A Web-Based Self-management App for Living Well With Dementia: User-Centered Development Study

Lee, Abigail Rebecca; Csipke, Emese; Yates, Lauren; Moniz-Cook, Esmé; McDermott, Orii; Taylor, Steven; Stephens, Michael; Kelleher, Daniel; Orrell, Martin

doi:10.2196/40785

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the UK government has shown support for the advancement of intelligent assistive technology for individuals with dementia [ 7 ]. This includes endorsing the development of mHealth apps specifically tailored to patients with early-stage dementia and their caregivers [ 8 ]. These apps are believed to have significant potential in aiding cognitive function and facilitating self-care among those living with dementia [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study

Chen,

Lee,

2024

JMIR Form Res

View full text Add to dashboard Cite

Background The adoption of mobile health (mHealth) apps among older adults (>65 years) is rapidly increasing. However, use of such apps has not been fully effective in supporting people with dementia and their caregivers in their daily lives. This is mainly attributed to the heterogeneous quality of mHealth apps, highlighting the need for improved app quality in the development of dementia-related mHealth apps. Objective The aims of this study were (1) to assess the quality and content of mobile apps for dementia management and (2) to investigate the relationship between app quality and download numbers. Methods We reviewed dementia-related mHealth apps available in the Google Play Store and Apple App Store in Taiwan. The identified mobile apps were stratified according to a random sampling approach and evaluated by five independent reviewers with sufficient training and proficiency in the field of mHealth and the related health care sector. App quality was scored according to the user version of the Mobile Application Rating Scale. A correlation analysis was then performed between the app quality score and number of app downloads. Results Among the 17 apps that were evaluated, only one was specifically designed to provide dementia-related education. The mean score for the overall app quality was 3.35 (SD 0.56), with the engagement (mean 3.04, SD 0.82) and information (mean 3.14, SD 0.88) sections of the scale receiving the lowest ratings. Our analyses showed clear differences between the top three– and bottom three–rated apps, particularly in the entertainment and interest subsections of the engagement category where the ratings ranged from 1.4 to 5. The top three apps had a common feature in their interface, which included memory, attention, focus, calculation, and speed-training games, whereas the apps that received lower ratings were found to be deficient in providing adequate information. Although there was a correlation between the number of downloads (5000 or more) and app quality (t15=4.087, P<.001), this may not be a significant determinant of the app’s perceived impact. Conclusions The quality of dementia-related mHealth apps is highly variable. In particular, our results show that the top three quality apps performed well in terms of engagement and information, and they all received more than 5000 downloads. The findings of this study are limited due to the small sample size and possibility of disregarding exceptional occurrences. Publicly available expert ratings of mobile apps could help people with dementia and their caregivers choose a quality mHealth app.

show abstract

Section: Introductionmentioning

confidence: 99%

Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study

Chen,

Lee,

2024

JMIR Form Res

View full text Add to dashboard Cite

show abstract

Self-Disclosure and Social Media Use among Younger and Older People with Dementia: An Internet-Mediated Mixed-Methods Study

Kohl,

Koh,

Scior

et al. 2023

International Journal of Human–Computer Interaction

View full text Add to dashboard Cite

Enhancing User Receptivity in E-Learning Environments through Gesture-Based Emotion Tracking as a Pathway to Mental Well-being and Health Equity (Preprint)

Heraz,

Chen,

Bhyravabhottla

2024

Preprint

View full text Add to dashboard Cite

BACKGROUND In the realm of e-learning, where optimizing user engagement is paramount, this study explores the efficacy of gesture-based emotion tracking in enhancing user receptivity. Departing from traditional multimodal approaches, we focus on non-intrusive methods to measure emotions solely through gesture analysis. By leveraging this innovative approach, we aim to dissect the intricate dynamics of user engagement and propose actionable recommendations for improving online educational experiences. Our investigation not only sheds light on the effectiveness of gesture-based emotion tracking but also offers valuable insights for stakeholders in the e-learning domain, contributing to the advancement of mental well-being and health equity. OBJECTIVE This research endeavors to advance our understanding of user receptivity in e-learning environments by employing gesture-based emotion tracking as a non-intrusive approach. Through rigorous analysis and experimentation, we seek to uncover the nuanced factors influencing user engagement and satisfaction on e-learning platforms. Our goal is to develop actionable recommendations informed by gesture analysis insights, empowering stakeholders to create more immersive and effective educational experiences. By adopting this innovative approach, we aim to contribute to the promotion of mental well-being and health equity in digital learning environments. METHODS The methods undertaken in our study encompass data collection, feature selection, training and testing of the AI model. For data collection, real-time emotion data from a live e-learning website is collected using the Emaww API. The data is obtained with user consent, and privacy is ensured. The dataset consists of 6,026 users across 35 countries over 53 weeks. Feature selection involves calculating users’ receptivity and extracting web pages' properties. Users are considered receptive (towards the content of a web page) if they are focused for at least 70% of the time. The properties of web pages are related to their content and style. The training and testing utilize various machine learning techniques, and their performance is evaluated using accuracy, recall, F1-score, precision, and ROC curve. RESULTS The models were trained and validated using 10-fold cross-validation, with a diverse set of algorithms selected for analysis. The results showed that the Light Gradient Boosting Machine (LightGBM) performed the best, with an accuracy of 83.20%. Time was found to be an important input feature, as excluding it led to overfitting issues. Incorporating time as a feature enhanced the model's ability to generalize and make accurate predictions. CONCLUSIONS This research underscores the imperative features essential for integration into a web page to ensure user concentration on the content. The research findings, particularly the receptivity score derived from web page attributes, serve as a valuable resource for UI/UX designers. By providing insights into user engagement levels, these findings empower designers to craft web pages with a meticulous layout, thereby enhancing the ability to captivate and maintain user focus on the content. Consequently, these findings can be integrated into design practices as a guiding principle to optimize user experience and overall web page effectiveness. CLINICALTRIAL N/A

show abstract

A Web-Based Self-management App for Living Well With Dementia: User-Centered Development Study

Cited by 3 publications

References 11 publications

Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study

Attributes, Quality, and Downloads of Dementia-Related Mobile Apps for Patients With Dementia and Their Caregivers: App Review and Evaluation Study

Self-Disclosure and Social Media Use among Younger and Older People with Dementia: An Internet-Mediated Mixed-Methods Study

Enhancing User Receptivity in E-Learning Environments through Gesture-Based Emotion Tracking as a Pathway to Mental Well-being and Health Equity (Preprint)

Contact Info

Product

Resources

About