This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.
MULTIFILE
This week, JMIR (Journal of Medical Internet Research) Cardio published our paper ‘Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring‘. In this blogpost, I’ll attempt to break down the paper’s key findings in relatively lay language.
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OBJECTIVES: To determine the number of steps taken by older patients in hospital and 1 week after discharge; to identify factors associated with step numbers after discharge; and to examine the association between functional decline and step numbers after discharge.DESIGN: Prospective observational cohort study conducted in 2015-2017.SETTING AND PARTICIPANTS: Older adults (≥70 years of age) acutely hospitalized for at least 48 hours at internal, cardiology, or geriatric wards in 6 Dutch hospitals.METHODS: Steps were counted using the Fitbit Flex accelerometer during hospitalization and 1 week after discharge. Demographic, somatic, physical, and psychosocial factors were assessed during hospitalization. Functional decline was determined 1 month after discharge using the Katz activities of daily living index.RESULTS: The analytic sample included 188 participants [mean age (standard deviation) 79.1 (6.7)]. One month postdischarge, 33 out of 174 participants (19%) experienced functional decline. The median number of steps was 656 [interquartile range (IQR), 250-1146] at the last day of hospitalization. This increased to 1750 (IQR 675-4114) steps 1 day postdischarge, and to 1997 (IQR 938-4098) steps 7 days postdischarge. Age [β = -57.93; 95% confidence interval (CI) -111.15 to -4.71], physical performance (β = 224.95; 95% CI 117.79-332.11), and steps in hospital (β = 0.76; 95% CI 0.46-1.06) were associated with steps postdischarge. There was a significant association between step numbers after discharge and functional decline 1 month after discharge (β = -1400; 95% CI -2380 to -420; P = .005).CONCLUSIONS AND IMPLICATIONS: Among acutely hospitalized older adults, step numbers double 1 day postdischarge, indicating that their capacity is underutilized during hospitalization. Physical performance and physical activity during hospitalization are key to increasing the number of steps postdischarge. The number of steps 1 week after discharge is a promising indicator of functional decline 1 month after discharge.
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.
