Habit and Goal-Related Constructs in Determining Toothbrushing Behavior: Two Sensor-Based Longitudinal Studies

HEALTH PSYCHOLOGY(2022)

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摘要
Objectives: Two longitudinal studies were conducted to examine how habits and goal-related constructs determine toothbrushing behavior from a dual-process perspective. We aimed to describe the variations of habit strength, intention, and attitude and to test their associations with actual behavior at both inter- and intraindividual levels. In addition, toothbrushing behavior was measured both by self-report and sensors with the goal to compare these measures. Method: In Study 1, 40 young adults were instructed to brush their teeth twice a day, and their behaviors were measured by accelerometers for 3 weeks. Participants also self-reported their instrumental and affective attitude, habit strength, and behavior frequency weekly. Effects of interest were estimated using structural equation modeling. Study 2 replicated Study 1 with a larger and more diverse sample (N = 79), adding a measure of behavioral intention. Results: Supporting the dual-process account, habit strength predicted future behavior in addition to goal-related constructs. Habit strength also attenuated the influences of goal-related constructs on behavior, but this pattern only emerged interindividually and for self-reported behavior. In addition, toothbrushing behavior was more strongly driven by affective rather than instrumental attitude. In both studies, associations among variables were weaker within-person and when sensor-measured behavior was modeled. Conclusions: The partial support for the dual-process account suggests the need of using habit-based interventions to complement intention-based interventions when attempting to change oral health routines. Our findings also highlight the importance of affective aspects of toothbrushing behavior and the potential to incorporate sensor-based objective measures in research and interventions.
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关键词
accelerometer, dental behavior, dual-process theory, habit formation, longitudinal data analysis
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