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      Smartphone-Based Physical Activity Telecoaching in Chronic Obstructive Pulmonary Disease: Mixed-Methods Study on Patient Experiences and Lessons for Implementation

      research-article
      , MSc 1 , 2 , 3 , , MD, PhD 4 , , PhD 1 , 2 , 5 , , PhD 1 , 6 , , MSc 7 , , BSc 4 , , PhD 8 , , PhD 9 , , PhD 5 , 10 , 11 , , MSc 1 , 2 , , BSc 7 , , MD, PhD 7 , 8 , , MSc 8 , , MSc 5 , 10 , 11 , , PhD 6 , 12 , , MD, PhD 5 , 10 , 11 , , MD, PhD 7 , , PhD 1 , 2 ,
      (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      physical activity, COPD, telemedicine, smartphone, patient adherence, patient satisfaction, outcome and process assessment (health care)

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          Abstract

          Background

          Telecoaching approaches can enhance physical activity (PA) in patients with chronic obstructive pulmonary disease (COPD). However, their effectiveness is likely to be influenced by intervention-specific characteristics.

          Objective

          This study aimed to assess the acceptability, actual usage, and feasibility of a complex PA telecoaching intervention from both patient and coach perspectives and link these to the effectiveness of the intervention.

          Methods

          We conducted a mixed-methods study based on the completers of the intervention group (N=159) included in an (effective) 12-week PA telecoaching intervention. This semiautomated telecoaching intervention consisted of a step counter and a smartphone app. Data from a project-tailored questionnaire (quantitative data) were combined with data from patient interviews and a coach focus group (qualitative data) to investigate patient and coach acceptability, actual usage, and feasibility of the intervention. The degree of actual usage of the smartphone and step counter was also derived from app data. Both actual usage and perception of feasibility were linked to objectively measured change in PA.

          Results

          The intervention was well accepted and perceived as feasible by all coaches present in the focus group as well by patients, with 89.3% (142/159) of patients indicating that they enjoyed taking part. Only a minority of patients (8.2%; 13/159) reported that they found it difficult to use the smartphone. Actual usage of the step counter was excellent, with patients wearing it for a median (25th-75th percentiles) of 6.3 (5.8-6.8) days per week, which did not change over time ( P=.98). The smartphone interface was used less frequently and actual usage of all daily tasks decreased significantly over time ( P<.001). Patients needing more contact time had a smaller increase in PA, with mean (SD) of +193 (SD 2375) steps per day, +907 (SD 2306) steps per day, and +1489 (SD 2310) steps per day in high, medium, and low contact time groups, respectively; P for-trend=.01. The overall actual usage of the different components of the intervention was not associated with change in step count in the total group ( P=.63).

          Conclusions

          The 12-week semiautomated PA telecoaching intervention was well accepted and feasible for patients with COPD and their coaches. The actual usage of the step counter was excellent, whereas actual usage of the smartphone tasks was lower and decreased over time. Patients who required more contact experienced less PA benefits.

          Trial Registration

          ClinicalTrials.gov NCT02158065; http://clinicaltrials.gov/ct2/show/NCT02158065 (Archived by WebCite at http://www.webcitation.org/73bsaudy9)

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          Most cited references21

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          Validity of Six Activity Monitors in Chronic Obstructive Pulmonary Disease: A Comparison with Indirect Calorimetry

          Reduced physical activity is an important feature of Chronic Obstructive Pulmonary Disease (COPD). Various activity monitors are available but their validity is poorly established. The aim was to evaluate the validity of six monitors in patients with COPD. We hypothesized triaxial monitors to be more valid compared to uniaxial monitors. Thirty-nine patients (age 68±7years, FEV1 54±18%predicted) performed a one-hour standardized activity protocol. Patients wore 6 monitors (Kenz Lifecorder (Kenz), Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod (MiniMod), and SenseWear Armband (SenseWear)) as well as a portable metabolic system (Oxycon Mobile). Validity was evaluated by correlation analysis between indirect calorimetry (VO2) and the monitor outputs: Metabolic Equivalent of Task [METs] (SenseWear, MiniMod), activity counts (Actiwatch), vector magnitude units (Actigraph, RT3) and arbitrary units (Kenz) over the whole protocol and slow versus fast walking. Minute-by-minute correlations were highest for the MiniMod (r = 0.82), Actigraph (r = 0.79), SenseWear (r = 0.73) and RT3 (r = 0.73). Over the whole protocol, the mean correlations were best for the SenseWear (r = 0.76), Kenz (r = 0.52), Actigraph (r = 0.49) and MiniMod (r = 0.45). The MiniMod (r = 0.94) and Actigraph (r = 0.88) performed better in detecting different walking speeds. The Dynaport MiniMod, Actigraph GT3X and SenseWear Armband (all triaxial monitors) are the most valid monitors during standardized physical activities. The Dynaport MiniMod and Actigraph GT3X discriminate best between different walking speeds.
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            Validity of physical activity monitors during daily life in patients with COPD.

