Harnessing the Potential of Wearable Activity Trackers for Heart Failure Self-Care

PURPOSE OF REVIEW

The purpose of this study was to explore the potential of wearable activity trackers to promote self-care management for physical activity in heart failure (HF).

RECENT FINDINGS

Exercise participation decreases hospital admissions and improves quality of life in HF, and activity tracking devices provide more precise means to assess free-living physical activity and thus enable tailored exercise instruction. Use of activity trackers by cardiac patients for self-monitoring and motivational purposes is associated with increased levels of physical activity and is predictive of disease severity. However, more research is required to establish the feasibility and validity of these devices in HF patients. It is also critical that the devices can be easily used to collect, process and utilise relevant data. Activity trackers have the potential to promote HF self-care because they provide monitoring of physical activity behaviours and the potential to generate habit formation and goal reinforcement, all of which foster physical activity.

Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies

Nowadays, the advancement of mobile technology in conjunction with the introduction of the concept of exposome has provided new dynamics to the exposure studies. Since the addressing of health outcomes related to environmental stressors is crucial, the improvement of exposure assessment methodology is of paramount importance. Towards this aim, a pilot study was carried out in the two major cities of Greece (Athens, Thessaloniki), investigating the applicability of commercially available fitness monitors and the Moves App for tracking people's location and activities, as well as for predicting the type of the encountered location, using advanced modeling techniques. Within the frame of the study, 21 individuals were using the Fitbit Flex activity tracker, a temperature logger, and the application Moves App on their smartphones. For the validation of the above equipment, participants were also carrying an Actigraph (activity sensor) and a GPS device. The data collected from Fitbit Flex, the temperature logger, and the GPS (speed) were used as input parameters in an Artificial Neural Network (ANN) model for predicting the type of location. Analysis of the data showed that the Moves App tends to underestimate the daily steps counts in comparison with Fitbit Flex and Actigraph, respectively, while Moves App predicted the movement trajectory of an individual with reasonable accuracy, compared to a dedicated GPS. Finally, the encountered location was successfully predicted by the ANN in most of the cases.