To evaluate whether a 12-week supervised exercise program promotes an active lifestyle throughout pregnancy in pregnant women with obesity. In this preliminary randomised trial, pregnant women (body mass index ≥ 30 kg/m²) were allocated to either standard care or supervised training, from 15 to 27 weeks of gestation. Physical activity was measured by accelerometry at 14, 28 and 36 weeks, while fitness (oxygen consumption (VO₂) at the anaerobic threshold), nutrition (caloric intake and macronutrients percentage) and anthropometry were assessed at 14 and 28 weeks of gestation. Analyses were performed using repeated measures ANOVA. A total of fifty (50) women were randomised, 25 in each group. There was no time-group interaction for time spent at moderate and vigorous activity (p_interaction = 0.064), but the exercise group’s levels were higher than controls’ at all times (p_{group effect} = 0.014). A significant time-group interaction was found for daily physical activity (p = 0.023); similar at baseline ((22.0 ± 6.7 vs 21.8 ± 7.3) x 10⁴ counts/day) the exercise group had higher levels than the control group following the intervention ((22.8 ± 8.3 vs 19.2 ± 4.5) x 10⁴ counts/day, p = 0.020) and at 36 weeks of gestation ((19.2 ± 1.5 vs 14.9 ± 1.5) x 10⁴ counts/day, p = 0.034). Exercisers also gained less weight than controls during the intervention period despite similar nutritional intakes (difference in weight change = -0.1 kg/week, 95% CI -0.2; -0.02, p = 0.016) and improved cardiorespiratory fitness (difference in fitness change = 8.1%, 95% CI 0.7; 9.5, p = 0.041). Compared with standard care, a supervised exercise program allows pregnant women with obesity to maintain fitness, limit weight gain and attenuate the decrease in physical activity levels observed in late pregnancy. ClinicalTrials.gov NCT01610323

Background: People with chronic diseases can benefit from wearable devices in managing their health and encouraging healthy lifestyle habits. Wearables such as activity trackers or blood glucose monitoring devices can lead to positive health impacts, including improved physical activity adherence or better management of type 2 diabetes. Few literature reviews have focused on the intersection of various chronic diseases, the wearable devices used, and the outcomes evaluated in intervention studies, particularly in the context of primary health care. Objective: This study aims to identify and describe (1) the chronic diseases represented in intervention studies, (2) the types or combinations of wearables used, and (3) the health or health care outcomes assessed and measured. Methods: We conducted a scoping review following the Joanna Briggs Institute guidelines, searching the MEDLINE and Web of Science databases for studies published between 2012 and 2022. Pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. We included interventions using wearables that automatically collected and transmitted data to adult populations with at least one chronic disease. We excluded studies with participants with only a predisposition to develop a chronic disease, hospitalized patients, patients with acute diseases, patients with active cancer, and cancer survivors. We included randomized controlled trials and cohort, pretest-posttest, observational, mixed methods, and qualitative studies. Results: After the removal of 1987 duplicates, we screened 4540 titles and abstracts. Of the remaining 304 articles after exclusions, we excluded 215 (70.7%) full texts and included 89 (29.3%). Of these 89 texts, 10 (11%) were related to the same interventions as those in the included studies, resulting in 79 studies being included. We structured the results according to chronic disease clusters: (1) diabetes, (2) heart failure, (3) other cardiovascular conditions, (4) hypertension, (5) multimorbidity and other combinations of chronic conditions, (6) chronic obstructive pulmonary disease, (7) chronic pain, (8) musculoskeletal conditions, and (9) asthma. Diabetes was the most frequent health condition (18/79, 23% of the studies), and wearable activity trackers were the most used (42/79, 53% of the studies). In the 79 included studies, 74 clinical, 73 behavioral, 36 patient technology experience, 28 health care system, and 25 holistic or biopsychosocial outcomes were reported. Conclusions: This scoping review provides an overview of the wearable devices used in chronic disease self-management intervention studies, revealing disparities in both the range of chronic diseases studied and the variety of wearable devices used. These findings offer researchers valuable insights to further explore health care outcomes, validate the impact of concomitant device use, and expand their use to other chronic diseases. Trial Registration: Open Science Framework Registries (OSF) s4wfm; https://osf.io/s4wfm

