Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Materials
- Software
- We created Experiencer [55], a GDPR-compliant ESM platform. The software is implemented in JavaScript, using Web API of Tizen OS suitable for Samsung smartwatches. In our experiments, we used the Samsung Galaxy Watch Active 2 devices. To ensure seamless data collection, our prototype is integrated with GameBus [56] (an mHealth platform developed for supporting the design, implementation, and evaluation of various health promotion campaigns [57,58]) (Figure 1).Experiencer was designed to support (1) dynamic configurabilty that facilities researchers with on-the-fly adjustments applied to the ESM parameters. (2) stand-alone operation to collect data in situ, and syncing the data upon detecting reliable network connectivity. (3) a user interface compliant with wearable usability standards so that participants can easily answer the questionnaires on the smartwatch screen (Figure 2).
3.2. Methods
3.2.1. Study Design
- Recruitment
- Our study was conducted in the context of the TU/e Samen Gezond program, an online program designed to promote healthy activities for the students and staff members of the Eindhoven University of Technology. During the program, participants received a set of healthy suggestions in a web application and were rewarded points in return for acting upon those suggestions. To enhance the experience of participants in the lifestyle program (by providing a steps tracker built on top of our ESM application), they also received Samsung Galaxy Watch Active 2 equipped with our prototyped ESM application.
- Duration
- The duration of the study was 5 weeks, which is as long as the TU/e Samen Gezond program lasted.
- Number of participants
- Constrained by the number of available smartwatches at the time of the study, and the recruitment process described, we could ultimately recruit participants.
- Treatment groups
- The participants were randomly assigned to two treatment groups which we called ’resting’ and ’active’: Half were assigned to the resting group who received beeps while not moving, and the other half to the active group who received beeps while being physically active (e.g., walking). Due to some early dropouts, ultimately the active group consisted and the resting group participants.
- Compensation
- Depending on the allocated treatment group in the TU/e Samen Gezond program, participants could be rewarded with a giveaway voucher of €25 in exchange for their points. Note that the participants were not rewarded for wearing the smartwatch neither for any other interactions with it (e.g., checking the smartwatch for notifications, replying to the questions they received, etc.). Rather they were rewarded for doing healthy activities that they could register via a separate web application dedicated to the TU/e Samen Gezond program or via unobtrusive sensing by the smartwatch.
- Schedule
- Following our hypothesis, the schedule of choice was event-contingent. The monitored event was the level of physical activity. As soon as a physical activity event was detected via our prototype, a beep was delivered to the participant’s smartwatch. The beeps were administered depending on the type of physical activity (e.g., walking, running, not moving), the treatment group a participant was in, and the defined inter notification time.
- Inquiry limit
- In our study, being event-contingent, sensible limits could reduce burden. According to the literature, around 7 beeps per day may yield an optimal balance of recall and annoyance [59]. Since we instructed participants to wear the smartwatch when they were awake, assuming one wears the smartwatch ∼12 h per day, an internotification time of 105 min (1.75 h) would result in inquiries per day, compliant with the literature.
- Inter notification time
- This notion is defined as the time in-between two consecutive notifications. In our case, since the schedule was event-contingent, there might be a situation that one is rarely or frequently beeped based on their level of physical activity and their treatment group. As as we described above, to prevent overwhelming the participants, we set a 105 min internotification time.
- Notification expiry
- There are many heuristics and hypotheses in the literature depending on different scenarios to determine notification expiry time (or lifetime) such as 5-min [60] or 3-min [61]. In this study, the notifications remained in the notifications area of the smartwatch, unless a participant cleared it, or the next beep from our prototyped ESM software arrived (our beeps did not stack up). This could also act as a reminder to the participant in case of an occasional visit to the notification area.
- Questionnaire
- To assess the impact of the event contingent strategy upon response rates, we chose to survey user emotions which is a typical case of ESM applications. Furthermore, we were motivated by earlier research that aims to infer emotions from wearable sensors (see [37,46,62]). Thus, at sampling moments, participants were requested to complete the Positive and Negative Affect Schedule (PANAS), which is a standard scale that consists of different words that describe feelings and emotions [63].
