Dynamic associations among motor activity, sleep, energy, and mood could suggest new focus for depression treatment: Study

The findings, published online December 12 in the journal JAMA Psychiatry, suggest new targets for depression treatment.

Previous research has shown the interrelated nature of many of the homeostatic systems of the body. For example, the regulation of motor activity has been found to be linked with other systems, such as those regulating sleep, activity, and emotional states. While dysregulation in these systems is often seen in people with mental disorders, it can be challenging for researchers to examine both the interrelationships between multiple systems and their directional influences using traditional clinical assessments based on retrospective recall.

Lead investigator Kathleen Merikangas, Ph.D., Chief of the Genetic Epidemiology Research Branch in the Intramural Research Program at the National Institute of Mental Health, and colleagues overcame these challenges by combining objectively estimated real-time measures of participants’ motor activity and sleep with subjective electronic diary measures detailing participants’ ratings of their mood and energy levels. Specifically, over a period of two weeks, the researchers used actigraphy monitors to collect minute-by-minute physical activity counts during wake and used these counts to estimate sleep duration from participants with bipolar-I disorder, bipolar-II disorder, major depressive disorder, or none of these three disorders. Participants also completed ecological momentary assessments (diary entries, recorded in real-time) of mood and energy levels four times a day during this two-week period. At each assessment, participants were asked to rate the degree to which they felt “very happy” to “very sad” and “very tired” to “very energetic.”

The researchers discovered a unidirectional relationship between motor activity and mood, suggesting that motor activity affects subsequent mood, but that mood does not affect subsequent motor activity. The researchers also found bidirectional relationships between motor activity and subjective energy and between motor activity and sleep duration. In addition, their findings indicated that the inter-relationships among motor activity, sleep, mood, and energy were substantially stronger in people with a history of bipolar-I disorder than in those without, suggesting that people with bipolar-I disorder may react more strongly to changes or disturbances in these systems.

The results suggest that motor activity may play a central role in mood regulation, and thus might be a target of intervention—one that can be more easily changed than mood states. Thus, novel pharmacological, physical, and behavioral approaches focused on increasing activity and energy may be more effective than current treatments that primarily focus on elevating and stabilizing mood. In addition, therapies that focus on enhancing more stable rhythms across these systems may be more effective than treatments that focus on one system alone.

“This work demonstrates the importance of combined passive monitoring to obtain objective measures of sleep and activity, and active assessment of emotional states and their contextual influences in real time to capture the variability and inter-relationships of systems underlying mood and other disorders,” said Dr. Merikangas. “The research team and I are currently conducting additional studies to understand these complex interactions better—repeating the assessments to study the generalizability of these findings over longer periods of time.”

In addition, the researchers have established a collaborative international consortium—the Motor Activity Research Consortium for Health (mMARCH)—to help examine the generalizability of these findings with respect to other conditions, sex differences, genetic and environmental influences on motor activity, and geographic and seasonal variation. The Consortium’s use of common methods is also enabling them to pool data to create larger databases with enough power to employ statistical- and machine-learning approaches to identify subgroups of interest and build prediction models tracking real-time changes in these systems.

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