Contact time between patients with COPD and coach during an activity telecoaching intervention: Impact on the intervention effect

Loeckx, Matthias; Louvaris, Zafeiris; Tanner, Rebecca; Rubio, Noah; Frej, Anja; Jong, Corina de; Santos, E.; Demeyer, Heleen; Spruyt, Maarten; Buttery, Sara C; Hopkinson, Nicholas S; Buesching, Gilbert; Strassmann, Alexandra; Serra, Ignasi; Rabinovich, Roberto; Vogiatzis, Giannis; Polkey, Michael I.; Garcia‐Aymerich, Judith; Troosters, Thierry

doi:10.1183/13993003.congress-2016.oa4817

Cited by 3 publications

(1 citation statement)

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“…The success of mHealth apps in improving PA and reducing sedentary time in COPD could potentially be increased if apps were used for tele-coaching ( 8 , 37 ). Previous research has shown positive results using tele-coaching for behavior change, but it has also been recognized that this type of intervention is not a “one size fits all” ( 9 , 38 ). Results of the present study are useful to inform the decision-making process of features to be considered in tele-coaching interventions provided through mHealth apps, taking into consideration the demographics, clinical and PA characteristics of the target audience.…”

Section: Discussionmentioning

confidence: 99%

Phenotyping Adopters of Mobile Applications Among Patients With COPD: A Cross-Sectional Study

Flora¹,

Hipólito²,

Brooks³

et al. 2021

Front. Rehabilit. Sci.

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Effectiveness of technology-based interventions to improve physical activity (PA) in people with COPD is controversial. Mixed results may be due to participants' characteristics influencing their use of and engagement with mobile health apps. This study compared demographic, clinical, physical and PA characteristics of patients with COPD using and not using mobile apps in daily life. Patients with COPD who used smartphones were asked about their sociodemographic and clinic characteristics, PA habits and use of mobile apps (general and PA-related). Participants performed a six-minute walk test (6MWT), gait speed test and wore an accelerometer for 7 days. Data were compared between participants using (App Users) and not using (Non-App Users) mobile apps. A sub-analysis was conducted comparing characteristics of PA–App Users and Non-Users. 59 participants were enrolled (73% Male; 66.3 ± 8.3 yrs; FEV1 48.7 ± 18.4% predicted): 59% were App Users and 25% were PA-App Users. Significant differences between App Users and Non-App Users were found for age (64.2 ± 8.9 vs. 69.2 ± 6.3yrs), 6MWT (462.9 ± 91.7 vs. 414.9 ± 82.3 m), Gait Speed (Median 1.5 [Q1–Q3: 1.4–1.8] vs. 2.0 [1.0–1.5]m/s), Time in Vigorous PA (0.6 [0.2–2.8] vs. 0.14 [0.1–0.7]min) and Self-Reported PA (4.0 [1.0–4.0] vs. 1.0 [0.0–4.0] Points). Differences between PA–App Users and Non-Users were found in time in sedentary behavior (764.1 [641.8–819.8] vs. 672.2 [581.2–749.4] min) and self-reported PA (4.0 [2.0–6.0] vs. 2.0 [0.0–4.0] points). People with COPD using mobile apps were younger and had higher physical capacity than their peers not using mobile apps. PA-App Users spent more time in sedentary behaviors than Non-Users although self-reporting more time in PA.

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Section: Discussionmentioning

confidence: 99%