We investigated whether the premenstrual experience (PME) is influenced by question framing using an online experiment. We manipulated the phrasing of the question asking for a description of the premenstrual experience (control: nothing specified; treatments: framing the question specifically asking about both negative and positive experiences in a different order). Drawing on the analysis of text data collected, the results show that (1) in both control and treatment groups, PME was overall described negatively, (2) framing the question with emotional valence specifying both positive and negative experiences, resulted in higher odds of reporting neutral compared to negative responses, and (3) the experience was described less negatively, and the breadth of symptoms described was higher (more diverse) in treatments compared to the control group. These results have implications for how the premenstrual experience is measured and challenges the idea that the premenstrual experience is solely negative.
The results show that, regardless of the phrasing of the question, the polarity of menstruating individual’s accounts of the premenstrual experience are overall negative, but not solely so. Our results are consistent with previous studies showing that menstruating individuals describe their premenstrual phase as a negative period [40, 41]. This suggests that inflammation associated with the menstrual cycle has a large impact on descriptions, leading menstruating individuals to experience the phase in a negative way. However, language and social constructions also play a role in the description of the experience. Negative premenstrual experiences may result from an inflammatory reaction triggered by dropping progesterone levels before menstruation.
In this line, premenstrual syndrome has been regarded as the outcome of a cycle of inflammation [42–44]. Yet, the negative premenstrual experience reported by individuals using hormonal contraception cannot be explained by cyclical inflammation, so other mechanisms might be at play.
The analysis shows that age and the use of hormonal contraception did not associate with the polarity of responses. This is surprising because both age and use of hormonal contraceptives are important in mediating cycle differences [45, 46]. The lack of effect on combined contraceptive users could stem from the fact that these types of contraceptives mimic the menstrual cycle and cyclical immunity, and it has also been suggested that these may produce PMS-like symptoms such as irritability, which are confused with PMS . The seven-day placebo pill causes a drop of hormones leading to a withdrawal bleed, which may have been obscured within the general ‘hormonal contraceptive’ user responses. We had expected progestin only methods to show lower levels of any premenstrual changes, as they do not cause cycling, however, they do impact inflammation and responses to infection. For example, it is interesting to note that in rodents, infection has been facilitated by the administration of progestin contraception , so progestin might heighten systemic inflammation. Therefore, those on progestin contraceptives have higher levels of systemic inflammation, which may mask any impact of cyclical inflammation. We also expected differences between IUD users and hormonal contraceptive users. However, while IUD users’ ovulation is not affected, any intra-uterine device causes uterine irritation and inflammation , which may contribute to systemic inflammation similarly to progestin only methods. While we were surprised by these results, one previous study showed minor differences in PMS severity between those who used and did not use hormonal contraceptives, and encouraged researchers to include hormonal contraceptive users from future studies on the premenstrual phase . It is important to note that we did not have data on how long people had been using their contraceptive methods, and whether they took them regularly. It is also possible that people on hormonal contraceptives were responding with their premenstrual experiences over their lifetime, or for cycles before they took contraception. Or, perhaps people on hormonal contraceptives are conflating negative side effects from contraceptive use with PMS.
The lack of differences between hormonal contraceptive and non contraceptive users could emphasise the importance of the social framing of premenstrual syndrome. It is possible that the negative stereotype of PMS is so pervasive, that people undergoing a withdrawal bleed (from hormonal contraception) rather than a true bleed also believe they are experiencing PMS.
Most respondents did not describe positive experiences, and some even reacted strongly against the idea of having these. In the advertising industry, framing has been shown to affect responses and behaviour , however, we did not find this to be the case in all parts of this study. We were surprised to find that the number of neutral responses was impacted by treatment, even though the number of positive and negative responses were not. This suggests that: i) whilst the premenstrual experience is generally a negative one; ii) when menstruating individuals are prompted specifically to consider positive and negative effects, they are more likely to respond with a neutral statement such as “I don’t get PMS” or “none”. Some research has shown that as the number of Likert-style options increase, the extreme responses decreased , suggesting that people opt for a neutral response to avoid the cognitive effort required to give a polarised response. This could explain the reduced spread of positive and negative responses in the treatment groups, given we don’t see the same results when there are free text boxes.
