Prior to initiation, this study will be reviewed and approved by the RTI International Institutional Review Board.

This study will make use of a panel of Mturkers via Amazon’s Mechanical Turk (Mturk) platform. Mturkers are a workforce of individuals willing to participate in online research studies in exchange for money deposited into their Amazon.com personal account. With the help of Mturk Prime, this study will use Mturkers as a basis for the sample. Mturk Prime operates as a team of online support staff who assist researchers in managing, communicating with, and collecting data from Mturkers (www.turkprime.com). Research using Mturkers has increased dramatically in the past few years due to the low cost and vast acquisition of data [9]. Additionally, studies show that Mturkers are more demographically diverse than standard convenience samples and samples from other online forms of data collection such as Twitter [10] and may be generalizable to the greater population [11,12]. In order for an Mturker to become a part of this study’s panel, the person will be required to either keep track of their own weight, diet, or exercise routine or keep track of their own blood pressure, blood sugar, sleep patterns, headaches, or some other health-related indicator. An eligible Mturker must also be at least 18 years of age, regularly wear a Fitbit, and be willing to give the research team access to their Fitbit data for the previous month and the upcoming month from the date of sign-up.

The sample will consist of 30 Mturk participants. Participants will read a short task description and compensation information on Mturk’s research studies advertisement page. Interested participants will be asked to click a link directing them to a series of eligibility questions. If they qualify for the study based on their answers, participants will complete an electronic informed consent and become part of the Mturk Prime panel. All panelists will receive a unique numeric participant ID. Once the panel is confirmed, all 30 panelists will complete a health questionnaire that assesses demographics, general health, physical activity, health tracking processes, and consumer technology (see Multimedia Appendix 1 for full questionnaire specifications including skip logic and response codes). Participants will be asked to enter their unique participant ID into a text box at the start of the questionnaire. At the end of the questionnaire, participants will be queried for their willingness to allow researchers to download their Fitbit data. All varieties and models of Fitbit will be allowed in this study (a range of devices is summarized in the research of Evenson et al [13]).

Upon consent to Fitbit data access, participants will be routed to a third-party data service provider called Fitabase LLC (San Diego, California). Using the Fitbit application programming interface, third-party services such as Fitabase can access and aggregate self-tracker data. Fitabase provides researchers with a connection to the Fitbit infrastructure to support data collection. The research team will generate unique Fitabase links for each participant. When respondents reach the end of the self-administered survey, they will click on the link to Fitabase that corresponds to their participant ID (Figure 1). Upon completion of the Fitabase sign-up, participants will be given $10 via Mturk Prime’s customer service team. Participants who complete the questionnaire but do not sign up with Fitabase will not receive the $10 incentive. Participants can refuse to participate or cancel registration at any time. We will contract with Fitabase to provide the research team with 30 days of retrospective data and 30 days of prospective data, both from the date of sign-up. After the 30 days of prospective data are complete, Fitabase will terminate the connection to the individuals’ Fitbit device.

Fitabase will be used to extract daily and intraday data from the linked Fitbit accounts. These variables include daily-level data on total steps, distance, calories burned, total sleep time, and daily active versus sedentary time. We will also obtain hourly-level data on calories burned, active versus sedentary time, heart rate, sleep, and step counts. The most granular output, intraday data, will include minute-level step counts. These data will be downloaded as both raw and aggregated files by day, per person.

This study provides a unique and innovative protocol for remote data collection using a common physical activity tracker. The study will be cost effective and easily manageable in that researchers do not need to meet with participants face-to-face at any point in the study and participants are able to use their own personal device to participate in the study.

With the acquisition of these data, we will be able to learn detailed information about the health of these individuals without meeting the participant face-to-face for an interview or in-person physiological assessments. Furthermore, we will learn more about the ease at which participants navigated the questionnaire-to-Fitabase linkage system by determining what proportion of participants were able to complete the online questionnaire but were unable to connect their personal Fitbit to the Fitabase platform. Finally, these data will indicate the frequency at which users sync their Fitbit, allowing us to learn more about the normal use and wearing habits of Fitbit users.

This feasibility study has several limitations. First, this study is targeted to a specific population, and Mturkers may not be generalizable to other populations. However, Mturkers are familiar with the online environment and therefore may be more adept at performing tasks with technology, thus making feasibility of the protocol administration more likely to be successful.

Second, this study will require respondents to have access to a Fitbit device in order to participate. Individuals who can afford and use Fitbit devices and other consumer wearables are more likely to be younger (between the ages of 18 and 34 years) and affluent [14], thus impacting generalizability. Third, the initial process of gathering a panel of participants will require respondents to complete a screener and then at a later date, complete a questionnaire in order to allow researchers to choose a varied participant pool who all use Fitbit devices. A more streamlined process would be preferred in future studies whereby participants would be able to fill out the screener and immediately begin the questionnaire if they are eligible, without the need to create a panel of participants. Unfortunately, this study will not be able to employ this methodology due to panel restrictions.

This study will demonstrate that activity tracker data (ie, Fitbit data) can be remotely gathered from participants without face-to-face contact and with the use of respondent’s personal consumer wearable devices. Future research could investigate the feasibility of remote data collection without the need of a 2-step data management process as well as assess the clinical validity of consumer wearable devices, like Fitbit, to ensure that the data are accurate. If effective, this methodology could be used as a guide for researchers to implement when setting up a remote data collection system and could be applied to other consumer wearable devices as well.