Background Low birth weight has been associated with increased risk of type 2 diabetes mellitus, cardiovascular disease, and hypertension, but the risk at high birth weight levels remains uncertain. This systematic review and meta‐analysis aimed to clarify the shape of associations between birth weight and aforementioned diseases in adults and assessed sex‐specific risks. Methods and Results We systematically searched PubMed, EMBASE , and Web of Science for studies published between 1980 and October 2016. Studies of birth weight and type 2 diabetes mellitus (T2 DM ), cardiovascular disease ( CVD ), and hypertension were included. Random‐effects models were used to derive the summary relative risks and corresponding 95% confidence intervals.We identified 49 studies with 4 053 367 participants assessing the association between birth weight and T2 DM , 33 studies with 5 949 477 participants for CVD , and 53 studies with 4 335 149 participants for hypertension and high blood pressure. Sex‐specific binary analyses showed that only females had an increased risk of T2 DM and CVD at the upper tail of the birth weight distribution. While categorical analyses of 6 birth weight groups and dose‐response analyses showed J‐shaped associations of birth weight with T2 DM and CVD , the association was inverse with hypertension. The lowest risks for T2 DM , CVD , and hypertension were observed at 3.5 to 4.0, 4.0 to 4.5, and 4.0 to 4.5 kg, respectively. Conclusions These findings indicate that birth weight is associated with risk of T2 DM and CVD in a J‐shaped manner and that this is more pronounced among females.

BackgroundMany modern smart watches and activity trackers feature an optical sensor that estimates the wearer’s heart rate. Recent studies have evaluated the performance of these consumer devices in the laboratory.ObjectiveThe objective of our study was to examine the accuracy and sensitivity of a common wrist-worn tracker device in measuring heart rates and detecting 1-min bouts of moderate to vigorous physical activity (MVPA) under free-living conditions.MethodsTen healthy volunteers were recruited from a large university in Singapore to participate in a limited field test, followed by a month of continuous data collection. During the field test, each participant would wear one Fitbit Charge HR activity tracker and one Polar H6 heart rate monitor. Fitbit measures were accessed at 1-min intervals, while Polar readings were available for 10-s intervals. We derived intraclass correlation coefficients (ICCs) for individual participants comparing heart rate estimates. We applied Centers for Disease Control and Prevention heart rate zone cut-offs to ascertain the sensitivity and specificity of Fitbit in identifying 1-min epochs falling into MVPA heart rate zone.ResultsWe collected paired heart rate data for 2509 1-min epochs in 10 individuals under free-living conditions of 3 to 6 hours. The overall ICC comparing 1-min Fitbit measures with average 10-s Polar H6 measures for the same epoch was .83 (95% CI .63-.91). On average, the Fitbit tracker underestimated heart rate measures by −5.96 bpm (standard error, SE=0.18). At the low intensity heart rate zone, the underestimate was smaller at −4.22 bpm (SE=0.15). This underestimate grew to −16.2 bpm (SE=0.74) in the MVPA heart rate zone. Fitbit devices detected 52.9% (192/363) of MVPA heart rate zone epochs correctly. Positive and negative predictive values were 86.1% (192/223) and 92.52% (2115/2286), respectively. During subsequent 1 month of continuous data collection (270 person-days), only 3.9% of 1-min epochs could be categorized as MVPA according to heart rate zones. This measure was affected by decreasing wear time and adherence over the period of follow-up.ConclusionsUnder free-living conditions, Fitbit trackers are affected by significant systematic errors. Improvements in tracker accuracy and sensitivity when measuring MVPA are required before they can be considered for use in the context of exercise prescription to promote better health.

Background Mobile health (mHealth) approaches are growing in popularity as a means of addressing low levels of physical activity (PA). Objective This study aimed to determine the validity of wearables in measuring step count and floor count per day and assess the feasibility and effects of a 6-week team challenge intervention delivered through smartphone apps. Methods Staff and students from a public university were recruited between 2015 and 2016. In phase 1, everyone wore a Fitbit tracker (Charge or Charge HR) and an ActiGraph for 7 days to compare daily step count estimated by the two devices under free-living conditions. They were also asked to climb 4 bouts of floors in an indoor stairwell to measure floor count which was compared against direct observation. In phase 2, participants were allocated to either a control or intervention group and received a Fitbit tracker synced to the Fitbit app. Furthermore, the intervention group participants were randomized to 4 teams and competed in 6 weekly (Monday to Friday) real-time challenges. A valid day was defined as having 1500 steps or more per day. The outcomes were as follows: (1) adherence to wearing the Fitbit (ie, number of days in which all participants in each group were classified as valid users aggregated across the entire study period), (2) mean proportion of valid participants over the study period, and (3) the effects of the intervention on step count and floor count determined using multiple linear regression models and generalized estimating equations (GEEs) for longitudinal data analysis. Results In phase 1, 32 of 40 eligible participants provided valid step count data, whereas all 40 participants provided valid floor count data. The Fitbit trackers demonstrated high correlations (step count: Spearman ρ=0.89; P<.001; floor count: Spearman ρ=0.98; P<.001). The trackers overestimated step count (median absolute error: 17%) but accurately estimated floor count. In phase 2, 20 participants each were allocated to an intervention or control group. Overall, 24 participants provided complete covariates and valid PA data for analyses. Multiple linear regressions revealed that the average daily steps was 15.9% higher for the intervention group (95% CI −8.9 to 47.6; P=.21) during the final two intervention weeks; the average daily floors climbed was 39.4% higher (95% CI 2.4 to 89.7; P=.04). GEE results indicated no significant interaction effects between groups and the intervention week for weekly step count, whereas a significant effect (P<.001) was observed for weekly floor count. Conclusions The consumer wearables used in this study provided acceptable validity in estimating stepping and stair climbing activities, and the mHealth-based team challenge interventions were feasible. Compared with the control group, the participants in the intervention group climbed more stairs, so this can be introduced as an additional PA promotion target in the context of mHealth strategies. Methodologically rigorous studies are warranted to further strengthen this study’s findings.