Adapting the intensity gradient for use across commonly derived accelerometer activity metrics: A LABDA Network project

The intensity gradient (IG) quantifies the distribution of time spent across accelerometer-assessed physical activity intensity and is positively associated with health. It was developed using the Euclidean Norm Minus One (ENMO) intensity metric. This study aimed to enable generation of comparable IGs across other metrics (mean amplitude deviation (MAD), monitor independent movement summary units (MIMS), and counts), by addressing a key step in the IG algorithm of dividing physical activity intensity into incremental intensity bins. Two methods of creating analogous bins for MAD, MIMS and counts were explored: 1) linear scaling ("naive"); 2) non-linear modelling ("modelled"). Generated IGs were compared to the original IG (IG_ENMO) using limits of agreement (LoA) and intra-class correlation (ICC). 43 adults (age, median [IQR]: 23 (21, 26), 61% female) were included. Relative to IG_ENMO, the modelled approach led to lower IGs (bias: -0.43, -1.23, -0.91 for MAD, MIMS, and counts, respectively). In contrast, the naive approach led to higher IGs (+0.27, +0.39, +0.54, respectively). For MAD and counts, LoA were slightly wider for naive bins (95% LoA: {+/-}0.26, {+/-}0.34) vs modelled bins ({+/-}0.21, {+/-}0.28), but for MIMS were slightly wider for modelled bins (modelled: {+/-}0.35, naive: {+/-}0.31). ICCs were higher for the modelled approach with IG_MAD most consistent (ICC 95% confidence interval: 0.72-0.91) and IG_MIMS least consistent (0.59-0.86). For the naive approach, IG_MAD was most consistent (0.49-0.82) and IG_counts least consistent (0.09-0.61). Results indicate that consistency of the IG between metrics is improved with appropriate scaling to create analogous intensity bins, but agreement is limited.