Evaluation of actical equations and thresholds to predict physical activity intensity in young children

This study examined the validity of current Actical activity energy expenditure (AEE) equations and intensity cut-points in preschoolers using AEE and direct observation as criterion measures. Forty 4–6-year-olds (5.3 ± 1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary behaviours (SBs), light intensity physical activities (LPAs) and moderate-to-vigorous intensity physical activities (MVPAs). AEE and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity and area under the receiver operating characteristic curve. The Pfeiffer equation significantly overestimated AEE during SB and underestimated AEE during LPA (P < 0.0125 for both). There was no significant difference between measured and predicted AEEs during MVPA. The Adolph cut-point showed significantly higher accuracy for classifying SB, LPA and MVPA than all others. The available Actical equation does not provide accurate estimates of AEE across all intensities in preschoolers. However, the Pfeiffer equation performed reasonably well for MVPA. Using cut-points of ≤6 counts · 15 s−1, 7–286 counts · 15 s−1 and ≥ 287 counts · 15 s−1 when classifying SB, LPA and MVPA, respectively, is recommended

Dietary outcomes of the Healthy Dads Healthy Kids randomised controlled trial

Fathers have not been exclusively targeted in family-based lifestyle programmes. The aim was to determine whether dietary intakes of fathers and children can be improved, following an intervention targeting fathers. Overweight and obese fathers (n=50, 21–65 years, body mass index [mean±standard deviation] 33.3±4.1) and their children (5–12 years) were recruited. Dietary intake was assessed at baseline and 6 months (n=35) by food frequency questionnaire. Linear mixed models determined differences by time. Fathers significantly reduced portion size (P=0.03) but not energy intakes, whereas children reduced energy intakes (kJ) (P=0.02). There is an opportunity to target fathers as to improve child intakes

Social determinants of injury-attributed mortality in Papua New Guinea: new data from the Comprehensive Health and Epidemiological Surveillance System

Objective This study reported the prevalence and sociodemographic distribution of mortalities attributed to injuries in Papua New Guinea (PNG). Setting As part of a longitudinal study, mortality data were collected from the population who live in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System, established in six major provinces in PNG. Verbal autopsy (VA) interviews were conducted by the surveillance team with close relatives of the deceased, using the WHO 2016 VA instrument from January 2018 to December 2020. Participant and Intervention Mortality data from 926 VA interviews were analysed, using the InterVA-5 diagnostic tool to assign specific cause of death (COD). Distributions of injury-attributed mortality were calculated and multinomial logistic regression analyses were conducted to identify sociodemographic factors and provide ORs, 95% CIs of estimates and p values. Result Injury-attributed deaths accounted for 13% of the total deaths recorded in the surveillance population, with the highest proportion in Madang (22%), followed by Port Moresby and Central Province (13%). Road traffic accidents were the leading COD, accounting for 43% of the total injury-attributed deaths, followed by assaults (25%) and accidental falls (10%). Young adults (aged 15-24 years) accounted the largest proportion of injury-attributed deaths (34%) and were nearly six times more likely to die from injuries than those aged 75+ years (OR: 5.89 (95% CI: 2.18 to 15.9); p\u3c0.001). Males were twice more likely to die from injuries than females (OR: 2.0 (95% CI: 1.19 to 3.36); p=0.009). Another significant sociodemographic factor associated with the increased injury-attributed mortalities included urban versus rural residence (OR: 2.0 (95% CI: 1.01 to 3.99); p=0.048). Conclusion Young adults, particularly those who live in urban areas, were at the highest risk of dying from injuries. Public health policies and interventions are needed to reduce premature mortality from injuries in PNG

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea

Objective InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).Design and setting This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Research in six major provinces.Methods The CHESS demographic team conducted verbal autopsy (VA) interviews with close relatives of the deceased, who died in communities within the catchment areas of CHESS, using the WHO 2016 VA instrument. COD of the deceased was assigned by InterVA-5 tool, and independently certified by the medical team. Consistency, difference and agreement between the InterVA-5 model and medical review were assessed. Sensitivity and positive predictive value (PPV) of the InterVA-5 tool were calculated with reference to the medical review method.Results Specific COD of 926 deceased people was included in the validation. Agreement between the InterVA-5 tool and medical review was high (kappa test: 0.72; p&lt;0.01). Sensitivity and PPV of the InterVA-5 were 93% and 72% for cardiovascular diseases, 84% and 86% for neoplasms, 65% and 100% for other chronic non-communicable diseases (NCDs), and 78% and 64% for maternal deaths, respectively. For infectious diseases and external CODs, sensitivity and PPV of the InterVA-5 were 94% and 90%, respectively, while the sensitivity and PPV of the medical review method were both 54% for classifying neonatal CODs.Conclusion The InterVA-5 tool works well in the PNG context to assign specific CODs of infectious diseases, cardiovascular diseases, neoplasms and injuries. Further improvements with respect to chronic NCDs, maternal deaths and neonatal deaths are needed