Seasonal mood changes in 250 normal women

77

Psychiatry Research, 49:77-87 Elsevier

Seasonal Susan

Harris

Received August

Mood

Changes

in 250

Normal

Women

and Bess Dawson-Hughes 7, 1992; revised version received February 12, 1993; accepted March 8, 1993.

Abstract. A l-year prospective study of seasonal mood changes was conducted in 250 female Boston area residents, aged 43 to 72, who were participants in a study of vitamin D supplementation. Each woman completed the Profile of Mood States questionnaire at four study visits. There were significant changes over the year in scores for Tension-Anxiety, Depression-Dejection, AngerHostility, Fatigue-Inertia, and Confusion-Bewilderment. These scores were all highest or “worst” in the fall and lowest in the spring or summer. Worse mood scores were associated with fewer hours of sleep. Serum thyroxine was positively associated with higher Depression-Dejection scores in August through November and with higher (better) Vigor-Activity scores in February through May. Supplementation with 400 IU of vitamin D did not appear to affect levels or changes in mood scores. Key Words. Annual rhythms, postmenopausal Mood States, depression, vitamin D.

women,

thyroxine,

Profile

of

It has been suggested that psychiatric disorders characterized by seasonal fluctuations in behavior and affect reflect increases in amplitude of comparable cycles in the general population (Kraepelin, 1921; Eastwood et al., 1985). As many as 92% of people living at Northern latitude 39” or higher may experience seasonal changes in mood and behavior, and about a fourth of them consider these changes to be a problem (Kasper et al., 19896). An understanding of seasonal patterns of mood change in normal populations may lead to better methods of preventing and treating affective disorders, and to modifications of environmental factors related to health and productivity of a large segment of the general population. The range in degree of seasonal change appears to be a continuum including, at one extreme, patients with seasonal affective disorder (SAD), a major affective disorder characterized in part by recurring fall or winter depression (Rosenthal et al., 1984), and at the other, individuals who do not report difficulties related to season. People experiencing mild SAD-type symptoms have been described as having subsyndromal SAD (Kasper et al., 1989~). The latter two groups constitute the “normal” population, from which the present study group is drawn.

Susan Harris, M.S., and Bess Dawson-Hughes, M.D., are in the Calcium and Bone Metabolism Laboratory at the USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA. Dr. Dawson-Hughes is Associate Professor of Medicine at Tufts University. (Reprint requests to Dr. B. Dawson-Hughes, Calcium and Bone Metabolism Laboratory, USDA Human Nutrition Research Center on Aging at Tufts University, 71 I Washington St., Boston, MA 021 II, USA.) 0165-1781/93/$06.00

@ 1993 Elsevier Scientific

Publishers

Ireland

Ltd.

78

The seasonal features associated with SAD include anergia, hypersomnia, increased appetite, weight gain, and carbohydrate craving (Rosenthal et al., 1984). Changes in these symptoms, as well as changes in levels of the hormone melatonin and changes in sleep patterns, have been induced by exposure to bright artificial light (Lewy et al., 1980; Skwerer et al., 1988; Kasper et al., 1989a; Hauri and Esther, 1990; Wehr, 1991), but attempts to identify the biochemical factors that distinguish SAD patients from normal subjects have been largely unsuccessful (Terman, 1988). It has been hypothesized that these factors may include, among others, thyroid stimulating hormone (TSH) and the thyroid hormones triiodothyronine (T,) and thyroxine (T4) (Terman, 1988; Kasper et al., 1989~). Although relationships between thyroid hormone status and major depression have been studied extensively (Bauer and Whybrow, 1988; Joffe, 1990), the effects of thyroid hormones on mood in nonpsychiatric patients have not. It is not known whether thyroid hormones or other biological parameters with possible connections to SAD are associated with seasonal mood changes in normal populations. Evidence of a positive effect of physical activity on mood and life satisfaction is mixed (Hughes, 1984; Stephens, 1988; Berger, 1989), and we are aware of only one study that examined the effects of physical activity on seasonal changes in mood (Suter et al., 1991). This study extends the work of other investigators who have examined seasonal mood changes in normal populations (Eastwood et al., 1985; Potkin et al., 1986; Kasper et al., 1989a, 19896; Rosen et al., 1989; Haggag et al., 1990) to a group that has not been the focus of previous studies: middle-aged and older women. In this group of 250 women, we report changes in six mood factors measured four times over a period of 1 year. Additionally, we have investigated interrelationships of mood, season, hours of sleep, physical activity, and, in a subset of the study group, TSH and the thyroid hormones. Since the study volunteers were participants in a vitamin D supplementation study, we have also examined possible effects of vitamin D status on mood and mood changes.

