The current study examined whether participation in the COPING study was associated with sociodemographic, mental health and physical health characteristics of the volunteers re-contacted from the GLAD Study. Assessing for potential participation biases in the COPING study invites future researchers recruiting from the GLAD Study, or other large scale mental health cohorts, to consider the influence of participation bias on research and findings. The present study also benefits the wider scientific community by scrutinising prior findings and assessing previously unexplored characteristics that may be related to participation.
Many of the same characteristics were independently associated with participation in the COPING baseline and follow-up surveys after controlling for all factors under investigation. For instance, older age, identifying as female, and providing a saliva kit for the GLAD Study were broadly associated with increased participation across the COPING study. Aside from the provision of genetic data, which was unique to this study, these findings are consistent with previous studies investigating participation in longitudinal research [3, 5, 6, 13, 14]. In contrast, having GCSEs or none of the specified educational qualifications, previously smoking, and higher alcohol consumption at the GLAD Study sign-up were negatively associated with participation across the COPING study. These patterns of associations are also comparable to previous research, which shows that lower educational attainment [3], smoking [5, 15] and higher alcohol consumption are related to reduced participation [5, 9, 15].
There were, however, some differences between the factors associated with participation in the COPING baseline and follow-up surveys. Notably, a broader range of factors were associated with participation bias in the baseline survey. For example, having one or more physical health disorders was associated with increased participation in the baseline, but not follow-up, surveys. This contradicts previous research that suggests individuals with poorer physical health have lower levels of participation [5, 7, 9, 15, 20, 21]. In contrast, having a Black or Asian ethnic identity or a non-binary or self-defined gender identity were associated with reduced participation in the COPING baseline survey only. Since these ethnic groups are minorities in the UK, this seems to contradict previous findings suggesting that people from ethnic minority groups broadly show lower levels of participation in research [3, 7]. On the other hand, to our knowledge, a non-binary or prefer to self-define gender identity has not previously been investigated in relation to participation bias. Finally, it is intresting that ADHD was only associated with reduced participation in the COPING follow-up surveys. Past research has shown that higher polygenic risk for ADHD is negatively associated with participation in longitudinal research [13, 14], and one may have expected this to be broadly associated with less participation in mental health research.
Several conclusions can be drawn from the findings from this study. Firstly, in line with previous literature, the GLAD Study volunteers’ participation after re-contact seems to follow a systematic, rather than random, process. Secondly, the results suggest that sociodemographic, physical health and saliva kit provision are the factors most strongly associated with participation bias when re-contacting GLAD Study volunteers. In contrast, other potential characteristics that have demonstrated relationships with participation in past research, including mental health factors [3, 6, 10, 11, 13, 14, 18, 19], employment status [4, 8] and partnership status [9, 10] were weakly or not significantly related to participation following re-contact. It is possible that these characteristics may be unrelated to participation after controlling for other factors. However, these findings need to be interpreted with caution, since GLAD is a mental health cohort constituted by volunteers with a generally severe presentation of anxiety and/or depression [1]. These results may therefore be specific to studies recruiting from GLAD or other mental health cohorts, and require replication in future research.
A pertinent finding for researchers interested in re-contacting mental health cohorts whose participants have provided genetic data, such as the GLAD Study volunteers, is that saliva kit provision was the strongest predictor of participation in COPING baseline and follow-up surveys. This may be unsurprising because providing a saliva kit is an element of participation in the GLAD Study, with participants returning a kit thereby showing a higher level of commitment to participation or to research more broadly. Furthermore, in the exploratory analyses, several characteristics were associated with saliva kit provision itself. For example, older age and being a student were associated with an increased odds of saliva kit provision, whereas A-levels or lower educational attainment and current or previous smoking were related to a decreased odds of provision. Previous studies have similarly found participation biases surrounding genetic data provision, such as provision increasing amongst persons coming from a higher socioeconomic status and those with a greater familial risk of schizophrenia [21]. In contrast, several psychiatric diagnoses have predominantly shown negative associations with the provision of genetic data [30], and the representation of minoritised ethnic groups has also historically been an issue for genetic research studies (e.g., [31]). Overall, researchers should consequently be mindful about participation bias when collecting genetic data or re-contacting volunteers in contexts where genetic data provision is relevant.
It is noteworthy that there were some differences in the factors associated with saliva kit provision compared to participation in COPING. For example, employment and partnership statuses were only associated with saliva kit provision. Furthermore, while having physical health disorders and a female gender identity were associated with increased participation in COPING, both characteristics were associated with a reduced odds of saliva kit provision. Collectively, this suggests that the characteristics associated with participation, and the direction of their relationship, may vary according to the form of participation.
This study supports previous recommendations to actively consider participation bias in research, such as by oversampling groups of volunteers that are associated with lower levels of participation [18], and conducting sensitivity analyses [30]. Such considerations could help to mitigate the negative consequences surrounding participation bias, such as the sample representativeness [3] and erroneous relationships between variables [32, 33].
There are several limitations that should be considered when interpreting these findings. Firstly, participation in the COPING study was defined as reaching the end of the survey, regardless of the amount of missing data. This overlooks the potential nuances of characteristics associated with different levels of missingness [4), such as full responders with no missing data compared to partial responders, which could be examined in future research. Secondly, the COPING study was conducted entirely online and only involved completing surveys. Therefore, this study’s results may not represent participation biases impacting other types of research, such as in-person studies or clinical trials [18]. Thirdly, this study investigated participation biases in a mental health orientated, COVID-19 study during a global pandemic, which involved nationwide experiences, such as rising unemployment and national lockdowns [34]. Consequently, some of the observed associations in this study may not generalise to participation biases in longitudinal health research outside the pandemic. Fourthly, it is noteworthy that the GLAD Study predominantly utilises online recruitment methods [1]. Therefore, the participation biases observed in this study may only relate to a specific group of volunteers, such as people who are enthusiastic about research and who can access the internet [35]. Finally, certain populations were underrepresented, such as people from ethnic minority backgrounds. As a result, these findings may not generalise to studies recruiting from the general population.
There are several future directions for investigations of participation biases in research. Firstly, researchers with data throughout the COVID-19 pandemic could examine whether fluctuations in sociodemographic or health characteristics, such as mental health symptoms and employment status, are related to changes in study participation throughout the pandemic. This was beyond the scope of the current study, which solely utilised pre-pandemic data from the GLAD Study sign-up survey. Secondly, future studies could replicate and/or extend our exploratory investigation into factors associated with the provision of genetic data. This would be useful because our exploratory analyses did show some discrepancies with the factors associated with genetic data provision compared to past research (e.g. [30]). Thirdly, future researchers could more closely examine the barriers and mechanisms underlying the associations between certain characteristics and participation, enabling researchers to combat these issues and address recruitment biases in future research.