Test-retest reliability of a smartphone app for measuring core stability for two dynamic exercises

Study design

A descriptive repeated-measures study was performed between April and June 2018.

Participants

Thirty-three active students (12 males and 21 females; age: 22.3 ± 5.9 years, body weight: 66.9 ± 11.3 kg, height: 167.8 ± 10. three cm) were recruited from the academic community to participate in this study. Exclusion criteria were (1) any cardiovascular, respiratory, abdominal, neurological, musculoskeletal, or other chronic disease and (2) any symptoms that could affect the musculoskeletal system. Before starting the procedure, all the participants read and signed an institutional informed consent. This research project was conducted according to the Declaration of Helsinki and was approved by the CyMO Research Institute granted Ethical approval to carry out the study (1.200.518).

Procedures

The participants completed two testing sessions with intervals of 48 h. In each testing session, the participants performed two exercises tests (partial range single leg deadlift [SLD] and variations of the bird-dog exercise [BD]). These were randomized between participants, with five minutes of rest between each exercise.

This is a methodological study carried out to test the reliability of the OCTOcore app. An iPhone^® model 6 (iPhone^® is a trademark of Apple Inc., Cupertino, CA, USA) was utilized. The iPhone^® app OCTOcore was used to collect data. The mobile phone was placed, through a belt, on the midline of the subject’s back at the level of the iliac crests at the level of the fourth lumbar vertebra. The headphones (EarPods; Apple Inc., Cupertino, CA, USA) were utilized to improve the concentration of the participants. According to Adusumilli et al. (2017) sampling rate was set constant at 100 Hz for both tests. The first and second repetition of each trial were discarded, analyzing a 3 s window for each repetition. The mean acceleration was calculated as the average of the acceleration magnitude data series. The acceleration data were analysed with subtraction of the gravitational component. The iPhone incorporates the Sensortec BMA280 3-axis accelerometer (Bosch Sensortec GmbH, Germany) with resolution (in ±2g range) of 0.244 mg and digital resolution of 14 bit. Several studies showed that iPhone’s accelerometer is valid and reliable tool for measuring human movements (Furness et al., 2018; Nolan, Mitchell & Doyle-Baker, 2014; Sun, Wang & Banda, 2014).

Partial range single leg deadlift (SLD)

Subjects were placed in a standing position with their backs to the wall. Next, they were located at a distance of two feet from the wall, with their feet straight and spaced to the width of their hips. Also, their arms were placed crossed on their chests, and they were instructed to look forward at all times. Once a subject was placed in the starting position, a mark was placed on the ground so that the subject had a reference for where it should be placed. According to the order given by the mobile app, “left” or “right”, the subject touched the right or left heel to the wall, keeping the trunk and leg straight while slightly tilting the trunk forward (Fig. 1). Then, the subject would return to the starting position to wait for the next order. This exercise provides an indication of the ability to simultaneously flex and extend at the hip with extended knees while maintaining neutral spinal alignment (Reid et al., 2015). This type of exercise produced a high or very high load (Ebert et al., 2017).

Variation of bird-dog (BD)

In the “bird-dog” or quadruped exercise, contralateral upper and lower extremities are lifted to horizontal from the quadruped position. The exercise was performed under the instruction that trunk motion was to be maintained to a minimum while keeping the lumbar spine and pelvis in a “neutral” position, knees bent to 90°, the toes faced forward, and the hands at the participant’s sides on the OctoBalance^® line (Check your MOtion^®, Albacete, Spain). Participants also learned to execute repetitions of the bird-dog exercise at the selected cadence, following the ticks emitted by OCTOcore app. According to the random order given by the app, “grey left” or “green right,” participants would raise their right or left arms to the side following the direction of the OctoBalance^® line while stretching the opposite leg with ankle dorsi-flexion (Fig. 2). This is a conventional core stabilisation exercise that generally produced low or moderate load (Ebert et al., 2017).

Each exercise (test) was performed for thirty repetitions as a familiarization trial and, after a break of three minutes, again for fifty repetitions as the measurement trial. Participants were instructed to perform the exercise at a moderate movement velocity, three seconds for each repetition per condition. All testing was performed at the same time of day to minimize the effect of circadian rhythms.

Statistical procedure

The Shapiro–Wilk test was used to check the normality of the data. Descriptive statistics were expressed as mean and standard deviation (SD). After checking the assumptions of parametric statistics, the dependent variables of the OCTOcore app (right side, left side, and composite accelerations) for two movement control tests on the different assessment days were compared by one-sample t-tests conducted for the mean difference between the two time points for each test. Additionally, effect size statistic, d, was analysed to determine the magnitude of the effect independent of sample size (Dankel & Loenneke, 2018). Differences were interpreted using Cohen’s (d) guidelines as trivial (<0.2), small (0.2–0.6), moderate (0.6–1.2), large (1.2–2.0), very large (2.0–4.0), and huge (>4.0 )(Hopkins et al., 2009). For the reliability, intraclass correlation coefficient (ICC_3,1) with 95% confidence intervals and coefficient of variation (CV% = SD/mean × 100) were calculated. ICC values were considered small if >0.25, low 0.26–0.49, moderate 0.50–0.69, high 0.70–0.89 and very high >0.90 (Hopkins, Schabort & Hawley, 2001). Bland–Altman plots were used to verify the agreement between measurements (Bland & Altman, 1986). The standard error of measurement (SEM) was calculated using the equation: $\mathrm{SEM}=SD\times \left(\sqrt{1}-ICC\right)$, where SD corresponds to the standard deviation from day two (Atkinson & Nevill, 1998). The minimum detectable difference (MDD) with 95% confidence interval was obtained with the equation: $\mathrm{MDD}=1.96\times \sqrt{}\left(2\times \mathrm{SEM}\right)$ (Popovic & Thomas, 2017). All analyses assumed statistical significance at p ≤ 0.05. Statistical procedures were performed with SPSS^® software version 23.0.