Device Worn Research Articles

P1460Seismocardiographic measurement of systolic time intervals in left ventricular dysfunction: validation with transthoracic echo

Abstract Background Systolic time intervals (STI) measured using Transthoracic Echo (TTE) have been shown to be sensitive indicators of contractile performance, but TTE is limited to a hospital setting. Recent advances in technology have enabled the simultaneous measurement of Electrocardiograms (ECG) and Seismocardiograms (SCG) using wearable devices. However, the use SCG to measure STIs has not been validated in a mobile setting, or on pathological subjects. Purpose To test the validity of an automated algorithm for measuring STIs using a wearable device recording ECG and SCG, in subjects with varying degrees of left ventricular dysfunction. Methods 179 Patients with suspected left ventricular dysfunction were assessed using TTE performed by a Cardiologist. Patients were simultaneously fitted with a novel wearable device worn on a chest strap, recording SCG and single-lead ECG data, while the cardiologist measured the pre-ejection period (PEP) and left ventricular ejection time (LVET) with the patient in a supine position. Of these subjects 29 (16.2%) were diagnosed with Dilated cardiomyopathy (DCM), and 109 (60.8%) had Ischemic Heart disease. The SCG and ECG data recorded on the wearable device was then analysed using a peak-detection algorithm, which detected the Q,R,S points on the ECG, and then determined the 4 most prominent peaks in the SCG signal corresponding to each R-peak. Heart rate, patient's age, gender and SCG time intervals were then used in a DecisionTree algorithm to determine the values of PEP and LVET, which were were then compared against those determined by the cardiologist using TTE. Results The correlation coefficient (r2) between PEP calculated using TTE, and the values obtained from the algorithm analyzing SCG data was 0.92 while the mean error was 7.47%. The r2 between the LVET calculated using the TTE and the algorithm was 0.75, while the mean error was 8.53% (p-value<0.001 for all cases). Results All Subjects With IHD Without IHD With DCM Without DCM Number of Subjects 178 109 69 29 149 PEP (r2) 0.92 0.89 0.94 0.88 0.91 PEP (% age error) 7.47 7.50 7.42 6.99 7.56 LVET (r2) 0.75 0.81 0.66 0.55 0.83 LVET (% age error) 8.53 6.69 11.4 20.42 6.16 DCM, Dilate Cardiomyopathy; IHD, Ischemic Heart Disease. ECG + SCG signals for a DCM patient Conclusion The algorithm-derived STIs measured by SCG correlate well with those measured by TTE across most patient groups, including those with Dilated Cardiomyopathy and Ischemic Heart Disease, opening prospects for continuous remote monitoring of STIs in a mobile setting. Acknowledgement/Funding Fourth Frontier Technologies

Pediatric cochlear implant wear time and early language development

Objectives: The purpose of this study is to analyze spoken language development in the first year of cochlear implant device use for pediatric recipients under five, and to determine the impact of device wear time on receptive and expressive spoken language outcomes using objective datalogging measures.Methods: Retrospective review of 37 children utilizing Cochlear™ processors capable of datalogging, who had completed speech and language testing before implantation and one year post. Hearing Hour Percentage (HHP) was created from datalogging results and two multiple regression analysis were performed including HHP and standard language scores.Results: HHP was found to be a predictor for receptive language outcomes one year after cochlear implant use. HHP was not found to be predictive of expressive language outcomes one year after cochlear implant use.Discussion: Datalogging is a vital objective measure that yields powerful data regarding a child’s device use and early receptive spoken language development.Conclusion: In the first year of cochlear implant use, datalogging using the HHP significantly predicted receptive language scores. Increased HHP yielded higher receptive language standard scores. Continued research is needed to see if HHP is predictive of expressive language outcomes after a longer period of cochlear implant use.

Recognizing Hand Gestures With Pressure-Sensor-Based Motion Sensing.

This paper proposes a novel framework to process pressure signals for real-time and robust gesture recognition, which includes an innovative segmentation scheme, a gesture recognition scheme and a pressure-parameter adaptive updating strategy. A prototype system, including a wearable gesture sensing device with four pressure sensors and the corresponding algorithmic framework, is developed to realize real-time gesture-based interaction. With the device worn on the wrist, the user can interact with the computer using 8 predefined gestures. Experimental results show that the delay of gesture recognition is about 100 ms, with the average accuracy of 95.28% in the experienced-user test and 86.20% in the inexperienced-user test. Finally, the system is evaluated by a mouse-controlling interaction task and performs well. Both experienced and inexperienced people can easily and quickly complete interactive tasks. These results demonstrate that a pressure-sensor based wristband can be used to classify hand gestures well and to control the mouse interaction. This approach provides an interactive way to replace the mouse for decreasing the risk of the carpal tunnel syndrome (CTS).

