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Fogg behaviour model. From "BJ Fogg's Behavior Model" by B. J. Fogg, 2018 (www.behaviormodel.org). Copyright 2018 by BJ Fogg.org LLC. Reprinted with permission

Fogg behaviour model. From "BJ Fogg's Behavior Model" by B. J. Fogg, 2018 (www.behaviormodel.org). Copyright 2018 by BJ Fogg.org LLC. Reprinted with permission

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eHealth interventions are widely used to support parents in managing children's health behaviours and could be beneficial in supporting physiotherapy home programmes for children with cerebral palsy. The use of technology in health crosses several disciplines, and a conceptual analysis of techniques and models used by these different disciplines co...

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... Fogg Behavior Model defines how technology can trigger behaviour though the interplay of three elements: 1) The person's inherent motivation; 2) Their ability; and 3) An appropriate trigger or prompt. This relationship is represented by the formula B=MAP where three elements, namely motivation (M), ability (A) and prompt (P) must converge at the same moment (above an activation threshold) in order for the desired behaviour (B) to occur (Figure 1) (Fogg, 2018). If the prompt (such as an email with direct advice) is delivered when the user has a level of motivation and ability that positions them above the activation line, it will elicit the desired behaviour (Fogg, 2009). ...

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Background Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users. Objective This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions. Methods An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics. Results The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; P<.001), with an adjusted R2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (P<.001) and ≥60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness. Conclusions This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category.