            Symptoms during physical activity and physical inactivity are hallmarks of chronic obstructive pulmonary disease (COPD). Our aim was to evaluate the validity and usability of six activity monitors in patients with COPD against the doubly labelled water (DLW) indirect calorimetry method. 80 COPD patients (mean ± sd age 68 ± 6 years and forced expiratory volume in 1 s 57 ± 19% predicted) recruited in four centres each wore simultaneously three or four out of six commercially available monitors validated in chronic conditions for 14 consecutive days. A priori validity criteria were defined. These included the ability to explain total energy expenditure (TEE) variance through multiple regression analysis, using TEE as the dependent variable with total body water (TBW) plus several physical activity monitor outputs as independent variables; and correlation with activity energy expenditure (AEE) measured by DLW. The Actigraph GT3X (Actigraph LLC, Pensacola, FL, USA), and DynaPort MoveMonitor (McRoberts BV, The Hague, the Netherlands) best explained the majority of the TEE variance not explained by TBW (53% and 70%, respectively) and showed the most significant correlations with AEE (r=0.71, p<0.001 and r=0.70, p<0.0001, respectively). The results of this study should guide users in choosing valid activity monitors for research or for clinical use in patients with chronic diseases such as COPD.
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              Three techniques for integrating data in mixed methods studies.

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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
                December 2018
                21 December 2018
                : 6
                : 12
                : e200
                Affiliations
                [1 ] Department of Rehabilitation Sciences KU Leuven Leuven Belgium
                [2 ] Respiratory Division University Hospitals Leuven Leuven Belgium
                [3 ] Department of Physiotherapy LUNEX International University of Health, Exercise and Sports Differdange Luxembourg
                [4 ] ELEGI Colt Laboratory, Centre for Inflammation Research The Queen's Medical Research Institute University of Edinburgh Edinburgh United Kingdom
                [5 ] ISGlobal Barcelona Spain
                [6 ] Faculty of Physical Education and Sports Sciences National and Kapodistrian University of Athens Athens Greece
                [7 ] National Institute for Health Research Respiratory Biomedical Research Unit Royal Brompton and Harefield National Health Services Foundation Trust and Imperial College London United Kingdom
                [8 ] Epidemiology, Biostatistics and Prevention Institute University of Zurich Zurich Switzerland
                [9 ] Groningen Research Institute for Asthma and Chronic Obstructive Pulmonary Disease-Primary Care Department of General Practice and Elderly Care University of Groningen, University Medical Center Groningen Groningen Netherlands
                [10 ] CIBER Epidemiología y Salud Pública Barcelona Spain
                [11 ] Universitat Pompeu Fabra Barcelona Spain
                [12 ] Department of Sport, Exercise and Rehabilitation Faculty of Health and Life Sciences Northumbria University Newcastle-upon-Tyne United Kingdom
                Author notes
                Corresponding Author: Thierry Troosters thierry.troosters@ 123456kuleuven.be
                Author information
                http://orcid.org/0000-0002-0397-4973
                http://orcid.org/0000-0002-8653-0373
                http://orcid.org/0000-0001-8925-0564
                http://orcid.org/0000-0001-8661-8546
                http://orcid.org/0000-0002-0243-9047
                http://orcid.org/0000-0001-5962-8650
                http://orcid.org/0000-0002-7134-1000
                http://orcid.org/0000-0002-9316-2548
                http://orcid.org/0000-0001-5149-2015
                http://orcid.org/0000-0002-2888-5651
                http://orcid.org/0000-0001-9410-414X
                http://orcid.org/0000-0003-3235-0454
                http://orcid.org/0000-0001-5410-8031
                http://orcid.org/0000-0001-8297-544X
                http://orcid.org/0000-0002-7715-509X
                http://orcid.org/0000-0002-3267-2624
                http://orcid.org/0000-0002-7097-4586
                http://orcid.org/0000-0003-1243-8571
                http://orcid.org/0000-0003-2767-5027
                Article
                v6i12e200
                10.2196/mhealth.9774
                6320438
                30578215
                8e61da9d-7bcd-49b6-b77e-5c123389cd22
                ©Matthias Loeckx, Roberto A Rabinovich, Heleen Demeyer, Zafeiris Louvaris, Rebecca Tanner, Noah Rubio, Anja Frei, Corina De Jong, Elena Gimeno-Santos, Fernanda M Rodrigues, Sara C Buttery, Nicholas S Hopkinson, Gilbert Büsching, Alexandra Strassmann, Ignasi Serra, Ioannis Vogiatzis, Judith Garcia-Aymerich, Michael I Polkey, Thierry Troosters. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 21.12.2018.

                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
                : 25 January 2018
                : 21 March 2018
                : 30 May 2018
                : 24 September 2018
                Categories
                Original Paper
                Original Paper

                physical activity,copd,telemedicine,smartphone,patient adherence,patient satisfaction,outcome and process assessment (health care)

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