Literature DB >> 21989632

The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

Bruce H Dobkin1, Andrew Dorsch.   

Abstract

Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.

Entities:  

Mesh:

Year:  2011        PMID: 21989632      PMCID: PMC4098920          DOI: 10.1177/1545968311425908

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  133 in total

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Journal:  Gait Posture       Date:  2006-11-13       Impact factor: 2.840

2.  Context-aware sensing of physiological signals.

Authors:  Winston H Wu; Maxim A Batalin; Lawrence K Au; Alex A T Bui; William J Kaiser
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3.  Ambulatory activity of stroke survivors: measurement options for dose, intensity, and variability of activity.

Authors:  Patricia J Manns; Evan Baldwin
Journal:  Stroke       Date:  2009-01-15       Impact factor: 7.914

4.  Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study.

Authors:  Rebecca L Birkenmeier; Eliza M Prager; Catherine E Lang
Journal:  Neurorehabil Neural Repair       Date:  2010-04-27       Impact factor: 3.919

5.  Effectiveness of modified constraint-induced movement therapy in children with unilateral spastic cerebral palsy: a randomized controlled trial.

Authors:  Pauline B Aarts; Peter H Jongerius; Yvonne A Geerdink; Jacques van Limbeek; Alexander C Geurts
Journal:  Neurorehabil Neural Repair       Date:  2010-04-27       Impact factor: 3.919

Review 6.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

7.  Task-oriented biofeedback to improve gait in individuals with chronic stroke: motor learning approach.

Authors:  Johanna Jonsdottir; Davide Cattaneo; Mauro Recalcati; Alberto Regola; Marco Rabuffetti; Maurizio Ferrarin; Anna Casiraghi
Journal:  Neurorehabil Neural Repair       Date:  2010-01-06       Impact factor: 3.919

8.  The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

Authors:  Si-Woon Park; Steven L Wolf; Sarah Blanton; Carolee Winstein; Deborah S Nichols-Larsen
Journal:  Neurorehabil Neural Repair       Date:  2008 Sep-Oct       Impact factor: 3.919

9.  Reinvestment and falls in community-dwelling older adults.

Authors:  W L Wong; R S W Masters; J P Maxwell; A B Abernethy
Journal:  Neurorehabil Neural Repair       Date:  2008-03-11       Impact factor: 3.919

10.  Influence of speed on walking economy poststroke.

Authors:  Darcy S Reisman; Katherine S Rudolph; William B Farquhar
Journal:  Neurorehabil Neural Repair       Date:  2009-01-06       Impact factor: 3.919

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  89 in total

Review 1.  The Specific Requirements of Neural Repair Trials for Stroke.

Authors:  Bruce H Dobkin; S Thomas Carmichael
Journal:  Neurorehabil Neural Repair       Date:  2015-09-10       Impact factor: 3.919

2.  Identifying activity levels and steps of people with stroke using a novel shoe-based sensor.

Authors:  George D Fulk; S Ryan Edgar; Rebecca Bierwirth; Phil Hart; Paulo Lopez-Meyer; Edward Sazonov
Journal:  J Neurol Phys Ther       Date:  2012-06       Impact factor: 3.649

3.  SIRRACT: An International Randomized Clinical Trial of Activity Feedback During Inpatient Stroke Rehabilitation Enabled by Wireless Sensing.

Authors:  Andrew K Dorsch; Seth Thomas; Xiaoyu Xu; William Kaiser; Bruce H Dobkin
Journal:  Neurorehabil Neural Repair       Date:  2014-09-26       Impact factor: 3.919

Review 4.  Physical activity and type 1 diabetes: time for a rewire?

Authors:  Sheri R Colberg; Remmert Laan; Eyal Dassau; David Kerr
Journal:  J Diabetes Sci Technol       Date:  2015-01-06

5.  Beyond acceptability and feasibility: moving mHealth into impact.

Authors:  Sheana Bull
Journal:  Mhealth       Date:  2016-12-19

Review 6.  Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice.

Authors:  Bruce H Dobkin; Clarisa Martinez
Journal:  Curr Neurol Neurosci Rep       Date:  2018-10-06       Impact factor: 5.081

Review 7.  Functional recovery following stroke: capturing changes in upper-extremity function.

Authors:  Lisa A Simpson; Janice J Eng
Journal:  Neurorehabil Neural Repair       Date:  2012-10-16       Impact factor: 3.919

Review 8.  "Big data" and the electronic health record.

Authors:  M K Ross; W Wei; L Ohno-Machado
Journal:  Yearb Med Inform       Date:  2014-08-15

9.  Prediction of responders for outcome measures of locomotor Experience Applied Post Stroke trial.

Authors:  Bruce H K Dobkin; Stephen E Nadeau; Andrea L Behrman; Samuel S Wu; Dorian K Rose; Mark Bowden; Stephanie Studenski; Xiaomin Lu; Pamela W Duncan
Journal:  J Rehabil Res Dev       Date:  2014

10.  Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice.

Authors:  Jenna Blumenthal; Andrea Wilkinson; Mark Chignell
Journal:  Physiother Can       Date:  2018       Impact factor: 1.037

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