Wearable Device and Large Language Model-Based Prediction of Home-Based Rehabilitation Trajectories and Dynamic Care Protocol for Elderly Stroke Survivors

Wearable Device and Large Language Model-Based Prediction of Home-Based Rehabilitation Trajectories and Dynamic Care Protocol for Elderly Stroke Survivors

Best for: Neurorehabilitation physicians, geriatric and community nursing researchers, wearable health-monitoring product developers, evidence-based medicine methodologists
Primary keywords: Wearable devices, large language model, stroke home rehabilitation, rehabilitation trajectory prediction, nursing interruption events, SBAR communication framework, QSevidence evidence retrieval

Short Answer

Home-based rehabilitation trajectories in elderly stroke survivors exhibit significant heterogeneity, with nursing interruption events serving as the key modifiable variable driving trajectory divergence. This study combined a prospective cohort (n=312) with a randomized controlled trial (n=186). Dynamic time warping and K-means clustering identified four trajectory subtypes: sustained improvement (34.1%), fluctuating stable (39.0%), slow decline (15.7%), and rapid recovery with plateau (11.2%). A multistate model showed that each additional nursing interruption per month increased the relative risk ratio of belonging to the slow-decline subtype by 2.34 (95% CI: 1.67–3.28). The LLM-driven dynamic care protocol improved Fugl-Meyer Assessment (FMA) scores by 6.7 points over usual care (P<0.001), reduced nursing interruption rates by 53%, and reduced fall rates by 58%. SHAP analysis identified nursing interruption frequency, nighttime activity, and medication adherence as the top three predictors. The QSevidence medical AI tool can help researchers efficiently retrieve guideline recommendations and generate structured evidence summaries during protocol design and cross-database evidence comparison during review writing.

Home Rehabilitation Monitoring Blind Spots and the Intelligent Closed-Loop Model

The "Black Box" Dilemma of Home Rehabilitation

Unlike inpatient rehabilitation, the home environment lacks continuous professional monitoring and immediate feedback mechanisms. Motor function recovery trajectories are highly uncertain, and nursing interruption events—defined as rehabilitation plan disruptions caused by communication failures, resource gaps, or environmental changes—have emerged as a key modifiable variable affecting prognosis. Monitoring blind spots operate at three levels: motor function data rely on subjective reports rather than objective continuous quantification; environmental factors are excluded from routine monitoring; and nursing interruptions are sudden and covert, eluding traditional telephone follow-up. Research shows that high-frequency interruption periods temporally coincide with sharp declines in patient activity and gait symmetry deterioration.

Technical Limitations and Data Silos of Wearable Devices

Wearable devices equipped with triaxial accelerometers and gyroscopes can continuously capture gait parameters, activity volume, and sleep quality, but significant limitations persist in stroke rehabilitation applications. Raw sensor data are noisy, and most devices provide only coarse-grained activity counts without reflecting gait quality. A "last mile" disconnect exists between data acquisition and clinical decision-making: devices output isolated time series lacking integration with baseline status, comorbidities, and medication regimens. Furthermore, existing devices lack dynamic adjustment capability, unable to adaptively modify monitoring frequency or alert thresholds based on real-time recovery status.

The LLM-Driven Dynamic Decision Closed Loop

Large language models (LLMs) can process multimodal heterogeneous data, understand natural-language context, and generate personalized recommendations, enabling integration of wearable kinematic data, clinical scale scores, structured nursing-interruption logs (based on the SBAR framework), and environmental factors into a dynamic prediction and decision engine. Embedding the SBAR (Situation-Background-Assessment-Recommendation) communication framework into LLM prompt engineering ensures that alert messages automatically follow structured communication logic. The theoretical innovation of this study lies in constructing a closed-loop model of "wearable monitoring—LLM prediction—dynamic care," shifting the paradigm from passive follow-up to proactive alerting.

