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Objective: Proper exercise form is critical for the safety and efficacy of therapeutic exercise. This research examines if a novel smartphone application, designed to monitor and provide real-time corrections during resistance training, can reduce performance errors and elicit a motor learning response. Design: Forty-two participants aged 18 to 65 years were randomly assigned to treatment and control groups. Both groups were tested for the number of movement errors made during a 10-repetition set completed at baseline, immediately after, and 1 to 2 weeks after a single training session of knee extensions. The treatment group trained with real-time, smartphone-generated feedback, whereas the control subjects did not. Group performance (number of errors) was compared across test sets using a 2-factor mixed-model analysis of variance. Results: No differences were observed between groups for age, sex, or resistance training experience. There was a significant interaction between test set and group. The treatment group demonstrated fewer errors on posttests 1 and 2 compared with pretest (P < 0.05). There was no reduction in the number of errors on any posttest for control subjects. Conclusion: Smartphone apps, such as the one used in this study, may enhance patient supervision, safety, and exercise efficacy across rehabilitation settings. A single training session with the app promoted motor learning and improved exercise performance. [ABSTRACT FROM AUTHOR]

Background: Therapeutic exercise is a central component in the management of many common conditions. It is imperative, therefore, that clinicians monitor and correct patient performance to facilitate the use of proper form both in the clinic and during home exercise programs. Although clinicians are trained to prescribe exercise and analyze form, there are many subtleties that may be missed by relying on visual assessment. This study investigated the accuracy and precision of a novel, exercise-training smartphone application (app), running on an iPhone® (Apple, Cupertino, CA) 4 and using its LIS331DLH accelerometer to dynamically measure and record movement during exercise. Materials and Methods: The iPhone, running the app, was mounted to the movement arm of a Biodex™ isokinetic dynamometer System 4 (Biodex Corp., Shirley, NY). Angle and time measurements taken by the app were compared with the dynamometer (gold standard) while rotating at 30°/s, 60°/s, 90°/s, 120°/s, and 150°/s. Accuracy was assessed using limits of agreement and fast Fourier transform analyses. Precision was assessed using the coefficient of variation. Results: The mean difference between the app and the Biodex recordings was less than 1°/s for all test velocities. The coefficient of variation was less than 3% at velocities from 30°/s to 120°/s and less than 7% at 150°/s. Conclusions: The app was highly accurate and precise. The validation of apps designed for motion tracking is a vital prerequisite to clinical implementation. The app described in this article is clinically identical to the Biodex dynamometer in its ability to accurately and precisely read angular movement over time. [ABSTRACT FROM AUTHOR]