Küppers, Lucas; Pfannenstiel, Richard; Bozorgmehr, Arezoo; Jonas, Stephan; Weltermann, Birgitta; Reimer, Lara Marie: Short physical performance battery : Pilot study of a human motion capture app (MobiSPPB). In: Digital health. 2025, vol. 11, 1-12.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/13675
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/13675
@article{handle:20.500.11811/13675,
author = {{Lucas Küppers} and {Richard Pfannenstiel} and {Arezoo Bozorgmehr} and {Stephan Jonas} and {Birgitta Weltermann} and {Lara Marie Reimer}},
title = {Short physical performance battery : Pilot study of a human motion capture app (MobiSPPB)},
publisher = {Sage},
year = 2025,
month = jul,
journal = {Digital health},
volume = 2025, vol. 11,
pages = 1--12,
note = {Background: A standardized fall risk assessment can guide targeted interventions. The widely used short physical performance battery (SPPB) for mobility assessment covers balance, gait speed, and lower limb strength, but is time-consuming and requires trained raters. The newly developed video-based smartphone application called MobiSPPB provides a rater-independent SPPB assessment. This study evaluated the technical validity and reliability of the MobiSPPB app compared to the standard rater-based SPPB. In addition, the ability to detect disease-related movement patterns was investigated.
Methods: Using a standardized experimental setting, 10 healthy participants performed the SPPB with and without movement impairments simulated by an instant aging suit. Two experienced raters rated the SPPB performance, and a smartphone recorded at the same time. The MobiSPPB app analyzed videos via vision-based human motion capture techniques. Spearman's correlations, the intraclass correlation coefficient (ICC), and receiver operating characteristic curves were calculated.
Results: There was a strong correlation between the app and standard SPPB (Spearman's Correlation of 0.869, 95% confidence interval (CI) of 0.79–0.92, p < 0.001). Compared with the standard assessment, the app presented a more significant ICC in the test–retest reliability analysis (0.936, 95% CI of 0.87-0.97, p < 0.001). Detecting disease-related movement patterns achieved high accuracy in capturing severe impairments such as hemiplegia (area under the curve (AUC) 93%). Inconsistencies between the raters indicated that the app provides more objective assessments.
Conclusions: The technical validation of the MobiSPPB app was successful in a standardized experimental setting and requires further testing in clinical practice.},
url = {https://hdl.handle.net/20.500.11811/13675}
}
author = {{Lucas Küppers} and {Richard Pfannenstiel} and {Arezoo Bozorgmehr} and {Stephan Jonas} and {Birgitta Weltermann} and {Lara Marie Reimer}},
title = {Short physical performance battery : Pilot study of a human motion capture app (MobiSPPB)},
publisher = {Sage},
year = 2025,
month = jul,
journal = {Digital health},
volume = 2025, vol. 11,
pages = 1--12,
note = {Background: A standardized fall risk assessment can guide targeted interventions. The widely used short physical performance battery (SPPB) for mobility assessment covers balance, gait speed, and lower limb strength, but is time-consuming and requires trained raters. The newly developed video-based smartphone application called MobiSPPB provides a rater-independent SPPB assessment. This study evaluated the technical validity and reliability of the MobiSPPB app compared to the standard rater-based SPPB. In addition, the ability to detect disease-related movement patterns was investigated.
Methods: Using a standardized experimental setting, 10 healthy participants performed the SPPB with and without movement impairments simulated by an instant aging suit. Two experienced raters rated the SPPB performance, and a smartphone recorded at the same time. The MobiSPPB app analyzed videos via vision-based human motion capture techniques. Spearman's correlations, the intraclass correlation coefficient (ICC), and receiver operating characteristic curves were calculated.
Results: There was a strong correlation between the app and standard SPPB (Spearman's Correlation of 0.869, 95% confidence interval (CI) of 0.79–0.92, p < 0.001). Compared with the standard assessment, the app presented a more significant ICC in the test–retest reliability analysis (0.936, 95% CI of 0.87-0.97, p < 0.001). Detecting disease-related movement patterns achieved high accuracy in capturing severe impairments such as hemiplegia (area under the curve (AUC) 93%). Inconsistencies between the raters indicated that the app provides more objective assessments.
Conclusions: The technical validation of the MobiSPPB app was successful in a standardized experimental setting and requires further testing in clinical practice.},
url = {https://hdl.handle.net/20.500.11811/13675}
}





