A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing.

BACKGROUND: A healthy lifestyle, including regular physical activity and a healthy diet, is increasingly part of type 2 diabetes (T2D) management. As many people with T2D have difficulty living and maintaining a healthy lifestyle, there is a need for effective interventions. eHealth interventions that incorporate behavior change theories and tailoring are considered effective tools for supporting a healthy lifestyle. The E-Supporter 1.0 digital coach contains eHealth content for app-based eHealth interventions and offers tailored coaching regarding physical activity and a healthy diet for people with T2D. OBJECTIVE: This study aimed to assess the acceptability of E-Supporter 1.0 and explore its limited efficacy on physical activity, dietary behavior, the phase of behavior change, and self-efficacy levels. METHODS: Over a span of 9 weeks, 20 individuals with T2D received daily motivational messages and weekly feedback derived from behavioral change theories and determinants through E-Supporter 1.0. The acceptability of the intervention was assessed using telephone-conducted, semistructured interviews. The interview transcripts were coded using inductive thematic analysis. The limited efficacy of E-Supporter 1.0 was explored using the Fitbit Charge 2 to monitor step count to assess physical activity and questionnaires to assess dietary behavior (using the Dutch Healthy Diet index), phase of behavior change (using the single-question Self-Assessment Scale Stages of Change), and self-efficacy levels (using the Exercise Self-Efficacy Scale). RESULTS: In total, 5 main themes emerged from the interviews: perceptions regarding remote coaching, perceptions regarding the content, intervention intensity and duration, perceived effectiveness, and overall appreciation. The participants were predominantly positive about E-Supporter 1.0. Overall, they experienced E-Supporter 1.0 as a useful and easy-to-use intervention to support a better lifestyle. Participants expressed a preference for combining E-Supporter with face-to-face guidance from a health care professional. Many participants found the intensity and duration of the intervention to be acceptable, despite the coaching period appearing relatively short to facilitate long-term behavior maintenance. As expected, the degree of tailoring concerning the individual and external factors that influence a healthy lifestyle was perceived as limited. The limited efficacy testing showed a significant improvement in the daily step count (z=-2.040; P=.04) and self-efficacy levels (z=-1.997; P=.046) between baseline and postintervention. Diet was improved through better adherence to Dutch dietary guidelines. No significant improvement was found in the phase of behavior change (P=.17), as most participants were already in the maintenance phase at baseline. CONCLUSIONS: On the basis of this explorative feasibility study, we expect E-Supporter 1.0 to be an acceptable and potentially useful intervention to promote physical activity and a healthy diet in people with T2D. Additional work needs to be done to further tailor the E-Supporter content and evaluate its effects more extensively on lifestyle behaviors.

A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study.

BACKGROUND: A healthy lifestyle, including regular physical activity and a healthy diet, is becoming increasingly important in the treatment of chronic diseases. eHealth interventions that incorporate behavior change techniques (BCTs) and dynamic tailoring strategies could effectively support a healthy lifestyle. E-Supporter 1.0 is an eCoach designed to support physical activity and a healthy diet in people with type 2 diabetes (T2D). OBJECTIVE: This paper aimed to describe the systematic development of E-Supporter 1.0. METHODS: Our systematic design process consisted of 3 phases. The definition phase included the selection of the target group and formulation of intervention objectives, and the identification of behavioral determinants based on which BCTs were selected to apply in the intervention. In the development phase, intervention content was developed by specifying tailoring variables, intervention options, and decision rules. In the last phase, E-Supporter 1.0 integrated in the Diameter app was evaluated using a usability test in 9 people with T2D to assess intervention usage and acceptability. RESULTS: The main intervention objectives were to stimulate light to moderate-vigorous physical activities or adherence to the Dutch dietary guidelines in people with T2D. The selection of behavioral determinants was informed by the health action process approach and theories explaining behavior maintenance. BCTs were included to address relevant behavioral determinants (eg, action control, self-efficacy, and coping planning). Development of the intervention resulted in 3 types of intervention options, consisting of motivational messages, behavioral feedback, and tailor-made supportive exercises. On the basis of IF-THEN rules, intervention options could be tailored to, among others, type of behavioral goal and (barriers to) goal achievement. Data on these variables could be collected using app data, activity tracker data, and daily ecological momentary assessments. Usability testing revealed that user experiences were predominantly positive, despite some problems in the fixed delivery of content. CONCLUSIONS: The systematic development approach resulted in a theory-based and dynamically tailored eCoach. Future work should focus on expanding intervention content to other chronic diseases and lifestyle behaviors, enhancing the degree of tailoring and evaluating intervention effects on acceptability, use, and cost-effectiveness.

Night to night variability of pulse oximetry features in children at home and at the hospital.

Objective. Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea.Approach. Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models.Main results. Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital.Significance. At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children.

