The activity tracking function of wearable devices is becoming more and more popular. A report from Insight indicated that 13 million wearable devices were carrying the activity tracking function, from a total of 19 million such devic [1]. It is clear from this that almost 69% of devices have the activity tracking function, which also shows the huge market for wearable devices, including smartwatches. Users adopt this function to record their daily activities, track their actions, and monitor their sleep duration and quality. Users could use this function to improve their habits and customs or remind them to do some exercise. However, even though the function is popular, some issues still exist. One significant phenomenon is the rapid adoption of activity tracking devices, but with little sustainable and long-term use. For example, a 2017 report found that around 30% to 70% activity tracking products were abandoned after only a few months [2]. This phenomenon – the rapid adoption of activity tracking devices but subsequent limited use of functions – is of interest to study both academically and practically.
The ‘tracker’ was first invented by Dr Yoshiro Hatano in 1956 and aimed to combat obesity by counting users’ steps and thus encouraging them to take more exercise. This is the embryonic form of activity tracking [3]. Modern activity tracking then appeared, applied to various devices, including mechanical machines and wearable devices. In recent years, with the spread of innovation in advanced electronic technology as a new popular lifestyle, Levy notes the increasing interest in and adoption of these tools [4]. Most activity tracking functions are carried on wearable devices, such as smartwatches and smartphones, of which the smartwatch is one of the most notable and widely worn examples [5]. Hence, when investigating activity tracking on wearable devices, studying the smartwatch could be more representative and convenient. Moreover, the research of Harrion argues that participants have started to give up using the activity tracking function on different devices, including the smartwatch [6]. This illustrates that there are barriers to the users adopting the activity tracking on smartwatches.
My own research investigates the adoption conditions of smartwatch-based activity tracking by identifying the facilitators and barriers. It employed a mixed-methods research approach that contains both quantitive and qualitative research, involving 10 semi-structured interviews and a questionnaire with 213 valid respondents. Through semi-structured interviews, data regarding personal usage from experience on the activity tracking function was gathered and analysed. We obtained key facilitators and barriers from the interview, and then used these as the main questions of the questionnaire, which was administered online with results being analysed using SPSS.
The survey shows that 96.7% of the responders’ adoption frequency was decreasing. This indicates that most users reduce their usage frequency over time. Also, 47% of participants were not satisfied with the activity tracking function, while only 9% were satisfied. 59% of participants agreed there are barriers that exist to the adoption of smartwatch-based activity tracking.
After the analysis, the identified key facilitators and barriers are detailed in Figure 1. The key facilitators are activity tracking capabilities, design, smartwatch functionality, interaction and improvement of living habits. Among these factors, ‘activity tracking capabilities’ and ‘improving lifestyle’ are the two most important. The main barriers include five perspectives: data, technical, interaction and user-friendliness, design and social comparison. Each of the perspectives contains its own sub-barriers.
Figure 1 Facilitators and barriers of smartwatch-based activity tracking adoption
Using ANOVA and T-test, we compared the different facilitators and different barriers. ‘Activity tracking capabilities’ and ‘improving living habits’ were regarded as the main points attractive to users, with 89.70% and 64.3% of participants supportive, respectively.
Table 1 Facilitating factors affecting activity tracking adoption.
As the table above indicates, during the long-term usage of smartwatch-based activity tracking, users consider ‘activity tracking capabilities’ as the most vital encouraging factor, while ‘smartwatch functionality’ was the least important. In addition, based on the different mean-values of the other three factors, their mean-value was equal to 4.16, 3.6 and 3.24, respectively (improve living habits > design and appearance > interaction and user-friendliness). In this case, among these five facilitators, ‘activity tracking capabilities’ and ‘improving lifestyle’ had more positive promotional effects of encouraging the users to adopt than the other three.
