winter sub-SAD, summer SAD, and summer sub-SAD categories was significantly different (χ 42 = 18.24; P b .01) (see Table 1), with higher percentages of females in all categories, except for summer SAD and no SAD, where males were prevalent. Aiming to analyze the sex distribution separately by nationality, we performed another χ 2 test (Table 1), observing a still significantly different sex distribution in Italians (χ 42 = 17.22; P b .01) but not in Indians (χ 42 = 2.26; P = .69). We analyzed the distribution of Italians and Indians across the aforementioned categories, carrying out an additional χ 2 test. We observed a still significantly different distribution (χ 24 = 80.69; P b .001), with higher frequency of winter SAD and winter sub-SAD in Italians and higher prevalence of summer SAD and summer sub-SAD in Indians (see Table 1). Moreover, Indians reported higher occurrence of no SAD than Italians (Table 1). Performing a multiple regression analysis, sex resulted to be the best predictor of GSS (β = .25; t1366 = 9.93; P b .001), followed by nationality (β = .24; t1366 = 8.97; P b .001). Age was not a significant predictor. Finally, at the logistic regression analysis, the entire model resulted significant (χ 32 = 28.77; P b .001); nationality was associated with the highest odds ratio (2.91), followed by sex (1.44) and age (.97).
Summer SAD
3. Results
3.91% (22) 7.30% (59) 5.91% (81)
Summer sub-SAD
No SAD
variable) and a maximum likelihood estimation of the logistic regression analysis, using the quasi-Newton estimate methods, (sex, age, and nationality as independent variables and SAD presence or absence as a dichotomous dependent variable), respectively.
89.74% (210) 87.59% (240) 88.58% (375)
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Males Females Total
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this case, latitude plays a modulating role on mood seasonality, adding indirect support to the latitude gradient hypothesis [15]. The significant interaction between sex and nationality on GSS highlighted that Italian males and females differed significantly, whereas this was not true for Indians; the lack of significant sex effect in Indians is discussed below. These results have been confirmed performing a multiple regression analysis, which showed that sex in the whole sample was the best predictor of GSS, followed by nationality. When we analyzed the extremes of the mood seasonality continuum, we found, in the whole sample, a significantly higher prevalence of females in all SAD and sub-SAD categories, except for summer SAD and no SAD. These data confirm the higher prevalence of SAD in females than males [1,2,16]. Because previous studies carried out in Asian populations (specifically in Chinese and Japanese populations) [8,9] failed to detect a preponderance of females in seasonal affected individuals, we analyzed the distribution of sexes in SAD categories separately for Indians and Italians. We observed a still significantly different sex distribution in Italians that was lacking in Indians. These results could be caused by cultural differences between Western and Eastern countries because a previous study [17] showed that the stereotype of females as more emotional is more valid in individualistic (such as Italy) than collectivistic (such as India) cultures. An alternative explanation of this differential sex effect between Italians and Indians could be related to the potentially different effects of photoperiod and temperature on males and females. Specifically, photoperiod could act as a synchronizer for human body (particularly for the sleep-wake cycle) especially when its excursion is great and its length is lower than a hypothetic minimum threshold value. Assuming that females may be more dependent on external stimuli, this could be the reason why they are more sensitive to the variation of the photoperiod length in Italy, which is more extreme than in India. On the other hand, we did not observe any significant sex difference in India, probably because the highest summer temperature could stress the overall homeostasis of human body, which is similar in males and females. The observed higher prevalence of winter SAD and winter sub-SAD in Italians and summer SAD and summer sub-SAD in Indians confirms our expectations based on the role played by the short winter photoperiod with great excursion [1] and hottest summer temperature [10] in the development of winter SAD and summer SAD, respectively. In Kalyani (India), the summer is followed by rainy season, so the last part of summer is very humid and uncomfortable, which could hypothetically contribute to the summer depression. The results of the χ 2 tests have been confirmed by the logistic regression analysis, which showed that nationality was the highest risk factor for the developing of SAD. As an alternative explanation to the
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short winter photoperiod (with great excursion) and hottest summer temperature, the different prevalence of winter and summer SAD in India and Italy could be related to cultural factors. Kasof [7] has shown that 2 dimensions of nation culture (ie, individualism and power distance) are related to winter SAD and summer SAD, with countries high in individualism and low in power distance (such as Italy) showing higher prevalence of winter SAD. On the contrary, countries characterized by low individualism and high power distance (this is the case of India) seem to be more affected by summer SAD. However, Kasof [7] himself acknowledged that his data can be caused by environmental factors such as winter photoperiod and summer temperature. Based on our data, it is possible to posit that the photoperiod plays a primary role on the development of winter SAD only when its excursion is great and when its length is lower than a hypothetic minimum threshold (in our case, 9.6 hours) (Fig. 1). On the other hand, it seems possible that summer temperature leads to a mental distress only when it goes over an assumed maximum cutoff value (in the present article, 33.8°C) (Fig. 2), altering the overall homeostatic process. Aiming to verify the plausibility of these hypotheses, future studies should explore the prevalence of winter and summer SAD in countries with extreme excursion in the temperature and minimum excursion in the photoperiod length and vice versa. Our data do not allow us to exclude an alternative hypothesis explaining the winter and summer SAD, the phase-shift hypothesis [18,19], which postulates abnormally delayed circadian rhythms in seasonal depressed patients. Future studies should try to verify this hypothesis using a biologic marker for internal circadian misalignment (eg, phase angle difference between the dim light melatonin onset and the midpoint of sleep) [18]. References [1] Kasper S, Wehr TA, Bartko JJ, Gaist PA, Rosenthal NE. Epidemiological findings of seasonal changes in mood and behaviour: a telephone survey of Montgomery County, Maryland. Arch Gen Psychiatry 1989;46:823-33. [2] Rosen LN, Targum SD, Terman M, Bryant MJ, Hoffman H, Kasper SF, et al. Prevalence of seasonal affective disorder at four latitudes. Psychiatry Res 1990;31:131-44. [3] Rosenthal NE, Bradt GH, Wehr TA. Seasonal Pattern Assesssment Questionnaire (SPAQ). Bethesda (Md): National Institute of Mental Health; 1984. [4] Rosenthal NE, Genhart M, Sack DA, Skwerer RG, Wher TA. Seasonal affective disorder and its relevance for the understanding and treatment of bulimia. In: Hudson JI, & Pope HG, editors. The psychobiology of bulimia. Washington (DC): American Psychiatric Press; 1987. p. 203-28. [5] Magnusson A, Friis S, Opjordsmoen S. Internal consistency of the Seasonal Pattern Assessment questionnaire (SPAQ). J Affect Disord 1997;42:113-6. [6] Natale V, Danesi E, Scapellato P. An Italian version of Seasonal Pattern Assessment Questionnaire (SPAQ). Test Psicometria Metodol 2003;10:33-42 [n Italian].
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