BackgroundChanging diet and physical activity behaviour is one of the cornerstones of type 2 diabetes treatment, but changing behaviour is challenging. The objective of this study was to identify behaviour change techniques (BCTs) and intervention features of dietary and physical activity interventions for patients with type 2 diabetes that are associated with changes in HbA1c and body weight.MethodsWe performed a systematic review of papers published between 1975–2015 describing randomised controlled trials (RCTs) that focused exclusively on both diet and physical activity. The constituent BCTs, intervention features and methodological rigour of these interventions were evaluated. Changes in HbA1c and body weight were meta-analysed and examined in relation to use of BCTs.ResultsThirteen RCTs were identified. Meta-analyses revealed reductions in HbA1c at 3, 6, 12 and 24 months of -1.11 % (12 mmol/mol), -0.67 % (7 mmol/mol), -0.28 % (3 mmol/mol) and -0.26 % (2 mmol/mol) with an overall reduction of -0.53 % (6 mmol/mol [95 % CI -0.74 to -0.32, P < 0.00001]) in intervention groups compared to control groups. Meta-analyses also showed a reduction in body weight of -2.7 kg, -3.64 kg, -3.77 kg and -3.18 kg at 3, 6, 12 and 24 months, overall reduction was -3.73 kg (95 % CI -6.09 to -1.37 kg, P = 0.002).Four of 46 BCTs identified were associated with >0.3 % reduction in HbA1c: ‘instruction on how to perform a behaviour’, ‘behavioural practice/rehearsal’, ‘demonstration of the behaviour’ and ‘action planning’, as were intervention features ‘supervised physical activity’, ‘group sessions’, ‘contact with an exercise physiologist’, ‘contact with an exercise physiologist and a dietitian’, ‘baseline HbA1c >8 %’ and interventions of greater frequency and intensity.ConclusionsDiet and physical activity interventions achieved clinically significant reductions in HbA1c at three and six months, but not at 12 and 24 months. Specific BCTs and intervention features identified may inform more effective structured lifestyle intervention treatment strategies for type 2 diabetes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-016-0436-0) contains supplementary material, which is available to authorized users.

BackgroundDesign processes such as human-centered design, which involve the end user throughout the product development and testing process, can be crucial in ensuring that the product meets the needs and capabilities of the user, particularly in terms of safety and user experience. The structured and iterative nature of human-centered design can often present a challenge when design teams are faced with the necessary, rapid, product development life cycles associated with the competitive connected health industry.ObjectiveWe wanted to derive a structured methodology that followed the principles of human-centered design that would allow designers and developers to ensure that the needs of the user are taken into account throughout the design process, while maintaining a rapid pace of development. In this paper, we present the methodology and its rationale before outlining how it was applied to assess and enhance the usability, human factors, and user experience of a connected health system known as the Wireless Insole for Independent and Safe Elderly Living (WIISEL) system, a system designed to continuously assess fall risk by measuring gait and balance parameters associated with fall risk.MethodsWe derived a three-phase methodology. In Phase 1 we emphasized the construction of a use case document. This document can be used to detail the context of use of the system by utilizing storyboarding, paper prototypes, and mock-ups in conjunction with user interviews to gather insightful user feedback on different proposed concepts. In Phase 2 we emphasized the use of expert usability inspections such as heuristic evaluations and cognitive walkthroughs with small multidisciplinary groups to review the prototypes born out of the Phase 1 feedback. Finally, in Phase 3 we emphasized classical user testing with target end users, using various metrics to measure the user experience and improve the final prototypes.ResultsWe report a successful implementation of the methodology for the design and development of a system for detecting and predicting falls in older adults. We describe in detail what testing and evaluation activities we carried out to effectively test the system and overcome usability and human factors problems.ConclusionsWe feel this methodology can be applied to a wide variety of connected health devices and systems. We consider this a methodology that can be scaled to different-sized projects accordingly.

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Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may\ud be the most incapacitating. FOG episodes may result in falls and reduce patients’\ud quality of life. Accurate assessment of FOG would provide objective information\ud to neurologists about the patient’s condition and the symptom’s characteristics,\ud while it could enable non-pharmacologic support based on rhythmic\ud cues.\ud This paper is, to the best of our knowledge, the first study to propose a\ud deep learning method for detecting FOG episodes in PD patients. This model\ud is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach\ud was evaluated using data collected by a waist-placed inertial measurement unit\ud from 21 PD patients who manifested FOG episodes. These data were also employed\ud to reproduce the state-of-the-art methodologies, which served to perform\ud a comparative study to our FOG monitoring system.\ud The results of this study demonstrate that our approach successfully outperforms\ud the state-of-the-art methods for automatic FOG detection. Precisely, the\ud deep learning model achieved 90% for the geometric mean between sensitivity\ud and specificity, whereas the state-of-the-art methods were unable to surpass the\ud 83% for the same metric.Peer ReviewedPostprint (published version