Background and Rationale:
Immobility during hospitalization is a widespread and often preventable cause of disability, particularly among older adults and patients recovering from critical illness. Research demonstrates that very modest activity confers significant benefits: walking just 275 steps per day reduces 30-day readmissions by 10% in older inpatients, and a single 20-minute cycling session on weekdays shortens length of stay and improves functional recovery in ventilated ICU patients. However, most patients remain largely immobile despite being physiologically capable of low-intensity exercise.
The Bedside Bike addresses key barriers to inpatient mobility by eliminating the need to transfer out of bed (reducing fall risk), requiring less than 1 minute of staff setup time (addressing staffing constraints), providing adjustable resistance for progressive exercise, and allowing patients to exercise independently without navigating hallways with IV lines and catheters. The device is FDA-cleared (Class I, product code ION, 21 CFR §890.5370) and has been tested with healthcare providers and healthy volunteers without safety concerns.
Study Design and Implementation:
This is a prospective, single-arm quality improvement implementation study conducted across three Indiana University Health hospital units: IU Methodist Post-ICU step-down unit, IU Bloomington ortho/neuro and medical/surgical units, and IU North medical/surgical unit. The study uses a pragmatic design embedded within routine clinical care, with contemporaneous historical unit-level metrics from 2025 serving as comparison benchmarks.
Device Setup and Operation:
After informed consent, the Bedside Bike is installed at the bedside and patients receive training on its use. The device clamps securely to the bed frame with pedals positioned approximately 10 cm above the mattress. Patients can adjust resistance via a dial and exercise at their own pace, targeting at least one 15-minute session daily. A quick-release lever allows the device to be disengaged in under 2 seconds if needed for patient transfers or clinical procedures.
The Bedside Bike includes a Bluetooth-enabled telemetry module that automatically records exercise metrics including duration, revolutions per minute, and resistance level. Data is encrypted and uploaded nightly to a secure REDCap database at Indiana University. Between patients, the device is cleaned with standard hospital disinfectant, and Clinical Engineering performs weekly quality assurance checks.
Integration with Standard Care:
Patients continue all standard medical care without restrictions. Physical therapy, occupational therapy, and progressive ambulation programs proceed as usual. The Bedside Bike is designed to augment-not replace-skilled therapy sessions. There are no restrictions on pain medications, sleep aids, or nutritional support. Cycling is temporarily paused during hemodialysis or when telemetry alarms require clinical attention.
Data Safety Monitoring:
The Principal Investigator leads weekly data safety monitoring huddles to review enrollment progress, device utilization patterns, and adverse events. Any unanticipated serious adverse event triggers immediate IRB notification within 48 hours. The study protocol includes automatic pause criteria: enrollment halts if any device-related serious adverse event occurs or if feasibility drops below 40% for two consecutive weeks, pending comprehensive safety review.
Duration and Follow-up:
The enrollment period spans 3 months (February through April 2026) with approximately 16 patients enrolled monthly across all sites. Each participant is followed through hospital discharge plus 60 days post-discharge. The final 60-day follow-up is completed by the end of June 2026, with database lock and analysis extending through September 2026.
Statistical Design:
With N=80 participants, the study provides 90% power (α=0.05, two-tailed, assuming SD of 2 days) to detect a clinically meaningful 0.2-day reduction in length of stay compared to historical unit averages. This sample size accounts for potential dropouts and incomplete data. Missing data under 5% will be handled using multiple imputation (m=5 iterations); higher rates of missingness will default to complete-case analysis.
This pragmatic feasibility study will provide critical data on device utilization, safety profile, and preliminary effectiveness signals to inform the design of larger randomized controlled trials and guide clinical implementation strategies for in-bed cycling technology across diverse inpatient populations.
