Fitbit wearable devices provide users with objective data on their physical activity and sleep habits. However, little is known about how users develop their usage patterns and the key mechanisms underlying the development of such patterns. In this article, we report results from a longitudinal analysis of Fitbit usage behavior among a sample of college students. Survey and Fitbit data were collected from 692 undergraduates at the University of Notre Dame across two waves. We use a structural equation modeling strategy to examine the relationships among three dimensions of Fitbit usage behavior corresponding to three elements of the habit loop model: trust in the accuracy of Fitbit physical activity and sleep data (cue), intensity of Fitbit device use (routine), and adjustment of physical activity and sleep behaviors based on Fitbit data (reward). More than 75 percent of participants trusted the accuracy of Fitbit data and nearly half of the participants reported they adjusted their physical activities based on the data reported by their devices. Participants who trusted the Fitbit physical activity data also tended to trust the sleep data, and those who intensively used Fitbit devices tended to adjust both their physical activities and then sleep habits. Psychological states and traits such as depression, extroversion, agreeableness, and neuroticism help predict multiple dimensions of Fitbit usage behaviors. However, we find little evidence that trust, Fitbit usage, or perceived adjustment of activity or sleep were associated with actual changes in levels of sleep and activity. We discuss the implications of these findings for understanding when and how this new monitoring technology results in changes in people's behavior.

中文翻译：

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大学生Fitbit使用行为纵向研究

Fitbit 可穿戴设备为用户提供有关其身体活动和睡眠习惯的客观数据。然而，关于用户如何发展他们的使用模式以及这些模式发展背后的关键机制知之甚少。在本文中，我们报告了对大学生样本中 Fitbit 使用行为的纵向分析结果。调查和 Fitbit 数据是从圣母大学的 692 名本科生中收集的，跨越两波。我们使用结构方程建模策略来检查 Fitbit 使用行为的三个维度之间的关系，这些维度对应于习惯循环模型的三个要素：对 Fitbit 身体活动和睡眠数据准确性的信任 ( cue )、Fitbit 设备使用强度( routine )), 以及根据 Fitbit 数据调整身体活动和睡眠行为 ( reward). 超过 75% 的参与者相信 Fitbit 数据的准确性，近一半的参与者表示他们根据设备报告的数据调整了身体活动。信任 Fitbit 身体活动数据的参与者也倾向于信任睡眠数据，而那些频繁使用 Fitbit 设备的参与者倾向于同时调整身体活动和睡眠习惯。抑郁、外向、宜人和神经质等心理状态和特征有助于预测 Fitbit 使用行为的多个维度。然而，我们发现几乎没有证据表明信任、Fitbit 的使用或活动或睡眠的感知调整与睡眠和活动水平的实际变化相关。