Stand and Move at Work

Abstract

Cardiovascular disease and diabetes are epidemic in the United States, and efforts to shift this trend have been largely ineffective. The greatest challenge that health care practitioners face is inspiring the lifestyle changes necessary to prevent or reverse these conditions. New evidence suggests that minimal activity, such as simply standing up periodically and moving around can reduce biomarkers of cardiovascular disease and diabetes. Given the challenge and temporary nature of inspiring habitually sedentary individuals to take on intensive exercise routines, this is an exciting prospect. With this new information, the question becomes: "How do we inspire individuals and populations to get up and move?" Dr. Matthew Buman and his group at Arizona State University are addressing this question through researching methods to inspire individuals and groups to stand at move at work. In support of Dr. Buman's work, we have leveraged subject generated postural data gathered by his group to create a pipeline that processes and analyzes the patterns of subject movement with the prompts they received in order to identify the most effective prompt that elicits standing and moving behavior. The pipeline helps researchers in his group visualize their data in an interactive way and to help inform statistical analyses. In future directions, this pipeline structure can be adopted by various aspects of clinical work such as diagnosis, selection of treatment options, monitoring for changes in a patient's conditions over time, evaluating efficacy of different treatment options, and promoting shared decision-making between providers and patients. They can range from novel ways to visualize PGD to help clinicians and patients identify important and actionable trends, to novel computational solutions, to using these data together with the traditional EHR data to provide clinical decision support that may or may not include visual presentations. Importantly, in this challenge, our focus is on integrating PGD with EHR to improve care within the clinical context, rather than on using these data outside of traditional care settings to promote health and wellness.

Figure 1.

Image of the first tab of the data visualization app showing the summary by week and by day for each subject across the study. Users can select specific starting and end dates and download this data as a .csv file in order to flag specific days.

Figure 2.

Image of activePAL^TM events file data visualization page from our Shiny application showing the percentage of time each subject spent in each of the three postural activities detected by the activPAL^TM device.

Figure 3.

Shiny application visualization of the distribution of time to positive action for each prompt and percentage of subjects that positively responded to each prompt.

Figure 4.

Image of the 4^th tab of the data visualization that shows the distribution of the time to action for each prompt. This is used to identify which variables need to be normalized before statistical analysis.

Figure 5.

Image of the 5^th and final tab of the data visualization that shows the statistical results for time to action for each prompt and the percentage of subjects that responded positively to each prompt.