Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Design
2.2. Participants
2.3. Data Acquisition
2.4. Procedures
2.5. How the Selftraining UMH Application Works
2.6. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Males (n = 10) | Females (n = 10) | |
---|---|---|
Age (y) | 27.70 ± 4.40 | 25.00 ± 2.67 |
Weight (kg) | 77.10 ± 4.71 | 62.43 ± 5.91 |
Height (m) | 1.77 ± 0.05 | 1.64 ± 0.08 |
BMI (kg∙m−2) | 24.54 ± 1.76 | 23.27 ± 2.08 |
Breathing frequency (bpm) | 9.50 ± 0.94 | 9.70 ± 1.06 |
Position | Device | Descriptive Data | MD | p | Effect Sizes (95%CI) |
---|---|---|---|---|---|
Supine | ECG | 3.995 ± 0.64 | |||
Selftraining UMH (kubios) | 3.998 ± 0.65 | −0.002 | 1.00 | −0.174 (−0.010, 0.006) | |
Selftraining UMH (app) | 3.964 ± 0.65 | 0.031 | 0.13 | 0.615 (−0.005, 0.068) | |
Sitting | ECG | 3.947 ± 0.66 | |||
Selftraining UMH (kubios) | 3.951 ± 0.66 | −0.004 | 1.00 | −0.236 (−0.016, 0.008) | |
Selftraining UMH (app) | 3.913 ± 0.67 | 0.034 | 0.86 | 0.405 (−0.026, 0.094) |
Device | Position | 1 min | 5 min | MD | p | Effect Sizes (95%CI) |
---|---|---|---|---|---|---|
ECG | Supine | 3.978 ± 0.66 | 3.970 ± 0.65 | 0.007 | 0.73 | 0.078 (−0.038, 0.053) |
Seated | 3.942 ± 0.71 | 3.918 ± 0.69 | 0.025 | 0.42 | 0.183 (−0.038, 0.087) | |
Selftraining UMH | Supine | 4.002 ± 0.66 | 3.981 ± 0.65 | 0.020 | 0.52 | 0.147 (−0.045, 0.086) |
Seated | 3.939 ± 0.70 | 3.920 ± 0.69 | 0.019 | 0.52 | 0.145 (−0.043, 0.082) |
Position | Device | Mean Difference | ICC (90%CI) | SEM (%) | MCD (%) |
---|---|---|---|---|---|
Supine | ECG | 0.10 ± 0.26 | 0.93 (0.82, 0.97) | 4.51 | 0.50 |
Selftraining UMH (kubios) | 0.09 ± 0.26 | 0.92 (0.82, 0.97) | 4.54 | 0.50 | |
Selftraining UMH (app) | 0.09 ± 0.26 | 0.92 (0.82, 0.97) | 4.66 | 0.51 | |
Sitting | ECG | 0.03 ± 0.12 | 0.99 (0.97, 0.99) | 2.11 | 0.23 |
Selftraining UMH (kubios) | 0.03 ± 0.11 | 0.99 (0.97, 1.00) | 1.99 | 0.22 | |
Selftraining UMH (app) | 0.01 ± 0.11 | 0.99 (0.97, 1.00) | 1.98 | 0.21 |
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Casanova-Lizón, A.; Sarabia, J.M.; Pastor, D.; Javaloyes, A.; Peña-González, I.; Moya-Ramón, M. Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training. Int. J. Environ. Res. Public Health 2023, 20, 1528. https://doi.org/10.3390/ijerph20021528
Casanova-Lizón A, Sarabia JM, Pastor D, Javaloyes A, Peña-González I, Moya-Ramón M. Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training. International Journal of Environmental Research and Public Health. 2023; 20(2):1528. https://doi.org/10.3390/ijerph20021528
Chicago/Turabian StyleCasanova-Lizón, Antonio, José M. Sarabia, Diego Pastor, Alejandro Javaloyes, Iván Peña-González, and Manuel Moya-Ramón. 2023. "Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training" International Journal of Environmental Research and Public Health 20, no. 2: 1528. https://doi.org/10.3390/ijerph20021528