Elastohydrodynamic lubrication line contact of piezoelectric materials

This paper investigates the elastohydrodynamic lubrication (EHL) line contact between a rigid cylindrical punch and a transversely isotropic piezoelectric half-plane. The punch is supposed to be a perfect conductor, and its electric potential is constant within the contact region. The lubricant is assumed as a kind of Newtonian fluid. The lubricant density and viscosity are considered as the function of the fluid pressure. An iterative method is developed to solve the film thickness equation, Reynolds equation, flow rheology equation and load balance equation simultaneously. The effects of the total electric charge, maximum normal pressure, entraining velocity and material properties on the EHL film thickness and fluid pressure are discussed. Furthermore, the EHL contact of the insulating punch is also discussed to analyze the influences of conductivity of punch and piezoelectric effect of materials. The present results could be applied to improve the wear and contact damage of piezoelectric devices.

Tolerability and acceptability of real-time continuous glucose monitoring and its impact on diabetes management behaviours in individuals with Type 2 Diabetes – A pilot study

IntroductionEmerging evidence suggests us of real-time continuous glucose monitoring systems (RT-CGM), can assist to improve glucose control in Type 2 Diabetes (T2D) treatment, however the impact of these devices on patients’ stress levels and behaviour is poorly understood. This study aimed to examine the effects of RT-CGM on tolerance and acceptability of device wear, stress and diabetes management and motivation to change. MethodsTwenty adults (10 men, 10 women) with T2D (aged 60.6 ± 8.4 years, BMI 34.2 ± 4.7 kg/m2), were randomised to a low-carbohydrate lifestyle plan whilst wearing a RT-CGM or an ‘offline-blinded’ (Blinded-CGM) monitoring system continuously for 12 weeks. Outcomes were glycaemic control (HbA1c), weight (kg) perceived stress scale (PSS), CGM device intolerance, acceptability, motivation to change and diabetes management behaviour questionnaires. ResultsBoth groups experienced significant reductions in body weight (RT-CGM −7.4 ± 4.5 kg vs. Blinded-CGM −5.5 ± 4.0 kg) and HbA1c (−0.67 ± 0.82% vs. −0.68 ± 0.74%). There were no differences between groups for perceived stress (P = 0.47) or device intolerance at week 6 or 12 (both P > 0.30). However, there was evidence of greater acceptance of CGM in the RT-CGM group at week 12 (P = 0.03), improved blood glucose monitoring behaviour in the RT-CGM group at week 6 and week 12 (P ≤ 0.01), and a significant time x group interaction (P = 0.03) demonstrating improved diabetes self-management behaviours in RT-CGM. ConclusionThis study provides preliminary evidence of improved behaviours that accompany RT-CGM in the context of diabetes management and glucose self-monitoring. RT-CGM may provide an alternative approach to glucose management in individuals with T2D without resulting in increased disease distress.

Reporting of Physical Activity Device Measurement and Analysis Protocols in Lifestyle Interventions.

This systematic review examined the extent to which lifestyle physical activity interventions that used wearable devices (eg, pedometers, accelerometers) reported on the length of device wear time requested in their protocols, criteria for analytic inclusion of data, and participant compliance with device use protocols. Literature were searches were conducted using PubMed, Cochrane Central Register, and PsychInfo. Studies were included if they were the main outcomes paper of a trial that reported on a randomized or quasi-randomized trial focused on increasing lifestyle physical activity and were published between January 1, 2006 and March 30, 2016. Titles and abstracts were screened by 2 independent reviewers; eligible full texts were retrieved and reviewed by 2 independent reviewers. A total of 104 studies used wearable devices (n = 57 pedometers, n = 47 accelerometers). Most studies (n = 65, 67.3%) asked participants to wear devices for 7 days. Almost half of the studies (n = 46, 44.2%) did not report minimum device wear time required for analytic inclusion of data, and variation existed among studies reporting these criteria. Most studies (n = 60, 57.7%) did not report average device wear time, or participant compliance with device wear. Overall, there was heterogeneity in reporting of physical activity device data. Refinement and streamlining of guidelines for device use, analysis, and reporting of data could improve comparability across studies.

