Ambulatory assessment for physical activity research: State of the science, best practices and future directions

Reichert, Markus; Giurgiu, Marco; Koch, Elena D.; Wieland, Lena M.; Lautenbach, Sven; Neubauer, Andreas B.; von Haaren-Mack, Birte; Schilling, Renè; Timm, Irina; Notthoff, Nanna; Marzi, Isabel; Hill, Holger; Brüßler, Sarah; Eckert, Tobias; Fiedler, Janis; Burchartz, Alexander; Anedda, Bastian; Wunsch, Kathrin; Gerber, Markus; Jekauc, Darko; ... (2020). Ambulatory assessment for physical activity research: State of the science, best practices and future directions. Psychology of sport and exercise, 50 Elsevier Science 10.1016/j.psychsport.2020.101742

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Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 “Physical Activity Assessment – State of the Science, Best Practices, Future Directions” where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessments near real-time, which minimize retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within-subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior.

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