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Application of N-of-1 Experiments to Test the Efficacy of Inactivity Alert Features in Fitness Trackers to Increase Breaks from Sitting in Older Adults

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Issue: 2017 (Vol. 56): Issue 6 2017
Pages: 427-436
Ahead of Print: 2017-08-16

Application of N-of-1 Experiments to Test the Efficacy of Inactivity Alert Features in Fitness Trackers to Increase Breaks from Sitting in Older Adults

Focus Theme - Original Article

Focus Theme: N-of-1

D. E. Rosenberg (1), E. Kadokura (2), M. E. Morris (3), A. Renz (4), R. M. Vilardaga (5)

(1) Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; (2) Washington State Department of Health, Seattle, WA, USA; (3) University of Washington, Seattle, WA, USA; (4) Group Health Research Institute, Seattle, WA, USA; (5) Duke University, Durham, NC, USA


physical activity, Sensors, aging, chronic conditions, sedentary behavior


Background: Frequent breaks from sitting could improve health. Many commercially available fitness trackers deliver vibration alerts that could be used to cue sitting breaks. As a potentially pragmatic approach to promote frequent breaks from sitting, we tested the efficacy of inactivity alerts among obese older adults, a highly sedentary population. Methods: We conducted 10 sequential N-of-1 (single-case) experimental ABA trials. Participants (mean age = 68, mean BMI = 35) were monitored for a baseline phase (“A1”) followed by an intervention phase (“B”). The intervention was then removed and participants were monitored to test an experimental effect (reversal “A2” phase). Total time in the study was limited to 25 days. During the intervention phase (“B”), participants used fitness trackers to stand up or move every time they received an alert (every 15 or 20 minutes of inactivity). Participants wore activPAL devices to measure breaks from sitting each day. Randomization tests were used to determine whether the number of breaks was significantly higher during the “B” phase than the two “A” phases. Results: Breaks were higher by 7.2 breaks per day during the “B” phase compared to the mean of the “A” phases. Seven out of 10 participants had more sitting breaks during the intervention phase which subsequently decreased during the reversal “A2” phase (combined p-value < .05). Conclusion: Inactivity alert features within commercially available devices are efficacious for promoting modest improvements in breaks from sitting among older adults with obesity and could be a simple health-promoting strategy in this population.

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