Sustainable Practices Within a School‐Based Intervention: A Report from Project Healthy Schools

Over the past three decades the proportion of students classified as overweight has almost tripled. This trend in childhood obesity is a cause for concern. Stakeholders have come together to stem growth and implement healthy habits in childhood to not only prevent obesity, but also future cardiovascular risk. School‐based health interventions have proven to be an effective medium to reach youth. Sustainable practices remain the largest determinant of long‐term success of these programs. Project Healthy Schools, a community–university collaborative school‐based health intervention program, sustainable practices have led to positive changes in participating middle schools. This collaborative has provided important insight on key factors needed for long‐term sustainability for a school‐based wellness program. These key factors are described under leadership, policy, finances, and reproducibility. Future school‐based programs may plan for success with sustainability while drawing from our experience.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96375/1/wmh36.pd

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development