A call to integrate menstrual cycle influences into just-in-time adaptive interventions for suicide prevention - Tauseef, Hafsah A.; Coppersmith, Daniel D. L.; Reid-Russell, Azure J.; Nagpal, Anisha; Ross, Jaclyn; Nock, Matthew K.; Eisenlohr-Moul, Tory (Frontiers in Psychiatry, 2024)
This paper discusses the scientific rationale and methodological considerations for incorporating the menstrual cycle as a time-varying intra-individual factor in personalized medicine models, such as Just-In-Time Adaptive Interventions (JITAIs). Among patients, accumulating evidence suggests that the normal hormone fluctuations of the menstrual cycle represent a time-varying factor that can trigger or exacerbate psychiatric symptoms, including but not limited to affective dysregulation, suicidality, and irritability. While only a minority of the general female population experiences significant cyclical changes, this hormone-sensitive response appears to be greater among patients with psychiatric disorders, with studies demonstrating that a majority of patients recruited for past-month suicidal ideation demonstrate worsening of their suicidality around menses. However, no interventions target suicidality during this monthly period of elevated risk despite evidence of a clear recurring biological trigger. This unique and recurrent "biotype" of suicidality is well-suited for JITAIs. In addition to providing a rationale for the inclusion of the cycle in JITAI, we provide illustrative options and examples regarding the measurement and implementation of cycle variables in JITAIs. We discuss how JITAIs might be leveraged to use menstrual cycle data to identify states of vulnerability within people and strategically select and deploy interventions based upon their receptivity at various phases in the cycle. Furthermore, we discuss how to integrate passive measures for tracking the menstrual cycle. Although much research is needed before implementation, we maintain that the menstrual cycle represents a critically understudied time-varying feature that may markedly improve the accuracy of JITAI models for predicting suicidality.