A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease
Identifieur interne : 000128 ( Main/Exploration ); précédent : 000127; suivant : 000129A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease
Auteurs : Taylor Chomiak ; Alexander Watts ; Nicole Meyer ; Fernando V. Pereira ; Bin HuSource :
- Medicine [ 0025-7974 ] ; 2017.
English descriptors
- KwdEn :
- MESH :
- rehabilitation : Parkinson Disease.
- Aged, Algorithms, Attention, Female, Humans, Male, Middle Aged, Music, Physical Therapy Modalities, Pilot Projects, Prospective Studies, Psychomotor Performance, Smartphone, Walking.
Abstract
Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT).
This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated.
While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01).
Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.
Url:
DOI: 10.1097/MD.0000000000005934
PubMed: 28151878
PubMed Central: 5293441
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease</title><author><name sortKey="Chomiak, Taylor" sort="Chomiak, Taylor" uniqKey="Chomiak T" first="Taylor" last="Chomiak">Taylor Chomiak</name></author><author><name sortKey="Watts, Alexander" sort="Watts, Alexander" uniqKey="Watts A" first="Alexander" last="Watts">Alexander Watts</name></author><author><name sortKey="Meyer, Nicole" sort="Meyer, Nicole" uniqKey="Meyer N" first="Nicole" last="Meyer">Nicole Meyer</name></author><author><name sortKey="Pereira, Fernando V" sort="Pereira, Fernando V" uniqKey="Pereira F" first="Fernando V." last="Pereira">Fernando V. 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Pereira</name></author><author><name sortKey="Hu, Bin" sort="Hu, Bin" uniqKey="Hu B" first="Bin" last="Hu">Bin Hu</name></author></analytic><series><title level="j">Medicine</title><idno type="ISSN">0025-7974</idno><idno type="eISSN">1536-5964</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Attention</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Music</term><term>Parkinson Disease (rehabilitation)</term><term>Physical Therapy Modalities</term><term>Pilot Projects</term><term>Prospective Studies</term><term>Psychomotor Performance</term><term>Smartphone</term><term>Walking</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Attention</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Music</term><term>Physical Therapy Modalities</term><term>Pilot Projects</term><term>Prospective Studies</term><term>Psychomotor Performance</term><term>Smartphone</term><term>Walking</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><sec sec-type="background"><title>Background:</title><p>Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT).</p></sec><sec sec-type="methods"><title>Methods:</title><p>This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated.</p></sec><sec sec-type="results"><title>Results:</title><p>While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01).</p></sec><sec sec-type="conclusion"><title>Conclusion:</title><p>Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Kelly, Ve" uniqKey="Kelly V">VE Kelly</name></author><author><name sortKey="Eusterbrock, Aj" uniqKey="Eusterbrock A">AJ Eusterbrock</name></author><author><name sortKey="Shumway Cook, A" uniqKey="Shumway Cook A">A Shumway-Cook</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Holtzer, R" uniqKey="Holtzer R">R Holtzer</name></author><author><name sortKey="Wang, C" uniqKey="Wang C">C Wang</name></author><author><name sortKey="Verghese, J" uniqKey="Verghese J">J Verghese</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yogev Seligmann, G" uniqKey="Yogev Seligmann G">G Yogev-Seligmann</name></author><author><name sortKey="Hausdorff, Jm" uniqKey="Hausdorff J">JM 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uniqKey="Chomiak T" first="Taylor" last="Chomiak">Taylor Chomiak</name><name sortKey="Hu, Bin" sort="Hu, Bin" uniqKey="Hu B" first="Bin" last="Hu">Bin Hu</name><name sortKey="Meyer, Nicole" sort="Meyer, Nicole" uniqKey="Meyer N" first="Nicole" last="Meyer">Nicole Meyer</name><name sortKey="Pereira, Fernando V" sort="Pereira, Fernando V" uniqKey="Pereira F" first="Fernando V." last="Pereira">Fernando V. 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