Masterarbeit, 2021
105 Seiten, Note: 1,2
1 Introduction
2 Research Topic
2.1 Relevance of the Topic
2.2 Description of the Research Subject
3 State of Research and Theory
3.1 State of Research
3.1.1 Quality Assessments of mHealth Apps
3.1.2 Behavior Change Technique Incorporation
3.2 Theoretical Background
3.2.1 Social Cognitive Theory
3.2.2 Theory of Reasoned Action / Planned Behavior
3.3.3 Information–Motivation–Behavioral Skills Model
3.3.4 Operant Conditioning
3.3.5 Control Theory
4 Research Problem
4.1 Conceptual Framework
4.2 Research Questions
5 Methods
5.1 Study Design
5.2 Operationalization and Instruments
5.2.1 Information Quality
5.2.2 System Quality
5.3 Sampling
5.4 Data Collection
5.5 Data Analysis
5.6 Research Ethics
6 Results
6.1 App Selection
6.2 App Characteristics
6.3 Information Quality - (RQ1)
6.4 System Quality - (RQ2)
6.5 Relationships - (RQ3)
6.6 Qualitative Examples
6.6.1 App Fastyle
6.6.2 App Fresh Tri
6.6.3 App GPlans
6.6.4 App YAZIO
6.6.5 App Zero
7 Discussion
7.1 Summary and Interpretation of Main Results
7.1.1 Information Quality - (RQ1)
7.1.2 System Quality - (RQ2)
7.1.3 Relationships - (RQ3)
7.2 Limitations of the Study
7.3 Discussion of Future Research and Applications
This study aims to evaluate the quality of mobile weight management applications by analyzing the presence of behavior change techniques (BCTs) and overall system quality using the Mobile App Rating Scale (MARS), while examining the correlations between these technical factors, user ratings, and download counts.
3.3.4 Operant Conditioning
Operant Conditioning (OC) is a type of learning that uses rewards and punishments to influence behavior. Through OC, a link is established between a behavior and its associated outcome (whether positive or negative) (Staddon & Certrutti, 2003). The operant is the behavior. The link between the discriminative stimulus, response, and reinforcer impacts the possibility of a behavior occurring again in the future. A reinforcer is a positive reinforcement or, in the case of negative outcomes, a punishment. OC emphasizes deliberate behaviors above unconscious and automatic ones and the role of rewards and punishments in the formation of behaviors. When pleasant consequences follow a behavior, it is more likely to be repeated, and when unpleasant consequences follow a behavior, it is less likely to be repeated.
1 Introduction: Provides background on global obesity trends and the rising importance of mobile health apps for weight management.
2 Research Topic: Outlines the clinical and economic relevance of shifting toward cost-effective digital weight loss interventions.
3 State of Research and Theory: Reviews existing literature on mHealth quality assessments and explains fundamental behavioral theories used to guide interventions.
4 Research Problem: Introduces the adapted Information Systems (IS) Success Model and specifies the core research questions driving the evaluation.
5 Methods: Details the systematic search, app inclusion criteria, and the use of the MARS and BCT taxonomy for the quantitative content analysis.
6 Results: Presents statistical findings regarding the BCT implementation, MARS quality scores, and correlations with App Store metrics.
7 Discussion: Interprets the findings in the context of previous research, acknowledges study limitations, and proposes directions for future app development and research.
mHealth, Weight Management, Mobile Apps, Behavior Change Techniques, MARS, Information Quality, System Quality, Obesity, Digital Interventions, App Evaluation, Health Promotion, User Engagement, Behavior Modification, Dietary Habits, Mobile Technology
The research focuses on evaluating the technical quality and the inclusion of behavior change techniques in popular mobile weight management apps.
The study intersects the fields of media psychology, health promotion, and information systems, specifically looking at how software design impacts health-related behavior change.
The primary goal is to determine how well commercial weight management apps integrate evidence-based behavioral strategies and how these features relate to app quality, user ratings, and popularity.
The study performs a quantitative content analysis using the Mobile App Rating Scale (MARS) and the BCT taxonomy developed by Abraham & Michie to code app content.
The work provides a thorough literature review, a theoretical framework based on behavioral science, a detailed methodology section, a results chapter with statistical correlations, and a qualitative discussion of top-rated apps.
The study is characterized by concepts such as mHealth, Behavior Change Techniques, mobile app quality evaluation, and weight management interventions.
BCTs are considered the 'active ingredients' of interventions that help users transition from passive information consumption to active habit modification and long-term behavioral change.
The study uses the adapted IS Success Model, looking at information and system quality alongside objective measures like download counts and average user star ratings from the App Store.
The analysis showed that top-rated apps like Fastyle and YAZIO distinguish themselves by combining high aesthetic appeal with a wide range of integrated BCTs that cater to user self-monitoring and engagement.
The study suggests that because many apps lack clinical validation, standardizing assessment frameworks is essential to help users choose effective tools and to encourage developers to incorporate more evidence-based content.
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