Goal-Setting for Knowledge Documentation using Persuasive Systems Design – Selection and Implementation of Design Principles

Merz, Marieluise; Hurm, Max

doi:10.24251/hicss.2022.680

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…These features were extracted and evaluated using the persuasive systems design (PSD) model [ 45 ]. The PSD model has been validated in several studies [ 37 ] and is predominately used in the design and evaluation of BCSSs [ 46 ]. The model consists of 28 system features that make up 4 categories of persuasive principles (namely primary task support, dialogue support, credibility support, and social support).…”

Section: Resultsmentioning

confidence: 99%

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Ekpezu,

Wiafe,

Oinas-Kukkonen

2023

JMIR AI

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Background There is a dearth of knowledge on reliable adherence prediction measures in behavior change support systems (BCSSs). Existing reviews have predominately focused on self-reporting measures of adherence. These measures are susceptible to overestimation or underestimation of adherence behavior. Objective This systematic review seeks to identify and summarize trends in the use of machine learning approaches to predict adherence to BCSSs. Methods Systematic literature searches were conducted in the Scopus and PubMed electronic databases between January 2011 and August 2022. The initial search retrieved 2182 journal papers, but only 11 of these papers were eligible for this review. Results A total of 4 categories of adherence problems in BCSSs were identified: adherence to digital cognitive and behavioral interventions, medication adherence, physical activity adherence, and diet adherence. The use of machine learning techniques for real-time adherence prediction in BCSSs is gaining research attention. A total of 13 unique supervised learning techniques were identified and the majority of them were traditional machine learning techniques (eg, support vector machine). Long short-term memory, multilayer perception, and ensemble learning are currently the only advanced learning techniques. Despite the heterogeneity in the feature selection approaches, most prediction models achieved good classification accuracies. This indicates that the features or predictors used were a good representation of the adherence problem. Conclusions Using machine learning algorithms to predict the adherence behavior of a BCSS user can facilitate the reinforcement of adherence behavior. This can be achieved by developing intelligent BCSSs that can provide users with more personalized, tailored, and timely suggestions.

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Section: Resultsmentioning

confidence: 99%

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Ekpezu,

Wiafe,

Oinas-Kukkonen

2023

JMIR AI

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“…The PSD model is the most-used framework for designing and evaluating persuasive systems [15]. Built on theories from psychology, information systems, and other disciplines, the PSD model guides the analysis of the persuasion context, including recognizing the intent of the persuasion, understanding the persuasion event, and defining the strategies in use [13].…”

Section: Introductionmentioning

confidence: 99%

Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials

Agyei,

Ekpezu,

Oinas-Kukkonen

2024

JMIR Cardio

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Background Behavior change support systems (BCSSs) have the potential to help people maintain healthy lifestyles and aid in the self-management of coronary heart disease (CHD). The Persuasive Systems Design (PSD) model is a framework for designing and evaluating systems designed to support lifestyle modifications and health behavior change using information and communication technology. However, evidence for the underlying design principles behind BCSSs for CHD has not been extensively reported in the literature. Objective This scoping review aims to identify existing health BCSSs for CHD, report the characteristics of these systems, and describe the persuasion context and persuasive design principles of these systems based on the PSD framework. Methods Using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 3 digital databases (Scopus, Web of Science, and MEDLINE) were searched between 2010 to 2022. The major inclusion criteria for studies were in accordance with the PICO (Population, Intervention, Comparison, and Outcome) approach. Results Searches conducted in the databases identified 1195 papers, among which 30 were identified as eligible for the review. The most interesting characteristics of the BCSSs were the predominant use of primary task support principles, followed by dialogue support and credibility support and the sparing use of social support principles. Theories of behavior change such as the Social Cognitive Theory and Self-Efficacy Theory were used often to underpin these systems. However, significant trends in the use of persuasive system features on par with behavior change theories could not be established from the reviewed studies. This points to the fact that there is still no theoretical consensus on how best to design interventions to promote behavior change in patients with CHD. Conclusions Our results highlight key software features for designing BCSSs for the prevention and management of CHD. We encourage designers of behavior change interventions to evaluate the techniques that contributed to the success of the intervention. Future research should focus on evaluating the effectiveness of the interventions, persuasive design principles, and behavior change theories using research methodologies such as meta-analysis.

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Goal-Setting for Knowledge Documentation using Persuasive Systems Design – Selection and Implementation of Design Principles

Cited by 2 publications

References 34 publications

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials

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