Realizing the Potential of Behavioral Intervention Technologies

Behavioral intervention technologies (BITs) apply behavioral and psychological intervention strategies by using digital media to target behaviors, cognitions, and emotions in support of physical and mental health. BITs offer promising opportunities to expand psychological practice. However, to realize the potential of BITs, psychologists must understand both the possibilities and the limitations associated with using technology to advance psychology. We review examples of the most cutting-edge BITs and discuss features that differentiate BITs from traditional modes of delivery. We highlight major challenges in designing BITs, including adherence, engagement, technological and interventional obsolescence, multidisciplinary collaboration, and a reliance on psychological skeuomorphisms. Psychologists who do not understand the conceptual and applied considerations involved in creating BITs risk being left out of the development of BITs in the future. Teaming psychologists with experts in technology will help to promote novel interventions rather than using technology to disseminate existing treatments.

[1]  Per Ola Kristensson,et al.  I did that! Measuring users' experience of agency in their own actions , 2012, CHI 2012.

[2]  J. Galen Buckwalter,et al.  A Controlled Clinical Comparison of Attention Performance in Children with ADHD in a Virtual Reality Classroom Compared to Standard Neuropsychological Methods , 2007, Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence.

[3]  J. Arnett.  The neglected 95%: why American psychology needs to become less American. , 2008, The American psychologist.

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

[5]  David W. McDonald,et al.  Theory-driven design strategies for technologies that support behavior change in everyday life , 2009, CHI.

[6]  G. Andersson,et al.  Individually-tailored, Internet-based treatment for anxiety disorders: A randomized controlled trial. , 2011, Behaviour research and therapy.

[7]  Robert C. Martin.  Agile Software Development, Principles, Patterns, and Practices , 2002 .

[8]  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.

[9]  D. Mohr.  Telemental Health: Reflections on How to Move the Field Forward , 2009 .

[10]  Deborah I Thompson,et al.  Food, Fun and Fitness Internet program for girls: influencing log-on rate. , 2007, Health Education Research.

[11]  Predrag V. Klasnja,et al.  Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research , 2013, CHI.

[12]  Adam A. Lewis,et al.  Multimodal E-Mental Health Treatment for Depression: A Feasibility Trial , 2010, Journal of medical Internet research.

[13]  R. Muñoz.  Using Evidence-Based Internet Interventions to Reduce Health Disparities Worldwide , 2010, Journal of medical Internet research.

[14]  Claudia Pagliari,et al.  Design and Evaluation in eHealth: Challenges and Implications for an Interdisciplinary Field , 2007, Journal of medical Internet research.

[15]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[16]  Glendon L. Moriarty.  Psychology 2.0: Harnessing social networking, user‐generated content, and crowdsourcing , 2010 .

[17]  G. Eysenbach.  The Law of Attrition , 2005, Journal of medical Internet research.

[18]  Kenneth R. Stern,et al.  Low vs. high-fidelity prototyping debate , 1996, INTR.

[19]  Issues in Designing Agents for Long Term Behavior Change , 2008 .

[20]  E. Perez-stable,et al.  Global Reach of an Internet Smoking Cessation Intervention among Spanish- and English-Speaking Smokers from 157 Countries , 2009, International journal of environmental research and public health.

[21]  Rosalind W. Picard,et al.  Crowdsourcing Collective Emotional Intelligence , 2012, ArXiv.

[22]  Lee M Ritterband,et al.  Efficacy of an Internet-based behavioral intervention for adults with insomnia. , 2009, Archives of general psychiatry.

[23]  M. Massagli,et al.  Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm , 2011, Nature Biotechnology.

[24]  Stephen M Schueller,et al.  From Online Randomized Controlled Trials to Participant Preference Studies: Morphing the San Francisco Stop Smoking Site into a Worldwide Smoking Cessation Resource , 2012, Journal of medical Internet research.

[25]  D. Mohr,et al.  Effect of telephone-administered vs face-to-face cognitive behavioral therapy on adherence to therapy and depression outcomes among primary care patients: a randomized trial. , 2012, JAMA.

[26]  Santosh Kumar,et al.  AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field , 2011, SenSys.

[27]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[28]  Lorien C Abroms,et al.  iPhone apps for smoking cessation: a content analysis. , 2011, American journal of preventive medicine.

[29]  Brian M. Kleiner,et al.  Empirically understanding trust in medical technology , 2009 .