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Abstract Background Physical activity can improve function and decrease healthcare spending among overweight and obese older adults. Although unstructured physical activity has been related to cardiometabolic improvements, the relationship between unstructured activity and movement quality is unclear. Aims This study aimed to evaluate the association of amount of unstructured free-living moderate-vigorous physical activity (MVPA) with measures of movement quality in overweight and obese older adults. Methods The association of MVPA with movement quality was assessed in 165 overweight and obese older adults (Age: 77.0(8.0) years; Body mass index (BMI): 29.2(5.3) kg/m 2 ). Participants performed overground walking, the Figure of 8 Walk test, and the Five-Times Sit to Stand. Weekly physical activity was measured using a waist-worn Actigraph activity monitor. Results Movement quality during straight path (gait speed (ρ = 0.30, p  < 0.01), stride length (ρ = 0.33, p  < 0.01), double-limb support time (ρ=-0.26, p  < 0.01), and gait symmetry (ρ = 0.17, p  = 0.02)) and curved path (F8W time (ρ=-0.22, p  < 0.01) and steps (ρ=-0.22, p  < 0.01)) walking were associated with weekly minutes of MVPA after controlling for age. Five-Times Sit to Stand performance was not significantly associated with weekly minutes of MVPA (ρ=-0.10, p  = 0.13). Conclusions Older adults with high BMIs who are less active also demonstrate poorer movement quality which should be targeted in interventions to promote healthy aging, decrease falls, and delay disability development. Future work should explore if these associations are observed in middle-aged adults so targeted interventions can be implemented even earlier in the disability development continuum.

A machine learning algorithm, developed to detect occlusion myocardial infarction with no-ST elevation from electrocardiogram, outperforms clinicians in diagnostic assessments. Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.