How to Measure Sedentary Behavior at Work?

Background: Prolonged sedentary behavior (SB) is associated with increased risk for chronic conditions. A growing number of the workforce is employed in office setting with high occupational exposure to SB. There is a new focus in assessing, understanding and reducing SB in the workplace. There are many subjective (questionnaires) and objective methods (monitoring with wearable devices) available to determine SB. Therefore, we aimed to provide a global understanding on methods currently used for SB assessment at work.Methods: We carried out a systematic review on methods to measure SB at work. Pubmed, Cochrane, Embase, and Web of Science were searched for peer-reviewed English-language articles published between 1st January 2000 and 17th March 2019.Results: We included 154 articles: 89 were cross-sectional and 65 were longitudinal studies, for a total of 474,091 participants. SB was assessed by self-reported questionnaires in 91 studies, by wearables devices in also 91 studies, and simultaneously by a questionnaire and wearables devices in 30 studies. Among the 91 studies using wearable devices, 73 studies used only one device, 15 studies used several devices, and three studies used complex physiological systems. Studies exploring SB on a large sample used significantly more only questionnaires and/or one wearable device.Conclusions: Available questionnaires are the most accessible method for studies on large population with a limited budget. For smaller groups, SB at work can be objectively measured with wearable devices (accelerometers, heart-rate monitors, pressure meters, goniometers, electromyography meters, gas-meters) and the results can be associated and compared with a subjective measure (questionnaire). The number of devices worn can increase the accuracy but make the analysis more complex and time consuming.

How Does a Nation Walk?

Activity trackers are being deployed in large-scale physical activity intervention programs, but analyzing their data is difficult due to the large data size and complexity. As such large datasets of steps become more available, it is paramount to develop analysis methods to deeply interpret them to understand the variety and changing nature of human steps behavior. In this work, we explored ways to analyze the heterogeneous steps activity data and propose a framework of dimensions and time aggregations to interpret how providing a city-wide population with activity trackers, and monetary incentives influences their wearing and steps behavior. We analyzed the daily step counts of 140,000 individuals, walking a combined 74 billion steps in 305 days of a city-wide public health campaign. We performed data mining clustering to identify 16 user segments, each with distinctive weekly streaks in patterns of device wear and recorded steps. We demonstrate that these clusters enable us to interpret how some users increased their steps level. Our key contributions are: a new analytic method to scalably interpret large steps data; the insights of our analysis about key user segments in our large intervention; demonstrating the power to predictive user outcomes from their first few days of tracking.

Accuracy of Activity Trackers during Treadmill Walking Versus Outdoor Walking

Establishing the accuracy of devices that measure daily activity is important in controlled lab settings and in real-life settings. PURPOSE: To assess the accuracy of a pedometer and 6 popular activity trackers at measuring steps while walking on a treadmill and walking outside. METHODS: Twenty-three college students (Mean±SD; 22.2±3.9yrs; 24.9±4.1kg/m2, 11 male) walked 500 steps at 3mph on a treadmill while wearing 7 different activity trackers (Pedometer (PED), Blaze (BLA), Charge HR (CHR), Alta (ALT), Flex (FLE), Zip (ZIP), One (ONE)). During a second visit, participants wore the devices while walking 400 meters at 3mph outside. Steps were counted by a trained researcher using a hand tally counter. Mean Absolute Percent Error (MAPE) values were calculated for each device relative to the tally counter and were correlated between each of the devices and the tally counter using Pearson correlations. Significance was set at p<0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. RESULTS: MAPE values were significantly correlated between the treadmill and outdoor protocol for the PED (r=0.634, p<0.001). The remaining devices were not correlated between protocols (p>0.05). The treadmill protocol produced underestimations in step counts for 5 devices (mean bias±SD: PED = -1.4±41.5steps; BLA = -35.3±70.8; CHR = -3.9±51.9; ALT = -86.5±74.4; FLE = -16.9±71.6) and slight overestimations for 2 devices (mean bias±SD: ZIP = 2.1±3.5; ONE = 0.3±2.2). The outdoor protocol produced step count overestimations for all devices. MAPE values were approximately twice as large or greater for all devices in the outdoor protocol compared to the treadmill protocol except for the PED (MAPE±SD: 4.0±7.2 treadmill vs. 3.5±5.2 outdoor). Besides the PED, the ONE was the most accurate during the treadmill protocol (MAPE±SD: 0.3±0.3) and the ZIP was the most accurate during the outdoor protocol (MAPE±SD: 14.7±6.6). CONCLUSION: The step counting devices in this study performed better in the controlled laboratory setting compared to the outdoor setting with a device worn on the waist producing the best results in each trial. These findings indicate that step counts in real-life settings from commercial activity devices may produce significant error.

