This proposal responds to PAR-12-198 ('Improving Diet and Physical Activity Assessment'). It will developand test novel multilevel statistical methods to examine the effects of subject-level parameters (variance andslope) of time-varying variables in ecological momentary assessment (EMA) studies of physical activity. Lowlevel of physical activity heightens the risk of numerous deadly diseases (e.g., heart disease, stroke, cancer,diabetes) throughout the life course. The use of EMA in physical activity research is growing rapidly becausereal-time data capture methods supply novel insights into determinants of this behavior. In EMA studies, it iscommon to have up to thirty or forty observations per subject, and this allows us to model subject-levelparameters such as variances (e.g., how erratic is a subject's mood?) and slopes (e.g., how much does asubject's mood change across contexts?) of time-varying variables. For example, in our recent EMA work, wehave found that more physically active children have greater positive and negative emotional stability thanchildren who are less physically active. However, current multilevel modeling strategies are restricted totreating subject-level variances and slopes as outcomes. As a consequence, statistical models do not have theability to test whether subject-level variance and slope parameters have predictive, mediating, and moderatingeffects on physical and sedentary activity. For example, we are unable to ask important research questionssuch as whether erratic mood mediates the effects of depression on physical activity, or whether the effects ofliving in a highly walkable neighborhood on physical activity are attenuated for individuals with unstable self-efficacy beliefs. This modeling restriction severely limits our ability to capitalize on the full potential of the time-varying nature of EMA data to enhance physical activity research. To address this critical methodological gap,we propose to develop multilevel models, software, and strategies to test for the effects of these parameters inEMA studies. We will apply these modeling strategies to secondary analyses of pooled data from five federally-and foundation-supported EMA studies of physical activity with a combined sample size of N = 553 participants(including children and adults). The primary aims are (1) to develop novel multilevel modeling strategies andsoftware to test whether subject-level variance and slope parameters have predictive, mediating, andmoderating effects on subject-level physical and sedentary activity outcomes and (2) to apply these novelmodeling strategies and software in secondary analyses of existing EMA datasets to examine the effects ofsubject-level variance and slopes of time-varying variables such as safety, stress, fatigue, and self-efficacy onphysical and sedentary activity. This study has the potential to make novel methodological and substantivecontributions for analysis of EMA data in physical activity research. The methods to be developed can easilygeneralize to a variety of chronic disease-relevant research areas.
This study will develop and test novel multilevel statistical methods to examine the effects of subject-level parameters (variance and slope) of time-varying variables in ecological momentary assessment (EMA) studies of physical activity. The results will have the potential to make notable methodological and substantive contributions for analysis of EMA data and in understanding the complex effects of contexts and psychological states on physical and sedentary activity. Given the detrimental health effects and enormous costs of physical inactivity; such information could be of significant value to efforts to improve public health.
| Lin, Xiaolei; Mermelstein, Robin J; Hedeker, Donald (2018) A 3-level Bayesian mixed effects location scale model with an application to ecological momentary assessment data. Stat Med 37:2108-2119 |
| Maher, Jaclyn P; Ra, Chaelin K; Leventhal, Adam M et al. (2018) Mean level of positive affect moderates associations between volatility in positive affect, mental health, and alcohol consumption among mothers. J Abnorm Psychol 127:639-649 |
| Hedeker, Donald; du Toit, Stephen H C; Demirtas, Hakan et al. (2018) A note on marginalization of regression parameters from mixed models of binary outcomes. Biometrics 74:354-361 |
| Dunton, Genevieve Fridlund (2017) Ecological Momentary Assessment in Physical Activity Research. Exerc Sport Sci Rev 45:48-54 |
| Hedeker, Donald; Mermelstein, Robin J; Demirtas, Hakan et al. (2016) A Mixed-effects Location-Scale Model for Ordinal Questionnaire Data. Health Serv Outcomes Res Methodol 16:117-131 |
