Motor Activity Research Consortium for Health (mMarch)

National Institute Of Mental Health

Investigators

Linked publications & trials

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 (GGIR, an R package to 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), and the Healthy Brain Network, NY; and supported the development of sophisticated applets in the MindLogger platform to enhance longitudinal data collection for cognitive testing, electronic diary applications, and tracking of mobile assessments in population-based research. Over the last year we collaborated to publish several key papers related to motor activity and daily rhythms monitoring. With researchers from Australia, we examined evidence that suggested a role of circadian dysrhythmia in the switch between activation states in bipolar 1 disorder (BD-1) (Hickie et al, 2023). We found that circadian dysrhythmia is a plausible driver of transitions into high- and low-activation states. This work has implications for indicated prevention, early intervention, and personalized treatment choices, suggesting prioritization in BD research. In collaboration with investigators in Switzerland, we published 3 studies using follow-up data from the CoLaus/PsyColaus study. We investigated the associations of major depressive disorder (MDD) and its subtypes with actigraphy-derived measures of sleep, physical activity and circadian rhythms and tested the potentially mediating role of sleep, physical activity and circadian rhythms in the well-established associations of the atypical MDD subtype with body mass index (BMI) and the metabolic syndrome (MeS) (Glaus et al, 2023). The sample included data from participants who used actigraphy measures as part of their physical evaluation. Results confirmed associations of MDD and its atypical subtype with sleep and physical activity, which are likely to partially mediate the associations of atypical MDD with BMI and MeS, although most of these associations are not explained by sleep and activity variables. Findings highlight the need to consider atypical MDD, sleep and sedentary behavior as cardiovascular risk factors. In a second publication, we investigated associations between ambient temperatures and bad daily mood and identified variables affecting the strength of these associations in the CoLaus/PsyCoLaus population (Bundo et al, 2023). Daily mood was collected using EMA as part of the physical examination. We found that rising temperatures may positively affect mood in the general population, but individuals with psychiatric disorders (such as anxiety, depression, and schizophrenia) may exhibit alternated responses to heat. This research suggests that tailored public health policies may help protect this vulnerable population. Lastly, we published a report on the associations between hunger and psychological outcomes (de Rivaz et al, 2022) using data from participants who completed EMA for one week in CoLaus/PsyCoLaus. Findings showed that positive psychological states and hunger can influence each other, while no association was found between hunger and negative psychological states. We continued to collaborate with investigators from the Netherlands, Norwegian and US (Yale) affiliated mMarch sites on several papers on motor activity. First, we investigated patterns of motor activity with comorbid attention-deficit/hyperactivity disorder (ADHD) and temperamental factors that may influence the clinical course and symptoms, including cyclothymic temperament (CT) (Syrstad et al, 2022). From the Netherlands site data, we reported findings from examining the bidirectional associations between accelerometry-derived physical activity level (PAL) and EMA-rated affect in a 3-hour time frame and evaluated whether associations differed between people with and without current or remitted depression or anxiety (Difrancesco et al, 2022). We found that higher PAL may improve affect, especially in patients with depression or anxiety. This study supports and extends previous findings on the bidirectional relationship between physical activity and mood. We collaborated on statistical and methodologic research on accelerometry data and its demographic and clinical correlates. With researchers from the University of Pennsylvania Biometrics team, we published a paper proposing nonparametric, graph-based two-sample tests for object data with the same structure of repeated measures (Zhang et al, 2022). The proposed tests were demonstrated to provide additional insights on the location, inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders. Additionally, our NIMH statistical group developed an open-source publicly available pipeline for loading and cleaning raw accelerometry that facilitates harmonization and enhances reproducibility of accelerometry data (Guo et al, 2022). 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) establishment of a new protocol "Rhythms and Blues: Multidomain Dynamics of Motor Activity and Mood" to test mechanisms for findings on the mechanisms underlying BD 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.