Comparison of Self-Reported and Device-Based Measured Physical Activity Using Measures of Stability, Reliability, and Validity in Adults and Children

Abstract

Quantification of physical activity (PA) depends on the type of measurement and analysis method making it difficult to compare adherence to PA guidelines. Therefore, test-retest reliability, validity, and stability for self-reported (i.e., questionnaire and diary) and device-based measured (i.e., accelerometry with 10/60 s epochs) PA was compared in 32 adults and 32 children from the SMARTFAMILY study to examine if differences in these measurement tools are systematic. PA was collected during two separate measurement weeks and the relationship for each quality criteria was analyzed using Spearman correlation. Results showed the highest PA values for questionnaires followed by 10-s and 60-s epochs measured by accelerometers. Levels of PA were lowest when measured by diary. Only accelerometry demonstrated reliable, valid, and stable results for the two measurement weeks, the questionnaire yielded mixed results and the diary showed only a few significant correlations. Overall, higher correlations for the quality criteria were found for moderate than for vigorous PA and the results differed between children and adults. Since the differences were not found to be systematic, the choice of measurement tools should be carefully considered by anyone working with PA outcomes, especially if vigorous PA is the parameter of interest.

mHealth Interventions to Reduce Physical Inactivity and Sedentary Behavior in Children and Adolescents: Systematic Review and Meta-analysis of Randomized Controlled Trials

“…A more appropriate approach would be to focus only on objective data or consider a combination of objective and subjective data, similar to the approach of Chen et al [ 62 ]. The use of only qualitative data can become a problem if an objective comparison with WHO recommendations has to be provided [ 86 ]. Therefore, we encourage researchers in the field of mHealth to use accelerometry-based measurements and more standardized outcome measures in future intervention studies.…”

Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth intervention

“…Furthermore, it is unclear whether the daily mean values for valence, energetic arousal and calmness are representative of the person’s average as they were answered once to multiple times per day and the different parameters of affect are known to change throughout the day (Reichert et al., 2020 ). Finally, the selection of epoch lengths is important to consider for PA estimations by accelerometers, especially if both adults and children are included in the study (Fiedler et al., 2021 ). The choice of another epoch length (e.g., 10 s epochs instead of 60 s epochs) might have led to differing findings for intensity-related parameters (i.e., MVPA).…”

“…( 2018 ) investigated the relationship between affect and PA within a single day and the results indicate that higher ratings of valence in the morning were associated with increased PA over the day. Here, both studies assessed PA by self-report, which may not represent changes within an individual in detail (Reichert et al., 2020 ) and often differs from device-based measured PA (Fiedler, Eckert, Burchartz, Woll, & Wunsch, 2021 ). Comparable results for the relation of PA and energetic arousal alone were also found in children between 9 and 13 years (Dunton et al., 2014 ) and for all three affective states in children between 12 and 17 years (Koch et al., 2018 ).…”

Analysis of self‐reported activities of daily living, motor performance and physical activity among children and adolescents with cancer: Baseline data from a randomised controlled trial assessed shortly after diagnosis of leukaemia or non‐Hodgkin lymphoma

“…Data on the validity of this device have been published previously. Compared to devices analysing raw data in form of counts in combination with single regression models, the Move 3 estimates activity‐related energy expenditure more accurately (1.5–15 times lower relative differences to calorimetry reference values) in daily life activities of moderate intensity and reliably detects gait speeds (Anastasopoulou et al, 2014; Fiedler et al, 2021).…”