Appreciating the Nuance of Daily Symptom Variation to Individualize Patient Care

INTRODUCTION: Improving symptoms for patients with chronic illness is difficult due to poor recall and imprecise assessments of therapeutic response to inform treatment decisions. Daily variation in symptoms may obscure subtle improvement or lead to erroneous associations between symptom changes and alteration in medication or dietary regimens. This may lead to mistaken impressions of treatment efficacy (or inefficacy). Mobile health technologies that collect daily patient reported outcome (PRO) data have the potential to improve care by providing more detailed information for clinical decision-making in practice and may facilitate conducting single subject (n-of-1) trials. METHODS: Interrupted time series to prototype mobile health enabled data collection for three patients. We recruited pediatric patients with established inflammatory bowel disease who had persistent symptoms. Based on their self-identified most troubling symptoms, patients were sent customized, daily-automated text messages to assess the extent of their symptoms. Standardized, PRO Measurement Information System (PROMIS) surveys were deployed weekly. Individual statistical process control charts were used to assess variation. Patients met with physicians regularly to interpret their data jointly. RESULTS: We report the experience of 3 patients with inflammatory bowel disease, each with different symptoms. Daily symptom monitoring uncovered important patterns, some of which even patients were unaware before reviewing their symptom data. Important associations were found between symptom variation and changes in medications and diet. PROMIS survey results assessed longitudinally accurately reflected changes in patient symptoms. CONCLUSIONS: We demonstrated how PROs can be implemented in practice. Monitoring and analyzing daily symptom data, using both customized and standard PROs, has the potential to detect meaningful variation in symptom patterns, which can inform clinical decision-making or can facilitate conducting formal n-of-1 trials to further improve outcomes.