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IntroductionDepression 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 glycaemic 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 individual’s behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.Methods and analysisIn a three-arm randomised 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 18–75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.Ethics and disseminationThe 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.Trial registration numberNCT03490253; pre-results.

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign.

IntroductionWe conducted this study to quantify how health professionals use Twitter to communicate about the human papillomavirus (HPV) vaccine.MethodsWe collected 193,379 tweets from August 2014 through July 2015 that contained key words related to HPV vaccine. We classified all tweets on the basis of user, audience, sentiment, content, and vaccine characteristic to examine 3 groups of tweets: 1) those sent by health professionals, 2) those intended for parents, and 3) those sent by health professionals and intended for parents. For each group, we identified the 7-day period in our sample with the most number of tweets (spikes) to report content.ResultsOf the 193,379 tweets, 20,451 tweets were from health professionals; 16,867 tweets were intended for parents; and 1,233 tweets overlapped both groups. The content of each spike varied per group. The largest spike in tweets from health professionals (n = 851) focused on communicating recently published scientific evidence. Most tweets were positive and were about resources and boys. The largest spike in tweets intended for parents (n = 1,043) centered on a national awareness day and were about resources, personal experiences, boys, and girls. The largest spike in tweets from health professionals to parents (n = 89) was in January and centered on an event hosted on Twitter that focused on cervical cancer awareness month.ConclusionUnderstanding drivers of tweet spikes may help shape future communication and outreach. As more parents use social media to obtain health information, health professionals and organizations can leverage awareness events and personalize messages to maximize potential reach and parent engagement.

Existing frameworks have identified a range of intervention design features that may facilitate adherence to eHealth interventions; however, empirical data are lacking on whether intervention design features can predict user adherence in the real world-where the public access available tools-and whether some design aspects of behavioral eHealth interventions are more important than others in predicting adherence. This study examined whether intervention design qualities predict user adherence to behavioral eHealth interventions in real-world use and which qualities matter the most. We correlated the online activities of users of 30 web-based behavioral interventions-collected from a proprietary data set of anonymized logs from consenting users of Microsoft Internet Explorer add-on-with interventions' quality ratings obtained by trained raters prior to empirical examination. The quality ratings included: Usability, Visual Design, User Engagement, Content, Therapeutic Persuasiveness (i.e., persuasive design and incorporation of behavior change techniques), and Therapeutic Alliance. We found Therapeutic Persuasiveness (i.e., the incorporation of persuasive design/behavior change principles) to be the most robust predictor of adherence (i.e., duration of use, number of unique sessions; 40 ≤ rs ≤ .58, ps ≤ .005), explaining 42% of the variance in user adherence in our regression model. Results indicated up to six times difference in the percentage of users utilizing the interventions for more than a minimum amount of time and sessions based on Therapeutic Persuasiveness. Findings suggest the importance of persuasive design and behavior change techniques incorporation during the design and evaluation of digital behavioral interventions.

Objective Eating disorders (EDs) compromise the health and functioning of affected individuals, but it can often take them several years to acknowledge their illness and seek treatment. Early identification of individuals with EDs is a public health priority, and innovative approaches are needed for such identification and ultimate linkage with evidence‐based interventions. This study examined whether Internet activity data can predict ED risk/diagnostic status, potentially informing timely interventions. Method Participants were 936 women who completed a clinically validated online survey for EDs, and 231 of them (24.7%) contributed their Internet browsing history. A machine learning algorithm used key attributes from participants' Internet activity histories to predict their ED status: clinical/subclinical ED, high risk for an ED, or no ED. Results The algorithm reached an accuracy of 52.6% in predicting ED risk/diagnostic status, compared to random decision accuracy of 38.1%, a relative improvement of 38%. The most predictive Internet search history variables were the following: use of keywords related to ED symptoms and websites promoting ED content, participant age, median browsing events per day, and fraction of daily activity at noon. Discussion ED risk or clinical status can be predicted via machine learning with moderate accuracy using Internet activity variables. This model, if replicated in larger samples where it demonstrates stronger predictive value, could identify populations where further assessment is merited. Future iterations could also inform tailored digital interventions, timed to be provided when target online behaviors occur, thereby potentially improving the well‐being of many individuals who may otherwise remain undetected.

The ability of modern web services such as news aggregators and search engines to tailor their results to the tastes of individuals, together with people's preference for reading opinions which reinforce their own viewpoints, have raised concerns that people are nowadays exposed to a narrow range of view-points, a phenomenon referred to as the ''filter bubble''. In this paper we focus on increasing exposure to varied political opinions with a goal of improving civil discourse. We develop a method to algorithmically encourage people to read diverse political opinions and test it when people actively seek information. First, analyzing data from a popular search engine we show that people are indeed more likely to read opinions consistent with their own. Interestingly, they are more likely to read news from opposing sites when the language model of a particular news item is close to the language model of their own political leaning. Based on this finding, we describe a method for assisting people to read divergent opinions by choosing documents of opposing viewpoints that have a language model closer to their own language model. We test our method on a number of web searchers and show that pages of the opposing side which were more similar than the average persons' own language model tended to be clicked 38% more than those below. We also describe the long-term effects of our method, showing that people who were shown more diverse results continued reading more diverse results and overall became more interested in news.

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