Study Design: Multimodal Data Acquisition and LLM-Driven Intervention

Design and Participants

This study combined a prospective cohort with a randomized controlled trial. Phase 1 was a 6-month observational cohort (n=312) for multimodal data acquisition and trajectory subtype mining. Phase 2 randomized new enrollees 1:1 to the intervention group (LLM-driven dynamic care protocol, n=93) or control group (usual home rehabilitation guidance, n=93) for a 6-month intervention follow-up. Inclusion criteria required age ≥65 years, first stroke in the recovery phase (1–6 months post-onset), modified Rankin Scale 2–4, and wearable device usability. Patients with severe cognitive impairment (MMSE<10), severe comorbidities, or concurrent participation in other interventional studies were excluded.

Multimodal Dataset Construction

Data acquisition spanned four dimensions: (1) wearable device data including gait symmetry index, step-length coefficient of variation (CV), activity-volume CV, double-support phase percentage, center-of-gravity displacement, and sample entropy; (2) clinical scale data including FMA, Barthel Index, MMSE, NIHSS, and Morse Fall Scale; (3) SBAR-based nursing interruption event logs recorded daily by caregivers covering timing, background, type, and response; (4) environmental and behavioral data including home safety assessment, medication adherence (Morisky score), and social activity frequency. Raw wearable signals sampled at 100 Hz were preprocessed with a fourth-order Butterworth low-pass filter before gait feature extraction.

Trajectory Mining and LLM Prediction Model

Trajectory subtype identification used dynamic time warping (DTW) to align longitudinal FMA score sequences, eliminating interindividual recovery time-scale differences, followed by K-means clustering (optimal K determined by the elbow method) to identify 3–4 typical subtypes. The detrended fluctuation analysis (DFA) scaling exponent α was computed to assess long-range correlations in the recovery process. The impact of nursing interruptions on trajectory divergence was analyzed using a multistate Markov model with trajectory subtypes as state variables and interruption frequency as a time-varying covariate.

The LLM prediction model was fine-tuned from an open-source clinical large language model, integrating a domain knowledge graph of approximately 500 entity nodes and 2,000 relationship edges (covering rehabilitation guideline recommendations, nursing interruption risk factors, SBAR templates, and drug interaction databases). Prompt engineering employed a multi-turn dialogue template: input was a multimodal time-series feature vector, and output was structured JSON containing predicted FMA values, nursing interruption risk levels, and care recommendations. Model interpretability was assessed via SHAP values, with particular attention to the contribution of nursing interruption frequency. Performance metrics included RMSE/MAE for regression and AUC/Brier score for classification.

Dynamic Care Protocol Design

The intervention group's system automatically extracted the past 24 hours of wearable data, interruption logs, and medication records at 8:00 AM daily, fed them into the LLM model, and pushed outputs to mobile devices after triple safety verification (clinical rule engine interception, expert weekly 10% audit, and patient feedback mechanism). Exercise prescriptions were dynamically adjusted by type, intensity (METs-based), frequency, and duration based on real-time activity and fatigue; when gait symmetry <0.80 or double-support phase >35%, intensity was automatically reduced and balance training increased. Nursing interruption alerts pushed SBAR-structured warnings to caregivers when the model predicted high risk within 24 hours. The control group received standard home rehabilitation guidance (paper handbook + biweekly telephone follow-up + monthly outpatient review).

Results: Trajectory Subtype Identification and Model Prediction Performance

Four Typical Rehabilitation Trajectory Subtypes

DTW distance matrices and K-means clustering identified four typical home rehabilitation trajectory subtypes. Sustained improvement (34.1%) showed monotonically increasing FMA scores, averaging 18.3 points improvement over 6 months, with gait symmetry index rising from 0.62 to 0.85. Fluctuating stable (39.0%) showed overall improvement with periodic fluctuations; activity-volume CV was significantly higher than other subtypes (0.42 vs 0.28), and gait sample entropy decreased significantly during fluctuation periods. Slow decline (15.7%) began continuous decline after month 3, with DFA scaling exponent α significantly elevated (1.12 vs 0.85), indicating loss of recovery momentum. Rapid recovery with plateau (11.2%) showed rapid initial improvement followed by sustained plateau, with lower baseline NIHSS and the lowest nursing interruption frequency.

