Digital health interventions are increasingly being designed to support health behaviors. Although digital health interventions informed by behavioral science theories, models, and frameworks (TMFs) ...are more likely to be effective than those designed without them, design teams often struggle to use these evidence-informed tools. Until now, little work has been done to clarify the ways in which behavioral science TMFs can add value to digital health design.
The aim of this study was to better understand how digital health design leaders select and use TMFs in design practice. The questions that were addressed included how do design leaders perceive the value of TMFs in digital health design, what considerations do design leaders make when selecting and applying TMFs, and what do design leaders think is needed in the future to advance the utility of TMFs in digital health design?
This study used a qualitative description design to understand the experiences and perspectives of digital health design leaders. The participants were identified through purposive and snowball sampling. Semistructured interviews were conducted via Zoom software. Interviews were audio-recorded and transcribed using Otter.ai software. Furthermore, 3 researchers coded a sample of interview transcripts and confirmed the coding strategy. One researcher completed the qualitative analysis using a codebook thematic analysis approach.
Design leaders had mixed opinions on the value of behavioral science TMFs in digital health design. Leaders suggested that TMFs added the most value when viewed as a starting point rather than the final destination for evidence-informed design. Specifically, these tools added value when they acted as a gateway drug to behavioral science, supported health behavior conceptualization, were balanced with expert knowledge and user-centered design principles, were complementary to existing design methods, and supported both individual- and systems-level thinking. Design leaders also felt that there was a considerable nuance in selecting the most value-adding TMFs. Considerations should be made regarding their source, appropriateness, complexity, accessibility, adaptability, evidence base, purpose, influence, audience, fit with team expertise, fit with team culture, and fit with external pressures. Design leaders suggested multiple opportunities to advance the use of TMFs. These included improving TMF reporting, design, and accessibility, as well as improving design teams' capacity to use TMFs appropriately in practice.
When designing a digital health behavior change intervention, using TMFs can help design teams to systematically integrate behavioral insights. The future of digital health behavior change design demands an easier way for designers to integrate evidence-based TMFs into practice.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on ...biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 – 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.
Digital health interventions have enormous potential to support patients and the public in achieving their health goals. Nonetheless, many digital health interventions are failing to effectively ...engage patients and the public. One solution that has been proposed is to directly involve patients and the public in the design process of these digital health interventions. Although there is consensus that involving patients and the public in collaborative design is valuable, design teams have little guidance on how to maximize the value of their collaborative design work. The main objective of this study was to understand how the value of patient and public involvement in digital health design can be maximized, from the perspective of design leaders and patient-public partners. Using a qualitative descriptive methodology, we conducted semi-structured interviews with 19 design leaders and 9 patient-public partners. Interviewees agreed that involving patients and the public was valuable, however, they questioned if current collaborative methods were optimized to ensure maximal value. Interviewees suggested that patient and public collaborative design can add value through four different mechanisms: (1) by allowing the design process to be an empowering intervention itself, (2) by ensuring that the digital health intervention will be effectively engaging for users, (3) by ensuring that the digital health intervention will be seamlessly implemented in practice, and (4) by allowing patient-public collaborations extend beyond the initial product design. Overall, interviewees emphasized that although collaborative design has historically focused on improving the digital health product itself, patients and the public have crucial insights on implementation planning as well as how collaborative design can be used as its own empowering intervention. The results of this paper provide clarity about the ways that patient and public collaborative design can be made more valuable. Digital health design teams can use these results to be more intentional about their collaborative design approaches.
While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke ...prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R
= 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.
Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by ...clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment.
The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images.
We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB).
In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB.
Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Many clinicians remain wary of machine learning because of longstanding concerns about “black box” models. “Black box” is shorthand for models that are sufficiently complex that they are not ...straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care, in which so many decisions are— literally—life and death issues. There has been a recent explosion of research in the field of explainable machine learning aimed at addressing these concerns. The promise of explainable machine learning is considerable, but it is important for cardiologists who may encounter these techniques in clinical decision-support tools or novel research papers to have critical understanding of both their strengths and their limitations. This paper reviews key concepts and techniques in the field of explainable machine learning as they apply to cardiology. Key concepts reviewed include interpretability vs explainability and global vs local explanations. Techniques demonstrated include permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence plots. We discuss several limitations with explainability techniques, focusing on the how the nature of explanations as approximations may omit important information about how black-box models work and why they make certain predictions. We conclude by proposing a rule of thumb about when it is appropriate to use black- box models with explanations rather than interpretable models.
