Dynamic patterns of healthy lifestyle awareness after COVID-19: a study using Google Trends and joinpoint regression - Shaluhiyah, Zahroh; Qatrannada, Shabrina Arifia; Mahato, Roshan Kumar; Agushybana, Farid; Handayani, Sri; Afriyanto, Dzul Fahmi; Rani, Usha; Sulistyorini, Dewie (Frontiers in Big Data, 2026)
IntroductionThe COVID-19 pandemic has significantly influenced public interest in health-related behaviors, as reflected in online search trends. Analyzing these trends provides insights into shifting health concerns and informing future public health strategies. This study examined Google Trends data to assess the changes in public interest in mental health, healthy diet, sleep, screen time, physical activity, and tobacco smoking before, during, and after the COVID-19 pandemic.MethodsGoogle Trends data (2019–2023) were analyzed using joinpoint regression to identify statistically significant shifts in relative search volume (RSV) over time. Additionally, the Mann–Whitney U test was conducted to examine differences in mean RSV across time period.ResultsAwareness that consistently increased during and after the pandemic was observed in mental health, particularly anxiety, and sleep patterns. These topics showed significant positive trends in joinpoint regression and higher mean RSVs, with statistically significant differences across time periods (p < 0.05). In contrast, some behaviors such as physical activity and screen time saw increased awareness only during the pandemic but did not sustain afterward. Whilst, dietary behavior and smoking either remained stagnant or declined, indicating limited or declining public interest despite their relevance to health outcomes.ConclusionDigital interest in health behaviors varied during and after COVID-19, with only mental health and sleep showing sustained concern. However, spikes in awareness often reflected personally relevant issues, highlighting Google Trends' potential as an early signal for health promotion efforts.