Stein Olav Skrøvseth

Mobile Health Applications to Assist Patients with Diabetes: Lessons Learned and Design Implications

Self-management is critical to achieving diabetes treatment goals. Mobile phones and Bluetooth® can support self-management and lifestyle changes for chronic diseases such as diabetes. A mobile health (mHealth) research platform—the Few Touch Application (FTA)—is a tool designed to support the self-management of diabetes. The FTA consists of a mobile phone-based diabetes diary, which can be updated both manually from user input and automatically by wireless data transfer, and which provides personalized decision support for the achievement of personal health goals. Studies and applications (apps) based on FTAs have included: (1) automatic transfer of blood glucose (BG) data; (2) short message service (SMS)-based education for type 1 diabetes (T1DM); (3) a diabetes diary for type 2 diabetes (T2DM); (4) integrating a patient diabetes diary with health care (HC) providers; (5) a diabetes diary for T1DM; (6) a food picture diary for T1DM; (7) physical activity monitoring for T2DM; (8) nutrition information for T2DM; (9) context sensitivity in mobile self-help tools; and (10) modeling of BG using mobile phones. We have analyzed the performance of these 10 FTA-based apps to identify lessons for designing the most effective mHealth apps. From each of the 10 apps of FTA, respectively, we conclude: (1) automatic BG data transfer is easy to use and provides reassurance; (2) SMS-based education facilitates parent-child communication in T1DM; (3) the T2DM mobile phone diary encourages reflection; (4) the mobile phone diary enhances discussion between patients and HC professionals; (5) the T1DM mobile phone diary is useful and motivational; (6) the T1DM mobile phone picture diary is useful in identifying treatment obstacles; (7) the step counter with automatic data transfer promotes motivation and increases physical activity in T2DM; (8) food information on a phone for T2DM should not be at a detailed level; (9) context sensitivity has good prospects and is possible to implement on todays phones; and (10) BG modeling on mobile phones is promising for motivated T1DM users. We expect that the following elements will be important in future FTA designs: (A) automatic data transfer when possible; (B) motivational and visual user interfaces; (C) apps with considerable health benefits in relation to the effort required; (D) dynamic usage, e.g., both personal and together with HC personnel, long-/short-term perspective; and (E) inclusion of context sensitivity in apps. We conclude that mHealth apps will empower patients to take a more active role in managing their own health.

Cost-effectiveness and quality of life in surgeon versus general practitioner-organised colon cancer surveillance: a randomised controlled trial

Objective To assess whether colon cancer follow-up can be organised by general practitioners (GPs) without a decline in the patients quality of life (QoL) and increase in cost or time to cancer diagnoses, compared to hospital follow-up. Design Randomised controlled trial. Setting Northern Norway Health Authority Trust, 4 trusts, 11 hospitals and 88 local communities. Participants Patients surgically treated for colon cancer, hospital surgeons and community GPs. Intervention 24-month follow-up according to national guidelines at the community GP office. To ensure a high follow-up guideline adherence, a decision support tool for patients and GPs were used. Main outcome measures Primary outcomes were QoL, measured by the global health scales of the European Organisation for Research and Treatment of Cancer QoL Questionnaire (EORTC QLQ C-30) and EuroQol-5D (EQ-5D). Secondary outcomes were cost-effectiveness and time to cancer diagnoses. Results 110 patients were randomised to intervention (n=55) or control (n=55), and followed by 78 GPs (942 follow-up months) and 70 surgeons (942 follow-up months), respectively. Compared to baseline, there was a significant improvement in postoperative QoL (p=0.003), but no differences between groups were revealed (mean difference at 1, 3, 6, 9, 12, 15, 18, 21 and 24-month follow-up appointments): Global Health; Δ−2.23, p=0.20; EQ-5D index; Δ−0.10, p=0.48, EQ-5D VAS; Δ−1.1, p=0.44. There were no differences in time to recurrent cancer diagnosis (GP 35 days vs surgeon 45 days, p=0.46); 14 recurrences were detected (GP 6 vs surgeon 8) and 7 metastases surgeries performed (GP 3 vs surgeon 4). The follow-up programme initiated 1186 healthcare contacts (GP 678 vs surgeon 508), 1105 diagnostic tests (GP 592 vs surgeon 513) and 778 hospital travels (GP 250 vs surgeon 528). GP organised follow-up was associated with societal cost savings (£8233 vs £9889, p<0.001). Conclusions GP-organised follow-up was associated with no decline in QoL, no increase in time to recurrent cancer diagnosis and cost savings. Trial registration ClinicalTrials.gov identifier NCT00572143.

Consistency and Standardization of Color in Medical Imaging: a Consensus Report

This article summarizes the consensus reached at the Summit on Color in Medical Imaging held at the Food and Drug Administration (FDA) on May 8–9, 2013, co-sponsored by the FDA and ICC (International Color Consortium). The purpose of the meeting was to gather information on how color is currently handled by medical imaging systems to identify areas where there is a need for improvement, to define objective requirements, and to facilitate consensus development of best practices. Participants were asked to identify areas of concern and unmet needs. This summary documents the topics that were discussed at the meeting and recommendations that were made by the participants. Key areas identified where improvements in color would provide immediate tangible benefits were those of digital microscopy, telemedicine, medical photography (particularly ophthalmic and dental photography), and display calibration. Work in these and other related areas has been started within several professional groups, including the creation of the ICC Medical Imaging Working Group.

Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes

Background In a growing number of intervention studies, mobile phones are used to support self-management of people with Type 2 diabetes mellitus (T2DM). However, it is difficult to establish knowledge about factors associated with intervention effects, due to considerable differences in research designs and outcome measures as well as a lack of detailed information about participants’ engagement with the intervention tool. Objective To contribute toward accumulating knowledge about factors associated with usage and usability of a mobile self-management application over time through a thorough analysis of multiple types of investigation on each participant’s engagement. Methods The Few Touch application is a mobile-phone–based self-management tool for patients with T2DM. Twelve patients with T2DM who have been actively involved in the system design used the Few Touch application in a real-life setting from September 2008 until October 2009. During this period, questionnaires and semistructured interviews were conducted. Recorded data were analyzed to investigate usage trends and patterns. Transcripts from interviews were thematically analyzed, and the results were further analyzed in relation to the questionnaire answers and the usage trends and patterns. Results The Few Touch application served as a flexible learning tool for the participants, responsive to their spontaneous needs, as well as supporting regular self-monitoring. A significantly decreasing (P<.05) usage trend was observed among 10 out of the 12 participants, though the magnitude of the decrease varied widely. Having achieved a sense of mastery over diabetes and experiences of problems were identified as reasons for declining motivation to continue using the application. Some of the problems stemmed from difficulties in integrating the use of the application into each participant’s everyday life and needs, although the design concepts were developed in the process where the participants were involved. The following factors were identified as associated with usability and/or usage over time: Integration with everyday life; automation; balance between accuracy and meaningfulness of data with manual entry; intuitive and informative feedback; and rich learning materials, especially about foods. Conclusion Many grounded design implications were identified through a thorough analysis of results from multiple types of investigations obtained through a year-long field trial of the Few Touch application. The study showed the importance and value of involving patient-users in a long-term trial of a tool to identify factors influencing usage and usability over time. In addition, the study confirmed the importance of detailed analyses of each participant’s usage of the provided tool for better understanding of participants’ engagement over time.

Automatic segmentation of dermoscopic images by iterative classification

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

A computer aided diagnostic system for malignant melanomas

We describe a system for automatic diagnosis of malignant melanoma based on digital dermoscopic images. The tool is designed for use with general practitioners, saving time and resources in the diagnostic process. A variety of indicative features are described mimicking the human approach for diagnosis. Segmentation, pattern recognition, and change detection are the important steps in our approach.

Phase transitions and localizable entanglement in cluster-state spin chains with Ising couplings and local fields

We consider a one-dimensional spin chain for which the ground state is the cluster state, capable of functioning as a quantum computational wire when subjected to local adaptive measurements of individual qubits, and investigate the robustness of this property to local and coupled (Ising-type) perturbations. We investigate the ground state both by identifying suitable correlation functions as order parameters, as well as numerically using a variational method based on matrix product states. We find that the model retains an infinite localizable entanglement length for Ising and local fields up to a quantum phase transition, but that the resulting entangled state is not simply characterized by a Pauli correction based on the measurement results.

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

Abstract Background: A mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patients devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article. Materials and Methods: We developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge design, both groups initially received an application without Diastat. Group 1 activated Diastat after 4 weeks, whereas Group 2 activated Diastat 12 weeks after startup (T1). End points were glycated hemoglobin (HbA1c) level and number of out-of-range (OOR) measurements (i.e., outside the range 72–270 mg/dL). Results: Thirty patients were recruited to the study, and 15 were assigned to each group after the initial meeting. There were no significant differences between groups at T1 in HbA1c or OOR events. Overall, all patients had a decrease of 0.6 percentage points in mean HbA1c (P<0.001) and 14.5 in median OOR events over 2 weeks (P<0.001). Conclusions: The study does not provide evidence that data-driven feedback improves glycemic control. The decrease in HbA1c was sizeable and significant, even though the study was not powered to detect this. The overall improvement in glycemic control suggests that, in general, mobile phone-based interventions can be useful in diabetes self-management.

Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

A Bayesian multiscale technique for the detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging.

Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods

OBJECTIVE In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs. MATERIAL AND METHODS We used linear and non-linear kernel methods individually for each data source, and leveraging the powerful multiple kernels for their effective combination. To validate the system, we used data from the EHR of the gastrointestinal department at a university hospital. RESULTS We first investigated the early prediction performance from each data source separately, by computing Area Under the Curve values for processed free text (0.83), blood tests (0.74), and vital signs (0.65), respectively. When exploiting the heterogeneous data sources combined using the composite kernel framework, the prediction capabilities increased considerably (0.92). Finally, posterior probabilities were evaluated for risk assessment of patients as an aid for clinicians to raise alertness at an early stage, in order to act promptly for avoiding AL complications. DISCUSSION Machine-learning statistical model from EHR data can be useful to predict surgical complications. The combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance. These results can be used as a framework for preoperative clinical decision support.