Motor Activity Research Consortium for Health (mMarch)

National Institute Of Mental Health

Investigators

Linked publications, trials & patents

Abstract

We have been expanding on intensive multimodal studies of youth and within families, following up samples to examine stability, and expanding on domains of assessment. We have developed a new processing platform called mMarch.AC pipeline (created through GGIR to transform and analyze accelerometer data); processed and analyzed actigraphy data from 6 sites; applied novel statistical methods including functional data analysis and Joint Individual Variance Explained (JIVE) to actigraphy cross-site data; collected and analyzed concomitant ecological momentary assessment (EMA) data; examined cross device features; expanded to new sites including Yale, Toronto (CAMH), South Korea, University of Pennsylvania, and the Healthy Brain Network, NY; and supported the development of sophisticated applets in the Curious platform (formerly MindLogger) to enhance longitudinal data collection for cognitive testing, electronic diary applications, and tracking of mobile assessments in population-based research. We have continued to utilize our knowledge of EMA and actigraphy to inform and advance research tools. Through a large collaboration with investigators from the University of Pennsylvania and others, an open data resource was created to characterize affective dynamics, sleep variation, and multimodal measures in brain development (Brook et al, 2025). This resource, which includes data from EMA and activity measures, will be updated regularly and will help to contribute to the identification of early markers of affective instability and emotional regulation in clinical populations, and will advance research in mood, sleep, and brain development. Over the last year, we worked with consortium collaborators to publish several key papers related to studies in motor activity, daily rhythms monitoring, and mental and physical health. We continued to explore associations between subtypes of major depressive disorder (MDD) and physical activity, sleep and circadian rhythmicity using wrist-worn accelerometers in CoLaus/PsyCoLaus participants in Switzerland (Kang et al, 2025). In this study, we investigated whether: (1) behavioral and cardiovascular risk factors (CVRFs) may influence the association of MDD with objectively assessed sleep, physical activity and circadian rhythmicity derived from accelerometry; (2) associations between both the average and variability of objectively assessed sleep, physical activity and circadian rhythmicity are associated with MDD; and (3) these objective measures differ between current and remitted MDD. The findings suggested that CVRFs, particularly cigarette smoking and body mass index (BMI), were significantly associated with both the average and variability of sleep, physical activity and circadian rhythmicity, and with MDD. Lower average and less variable physical activity, as well as more variable circadian rhythmicity were associated with remitted MDD, whereas later sleep midpoint and greater variation of sleep duration variability were associated with depressive states. The findings demonstrated the importance of considering both the average and variability of accelerometry-derived phenotypes that may distinguish state from trait manifestations of MDD. Further, the confounding influence of smoking, particularly on state manifestations of MDD, should be considered in studies of accelerometer derived phenotypes, particularly sleep characteristics. With researchers from Australia, we examined actigraphy-derived estimates of 24-hour sleep-wake patterns, circadian rhythms, and physical activity in youth with mood, anxiety, or psychotic syndromes (Carpenter et al, 2025). The findings suggested differences in sleep-wake and rest-activity patterns according to clinical stage and proposed illness trajectory subtypes, and highlight a need for future longitudinal research into interactions of sleep-wake patterns, circadian rhythms , treatment, and progression of both mental and physical illness. These collaborations illustrated the value of utilizing activity measures in evaluating subtypes of mental health disorders and highlighted the importance of consideration of the full 24-hour sleep-wake cycle in current and future research. Public Health Impact: The formation and continuation of the mMARCH initiative will enable groups to efficiently share and combine data to learn more about how activity affects different disorders and diseases across many populations, including mood disorders, sleep patterns, circadian rhythms, genetic studies, emotion, eating, and other disorders that impact public health. This work will also define targets for prevention and intervention studies. Future Plans: We plan to expand the network using the common procedures of actigraphy and EMA to include more sites that can conduct common data analyses, continued development of analytic models including multi-level dynamic models of intensive repeated measures data, and machine learning approaches that classify the structure of inter-relationships among the regulatory domains under investigation. We will also report the findings of our analyses of several projects that investigate the heritability of actigraphy phenotypes and their associations with clinical and health measures in the NIMH and CoLaus family studies, and genetic association studies of these phenotypes in the CoLaus cohort. We will focus on six major activities: 1) joint analysis of the mMARCH core group data including the CoLaus/PsyCoLaus study of comorbidity of depression and cardiovascular disease, the NESDA study in the Netherlands, the Australian studies of twin and youth with emerging mood disorders, the Hong Kong circadian rhythms study and a cohort study of Brazilian youth; 2) implementation of a new protocol “Rhythms and Blues: Multidomain Dynamics of Motor Activity and Mood” to test mechanisms for findings on the mechanisms underlying bipolar disorder from NIMH Family Study; 3) addition of several sites with actigraphy and EMA data in both adults and youth with mood disorders; 4) initiation of new studies of youth in seven sites (miniMARCH collaboration); 5) development of translational studies to identify the regulatory systems underlying motor activity and sleep across species, where we also plan to examine the cross-domain inter-relationships and their directional influences using real-time tracking and experimental paradigms in the NIH Rhythms and Blues Program; and 6) establishment of methodologic workgroups to address challenges in analysis of multidomain, multilevel intensive repeated measures data from mobile assessments, and another designed to build aggregate environmental data bases on light and temperature to address the impact of climate change on mental health and underlying domains.