Methods Subjects and Study Design. The 250 women in this study, residents of the greater Boston area, ranged in age from 43 to 72 years (mean age = 62 years, SD = 5) in June or July of 1989 when they were enrolled in a randomized clinical trial designed to examine the effects of supplementation with vitamin D on overall and seasonal rates of bone loss (Dawson-Hughes et al., 1991). Eligibility criteria for enrollment have been reported previously (Dawson-Hughes et al., 1991) and included, in brief, white race, good general health, at least 6 months since last menses, low to moderate dietary calcium intake, and spinal bone density within 2 SD of the reference mean. All volunteers received 377 mg of stipplemental calcium daily during the trial, and half the women also received 400 IU of vitamin D daily. The protocol was approved by the Human Investigation Review Committee of Tufts University, and written informed consent was obtained from each woman. Table 1 presents demographic characteristics of the volunteers. Each volunteer came to the USDA/Tufts Human Nutrition Research Center on Aging five times during the l-year study for bone scans and other measurements including hours of sleep, level of physical activity, and determination of hormone levels. Volunteers were familiar with most of the measurement procedures from participation in a previous study (Dawson-Hughes et al., 1990), and the procedures were neither painful nor difficult. The Profile of Mood States

79 Table 1. Characteristics of 250 women Age’ Under 55

1 1%

55-65

58%

Over 65

30%

Marital status Married

60%

Widowed

10% 6%

Divorced or separated

23%

Never married

Grade or high school

25%

College

29%

Graduate school

23%

Other

24%

Treatment

for depression

Current

1%

Past

5% 94%

Never Treatment Current Past Never

for other psychiatric

condition 1% 1% 98%

1. Age was recorded at the first visit of the current study. Other characteristics were recorded in interviews conducted approximately 3 years earlier.

(POMS; McNair et al., 1979) was completed at each of the last four study visits. These visits took place during the four periods of August through November, December or January, February through May, and June or July. The POMS was self-administered, and volunteers were instructed to complete it for the “previous week including today.” Measurements. The POMS is an adjective rating scale that measures six mood factors derived from repeated factor analyses, and has been tested for reliability and validity (McNair et al., 1979). It has been used in prospective studies of mood change both in psychiatric outpatients (Lorr et al., 1963; Haskell et al., 1969) and in people without known psychiatric conditions (Gordon et al., 1986; Kasper et al., 1989a, 19896). As described by the developers of the POMS (McNair et al., 1979), Tension-Anxiety measures heightened musculoskeletal tension; Depression-Dejection measures depression accompanied by a sense of personal inadequacy; Anger-Hostility measures anger and antipathy toward others; Vigor-Activity measures vigorousness, ebullience, and high energy; Fatigue-Inertia measures weariness, inertia, and low energy level; and Confusion-Bewilderment measures bewilderment and “muddleheadedness.” Although the mood factors are correlated with each other, they have been determined from reliability analysis to be independent measures of mood states (McNair et al., 1979). Current physical activity was assessed at the August-November and February-May visits with a questionnaire adapted from Kriska et al. (1988) and is reported as total kilocalories per day expended for activities including walking, stair-climbing, sports, exercise, housework, and gardening. Demographic information and self-reported history of treatment for depression or other psychiatric condition were obtained by interview at the beginning of a previous study (Dawson-Hughes et al., 1990). The average number of hours of sleep per night, as reported by