102-LB: GP-OSMOTIC: An RCT to Determine the Effect of 3-Monthly Retrospective Continuous Glucose Monitoring (rCGM) on 12-Month HbA1c in Adults with Type 2 Diabetes (T2D) in Primary Care

Introduction: Reviewing rCGM patterns may prompt lifestyle or therapeutic changes to achieve glycaemic targets but evidence for rCGM use in primary care management of T2D is limited. Methods: Two-arm RCT. Participants: Adults with T2D, HbA1c ≥0.5% above target, prescribed ≥2 non-insulin glycemia medications or insulin. Intervention: 1-hour diabetes education and (at 0/3/6/9/12 months): HbA1c assessment + wearing FreeStyle Libre Pro (Abbott) for up to 14 days prior to being discussed at a clinic visit. Physicians were trained in interpreting ambulatory glucose profiles. Control: 3-monthly ‘usual care’ clinic visits + r-CGM device worn (blinded, research data) at 0 and 12 months. Primary outcome: difference in mean HbA1c at 12 months. Secondary outcomes: mean differences in time in range (TIR: 4-10 mmol/L) and diabetes-specific distress (PAID) at 12 months, and HbA1c at 6 months. ITT analysis. Results: In 25 primary care practices, participants were: 299 adults with T2D, aged (mean(SD)) 60(10) years, HbA1c: 8.9(1.2)%, diabetes duration (median(IQR)) of 12(8,20) years. At 12 months, the between-group difference in mean HbA1c was 0.2% (p=0.112). The estimated mean percentage TIR was 8.4% higher in the intervention than the control arm (p=0.004). Diabetes-specific distress did not differ between arms (0.5; p=0.71). At 6 months, HbA1c was significantly lower in the intervention arm (0.5%; p&amp;lt;0.001). We found little evidence of changes in the number of glycemic medications in either arm. Discussion: Use of 3-monthly rCGM in adults with T2D in primary care does not improve HbA1c at 12 months. We showed a statistically and clinically significant reduction in HbA1c at 6 months in the intervention group, as well as significant improvements in TIR at 12 months, with no change in diabetes distress. Our findings suggest the primary impact of r-CGM use on HbA1c in this setting is short term, and not associated with increase in the number of diabetes medications. Disclosure J. Furler: Research Support; Self; Abbott, Sanofi. D.N. O’Neal: None. J. Speight: Research Support; Self; AstraZeneca, Medtronic, Sanofi. Speaker’s Bureau; Self; Novo Nordisk A/S, Roche Diabetes Care. J.E. Manski-Nankervis: Research Support; Self; Australian National Health and Medical Research Council, Boehringer Ingelheim International GmbH, Diabetes Australia, Eli Lilly and Company. Speaker’s Bureau; Self; RACGP. S. Thuraisingam: None. E. Holmes-Truscott: Research Support; Self; Sanofi. Speaker’s Bureau; Self; Novo Nordisk Inc. K.R. De La Rue: None. L.E. Ginnivan: None. R.C. Doyle: None. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp &amp; Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp &amp; Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp &amp; Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker’s Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp &amp; Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. M. Catchpool: None. K. Dalziel: None. J.I. Chiang: None. I. Blackberry: None. R. Audehm: Advisory Panel; Self; AstraZeneca, Novartis Pharmaceuticals Corporation. M. Kennedy: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp &amp; Dohme Corp., Novo Nordisk Inc., Sanofi. M.J. Clark: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. A.S. Januszewski: None. D. Liew: Advisory Panel; Self; AstraZeneca, Bayer AG. Research Support; Self; AbbVie Inc., AstraZeneca, Bristol-Myers Squibb Company, CSL Behring, Pfizer Inc. P.M. Clarke: None. J.D. Best: Consultant; Self; Abbott. Funding National Health and Medical Research Council of Australia (APP1104241); Sanofi Australia; Abbott Diabetes Care

Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study.

BackgroundIndividuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends.ObjectiveThe purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram.MethodsThis exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag #dexcom over a 3-month period—December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes.ResultsA total of 2923 photographs were manually screened; 12.08% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7%) of the photographs showed a CGM site in an off-label location, while 26.2% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity.ConclusionsOur results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study.

On Placement, Location and Orientation of Wrist-Worn Tri-Axial Accelerometers during Free-Living Measurements.

Wearable accelerometers have recently become a standalone tool for the objective assessment of physical activity (PA). In free-living studies, accelerometers are placed by protocol on a pre-defined body location (e.g., non-dominant wrist). However, the protocol is not always followed, e.g., the sensor can be moved between wrists or reattached in a different orientation. Such protocol violations often result in PA miscalculation. We propose an approach, PLOE (“Placement, Location and Orientation Evaluation method”), to determine the sensor position using statistical features from the raw accelerometer measurements. We compare the estimated position with the study protocol and identify discrepancies. We apply PLOE to the measurements collected from 45 older adults who wore ActiGraph GT3X+ accelerometers on the left and right wrist for seven days. We found that 15.6% of participants who wore accelerometers violated the protocol for one or more days. The sensors were worn on the wrong hand during 6.9% of the days of simultaneous wearing of devices. During the periods of discrepancies, the daily PA was miscalculated by more than 20%. Our findings show that correct placement of the device has a significant effect on the PA estimates. These results demonstrate a need for the evaluation of sensor position.

The Level of Compliance With the Use of Personal Radiation Monitoring Devices by Qualified Radiographers at Provincial Hospitals in the Tshwane District Area.

When researchers accompanied radiography students during their work integrated learning, it was observed that there is non-compliance with regards the use of personal radiation monitoring devices by radiographers in the Radiology Departments at Provincial hospitals in the Tshwane district area. The aim of this research study was to identify the level of compliance with the use of radiation monitoring devices by radiographers. A quantitative descriptive study method was used, and the data collection tool was questionnaires that were hand delivered to all settings. The sample size of radiographers was 96, and consent was received from 61 participants. The data was collated in a Microsoft Excel® spreadsheet, and the variables were statistically analyzed for frequencies and percentages. The Fisher's exact test was used for association to answer the level of compliance and management of the radiation monitoring device. Results showed compliance of radiographers in the wearing of radiation monitoring devices but inconsistency as to where the radiation monitoring device should be worn. A lack of awareness about policies from the Radiation Board was also noted.

Concurrent validity of Nokia Go activity tracker in walking and free-living conditions.

Consumer-based activity trackers aim at quantifying physical activity in a wide range of contexts. Nevertheless, they need to be validated before they are confidently used. This study assessed the concurrent validity of the Nokia Go against reference devices, according to different sensor locations, in two measurement conditions: during a walking task and during a 24-hour free-living condition. We examined the agreement between devices and between locations in the number of steps and total sleep time by using intraclass correlation coefficient and Bland-Altman method. In the walking task, the agreement is good to excellent for steps between the Nokia Go and the reference device. In the free-living condition, there is a systematic underestimation of steps in comparison with the ActiGraph. Excellent agreement was found between locations. The device worn at the hip indicated the lowest number of steps, and the device located at the dominant wrist indicated the greatest number of steps. There are high discrepancies in step count between devices because of the different types of activities in daily life. The Nokia Go may be confidently used for step counting during pure walking tasks, at different locations. However, the lack of concurrent validity with ActiGraph call for caution regarding their use in daily living conditions.

Impact of professional continuous glucose monitoring by clinical pharmacists in an ambulatory care setting