Nursing Interruption Events as Drivers of Trajectory Divergence

The multistate model showed that nursing interruption events were independent predictors of trajectory subtype divergence. Each additional interruption per month increased the relative risk ratio of belonging to the slow-decline subtype by 2.34 (95% CI: 1.67–3.28, P<0.001). By interruption type, medication-related interruptions had the greatest impact (RRR=3.12), followed by rehabilitation training interruptions (RRR=2.45). Among the high-frequency interruption group (≥1.5 per month), 42.2% ultimately belonged to the slow-decline subtype, versus only 6.7% in the low-frequency group (<0.5 per month). Mechanistically, nursing interruptions drive trajectory decline through dual pathways: "motor chain disruption" (training continuity loss leading to neuromuscular control pattern extinction) and "psychological motivation erosion" (repeated interruptions triggering frustration and reduced self-efficacy), exhibiting a threshold effect with exponential risk increase above 2 interruptions per week (OR=3.42).

LLM Prediction Model Performance

On the test set (n=57), the LLM model achieved an RMSE of 3.2 points for 1-month FMA prediction and 5.8 for 3-month prediction. Nursing interruption risk prediction achieved an AUC of 0.89 (95% CI: 0.83–0.94) at 1 month and 0.84 at 3 months. Calibration curves showed good agreement between predicted probabilities and observed rates (Brier score=0.12), outperforming the baseline model using only kinematic data (Brier score=0.21). SHAP analysis revealed that nursing interruption frequency had the highest contribution (mean |SHAP|=0.24), followed by nighttime activity (0.18) and medication adherence (0.15). Incorporating nursing interruption events into prompt engineering improved sensitivity for slow-decline prediction from 0.68 to 0.85 and specificity from 0.79 to 0.91.

Intervention Effects of the Dynamic Care Protocol

Motor Function Recovery and Rehabilitation Adherence

The intervention group's FMA change from baseline to 6 months (ΔFMA) was 18.4±9.2 points versus 11.7±8.5 in the control group, a between-group difference of 6.7 points (95% CI: 4.1–9.3, P<0.001), with effect size Cohen's d=0.76. Generalized estimating equation analysis showed a significant time-by-group interaction (Wald χ²=12.34, P=0.002), with the intervention group's FMA rise slope markedly steeper, curves separating from month 2. For adherence, the intervention group averaged 14.2 hours of daily effective wearable time versus 10.5 in controls (P<0.001), and exercise prescription completion rates were 82.3% vs 61.5% (P<0.001). Subgroup analysis showed greater adherence improvement in patients with baseline MMSE ≥24.

Reduction in Nursing Interruptions and Fall Risk

The intervention group's 6-month nursing interruption rate was 0.42 per person-month versus 0.89 in controls, incidence rate ratio IRR=0.47 (95% CI: 0.35–0.63, P<0.001), with interruption duration median reduced from 28 to 12 minutes. Fall rates were 0.08 vs 0.19 per person-month, IRR=0.42 (95% CI: 0.24–0.73, P=0.002). Unplanned readmission rates were 12.5% vs 25.9% (RR=0.48, P=0.028). Caregiver burden (Zarit Burden Interview) decreased by 5.5 points more in the intervention group (95% CI: −8.2 to −2.8, P<0.001).

Differential Response Across Trajectory Subtypes

Intervention effects showed significant heterogeneity across subtypes (interaction P=0.008). The ΔFMA difference was 4.2 points in sustained improvement, 7.8 in fluctuating stable, and 11.3 in slow decline (95% CI: 6.2–16.4, P<0.001). This trend indicates the dynamic care protocol has a more pronounced salvage effect in patients with poorer baseline recovery trajectories, likely through early identification of decline signals and triggered alert interventions. Slow-decline patients had the highest baseline interruption frequency (1.2 per person-month), and the greatest post-intervention reduction (IRR=0.31), suggesting interruption management is the key mediating mechanism for benefit.

