{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:47:45Z","timestamp":1764704865288,"version":"3.46.0"},"reference-count":111,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"crossref","award":["00021_178978"],"award-info":[{"award-number":["00021_178978"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>\n                    Online surveys are used extensively in fitness-tracking research. One common limitation is that researchers cannot assess the veracity of participants' self-reported physical activity data. One promising response to this limitation is using data-driven surveys that integrate participants' online account data (e.g., Fitbit). In this paper, we evaluate using data-driven surveys for fitness-tracking research by examining how participants perceive them, how participants' self-reported data compares to their Fitbit data, and what monetary incentives could motivate participation. To this end, we integrated the participants' Fitbit data in a survey and conducted a three-group study with\n                    <jats:italic toggle=\"yes\">N =<\/jats:italic>\n                    300 participants. We discuss the two main findings: First, although participants were receptive to data-driven surveys, the groups that had to share their data had lower completion rates. Second, there was a large discrepancy between the self-reported data and the data in the participants' Fitbit accounts (e.g., only 38% self-reported a consistent typical weekly exercise-time). We provide suggestions for researchers who conduct online surveys, including data-driven ones, that collect online-account data.\n                  <\/jats:p>","DOI":"10.1145\/3770665","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Potential of Data-Driven Surveys for Fitness-Tracking Research"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7343-2731","authenticated-orcid":false,"given":"Lev","family":"Velykoivanenko","sequence":"first","affiliation":[{"name":"Department of Information Systems, University of Lausanne, Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4438-3544","authenticated-orcid":false,"given":"Kavous","family":"Salehzadeh Niksirat","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Security and Privacy, Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5789-9331","authenticated-orcid":false,"given":"Amro","family":"Abdrabo","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Lausanne, Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7147-1828","authenticated-orcid":false,"given":"K\u00e9vin","family":"Huguenin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Lausanne, Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357236.3395448"},{"key":"e_1_2_1_2_1","volume-title":"Reducing Default and Framing Effects in Privacy Decision-Making. 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