Long-term Compliance Habits: What Early Data Tells Us

TL;DR

This study analyzes early compliance data from college students using activity trackers to predict long-term participation and dropout risk, providing insights for designing more effective long-term health studies.

Contribution

It demonstrates that early compliance patterns can reliably predict long-term adherence and dropout, aiding in study design and participant management.

Findings

Early compliance correlates with dropout risk (p < .001)

Early data predicts long-term compliance (p < .001)

Insights support improved study retention strategies

Abstract

The rise in popularity of physical activity trackers provides extensive opportunities for research on personal health, however, barriers such as compliance attrition can lead to substantial losses in data. As such, insights into student's compliance habits could support researcher's decisions when designing long-term studies. In this paper, we examined 392 students on a college campus currently two and a half years into an ongoing study. We find that compliance data from as early as one month correlated with student's likelihood of dropping out of the study (p < .001) and compliance long-term (p < .001). The findings in this paper identify long-term compliance habits and the viability of their early detection.