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Using Compositional Data Analysis to Explore Movement Behaviors in People Who Have Experienced a Stroke
George D Fulk1, Karen J Klingman, Sandra A Billinger
1Division of Physical Therapy, Department of Rehabilitation Medicine, School of Medicine, Emory University, Atlanta, Georgia (G.D.F., K.B.); College of Nursing, State University of New York, Upstate Medical University, Syracuse, New York (K.J.H., M.F.); Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas (S.A.B., B.L.B., A.J.B.-C.); Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina (P.W.D.); and Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia (E.P.).
Compositional data analysis reveals that the balance of daily activity, sedentary time, and sleep significantly impacts physical function after stroke, outperforming traditional methods for stroke recovery insights.
Area of Science:
- Rehabilitation Science
- Biostatistics
- Movement Science
Background:
- Stroke survivors often exhibit low physical activity, prolonged sedentary behavior, and disrupted sleep patterns, negatively affecting recovery.
- Traditional analyses of movement behaviors in stroke populations often overlook the interdependent nature of daily time allocation.
- Compositional data analysis (CoDA) offers a novel approach to analyze the relative proportions of different behaviors within a 24-hour period.
Purpose of the Study:
- To investigate the association between the composition of daily movement behaviors (active, sedentary, sleep) and physical function in individuals post-stroke.
- To compare the efficacy of CoDA in analyzing movement behavior composition against traditional methods that treat behaviors independently.
- To determine if CoDA enhances the understanding of how movement behavior patterns relate to physical function outcomes in stroke survivors.
Main Methods:
- Sixty-eight participants post-stroke (60 days) wore activity monitors to quantify time spent in active, sedentary, and sleep behaviors.
- Compositional data analysis, utilizing isometric log-ratios, was applied to the movement behavior data.
- Linear regression models were used to assess the association between movement behavior composition and the Stroke Impact Scale (SIS-16), comparing results with traditional analyses.
Main Results:
- The average daily movement composition for participants was 7.3% active, 58.1% sedentary, and 34.6% sleep.
- The composition of movement behaviors demonstrated a significant association with physical function as measured by the SIS-16 (adjusted R² = 0.5007, P < 0.05).
- CoDA yielded a stronger association (adjusted R² = 0.5007) compared to traditional analyses considering individual behaviors (adjusted R² = 0.3926).
Conclusions:
- The proportional composition of daily active, sedentary, and sleep behaviors is significantly associated with physical function in people with stroke.
- Applying CoDA to movement behavior data improves data interpretability by accounting for the inherent dependencies between behavioral components.
- Clinicians should consider holistic interventions targeting increases in physical activity, improvements in sleep quality, and reductions in sedentary time for stroke rehabilitation.
