We refer to emergent methods as those methods that…
Studies have shown that daily self-monitoring can promote healthy lifestyles and weight loss over an extended period of time. While the act of recording daily activity is in itself beneficial, using mobile applications to monitor fitness makes it easier for people to change their lifestyle habits and adhere to their fitness goals1. However, many of these studies also rely on extensive coaching, incentives, and feedback to promote behavioral changes.
With the advent of numerous fitness applications available for mobile devices, people are able to collect quantitative data on their day-to-day activity patterns and behaviors. Applications like Fitbit connect a biometric wearable to a mobile device to keep track of daily step count, distance walked, calories burned, and sleep patterns. As the collection of personal data becomes more popular and present in new technology, we seek to study how knowledge of this data would help create positive lifestyle changes. We would also like to further understand features that can help motivate users and increase the effectiveness of the Fitbit application.
Positive motivation is applied in specific ways in the current Fitbit user experience. However, encouragement is known to help with goal attainment and perception (Burke 2012). Perhaps an expanded suite of motivational tactics will help increase the likelihood that positive motivation be beneficial for Fitbit users as they work to attain their fitness goals.
One other common barrier to entry is that people often find it difficult to make time for exercise. A simple daily reminder to exercise could aid in increasing activity. It would be interesting to see if awareness of quantified personal fitness data along with daily reminders and positive reinforcement to walk would increase exercise levels. Are interactions with the Fitbit wearable and mobile application, as well as daily prompts and positive messages, enough to enact real changes in activity level?
Do wearables, when paired with a fitness app for monitoring, promote positive behavior change, and can this behavior be sustained over a period of time?
According to studies regarding the Fitbit user base (Sadarangani, 2015), their customer persona is someone who has a desk job, has an expendable income, possibly an early adopter of technology. Despite being comfortable with and owning technology they are value conscious. They are willing to pay for a product that would help them improve health habits and want to work out more. The Fitbit consumer owns a smartphone and is active on social media. They are more likely to believe in a product that has good reviews and is from an established company.
The sample of participants that were recruited for this study were selected based on their ownership and usage of a Fitbit wearable tracker and application. Having a smartphone was also a requirement for this study, as it would allow a medium by which to set reminders for their daily exercise. Participants should have a fairly stable baseline activity level. We also recruited participants who demonstrated a desire to add more activity in their lives, regardless of their availability to actually do so.
Studies show that participants who used a mobile device to monitor their diets while receiving daily remote feedback lost weight over a period of two years. Software monitoring of diet along with automatic feedback showed to be more effective than traditional paper diaries for weight loss (Burke, 2012).
– Automatic feedback helped with adherence to study participation and weight loss.
– Required regular group meetings as part of the study intervention.
– Our study will use reminders to keep participants on track with their exercise routines.
Since automatic feedback proved to be an important factor for weight loss, providing positive reinforcement may be a way to motivate people to continue pursuing fitness goals.
– Positive reinforcement in the form of encouraging statements is known to help with goal attainment and perception (Weinburg 1990).
– Use of pre-set positive statements to keep positive messages consistent.
We used a number of different methodologies to collect our data: pre-screened surveys, self-reported step counts before and during study, pre-study survey, and post-study survey.
Participants were screened via an online qualifying survey using Qualtrics as our primary survey data collection platform. We recruited 12 smartphone users from Des Moines, IA, Los Angeles, CA, San Francisco, CA and Seattle, WA to pre-qualify for our study. The data from this survey was used to determine if the potential study participants already had the Fitbit fitness tracker and mobile app, to understand their current usage of mobile technologies, and to understand their fitness goals and aspirations.
There were 9 out of 12 participants who were selected to join the study. The participants were sent a pre-study survey so that we could measure their current perceptions regarding self-image, motivation to exercise, their goals and aspirations for participating in our study, and what they’ve found to be challenging with regards to their exercise goals. Since we were working with human participants, we provided each person with an informed consent form that outlined the purpose of the study. Participants received detailed instructions on how to report their daily steps and how to set up notifications and alerts on their smartphone calendars.
In order to establish a threshold of behavior and perception change, we designed a short baseline period by which the step measurements from the first two days of the study would act as a baseline activity level. For this baseline period, participants reported their step counts from their Fitbit mobile application, but did not receive any reply from their study contacts.