De nombreux cliniciens restent méfiants envers l’apprentissage automatique en raison de préoccupations de longue date concernant les modèles à « boîte noire ». Le terme « boîte noire » sert à désigner des modèles suffisamment complexes pour échapper à une interprétation simple par un humain. Le manque d’interprétabilité des modèles prédictifs peut miner la confiance en ces modèles, en particulier dans le domaine des soins de santé, où tant de décisions sont littéralement des questions de vie ou de mort. Il y a eu récemment une explosion de la recherche consacrée à l’apprentissage automatique explicable visant à répondre à ces préoccupations. L’apprentissage automatique explicable est très prometteur, mais il importe que les cardiologues aient une compréhension critique de ses forces et de ses limites puisqu’ils pourraient le retrouver dans des outils d’aide à la décision clinique ou des rapports de recherche de pointe. Cet article passe en revue les concepts et éléments techniques clés de l’apprentissage automatique explicable, tels qu’ils s’appliquent à la cardiologie. Les concepts clés que nous examinons comprennent l’interprétabilité par rapport à l’explicabilité et les explications globales par rapport aux explications locales. Au nombre des éléments techniques présentés figurent la mesure d’importance des variables à partir de permutations, les arbres de décision de substitution, l’algorithme LIME (local interpretable model-agnostic explanations) et les tracés de dépendance partielle. Nous abordons plusieurs limites des éléments techniques d’explicabilité, en attirant l’attention sur le fait que la nature approximative des explications peut mener à l’omission d’informations importantes sur la façon dont les modèles à boîte noire fonctionnent et les fondements de certaines prévisions obtenues à l’aide de ces modèles. Pour conclure, nous proposons une méthode empirique permettant de déterminer quand il est approprié d’utiliser des modèles à boîte noire assortis d’explications plutôt que des modèles interprétables.
Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support ...the development of more effective mHealth interventions. Behavioral Design (BD) and Design Thinking (DT) have emerged as best practice approaches in their respective fields. Until now, little work has been done to examine how BD and DT can be integrated throughout the mHealth design process.
The aim of this scoping review was to map the evidence on how insights from BD and DT can be integrated to guide the design of mHealth interventions. The following questions were addressed: (1) what are the main characteristics of studies that integrate BD and DT during the mHealth design process? (2) what theories, models, and frameworks do design teams use during the mHealth design process? (3) what methods do design teams use to integrate BD and DT during the mHealth design process? and (4) what are key design challenges, implementation considerations, and future directions for integrating BD and DT during mHealth design?
This review followed the Joanna Briggs Institute reviewer manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Studies were identified from MEDLINE, PsycINFO, Embase, CINAHL, and JMIR by using search terms related to mHealth, BD, and DT. Included studies had to clearly describe their mHealth design process and how behavior change theories, models, frameworks, or techniques were incorporated. Two independent reviewers screened the studies for inclusion and completed the data extraction. A descriptive analysis was conducted.
A total of 75 papers met the inclusion criteria. All studies were published between 2012 and 2021. Studies integrated BD and DT in notable ways, which can be referred to as "Behavioral Design Thinking." Five steps were followed in Behavioral Design Thinking: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users' needs and for its behavior change potential. The key challenges experienced during mHealth design included meaningfully engaging patient and public partners in the design process, translating evidence-based behavior change techniques into actual mHealth features, and planning for how to integrate the mHealth intervention into existing clinical systems.
Best practices from BD and DT can be integrated throughout the mHealth design process to ensure that mHealth interventions are purposefully developed to effectively engage users. Although this scoping review clarified how insights from BD and DT can be integrated during mHealth design, future research is needed to identify the most effective design approaches.
Objective
To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications.
Research Design and Methods
...Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique. Cross‐validation was used to develop and test the ability of models to predict five5 prespecified states (diabetes, high glycosylated hemoglobin, cardiovascular event, retinopathy, albuminuria, and hypertension). The performance of each model for a particular state was assessed by estimating areas under the receiver operating characteristics curves (AUROC) and precision recall curves (AUPR).
Results
A total of 5236 nailfold images were acquired from 120 participants (mean 44 images per participant) and were all available for analysis. Models were able to accurately identify the presence of diabetes, with AUROC 0.84 (95% confidence interval CI 0.76, 0.91) and AUPR 0.84 (95% CI 0.78, 0.93), respectively. Models were also able to predict a history of cardiovascular events in patients with diabetes, with AUROC 0.65 (95% CI 0.51, 0.78) and AUPR 0.72 (95% CI 0.62, 0.88) respectively.
Conclusions
This proof‐of‐concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images, and for possibly identifying those most likely to have diabetes‐related complications.
