Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar

We present a segmentation algorithm capable of segmenting exercise repetitions in real-time. This approach uses subsequence dynamic time warping and requires only a single exemplar repetition of an exercise to correctly segment repetitions from other subjects, including those with limited mobility. This approach is invariant to low range of motion, instability in movements and sensor noise while remaining selective to different exercises. This algorithm enables responsive feedback for technology-assisted physical rehabilitation systems. We evaluated the algorithm against a publicly available dataset (CMU) and against a healthy population and stroke patient population performing rehabilitation exercises captured on a consumer-level depth sensor. We show the algorithm can consistently achieve correct segmentation in real-time

An explanatory randomised controlled trial of a nurse-led, consultation-based intervention to support patients with adherence to taking glucose lowering medication for type 2 diabetes.

BACKGROUND: Failure to take medication reduces the effectiveness of treatment leading to increased morbidity and mortality. We evaluated the efficacy of a consultation-based intervention to support objectively-assessed adherence to oral glucose lowering medication (OGLM) compared to usual care among people with type 2 diabetes. METHODS: This was a parallel group randomised trial in adult patients with type 2 diabetes and HbA1c ≥ 7.5% (58 mmol/mol), prescribed at least one OGLM. Participants were allocated to a clinic nurse delivered, innovative consultation-based intervention to strengthen patient motivation to take OGLM regularly and support medicine taking through action-plans, or to usual care. The primary outcome was the percentage of days on which the prescribed dose of medication was taken, measured objectively over 12 weeks with an electronic medication-monitoring device (TrackCap, Aardex, Switzerland). The primary analysis was intention-to-treat. RESULTS: 211 patients were randomised between July 1, 2006 and November 30, 2008 in 13 British general practices (primary care clinics). Primary outcome data were available for 194 participants (91.9%). Mean (sd) percentage of adherent days was 77.4% (26.3) in the intervention group and 69.0% (30.8) in standard care (mean difference between groups 8.4%, 95% confidence interval 0.2% to 16.7%, p = 0.044). There was no significant adverse impact on functional status or treatment satisfaction. CONCLUSIONS: This well-specified, theory based intervention delivered in a single session of 30 min in primary care increased objectively measured medication adherence, with no adverse effect on treatment satisfaction. These findings justify a definitive trial of this approach to improving medication adherence over a longer period of time, with clinical and cost-effectiveness outcomes to inform clinical practice.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Blood pressure self-monitoring in pregnancy (BuMP) feasibility study; a qualitative analysis of women's experiences of self-monitoring

Background Hypertensive disorders in pregnancy are a leading cause of maternal and fetal morbidity worldwide. Raised blood pressure (BP) affects 10% of pregnancies worldwide, of which almost half develop pre-eclampsia. The proportion of pregnant women who have risk factors for pre-eclampsia (such as pre-existing hypertension, obesity and advanced maternal age) is increasing. Pre-eclampsia can manifest itself before women experience symptoms and can develop between antenatal visits. Incentives to improve early detection of gestational hypertensive disorders are therefore strong and self-monitoring of blood pressure (SMBP) in pregnancy might be one means to achieve this, whilst improving women’s involvement in antenatal care. The Blood Pressure Self-Monitoring in Pregnancy (BuMP) study aimed to evaluate the feasibility and acceptability of SMBP in pregnancy. Methods To understand women’s experiences of SMBP during pregnancy, we undertook a qualitative study embedded within the BuMP observational feasibility study. Women who were at higher risk of developing hypertension and/or pre-eclampsia were invited to take part in a study using SMBP and also invited to take part in an interview. Semi-structured interviews were conducted at the women’s homes in Oxfordshire and Birmingham with women who were self-monitoring their BP as part of the BuMP feasibility study in 2014. Interviews were conducted by a qualitative researcher and transcribed verbatim. A framework approach was used for analysis. Results Fifteen women agreed to be interviewed. Respondents reported general willingness to engage with monitoring their own BP, feeling that it could reduce anxiety around their health during pregnancy, particularly if they had previous experience of raised BP or pre-eclampsia. They felt able to incorporate self-monitoring into their weekly routines, although this was harder post-partum. Self-monitoring of BP made them more aware of the risks of hypertension and pre-eclampsia in pregnancy. Feelings of reassurance and empowerment were commonly reported by the women in our sample

Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applciations. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data