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

The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN\u27s efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first “prescription digital therapeutic” authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare

Big Data Techniques to Improve Learning Access and Citizen Engagement for Adults in Urban Environments

This presentation explores the emerging concept of ‘Big Data in Education’ and introduces novel technologies and approaches for addressing inequalities in access to participation and success in lifelong learning, to produce better life outcomes for urban citizens. It introduces the work of the new Urban Big Data Centre (UBDC) at the University of Glasgow, presenting a case study of its first data product – the integrated Multimedia City Data (iMCD) project. Educational engagement and predictive factors are presented for adult learners, and older adult learners, in a representative survey of 1500 households. This was followed up with mobility tracking data using GPS data and wearable camera images, as well as one year’s worth of contextual data from over one hundred web sources (social media, news, weather). The chapter introduces the complex dataset that can help stakeholders, academics, citizens and other external users examine active aging and citizen learning engagement in the modern urban city, and thus support the development of the learning city. It concludes with a call for a more three-dimensional view of citizen-learners’ daily activity and mobility, such as satellite, mobile phone and active travel application data, alongside administrative data linkage to further explore lifelong learning participation and success. Policy implications are provided for addressing inequalities, and interventions proposed for how cities might promote equal and inclusive adult learning engagement in the face of continued austerity cuts and falling adult learner numbers

The Design of a Digital Behaviour Change Intervention for Third-Level Student Illicit Substance Use: A Persona Building Approach

Illicit substance use among third-level students is an issue of increasing concern. Digital behavioural change interventions have been developed to target this population, but reports of their effectiveness are mixed. The importance of end-user involvement in digital intervention development has been well established, but it appears that many interventions in this area did not engage end-users during development. This absence may have affected engagement, undermining their potential effectiveness. This paper describes the process and contributions of a persona-building approach in the development of a digital behaviour change intervention tailored to the needs of third-level students. Nine exploratory persona-building workshops were carried out with 31 students, and 7 project team members to develop personas for heavy, occasional and non-substance using third-level students. Early analysis has identified five archetypes which will contribute to the design of an acceptable and user-friendly intervention, and to the identification of targeted behavioural change techniques

Outcome Evaluation of the work of the CGIAR Research Program on Water, Land and Ecosystems (WLE) on soil and water management in Ethiopia

In 2019, the CGIAR Research Program on Water, Land and Ecosystems (WLE) Leadership chose to evaluate WLE’s work in Ethiopia as one of its countries where it has had most success. The objectives of the evaluation are: To determine how and in what ways WLE contributed to the achievement of intended/unintended outcomes; Based on the findings of the evaluation, make recommendations of how WLE (and its partners) can become more effective in supporting soil and water management in Ethiopia; To serve as a participatory learning experience for WLE and its partners. This report describes the evaluation process, findings, conclusions and recommendations

Piloting and producing a map of Millennium Cohort Study Data usage: Where are data underutilised and where is granularity lost?

The UK Millennium Cohort Study (MCS) is a longitudinal interdisciplinary study following the lives of 19,000 children born in the UK in 2000/1. Information has been collected at 9 months, 3, 5, 7 and 11 years, with the next sweep of data collection underway among study members who are aged 14 years. A wide range of data have been collected from children, parents and guardians, the partners of parents/guardians, older siblings and teachers, as well as sub-studies that collected data from health visitors; these include self-reported and objectively measured/verified data. This study sets out to examine how MCS data are utilised. To fit within the remit of the study, we hone in on ten priority question areas (Strengths and Difficulties Questionnaire, Child Social Behaviour Questionnaire, Diet, BMI, Immunisations, School Dis/like, Self-reported Friendships, Self-reported feelings, Screen Time, Hobbies). In total we found 481 unique studies that were using MCS data and undertaking primary analysis up to July 2015. Data that are collected through a recognised scale with defined thresholds or cut-off points for identifying constructs of interest and/or data that can provide a unique insight into a policy-relevant issue, are those most widely used in the MCS data. Measures that have been collected across sweeps – diet, BMI, SDQ and screen time - are all comparatively well used. Those measures that have started to be collected at age 7 (and first made available in 2010) have had lower usage. Data that were collected from the child’s own reports (e.g. friendships and feelings) have seldom been utilised in comparison to data collected through parental reports (e.g. SDQ). Collection of data from multiple informants did not always correlate with higher levels of usage. Imposing thresholds on data was found to be problematic in some cases, for example for BMI, where a number of different thresholds for overweight and obesity were in use. The use of different thresholds can lead to substantial differences in the results obtained. This is the first review using systematic methods that has explored MCS data use. We set out a number of ideas for good practice around the use of and reporting of MCS data

Study protocol for a randomized controlled trial of supportive parents – coping kids (SPARCK)—a transdiagnostic and personalized parent training intervention to prevent childhood mental health problems

Background: To meet the scientific and political call for effective prevention of child and youth mental health problems and associated long-term consequences, we have co-created, tested, and optimized a transdiagnostic preventive parent-training intervention, Supportive parents – coping kids (SPARCK), together with and for the municipal preventive frontline services. The target group of SPARCK is parents of children between 4 and 12 years who display symptoms of anxiety, depression, and/or behavioral problems, that is, indicated prevention. The intervention consists of components from various empirically supported interventions representing different theorical models on parent–child interactions and child behavior and psychopathology (i.e., behavioral management interventions, attachment theory, emotion socialization theory, cognitive-behavioral therapy, and family accommodation intervention). The content and target strategies of SPARCK are tailored to the needs of the families and children, and the manual suggests how the target strategies may be personalized and combined throughout the maximum 12 sessions of the intervention. The aim of this project is to investigate the effectiveness of SPARCK on child symptoms, parenting practices, and parent and child stress hormone levels, in addition to later use of specialized services compared with usual care (UC; eg. active comparison group). Methods: We describe a randomized controlled effectiveness trial in the frontline services of child welfare, health, school health and school psychological counselling services in 24 Norwegian municipalities. It is a two-armed parallel group randomized controlled effectiveness and superiority trial with 252 families randomly allocated to SPARCK or UC. Assessment of key variables will be conducted at pre-, post-, and six-month follow-up. Discussion: The current study will contribute with knowledge on potential effects of a preventive transdiagnostic parent-training intervention when compared with UC. Our primary objective is to innovate frontline services with a usable, flexible, and effective intervention for prevention of childhood mental health problems to promote equity in access to care for families and children across a heterogeneous service landscape characterized by variations in available resources, personnel, and end user symptomatology. Trial registration: ClinicalTrials.gov ID: NTCT0580052