A study in Nature revealed how patterns in data continuously collected during routine activities through wearable devices could reveal early signs of metabolic dysfunction.
Using longitudinal signals collected from Fitbit and Google Pixel watch devices integrated with demographic characteristics and readily available blood biomarkers, the researchers trained a computational model against the homeostatic model assessment of IR (HOMA-IR).
Using a HOMA-IR cut-off of 2.9, the multimodal model was robust in detecting IR when combining data from the wearable devices, together with demographic and routine blood biomarker data.
The findings suggest that collecting data in this way can reveal early physiological stress that intermittent assessments are poorly suited to detect.
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