摘要
目的:探讨视频毛细血管镜获取的甲襞毛细血管图像能否提供糖尿病及其并发症的诊断信息。
方法:对120例成人患者进行视频甲襞毛细血管镜检查, 患者有或无1型或2型糖尿病, 有或无心血管疾病。使用卷积神经网络(CNNs), 一种深度学习技术对甲襞图像进行分析。交叉验证用于开发和检验模型预测5种预设状态(糖尿病、高HbA1c、心血管事件、视网膜病变、白蛋白尿和高血压)的能力。通过评估受试者工作特征曲线下面积(AUROC)和精确召回曲线(AUPR)来评估每个模型对特定状态的性能。
结果:120名参与者共获得5,236张甲襞图像(平均每位参与者44张), 所有图像均可用于分析。模型能够准确识别糖尿病的存在, AUROC为0.84 (95% CI 0.76, 0.91), AUPR为0.84 (95% CI 0.78, 0.93)。模型还能够预测糖尿病患者的心血管事件史, AUROC为0.65 (95% CI 0.51, 0.78), AUPR为0.72 (95% CI 0.62, 0.88)。
结论:这项概念验证研究证明了机器学习在基于甲襞图像从糖尿病中识别微血管毛细血管改变人群的潜力, 并可能识别出最有可能发生糖尿病相关并发症的人群。
Highlights
Nailfold capillary images were obtained by performing nailfold video capillaroscopy in 120 adult patients with and without diabetes.
Over 5000 photographs were analyzed using machine‐learning approaches, and these models were clearly able to distinguish people based on diabetes status.
This proof‐of‐concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images and for possibly identifying those likely to have diabetes‐related complications.
To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record ...linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served.
We aimed to develop and evaluate a machine learning-based software tool, which automatically configures a patient matching algorithm by learning from pairs of patient records previously linked by humans already present in the database.
We built a free and open-source software tool to optimize record linkage algorithm parameters based on historical record linkages. The tool uses Bayesian optimization to identify the set of configuration parameters that lead to optimal matching performance in a given patient population, by learning from prior record linkages by humans. The tool is written assuming only the existence of a minimal HTTP application programming interface (API), and so is agnostic to the choice of MPI software, record linkage algorithm, and patient population. As a proof of concept, we integrated our tool with SantéMPI, an open-source MPI. We validated the tool using several synthetic patient populations in SantéMPI by comparing the performance of the optimized configuration in held-out data to SantéMPI's default matching configuration using sensitivity and specificity.
The machine learning-optimized configurations correctly detect over 90% of true record linkages as definite matches in all data sets, with 100% specificity and positive predictive value in all data sets, whereas the baseline detects none. In the largest data set examined, the baseline matching configuration detects possible record linkages with a sensitivity of 90.2% (95% CI 88.4%-92.0%) and specificity of 100%. By comparison, the machine learning-optimized matching configuration attains a sensitivity of 100%, with a decreased specificity of 95.9% (95% CI 95.9%-96.0%). We report significant gains in sensitivity in all data sets examined, at the cost of only marginally decreased specificity. The configuration optimization tool, data, and data set generator have been made freely available.
Our machine learning software tool can be used to significantly improve the performance of existing record linkage algorithms, without knowledge of the algorithm being used or specific details of the patient population being served.
Background
Health coaching is an emerging intervention that has been shown to improve clinical and patient-relevant outcomes for type 2 diabetes. Advances in artificial intelligence may provide an ...avenue for developing a more personalized, adaptive, and cost-effective approach to diabetes health coaching.
Objective
We aim to apply Q-learning, a widely used reinforcement learning algorithm, to a diabetes health-coaching data set to develop a model for recommending an optimal coaching intervention at each decision point that is tailored to a patient’s accumulated history.
Methods
In this pilot study, we fit a two-stage reinforcement learning model on 177 patients from the intervention arm of a community-based randomized controlled trial conducted in Canada. The policy produced by the reinforcement learning model can recommend a coaching intervention at each decision point that is tailored to a patient’s accumulated history and is expected to maximize the composite clinical outcome of hemoglobin A1c reduction and quality of life improvement (normalized to 0, 1 , with a higher score being better). Our data, models, and source code are publicly available.
Results
Among the 177 patients, the coaching intervention recommended by our policy mirrored the observed diabetes health coach’s interventions in 17.5% (n=31) of the patients in stage 1 and 14.1% (n=25) of the patients in stage 2. Where there was agreement in both stages, the average cumulative composite outcome (0.839, 95% CI 0.460-1.220) was better than those for whom the optimal policy agreed with the diabetes health coach in only one stage (0.791, 95% CI 0.747-0.836) or differed in both stages (0.755, 95% CI 0.728-0.781). Additionally, the average cumulative composite outcome predicted for the policy’s recommendations was significantly better than that of the observed diabetes health coach’s recommendations (tn-1=10.040; P<.001).
Conclusions
Applying reinforcement learning to diabetes health coaching could allow for both the automation of health coaching and an improvement in health outcomes produced by this type of intervention.
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