Aim To report the differences between Performance of Upper Limb (PUL) versions 1.2 and 2.0, compare the measurement ability of the two versions, and compare their longitudinal performance in Duchenne muscular dystrophy. Method Rasch analysis was performed on the dual data from three centres to confirm whether the two scales measure the same construct. Change scores in natural history for the different domains were compared for the two versions. Results Rasch analysis demonstrated that both versions measure the same construct and that the PUL 2.0 was a better fit to the construct of motor performance and better able to detect change at 12 months in all levels of ability than the PUL 1.2. This was also true when change scores were reviewed over 2 years. Interpretation Our results confirm that the PUL 1.2 and 2.0 versions detect change in all domains over 2 years. They also demonstrate that simplifying the original scoring of the PUL 1.2 for the revised PUL 2.0 maintains the validity of the construct and enhances the scale measurement qualities. What this paper adds The original and revised Performance of Upper Limb (PUL) scales measure the same construct. Both scales detected change in all domains over 2 years. The PUL 2.0 enhances the measurement qualities of the scale.

The field of translational research in Duchenne muscular dystrophy (DMD) has been transformed in the last decade by a number of therapeutic targets, mostly studied in ambulant patients. A paucity of studies focus on measures that capture the non-ambulant stage of the disease, and the transition between the ambulant and non-ambulant phase. In this prospective natural history study, we report the results of a comprehensive assessment of respiratory, upper limb function and upper limb muscle strength in a group of 89 DMD boys followed in 3 European countries, 81 receiving corticosteroids, spanning a wide age range (5-18 years) and functional abilities, from ambulant (n=60) to non-ambulant (n=29).

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Scientists have known for decades that persons who volunteer for behavioral research may be different from those who decline participation and that characteristics differentiating volunteers from non-volunteers may vary depending on the nature of the research. There is evidence that volunteer self-selection can impact representativeness of samples in studies involving physically or psychologically stressful procedures, such as electric shocks, sensory isolation, or drug effects. However, the degree to which self-selection influences sample characteristics in “stressful” studies involving positron emission tomography (PET) has not been evaluated. Since estimation of population parameters, robustness of findings, and validity of inferred relationships can all be impacted by volunteer bias, it is important to determine if self-selection may act as an unrecognized confound in such studies. In the present investigation, we obtained baseline data on 114 M, F subjects who participated in a study involving completion of several self-report questionnaires and behavioral performance tasks. Participants were later given the opportunity to enroll in an [11C]raclopride PET study involving intravenous amphetamine (AMPH) administration. Demographic characteristics, personality traits, and task performance of subjects who consented to the latter study were compared with those who declined participation. Findings showed that the principal personality trait that distinguished the two groups was sensation-seeking; volunteers scored significantly higher on this dimension than non-volunteers. Males were more likely to volunteer than females. However, results of mediation analysis suggested that the relationship between gender and volunteer status was mediated by greater sensation-seeking traits in the males. Implications of these findings are discussed.

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy.