A taxonomy for visualisations of personal physical activity data on self-tracking devices and their applications

Self-tracking devices and apps have been widely used for personal data collection, with particular focus on health and physical activity (PA) monitoring. Despite their pervasive use, data representation and data sharing on these devices and apps are still in their infancy. With the aim of contributing towards structuring the design space of personal health visualisation, we present an overview focused on visualisation methods and the typology of tracked data in the most popular health and PA tracking devices and their companion apps/dashboards. Our research method of data collection is based not only on a review of scientific literature in the field, but also on autoethnography, information collected from manufacturers' websites and user manuals as well as online communities and reviews. We then discuss the major issues and limitations users face with regards to health and PA data interpretation and sharing.</p

COVID19 and the city; from the short term to the long term

[Extract] The current COVID19 pandemic cause an enormous number of cases of diseases and premature deaths (EPIWIN, 2020). The number of cases tests health care systems and has led to dramatic actions in many countries e.g. China, Singapore, Japan, Italy, Spain and many other countries. The actions had considerable success in some countries such as China, South Korea and Japan, but still led to a large impact in others like Spain and Italy

Inter-instrument reliability and agreement of fitbit charge measurements of heart rate and activity at rest, during the modified Canadian aerobic fitness test, and in recovery

© 2019, University of Toronto Press Inc.. All rights reserved. Purpose: We determined the inter-instrument reliability and agreement parameters of the Fitbit Charge Heart Rate (Charge HR) device during three phases: rest, modified Canadian Aerobic Fitness Test (mCAFT), and recovery. Method: We recruited 60 participants for this cross-sectional measurement study using convenience and snowball sampling approaches. The performance of the Charge HR was assessed throughout the rest, mCAFT, and recovery phases. To establish inter-instrument reliability, the Charge HR variables – heart rate, steps taken, and energy expenditures – were compared with those for two other devices: the Zephyr BioHarness (ZB) for heart rate and the Fitbit One for steps taken and energy expenditure. Measurements were recorded every 30 seconds. Results: At rest, the inter-instrument intra-class correlation coefficient (ICC) (standard error of measurement [SEM]) for the Charge HR versus the ZB was \u3e 0.97 (range, min–max, 1.02–1.32). During the mCAFT and in recovery, the ICCs (SEMs) for the Charge HR and the ZB were \u3e 0.89 (range, min–max, 1.30–3.98) and \u3e 0.68 (range, min–max, 3.58–8.35), respectively. During the mCAFT only, the number of steps taken and the energy expenditure recorded by the Charge HR and the Fitbit One displayed ICCs (SEMs) of 0.97 (83.00) and 0.77 (14.70), respectively. The average agreement differences in heart rate in this pair-wise device comparison indicated mean differences of –0.20, 4.00, and 1.00 beats per minute at rest, during the mCAFT, and in recovery, respectively. Conclusions: The Charge HR heart rate variable demonstrated excellent inter-instrument reliability compared with the ZB and provided good levels of agreement. The steps taken and energy expenditure variables displayed excellent reliability measures between Charge HR and Fitbit One. Our findings may be used to capture field-based wireless measures of heart rate in various phases and provide information about possibly using the Charge HR and ZB devices interchangeably