Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management

The experience of self-managing from the perspective of persons with lower limb loss, prosthetists, and physical therapists

PURPOSE

Persons with lower limb loss (PwLLL) must self-manage their residual limb and their prosthesis to prevent self-management related complications (SMRC). However, the experience of PwLLL as it relates to self-management has not been reported. Thus, the purpose of this study was to explore the experience of self-management from the perspective of PwLLL, prosthetists, and physical therapists.

METHODS

This study had a qualitative design. Twenty-three participants were interviewed (PwLLL = 10, prosthetists = 7, physical therapists = 6). Interviews were transcribed and then coded using constant comparison.

RESULTS

Four prominent themes were developed from the transcripts: (1) embodying the duty of self-management, (2) being a vigilant self-advocate, (3) setting goals collaboratively, and (4) making informed decisions. Each of the four themes were influenced by the health beliefs of the PwLLL, specifically motivation and presence of an internal locus of control.

CONCLUSION

Clinicians should emphasize the therapeutic relationship, including open communication, collaborative goal setting, and promoting an internal locus of control in interactions with PwLLL, as it may play a role in decreasing SMRC and improving clinical outcomes.Implications for rehabilitationSelf-management is a crucial aspect of preventing secondary complications associated with limb loss and prosthesis use.Self-management requires an internal locus of control, problem-solving abilities, and foundational knowledgeClinicians can promote self-management through collaborative goal setting and systematic education.

Development and validation of the Self-Management Assessment for the Residuum and prosThesis system designed for persons with limb loss (SMART)

OBJECTIVE

To develop a system of reliable and valid knowledge assessments of self-management in persons with lower limb loss, along with the accompanying targeted educational interventions (TEIs), known as the Self-Management Assessment for the Residuum and prosThesis (SMART) system.

DESIGN

This 2-phase study used mixed methodology. Phase 1 was development, face validation, and content validation of the 60-item knowledge assessment measure (SMART 60) and the TEI. Phase 2 assessed internal consistency reliability using Kuder-Richardson Formula 20 and the creation of the SMART system, consisting of modules developed from the SMART 60. Validity of the measures using known groups' comparison was analyzed by comparing clinicians (prosthetists and physical therapists) with persons with lower limb loss. Participants were recruited from the Amputee Coalition National Conference in 2018 and 2019.

RESULTS

A total of 140 participants completed this study. Four modules from the SMART 60 were created and designed to integrate as a system. Face validity survey average scores found that 9/10 participants either agreed or strongly agreed that the SMART system has high readability, perceived usefulness, and value for both new and experienced prosthetic users. Measure length ranged from 10 to 45 items with a reliability ranging from Kuder-Richardson Formula 20 = 0.70-0.82. The SMART system demonstrated known-groups validity ( p < 0.05).

CONCLUSION

The SMART system is an integrated series of self-management knowledge assessments with reasonable to good internal consistency reliability and known-groups validity. The TEIs provide directed solutions to identified knowledge gaps on the assessments.

Effectiveness of a web-based program on self-care behaviors and glycated hemoglobin in patients with type 2 diabetes: Study protocol of a randomized controlled trial

BACKGROUND

Type 2 diabetes (T2DM) decreases the life expectancy and quality of life of diabetics and causes economic and societal problems. For this purpose, diabetes self-management education and support (DSMES) has been designed for many years, which is recently provided through technology-assisted education. Therefore, we developed a web-based program in accordance with DSMES to assess its effect on self-care behaviors and glycated hemoglobin (HbA1c) for patients with T2DM during the coronavirus disease (COVID-19) pandemic, which is described in detail in this paper.

