Objective
The impact of a text messaging‐assisted lifestyle weight loss intervention on weight change among overweight adults in Beijing was examined.
Methods
It was a 6‐month randomized two arm clinical trial. The control group received a brief advice session after randomization. The intervention group received three group sessions, five coaching calls, and a daily text message prompting participants to follow predetermined lifestyle goals.
Results
A total of 123 participants were randomized. At 6 months, controls gained 0.24 ± 0.28 kg (0.21% ± 0.38%) (NS) while intervention participants lost 1.6 ± 0.28 kg (2.31% ± 0.38%) (p < 0.0001). Intervention participants decreased waist circumference (WC) (−2.69 ± 0.43 cm, p < 0.0001), percent body fat (%BF) (−0.66% ± 0.19%, p = 0.0007), and systolic/diastolic blood pressure (SBP/DBP) significantly (−1.71 ± 1.12/−3.24 ± 0.87 mmHg), while the controls had no change in WC and %BF and increased SBP/DBP by 2.43 ± 1.14/1.20 ± 0.88 mmHg (between groups: p = 0.01/p = 0.0004).
Conclusions
This text message‐assisted lifestyle intervention was effective in reducing weight, WC, %BF, and improving BP. Coupled with the scalable feature of the intervention, this finding is intriguing in light of the potential reach of the intervention for countries like China where mobile phone penetration is high and the obesity rate continues to rise.
The interdependence of multiple traits allows plants to perform multiple functions. Acquiring an accurate representation of the interdependence of plant traits could advance our understanding of the adaptative strategies of plants. However, few studies focus on complex relationships among multiple traits. Here, we proposed use of leaf trait networks (LTNs) to capture the complex relationships among traits, allowing us to visualize all relationships and quantify how they differ through network parameters. We established LTNs using six leaf economic traits. It showed that significant differences in LTNs of different life forms and growth forms. The trait relationships of broad-leaved trees were tighter than conifers; thus, broad-leaved trees could be more efficient than conifers. The trait relationships of shrubs were tighter than trees because shrubs require multiple traits to co-operate efficiently to perform multiple functions for thriving in limited resources. Furthermore, leaf nitrogen concentration and life span had the highest centrality in LTNs; consequently, the environmental selection of these two traits might impact the whole phenotype. In conclusion, LTNs are useful tools for identifying key traits and quantifying the interdependence of multiple traits.
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