App-based persuasive technologies emerged as promising tools to promote sustainable travel behavior. However, the opt-in, self-selection framework characterizing their use in real-life conditions might actually lead to wrongly estimate their potential and actual impact in analyses that do not rely on strict randomized controlled trials (RCTs). To investigate evidence of such biases, we analyze mobility data gathered from users of a persuasive app promoting public transport and active mobility launched in 2018 in Bellinzona (Switzerland). We consider the users' baseline mobility data: km per day (total and by car) traveled during the app validation period, when behavior change motivational features were not enabled. To estimate the possible self-selection bias, we compare these data with the reference population, using data from the Swiss Mobility and Transport Census; to study the possible attrition bias, we look at the relations between baseline mobility and the number of weeks of app's active use. We find evidence of neither self-selection nor critical attrition biases. This strengthens findings by earlier non RCT-based analyses and confirms the relevance of app-based persuasive technologies for mobility behavior change.

基于应用程序的说服技术成为促进可持续旅行行为的有前途的工具。然而，选择加入、自我选择的框架表征了它们在现实生活中的使用，实际上可能会导致错误地估计它们在不依赖严格随机对照试验 (RCT) 的分析中的潜在和实际影响。为了调查此类偏见的证据，我们分析了从 2018 年在贝林佐纳（瑞士）推出的促进公共交通和主动移动的说服性应用程序用户收集的移动数据。我们考虑用户的基线移动数据：在应用验证期间，当行为改变激励功能未启用时，每天行驶的公里数（总计和乘车）。为了估计可能的自我选择偏差，我们将这些数据与参考人群进行比较，使用来自瑞士交通运输普查的数据；为了研究可能的损耗偏差，我们研究了基线移动性与应用程序活跃使用周数之间的关系。我们发现既没有自我选择也没有批判性磨损偏见的证据。这加强了早期非 RCT 分析的发现，并证实了基于应用程序的说服技术与移动行为改变的相关性。