Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies

In recent years, there has been increasing discussion of the limitations of traditional randomized controlled trial (RCT) methodologies for the evaluation of eHealth and mHealth interventions, and in particular, the requirement that these interventions be locked down during evaluation. Locking down these interventions locks in defects and eliminates the opportunities for quality improvement and adaptation to the changing technological environment, often leading to validation of tools that are outdated by the time that trial results are published. Furthermore, because behavioral intervention technologies change frequently during real-world deployment, even if a tested intervention were deployed in the real world, its shelf life would be limited. We argue that RCTs will have greater scientific and public health value if they focus on the evaluation of intervention principles (rather than a specific locked-down version of the intervention), allowing for ongoing quality improvement modifications to the behavioral intervention technology based on the core intervention principles, while continuously improving the functionality and maintaining technological currency. This paper is an initial proposal of a framework and methodology for the conduct of trials of intervention principles (TIPs) aimed at minimizing the risks of in-trial changes to intervention technologies and maximizing the potential for knowledge acquisition. The focus on evaluation of intervention principles using clinical and usage outcomes has the potential to provide more generalizable and durable information than trials focused on a single intervention technology.

[1]  P. Kaufmann,et al.  Efficiency perspectives on adaptive designs in stroke clinical trials. , 2011, Stroke.

[2]  D. Ben-Zeev,et al.  Strategies for mHealth Research: Lessons from 3 Mobile Intervention Studies , 2015, Administration and Policy in Mental Health and Mental Health Services Research.

[3]  D. Mohr,et al.  Behavioral intervention technologies: evidence review and recommendations for future research in mental health. , 2013, General hospital psychiatry.

[4]  G. Andersson,et al.  Internet-based self-help for depression: randomised controlled trial , 2005, British Journal of Psychiatry.

[5]  Gwen L. Alexander,et al.  Tailoring a Fruit and Vegetable Intervention on Novel Motivational Constructs: Results of a Randomized Study , 2008, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[6]  A. Jadad,et al.  What Is eHealth (3): A Systematic Review of Published Definitions , 2005, Journal of medical Internet research.

[7]  Bonnie Spring,et al.  Practical behavioral trials to advance evidence-based behavioral medicine , 2006, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[8]  B. Muthén,et al.  Adaptive designs for randomized trials in public health. , 2009, Annual review of public health.

[9]  Elizabeth A Stuart,et al.  Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. , 2010, Psychological methods.

[10]  David A. Chambers,et al.  The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change , 2013, Implementation Science.

[11]  Xuan Cai,et al.  A Randomized Controlled Trial Evaluating a Manualized TeleCoaching Protocol for Improving Adherence to a Web-Based Intervention for the Treatment of Depression , 2013, PloS one.

[12]  J. Kestle Clinical Trials , 2014, World Journal of Surgery.

[13]  Clayton M. Christensen,et al.  Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns , 2008 .

[14]  Tiffany Barnes,et al.  BeadLoom Game: adding competitive, user generated, and social features to increase motivation , 2011, FDG.

[15]  P. Cuijpers,et al.  Supportive Accountability: A Model for Providing Human Support to Enhance Adherence to eHealth Interventions , 2011, Journal of medical Internet research.

[16]  Charles Abraham,et al.  Strengthening evaluation and implementation by specifying components of behaviour change interventions: a study protocol , 2011, Implementation science : IS.

[17]  Rik Crutzen,et al.  Reminders in Web-Based Data Collection , 2012 .

[18]  Wanda Pratt,et al.  How to evaluate technologies for health behavior change in HCI research , 2011, CHI.

[19]  Barbara Resnick,et al.  Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. , 2004, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[20]  Geoff Sutcliffe,et al.  A Computational Future for Preventing HIV in Minority Communities: How Advanced Technology Can Improve Implementation of Effective Programs , 2013, Journal of acquired immune deficiency syndromes.

[21]  Peter F Thall,et al.  Continuous Bayesian adaptive randomization based on event times with covariates , 2006, Statistics in medicine.

[22]  Dennis D. Embry,et al.  Evidence-based Kernels: Fundamental Units of Behavioral Influence , 2008, Clinical child and family psychology review.

[23]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[24]  Dhavan V. Shah,et al.  How Can Research Keep Up With eHealth? Ten Strategies for Increasing the Timeliness and Usefulness of eHealth Research , 2014, Journal of medical Internet research.

[25]  J. Wyatt,et al.  Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide , 2014, BMJ : British Medical Journal.

[26]  Parisa Rashidi,et al.  The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions , 2014, Journal of medical Internet research.

[27]  Naihua Duan,et al.  Design of implementation studies for quality improvement programs: an effectiveness-cost-effectiveness framework. , 2014, American journal of public health.

[28]  Wei Wang,et al.  Methods for Synthesizing Findings on Moderation Effects Across Multiple Randomized Trials , 2011, Prevention Science.

[29]  Susan A. Murphy,et al.  A Generalization Error for Q-Learning , 2005, J. Mach. Learn. Res..

[30]  G. Eysenbach CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions , 2011, Journal of medical Internet research.

[31]  Heleen Riper,et al.  Internet-Based Treatment for Adults with Depressive Symptoms: Randomized Controlled Trial , 2008, Journal of medical Internet research.

[32]  Ree Dawson,et al.  Dynamic treatment regimes: practical design considerations , 2004, Clinical trials.

[33]  Alireza Ahmadvand CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions , 2011 .

[34]  Louise M. Wallace,et al.  Applying the Behavioural Intervention Technologies model to the development of a smartphone application (app) supporting young peoples’ adherence to anaphylaxis action plan , 2015, BMJ Innovations.

[35]  D. Berry,et al.  Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. , 1995, Statistics in medicine.

[36]  H. Eysenck The effects of psychotherapy: an evaluation. , 1952, Journal of consulting psychology.

[37]  D. Mackinnon Introduction to Statistical Mediation Analysis , 2008 .

[38]  David C Mohr,et al.  Continuous evaluation of evolving behavioral intervention technologies. , 2013, American journal of preventive medicine.

[39]  Amy P Abernethy,et al.  Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise , 2013, Clinical and Translational Medicine.

[40]  M. Petticrew,et al.  Developing and evaluating complex interventions: the new Medical Research Council guidance , 2008, BMJ : British Medical Journal.

[41]  B. Chorpita,et al.  Mapping evidence-based treatments for children and adolescents: application of the distillation and matching model to 615 treatments from 322 randomized trials. , 2009, Journal of consulting and clinical psychology.

[42]  C. Abraham,et al.  The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions , 2013, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[43]  Audie A Atienza,et al.  Mobile health technology evaluation: the mHealth evidence workshop. , 2013, American journal of preventive medicine.

[44]  Vijay N. Nair,et al.  A strategy for optimizing and evaluating behavioral interventions , 2005, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[45]  Martin Hilbert,et al.  The World’s Technological Capacity to Store, Communicate, and Compute Information , 2011, Science.

[46]  Bruce F Chorpita,et al.  Identifying and Selecting the Common Elements of Evidence Based Interventions: A Distillation and Matching Model , 2005, Mental health services research.