Feasibility of Therapist-Guided Personalized Music Listening for Fibromyalgia: A Pilot Study of Self-Directed Home-Based Intervention.

Most diseases of aging, including cardiovascular illness, neurodegeneration, and many types of cancers, develop gradually over years, offering an attractive window for early detection and potential mitigation. The explosive development of ever-more sophisticated measurement technologies, from imaging to -omics to wearables, has provided the opportunity to collect dense molecular, structural, and physiological data, while rapid advances in AI offer powerful analytic capabilities to help parse these multimodal parameters. The promise of advanced detection of a wellness-to-disease transition has attracted many adults to consumer health platforms that offer comprehensive testing. This trend worries many doctors, who learned in their medical school biostatistics coursework that extensive testing of unselected individuals for uncommon conditions generally produces far more false alarms than actionable signal – a basic consequence of Bayes’ Theorem. Concerned physicians point to the challenge of “incidentalomas,” inadvertently discovered anomalies that (while typically benign) can increase patient anxiety, spawn elaborate workups, and introduce the possibility of iatrogenic harm. The result is a dilemma: how to productively leverage our increasingly rich collection of measurement tools and analytic technologies to detect early signs of disease without incurring the penalties that can seem inextricably bound up with such comprehensive testing?