Accurate wear-time detection is essential for valid digital mobility assessment, yet existing lower-back device algorithms rely on heuristic thresholds validated only against stationary non-wear time, with no study benchmarking these methods on a common dataset. We benchmarked the full spectrum of wear-time detection approaches for lower-back devices, from signal processing to foundation models, using three complementary datasets: real-world recordings from 14 younger adults with labelled wear/non-wear periods, 12 recordings of non-wear with artificial device movement, and the Mobilise-D Technical Validation Study (n=101, target clinical population, 100% wear). Twenty-eight methods were evaluated using a common validation framework: 12 existing literature algorithms, a novel gyroscope-based signal processing method, and five machine learning, deep learning, and foundation model approaches, including two novel foundation models (SIGMA and ATLAS). Performance increased with model complexity: the best existing algorithm achieved a performance index of 0.866, the novel gyroscope-based signal processing method improved on this (0.889), and machine learning, deep learning, and foundation model approaches performed best (0.871-0.961), reducing daily wear-time duration error to as little as 4-5 minutes. Best-performing methods were implemented in the Mobilise-D pipeline (Mobgap), with models openly available for benchmarking and future development.