Designing Adaptive User Interfaces for mHealth Applications Targeting Chronic Disease: A User-Centered Approach
- URL: http://arxiv.org/abs/2405.08302v2
- Date: Fri, 09 May 2025 23:34:57 GMT
- Title: Designing Adaptive User Interfaces for mHealth Applications Targeting Chronic Disease: A User-Centered Approach
- Authors: Wei Wang, John Grundy, Hourieh Khalajzadeh, Anuradha Madugalla, Humphrey O. Obie,
- Abstract summary: Mobile Health (mHealth) applications have demonstrated considerable potential in supporting chronic disease self-management.<n>They remain under-utilised due to low engagement, limited accessibility, and poor long-term adherence.<n>This paper presents a two-stage study to develop and validate actionable Adaptive User Interfaces (AUIs) for mHealth applications.
- Score: 7.014860609693923
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Mobile Health (mHealth) applications have demonstrated considerable potential in supporting chronic disease self-management; however, they remain under-utilised due to low engagement, limited accessibility, and poor long-term adherence. These issues are particularly prominent among users with chronic disease, whose needs and capabilities vary widely. To address this, Adaptive User Interfaces (AUIs) offer a dynamic solution by tailoring interface features to users' preferences, health status, and contexts. This paper presents a two-stage study to develop and validate actionable AUI design guidelines for mHealth applications. In stage one, an AUI prototype was evaluated through focus groups, interviews, and a standalone survey, revealing key user challenges and preferences. These insights informed the creation of an initial set of guidelines. In stage two, the guidelines were refined based on feedback from 20 end users and evaluated by 43 software practitioners through two surveys. This process resulted in nine finalized guidelines. To assess real-world relevance, a case study of four mHealth applications was conducted, with findings supported by user reviews highlighting the utility of the guidelines in identifying critical adaptation issues. This study offers actionable, evidence-based guidelines that help software practitioners design AUIs in mHealth to better support individuals managing chronic diseases
Related papers
- Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
arXiv Detail & Related papers (2025-07-12T09:03:30Z) - Medical Red Teaming Protocol of Language Models: On the Importance of User Perspectives in Healthcare Settings [51.73411055162861]
We introduce a safety evaluation protocol tailored to the medical domain in both patient user and clinician user perspectives.<n>This is the first work to define safety evaluation criteria for medical LLMs through targeted red-teaming taking three different points of view.
arXiv Detail & Related papers (2025-07-09T19:38:58Z) - Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
Large language models (LLMs) often struggle with open-ended medical questions.
We propose a novel approach utilizing structured medical reasoning.
Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
arXiv Detail & Related papers (2025-03-05T05:24:55Z) - 'Being there together for health': A Systematic Review on the Feasibility, Effectiveness and Design Considerations of Immersive Collaborative Virtual Environments in Health Applications [3.7285188483791365]
We systematically searched MEDLINE, PsycINFO, and Emcare databases for peer-reviewed original reports.<n>All studies using immersive extended reality technologies while engaging more than one participant in an intervention with direct health benefits were included.<n>Findings indicated varying degrees of positive health outcomes, for engagement in rehabilitation, meaningful interactions across distances, positive affect, transformative experiences, mental health therapies, and motor skill learning.
arXiv Detail & Related papers (2024-12-06T03:58:51Z) - SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources.
This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
arXiv Detail & Related papers (2024-10-17T22:04:32Z) - SouLLMate: An Adaptive LLM-Driven System for Advanced Mental Health Support and Assessment, Based on a Systematic Application Survey [9.146311285410631]
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources.
This study aims to provide accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
arXiv Detail & Related papers (2024-10-06T17:11:29Z) - Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment [22.983780823136925]
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models.
We argue that the primary challenges in current RLAIF research for healthcare are the limitations of automated evaluation methods.
We present a new evaluation framework based on standardized patient examinations.
arXiv Detail & Related papers (2024-10-05T10:29:19Z) - Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks [16.099253839889148]
Large language models (LLMs) are emerging as promising tools for mental health care, offering scalable support through their ability to generate human-like responses.
