0294 How Different Smartphone Application Categories Impact Sleep in College Students

Abstract Introduction Sleep plays a pivotal role in academic success, physical health, and mental health, yet college students often compromise their sleep through irregular sleep schedules and heightened stress. Smartphone use prior to sleep onset can delay circadian rhythms, reduce melatonin production, and contribute to fatigue. Not all screen time affects sleep the same way; different application categories influence distinct brain systems. Social media rapidly releases dopamine, increasing irritation when deprived and contributing to mental fatigue. Gaming increases heart rate and emotional arousal. These physiological and psychological responses demonstrate that not all smartphone activities have equal impact on sleep health. With smartphones acting as a constant source of stimulation, and most college students owning one, understanding how specific application types affect sleep is essential. Methods Participants will be college students at least 18 years old enrolled at Montana State University. Recruitment will include flyers and will aim for diversity in background, age, and lived experiences. Data from a previous pilot study will be combined with new data. Participants will complete an intake survey assessing sleep, screen time habits, stress, and mental health. For four weeks, participants will wear a GENEActiv device to collect sleep-related movement, temperature, and light data. Screen time will be recorded using an approved tracking application measuring total time, hourly patterns, and app categories. Daily surveys will capture emotions and prior-night sleep. After data collection, participants will complete a one-on-one interview about their screen time and sleep patterns. Device data will be processed using a validated program, and screen time data will be synthesized using Python. Results The dataset will include objective phone-use metrics, subjective sleep and well-being measures, demographic variables, longitudinal patterns, and covariates such as bedtime phone use and nighttime awakenings. These combined layers will allow examination of how stress, application categories, and daily fluctuations influence sleep continuity and quality. Conclusion Together, these factors illustrate how the interaction between stress, technology use, and neurophysiology places college students at heightened risk for disrupted sleep. Understanding how specific application categories affect sleep will guide evidence-based recommendations for improving both technology habits and sleep health. Support (if any)