Literature DB >> 24050429

Continuous evaluation of evolving behavioral intervention technologies.

David C Mohr1, Ken Cheung, Stephen M Schueller, C Hendricks Brown, Naihua Duan.   

Abstract

Behavioral intervention technologies (BITs) are web-based and mobile interventions intended to support patients and consumers in changing behaviors related to health, mental health, and well-being. BITs are provided to patients and consumers in clinical care settings and commercial marketplaces, frequently with little or no evaluation. Current evaluation methods, including RCTs and implementation studies, can require years to validate an intervention. This timeline is fundamentally incompatible with the BIT environment, where technology advancement and changes in consumer expectations occur quickly, necessitating rapidly evolving interventions. However, BITs can routinely and iteratively collect data in a planned and strategic manner and generate evidence through systematic prospective analyses, thereby creating a system that can "learn." A methodologic framework, Continuous Evaluation of Evolving Behavioral Intervention Technologies (CEEBIT), is proposed that can support the evaluation of multiple BITs or evolving versions, eliminating those that demonstrate poorer outcomes, while allowing new BITs to be entered at any time. CEEBIT could be used to ensure the effectiveness of BITs provided through deployment platforms in clinical care organizations or BIT marketplaces. The features of CEEBIT are described, including criteria for the determination of inferiority, determination of BIT inclusion, methods of assigning consumers to BITs, definition of outcomes, and evaluation of the usefulness of the system. CEEBIT offers the potential to collapse initial evaluation and postmarketing surveillance, providing ongoing assurance of safety and efficacy to patients and consumers, payers, and policymakers.
© 2013 American Journal of Preventive Medicine.

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Year:  2013        PMID: 24050429      PMCID: PMC3828034          DOI: 10.1016/j.amepre.2013.06.006

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  22 in total

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Journal:  Adm Policy Ment Health       Date:  2012-07

2.  Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition.

Authors:  Russell E Glasgow; Edward Lichtenstein; Alfred C Marcus
Journal:  Am J Public Health       Date:  2003-08       Impact factor: 9.308

3.  Patient preferences and randomised clinical trials.

Authors:  C R Brewin; C Bradley
Journal:  BMJ       Date:  1989-07-29

4.  Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal.

Authors:  M O Finkelstein; B Levin; H Robbins
Journal:  Am J Public Health       Date:  1996-05       Impact factor: 9.308

5.  Mobile health technology evaluation: the mHealth evidence workshop.

Authors:  Santosh Kumar; Wendy J Nilsen; Amy Abernethy; Audie Atienza; Kevin Patrick; Misha Pavel; William T Riley; Albert Shar; Bonnie Spring; Donna Spruijt-Metz; Donald Hedeker; Vasant Honavar; Richard Kravitz; R Craig Lefebvre; David C Mohr; Susan A Murphy; Charlene Quinn; Vladimir Shusterman; Dallas Swendeman
Journal:  Am J Prev Med       Date:  2013-08       Impact factor: 5.043

Review 6.  Behavioral intervention technologies: evidence review and recommendations for future research in mental health.

Authors:  David C Mohr; Michelle Nicole Burns; Stephen M Schueller; Gregory Clarke; Michael Klinkman
Journal:  Gen Hosp Psychiatry       Date:  2013-05-08       Impact factor: 3.238

7.  Design of implementation studies for quality improvement programs: an effectiveness-cost-effectiveness framework.

Authors:  Ken Cheung; Naihua Duan
Journal:  Am J Public Health       Date:  2013-11-14       Impact factor: 9.308

Review 8.  What is eHealth (3): a systematic review of published definitions.

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Review 9.  Adherence in internet interventions for anxiety and depression.

Authors:  Helen Christensen; Kathleen M Griffiths; Louise Farrer
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10.  Scaling up mHealth: where is the evidence?

Authors:  Mark Tomlinson; Mary Jane Rotheram-Borus; Leslie Swartz; Alexander C Tsai
Journal:  PLoS Med       Date:  2013-02-12       Impact factor: 11.069

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  65 in total

1.  Rethinking the Endpoints of mHealth Intervention Research in Diabetes Care.

Authors:  Katherine L Modzelewski; Mary-Catherine Stockman; Devin W Steenkamp
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2.  Treatment Burden and Treatment Fatigue as Barriers to Health.

Authors:  Bryan W Heckman; Amanda R Mathew; Matthew J Carpenter
Journal:  Curr Opin Psychol       Date:  2015-10-01

3.  A systematic review of mHealth interventions for HIV prevention and treatment among gay, bisexual, and other men who have sex with men.

Authors:  Kimberly M Nelson; Nicholas S Perry; Keith J Horvath; Laramie R Smith
Journal:  Transl Behav Med       Date:  2020-10-12       Impact factor: 3.046

4.  Implementation strategies for digital mental health interventions in health care settings.

Authors:  Andrea K Graham; Emily G Lattie; Byron J Powell; Aaron R Lyon; Justin D Smith; Stephen M Schueller; Nicole A Stadnick; C Hendricks Brown; David C Mohr
Journal:  Am Psychol       Date:  2020-11

5.  Building partnerships: a pilot study of stakeholders' attitudes on technology disruption in behavioral health delivery and research.

Authors:  Madalina Sucala; Wendy Nilsen; Frederick Muench
Journal:  Transl Behav Med       Date:  2017-12       Impact factor: 3.046

6.  Behavioral and Social Sciences at the National Institutes of Health: adoption of research findings in health research and practice as a scientific priority.

Authors:  William T Riley
Journal:  Transl Behav Med       Date:  2017-06       Impact factor: 3.046

7.  Pilot Randomized Trial of a Self-Help Behavioral Activation Mobile App for Utilization in Primary Care.

Authors:  Jennifer Dahne; C W Lejuez; Vanessa A Diaz; Marty S Player; Jacob Kustanowitz; Julia W Felton; Matthew J Carpenter
Journal:  Behav Ther       Date:  2018-12-19

8.  The use of virtual reality in psychosis research and treatment.

Authors:  Lucia Valmaggia
Journal:  World Psychiatry       Date:  2017-10       Impact factor: 49.548

9.  Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement.

Authors:  Ken Cheung; Wodan Ling; Chris J Karr; Kenneth Weingardt; Stephen M Schueller; David C Mohr
Journal:  J Am Med Inform Assoc       Date:  2018-08-01       Impact factor: 4.497

10.  Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

Authors:  Ying Kuen Cheung; Pei-Yun Sabrina Hsueh; Min Qian; Sunmoo Yoon; Laura Meli; Keith M Diaz; Joseph E Schwartz; Ian M Kronish; Karina W Davidson
Journal:  Methods Inf Med       Date:  2018-02-10       Impact factor: 2.176

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