COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations.

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.

Correction: Beristain Iraola et al. User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model. Int. J. Environ. Res. Public Health 2021, 18, 6868.

The author would like to change the authorship in the previous publication [...].

User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model.

Preventive care and telemedicine are expected to play an important role in reducing the impact of an increasingly aging global population while increasing the number of healthy years. Virtual coaching is a promising research area to support this process. This paper presents a user-centered virtual coach for older adults at home to promote active and healthy aging and independent living. It supports behavior change processes for improving on cognitive, physical, social interaction and nutrition areas using specific, measurable, achievable, relevant, and time-limited (SMART) goal plans, following the I-Change behavioral change model. Older adults select and personalize which goal plans to join from a catalog designed by domain experts. Intervention delivery adapts to user preferences and minimizes intrusiveness in the user's daily living using a combination of a deterministic algorithm and incremental machine learning model. The home becomes an augmented reality environment, using a combination of projectors, cameras, microphones and support sensors, where common objects are used for projection and sensed. Older adults interact with this virtual coach in their home in a natural way using speech and body gestures on projected user interfaces with common objects at home. This paper presents the concept from the older adult and the caregiver perspectives. Then, it focuses on the older adult view, describing the tools and processes available to foster a positive behavior change process, including a discussion about the limitations of the current implementation.

TAQIH, a tool for tabular data quality assessment and improvement in the context of health data.

BACKGROUND AND OBJECTIVES: Data curation is a tedious task but of paramount relevance for data analytics and more specially in the health context where data-driven decisions must be extremely accurate. The ambition of TAQIH is to support non-technical users on 1) the exploratory data analysis (EDA) process of tabular health data, and 2) the assessment and improvement of its quality. METHODS: A web-based tool has been implemented with a simple yet powerful visual interface. First, it provides interfaces to understand the dataset, to gain the understanding of the content, structure and distribution. Then, it provides data visualization and improvement utilities for the data quality dimensions of completeness, accuracy, redundancy and readability. RESULTS: It has been applied in two different scenarios. (1) The Northern Ireland General Practitioners (GPs) Prescription Data, an open data set containing drug prescriptions. (2) A glucose monitoring tele health system dataset. Findings on (1) include: Features that had significant amount of missing values (e.g. AMP_NM variable 53.39%); instances that have high percentage of variable values missing (e.g. 0.21% of the instances with > 75% of missing values); highly correlated variables (e.g. Gross and Actual cost almost completely correlated (∼ + 1.0)). Findings on (2) include: Features that had significant amount of missing values (e.g. patient height, weight and body mass index (BMI) (> 70%), date of diagnosis 13%)); highly correlated variables (e.g. height, weight and BMI). Full detail of the testing and insights related to findings are reported. CONCLUSIONS: TAQIH enables and supports users to carry out EDA on tabular health data and to assess and improve its quality. Having the layout of the application menu arranged sequentially as the conventional EDA pipeline helps following a consistent analysis process. The general description of the dataset and features section is very useful for the first overview of the dataset. The missing value heatmap is also very helpful in visually identifying correlations among missing values. The correlations section has proved to be supportive as a preliminary step before further data analysis pipelines, as well as the outliers section. Finally, the data quality section provides a quantitative value to the dataset improvements.