Machine Learning of Lifestyle Data for Diabetes

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data. We believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations. The most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective. A lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works. GlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management

The development of My Care Hub mobile-phone app to support self-management in Australians with type 1 or type 2 diabetes

Non-adherence to self-management poses a serious risk to diabetes complications. Digital behavioural change interventions have the potential to provide education and motivate users to regularly engage with self-management of diabetes. This paper describes the development of My Care Hub mobile phone application (app) aimed at supporting self-management in people with type 1 or type 2 diabetes. The development of My Care Hub involved a comprehensive process of healthy behavioural change identification, end users’ needs, expert consensus, data security and privacy considerations. The app translation was a highly iterative process accompanied by usability testing and design modification. The app development process included: (1) behaviour change strategy selection; (2) users’ 31 involvement; (3) expert advisory involvement; (4) data security and privacy considerations; (5) design creation and output translation into a smartphone app and (6) two usability testings of the app prototype version. The app features include self-care activities documentation, analytics, personalized and generalized messages for diabetes self-management as well as carbohydrate components of common foods in Australia. Twelve respondents provided feedback on the usability of the app. Initially, a simplification of the documentation features of the app was identified as a need to improve usability. Overall, results indicated good user satisfaction rate

Enhancing diabetes self-management through mobile phone application

Mary Adu adopted a systematic health behavioural framework and user engagement process to develop and explore the efficacy of a novel mobile-phone app for diabetes self-management. Reported benefits of the app provide empirical evidence of support for its multi-feature functionality and comprehensive interventional role in diabetes self-management education and support

User retention and engagement with a mobile app intervention to support self-management in Australians with type 1 or type 2 diabetes (My Care Hub): mixed methods study

Background: Mobile health apps are commonly used to support diabetes self-management. However, there is limited research assessing if such apps are able to meet the basic requirements to retain and engage users. Objective: In this study, we aimed to evaluate participants' retention and engagement with My Care Hub (MCH), a mobile app for diabetes self-management. Methods: This study employed an explanatory mixed-method design. Participants were people with type 1 or type 2 diabetes who used the health app intervention for 2 weeks. Retention was measured by completion of the post-intervention survey. Engagement was measured using system log indices and interviews. Retention and system log indices were presented using descriptive statistics. Transcripts were analyzed using content analysis to develop themes interpreted according to the Behavioural Intervention Technology theory. Results: Of the 50 individuals enrolled, 42 (84%) adhered to the study protocol. System usage data showed multiple and frequent interaction with the app by most of the enrolled participants (84%, 42/50). Participants used the app on an average of 11 out of the 14 intervention days (range 2-14 days); where two-thirds of participants who inputted data returned to use the app after week 1 (85%, 36/42) and week 2 (71.4%, 30/42) of installation. Most daily used features were tracking of blood glucose (BG) (67.5%, 28/42) and accessing educational information (12.57%, 6/42). The interview results revealed the app's potential as a behavioural change intervention tool, particularly because it eased participants' self-care effort and improved their engagement with diabetes self-management activities such as BG monitoring, physical exercise and healthy eating. Participants suggested extra functionalities such as extended access to historical analytic data, automated data transmission from BG meter as well as periodic update of meals and corresponding nutrients to further enhance engagement with the app. Conclusions: The findings of this short-term intervention study suggested acceptable levels of participant retention and engagement with MCH, indicating that is a promising tool for extending diabetes self-management support and education beyond the confines of a physical clinic

Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation

The design of nutrition labels

The main objective of this dissertation was to expose the process involved in the design and the actual design of a nutrition label for restaurant’s menus. The increasing overweight and obesity rates are a major concern for health organizations and governments. In order to fight this epidemic, the Commission of the European Communities outlined a Strategy for Europe on Nutrition, Overweight and Obesity related to health issues determining, among others, that providing nutritional information to consumers is a critical factor that may influence healthier food choices. Since the habit of eating out-of-home is related to the increasing overweight and obesity rates as well, the disclosure of nutritional information in restaurants (or mass caterers in general) can provide guidance to consumers while choosing their food. The process of designing a nutrition label for restaurant’s menu was divided in to parts. The first part addressed research on communication, infographics and on the state-of-the-art of nutrition labels. The second part, and supported in the collected information and case studies’ analysis, was related with determining the type of information to include in the label and the nutritional criteria in which it was going to be based. It also included the actual design decisions related to the model of a nutrition label for restaurants’ menus, to be software generated. As future work it was pointed the necessity of testing it within the market and consumers and to develop an interactive solution for providing customized nutritional guidance.O principal objectivo desta dissertação consistia em expor o processo envolvido no design de um rótulo nutricional assim como desenvolver o seu próprio design. O aumento das taxas de excesso de peso e de obesidade são uma das grandes preocupações das organizações de saúde e dos governos. De forma a combater esta epidemia, a Comissão Europeia delineou o documento Strategy for Europe on Nutrition, Overweight and Obesity related to health issues que determina, entre outras decisões, que a provisão de informação nutricional aos consumidores é um factor crítico para influenciar a escolha de alimentos mais saudáveis. Dado que o hábito de consumir refeições fora de casa também está relacionado com o aumento das taxas de excesso de peso e de obesidade, a provisão de informação nutricional em restaurantes (ou estabelecimentos de restauração colectiva) pode orientar os consumidores na escolha das suas refeições. O processo de desenhar um rótulo nutricional para incluir no menu de restaurantes foi divido em duas partes. A primeira parte envolveu pesquisa nas áreas de comunicação, infografia e do estado da arte da rotulagem nutricional. Na segunda parte, e com base na informação reunida e análise de casos de estudo, foi determinado o tipo de informação a incluir no rótulo e os critérios nutricionais em que se fundamentaram. Incluiu também as decisões relativas ao design do modelo de rótulo nutricional para menus, que será gerado digitalmente. Como trabalho futuro foi apontada a necessidade de testar o rótulo nutricional no mercado e juntos dos consumidores e também a de desenvolvimento de uma solução interactiva para a provisão de orientação nutricional personalizada

