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BackgroundPhysical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem. ObjectiveThis study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers. MethodsWrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion. ResultsIn total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ≤2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ≥14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA. ConclusionsThis study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies.

Objective: The study was to determine the prevalence of baseline risk factors for cardiovascular outcomes and cancer among commercially‐insured patients with rheumatoid arthritis (RA) during their first dispensed treatment for either tumor necrosis factor inhibitors (TNFi) or JAK inhibitors (JAKi). Methods: Patients with RA from August 16, 2019 to March 31, 2022 were identified in the Merative MarketScan Commercial and Medicare databases. The first date that a TNFi or JAKi was dispensed was the index date, and baseline risk factors were assessed among patients continuously eligible for 12 months before the index date. Patients who had the following were stratified into an elevated risk category: age ≥65 years, smoking, or a history of a major adverse cardiovascular event, venous thromboembolism, or cancer. The prevalence of modifiable risk factors was also reported: hypertension, hyperlipidemia, obesity, and diabetes. The crude prevalence and prevalence difference (PD) were reported. Results: A total of 12,673 patients (TNFi [n = 7,748; 61%] and JAKi [n = 4,925; 39%]) met inclusion criteria. The prevalence of elevated risk was the same for all patients using TNFi (n = 2,051; 26%) and JAKi (n = 1,262; 26%). Compared with patients having low risk, patients with an elevated risk also had a higher prevalence of at least one primary modifiable risk factor for both patients using JAKi (79% vs 58%; PD 21%, 95% confidence interval [CI] 18%–24%) and TNFi (81% vs 60%; PD 21%, 95% CI 19%–23%). Conclusion: In recent years, JAKi and TNFi were used in similar proportions to treat RA among commercially‐insured patients at elevated cardiovascular and cancer risk. Because uncontrolled disease, modifiable comorbidities, and treatment with JAKi are associated with these adverse events, future studies evaluating how practice patterns may be affected by the emergence of safety data will be of value. [ABSTRACT FROM AUTHOR]

ObjectivesParents of children with autism spectrum disorders (ASDs) often report gastrointestinal (GI) dysfunction in their children. The objectives of the present study were to determine whether infants at high risk for developing ASD (ie, siblings of children diagnosed as having ASD) show greater prevalence of GI problems and whether this prevalence is associated with diet and age at weaning from breast milk.MethodsUsing questionnaires, diet history and GI problems were tracked prospectively and retrospectively in 57 high-risk infants and for comparison in 114 low-risk infants (infants from families without ASD history).ResultsIn low-risk infants, prevalence of GI symptoms, in aggregate, did not vary with diet or age of weaning. By contrast, high-risk infants with GI symptoms were weaned earlier than those without symptoms (P