IntroductionUse of continuous glucose monitoring (CGM) in the management of diabetes continues to grow. Despite professional‐use CGM serving as an opportunity for clinical pharmacists to improve care and generate revenue for managing patients with diabetes, there is limited literature describing their involvement with this technology.ObjectivesThe primary objective was to determine if professional CGM improves measures of glycemic control including percentage of time in target glycemic range, change in estimated average interstitial glucose, and change in hemoglobin A1c (HbA1c) from baseline to postintervention. Secondary objectives were to evaluate revenue generation measured by reimbursement rates for Current Procedural Terminology (CPT) codes 95250 and 95251 and utilization of clinical pharmacist services related to professional CGM.MethodsThis was a quasi‐experimental, retrospective, pre‐post intervention analysis. Patients that had a professional CGM placed and more than 24 hours of data interpreted by a clinical pharmacist were included for analysis. All clinical data were extracted from patients' electronic medical records, while reimbursement data were provided by the organization's billing department.ResultsTwenty‐nine patients that received professional CGM were included for analysis. Patients' mean baseline and post‐intervention HbA1c were 9.0% and 8.3%, respectively (P = 0.156). There was no difference in the mean percentage of time in target glycemic range (P = 0.966) or mean estimated average interstitial glucose (P = 0.779) from baseline to post‐intervention. Patients met with clinical pharmacists for a total of 68 follow‐up visits during their individual 14‐day wear periods, 14 of which were unanticipated visits. The mean payment amount for CPT code 95250 was $126.87, while $39.17 was received for 95251.ConclusionsClinical pharmacist‐led professional CGM did not demonstrate a statistically significant difference in measures of glycemic control; however, the 14‐day device wear period provided opportunities for optimization of antihyperglycemic therapy that resulted in a clinically significant reduction in HbA1c and reimbursement for clinical pharmacy services.

Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X+

BackgroundWrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting.MethodsSixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings.ResultsMean wear time was over 1420 min per day for both devices on all days. Lin’s concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were − 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were − 17.5 ± 10% in Troiano and − 0.8 ± 4.6% in Choi.ConclusionsHip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level.

Real-time Atrial Fibrillation Detection Using Artificial Neural Network on a Wearable Electrocardiogram

Providing equal healthcare quality on heart diseases are an issue in developing countries, especially in Indonesia, due to is wide-spread areas. It is founded that the heart diseases occur not only in big cities but also in rural areas, that is caused by unhealthy lifestyle and foods. Heart disease itself is a disease with gradually symptoms changes that can be seen based on the hearts' electrical activity or electrocardiogram signals. Now, wearable medical devices are capable to be worn daily, so that, it can monitor our heart condition and alert if there is an abnormality. An embedded device worn on the chest can be used to perform a real-time data acquisition and processing of the electrocardiogram, that consists of a 1-lead ECG, an ARM processor, a Bluetooth module, an SD card, and rechargeable batteries. Also, by performing a digital filter and Tompkins algorithm, we obtain the P-wave presences and the heart rate variability values (heartbeat, average heartbeat, standard deviation, and root mean square) then by using an artificial neural network with 4 input, 6 hidden, and 1 output layers that has multi-layer perceptrons and backpropagation. We are able to perform a pre-diagnosis of atrial fibrillation, that is one of the common arrhythmias, from 41 recorded training samples (Physionet MIT/BIH AFDB and NSRDB) and 6 healthy subjects as test samples. The neural network has 0.1% error rate and needed 31548 epochs to train itself for classification the heart disease. Based on the results, this prototype can be used as a medical-grade wearable device thatcan help cardiologist in giving an early warning on the user's heart condition, so that it can prevent sudden death due to heart diseases in rural areas.

Abstract TP173: Feasibility and Utility of Home-Based Gait Analysis Using Body-Worn Sensors

Background: Gait impairment is one of the most common post-stroke deficits and an important rehabilitation target for patients. Standardized gait assessments typically capture a brief snapshot of a patient’s gait in the clinical setting, but do not provide information about the trajectory of gait recovery in daily life. We assessed the feasibility of home-based gait analysis in patients with stroke-related gait impairment using the Sensoplex “Rover”, a small device worn on the ankles that measures leg movement. Methodology: Thirty-four chronic stroke patients were included in a prospective cohort study. Patients could elect 1 or 4 weeks of gait monitoring and were instructed to wear one device on each ankle during waking hours in this period. Patients completed a timed 10-meter walk test (10m) at the start of the monitoring period. Performance on the 10m was correlated with average gait speed at home assessed with the Rover during the entire monitoring duration. Patients completed a user experience survey at the end of the study. Results: Reliable gait monitoring data for at least 1 day was obtained in 22 of the 34 patients. In this group, there was a good correlation between walking speed measured at home and in clinic (R 2 =0.58), but walking speed measured at home was slower than in clinic (p&lt;0.0001; figure). Sixteen out of 34 patients (47%) wore the device for the entire chosen monitoring duration. This compliance was higher for patients who chose to wear the device for only 1 week (13 of 19; 68%) compared to patients who chose 4 weeks (3 of 15; 20%). On the user survey, 45% of patients reported having trouble donning/doffing the device, 60% wanted a slimmer design, and 60% wanted a mobile app to track progress. Conclusion: Home-based gait monitoring with the Rover device is feasible and correlates well with clinic-based assessments. Design improvements are needed to increase compliance and make home-based gait monitoring a useful additional outcome measure in studies of stroke recovery.

