An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

Abstract

People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results (Preprint)

“…Participants were eligible for the TIME Study main trial if they (1) owned an Android smartphone running Android version ≥6.0 as their only personal mobile phone with no intention to switch to a non-Android smartphone, (2) did not wear a smartwatch already, (3) were aged 18-29 years and living in the United States, (4) were currently engaged in recommended levels of PA (or intended to within the next 12 months) [55], (5) spoke and read English, (6) resided in an area with Wi-Fi connectivity, (7) did not have any physical or cognitive limitations that prevented participation, and (8) were able wear a smartwatch and answer real-time smartphone and smartwatch surveys. All study procedures were approved by the institutional review board at the University of Southern California (USC; HS-18-00605).…”

“…State or behavior assessment and personalized interventions both require computational models that can represent the interrelationships between different behaviors and states unique to individuals. Such models could be created by using (1) a hypothetico-deductive approach to study relationships between predictors and outcomes of interest based on prior knowledge [6,7], (2) data-driven discovery from a large number of constructs measured in intensive longitudinal data (ILD) studies [8,9], or (3) an appropriate combination of both [10,11]. Creating models of behavior that capture relationships between behavior and context that can happen quickly and many times in a day requires methods for sustainable data gathering on, and modeling of, within-day changes of variables (eg, physical activity [PA], sleep, sedentary behavior [SB], and affect) [12,13].…”

Momentary within-subject associations of affective states and physical behavior are moderated by weather conditions in real life: an ambulatory assessment study

“…Weather conditions might not only influence individuals' immediate decisions regarding PA but also might act as a potential moderator in the overall affective states and PB relationship. Several contextual factors such as natural, built, or social environments that may influence the time-dynamic, within-subject affect-behavior relationship have been investigated [27][28][29]. Self-perceived weather, such as too rainy, was shown to have a negative effect on affect, or perceived too cold weather reduced positive affect [30].…”

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results

“…State or behavior assessment and personalized interventions both require computational models that can represent the interrelationships between different behaviors and states unique to individuals. Such models could be created by using (1) a hypothetico-deductive approach to study relationships between predictors and outcomes of interest based on prior knowledge [ 6 , 7 ], (2) data-driven discovery from a large number of constructs measured in intensive longitudinal data (ILD) studies [ 8 , 9 ], or (3) an appropriate combination of both [ 10 , 11 ]. Creating models of behavior that capture relationships between behavior and context that can happen quickly and many times in a day requires methods for sustainable data gathering on, and modeling of, within-day changes of variables (eg, physical activity [PA], sleep, sedentary behavior [SB], and affect) [ 12 , 13 ].…”