Exploring the Relationship Between Smartphone GPS Patterns and Quality of Life in Patients with Advanced Cancer and their Family Caregivers: A Longitudinal Study (Preprint)

BACKGROUND Patients with advanced cancer and their family caregivers often experience poor quality of life. Self-report measures are commonly used to quantify quality of life of family caregivers but may have limitations such as recall bias and social desirability bias. Variables derived from passively obtained smartphone GPS data are a novel approach to measuring quality of life that may overcome these limitations, and enable detection of early signs of mental and physical health deterioration. OBJECTIVE This study explored an innovative method for predicting QOL levels using passively-collected smartphone GPS data. METHODS This was a secondary analysis involving 7 family caregivers and 4 patients with advanced cancer that assessed correlations between GPS sensor data captured by a personally-owned smartphone and QOL self-report measures over 12 weeks through linear correlation coefficients. QOL as measured by the PROMIS Global Health 10 was collected at baseline, 6, and 12 weeks. Using a Beiwe smartphone app, GPS data were collected and processed into variables including total distance, time spent at home, transition time, and number of significant locations. RESULTS The study identified relevant temporal correlations between QOL and smartphone GPS data across specific time periods. For instance, in terms of physical health, associations were observed with the total distance traveled (12 and 13 weeks, with r's ranging 0.37 to 0.38), time spent at home (-4 to -2 weeks, with r's ranging from -0.41 to -0.49), and transition time (-4 to -2 weeks, with r's ranging -0.38 to -0.47). CONCLUSIONS This research offers insights into utilizing passively obtained smartphone GPS data as a novel approach for assessing and monitoring QOL among family caregivers and advanced cancer patients, presenting potential advantages over traditional self-report measures. The observed correlations underscore the potential of this method to detect early signs of deteriorating mental and physical health, providing opportunities for timely intervention and support.