However, the effectiveness of these models in clinical settings remains unclear.
This scoping review focused on studies where these models were tested with human participants in real-world scenarios.
arXiv Detail & Related papers (2024-08-21T02:21:59Z) - Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary
Task Integration [54.76511683427566]
This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information.
A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction.
The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
arXiv Detail & Related papers (2024-02-16T05:16:20Z) - From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models [21.427976533706737]
We take a novel approach that leverages large language models to synthesize clinically useful insights from multi-sensor data.
We develop chain of thought prompting methods that use LLMs to generate reasoning about how trends in data relate to conditions like depression and anxiety.
We find models like GPT-4 correctly reference numerical data 75% of the time, and clinician participants express strong interest in using this approach to interpret self-tracking data.
arXiv Detail & Related papers (2023-11-21T23:53:27Z) - Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
Time-series learning is the bread and butter of data-driven *clinical decision support*
Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit.
Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
arXiv Detail & Related papers (2023-10-28T12:08:03Z) - Post-COVID Highlights: Challenges and Solutions of AI Techniques for
Swift Identification of COVID-19 [6.927994520150374]
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools.
This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic.
arXiv Detail & Related papers (2023-09-24T15:59:39Z) - Adaptive questionnaires for facilitating patient data entry in clinical
decision support systems: Methods and application to STOPP/START v2 [1.8374319565577155]
We propose an original solution to simplify patient data entry using an adaptive questionnaire.
Considering a rule-based decision support systems, we designed methods for translating the system's clinical rules into display rules.
We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire.
arXiv Detail & Related papers (2023-09-19T07:59:13Z) - Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on
Summarizing Patients' Active Diagnoses and Problems from Electronic Health
Record Progress Notes [5.222442967088892]
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum)
The goal for participants is to develop models that generated a list of diagnoses and problems using input from the daily care notes collected from the hospitalization of critically ill patients.
Eight teams submitted their final systems to the shared task leaderboard.
arXiv Detail & Related papers (2023-06-08T15:19:57Z) - A Comprehensive Picture of Factors Affecting User Willingness to Use
Mobile Health Applications [62.60524178293434]
The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps.
Users' digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information.
Users' demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect.
arXiv Detail & Related papers (2023-05-10T08:11:21Z) - Large Language Models for Healthcare Data Augmentation: An Example on
Patient-Trial Matching [49.78442796596806]
We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM)
Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
arXiv Detail & Related papers (2023-03-24T03:14:00Z) - A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications.
These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
arXiv Detail & Related papers (2022-11-07T17:20:39Z) - What Do End-Users Really Want? Investigation of Human-Centered XAI for
Mobile Health Apps [69.53730499849023]
We present a user-centered persona concept to evaluate explainable AI (XAI)
Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences.
Our insights bring an interactive, human-centered XAI closer to practical application.
arXiv Detail & Related papers (2022-10-07T12:51:27Z) - Adaptive Identification of Populations with Treatment Benefit in
Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
arXiv Detail & Related papers (2022-08-11T14:27:49Z) - StudyMe: A New Mobile App for User-Centric N-of-1 Trials [68.8204255655161]
N-of-1 trials are multi-crossover self-experiments that allow individuals to systematically evaluate the effect of interventions on their personal health goals.
We present StudyMe, an open-source mobile application that is freely available from https://play.google.com/store/apps/details?id=health.studyu.me.
arXiv Detail & Related papers (2021-07-31T20:43:36Z) - Adversarial Sample Enhanced Domain Adaptation: A Case Study on
Predictive Modeling with Electronic Health Records [57.75125067744978]
We propose a data augmentation method to facilitate domain adaptation.
adversarially generated samples are used during domain adaptation.
Results confirm the effectiveness of our method and the generality on different tasks.
arXiv Detail & Related papers (2021-01-13T03:20:20Z) - Predictive Modeling of ICU Healthcare-Associated Infections from
Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling
Approach [55.41644538483948]
This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units.
The aim is to support decision making addressed at reducing the incidence rate of infections.
arXiv Detail & Related papers (2020-05-07T16:13:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.