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Zhou L, Schneider J, Arnrich B, Konigorski S. Analyzing population-level trials as N-of-1 trials: An application to gait. Contemp Clin Trials Commun 2024; 38:101282. [PMID: 38533473 PMCID: PMC10964044 DOI: 10.1016/j.conctc.2024.101282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 03/28/2024] Open
Abstract
Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.
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Affiliation(s)
- Lin Zhou
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Juliana Schneider
- Digital Health & Machine Learning, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Stefan Konigorski
- Digital Health & Machine Learning, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Department of Statistics, Harvard University, Cambridge, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, NY, USA
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Konak O, van de Water R, Döring V, Fiedler T, Liebe L, Masopust L, Postnov K, Sauerwald F, Treykorn F, Wischmann A, Gjoreski H, Luštrek M, Arnrich B. HARE: Unifying the Human Activity Recognition Engineering Workflow. SENSORS (BASEL, SWITZERLAND) 2023; 23:9571. [PMID: 38067946 PMCID: PMC10708727 DOI: 10.3390/s23239571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE's multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.
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Affiliation(s)
- Orhan Konak
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Robin van de Water
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Valentin Döring
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Tobias Fiedler
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Lucas Liebe
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Leander Masopust
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Kirill Postnov
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Franz Sauerwald
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Felix Treykorn
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Alexander Wischmann
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
| | - Hristijan Gjoreski
- Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia;
| | - Mitja Luštrek
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; (R.v.d.W.); (V.D.); (T.F.); (L.L.); (L.M.); (K.P.); (F.S.); (F.T.); (A.W.); (B.A.)
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