Short physical performance batteryPilot study of a human motion capture app (MobiSPPB)
Short physical performance battery
Pilot study of a human motion capture app (MobiSPPB)

| dc.contributor.author | Küppers, Lucas | |
| dc.contributor.author | Pfannenstiel, Richard | |
| dc.contributor.author | Bozorgmehr, Arezoo | |
| dc.contributor.author | Jonas, Stephan | |
| dc.contributor.author | Weltermann, Birgitta | |
| dc.contributor.author | Reimer, Lara Marie | |
| dc.date.accessioned | 2025-11-13T10:43:45Z | |
| dc.date.available | 2025-11-13T10:43:45Z | |
| dc.date.issued | 02.07.2025 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11811/13675 | |
| dc.description.abstract | 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. | en |
| dc.format.extent | 12 | |
| dc.language.iso | eng | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Geriatric assessment | |
| dc.subject | mobility assessment | |
| dc.subject | falls | |
| dc.subject | frailty | |
| dc.subject | sarcopenia | |
| dc.subject | short physical performance battery | |
| dc.subject | ision-based human motion capture technology | |
| dc.subject | mHealth | |
| dc.subject.ddc | 610 Medizin, Gesundheit | |
| dc.title | Short physical performance battery | |
| dc.title.alternative | Pilot study of a human motion capture app (MobiSPPB) | |
| dc.type | Wissenschaftlicher Artikel | |
| dc.publisher.name | Sage | |
| dc.publisher.location | Thousand Oaks, CA | |
| dc.rights.accessRights | openAccess | |
| dcterms.bibliographicCitation.volume | 2025, vol. 11 | |
| dcterms.bibliographicCitation.pagestart | 1 | |
| dcterms.bibliographicCitation.pageend | 12 | |
| dc.relation.doi | https://doi.org/10.1177/20552076251346575 | |
| dcterms.bibliographicCitation.journaltitle | Digital health | |
| ulbbn.pubtype | Zweitveröffentlichung | |
| dc.version | publishedVersion | |
| ulbbn.sponsorship.oaUnifund | OA-Förderung Universität Bonn |
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