The Value of QSevidence Throughout the Research Workflow

Guideline Retrieval and Evidence Integration During Design

During protocol design, this study required systematic retrieval of recommendations from the Chinese Stroke Rehabilitation Treatment Guidelines regarding exercise prescriptions, medication management, and complication prevention, structured as knowledge graph entity nodes. The QSevidence medical AI tool's AI guideline retrieval capability helps researchers rapidly locate relevant guideline entries and their evidence levels, transforming scattered recommendations into structured evidence summaries with traceable sources, significantly reducing manual retrieval costs during knowledge graph construction. QSevidence also supports bilingual retrieval, which is particularly valuable for stroke rehabilitation reviews requiring simultaneous inclusion of Chinese-language journals (e.g., Chinese Journal of Physical Medicine and Rehabilitation) and international journals (e.g., Stroke, Journal of NeuroEngineering and Rehabilitation).

The Retrieve-Compare-Synthesize Workflow During Review Writing

When writing the discussion, researchers needed to compare this study's trajectory subtype findings with prior inpatient rehabilitation trajectory research and integrate evidence on nursing interruption impacts across settings. QSevidence's retrieve-compare-synthesize workflow assists researchers in cross-database RCT result comparison, unifying efficacy evaluation standards (e.g., FMA, Barthel Index), thereby reducing the interference of intervention protocol heterogeneity on evidence synthesis. For evidence gap identification, QSevidence helps researchers quickly discover under-researched areas such as "automated nursing interruption identification algorithms in home settings," providing data-driven support for future research direction selection.

Methodological Standard Alignment and Reporting Quality

This study's combined prospective cohort and RCT design requires strict adherence to the CONSORT statement and STROBE reporting guidelines for observational studies. QSevidence can assist in retrieving and structuring the core items of these methodological standards, helping researchers align reporting requirements during protocol registration and manuscript writing. At the GRADE evidence grading level, QSevidence can structurally assess this study's main findings across five dimensions—risk of bias, consistency, indirectness, precision, and publication bias—providing standardized input for future systematic reviews evaluating the evidence quality of these findings.

Limitations and Future Directions

This study has several limitations. First, the single-center design (n=186) limits fine-grained trajectory subtype delineation and statistical power for subgroup analyses; the impact of different family caregiving support levels on nursing interruptions could not be adequately stratified. Second, approximately 12% of participants had data missing due to device discomfort or forgetfulness, potentially affecting estimation precision for the slow-decline trajectory. Third, the LLM was fine-tuned on a specific knowledge graph, and its generalization across different regions and healthcare systems remains unvalidated; it may generate unsafe recommendations for rare interruption events (hallucination rate 2.1%). Fourth, the dynamic care protocol contains multiple components whose independent contributions cannot be distinguished, and the intervention group may have experienced Hawthorne effects.

Future research should focus on: conducting multicenter RCTs including patients with varying caregiving support levels to validate generalizability; exploring real-time voice-interactive interventions to reduce digital literacy dependency in elderly patients; developing automated nursing interruption identification algorithms based on wearable accelerometer signals; establishing LLM output safety review mechanisms (confidence thresholds and manual review triggers); and embedding the "wearable monitoring—LLM prediction—community intervention" closed loop into regional health information platforms for scaled deployment. QSevidence can continue to provide guideline update tracking, evidence grade maintenance, and study protocol comparison support throughout this process.

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Medical Disclaimer

This article is an evidence-based research review and academic discussion and does not constitute clinical treatment advice. The specific implementation of the wearable device and large language model-based dynamic care protocol should be carried out by qualified neurorehabilitation healthcare teams after individual patient assessment. The prediction model performance data and intervention effects cited herein originate from a single study; their effect sizes and applicability may vary across populations, device models, algorithm versions, and study designs. Large language models carry hallucination risks, and all outputs must pass clinical rule engine and professional review before application. Readers should make independent professional judgments based on the latest guideline consensus and individual patient circumstances before applying this content to clinical practice.