Analysis of 90,237 UK Biobank participants revealed striking differences in how physical activity appears to protect against cardiovascular disease depending on accelerometer data processing methods. Machine learning algorithms showed linear stroke risk reduction with moderate-vigorous activity, while traditional filtered methods and activity counts showed no clear relationship. For heart attacks, machine learning and filtered methods revealed curvilinear benefits, but activity counts suggested linear protection across 1,298 strokes and 2,031 heart attacks over nine years. These disparities highlight a critical methodological challenge in wearable device research that could fundamentally alter clinical recommendations. The finding suggests current physical activity guidelines based on accelerometer studies may be unreliable if processing methods aren't standardized. This creates uncertainty for both researchers and consumers relying on fitness trackers for health insights. Since this is a preprint awaiting peer review, these methodology-dependent results require validation before reshaping how we interpret wearable fitness data. The work represents an incremental but important step toward standardizing accelerometer research, though it raises more questions than answers about optimal activity measurement approaches.