Seasonality of mood and affect in a large general population sample: Only participants higher on neuroticism showing seasonality

Seasonality of mood and affect in a large general population sample. Wim H. Winthorst ,Elisabeth H. Bos,Annelieke M. Roest,Peter de Jonge. PLoS, September 14, 2020. https://doi.org/10.1371/journal.pone.0239033

Rolf Degen's take: https://twitter.com/DegenRolf/status/1305747281346519041

Abstract: Mood and behaviour are thought to be under considerable influence of the seasons, but evidence is not unequivocal. The purpose of this study was to investigate whether mood and affect are related to the seasons, and what is the role of neuroticism in this association. In a national internet-based crowdsourcing project in the Dutch general population, individuals were invited to assess themselves on several domains of mental health. ANCOVA was used to test for differences between the seasons in mean scores on the Positive and Negative Affect Schedule (PANAS) and Quick Inventory of Depressive Symptomatology (QIDS). Within-subject seasonal differences were tested as well, in a subgroup that completed the PANAS twice. The role of neuroticism as a potential moderator of seasonality was examined. Participants (n = 5,282) scored significantly higher on positive affect (PANAS) and lower on depressive symptoms (QIDS) in spring compared to summer, autumn and winter. They also scored significantly lower on negative affect in spring compared to autumn. Effect sizes were small or very small. Neuroticism moderated the effect of the seasons, with only participants higher on neuroticism showing seasonality. There was no within-subject seasonal effect for participants who completed the questionnaires twice (n = 503), nor was neuroticism a significant moderator of this within-subjects effect. The findings of this study in a general population sample participating in an online crowdsourcing study do not support the widespread belief that seasons influence mood to a great extent. For, as far as the seasons did influence mood, this only applied to highly neurotic participants and not to low-neurotic participants. The underlying mechanism of cognitive attribution may explain the perceived relation between seasonality and neuroticism.

Discussion

The purpose of this study was to investigate whether mood and affect are related to the seasons. Secondly, we examined the role of neuroticism as a potential moderator of seasonality. The main findings of this study were: on a population level, participants scored higher on positive affect in spring compared to the other seasons, lower on negative affect in spring compared to autumn, and lower on QIDS depressive symptoms in spring compared to the other seasons. The same pattern was visible in the separate “seasonality-related” questions of the QIDS (except for weight change and increased appetite): participants felt less sad, slept less, had more energy, more general interest in spring compared to the other seasons, mainly autumn and winter. In summary, this study shows that participants, in general, feel better in spring compared to the other seasons, but effect sizes were small or very small. The personality factor neuroticism moderated the effect of the season in all three outcomes. There were no within-subject seasonal differences in the scores of positive and negative affect, as shown in the repeated measures analysis in participants who filled out the questionnaires twice. The power of these analyses may have been insufficient to detect significant seasonal differences, due to smaller numbers and the fact that effect sizes were already very small or small in the first group. This may also explain that neuroticism did not moderate within-subject seasonal differences.

The finding that seasonal differences were only seen in the group of high-neurotic participants is in line with our previous study, in which we hypothesised that subjects who score high on neuroticism tend to attribute their symptoms and unhappiness to the seasons [26]. This finding is also in line with the findings of Rosellini and Nooteboom that the symptoms of depression are related to the personality trait neuroticism [58, 59].

In the crowdsourcing study HND procedure, the general public volunteered to assist in scientific research. In return, participants received feedback on their scores and were able to follow the results of the research on the internet [31]. Brabham described the internet crowdsourcing procedure as a relatively new model for application in public health [60]. Possible advantages mentioned by Bevelander are that by this sampling methodology already existing hypotheses can be reproduced but also that this methodology can generate ideas that are less well-documented or otherwise tend to be overlooked [61]. In previous crowdsourcing studies, the participants recruited were more diverse than in other means of recruitment [62]. Possible disadvantages of this method are selection bias and the impossibility to calculate non-response percentages, as it is not possible to know how many people have heard of the project or visited the website but did not enter the study [63, 64]. In order to find a group of participants for HND that could be representative for the general population (and thereby attempting to reduce the limitation of selection bias), publicity for HND was sought using several newspaper articles, magazine articles, public lectures, radio interviews, and other media. In order to examine possible selection effects, Van der Krieke et al. [31] compared the HND sample with the governmental data of the general Dutch population (Central Bureau of Statistics) and two large population studies: the Netherlands Mental Health Survey (NEMESIS-2) and the Lifelines population study [32,33]. They confirmed a certain selection bias. Compared to the general Dutch population, the HND participants were more often women (65.2% versus 50.5%; NEMESIS = 55.2%, Lifelines = 57,9%), on average 6 years older (45 versus 39 years; NEMESIS = 44, Lifelines = 42), more often with a partner (74% versus 58%;), more often living together (61 versus 47%; NEMESIS = 68%) and had higher education levels (> 20 years 76% versus 35%; NEMESIS = 35%) [31].

This selection bias clearly is a limitation of the present research. Moreover, in our study, a majority of the participants completed the questionnaires in spring. Although we adjusted for the differences between the seasons due to this selective inclusion by using the demographic variables as covariates, we cannot rule out the possibility that the results were still partly due to some unmeasured confounder. Since our sample was a general population sample, another potential limitation is that the proportion of participants suffering from SAD can be expected to be low (ranging from 3%–10%), implying that the contribution of SAD patients to our study results will be limited[11].

Depressive disorders and anxiety disorders show a high comorbidity [65, 66]. For this reason, it would have been interesting to include a measure of anxiety. However, in a previous article, we showed that the administered depression scale (QIDS) and the BAI (Becks Anxiety Inventory) showed a correlation of 0.80 [27]. In the HND study, the Anxiety subscale of the Depression Anxiety Stress Scale (DASS) was used to assess anxiety over the previous week. In our data, there was a correlation of 0.70 between the QIDS and the anxiety scale of the DASS (S1 Table). Since our main objective was to investigate the seasonality of depression and positive and negative affect, we did not include this measure of anxiety as a confounder because it could have masked the seasonal effect on depression.

A strength of this study is its large sample size for the analyses in the entire group and the spring–winter group in the repeated measures analyses. Other strengths are the use of validated instruments, comparability with other Dutch population studies, the use of questionnaires covering a short period guaranteeing a relative absence of memory bias, and the inclusion of a personality factor in the analyses.

The mechanism of cognitive attribution may underlie the relation between (perceived) seasonality and neuroticism [27, 67, 68]. For future studies on seasonality of mood and behaviour, we recommend including the personality measure neuroticism and a measure to establish the attribution style. Other confounding factors like presence or absence of pre-existing physical or mental health conditions, treatment and stressful life events should be measured as well. The objective then is to further disentangle the relationship between neuroticism, attribution style and (perceived) seasonality of mood and behaviour.