For the next eight-day period, participants were then asked to send us their daily step count as well as to add daily timed alerts on their smartphone calendar to remind them to take a walk every day for at least 15 minutes. During this eight day study phase, participants were provided positive reinforcement in the form of an encouraging text or email message soon after they report their daily step count. A predetermined list of encouraging statements were used for all participants to keep messages consistent. An example of positive reinforcement messages used for the study were, “Good job!”, “Excellent!” and “You’re making good progress!” along with their daily step count number.
At the conclusion of the study phase, participants were given a final online survey that mirrored the pre-study survey to measure changes in impressions before and after the study as well as new questions about their feelings on positive reinforcement and their feelings of goal attainment.
Pre-Screened Survey Results
There were 12 participants, 7 male, 5 female from Iowa, LA, SF and Seattle participated in an online pre-screen survey. The survey was used to determine participants’ eligibility and willingness to complete the rest of the study. The results of the pre-screen survey revealed that 100% of the participants use a mobile phone. 7 out of 12 like to plan fitness activities in advance. Their methods of planning exercise were using their phone calendar, training program website, or to rely on their memory. 7 out of 12 of the pre-screened group used Fitbit to track their fitness activities, and 8 out of 12 used the Fitbit mobile application. Based on their answers, 9 people were selected to participate in the study.
Self Reported Activity
Calculations based on the total step count of 9 participants over 11 days. 2 days of baseline step count, without study intervention (interaction with Fitbit application alone) and 9 days with study intervention (interaction with Fitbit application, positive messages and reminders to walk).
6 out of 9 participants showed an increase in step count relative to baseline. The average participant increased their step count over baseline by 19%.
With weekend days omitted, the average participant increased their step count over baseline by 28% and 8 out of 9 showed an increase in step count. Participants step count varied greatly for the weekend due to changes in personal schedules. The baseline step count does not take weekend variation into account since it was recorded over two consecutive weekdays. The one participant (#4) that did not show a change in step count, had both a high baseline as well as variability throughout all days of the study. Adding 15 minutes of extra activity may not be enough to see a change in step count.
Perception of Achievement
The study participants received a pre-study survey as well as a post-study survey. These surveys contained several questions that could be compared to view participants’ possible changes in perception before and after the study. Pre-study, we asked them what they would like to achieve through their participation in the study (Graph 1a). Post-study, we then asked what they felt they did achieve during the study (Graph 1b). The two graphs look somewhat similar, with the majority hoping to improve their health and wellness before the study, and most people reporting that they did achieve improvement of health and wellness after the study.
Perception of Participant Exercise Routines
Pre-study, participants reported their current weekly exercise frequency and their ideal exercise frequency (Graph 2a).
Most participants felt that they needed to increase their exercise frequency. However, after the study, most of the participants felt that they were meeting their weekly exercise goal (Graph 2b). Participants increased their daily activity level and felt that they met their exercise goals while taking part in the study.
Perception of Motivation
In the pre-study survey, all participants felt moderately to very motivated to pursue their exercise goals (Graph 3a). However, post-study, the majority of participants felt very motivated to pursue their exercise goals (Graph 3b). Their motivation to exercise increased during the study, perhaps due to positive reinforcement or the benefits of additional exercise on their attitudes and personal achievement.
The participants were asked to rate several statements relating to their self-perception before and after the study. Pre-study, most participants agreed that adding exercise would make them feel more satisfied with the way they look (Graph 4a). Post-study, however, participants had a wider range of answers, and were less sure that exercise would make them feel more satisfied about their physical appearance.
Pre-study, the bulk of participants all agreed that exercise would make them feel better in general. Post-study, participants stated that they strongly agreed that exercise made them feel better (Graph 4b). This shows that the perception of feeling good was not directly tied to their feelings about their physical appearance alone.
Participant Perception of Impact of Positive Reinforcement
In the post-study survey, participants were asked how they felt about the positive messages that they received after they self-reported their step count. 6 out of 9 participants felt that the positive statements motivated them to exercise. (Graph 5a). Only one participant felt that positive messages were not motivating. The positive messages were sent to each participant soon after their step count reporting via email or text message. Participants were then asked if the frequency and method of delivering positive reinforcement was disruptive or annoying to them. More than half of the participants felt that the frequent positive messages were not disruptive or annoying. Positive reinforcement seemed to be well received and beneficial to most of the participants.