Lessons Learned From a Living Lab on the Broad Adoption of eHealth in Primary Health Care.

BACKGROUND: Electronic health (eHealth) solutions are considered to relieve current and future pressure on the sustainability of primary health care systems. However, evidence of the effectiveness of eHealth in daily practice is missing. Furthermore, eHealth solutions are often not implemented structurally after a pilot phase, even if successful during this phase. Although many studies on barriers and facilitators were published in recent years, eHealth implementation still progresses only slowly. To further unravel the slow implementation process in primary health care and accelerate the implementation of eHealth, a 3-year Living Lab project was set up. In the Living Lab, called eLabEL, patients, health care professionals, small- and medium-sized enterprises (SMEs), and research institutes collaborated to select and integrate fully mature eHealth technologies for implementation in primary health care. Seven primary health care centers, 10 SMEs, and 4 research institutes participated. OBJECTIVE: This viewpoint paper aims to show the process of adoption of eHealth in primary care from the perspective of different stakeholders in a qualitative way. We provide a real-world view on how such a process occurs, including successes and failures related to the different perspectives. METHODS: Reflective and process-based notes from all meetings of the project partners, interview data, and data of focus groups were analyzed systematically using four theoretical models to study the adoption of eHealth in primary care. RESULTS: The results showed that large-scale implementation of eHealth depends on the efforts of and interaction and collaboration among 4 groups of stakeholders: patients, health care professionals, SMEs, and those responsible for health care policy (health care insurers and policy makers). These stakeholders are all acting within their own contexts and with their own values and expectations. We experienced that patients reported expected benefits regarding the use of eHealth for self-management purposes, and health care professionals stressed the potential benefits of eHealth and were interested in using eHealth to distinguish themselves from other care organizations. In addition, eHealth entrepreneurs valued the collaboration among SMEs as they were not big enough to enter the health care market on their own and valued the collaboration with research institutes. Furthermore, health care insurers and policy makers shared the ambition and need for the development and implementation of an integrated eHealth infrastructure. CONCLUSIONS: For optimal and sustainable use of eHealth, patients should be actively involved, primary health care professionals need to be reinforced in their management, entrepreneurs should work closely with health care professionals and patients, and the government needs to focus on new health care models stimulating innovations. Only when all these parties act together, starting in local communities with a small range of eHealth tools, the potential of eHealth will be enforced.

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care.

BACKGROUND: Most people experience low back pain (LBP) at least once in their life and for some patients this evolves into a chronic condition. One way to prevent acute LBP from transiting into chronic LBP, is to ensure that patients receive the right interventions at the right moment. We started research in the design of a clinical decision support system (CDSS) to support patients with LBP in their self-referral to primary care. For this, we explored the possibilities of using supervised machine learning. We compared the performances of the three classification models - i.e. 1. decision tree, 2. random forest, and 3. boosted tree - to get insight in which model performs best and whether it is already acceptable to use this model in real practice. METHODS: The three models were generated by means of supervised machine learning with 70% of a training dataset (1288 cases with 65% GP, 33% physio, 2% self-care cases). The cases in the training dataset were fictive cases on low back pain collected during a vignette study with primary healthcare professionals. We also wanted to know the performance of the models on real-life low back pain cases that were not used to train the models. Therefore we also collected real-life cases on low back pain as test dataset. These cases were collected with the help of patients and healthcare professionals in primary care. For each model, the performance was measured during model validation - with 30% of the training dataset -as well as during model testing - with the test dataset containing real-life cases. The total observed accuracy as well as the kappa, and the sensitivity, specificity, and precision were used as performance measures to compare the models. RESULTS: For the training dataset, the total observed accuracies of the decision tree, the random forest and boosted tree model were 70%, 69%, and 72% respectively. For the test dataset, the total observed accuracies were 71%, 53%, and 71% respectively. The boosted tree appeared to be the best for predicting a referral advice with a fair accuracy (Kappa between 0.2 and 0.4). Next to this, the measured evaluation measures show that all models provided a referral advice better than just a random guess. This means that all models learned some implicit knowledge of the provided referral advices in the training dataset. CONCLUSIONS: The study showed promising results on the possibility of using machine learning in the design of our CDSS. The boosted tree model performed best on the classification of low back pain cases, but still has to be improved. Therefore, new cases have to be collected, especially cases that are classified as self-care cases. This to be sure that also the self-care advice can be predicted well by the model.

Advancing Telemedicine Services for the Aging Population: The challenge of Interoperability.

We reflect on our experiences in two projects in which we developed interoperable telemedicine applications for the aging population. While technically data exchange could be implemented technically, uptake was impeded by a lack of working procedures. We argue that development of interoperable health technology for the aging population should go accompanied by a thorough study into working protocols by consulting all end-users and stakeholders.