Gross motor performance of infants with an at-home wearable measurement and the Alberta Infant Motor Scale: A...

To evaluate the concurrent validity of assessing infants' gross motor performance with an at-home wearable measurement versus the Alberta Infant Motor Scale (AIMS).

This observational study used a new multi-sensor wearable Motor Assessment of Infants with a JUmpsuit (MAIJU) for 67 at-home measurements of 42 infants (60% males; mean age 11.5 months, SD 3.7 months, range 4-18 months). The study collates a normative cohort of typically developing volunteers (n = 17, 65% males; 27 measurements, mean age 11.8 months, SD 3.1 months, range 7-18 months) and a clinical cohort recruited from a neurodevelopmental follow-up clinic (n = 25, 56% males; mean age 11.2 months, SD 3.8 months, range 4-17 months). We correlated the expert-assessed total AIMS score to a holistic assessment of motor development (the BABA Infant Motor Score [BIMS]) from the MAIJU measurements. Additionally, detailed MAIJU-derived metrics were compared to the AIMS scores, to train machine learning-based AIMS predictions.

There was a very strong correlation (Spearman's rank correlation coefficient [rho] = 0.97, p < 10^-40) between BIMS and AIMS. Centile-based detection of low-performing individuals (cut-offs of 5% and 10%) showed high agreement between AIMS and BIMS (Cohen's kappa = 0.81). Performance of a machine learning-based AIMS prediction from the wearable data was comparable (rho = 0.96, p < 10^-37) to direct use of the BIMS score.

Assessing motor performance with scalable, at-home wearable measurements is highly comparable to the widely used AIMS assessment. An objective, quantitative, and expert-independent motor assessment holds promise for providing benchmarks in geographically distributed health care or clinical trials.