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Interoception refers to the central nervous system's processing of internal physiological signals, such as heartrate and appetite signals (Khalsa et al., 2018). Fibromyalgia syndrome (FMS) is a disorder characterised by chronic widespread pain, in addition to fatigue, sleep disturbances, and autonomic disturbances (Sarzi-Puttini et al., 2020). Previous research suggests that FMS might be associated with altered interoceptive processing, across various components of interoception. However, the patterns of findings are complex, and appear to be dependent on the component of interoception that is being measured. For example, some studies have found group differences in levels of interoceptive sensibility (i.e., the self-reported tendency to focus on interoceptive signals) between people with fibromyalgia and healthy matched participants. The direction of these group differences varies depending on the sub-facet of interoceptive sensibility that is being examined (Borg et al., 2018; Valenzuela-Moguillansky et al., 2017; Schmitz et al., 2021; for further discussion of interoception measures, see Desmedt et al., 2021). Regarding the accuracy component of interoception, some studies have found that people with FMS evidence lower levels of cardiac accuracy (as measured using the heartbeat counting task) compared to matched healthy participants (Di Lernia et al., 2020; Duschek et al., 2015). However, several other studies have failed to demonstrate this group difference (Borg et al., 2018; Rost et al., 2017; Valenzuela-Moguillansky et al., 2017). Within this body of research, a negative association between FMS symptom severity and heartbeat counting task scores has been identified (Duschek et al., 2017), and another study found a negative association between meta-cognitive interoceptive awareness (confidence-accuracy correspondence) and pain symptoms in FMS (Borg et al., 2018). Finally, recent research suggests that a soft-touch paradigm (which activates interoceptive unmyelinated C-Tactile afferents) can reduce pain symptoms in chronic pain conditions, including FMS (Di Lernia et al., 2020). In sum, previous research suggests that FMS might be associated with altered interoceptive processing, but there have been some difficulties in replicating extant findings. In tandem with this issue, the validity of existing measures of cardiac accuracy has been widely challenged recently, with evidence indicating that they are susceptible to both physiological and psychological confounds (e.g., Desmedt et al., 2020; Murphy et al., 2018; Ring et al., 2015; Zamariola et al., 2018). To address these problems, the aim of the present study is to examine cardiac interoceptive accuracy in adults with fibromyalgia using a recently-developed method: the Phase Adjustment Task (Plans, Ponzo et al., 2020). In this task, participants’ heartrate is detected using a smartphone camera, and participants use a dial to advance or delay auditory tones until they perceive the tones to be synchronous (in-phase) with their heartbeat. The task appears to be free from the confounds of other cardiac measures in healthy adults (Plans, Ponzo et al., 2020), and can be completed at home using an iPhone app, making it far more accessible than traditional heartbeat perception tasks. We are aiming to examine whether subgroups of people with FMS can be identified according to interoceptive accuracy levels, and to explore the factors of FMS symptomology that might characterise these groups. We will also gather feedback on the usability of the task. If we find that this task is valid for use with people with FMS, we might be able to examine the links between internal bodily awareness and fibromyalgia symptoms more closely in future research (e.g., using longitudinal methods), which could lead to improvements in the diagnosis and monitoring FMS.

Supplemental material, sj-docx-1-dhj-10.1177_20552076221145852 for Menstrual cycle-associated symptoms and workplace productivity in US employees: A cross-sectional survey of users of the Flo mobile phone app by Sonia Ponzo, Aidan Wickham, Ryan Bamford, Tara Radovic, Liudmila Zhaunova, Kimberly Peven, Anna Klepchukova and Jennifer L Payne in Digital Health