Improved calibration of area detectors using multiple placements.

Calibration of area detectors from powder diffraction standards is widely used at synchrotron beamlines. From a single diffraction image, it is not possible to determine both the sample-to-detector distance and the wavelength, but, with images taken from multiple positions along the beam direction and where the relative displacement is known, the sample-to-detector distance and wavelength can both be determined with good precision. An example calibration using the GSAS-II software package is presented.

Dual-Task Assessments for Predicting Future Falls in Neurologic Conditions: A Systematic Review.

ABSTRACT: This review investigated the ability of dual-task tests to predict falls in people with neurological disorders. Databases were searched to identify prospective cohort studies that analyzed dual-task testing and falls in people with neurological disorders. Reviewers screened studies for eligibility and extracted key information like participant characteristics, intervention details, outcome measures, and significant outcomes. Reviewers assessed methodological quality of eligible studies using the Standard Quality Assessment Criteria. Eighteen studies of strong methodological qualified with 1750 participants were included in the review. Dual-task performances were predictive of future falls in people with Huntington's disease, spinal cord injury, and moderate cognitive impairment, although only one independent study was included for each disability type. In people with stroke, 37% of eligible studies showed dual-task assessments to be predictive of future falls. No dual-task tests predicted prospective falling in people with Alzheimer's or Parkinson's disease. Complex dual tasks seemed to be more predictive of fall risk than simpler dual tasks. Results suggest that disability type, severity of disability, and task complexity play a role in the predictive ability of dual-task assessments and future falling in neurological disorders. Future studies may benefit from using this review to guide the design of effective dual-task assessments and fall interventions.

Optimizing Detection and Prediction of Cognitive Function in Multiple Sclerosis With Ambulatory Cognitive Tests: Protocol for the Longitudinal Observational CogDetect-MS Study.

BACKGROUND: Cognitive dysfunction is a common problem in multiple sclerosis (MS). Progress toward understanding and treating cognitive dysfunction is thwarted by the limitations of traditional cognitive tests, which demonstrate poor sensitivity and ecological validity. Ambulatory methods of assessing cognitive function in the lived environment may improve the detection of subtle changes in cognitive function and the identification of predictors of cognitive changes and downstream effects of cognitive change on other functional domains. OBJECTIVE: This paper describes the study design and protocol for the Optimizing Detection and Prediction of Cognitive Function in Multiple Sclerosis (CogDetect-MS) study, a 2-year longitudinal observational study designed to examine short- and long-term changes in cognition, predictors of cognitive change, and effects of cognitive change on social and physical function in MS. METHODS: Participants-ambulatory adults with medically documented MS-are assessed over the course of 2 years on an annual basis (3 assessments: T1, T2, and T3). A comprehensive survey battery, in-laboratory cognitive and physical performance tests, and 14 days of ambulatory data collection are completed at each annual assessment. The 14-day ambulatory data collection includes continuous wrist-worn accelerometry (to measure daytime activity and sleep); ecological momentary assessments (real-time self-report) of somatic symptoms, mood, and contextual factors; and 2 brief, validated cognitive tests, administered by smartphone app 4 times per day. Our aim was to recruit 250 participants. To ensure standard test protocol administration, all examiners passed a rigorous examiner certification process. Planned analyses include (1) nonparametric 2-tailed t tests to compare in-person to ambulatory cognitive test scores; (2) mixed effects models to examine cognitive changes over time; (3) mixed effects multilevel models to evaluate whether ambulatory measures of physical activity, sleep, fatigue, pain, mood, and stress predict changes in objective or subjective measures of cognitive functioning; and (4) mixed effects multilevel models to examine whether ambulatory measures of cognitive functioning predict social and physical functioning over short (within-day) and long (over years) time frames. RESULTS: The study was funded in August 2021 and approved by the University of Michigan Medical Institutional Review Board on January 27, 2022. A total of 274 adults with MS (first participant enrolled on May 12, 2022) have been recruited and provided T1 data. Follow-up data collection will continue through March 2026. CONCLUSIONS: Results from the CogDetect-MS study will shed new light on the temporal dynamics of cognitive function, somatic and mood symptoms, sleep, physical activity, and physical and social function. These insights have the potential to improve our understanding of changes in cognitive function in MS and enable us to generate new interventions to maintain or improve cognitive function in those with MS. TRIAL REGISTRATION: ClinicalTrials.gov NCT05252195; https://clinicaltrials.gov/study/NCT05252195. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59876.

Unreliable Findings Due to Miscalculations and Errors. Comment on Nally et al. The Effectiveness of School-Based Interventions on Obesity-Related Behaviours in Primary School Children: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Children 2021, 8, 489.

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