Date Published: March 9, 2017
Publisher: Public Library of Science
Author(s): Ben Bullock, Greg Murray, Denny Meyer, Marianna Mazza.
Seasonal variation of manic and depressive symptoms is a controversial topic in bipolar disorder research. Several studies report seasonal patterns of hospital admissions for depression and mania and variation in symptoms that appear to follow a seasonal pattern, whereas others fail to report such patterns. Differences in research methodologies, data analysis strategies, and temporal resolution of data may partly explain the variation in findings between studies. The current study adds a novel perspective to the literature by investigating specific meteorological factors such as atmospheric pressure, hours of sunshine, relative humidity, and daily maximum and minimum temperatures as more proximal predictors of self-reported daily mood change in people diagnosed with bipolar disorder. The results showed that daily maximum temperature was the only meteorological variable to predict clinically-relevant mood change, with increases in temperature associated with greater odds of a transition into manic mood states. The mediating effects of sleep and activity were also investigated and suggest at least partial influence on the prospective relationship between maximum temperature and mood. Limitations include the small sample size and the fact that the number and valence of social interactions and exposure to natural light were not investigated as potentially important mediators of relationships between meteorological factors and mood. The current data make an important contribution to the literature, serving to clarify the specific meteorological factors that influence mood change in bipolar disorder. From a clinical perspective, greater understanding of seasonal patterns of symptoms in bipolar disorder will help mood episode prophylaxis in vulnerable individuals.
Many people intuit that bad weather makes us sad and good weather makes us happy. Scientific investigation has largely failed to support such associations however, with variations in meteorological variables either showing no [1, 2] or weak  relationships with variations in normal mood. A likely explanation for these weak experimental findings is that weather affects mood for only a subset of the population, and the strength and direction of these effects vary between individuals. Indeed, Klimstra et al.  identified four subtypes of people in their Dutch sample according to how individuals’ mood was affected by the weather—Summer Lovers, Summer Haters, Rain Haters, and Unaffected types. The latter subtype, consisting of those that do not experience changes in mood with the weather, was by far the most populated group in the Klimstra et al. sample and goes some way to explaining why average effects of weather on mood across large groups are often not found.
Table 1 shows the transition frequencies for mood on consecutive days. Depressed mood days were followed by another depressed mood day for 49.1% of such days with a switch to normal mood for 39.6% of such days and a switch to manic mood for 11.3% of such days. Normal mood days were followed by another normal mood day for 83.4% of such days with a switch to depressed mood for 8.9% of such days and a switch to manic mood for 7.6% of such days. Manic mood days were followed by another manic mood day for 41.1% of such days with a switch to normal mood for 41.7% of such days and a switch to depressed mood for 17.3% of such days. Mood was in the normal range for 70.7% of all days.
Overall, the results supported effects of two meteorological variables on mood. Specifically, higher maximum temperature was associated with an increase in the odds of a transition from depressed mood to manic mood on consecutive days. In addition, improvement in mood from depressed to normal on consecutive days was more likely the lower the recorded sea-level pressure was on that day. Effects of sunshine hours, humidity, and minimum temperature on transitions in mood were not found.