Narrative Review of the Role of Wearable Devices in Promoting Health Behavior: Based on Health Belief Model

Fei, Dingzhou; Wang, Xia

doi:10.1007/978-3-030-39512-4_68

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Of equal importance is consumers' perceptions of its quality. When users cannot effectively discern self‐tracking information quality (IQ), inaccurate understanding and belief of one's health and wellness may develop, which may negatively impact health behaviors and decisions (Fei & Wang, 2020). The system success model suggests that perceived IQ directly affects users' intention to use and satisfaction with an information system (DeLone & McLean, 1992).…”

Section: Introductionmentioning

confidence: 99%

The quality of health and wellness self‐tracking data: A consumer perspective

Zhang

Trace

2021

Asso for Info Science & Tech

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Information quality (IQ) is key to users' satisfaction with information systems. Understanding what IQ means to users can effectively inform system improvement. Existing inquiries into self-tracking data quality primarily focus on accuracy. Interviewing 20 consumers who had self-tracked health indicators for at least 6 months, we identified eight dimensions that consumers apply to evaluate self-tracking data quality: value-added, accuracy, completeness, accessibility, ease of understanding, trustworthiness, aesthetics, and invasiveness. These dimensions fell into four categories-intrinsic, contextual, representational, and accessibility-suggesting that consumers judge self-tracking data quality not only based on the data's inherent quality but also considering tasks at hand, the clarity of data representation, and data accessibility. We also found that consumers' self-tracking data quality judgments are shaped primarily by their goals or motivations, subjective experience with tracked activities, mental models of how systems work, self-tracking tools' reputation, cost, and design, and domain knowledge and intuition, but less by more objective criteria such as scientific research results, validated devices, or consultation with experts.Future studies should develop and validate a scale for measuring consumers' perceptions of self-tracking data quality and commit efforts to develop technologies and training materials to enhance consumers' ability to evaluate data quality.

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Section: Introductionmentioning

confidence: 99%

The quality of health and wellness self‐tracking data: A consumer perspective

Zhang

Trace

2021

Asso for Info Science & Tech

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Understanding Coping Intentions of Fitness Tracker Users: An Empirical Investigation Using Fear Appeals

Baskaran

Mathew

2022

International Journal of Human–Computer Interaction

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Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking

Aissaoui Ferhi,

Ben Amar,

Choubani

et al. 2024

Front. Artif. Intell.

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IntroductionThe development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples.MethodsThe dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models.ResultsThe performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses.DiscussionThe study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement.ConclusionThis study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

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Narrative Review of the Role of Wearable Devices in Promoting Health Behavior: Based on Health Belief Model

Cited by 3 publications

References 10 publications

The quality of health and wellness self‐tracking data: A consumer perspective

The quality of health and wellness self‐tracking data: A consumer perspective

Understanding Coping Intentions of Fitness Tracker Users: An Empirical Investigation Using Fear Appeals

Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking

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