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Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.
The investigators identified 4 distinct subgroups—earlier rising/robust, shorter active period/less modelable, shorter active period/very weak, and later settling/very weak.
An older individual’s 24-hour activity pattern could help forecast their depressive symptom subgroup.
A team, led by Stephen F. Smagula, PhD, Department of Psychiatry, School of Medicine, University of Pittsburgh Medical Center Western Psychiatric Hospital, identified subgroups of older adults with similar 24-hour activity rhythm characteristics and characterized associated depression symptoms and cognitive performance.
There is not much information on the nature and prevalence of 24-hour activity pattern phenotypes in older adults, particularly those related to depression symptoms and cognition. A better understanding of this is needed to guide the development of new targeted mechanism research and behavioral interventions.
In the cross-sectional analysis, the investigators looked at data from the 2011-2014 National Health and Nutrition Examination and Survey (NHANES) accelerometer study, which used a multistage probability sample defined to be a representative of noninstitutionalized adults in the US.
The investigators included only participants aged 65 years and older who had accelerometer and depression measures weighted to represent approximately 32 million older adults.
The investigators performed a latent profile analysis to identify subgroups with similar 24-hour activity pattern characteristics as measured using extended-cosine and nonparametric methods.
The team sought main outcomes of covariate-adjusted sample-weighted regressions assessed associations of subgroup membership with depressive symptoms defined as a 9-Item Patient Health Questionnaire (PHQ-9) scores of 10 or greater (PHQ-9) and having at least psychometric mild cognitive impairment (p-MCI) defined as scoring less than 1 SD below the mean on a composite cognitive performance score.
In the study, the actual clustering sample size was 1800, with a weighted mean age of 72.9 years.
The investigators identified 4 distinct subgroups—earlier rising/robust (37.6%; n = 677), shorter active period/less modelable (32.6%; n = 587), shorter active period/very weak (9.8%; n = 177), and later settling/very weak (20.0%; n = 359).
However, the prevalence of a PHQ-9 score of at least 10 differed across the different subgroups significantly (cluster 1, 3.5%; cluster 2, 4.7%; cluster 3, 7.5%; cluster 4, 9.0%; χ2 P = .004). The prevalence of having at least p-MCI also was widely different across the different subgroups (cluster 1, 7.2%; cluster 2, 12.0%; cluster 3, 21.0%; cluster 4, 18.0%; χ2 P < .001).
Finally, 5 of the 9 depressive symptoms differed significantly across the different subgroups.
“In this cross-sectional study, findings indicate that approximately 1 in 5 older adults in the US may be classified in a subgroup with weak activity patterns and later settling, and approximately 1 in 10 may be classified in a subgroup with weak patterns and shorter active duration,” the authors wrote. “Future research is needed to investigate the biologic processes related to these behavioral phenotypes, including why earlier and robust activity patterns appear protective, and whether modifying disrupted patterns improves outcomes.”
The study, “Association of 24-Hour Activity Pattern Phenotypes With Depression Symptoms and Cognitive Performance in Aging,” was published online in JAMA Psychiatry.