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Monitoring training activity during gait-related balance exercise in individuals with Parkinson’s disease: a proof-of-concept-study

Overview of attention for article published in BMC Neurology, January 2017
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Title
Monitoring training activity during gait-related balance exercise in individuals with Parkinson’s disease: a proof-of-concept-study
Published in
BMC Neurology, January 2017
DOI 10.1186/s12883-017-0804-7
Pubmed ID
Authors

David Conradsson, Håkan Nero, Niklas Löfgren, Maria Hagströmer, Erika Franzén

Abstract

Despite the benefits of balance exercise in clinical populations, balance training programs tend to be poorly described, which in turn makes it difficult to evaluate important training components and compare between programs. However, the use of wearable sensors may have the potential to monitor certain elements of balance training. Therefore, this study aimed to investigate the feasibility of using wearable sensors to provide objective indicators of the levels and progression of training activity during gait-related balance exercise in individuals with Parkinson's disease. Ten individuals with Parkinson's disease participated in 10 weeks of group training (three sessions/week) addressing highly-challenging balance exercises. The training program was designed to be progressive by gradually increasing the amount of gait-related balance exercise exercises (e.g. walking) and time spent dual-tasking throughout the intervention period. Accelerometers (Actigraph GT3X+) were used to measure volume (number of steps/session) and intensity (time spent walking >1.0 m/s) of dynamic training activity. Training activity was also expressed in relation to the participants' total daily volume of physical activity prior to the training period (i.e. number of steps during training/the number of steps per day). Feasibility encompassed the adequacy of data sampling, the output of accelerometer data and the participants' perception of the level of difficulty of training. Training activity data were successfully obtained in 98% of the training sessions (n = 256) and data sampling did not interfere with training. Reflecting the progressive features of this intervention, training activity increased throughout the program, and corresponded to a high level of the participants' daily activity (28-43%). In line with the accelerometer data, a majority of the participants (n = 8) perceived the training as challenging. The findings of this proof-of-concept study support the feasibility of applying wearable sensors in clinical settings to gain objective informative measures of gait-related balance exercise in individuals with Parkinson's disease. Still, this activity monitoring approach needs to be further validated in other populations and programs including gait-related balance exercises. NCT01417598 , 15th August 2011.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 282 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 282 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 45 16%
Student > Master 40 14%
Student > Ph. D. Student 28 10%
Student > Doctoral Student 16 6%
Student > Postgraduate 15 5%
Other 50 18%
Unknown 88 31%
Readers by discipline Count As %
Nursing and Health Professions 57 20%
Medicine and Dentistry 32 11%
Sports and Recreations 21 7%
Neuroscience 18 6%
Engineering 14 5%
Other 43 15%
Unknown 97 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2017.
All research outputs
#20,400,885
of 22,950,943 outputs
Outputs from BMC Neurology
#2,156
of 2,453 outputs
Outputs of similar age
#355,962
of 420,224 outputs
Outputs of similar age from BMC Neurology
#29
of 34 outputs
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