BACKGROUND
Currently, changing behaviors with the assistance of mobile applications has been popularized. However, most of the participants are unable to persist in participating in behavior-changing activities for a long time. Some researchers have studied what factors motivate people to maintain behaviors-changing actions. There has been controversy about whether the commonly used triggers, negative results or competitions, could motivate behavior changes. In the meantime, the main methodology these researchers have been using is to conduct experiments, from which data was collected from subjects’ recalling previous behavior changing. The experiments are time-consuming, and the results can be unreliable. To resolve this problem, the Ecological Momentary Assessment (EMA) was developed to record real-time feedback. However, the EMA unavoidably increases the workload of the subjects.
OBJECTIVE
This study investigated the factors affecting behavior change, especially from the motivation aspect. Additionally, this paper attempted to identify a way to record human behavior changes without increasing the subjects’ workload.
METHODS
The methodology of “self-report” was adopted to report how people’s views regarding the behavior-changing intervention. To achieve a balance between workload and being timely, the self-reporting data was recorded once a day. After the 28-day “self-report” experiment, the “focus group” method was used to gather people’s feedback on behavior changing process.
RESULTS
This paper identified 9 factors: cooperation, competition, award, understandable graphic, reminder and alarm, trust and willing, gender, relation with disease and environmental factors). These factors could affect motivation of behavior changing. Besides, we found that negative results could be a motivation for behavior changing. In the experiment, we also found that a small number of subjects tended to cheat for a more “beautiful” result. The last part of the paper has presented possible implications for technology design to facilitate behavior-changing.
CONCLUSIONS
In particular, (i) the research promoted the possibility of cheating when recording data which is ignored by existing research and will make the digital applications less useful; (ii) the results show that not all cooperation is needed to lead to a positive effect; (iii) the research identified the negative results caused by over-competition in behavior change. Finally, the paper proposes technology design directions should focus on giving motivation through keeping dairy, negative results feedback and avoid cheating.
Lie Group Method for Solving the Negative-Order Kadomtsev–Petviashvili Equation (nKP)
