Evolution of Do-It-Yourself Remote Monitoring Technology for Type 1 Diabetes

Improving Care for People Living With Dementia and Diabetes: Applying the Human-Centered Design Process to Continuous Glucose Monitoring

People with Alzheimer's disease or related dementias and diabetes mellitus (ADRD-DM) are at high risk for hypoglycemic events. Their cognitive impairment and psychosocial situation often hinder detection of hypoglycemia. Extending use and benefits of continuous glucose monitoring (CGM) to people with ADRD-DM could improve hypoglycemia detection, inform care, and reduce adverse events. However, cognitive impairment associated with ADRD presents unique challenges for CGM use. This commentary proposes applying the human-centered design process to CGM, investigating design solutions or interventions needed to integrate CGM into the health care of patients with ADRD-DM. With this process, we can identify and inform CGM designs for people with ADRD-DM, broadening CGM access, increasing detection and treatment of the silent threat posed by hypoglycemia.

Objectives and outcomes of patient-driven innovations published in peer-reviewed journals: a qualitative analysis of publications included in a scoping review

OBJECTIVES

The aim of this study was to gain a deeper understanding of the objectives and outcomes of patient-driven innovations that have been published in the scientific literature, focusing on (A) the unmet needs that patient-driven innovations address and (B) the outcomes for patients and healthcare that have been reported.

METHODS

We performed an inductive qualitative content analysis of scientific publications that were included in a scoping review of patient-driven innovations, previously published by our research group. The review was limited to English language publications in peer-reviewed journals, published in the years 2008-2020.

RESULTS

In total, 83 publications covering 21 patient-driven innovations were included in the analysis. Most of the innovations were developed for use on an individual or community level without healthcare involvement. We created three categories of unmet needs that were addressed by these innovations: access to self-care support tools, open sharing of information and knowledge, and patient agency in self-care and healthcare decisions. Eighteen (22%) publications reported outcomes of patient-driven innovations. We created two categories of outcomes: impact on self-care, and impact on peer interaction and healthcare collaboration.

CONCLUSIONS

The patient-driven innovations illustrated a diversity of innovative approaches to facilitate patients' and informal caregivers' daily lives, interactions with peers and collaborations with healthcare. As our findings indicate, patients and informal caregivers are central stakeholders in driving healthcare development and research forward to meet the needs that matter to patients and informal caregivers. However, only few studies reported on outcomes of patient-driven innovations. To support wider implementation, more evaluation studies are needed, as well as research into regulatory approval processes, dissemination and governance of patient-driven innovations.

The Enterprise's Willingness to Use Remote Monitoring Technology Under the Background of Green Operation and Service-Oriented Manufacturing

The current development of remote monitoring technology (RMT) has become increasingly mature. The key to implementing this technology lies in the user's willingness to use it. In order to study the influencing factors of using RMT in green operation and service-oriented manufacturing enterprises, based on organizational behavior, this exploration discusses the reasons that affect the introduction of new technologies into enterprises from the perspectives of perceived risk, conformity and technology acceptance. Moreover, a series of data is obtained through the questionnaire and the results are obtained by analyzing the data. Suggestions to improve the use of RMT in enterprises are put forward. The results show that technology itself, external environment and organizational characteristics can all affect the decision-making of enterprises on new technology.

Identifying Continuous Glucose Monitoring Data Using Machine Learning

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.

Patient-driven innovations reported in peer-reviewed journals: a scoping review

BACKGROUND

Awareness of patients' innovative capabilities is increasing, but there is limited knowledge regarding the extent and nature of patient-driven innovations in the peer-reviewed literature.

OBJECTIVES

The objective of the review was to answer the question: what is the nature and extent of patient-driven innovations published in peer-reviewed scientific journals?

ELIGIBILITY CRITERIA

We used a broad definition of innovation to allow for a comprehensive review of different types of innovations and a narrow definition of 'patient driven' to focus on the role of patients and/or family caregivers. The search was limited to years 2008-2020.

SOURCES OF EVIDENCE

Four electronic databases (Medline (Ovid), Web of Science Core Collection, PsycINFO (Ovid) and Cinahl (Ebsco)) were searched in December 2020 for publications describing patient-driven innovations and complemented with snowball strategies.

CHARTING METHODS

Data from the included articles were extracted and categorised inductively.

RESULTS

A total of 96 articles on 20 patient-driven innovations were included. The number of publications increased over time, with 69% of the articles published between 2016 and 2020. Author affiliations were exclusively in high income countries with 56% of first authors in North America and 36% in European countries. Among the 20 innovations reported, 'Do-It-Yourself Artificial Pancreas System' and the online health network 'PatientsLikeMe', were the subject of half of the articles.

CONCLUSIONS

Peer-reviewed publications on patient-driven innovations are increasing and we see an important opportunity for researchers and clinicians to support patient innovators' research while being mindful of taking over the work of the innovators themselves.

Youth and parent preferences for an ideal AP system: It is all about reducing burden

BACKGROUND

As new diabetes technologies improve to better manage glucose levels, users' priorities for future technologies may shift to prioritize burden reduction and ease of use. We used qualitative methods to explore youth and parent desired features of an "ideal" artificial pancreas (AP) system.

METHODS

We conducted semi-structured interviews with 39 youth, ages 10-25 years, and 44 parents. Interviews were audio-recorded, transcribed, and coded using thematic analysis.

RESULTS

Youth (79% female, 82% non-Hispanic white) were (M ± SD) ages 17.0 ± 4.7 years, with diabetes for 9.4 ± 4.9 years, and HbA1c of 8.4 ± 1.1%; 79% were pump-treated and 82% used CGM. Of parents, 91% were mothers and 86% were non-Hispanic white. Participants suggested various ways in which an ideal AP system could reduce physical and emotional burdens of diabetes. Physical burdens could be reduced by lessening user responsibilities to manage glucose for food and exercise, and wear or carry devices. Emotional burden could be reduced by mitigating negative emotional reactions to sound and frequency of alerts, while increasing feelings of normalcy. Youth and parents differed in their suggestions to reduce emotional burden. Participants suggested features that would improve glycemia, but nearly always in the context of how the feature would directly reduce their diabetes-specific burden.

CONCLUSIONS

Although participants expressed interest in improving glucose levels, the pervasive desire among suggested features of an ideal AP system was to minimize the burden of diabetes. Understanding and addressing users' priorities to reduce physical and emotional burden will be necessary to enhance uptake and maintain use of future AP systems.

Human Factors and Organizational Issues Section Synopsis IMIA Yearbook 2021

OBJECTIVE

To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2020.

METHODS

A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2020 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 1,562 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.

RESULTS

The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance in the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Emerging themes included several which discussed physician burnout, mobile health, and health equity. Those concerning the Corona Virus Disease 2019 (Covid-19) were included as part of that section.

CONCLUSION

The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.