Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement

Santosh Ray; Pratik K. Mishra; Ali Abedi; Charlene H. Chu; Amir Ahmad; Shehroz S. Khan

cs.CVAug 24, 2026

Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement

Authors: Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan

Organizations: Faculty of Information Technology, Liwa University, Abu Dhabi, United Arab Emirates · Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada · KITE Research Institute - Toronto Rehabilitation Institute, University Health Network, Toronto, Canada · Peter Munk Cardiac Centre, University Health Network, Toronto, Canada ·

Abstract

Older adults recovering after lower-limb fracture or hip replacement may experience complex recovery trajectories. Most of the time, these clinical aspects are studied in isolation, masking their joint impact on recovery. This study used the MAISON-LLF dataset, which contains multimodal sensor and clinical assessment data from 18 older adults recovering in the community after lower-limb fracture or hip replacement. Participants were monitored for up to eight weeks, corresponding to a maximum of 1,008 participant-days of sensor monitoring. Forty-six daily features were extracted from indoor motion, acceleration, step count, heart rate, out-of-home mobility, and sleep data. Five clinical outcomes were assessed every two weeks: the Social Isolation Scale, Oxford Hip Score, Oxford Knee Score, Timed Up and Go test, and 30-second Chair Stand test. We utilize an inherent relationship between multi-modal sensor data and different clinical scores and formulate it as a multi-output regression problem. We tested various machine learning and deep learning single- and multi-output regression algorithms to predict these scores simultaneously. The results showed that predicting clinical scores jointly was better than separately. The tabular DL multi-output regressor, NODE, gave a remarkable performance of MSE=3.96 and MAE=1.02 in comparison to other multi- and single-output regressors. The SHAP feature analysis further showed the importance of including multimodal sensors to provide a good estimate of patients' recovery trajectory. This work may support the simultaneous assessment of functional recovery and social engagement among community-dwelling older adults and ultimately help improve their care and quality of life.

Figures & tables

Data modality 1	Feature 1	Description
acceleration-	count, entropy, kurtosis, mean, skew, std, and sum	The total count, entropy, kurtosis, mean, skewness, standard deviation, and sum of acceleration data in a day.
	coefficient-of-variation	Measures the relative variability of daily physical activity, indicating how consistently an individual moves throughout the day in relation to their average activity level.
	minutes-with-data	Represents the total number of minutes within a 24-hour period (from 12:00 AM to the following 12:00 AM) during which acceleration data was recorded, providing insight into the smartwatch’s active recording time, even if it was not necessarily worn on the wrist.
	hours-with-data	Represents the total number of hours within a 24-hour period (from 12:00 AM to the following 12:00 AM) during which acceleration data was recorded, providing insight into the smartwatch’s active recording time, even if it was not necessarily worn on the wrist.
	movement-events-00to06, movement-events-06to12, movement-events-12to18, movement-events-18to24	Represents the number of detected movement events between 12:00 AM and 6:00 AM, 6:00 AM and 12:00 PM, 12:00 PM and 18:00 PM, 18:00 PM and 12:00 AM, based on acceleration values exceeding a dynamic threshold, and reflects physical activity or restlessness.
	movement-events-24h	Represents the total number of detected movement events over a full 24-hour period (from 12:00 AM to the following 12:00 AM), based on acceleration values exceeding a dynamic threshold, and provides an overall measure of daily physical activity.

TABLE I: The data modalities, feature names, and their description. 1 Feature naming convention in the dataset: ‘data modality’ +‘-’+‘feature’.

Fig. 1: Correlation matrix of the clinical outcomes for the overall dataset.

Fig. 2: Correlation matrix of the outcomes for each individual fold as part of the leave-one-participant-out cross validation.

TABLE II: Results of multi-output and single-output regression for leave-one-person-out cross-validation in terms of (a) MSE and (b) MAE. Unless the errors are equivalent, the best values for each outcome are highlighted in bold. The blue shaded cells signify the better values between single and multi-output regression.

TABLE III: Percentage improvement of multi-output over single-output regressors for leave-one-person-out cross-validation. The best values for each outcome across all the regressors are highlighted in bold.

TABLE IV: Percentage improvement of multi-output regressors with respect to LR baseline for leave-one-person-out cross-validation. The best values for each outcome across all the regressors are highlighted in bold.

TABLE V: Results of multi-output and single-output regression for leave-one-week-out cross-validation in terms of (a) MSE and (b) MAE. Unless the errors are equivalent, the best values for each outcome are highlighted in bold. The blue shaded cells signify the better values between single and multi-output regression.

TABLE VI: Percentage improvement of multi-output over single-output regressors for leave-one-week-out cross-validation. The best values for each outcome across all the regressors are highlighted in bold.

TABLE VII: Percentage improvement of multi-output regressors with respect to LR baseline for leave-one-week-out cross-validation. The best values for each outcome across all the regressors are highlighted in bold.

Panel of Table 10 — TABLE VIII: SHAP-based ranking of features for NODE multi-output regressor for leave-one-person-out cross-validation.

Panel of Figure 11 — Fig. 3: Outlier feature plots.

Panel of Figure 12 — Fig. 4: Outlier feature plots (continued).

Clinical Outcome Prediction

Clinical outcome prediction uses patient records, imaging, signals, and biomarkers to estimate risks, disease trajectories, treatment response, and survival.

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Clinical Outcome Prediction Wearable Sensing Clinical Prediction Wearable Health Monitoring Multimodal Sensor Fusion

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Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a longitudinal multimodal study of 66 older adults conducted in real-world conditions and combining wearable sensing, behavioral monitoring, and clinical assessments. This setting provides a rare opportunity to study an underrepresented population in long-term, into-the-wild conditions. Building on this dataset, we investigate how the alignment between sensed signals and target variables affects predictive performance across health-related tasks. We design a unified evaluation framework spanning tasks with increasing levels of observability, including Activity Levels prediction, Sleep Duration estimation, and Sleep Apnea Severity classification. Our results reveal a clear gradient of predictability: highly observable behavioral targets achieve robust performance (macro-F1 65%), while more abstract outcomes remain challenging despite consistent improvements over baseline models. Moreover, through explainability analysis, we show that historical features consistently emerge as the most informative predictors, highlighting the central role of longitudinal information.

Abstract

Clinical Outcome Prediction

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