Introduction Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individuals behavior and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. Methods and analysis: In a three-arm randomized controlled trial we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18 to 75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (PHQ-8 >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent HbA1c from medical records at baseline and at intervention completion at 6-month follow-up. Ethics and dissemination The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our User Designed Methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.

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Medical research based on internet archive data, which in some ways is quite different from other data-based studies, is becoming more and more common. Despite its uniqueness and the challenges that characterize it, clear ethical rules designed to guide practitioners in this field have not yet been written. This article points to the lacuna that exists in legal and ethical texts today and offers an ethically balancing alternative. Among other features, the balance is based on the famous three laws of robotics by Asimov and a series of values, including transparency, accountability, fairness, and privacy.

Introduction: Diet forums in social media websites provide an opportunity to glimpse the experience of different weight loss diet strategies reported by tens of thousands of individuals. Methods: We analyzed all postings with weight information from the six major Reddit weight-loss diet forums (“subreddits”) as reported by forum participants. Results: Data were collected from January 2011 to April 2020 from all 55,900 users posting weight information. Average start BMI was in the overweight or obese range (26–34 kg/m2), and average goal BMI was in the normal range (21.5–24.5 kg/m2) for all subreddits. There is correlation between start BMI and goal BMI (R2=0.63, p<10-10) and between planned weight loss and reported weight loss (R2=0.56, p<10-10). Approximately 80% of forum participants reported a weight loss that was greater than 5% of their initial body weight. Actual reported weight loss was less than half of goal weight loss. Average reported weight loss and adherence were highest in the keto and loseit subreddits. More upvotes and fewer downvotes were associated with higher reported weight loss in five of the six subreddits. Conclusions: Despite the need for cautious interpretation of this data due to self-selection of users who updated weight loss and the possibility of unreliable weight reports, the study has several findings. Average goal BMI was in the normal weight range, demonstrating a highly unrealistic perception, in a very large lay-public cohort, of the plausibility of losing all excess weight. The success in weight loss and maintenance in self-selected individuals who continued reporting weight for many months may demonstrate the subjective value some individuals can obtain from forum participation.

UNSTRUCTURED Medical research based on Internet archive data, which in some ways is quite different from other data-based studies, is becoming more and more common. Despite its uniqueness and the challenges that characterize it, clear ethical rules designed to guide practitioners in this field have not yet been written. This article points to the lacuna that exists in legal and ethical texts today and offers an ethically balancing alternative. Among other features, the balance is based on the famous Three Laws of Robotics by Asimov and a series of values, including Transparency, Accountability, Fairness, and Privacy.