Table 1.
Research studies on smartphone sensor-based physical activity intervention applications
| Study | Subjects | Sample size | Study design | Study duration | App name | App purpose | Primary outcome | Pros and cons |
|---|---|---|---|---|---|---|---|---|
| Harries et al., 2016 (United Kingdom) | Healthy males (22–40 years) | 165 | Randomized controlled trial | 6 Weeks | bActive | Promotes PA | Steps per day | Individual and group compared feedback showed 60% & 69% higher step-counts |
| Seto et al., 2016 (China) | College students (mean age, 24 years) | 12 Males | Cohort study | 2 Weeks | CalFit Chi and Dong (Phone accelerometry, GPS) | Promotes PA& diet | PA, food intake for energy balance assessment | Voice-annotated videos of meals |
| Vosa et al., 2016 (The Netherland) | Experience vs. no experience runners | 28 Experienced vs. unexperienced runners | Randomized controlled trial with 4 focus groups | 12 Months (~2 months per group) | Inspirun (GPS) May add Bluetooth HR monitor | Supports personalized running experience | Application usability by survey | Possible promotion of PA & participation |
| Furrer et al., 2015 (Switzerland) | Health adults (mean age, 27.4 years) | 10 Males 12 Males |
Cross-sectional study | Accelerometer | Gait analysis | CoM displacement & step duration | Comparison between smartphone vs. motion capture system, displacement ICC (0.71–0.80); time ICC (0.79–0.86) | |
| Nolan et al., 2014 (Canada) | Healthy adults | 25 | Randomized controlled trial | Apple iPhone app, accelerometer | PA & MET analysis | Walking, running & EE accuracy | Bias of 0.02 & −0.03 km/hr, Bias of 0.35 and −0.43 METs for walking and running (99% accuracy compared with treadmill) |
App, application; CoM, vertical center of mass; METs, metabolic equivalents; PA, physical activity; GPS, global positioning system; HR, heart rate; EE, energy expenditure; ICC, intraclass correlation coefficient.