Developing digital interventions: a methodological guide.

Digital interventions are becoming an increasingly popular method of delivering healthcare as they enable and promote patient self-management. This paper provides a methodological guide to the processes involved in developing effective digital interventions, detailing how to plan and develop such interventions to avoid common pitfalls. It demonstrates the need for mixed qualitative and quantitative methods in order to develop digital interventions which are effective, feasible, and acceptable to users and stakeholders

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments

Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through inclusive culturally targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments

Integrating Taxonomies into Theory-Based Digital Health Interventions for Behavior Change: A Holistic Framework

Digital health interventions have been emerging in the last decade. Due to their interdisciplinary nature, digital health interventions are guided and influenced by theories (e.g., behavioral theories, behavior change technologies, persuasive technology) from different research communities. However, digital health interventions are always coded using various taxonomies and reported in insufficient perspectives. The inconsistency and incomprehensiveness will bring difficulty for conducting systematic reviews and sharing contributions among communities. Based on existing related work, therefore, we propose a holistic framework that embeds behavioral theories, behavior change technique (BCT) taxonomy, and persuasive system design (PSD) principles. Including four development steps, two toolboxes, and one workflow, our framework aims to guide digital health intervention developers to design, evaluate, and report their work in a formative and comprehensive way

Digital support interventions for the self-management of low back pain: a systematic review

Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health. Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively. Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm. Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak

Beyond the trial: A systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety

Background: Digital self-help interventions (including online or computerized programs and apps) for common mental health issues have been shown to be appealing, engaging, and efficacious in randomized controlled trials. They show potential for improving access to therapy and improving population mental health. However, their use in the real world, that is, as implemented (disseminated) outside of research settings, may differ from that reported in trials, and implementation data are seldom reported. Objective: We aimed to review peer-reviewed articles reporting user uptake and/or ongoing use, retention, or completion data (hereafter ‘usage data’ or, for brevity, ‘engagement’) from implemented pure self-help (unguided) digital interventions for depression, anxiety, or the enhancement of mood. Methods: We conducted a systematic search of the Scopus, Embase, MEDLINE, and PsychINFO databases for studies reporting user uptake and/or usage data from implemented digital self-help interventions for the treatment or prevention of depression or anxiety, or the enhancement of mood, from 2002 to 2017. Additionally, we screened the reference lists of included articles, citations of these articles, and the titles of articles published in Internet Interventions, Journal of Medical Internet Research (JMIR), and JMIR Mental Health since their inception. We extracted data indicating the number of registrations or downloads and usage of interventions. Results: After the removal of duplicates, 970 papers were identified, of which ten met the inclusion criteria. Hand-searching identified one additional article. The included articles reported on seven publically available interventions. There was little consistency in the measures reported. The number of registrants or downloads ranged widely, from eight to over 40,000 per month. From 21% to 88% of users engaged in at least minimal use (e.g. used the intervention at least once or completed one module or assessment), while 7–42% engaged in moderate use (completing between 40% and 60% of modular fixed-length programs or continuing to use apps after four weeks). Indications of completion or sustained use (completion of all modules or the last assessment or continuing to use apps after six weeks or more) varied from 0.5% to 28.6%. Conclusions: Available data suggest that uptake and engagement vary widely among the handful of implemented digital self-help apps and programs which have reported this, and that usage may vary from that reported in trials. Implementation data should be routinely gathered and reported to facilitate improved uptake and engagement, arguably among the major challenges in digital health

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Social housing and digital exclusion

This report by the National Housing Federation Futures Project analyses the concept of digital exclusion, its effects, the role of housing associations in tackling its consequences and possible interventions that may arise

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Social Deprivation and Digital Exclusion in England

Issues of digital exclusion are now increasingly considered alongside those of material deprivation when formulating interventions in neighbourhood renewal and other local policy interventions in health, policing and education. In this context, this paper develops a cross classification of material deprivation and lack of digital engagement, at a far more spatially disaggregate level than has previously been attempted. This is achieved my matching the well known 2004 Index of Multiple Deprivation (IMD) with a unique nationwide geodemographic classification of access and use of new information and communications technologies (ICTs), aggregated to the unit postcode scale. This ‘E-Society’ classification makes it possible for the first time to identify small areas that are ‘digitally unengaged’, and our cross classification allows us to focus upon the extent to which the 2004 summary measure of material deprivation in England coincides with such lack of engagement. The results of the cross classification suggest that lack of digital engagement and material deprivation are linked, with high levels of material deprivation generally associated with low levels of engagement with ICTs and vice versa. However, some neighbourhoods are ‘digitally unengaged’ but not materially deprived, and we investigate the extent to which this outcome may be linked to factors such as lack of confidence, skills or motivation. Our analysis suggests that approximately 5.61 million people in England are both materially deprived and digitally unengaged. As with material deprivation, there are distinctive regional and local geographies to digital unengagement that have implications for digital policy implementation

A systematic review of digital interventions for improving the diet and physical activity behaviors of adolescents

Many adolescents have poor diet and physical activity behaviors, which can lead to the development of noncommunicable diseases in later life. Digital platforms offer inexpensive means of delivering health interventions, but little is known about their effectiveness. This systematic review was conducted to synthesize evidence on the effectiveness of digital interventions to improve diet quality and increase physical activity in adolescents, to effective intervention components and to assess the cost-effectiveness of these interventions. Following a systematic search, abstracts were assessed against inclusion criteria, and data extraction and quality assessment were performed for included studies. Data were analyzed to identify key features that are associated with significant improvement in behavior. A total of 27 studies met inclusion criteria. Most (n = 15) were Web site interventions. Other delivery methods were text messages, games, multicomponent interventions, emails, and social media. Significant behavior change was often seen when interventions included education, goal setting, self-monitoring, and parental involvement. None of the publications reported cost-effectiveness. Due to heterogeneity of studies, meta-analysis was not feasible.It is possible to effect significant health behavior change in adolescents through digital interventions that incorporate education, goal setting, self-monitoring, and parental involvement. Most of the evidence relates to Web sites and further research into alternate media is needed, and longer term outcomes should be evaluated. There is a paucity of data on the cost-effectiveness of digital health interventions, and future trials should report these data