Addressing challenges in diagnosis of premenstrual disorders through data driven approaches

Active Project Grant Awarded Last action

Award ID
F32MH143367
Recipient
Awarding agency
Obligated
$78,491

Award facts

Assistance listing
93.242 Mental Health Research Grants
Completion date 0% complete
Place of performance
Providence, RI 02903, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Premenstrual dysphoric disorder (PMDD) involves debilitating mood, behavioral, and physical symptoms that emerge in the week prior to menses and fully remit during or immediately after menses. Only a provisional diagnosis of PMDD can be made based on retrospective self-report alone, given evidence that relying on patients' retrospective reports results in a high rate of false positives when compared to prospective daily symptom tracking. Accordingly, a definitive diagnosis of PMDD requires at least 2 months of daily symptom tracking to ensure symptom severity and onset/offset meet strict diagnostic requirements. This daily symptom tracking is burdensome to patients and providers, potentially delaying diagnosis and treatment. IT is yet unclear why retrospective reports carry such a high false positive rate. There may be patterns of premenstrual symptoms that do not align with strict PMDD requirements, resulting in apparent false positives. However, some patients may have an elevated baseline level of symptoms, wherein symptoms significantly exacerbate during the luteal stage, but may not fully remit during/after menses (partial clearance). Further, other patients' symptoms may fully remit, but may have a delayed offset (i.e., remitting during the follicular stage). The current study will combine two large studies on cycle-related symptoms and will allow for the development of a flexible diagnostic tool, that allows for varied subtypes of symptom presentation (i.e., PMDD, PMDD with partial clearance, PMDD with delayed offset). Using the retrospective interview and daily symptom tracking data collected by each of the studies, I hope to determine whether retrospective reports of menstrual cycle symptoms can be a reliable measure of impairment, by comparing traditional and novel (developed for this project) methods. I hypothesize that a significant portion of false positives are due to strict PMDD requirements and no current approach for assessing partial clearance or delayed offset of symptoms, as opposed to patients' inability to accurately represent their cyclical symptoms. Aim 1 is to examine the impact of modifying current methods of assessment (retrospective interviews and daily symptom tracking) on the false positive rate of interviews. I hypothesize that a modified approach to diagnostic interviewing and daily symptom tracking (allowing for partial clearance and delayed offset of symptoms) will result in a lower false positive rate than traditional methods. Aim 2 is to model patterns of symptom expression across the cycle for those who report cycle-related mental health symptoms, but would not be diagnosed with PMDD per daily symptom tracking. While I have no a priori hypothesis, I will employ a variety of modeling techniques, including cutting-edge methodology and traditional approaches, allowing for both nomothetic and idiographic approaches to understanding cyclical symptom fluctuations. The findings of the proposed project will have key theoretical and practical implications for research on and assessment of premenstrual disorders and will provide the training needed to launch my research career.

Quick answers

Who received award F32MH143367?

F32MH143367 was awarded to Brown University by National Institute of Mental Health (National Institutes of Health) on December 1, 2026.

How much is award F32MH143367 worth?

$78,491 has been obligated on F32MH143367.

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