%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e63090
%T Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study
%A Kriara,Lito
%A Dondelinger,Frank
%A Capezzuto,Luca
%A Bernasconi,Corrado
%A Lipsmeier,Florian
%A Galati,Adriano
%A Lindemann,Michael
%+ , F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, CH-4070, Switzerland, 41 61 687 10 20, lito.kriara@roche.com
%K Floodlight Open
%K multiple sclerosis
%K smartphone
%K sensors
%K mobile phone
%K wearable electronic devices
%K digital health
%K equivalence
%K device equivalence
%K cognition
%K gait
%K upper extremity function
%K hand motor function
%K balance
%K digital biomarker
%K variability
%K mHealth
%K mobile health
%K autoimmune disease
%K motor
%K digital assessment
%D 2025
%7 3.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population. Objective: This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models. Methods: Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a “bring-your-own-device” (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor–based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status. Results: Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03). Conclusions: Consistent with the hypothesis that smartphone sensor–based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed. 
%R 10.2196/63090
%U https://www.jmir.org/2025/1/e63090
%U https://doi.org/10.2196/63090