A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study

Background The prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity. Methods Sixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a ‘Posture’ protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an ‘Activity’ protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a ‘Proof of Concept’ model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations. Results The WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application. Conclusions The study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores

Factor structure of the Shoulder Pain and Disability Index in patients with adhesive capsulitis

<p>Abstract</p> <p>Background</p> <p>The Shoulder Pain and Disability Index (SPADI) is a self-administered questionnaire that aims to measure pain and disability associated with shoulder disease. It consists of a pain section and a disability section with 13 items being responded to on visual analogue scales. Few researchers have investigated SPADI validity in specified diagnostic groups, although the selection of an evaluative instrument should be based on evidence of validity in the target patient group. The aim of the present study was to investigate factor structure of the SPADI in a study population of patients with adhesive capsulitis.</p> <p>Methods</p> <p>The questionnaire was administered to 191 patients with adhesive capsulitis. Descriptive statistics for items and a comparison of scores for the two subscales were produced. Internal consistency was analyzed by use of the Cronbach alpha and a principal components analysis with varimax rotation was conducted. Study design was cross-sectional.</p> <p>Results</p> <p>Two factors were extracted, but the factor structure failed to support the original division of items into separate pain and disability sections.</p> <p>Conclusion</p> <p>We found minimal evidence to justify the use of separate subscales for pain and disability. It is our impression that the SPADI should be viewed as essentially unidimensional in patients with adhesive capsulitis.</p