Defining Effective Engagement For Enhancing Cancer Patients' Well-being with Mobile Digital Behavior Change Interventions
- URL: http://arxiv.org/abs/2403.12007v3
- Date: Fri, 19 Apr 2024 12:47:27 GMT
- Title: Defining Effective Engagement For Enhancing Cancer Patients' Well-being with Mobile Digital Behavior Change Interventions
- Authors: Aneta Lisowska, Szymon Wilk, Laura Locati, Mimma Rizzo, Lucia Sacchi, Silvana Quaglini, Matteo Terzaghi, Valentina Tibollo, Mor Peleg,
- Abstract summary: Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors.
This study aims to define effective engagement with DBCIs for supporting cancer patients in enhancing their quality of life.
- Score: 0.25296764467138544
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors. Evaluating their effectiveness is crucial for their improvement and understanding of success factors. However, comprehensive guidance for developers, particularly in small-scale studies with ethical constraints, is limited. Building on the CAPABLE project, this study aims to define effective engagement with DBCIs for supporting cancer patients in enhancing their quality of life. We identify metrics for measuring engagement, explore the interest of both patients and clinicians in DBCIs, and propose hypotheses for assessing the impact of DBCIs in such contexts. Our findings suggest that clinician prescriptions significantly increase sustained engagement with mobile DBCIs. In addition, while one weekly engagement with a DBCI is sufficient to maintain well-being, transitioning from extrinsic to intrinsic motivation may require a higher level of engagement.
Related papers
- Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study [4.704094564944504]
The Personalized Context-aware intervention selection algorithm improves engagement and efficacy of mHealth interventions.
Even brief, one-minute interventions can significantly reduce perceived stress levels.
Our study contributes to the literature by introducing a personalized context-aware intervention selection algorithm.
arXiv Detail & Related papers (2024-07-16T11:22:22Z) - Dyadic Reinforcement Learning [7.528761100894881]
Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life.
Dyadic RL is an online reinforcement learning algorithm designed to personalize intervention delivery based on contextual factors and past responses.
We demonstrate dyadic RL's empirical performance through simulation studies on both toy scenarios and on a realistic test bed constructed from data collected in a mobile health study.
arXiv Detail & Related papers (2023-08-15T15:43:12Z) - TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic
Tree-Based Memory Network [54.332862955411656]
Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment.
In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials.
We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
arXiv Detail & Related papers (2023-07-19T12:35:09Z) - Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning
Approach to Critical Care [68.8204255655161]
We introduce a deep Q-learning approach able to obtain more reliable critical care policies.
We achieve this by first pruning the action set based on all available rewards, and second training a final model based on the sparse main reward but with a restricted action set.
arXiv Detail & Related papers (2023-06-13T18:02:57Z) - A Self-supervised Framework for Improved Data-Driven Monitoring of
Stress via Multi-modal Passive Sensing [7.084068935028644]
We propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response.
Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices.
We perform training experiments using a corpus of real-world data on perceived stress.
arXiv Detail & Related papers (2023-03-24T20:34:46Z) - 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) - SPeC: A Soft Prompt-Based Calibration on Performance Variability of
Large Language Model in Clinical Notes Summarization [50.01382938451978]
We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization.
Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
arXiv Detail & Related papers (2023-03-23T04:47:46Z) - COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for
COVID-19 Patients via Explainability and Trust Quantification [71.80459780697956]
We introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient clinical data.
The proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients.
We conducted system-level insight discovery using a quantitative explainability strategy to study the decision-making impact of different clinical features.
arXiv Detail & Related papers (2021-09-14T14:16:32Z) - Health improvement framework for planning actionable treatment process
using surrogate Bayesian model [1.2468700211588881]
This study proposes a novel framework to plan treatment processes in a data-driven manner.
A key point of the framework is the evaluation of the "actionability" for personal health improvements.
arXiv Detail & Related papers (2020-10-30T06:02:49Z) - Nine Recommendations for Decision Aid Implementation from the Clinician
Perspective [0.0]
Time pressure and patient characteristics were cited as major barriers by 55% of the clinicians we interviewed.
Structural factors such as external quotas for certain treatment procedures were also considered as barriers by 44% of the clinicians.
Our findings suggest a role for external stakeholders such as healthcare insurers in creating economic incentives to facilitate implementation.
arXiv Detail & Related papers (2020-07-21T13:40:23Z) - Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost
Functions [80.12620331438052]
deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features.
Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets.
We argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance.
arXiv Detail & Related papers (2020-06-25T08:46:37Z)
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.