Participant Perception of Difficulties in Maintaining Exercise Routine
Participants were mostly unsure before the study started if it would be difficult to adhere to an exercise regimen. However, after the study, more than half of the participants agreed that it was difficult to stick to an exercise regimen, even with study interventions
After the study concluded, participants were asked what prevented them from taking a daily walk. The main reason was variation in schedule, due to unforeseen or unchangeable circumstances such as commute times, job duties or illnesses. (Graph 6b). None of the participants said that they did not use reminders and simply forgot, showing that this was an effective way of ensuring increased activity as long as their schedule remained predictable.
Participant Perception of Continuing Increased Activity Levels
Post-study, participants were asked if they plan to continue their daily walks. 8 out of 9 participants said they planned to continue with their exercise regimen. The study appeared to be a positive experience for the majority of participants and they felt motivated to continue to increase their activity level. Most of the participants expressed intent to make positive changes in their activity level that may translate into lasting routines.
They also had aspirations to keep improving their health and endurance as a motivator to keep increasing their activity (Graph 7b).
What Participants Learned from the Study
Participants were asked in the post-study survey, “What did you learn from this study?” As a direct result of increased walking, one participant replied, “I enjoy walking and want to continue to do it more.” Two participants noted that interaction with the Fitbit mobile application and being aware of their daily steps was motivating. One participant stated, “Tracking/counting steps can be an exercise motivator” and another wrote, “I felt like being aware of my step count and how often I was exercising made me want to exercise more.” Participants also learned that having reminders to walk was a good way for them to schedule in exercise time, “I learned that I need to walk more often and that setting a schedule or alarm is a great way to ensure I actually end up walking.” Participants responded well to adding timed reminders to walk every day and reinforcing awareness of step counts as motivation to increase their activity levels.
Participants achieved their ideal exercise amount with daily Fitbit interaction, reminders, and positive reinforcement. The average participant increased their step count over baseline by 19%. Weekend step counts varied much more than weekday step counts, due to changes in routines and schedules that greatly affected activity (With weekends omitted, the average participant increased their step count over baseline by 28%).
The participants had an overall positive opinion on step tracking (Graph 8a), allowing them to feel more accountable to their new exercise regimen. The survey results also reported that participants’ awareness of their daily step count was motivating to them (Graph 8b).
Participants found positive reinforcement motivating as well, they also reported that they felt less self-conscious about others viewing their step count data after the study (Graph 9).
The participants reported that the biggest source of difficulty in adhering to a new exercise regimen was variability in daily schedules. A program with a remote coach sending encouraging statements based on step count such as the one piloted in this study, was recommended by the participants as a possibility for helping people keep to an exercise routine.
While our study was not long enough to measure statistically significant changes in behavior, our participants responded to reminders to walk, interaction with the Fitbit wearable/app, and positive reinforcements to increase their activity. The study interventions supported our participants’ ambitions to achieve their exercise goals. Most participants seem to be motivated to continue their new, daily exercise routine.
The 15 minutes goal of extra walking may not have been enough to see an average difference considering the length of time for this study.
Participants who experienced variations in daily routines and changes in schedules may have found it difficult to find time to exercise. Other unexpected daily challenges (work, school, family etc.) could also affect step count measurements, especially if their baseline was high. A larger sample size could have produced different results, but because of the study time lines it was not possible to gather a larger sample size to discover other relationships in the data. Another limitation to this study was that we did not having a sample of participants who represented a population that reported very low activity levels so that a relationship could have been gathered based on prompts to exercise.
Participants preferences to workout outdoors or indoors may have also affected the overall results. The environmental conditions, for example weather, was also not taken into account for this study. Participants who prefer outdoor workouts without having an indoor alternative and had bad weather during the study may have had their step counts adversely affected just due to circumstance which cannot be accounted for in the results. Self reported data can also be biased and since a post survey was conducted for this study these reports may be providing data that is bias. While participants report that they intend to continue with their new exercise regimen, the study must be expanded and contain a follow up period to see if people made actual long term lifestyle changes. This study, as was conducted, was not long enough to enact real behavior modifications.
1. Burke LE, Styn MA, Sereika SM, et al. Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial. American journal of preventive medicine. 2012;43(1):20-26. doi:10.1016/j.amepre.2012.03.016.
2. Weinberg, R. S., Bruya, L., Garland, H., & Jackson, A. (1990). Effect of goal difficulty and positive reinforcement on endurance performance. Journal of Sport & Exercise Psychology, 12(2
3. Sadarangani, Pranay. Fitbit Go-To Market Plan. 2014. Retrieved from http://www.slideshare.net/pranaysadarangani/fit-bit-gtm-spring-2014
Computer Generated Data