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Improving Data Collection from Fitness Trackers

Fitness trackers, when launched, attracted numerous potential buyers associated with sports, fitness, or nutrition. The devices promised to count burned calories, and heart rate during exercise and measure the number of steps per day. As a result, not only teenagers but also adults opted for purchasing the devices, “1.2% of the people use fitness trackers” (Alexander, 2018, para. 4). However, as the heart rate and activity are not challenging to measure, the devices’ calories counting function seems weak. It appears almost impossible to accurately estimate the number of calories a person burns daily with just a tiny watch on one’s wrist if one thinks about it. In other words, the device can measure heart rate through the veins on the wrist, but the same method does not apply to counting calories. The devices provide the number that is just an average drawn from a person’s daily activity, but the trackers do not consider the user’s body characteristics.

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Furthermore, people strongly associated with sports and nutrition might suffer from the inaccurate data calculated by the fitness tracker. It seems that unreliable information results from users’ disparities in body characteristics and individual lifestyle activity (Seshadri et al., 2019). As a matter of fact, professional sportspeople tend to thoroughly track their activity and the burned calories statistics to retain or enhance their shape (Seshadri et al., 2019). However, since the devices provide users with unreliable information, people suffer from not meeting their expectations regarding weight, shape, nutrition, and muscular form.

The human-centered solution to this problem must cater to the users’ needs, like providing them with accurate data on the number of burnt calories. The possible variant that companies must develop is devices’ personalization to adjust the average calorie count measured from heart rate and activity. For instance, after purchasing the fitness tracker, people would be required to provide the following information about themselves to improve the accuracy of the devices: age, sex, body type, height, and weight. According to Turki et al. (2021), the Apple brand is already working on implementing such functions into Apple Watches to tackle data inaccuracy. Such a solution seems human-centered since it effectively meets central users’ needs (Chung, 2017). Nonetheless, the implementation of AI in Apple Watches has been unfavorable to the organization since it might raise its prices for devices even more. Considering that the Apple brand launches products not affordable for every person, the newly advanced smartwatches might even decrease the number of people who can purchase such a device. In addition, the company would need to educate the users on the proper usage of improved trackers to reach accurate data collection of their burned calories, activity, and heart rate.

In addition, to conclude more reliable data that sportspeople can use in their training the organizations need to advance the following technological variant: the fitness trackers sync the information from similar applications. In other words, when purchasing a fitness tracker, people should be required to download similar apps that track the number of burnt calories (Seshadri et al., 2019). Consequently, the devices might use data from the connected phone and count the average to reach more reliable outcomes (Seshadri et al., 2019). However, the solution might not seem beneficial for companies as users might be irritated by the need to download other applications and use more of their phones’ storage. As a result, even though the technical side of the solution appears indeed relevant, this method cannot be utterly considered a human-centered one (Chung, 2017). Overall, this way, the companies might cater to the users needs, though still creating an obstacle in the way of reaching reliable data from trackers.

Nevertheless, the most favorable option for companies is to implement a technological variant of new sensors to measure the data required to provide users with an accurate number of burnt calories. To be more exact, it is vital to cover other aspects crucial for concluding more reliable data, such as non-exercise and exercise activity thermogenesis, basal metabolic rate, and lastly, diet-induced thermogenesis (Midland, 2021). Trackers can collect the data by counting calories according to the method that includes using measured metabolic equivalent for the task, user’s weight, and duration of activity (Midland, 2021). Such an advancement would be less expensive for manufacturers to optimize and for customers to purchase eventually. However, companies still might trace a constraint in the form of long research and experiments to prove the efficiency of the sensors. Overall, by implementing such technology, companies would benefit by affordably pricing the advance tracker, expanding the number of potential buyers.

Overall, the issue of inaccurate data seems critical to sportspeople as they suffer from not reaching their set goals. Therefore, companies should adjust fitness trackers to meet users’ requirements and expectations regarding the devices. As a result, the organizations might employ the following methods, though every one of them includes constraints for the manufacturers: AI implementation, developing of new sensors and syncing data from external applications from the phone. Furthermore, the companies need to test the possible solutions that include numerous technological advancements to identify the most beneficial option.


Alexander, E. (2018). People who use fitness trackers tend to stick with them for at least 6 months. There’s just 1 Problem. Men’s Journal.

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Chung, G. (2017). Practicing human-centered design (HCD) for innovation. OD Practitioner, 49(3), 82–85.

Midland, N. (2021). Calories burned calculator: A simple way to find out how many calories you burn daily. BetterMe Blog. Web.

Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the internal and external workload of the athlete. NPJ digital medicine, 2(1), 1-18.

Turki, A., Behbehani, K., Ding, K., Zhang, R., Li, M., & Bell, K. (2021, June). Estimation of Heart Rate Variability Measures Using Apple Watch and Evaluating Their Accuracy: Estimation of Heart Rate Variability Measures Using Apple Watch. In The 14th PErvasive Technologies Related to Assistive Environments Conference (pp. 565-574).

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