Context: Both excess visceral adipose tissue (VAT) and low cardiorespiratory fitness (CRF) levels are associated with a deteriorated cardiometabolic risk profile. Objective: The aim of the study was to examine the respective contributions of changes in VAT accumulation vs. changes in CRF to 6-yr longitudinal changes in cardiometabolic risk markers. Design, Settings, and Participants: We conducted a prospective, population-based study with an average follow-up of 5.9 ± 0.8 yr. We followed 132 middle-aged participants from the Quebec Family Study (mean age, 35.3 ± 13.9 yr). VAT was measured by computed tomography, whereas the level of CRF was assessed by a submaximal physical working capacity test at baseline and at follow-up. A complete cardiometabolic risk profile, including systolic and diastolic blood pressure, fasting glucose and insulin levels, C-reactive protein (n = 72), as well as a standard lipoprotein-lipid profile, was obtained at baseline and at follow-up. Main Outcome Measures: We measured changes in CRF, VAT, and cardiometabolic risk profile over 6 yr. Results: After adjusting for age and sex, 6-yr changes in VAT were negatively correlated with changes in CRF (r = −0.38; P < 0.001). In a multivariate model that included age, sex, changes in VAT, changes in CRF, as well as baseline levels of the above cardiometabolic risk factors, 6-yr changes in VAT were the most important predictor of the change in the metabolic syndrome score (R² = 13.2%; P < 0.001). Adding 6-yr changes in CRF levels significantly improved the predictability of the model (R² = 19.7%; P = 0.002). Conclusions: Changes in both VAT and CRF levels observed over 6 yr are associated with changes in parameters of the lipoprotein-lipid profile, glucose-insulin homeostasis, and inflammatory markers. Thus, maintaining a low level of VAT and a high level of CRF are important targets for maintenance of cardiometabolic health.

Background: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals’ personal or protected attributes. Objective: This study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance. Methods: We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies. Results: After removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as “male versus female” in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a “human-in-the-loop” approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. Conclusions: The results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm’s preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings. Trial Registration: OSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/ International Registered Report Identifier (IRRID): RR2-10.2196/46684

Obesity is associated with a greater prevalence of cardiovascular risk factors and a higher risk of cardiovascular events, and contributes to the rise in cardiovascular morbidity and mortality worldwide. Increased BMI is established as an independent risk factor for cardiovascular disease (CVD). Attention has recently been drawn to alternate measures of adiposity/obesity, such as waist circumference, waist-to-hip ratio and waist-to-height ratio, that provide information regarding body fat distribution. Although BMI is the established clinical measurement to estimate CVD risk associated with excess bodyweight, there is evidence suggesting that abdominal obesity could represent a better marker of CVD risk than BMI. It is now recognized that abdominally obese individuals tend to have higher blood pressure. A major cardiovascular risk factor associated with stroke is systemic hypertension followed closely by obesity. Clinical adiposity indices used to describe obesity linked with systemic hypertension and stroke incidence are reviewed in this article. In summary, BMI, waist circumference, waist-to-hip ratio and waist-to-height ratio measurements are all useful tools for assessing adiposity/obesity in clinical practice, and should be evaluated with other cardiometabolic risk factors to refine cardiovascular risk stratification.

DECISION + 2, a training program for physicians, is designed to implement shared decision making (SDM) in the context of antibiotics use for acute respiratory tract infections (ARTIs). We evaluated the impact of DECISION + 2 on SDM implementation as assessed by patients and physicians, and on physicians’ intention to engage in SDM.