80 the volunteers, was recorded for each of samples for hormone assays were collected As previously described (Dawson-Hughes (25[OH]D) were measured by the method (1,25[OH],D) by the method of Reinhardt

the 4 weeks that they completed the POMS. Blood at the August-November and February-May visits. et al., 1991), plasma levels of 25-hydroxyvitamin D of Preece et al. (1974) and 1,25_dihydroxyvitamin D et al. (1984). TSH, T,, and T, were measured in the subset of the study group that received a vitamin D supplement (n = 106). Serum TSH was measured with Allegro HS-TSH kits from Nichols Institute Diagnostics (San Juan Capistrano, CA) which have intra-assay coefficients of variation (CVs) of 5.9% and interassay CVs of 7.6%. T, and T, were measured in serum with Magic Radioimmunoassay kits from Ciba Corning Diagnostics (Medfield, MA). Intra-assay and interassay CVs are 2.2% and 6.5%, respectively, for the T, kit and 5.2% and 6.7% for the T4 kit. Statistical Analysis. With the exception of Vigor-Activity, the POMS mood scores were not normally distributed. There was a high percentage of zero values (up to 29% for each mood score at any single visit, and up to 7% across all 4 visits), and there was a strong positive skew. For these reasons, nonparametric statistical tests and measures of association were used to compare POMS scores and relate them to other variables (Conover, 1971; Ott, 1984). One exception was the use of partial correlation coefficients obtained from ordinary least squares regression to assess the multivariate relationship between sleep, fatigue, and other POMS scores. Friedman’s two-way analysis of variance was used to assess overall differences among mood measurements taken at each of the four time points. The Mann-Whitney U test was used to compare mood scores of independent groups. Spearman’s rank order correlation was used to assess relationships between mood scores and other variables including sleep, physical activity, and hormone levels. Changes across seasons in hours of sleep, physical activity, TSH, and the thyroid hormones were assessed with paired t tests. Significance tests were conducted at the 0.05 level.

Results Fig. 1 shows POMS scores for the six mood factors. In this group of 250 women, there were significant overall differences among the four measurements of all the POMS scores except Vigor-Activity (Table 2). Among the five scores with seasonal variation, the highest or “worst” scores occurred in August-November and the lowest or “best” scores in either February-May or June-July. These results were unchanged when the 18 women ever treated for depression or other psychiatric condition were excluded from analyses. POMS scores of individuals measured during the first half of each 4-month period were compared with those of individuals measured in the second half. The 132 subjects measured in August and September had significantly lower scores for Depression-Dejection (p = 0.032), Anger-Hostility (p < O.OOl), ConfusionBewilderment (p = 0.043), and Tension-Anxiety (p = 0.039) than the 118 subjects measured in October or November. In contrast, POMS scores of subjects measured in February-March did not differ from those of subjects measured in April-May. Possible effects of age, hours of sleep (measured at all four study visits), and physical activity (measured at the August-November and February-May visits) on mood were examined by correlation of these variables with mood scores at corresponding visits, and were similar in magnitude across time points. Table 3 presents correlations of mean sleep and physical activity levels with mean mood scores. There were weak but highly significant inverse correlations between hours of sleep and all the mood scores except Vigor-Activity. Women with mean hours of sleep

81

Fig. 1. Mean Profile of Mood States (POMS) scores by month in 250 women

The December/January values are double plotted. The T bars show standard errors of the means. The open circles show mean values for subsets of women measured in the first and second halves of the February through May and the August through November measurement periods.

Table 2. Comoarison of mean mood scores on the Profile of Mood States of 250 women’measured four times in 1 year Aug-Nov

Tension-Anxiety

(T)*

Depression-Dejection Anger-Hostility Vigor-Activity Fatigue-Inertia

(D)

(A) (V) (F)

Confusion-Bewilderment

(C)

Dee- Jan

Feb-May

Jun-Jul

Mean

SD

Mean

SD

Mean

SD

Mean

4.1

6.0

3.2

6.1

2.7

5.4

2.5

SD 5.6

5.3

6.3

5.1

6.5

4.4

5.6

4.4

6.0

0.017

5.1

5.9

4.7

5.3

3.9

4.5

3.9

5.4

< 0.001

19.6

5.9

19.1

6.1

19.4

5.9

19.9

5.9

0.153

6.6

5.7

6.3

5.5

5.5

5.6

6.0

5.4

0.015

0.6

3.4

0.4

3.1

0.3

3.2

0.2

3.1

0.024

< 0.001

1. Probability of an overall difference, Friedman’s test. 2. Reference values from normative samples (McNair et al., 1979) of female college students and female psychiatric outpatients, respectively:T: 13.9, 20.7; D: 14.8.28.0:A: 9.3, 14.9;V: 15.6, 9.3; F:10.7, 13.0; C: 11.7, 13.3.