Wrapped Up: Talismanic Garments in Early Modern Islamic Culture

This study examines clothing inscribed with religious scripture and mystic symbols as a talisman in Early Modern Islamic culture. The objects include extant examples of talismanic clothing from Iran, Turkey, and India created between the late fifteenth century and mid-eighteenth centuries. The premise for scripture as talisman in Islam, the relationship between text and textile, and pre-Islamic relationships to cloth and spirituality in these regions are included in the discussion. Though recent scholarship accepts these textiles as protective devices worn on the battlefield, this study suggests several additional contexts for the use of these talismanic garments. In addition, Qur’an-inscribed garments that emerge as a phenomenon in the Early Modern period are examined in relation to the origin of talismanic clothing in the belief systems that preceded the Muslim era. Specifically addressing the traditions of Zoroastrian, Hindu, and Shamanistic Turkic religious practices, the relationship between cloth, thread, and sacred teachings is explored in relation to the later empires that emerged from these regions. Specific verses from the Qur’an are included as an analytic device for understanding the Islamic perception of textiles and garments on a literal and metaphorical level, as well as spiritual and political practices involving cloth and power.

SU29 - GENETIC DISSECTION OF SLEEP DISTURBANCE IN BIPOLAR DISORDER

Background Bipolar Disorder (BD) is often associated with sleep disturbances such as insomnia or excessive sleep, even when mood is stable. Sleep loss may trigger the first episode of BD or subsequent episodes. Sleep disturbances have attracted much attention in the field of BD research, but also in clinical practice, demonstrated in the recent development of therapies manipulating sleep-wake rhythms. Sleep has been proposed as a biomarker of BD and its disturbance has been implicated in the biology of mood disorders. However, the mechanisms that link mood and sleep are not fully understood. For example, sleep loss may be a cause for BD, but the converse is also true. Alternatively, sleep disorders and BD may co-occur because they have risk factors in common, with no strong causal relationship between them. Genomics has already provided important insights into the relationship between psychiatric disorders, and may help disentangle the intricate relationship between sleep and mood. Methods This research project will take advantage of an existing dataset, the Bipolar Disorder Research Network (BDRN) collection of clinical and genetic data (available for over 5700 participants with BD) to evaluate: (1) the methods researchers and clinicians use to measure sleep in BD, (2) whether sleep characteristics can help identify biologically valid subgroups of individuals with BD, using genomic approaches.We will evaluate a range of sleep measures, from low intensity, easy to fill, one-off questionnaires to more intensive daily sleep and mood diaries. We will also include objective measures gathered through actigraphs. Actigraphs are devices worn on the wrist that record movements and can be used to estimate sleep characteristics. For each person with BD, we will calculate scores for their genetic risk for sleep disturbances based on the findings of large genetic studies of sleep in the UK general population. We will then compare the scores in individuals with BD with and without sleep disturbances to test the underpinning biological validity of measured sleep disturbance in BD. Results Participants (N=5,728) have been (and continue to be) recruited across the United Kingdom. To date, 5,549 participants have been genotyped using the Affymetrix (n=1,868), PsychChip (n=1,104) and Omni-Express (n=2,577) chips. All are at least 18 years old, provide written informed consent, and have a DSM-IV diagnosis of BD (I, II, not otherwise specified) or schizoaffective disorder, bipolar subtype. To date, over 1,500 individuals with BD have completed questionnaires on circadian preference and sleep disorders. Over 100 individuals with BD have completed 6-weeks of high-intensity sleep and mood monitoring. Preliminary analyses on 36 individuals with BD have shown moderate agreement (0.40 | rs | 0.59) between the low-intensity measures (e.g. sleep questions of the Beck Depression Inventory) and high-intensity measures such as actigraphy parameters. Discussion The proposed research will be the first to systematically evaluate a range of sleep measures in the largest study of individuals with BD in the world to date. The results will inform the choice and use of sleep measures in large-scale BD studies. The genetic analyses planned are well powered to provide insights into the mood-sleep relationship, and might pave the way to personalized diagnosis and treatment.