Making the best use of new technologies in the National Diet and Nutrition Survey: a review

.Background Dietary assessment is of paramount importance for public health monitoring. Currently in the UK, the population’s diets are examined by the National Diet and Nutrition Survey Rolling Programme (NDNS RP). In the survey, diet is assessed by a four-day paper-based dietary diary, with accompanying interviews, anthropometric measurements and blood and urine sampling. However, there is growing interest worldwide in the potential for new technologies to assist in data collection for assessment of dietary intake. Published literature reviews have identified the potential of new technologies to improve accuracy, reduce costs, and reduce respondent and researcher burden by automating data capture and the nutritional coding process. However, this is a fast-moving field of research, with technologies developing at a rapid pace, and an updated review of the potential application of new technologies in dietary assessment is warranted. This review was commissioned to identify the new technologies employed in dietary assessment and critically appraise their strengths and limitations in order to recommend which technologies, if any, might be suitable to develop for use in the NDNS RP and other UK population surveys. Objectives The overall aim of the project was to inform the Department of Health of the range of new technologies currently available and in development internationally that have potential to improve, complement or replace the methods used in the NDNS RP. The specific aims were: to generate an itinerary of new and emerging technologies that may be suitable; to systematically review the literature and critically appraise new technologies; and to recommend which of these new technologies, if any, would be appropriate for future use in the NDNS RP. To meet these aims, the project comprised two main facets, a literature review and qualitative research. Literature review data sources The literature review incorporated an extensive search of peer-reviewed and grey literature. The following sources were searched: Cochrane Database of Systematic Reviews (CDSR), Database of Abstracts of Reviews of Effectiveness (DARE), Web of Science Core Collection, Ovid MEDLINE, Ovid MEDLINE In-Process, Embase, NHS EED (Economic Evaluation Database), National Cancer Institute (NCI) Dietary Assessment Calibration/Validation Register, OpenGrey, EPPI Centre (TRoPHI), conference proceedings (ICDAM 2012, ISBNPA 2013, IEEE Xplore, Nutrition Society Irish Section and Summer Meetings 2014), recent issues of journals (Journal of Medical Internet Research, International Journal of Medical Informatics), grants registries (ClinicalTrials.gov, BBSRC, report), national surveys, and mobile phone application stores. In addition, hand-searching of relevant citations was performed. The search also included solicitation of key authors in the field to enquire about Making the best use of new technologies in the NDNS: a review 4 as-yet unpublished articles or reports, and a Bristol Online Survey publicised via social media, society newsletters and meetings. Literature review eligibility criteria Records were screened for eligibility using a three-stage process. Firstly, keyword searches identified obviously irrelevant titles. Secondly, titles and abstracts were screened against the eligibility criteria, following which full-text copies of papers were obtained and, in the third stage of screening, examined against the criteria. Two independent reviewers screened each record at each stage, with discrepancies referred to a third reviewer. Eligibility criteria were pre-specified and agreed by the project Steering Group (Section 1.6). Eligible records included: studies involving technologies, new to the NDNS RP, which can be used to automate or assist the collection of food consumption data and the coding of foods and portion sizes, currently available or beta versions, public domain or commercial; studies that address the development, features, or evaluation of new technology; technologies appropriate for the requirements of the NDNS RP in terms of nutritional analysis, with capacity to collect quantifiable consumption data at the food level; primary sources of information on a particular technology; and journal articles published since the year 2000 or grey literature available from 2011 onwards. The literature search was not limited to Englishlanguage publications, which are included in the itinerary, although data were not extracted from non-English studies. Literature synthesis and appraisal New technologies were categorised into eleven types of technology, and an itinerary was generated of tools falling under each category type. Due to the volume of eligible studies identified by the literature searches, data extraction was limited to the literature focussing on selected exemplar tools of five technology categories (web-based diet diary, web-based 24- hour recall, handheld devices (personal digital assistants and mobile phones), nonautomated cameras to complement traditional methods, and non-automated cameras to replace traditional methods). For each category, at least two exemplars were chosen, and all studies involving the exemplar were included in data extraction and synthesis. Exemplars were selected on the basis of breadth of evidence available, using pre-specified criteria agreed by the Steering Group. Data were extracted by a single reviewer and an evidence summary collated for each exemplar. A quality appraisal checklist was developed to assess the quality of validation studies. The checklist was piloted and applied by two independent reviewers. Studies were not excluded on the basis of quality, but study quality was taken into account when judging the strength of evidence. Due to the heterogeneity of the literature, meta-analyses were not performed. References were managed and screened using the EPPI Reviewer 4 systematic review software. EPPI Reviewer was also used to extract data