Table 2 The degree of influence of the barriers
Table 2 above provides evidence to explain the degree of influence of the five barriers. Thus, the mean of each factor shows the degree of influence compared to the others. The data indicate that ‘technology’ and ‘data’ were the most important barriers to users’ adoption of the smartwatch-based activity tracking function. However, according to participants, the barrier ‘social comparison’ had least impact on the use of this function.
Figure 2 shows the degree of influence of all sub-barriers on participants’ adoption of the activity tracking function on smartwatches using ANOVA and T-test. We set 1 to equal ‘strongly not influence’ and 5 to equal ‘strongly influence’.
Figure 2 Users’ sub-barrier scores
To conclude, in order to enhance users’ experience, application producers should develop the facilitators and pay attention to solving the issues of the main barriers. The key factors that encourage users’ long-term adoption of activity tracking are a) activity tracking capabilities, b) design, c) smartwatch functionality, d) interaction and e) improving the living habits. The ‘activity tracking capabilities’ was the best performing factor to motivate the users’ long-term usage. The second most important factor was ‘improving lifestyle’, which indicates that users pay attention to their habits and behaviours via the activity tracking function. Also, to the researcher’s surprise, ‘design and appearance’ and ‘interaction’ were far behind as facilitating factors. However, ‘smartwatch functionality’ was the least important factor that stimulated users’ long-term usage. Also, female users are attracted more by ‘smartwatch functionality’ and ‘interaction and user-friendliness’ factors than male users.
In terms of the research into barriers, ‘technology’ and ‘data’ have the largest influence on usage. Among ‘technology’, ‘battery issues’ and ‘pairing’ factors had quite a large impact on usage. In addition, the second most significant barrier to usage was ‘data’, specifically ‘data inaccuracy’ and ‘insufficient data categories’ being the two most influential factors. Moreover, the perspective of ‘interaction’ and ‘design’ was almost equally as important in preventing users’ adoption. However, ‘social comparison’ fell far behind, which was less than half as important as the most important perspective. This indicates that ‘social comparison’ has not hindered usage too much. Additionally, female users consider ‘data’ and ‘technology’ have more degree of preventing influence than male users. The user who goes to the gym seems to regard ‘data’ and ‘technology’ as the more serious barriers when compared to the users who do not go to the gym.
In practical terms, the product should increase the accuracy and integrity of the data produced by devices. Producers could add more abundant data categories for sports, such as tennis or basketball. The battery issues, including battery life, heating, and rechargeability, were shown to be vital by this study’s respondents. The producers and designers should provide more charging methods, such as solar charging, to increase convenient usage. Employing more smart voice control to replace Bluetooth is another method worthy of further enhancement given pairing issues. The use of holograms could also be seen as an ideal way to solve existent screen size or quality limitations. In improving interaction to enhance lifestyles, designers might, in future, focus on smart or customised feedback to enhance user experience. For example, calculating daily calorie intake and providing recipes or dividing data between aerobic and anaerobic exercise would represent novel developments. More generally, the long-term use of smartwatch-based activity tracking could be enhanced by strengthening the facilitators and addressing the barriers identified by this study.
[1] Berg Insight. 2019. Shipments of connected wearables will reach 168 million in 2019. Berg Insight. Retrieved from: http://www.berginsight.com/News.aspx.
[2] H. Lee, and Y. Lee, “A look at wearable abandonment. In MDM 2017: 18th IEEE International Conference on Mobile Data Management,” IEEE, pp. 392-393, 2017.
[3] Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M. (2006). Activity recognition and monitoring using multiple sensors on different body positions. International Workshop on Wearable and Implantable Body Sensor Networks (BSN’06), 4–7.
[4] Levy, H. (2015). Wearable Technology Beyond Smartwatches. Retrieved from: https://www.gartner.com/smarterwithgartner/wearable-technology-beyond- smartwatches 3/
[5] Page, T. (2015). Barriers to the Adoption of Wearable Technology. Journal on Information Technology, 4(3), 1–13.
[6] Harrion, D., Marshall, P., Bianchi-Berthouze, N., & Bird, J. (2015). Activity tracking. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp ’15, 617–621.
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