Digital health technology and diabetes management

Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients

BACKGROUND

Deficits in gait and balance are common among neurological inpatients. Currently, assessment of these patients is mainly subjective. New assessment options using wearables may provide complementary and more objective information.

METHODS

In this prospective cross-sectional feasibility study performed over a four-month period, all patients referred to a normal neurology ward of a university hospital and aged between 40 and 89 years were asked to participate. Gait and balance deficits were assessed with wearables at the ankles and the lower back. Frailty, sarcopenia, Parkinsonism, depression, quality of life, fall history, fear of falling, physical activity, and cognition were evaluated with questionnaires and surveys.

RESULTS

Eighty-two percent (n = 384) of all eligible patients participated. Of those, 39% (n = 151) had no gait and balance deficit, 21% (n = 79) had gait deficits, 11% (n = 44) had balance deficits and 29% (n = 110) had gait and balance deficits. Parkinson's disease, stroke, epilepsy, pain syndromes, and multiple sclerosis were the most common diseases. The assessment was well accepted.

CONCLUSIONS

Our study suggests that the use of wearables for the assessment of gait and balance features in a clinical setting is feasible. Moreover, preliminary results confirm previous epidemiological data about gait and balance deficits among neurological inpatients. Evaluation of neurological inpatients with novel wearable technology opens new opportunities for the assessment of predictive, progression and treatment response markers.

Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment

Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called "wearables") are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90-180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001). Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.

A Diabetes Mobile App With In-App Coaching From a Certified Diabetes Educator Reduces A1C for Individuals With Type 2 Diabetes

Purpose There are currently many diabetes apps available, but there is limited evidence demonstrating clinical impact. The purpose of this study is to evaluate the impact of a diabetes mobile app with in-app coaching by a certified diabetes educator on glycemic control for individuals with type 2 diabetes. Methods A 12 week-long single-arm intent-to-treat trial evaluated the impact of a diabetes mobile app and coaching program (One Drop | Mobile With One Drop | Experts), which facilitated tracking of self-care and included an in-app diabetes education program, on A1C for individuals with type 2 diabetes and an A1C ≥7.5% (58 mmol/mol). An online study platform (Achievement Studies, Evidation Health Inc, San Mateo, CA) was used to screen, consent, and enroll participants; collect study data; and track participants' progress throughout the study. Baseline and study end A1C measurements as well as questionnaire data from participants were collected. Results Participants (n = 146) were 52 ± 9 years old, 71% female, 25% black or Hispanic, diagnosed with diabetes for 11 ± 7 years, and with a mean baseline A1C of 9.87% ± 2.0 (84 mmol/mol). In adjusted repeated measures models, mean A1C improved by -0.86% among study completers (n = 127), -0.96% among active users of the app and coaching program (n = 93), and -1.32% among active users with a baseline A1C ≥9.0% (75 mmol/mol) (n = 53). Conclusions This program was associated with a clinically meaningful and significant reduction in A1C and can potentially increase access to effective diabetes self-management education and support for individuals with diabetes.