Emotionally Responsive Virtual Counselor for Behavior-Change Health Interventions

BACKGROUND Dual process theories propose that the brain uses 2 types of thinking to influence behavior: automatic processing and reflective processing. Automatic processing is fast, immediate, nonconscious, and unintentional, whereas reflective processing focuses on logical reasoning, and it is slow, step by step, and intentional. Most digital psychological health interventions tend to solely target the reflective system, although the automatic processing pathway can have strong influences on behavior. Laboratory-based research has highlighted that automatic processing tasks can create behavior change; however, there are substantial gaps in the field on the design, implementation, and delivery of automatic processing tasks in real-world settings. It is important to identify and summarize the existing literature in this area to inform the translation of laboratory-based research to real-world settings. OBJECTIVE This scoping review aims to explore the effectiveness of automatic training tasks, types of training tasks commonly used, mode of delivery, and impacts of gamification on automatic processing tasks designed for digital psychological health interventions in real-world settings among adults. METHODS The scoping review methodology proposed by Arskey and O’Malley and Colquhoun was applied. A scoping review was chosen because of the novelty of the digital automatic processing field and to encompass a broad review of the existing evidence base. Electronic databases and gray literature databases were searched with the search terms “automatic processing,” “computerised technologies,” “health intervention,” “real-world,” and “adults” and synonyms of these words. The search was up to date until September 2018. A manual search was also completed on the reference lists of the included studies. RESULTS A total of 14 studies met all inclusion criteria. There was a wide variety of health conditions targeted, with the most prevalent being alcohol abuse followed by social anxiety. Attention bias modification tasks were the most prevalent type of automatic processing task, and the majority of tasks were most commonly delivered over the web via a personal computer. Of the 14 studies included in the review, 8 demonstrated significant changes to automatic processes and 4 demonstrated significant behavioral changes as a result of changed automatic processes. CONCLUSIONS This is the first review to synthesize the evidence on automatic processing tasks in real-world settings targeting adults. This review has highlighted promising, albeit limited, research demonstrating that automatic processing tasks may be used effectively in a real-world setting to influence behavior change.

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Persuasive System Design Principles and Behavior Change Techniques to Stimulate Motivation and Adherence in Electronic Health Interventions to Support Weight Loss Maintenance: Scoping Review

Journal of Medical Internet Research ◽

10.2196/14265 ◽

2019 ◽

Vol 21 (6) ◽

pp. e14265 ◽

Cited By ~ 16

Author(s):

Rikke Aune Asbjørnsen ◽

Mirjam Lien Smedsrød ◽

Lise Solberg Nes ◽

Jobke Wentzel ◽

Cecilie Varsi ◽

...

Keyword(s):

Weight Loss ◽

Behavior Change ◽

System Design ◽

Scoping Review ◽

Weight Loss Maintenance ◽

Design Principles ◽

Health Interventions ◽

Behavior Change Techniques ◽

Support Weight ◽

And Behavior

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The Adaptive Behavioral Components (ABC) Model for Planning Longitudinal Behavioral Technology-Based Health Interventions: A Theoretical Framework

Journal of Medical Internet Research ◽

10.2196/15563 ◽

2020 ◽

Vol 22 (6) ◽

pp. e15563 ◽

Cited By ~ 1

Author(s):

Sean D Young

Keyword(s):

Behavior Change ◽

Digital Technology ◽

Behavioral Interventions ◽

Health Interventions ◽

Intervention Design ◽

Abc Model ◽

Behavior Change Intervention ◽

Hiv Prevention Intervention ◽

Behavioral Technology ◽

Over Time

A growing number of interventions incorporate digital and social technologies (eg, social media, mobile phone apps, and wearable devices) into their design for behavior change. However, because of a number of factors, including changing trends in the use of technology over time, results on the efficacy of these interventions have been mixed. An updated framework is needed to help researchers better plan behavioral technology interventions by anticipating the needed resources and potential changes in trends that may affect interventions over time. Focusing on the domain of health interventions as a use case, we present the Adaptive Behavioral Components (ABC) model for technology-based behavioral interventions. ABC is composed of five components: basic behavior change; intervention, or problem-focused characteristics; population, social, and behavioral characteristics; individual-level and personality characteristics; and technology characteristics. ABC was designed with the goals of (1) guiding high-level development for digital technology–based interventions; (2) helping interventionists consider, plan for, and adapt to potential barriers that may arise during longitudinal interventions; and (3) providing a framework to potentially help increase the consistency of findings among digital technology intervention studies. We describe the planning of an HIV prevention intervention as a case study for how to implement ABC into intervention design. Using the ABC model to plan future interventions might help to improve the design of and adherence to longitudinal behavior change intervention protocols; allow these interventions to adapt, anticipate, and prepare for changes that may arise over time; and help to potentially improve intervention behavior change outcomes. Additional research is needed on the influence of each of ABC’s components to help improve intervention design and implementation.

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