Question phrasing influences the number and type of words used to describe the PME. We found that the diversity of language used in treatment groups one and two is higher than in the control group, which had fewer unique words than treatment groups one and two. This suggests more repetition in the responses within the control group. This indicates that the diversity of language used in treatment groups one and two is higher than that in the control group, suggesting that when participants are directly asked to describe different experiences, they have more to say. However, the number of words used does not differ between the groups, rather, they describe different experiences. While some words were recurrent in all three groups, in particular words depicting physical pain (e.g., ‘breast,’ ‘cramp’ and ‘bloat’), some were unique to the control group such as ‘cry’ and ‘sensitive’. ‘Swing’ and ‘acne’ were found to be among the most frequent words used in treatment groups one and two. This suggests that the control elicits negative emotional and psychological symptoms, while the treatments elicited more descriptive bodily symptoms. Note that the number of boxes is unlikely to explain differences in word density: the control group with the single text box had almost double the word count compared to the others. Control responses had longer sentences with more descriptive words, while responses in treatment groups read more often as a list of symptoms. The responses in the treatments being split into ‘positive’, ‘negative’ and ‘other’ may have resulted in a ‘list style’ response of specific symptoms, while responses in the control group were more verbose, resulting in more general words with fewer distinct words.
Contrary to other studies, we have not found that the order of the wording impacted responses [38, 54]. This could be because participants could see all the words in the same question, regardless of the question order, while other studies administering different orders of positive and negative surveys were done by entirely separate questionnaires .
Using sentiment analysis permits the analysis of a large number of responses, many more than would have been feasible if these were being individually analysed by a thematic analysis method [55–57]. Indeed, the large number of respondents led to the collection of the largest and most diverse PMS experiences to date. However, our methodology was limited by the dictionary used in text analysis, which was an adapted dictionary lookup. A tailor-made sentiment dictionary would have been more accurate. However, we are not aware of any annotated training data created for female or reproductive health. Creating a more advanced model for this paper, using deep neural networks was beyond the scope of this study, indeed very few studies have used deep learning to address issues of cross-domain sentiment classification . However, our study has highlighted the need for a new lexicon that encompasses words relevant to female reproductive health.
Another limitation was that we did not have any data on BMI, as obesity has been shown to be a risk factor for PMS . However, due to the large number of respondents which were randomly allocated into the three response groups, any effect due to BMI would have been the same in each group. We opted not to record BMI as sensitive questions can lead to higher survey drop-out rates .
While biomedical perspectives may provide mechanistic explanations of PMS, there is no single experience of the premenstrual phase. Using an inductive, feminist approach that places participants experiences and their unique words at the centre of the research could give rise to hypotheses for why such variation in PMS is observed, for example highlighting the presence of neutral responses. It is clear that the premenstrual phase is negative for a majority of people, but this work highlights the existence of neutral and positive dimensions that should also be considered by healthcare providers. The current medical literature emphasises negative cycle changes, while positive ones get very little attention. Discussing the premenstrual phase as “changes” instead of “syndrome” could help break down the concept of PMS, to reduce the negative connotations that it invokes [61, 62].
Moreover, the wording of the question about the premenstrual phase can allow a greater diversity of responses to emerge from participants. Calls for future research should focus on the development of a reproductive health lexicon, with a focus on menstrual cycles. By using this, and ensuring enough models are trained in different languages, we will be able to quickly capture and analyse huge numbers of responses. This in turn will enable researchers to assess different risk factors for a negative premenstrual phase (we suggest inflammatory illnesses and stress levels) while also determining ‘risk factors’ for positive and neutral premenstrual phases. In this way, our findings have implications for how the premenstrual experience is quantified. Understanding which words are the most frequently used can help researchers build specific tools to capture the premenstrual experience in future, and to establish ways in to improve the premenstrual experiences, for example targeting anti-inflammatory medications at specific cycle points for those with negative premenstrual changes.