Data collected from fitness trackers worn by employees could be very useful for businesses. The sharing of this data with employers is already a well-established practice in the United States, and companies in Europe are showing an interest in the introduction of such devices among their workforces. Our argument is that employers processing their employees’ fitness trackers data is unlikely to be lawful under the General Data Protection Regulation (GDPR). Wearable fitness trackers, such as Fitbit and AppleWatch devices, collate intimate data about the wearer’s location, sleep and heart rate. As a result, we consider that they not only represent a novel threat to the privacy and autonomy of the wearer, but that the data gathered constitutes ‘health data’ regulated by Article 9. Processing health data, including, in our view, fitness tracking data, is prohibited unless one of the specified conditions in the GDPR applies. After examining a number of legitimate bases which employers can rely on, we conclude that the data processing practices considered do not comply with the principle of lawfulness that is central to the GDPR regime. We suggest alternative schema by which wearable fitness trackers could be integrated into an organization to support healthy habits amongst employees, but in a manner that respects the data privacy of the individual wearer.
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Background: Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.Method: Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC).Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.Results: Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.Conclusion: The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.
Aim: The aim of this study was to describe the experience with commercially available activity trackers embedded in the physiotherapy treatment of patients with a chronic disease. Methods: In a qualitative study, 29 participants with a chronic disease participated. They wore an activity tracker for two to eight weeks. Data were collected using 23 interviews and discussion with 6 participants. A framework analysis was used to analyze the data. Results: The framework analysis resulted in seven categories: purchase, instruction, characteristics, correct functioning, sharing data, privacy, use, and interest in feedback. The standard goal of the activity trackers was experienced as too high, however the tracker still motivated them to be more active. Participants would have liked more guidance from their physiotherapists because they experienced the trackers as complex. Participants experienced some technical failures, are willing to share data with their physiotherapist and, want to spend a maximum of e50,-. Conclusion: The developed framework gives insight into all important concepts from the experiences reported by patients with a chronic disease and can be used to guide further research and practice. Patients with a chronic disease were positive regarding activity trackers in general. When embedded in physiotherapy, more attention should be paid to the integration in treatment.
Wheelchair users with a spinal cord injury (SCI) or amputation generally lead an inactive lifestyle, associated with reduced fitness and health. Digital interventions and sport and lifestyle applications (E-platforms) may be helpful in achieving a healthy lifestyle. Despite the potential positive effects of E-platforms in the general population, no studies are known investigating the effects for wheelchair users and existing E-platforms can not be used to the same extent and in the same manner by this population due to differences in physiology, body composition, exercise forms and responses, and risk injury. It is, therefore, our aim to adapt an existing E-platform (Virtuagym) within this project by using existing data collections and new data to be collected within the project. To reach this aim we intend to make several relevant databases from our network available for analysis, combine and reanalyze these existing databases to adapt the existing E-platform enabling wheelchair users to use it, evaluate and improve the use of the adapted E-platform, evaluate changes in healthy active lifestyle parameters, fitness, health and quality of life in users of the E-platform (both wheelchair users and general population) and identify determinants of these changes, identify factors affecting transitions from an inactive lifestyle, through an intermediate level, to an athlete level, comparing wheelchair users with the general population, and comparing Dutch with Brazilian individuals. The analysis of large datasets of exercise and fitness data from various types of individuals with and without disabilities, collected over the last years both in the Netherlands and Brazil, is an innovative and potentially fruitful approach. It is expected that the comparison of e.g. wheelchair users in Amsterdam vs. Sao Paulo or recreative athletes vs. elite athletes provides new insight in the factors determining a healthy and active lifestyle.
