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      Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

      research-article
      , PhD 1 , , PhD 1 , , PhD 1 , , PhD 2 , , BEng, PhD 3 , , MA 1 , , PhD 1 , , Dr med 4 , , Dr med 4 , , Dr med, Prof Dr 1 , 4 , , Dr med, Prof Dr 4 , , MD, MHS 1 , , PhD 1 , , MD, PhD 5 , , PhD 1 ,
      (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      gait, walking speed, mobility limitation, accelerometry, clinical trials, frailty, wearable electronic devices, algorithms, open source data, data collection, dataset

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          Abstract

          Background

          Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches.

          Objective

          This study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community.

          Methods

          Data described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits.

          Results

          We have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively).

          Conclusions

          This study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence.

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          Most cited references21

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          Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

          Background Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. Methods We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. Results We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. Conclusions New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.
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            Treatment of Sarcopenia with Bimagrumab: Results from a Phase II, Randomized, Controlled, Proof-of-Concept Study.

            To assess the effects of bimagrumab on skeletal muscle mass and function in older adults with sarcopenia and mobility limitations.
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              • Article: not found

              Short-Physical Performance Battery (SPPB) score is associated with falls in older outpatients

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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                November 2019
                27 November 2019
                : 7
                : 11
                : e15191
                Affiliations
                [1 ] Novartis Institutes for BioMedical Research Basel Switzerland
                [2 ] Biostatistics and Pharmacometrics Novartis Pharmaceuticals Corporation East Hannover, NJ United States
                [3 ] Novartis Business Services Novartis Ireland Ltd Dublin Ireland
                [4 ] University Hospital Ludwigs-Maximillians Universität Munich Germany
                [5 ] Novartis Institutes for BioMedical Research Cambridge, MA United States
                Author notes
                Corresponding Author: Ieuan Clay ieuan.clay@ 123456novartis.com
                Author information
                https://orcid.org/0000-0001-6551-2283
                https://orcid.org/0000-0003-1209-8634
                https://orcid.org/0000-0002-8227-4149
                https://orcid.org/0000-0001-5122-7190
                https://orcid.org/0000-0001-8787-6889
                https://orcid.org/0000-0002-0718-8910
                https://orcid.org/0000-0002-8346-3654
                https://orcid.org/0000-0001-5529-1632
                https://orcid.org/0000-0002-1800-5278
                https://orcid.org/0000-0002-1230-3653
                https://orcid.org/0000-0003-0554-0201
                https://orcid.org/0000-0002-3959-3179
                https://orcid.org/0000-0002-3398-8225
                https://orcid.org/0000-0002-1121-3517
                https://orcid.org/0000-0001-9722-8834
                Article
                v7i11e15191
                10.2196/15191
                6906618
                31774406
                725446a2-4a0d-4764-bdd0-09f345029609
                ©Arne Mueller, Holger Alfons Hoefling, Amir Muaremi, Jens Praestgaard, Lorcan C. Walsh, Ola Bunte, Roland Martin Huber, Julian  Fürmetz, Alexander Martin Keppler, Matthias Schieker, Wolfgang Böcker, Ronenn Roubenoff, Sophie Brachat, Daniel S. Rooks, Ieuan Clay. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 27.11.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 26 June 2019
                : 31 August 2019
                : 9 September 2019
                : 24 September 2019
                Categories
                Original Paper
                Original Paper

                gait,walking speed,mobility limitation,accelerometry,clinical trials,frailty,wearable electronic devices,algorithms,open source data,data collection,dataset

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