82

Table 3. Spearman correlations of sleep (hours/day) and physical activity (KcaVday) with mean scores on the Profile of Mood States Sleep

Physicalactivity

r

P

r

P

Tension-Anxiety

-0.16

0.011

-0.03

0.597 0.316

Depression-Dejection

-0.18

0.005

-0.06

Anger-Hostility

-0.17

0.007

-0.06

0.375

Vigor-Activity

-0.01

0.827

0.34

< 0.001

Fatigue-Inertia

-0.20

0.001

-0.12

0.062

Confusion-Bewilderment

-0.19

0.002

-0.04

0.489

below the median (7.1 hours per night) had higher POMS scores than women who slept more, not only for Fatigue-Inertia (p = 0.019) but also for DepressionDejection (p = 0.045), Anger-Hostility (p = 0.044) and Confusion-Bewilderment (p = 0.024). When Fatigue-Inertia was controlled for, hours of sleep no longer correlated with other POMS scores. Physical activity was significantly correlated only with Vigor-Activity, though the inverse correlation with Fatigue-Inertia was almost significant. The correlations of physical activity with Vigor-Activity and Fatigue-Inertia were more pronounced in the 135 women with sleep hours below the median (Vigor-Activity: p = 0.43, p < 0.001; Fatigue-Inertia: p = -0.20, p = 0.019), but there was no evidence of a relationship between physical activity and the other POMS scores in either sleep group. Physical activity levels were higher in August-November than in FebruaryMay (p < O.OOO), but the change in activity level was not correlated with changes in mood scores over the same period. Mean hours of sleep did not change across the four measurement periods. Physical activity and sleep were not correlated with each other. Age was not significantly correlated with any of the POMS scores. Values of TSH and the thyroid hormones, for the 106 subjects in whom these measures were made, were within the normal age-specific ranges for the assays used. The group means of individual mean values (from two study visits) were 3.0 (SD = 1.2) Mu/l for TSH, 2.5 + 0.4 nmol/l for T,, and 105 f 15 nmol/l for T,. Correlations of TSH and T, with POMS scores measured in the same period were both nonsignificant and weak (-0.17

83 August-November or February-May. Changes in vitamin D hormonal status were also not correlated with changes in POMS scores over the same periods. Correlations of mean POMS scores for Tension-Anxiety, Depression-Dejection, Anger-Hostility, Fatigue-Inertia, and Confusion-Bewilderment with each other ranged from 0.50 to 0.74 (p < 0.01). The correlations of the Vigor-Activity score with other scores were weaker (r = -0.25 to 4.38, p < 0.01).

Discussion To our knowledge, this is the first large study of a normal population in which seasonal changes in mood were identified by repeated measures in the same individuals, and the first to focus on middle-aged and older women. There are no normative data for this age group for comparison, but the mean POMS scores of women in this sample tend to be lower than those in a normative sample of female college students (McNair et al., 1979) and similar to those of 40 normal men and women with a mean age of 40 (Kasper et al., 1989~). As expected, the mood scores reported here are lower (except for the Vigor-Activity score, which is higher) than those of psychiatric outpatients (Haskell et al., 1969). Several large studies have shown that most of the general population living in the United States notices seasonal changes in mood and behavior (Terman, 1988; Kasper et al., 19896) and that the severity of symptoms associated with these seasonal changes (i.e., the prevalence of SAD) increases with decreasing latitude from the southern to northern states (Potkin et al., 1986; Rosen et al., 1989). In these studies, participants were asked to perform a retrospective evaluation of selected feelings and behaviors by month or season. A potential problem with this approach is that it may suggest to study participants that seasonal changes are expected, and thereby bias the results in favor of positive findings. In the present prospective study, participants were asked to evaluate their moods at four different times of year, and no reference was made by the study staff to months, seasons, or changing patterns. The general agreement of results obtained by the two methods suggests that bias was not the primary reason for the seasonal patterns observed in the earlier studies. While the existence of seasonal mood changes in the general population appears evident, there is little consistency among studies in the observed timing of these changes. We did not ask volunteers to state their own feelings about particular months or seasons, but the finding that mood scores were worst in August through November, particularly in October and November, differs somewhat from that of the Maryland study (Kasper et al., 19896) in which the peak “feeling worst” month was February. Similarly, when a depression self-rating inventory was used in a large normal Swiss population, peak values for depressive symptoms occurred in spring (Lacoste and Wirz-Justice, 1987). In contrast, a study in northern Norway (latitude 69”) found self-reported depression scores to be higher in December than in June (Haggag et al., 1990). Smaller studies (n < 30) of SAD patients followed longitudinally address a slightly different question by showing peak months for percent of subjects experiencing depression to be in November/ December (Switzerland, latitude 47’) (Wirz-Justice et al., 1986) and January (Washington, D.C., latitude

