Validation of a Smartphone App for the Assessment of Sedentary and Active Behaviors

Description

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus,…

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.

Objective: This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).
Methods: Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).

Results: Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.

Conclusions: Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.