Does feedback on daily activity level from a Smart watch during inpatient stroke rehabilitation increase physical activity levels? Study protocol for a randomized controlled trial

Dong, Yi; Steins, Dax; Sun, Shibo; Li, Fēi; Amor, James D.; James, Christopher J.; Xia, Zhidao; Dawes, Helen; Izadi, Hooshang; Yi, Cao; Wade, Derick T

doi:10.1186/s13063-018-2476-z

Cited by 10 publications

(3 citation statements)

References 27 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Focusing next on physical activity monitoring, from Table 7, 85% of the research articles have used accelerometers as sensors. In this set, except for four articles, 54–57 all other articles used accelerometers placed at the lower extremities of the body. The step count, the monitoring of sedentary activity, and activity recognition were the most important parameters measured.…”

Section: Discussionmentioning

confidence: 99%

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

Mathunny

Karthik

Devaraj

et al. 2023

Proc Inst Mech Eng H

View full text Add to dashboard Cite

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.

show abstract

Section: Discussionmentioning

confidence: 99%

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

Mathunny

Karthik

Devaraj

et al. 2023

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…Then, specific protocols (standard operating procedures) were written, ethical applications sought in local institutional settings, and a 2-week formal training in the execution and governance of the protocols, procedures for data collection and recruitment was given to the ward staff and the research assessors. The full protocol can be accessed online [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a smartwatch-based intervention providing feedback of daily activity within a research-naive stroke ward: a pilot randomised controlled trial

Lawrie

Dong

Steins

et al. 2018

Pilot Feasibility Stud

Self Cite

View full text Add to dashboard Cite

BackgroundThe majority of stroke patients are inactive outside formal therapy sessions. Tailored activity feedback via a smartwatch has the potential to increase inpatient activity. The aim of the study was to identify the challenges and support needed by ward staff and researchers and to examine the feasibility of conducting a randomised controlled trial (RCT) using smartwatch activity monitors in research-naive rehabilitation wards. Objectives (Phase 1 and 2) were to report any challenges and support needed and determine the recruitment and retention rate, completion of outcome measures, smartwatch adherence rate, (Phase 2 only) readiness to randomise, adherence to protocol (intervention fidelity) and potential for effect.MethodsFirst admission, stroke patients (onset < 4 months) aged 40–75, able to walk 10 m prior to stroke and follow a two-stage command with sufficient cognition and vision (clinically judged) were recruited within the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine. Phase 1: a non-randomised observation phase (to allow practice of protocol)—patients received no activity feedback. Phase 2: a parallel single-blind pilot RCT. Patients were randomised into one of two groups: to receive daily activity feedback over a 9-h period or to receive no activity feedback. EQ-5D-5L, WHODAS and RMI were conducted at baseline, discharge and 3 months post-discharge. Descriptive statistics were performed on recruitment, retention, completion and activity counts as well as adherence to protocol.ResultsOut of 470 ward admissions, 11% were recruited across the two phases, over a 30-week period. Retention rate at 3 months post-discharge was 48%. Twenty-two percent of patients dropped out post-baseline assessment, 78% completed baseline and discharge admissions, from which 62% were assessed 3 months post-discharge. Smartwatch data were received from all patients. Patients were correctly randomised into each RCT group. RCT adherence rate to wearing the smartwatch was 80%. Baseline activity was exceeded for 65% of days in the feedback group compared to 55% of days in the no feedback group.ConclusionsDelivery of a smartwatch RCT is feasible in a research-naive rehabilitation ward. However, frequent support and guidance of research-naive staff are required to ensure completeness of clinical assessment data and protocol adherence.Trials registrationClinicalTrials.gov Identifier, NCT02587585–30th September 2015Electronic supplementary materialThe online version of this article (10.1186/s40814-018-0345-x) contains supplementary material, which is available to authorized users.

show abstract

“…The definition of ''smart city'' comes from the vision of ''smart earth'' [15]. ''Smart Earth'' actually refers to the full use of IT technology in various industries, to embed and equip sensors in hospitals, power grids, railways, bridges, tunnels, roads, buildings and other objects in every corner of the world.…”

Section: Overview Of Smart City Technologymentioning

confidence: 99%

Research on Resource Allocation Optimization of Smart City Based on Big Data

2020

View full text Add to dashboard Cite

The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation.

show abstract

Does feedback on daily activity level from a Smart watch during inpatient stroke rehabilitation increase physical activity levels? Study protocol for a randomized controlled trial

Cited by 10 publications

References 27 publications

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

Evaluation of a smartwatch-based intervention providing feedback of daily activity within a research-naive stroke ward: a pilot randomised controlled trial

Research on Resource Allocation Optimization of Smart City Based on Big Data

Contact Info

Product

Resources

About