84 39’) (Rosenthal et al., 1984). Identification of peak months for affective symptoms is likely to be highly dependent on study design and methods, but differences between locations in photoperiod, number of sunny days (Wirz-Justice et al., 1986), and temperature may all play roles in the timing of mood changes. The positive correlation of T, with vigor in spring is not surprising since hyperthyroidism is often associated with high energy levels (Utiger, 1987), and its positive correlation with depression in fall may parallel the relative increases in measures of T, that have been observed in primary affective illness (Bauer and Whybrow, 1988). Relative increases in T, during depression have been interpreted both as a compensatory mechanism for restoring affective homeostasis (Whybrow and Ferrell, 1974) and, more recently, as pathogenic in nature (Joffe et al., 1984). Relationships between thyroid hormones and mood have been little studied in euthyroid, nonpsychiatric patients, but Kasper et al. (1989~) reported that subjects with subsyndromal SAD had T, levels similar to those of control subjects, and higher levels of T, (within the normal range). Depression has been the primary focus of SAD research to date, but our results suggest that, among normal subjects, patterns in other mood factors, particularly tension, anger, and confusion, may follow patterns that are similar to those of depression. Kasper et al. (1989a), who used the POMS in a study of light therapy in 40 normal individuals, saw nonsignificant but suggestive declines in the Tension, Confusion, and Anxiety scores of some subjects, particularly those with subsyndromal SAD who were treated with bright lights for 5 hours a day. The number of hours of sleep was inversely correlated with the mood scores and appeared to have no effect independent of fatigue. Lack of sleep and resulting fatigue may reflect a higher incidence of sleep difficulties among women with higher mood scores or may have a direct negative effect on mood. Alternatively, these correlations may simply reflect common associations with underlying medical conditions or other factors. The estimated physical activity level in this study is validated to some extent by its correlation with the POMS Vigor-Activity scale. It was not correlated with levels or changes in other mood scores, but a positive effect of exercise can by no means be ruled out. Many (Hughes, 1984) but not all (Berger, 1989) experimental studies have failed to show a positive effect of physical activity on mood. However, an analysis of four large population studies showed a highly significant but very small beneficial effect of physical activity on several measures of psychological well-being (Stephens, 1988). This effect was more pronounced when housework and other chores (both included in our activity estimate) were excluded from the analysis. Additionally, Swiss investigators found that jogging diminished the wintertime deterioration of mood observed in 94 men and women randomized to jogging or no exercise (Suter et al., 1991). Prospective studies of seasonal mood changes provide an advantage over crosssectional studies by eliminating interindividual variation as a potential confounder of the seasonal effect. The method, however, is limited by the number of times it is possible or desirable to administer the same instrument to the same individual and the fact that it cannot be administered to everyone simultaneously. The longer the

85 measurement periods are, the more likely the estimated seasonal changes will be blunted relative to true values. Measurements collected over a single month might have shown a pattern of greater excursion than did our measurements collected over 2 months and especially those collected over 4 months. While the mood scores tended to be worst between August and November, the exact timing and magnitude of seasonal peaks within that period cannot be determined from this study. The fact that subjects measured in late fall had worse scores than those measured in early fall is suggestive but, as a cross-sectional comparison, should be viewed with caution. In conclusion, this l-year longitudinal study of mood changes in 250 normal women demonstrates that seasonal mood changes occur in normal middle-aged and older women living in New England. The mood scores appear to be inversely related to day length, with “worst”mood scores occurring in the fall and “best”mood scores in spring or summer. Worse mood scores were associated with fewer hours of sleep, and the vigor score was positively associated with physical activity. The thyroid hormone T, was positively associated with higher depression levels in August through November when depression levels were greatest, and also with greater vigor in February through May. Vitamin D supplementation did not appear to affect levels or changes in mood scores. Acknowledgment. This study was supported by the USDA Human Nutrition Research Center on Aging at Tufts University (Contract No. 53-3K06-5-10). The contents of this publication do not necessarily reflect the views or policies of the U.S. Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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