We summarize the key learnings from two large-scale fully mobile clinical trials targeting (> 2,000 enrolled people) depressed individuals. BRIGHTEN v1 was open to the general US population and BRIGHTEN v2 was designed to enroll both English-speaking and an underserved Latino/Hispanic population. Noticeable differences in user recruitment, engagement and daily self reported mood observed across two BRIGHTEN studies are highlighted here. Data Release We plan to release both passive and active self reported mood data from BRIGHTEN v1 and v2 studies by Q1-2018. For more information and collaboration opportunities, please contact us apratap@uw.edu www.brightenstudy.com/spa data collection ended-May 2017 www.brightenstudy.com BRIGHTEN v1 BRIGHTEN v2 keywords-mHealth, Depression, smartphone, passive sensing

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This study responds to recent calls for information about how personal health expenditures from the National Health Expenditure Accounts are distributed across medical conditions. It provides annual estimates from 1996 through 2005 for thirty-two conditions mapped into thirteen all-inclusive diagnostic categories. Circulatory system spending was highest among the diagnostic categories, accounting for 17 percent of spending in 2005. The most costly conditions were mental disorders and heart conditions. Spending growth rates were lowest for lung cancer, chronic obstructive pulmonary disease, pneumonia, coronary heart disease, and stroke, perhaps reflecting benefits of preventive care.[Health Affairs 28, no.

Some prior research has suggested that health spending for many diseases has been driven more by increases in so-called treated prevalence-the number of people receiving treatment for a given condition-than by increases in cost per case. Our study reached a different conclusion. We examined treated prevalence, clinical prevalence-the number of people with a given disease, treated or notand cost per case across all medical conditions between 1996 and 2006. Over this period, three-fourths of the increase in real per capita health spending was attributable to growth in cost per case, while treated prevalence accounted for about one-fourth of spending growth. Our evidence suggests that most of the treated-prevalence effect is due to an increase in the share of eligible people being treated rather than an increase in clinical prevalence of diseases. We conclude that efforts to curb health spending should focus more on reining in cost per case. C oncern about rising US health spending has existed for many years and has spawned substantial research into the factors that have caused it. 1 A 2008 report by the nonpartisan Congressional Budget Office identified seven key factors driving the historical growth of health care spending: aging of the population; changes in third-party payment; personal income growth; health sector prices; administrative costs; defensive medicine and supplier-induced demand; and technology-related changes in medical practice.2 Researchers have identified the latter as a leading factor, responsible for anywhere from one-third to two-thirds of the growth in real per capita health care spending. 1,3 Not listed among the seven key factors above is the effect on spending of changes in the prevalence of medical conditions beyond that caused by population aging. A number of recent studies, however, have pointed to a rise in the prevalence of conditions associated with the increase in obesity as an additional driver of spending. [4][5][6]