MATERIALS AND METHODS

This randomized controlled trial (RCT) was performed on 70 diabetic patients in Al-Zahra Hospital for three months. After random allocation, web-based educational content (including videos, lectures, educational motion graphics, text files, educational posters, and podcasts) according to DSMES was provided for the intervention group to improve self-care behaviors and HbA1c levels. The control group received routine educational pamphlets. A diabetes self-management questionnaire (21 questions) with a Likert scale was completed to assess self-care behaviors scores before and after intervention and three months later. Also, HbA1c was determined before and after the intervention. Analysis of variance with repeated measurements will be applied to compare mean scores of self-care behaviors components three times, and an independent t-test analyzed mean differences of HbA1c values.

CONCLUSION

The obtained results of this study might be useful for promoting self-care behaviors and assessing HbA1c in diabetic patients.

Examining AI Methods for Micro-Coaching Dialogs

Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition - brief coaching conversations related to specific meals, to support achievement of nutrition goals - and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

Automated vs. Human Health Coaching: Exploring Participant and Practitioner Experiences

Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.

A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study

INTRODUCTION

Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data.

MATERIALS AND METHODS

We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine's output to clinical narratives and gold standards developed by expert clinicians.

RESULTS

To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63 % consistent with the gold standard (range = 42 %-75 %) and 74 % consistent with narrative clinical observations (range = 63 %-83 %).

DISCUSSION

Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge.

CONCLUSION

New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.

Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis

Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.

Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus

Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.

Towards Supporting Patient Decision-making In Online Diabetes Communities

As of 2014, 29.1 million people in the US have diabetes. Patients with diabetes have evolving information needs around complex lifestyle and medical decisions. As their conditions progress, patients need to sporadically make decisions by understanding alternatives and comparing options. These moments along the decision-making process present a valuable opportunity to support their information needs. An increasing number of patients visit online diabetes communities to fulfill their information needs. To understand how patients attempt to fulfill the information needs around decision-making in online communities, we reviewed 801 posts from an online diabetes community and included 79 posts for in-depth content analysis. The findings revealed motivations for posters' inquiries related to decision-making including the changes in disease state, increased self-awareness, and conflict of information received. Medication and food were the among the most popular topics discussed as part of their decision-making inquiries. Additionally, We present insights for automatically identifying those decision-making inquiries to efficiently support information needs presented in online health communities.

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

OBJECTIVE

To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes.

MATERIALS AND METHODS

We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14).

RESULTS

The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals' activities and changes in their blood glucose levels that the participants referred to as "cause and effect". This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors).

DISCUSSION

The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes.

CONCLUSIONS

Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions.

Baseline Characteristics and Technology Training of Underserved Adults With Type 2 Diabetes in the Mobile Diabetes Detective (MoDD) Randomized Controlled Trial

Purpose The purpose of this study is to describe the characteristics and technology training needs of underserved adults with type 2 diabetes mellitus (T2DM) who participated in a health information technology (HIT) diabetes self-management education (DSME) intervention. Methods The baseline physiological, psychosocial, and technology use characteristics for 220 adults with poorly controlled T2DM were evaluated. Intervention participants received a 1-time intervention training, which included basic technology help, introduction to the Mobile Diabetes Detective (MoDD) website and text message features, and account activation that included subject-specific tailoring. Four additional on-site sessions for participants needing computer or Internet access or technology support were made available based on need. Data regarding on-site visits for usual care were collected. Data were analyzed using descriptive statistics and bivariate analysis. Results The participants were predominately Hispanic and female with a baseline mean A1C of 10% (86 mmol/mol). Only half of the participants regularly used computers or text messages in daily life. The average introductory MoDD training session lasted 73.6 minutes. Following training, approximately one-third (35%) of intervention participants returned for basic and MoDD-specific technology assistance at their federally qualified health center. The most frequently reported duration for the extra training sessions was 30 to 45 minutes. Conclusions Training and support needs were greater than anticipated. Diabetes educators should assess technology abilities prior to implementing health information technology (HIT) diabetes self-management education (DSME) in underserved adults. Future research must invest resources in technology access, anticipate subject training, and develop new training approaches to ensure HIT DSME use and engagement.

A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs

BACKGROUND

The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians' workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes.

