Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer - Howell, Kristen E.; Shaw, Marian; Santucci, Aimee K.; Rodgers, Kristy; Ortiz Rodriguez, Izeris; Taha, Danah; Laclair, Sara; Wolder, Carol; Cooper, Christie; Moon, Wonjong; Vukadinovich, Christopher; Erhardt, Matthew J.; et al. (Frontiers in Oncology, 2024)
Childhood cancer survivors face a range of potential health issues later in life, caused by their cancer treatment and other factors such as genetic predispositions, behavior, and socio-demographic conditions. These late effects include the development of subsequent cancers, chronic health conditions, and premature death. For optimal survivorship care, it is crucial to predict specific risks of late effects that survivors may face, discuss how to manage these risks, and offer early preventive measures and interventions. Our research utilizes mobile health technology to gather health data directly from survivors, including their daily symptoms, physical activity intensity, sleep quality, and various biometric measurements such as resting heart rate, heart rate variability, oxygen saturation, skin temperature, and physical stress. By analyzing these data, we aim to develop and validate riskprediction models for poor quality of life and adverse health outcomes for adult survivors of childhood cancer. Furthermore, integrating survivor-specific risk scores into electronic health records could enhance survivor-clinician communications. This integration could facilitate clinical decision-making, leading to improved management and care of late effects. Ultimately, this approach could provide the opportunity for early intervention and better health outcomes for childhood cancer survivors. We report here the design and plan of our study.