3.2.2. Data Analysis and Cleaning
- Physical activity recognition
- To detect the physical activity levels of participants, we utilized the built-in Samsung pedometer API that applies its proprietary algorithm for physical activity detection. We adopted such an API to capture changes in physical activity in real-time and to manage sending beeps based on the physical activity levels of our participants across the active and resting treatment groups. More specifically, the pedometer API of the smartwatch is able to detect and distinguish not moving, walking, and running activities [64]. In the case that the algorithm fails to categorize a physical activity, it marks it as unknown. In our study, in the active group, the beeps were sent as soon as either walking, or running were detected and only if the internotification time was passed. In contrast, in the resting group, the beeps were sent when the not moving activity was detected in accordance with the inter notification time constraint. The internotification time was set to control the number of notifications sent to the participants. That is to avoid overwhelming the participants by sending a beep at any moment that the pedometer detects a physical activity. By setting such constraints, the participants received at most about 7 bees per day. Additionally, to capture a wider range of physical activities, we also leveraged detection of activities that fell under the unknown category. The details of such inclusion are described below in the Analysis section.
- Analysis
- The response rate is calculated as the ratio of the number of self-reports over the total number of received beeps. In the results section, we do so at the treatment group level both for the whole study period and on each week:The built-in physical activity monitor API of our smartwatch could detect walking, running, and not-moving activities. Additionally, to capture other physical activities (such as householding) we also enabled the detection of the built-in unknown physical activity [64]. By doing so, we were able to capture a wider range of physical activities (other than just walking and running) in line with our methodological decisions. The unknown event includes a spectrum of physical activities from subtle to vigorous and is triggered whenever the built-in activity monitor in the smartwatch fails to categorize a physical activity into either not moving, walking, or running. The unknown event may be detected both in lower (resting) or higher (active) levels of physical activity. Accordingly, we also checked the speed property of unknown events so that beeps were only delivered at intended levels of physical activity (e.g., for a participant in the active group, if an unknown activity of high speed were detected, a beep could be delivered).
- Cleaning
- The gathered data consisted of beep-related information, self-reports, and sensor data. The beep-related information consisted of timestamps of when a beep was received and when a beep was read. The self-report data included the timestamps of when the self-report was submitted, and the selected emotion from the PANAS scale along with its corresponding intensity (the different intensity levels are “very slightly or not at all”, “a little”, “moderately”, “quite a bit”, or “extremely”). The sensor recordings included the physiological data monitored to detect interesting events. i.e., active and resting states.As discussed in previous sections, the internotification time and the inquiry limit were set to specific values congruent with the common strategies in the literature (see [3,65]). However, at the beginning of our experiment, a technical malfunction in our first version of the prototype caused some constraint violations concerning the inquiry limits and inter notification times. That led to receiving beeps sooner than the intended inter notification time and more than the inquiry limit. In other words, participants received more beeps than intended. Although the issue was fixed during the first week of the study, some noisy data was generated. To clean such noises, in our analysis, for each participant, we only considered the first 7 beeps that were delivered on each day. Having cleaned data, we tested our hypothesis by calculating and then comparing the response rates of our treatment groups.
4. Results
4.1. Response Rate
4.2. Dropouts
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Khanshan, A.; Van Gorp, P.; Nuijten, R.; Markopoulos, P. Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices. Int. J. Environ. Res. Public Health 2021, 18, 10593. https://doi.org/10.3390/ijerph182010593
Khanshan A, Van Gorp P, Nuijten R, Markopoulos P. Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices. International Journal of Environmental Research and Public Health. 2021; 18(20):10593. https://doi.org/10.3390/ijerph182010593
Chicago/Turabian StyleKhanshan, Alireza, Pieter Van Gorp, Raoul Nuijten, and Panos Markopoulos. 2021. "Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices" International Journal of Environmental Research and Public Health 18, no. 20: 10593. https://doi.org/10.3390/ijerph182010593