METHODS

A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient's metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety of Sinedie interventions versus standard care and its impact in the professionals' workload in terms of the clinician's time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients' compliance to self-monitoring; and patients' satisfaction.

RESULTS

Sinedie was clinically evaluated at "Parc Tauli University Hospital" in Spain during 17 months with the participation of 90 patients with gestational diabetes. Sinedie detected all situations that required a therapy adjustment and all the generated recommendations were safe. The time devoted by clinicians to patients' evaluation was reduced by 27.389% and face-to-face visits per patient were reduced by 88.556%. Patients reported to be highly satisfied with the system, considering it useful and trusting in being well controlled. There was no monitoring loss and, in average, patients measured their glycaemia 3.890 times per day and sent their monitoring data every 3.477days.

CONCLUSIONS

Sinedie generates safe advice about therapy adjustments, reduces the clinicians' workload and helps physicians to identify which patients need a more urgent or more exhaustive examination and those who present good metabolic control. Additionally, Sinedie saves patients unnecessary displacements which contributes to medical centres' waiting list reduction.

A Critical Examination of the Use of Trained Health Coaches to Decrease the Metabolic Syndrome for Participants of a Community-Based Diabetes Prevention and Management Program

The epidemic of obesity and diabetes in the United States poses major challenge to the prevention and management of chronic diseases. Furthermore, when this is viewed in other components of the metabolic syndrome (i.e., the burden of high cholesterol and hypertension), the prevalence of the metabolic syndrome continues to rise in the USA continued challenge is how to deal with this epidemic from a medical and public health standpoint. Community Based Participatory Research (CBPR) is a unique approach and offers a novel perspective for answering this challenge. A critical set of goals for CBPR is to address health disparities and social inequalities while getting community members engaged in all aspects of the research process. Utilizing the West Virginia Diabetes Prevention and Management Program and trained Health Coaches as a model, we discuss topics of consideration related to CBPR, involving trained health coaches, optimizing early adoption of healthy lifestyle behaviors, and enhancing participation. Through careful project planning and design, questions regarding disparities increasing susceptibility and preventive efforts within the community can be addressed successfully. These topics are part of a broader integration of theories such as participatory research, community engagement, and outcomes measurement. The understanding of the pathophysiology and epidemiology of the metabolic syndrome can help frame an appropriate strategy for establishing long-term community-wide changes that promote health. In order to continue to improve investigations for preventing the metabolic syndrome, it will be necessary to have aggressive efforts at the individual and population level for developing culturally sensitive programs that start early and are sustainable in practical environments such as the workplace. In this comprehensive review, we will discuss practical considerations related to project design, implementation, and how to measure effectiveness in regards to reducing the burden of the metabolic syndrome.

Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective

OBJECTIVE

To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD).

METHODS

In this qualitative study, researchers conducted semi-structured interviews with individuals from economically disadvantaged communities and ethnic minorities who are participating in a randomized controlled trial of MoDD. The transcripts of the interviews were analyzed using inductive thematic analysis; usage logs were analyzed to determine how actively the study participants used MoDD.

RESULTS

Fifteen participants in the MoDD randomized controlled trial were recruited for the qualitative interviews. Usage log analysis showed that, on average, during the 4 weeks of the study, the study participants logged into MoDD twice per week, reported 120 blood glucose readings, and set two behavioral goals. The qualitative interviews suggested that individuals used MoDD to follow the steps of the problem-solving process, from identifying problematic blood glucose patterns, to exploring behavioral triggers contributing to these patterns, to selecting alternative behaviors, to implementing these behaviors while monitoring for improvements in glycemic control.

DISCUSSION

This qualitative study suggested that informatics interventions for reflection and problem solving can provide structured scaffolding for facilitating these processes by guiding users through the different steps of the problem-solving process and by providing them with context-sensitive evidence and practice-based knowledge related to diabetes self-management on each of those steps.

CONCLUSION

This qualitative study suggested that MoDD was perceived as a useful tool in engaging individuals in self-monitoring, reflection, and problem solving.