Objective functional assessment in orthopedics: The emerging role of wearable technologies

Abstract

Functional outcome assessment in orthopedic surgery has long relied on patient-reported measures and intermittent clinical evaluations. While these tools provide valuable insight, they are often limited by subjectivity and may not accurately reflect real-world functional performance. Wearable technologies (“wearables”) have emerged as a promising means to capture objective, continuous, and ecologically valid data on mobility, activity levels, and joint mechanics. These devices, which range from consumer-grade fitness trackers to specialized medical-grade sensors, can enable detailed monitoring of recovery across various orthopedic contexts, including joint arthroplasty, fracture care, spine surgery, and sports injuries. By quantifying parameters such as gait patterns, step count, range of motion, and activity intensity, wearables offer a more nuanced understanding of functional recovery over time. Early evidence supports their ability to complement traditional assessments and enhance postoperative monitoring. However, challenges remain, including variability in device accuracy, patient adherence, data interpretation, and integration into clinical practice. As these technologies continue to evolve, they hold significant potential to personalize rehabilitation, support remote care models, and improve outcome measurement in orthopedic surgery. Ongoing research and standardization efforts will be critical to unlocking their full clinical utility.

Key Words: Orthopedics; Wearable devices; Arthroplasty; Patient-reported outcomes measures; Outcomes; Functional status

Core Tip: Traditional outcome measures in orthopedic surgery rely heavily on patient-reported scores and periodic clinical evaluations, which may not fully capture real-world function. Wearable technologies provide continuous, objective, and ecologically valid data on mobility, activity, and joint mechanics. From fitness trackers to medical-grade sensors, these devices enable detailed monitoring of recovery after arthroplasty, fracture care, spine surgery, and sports injuries. By quantifying gait, step count, range of motion, and activity intensity, wearables complement traditional assessments and support personalized rehabilitation. Despite challenges with accuracy, adherence, and integration, ongoing research suggests they may transform postoperative monitoring and functional outcome evaluation.

INTRODUCTION

Orthopedics within the United States is shifting to a model that increasingly rewards value over volume in healthcare delivery[1-3]. Outcome measures are utilized to demonstrate value, specifically by measuring the degree of pain relief and improved physical function a patient experiences after surgical intervention[4]. Patient-reported outcome measures (PROMs) rely on validated, evidence-based questionnaires to accurately assess a patient’s pain, stiffness, and function, while incorporating both physical and emotional health. These must contain sufficient reliability, validity, and responsiveness to be useful for medical research and patient care, and many healthcare systems are implementing PROMs into routine clinical practice to track patient progress and their own clinical practice[5]. Ideally collected before the encounter with the physician, PROMs should be scored and available at the time of the encounter with the surgeon to assist with shared medical decision making[6,7]. There are multiple PROMs that are utilized for research or clinical practice [such as the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS, JR), Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS, HR), EQ-5D, KSS, WOMAC, LEFS, NPRS, GROC etc.]; however, it is critical to identify an appropriate instrument for a specific purpose, such as a diagnosis specific, regionally specific, or generic quality of life PROM[6,7].

Despite their benefits, PROMs present with inherent limitations. One major limitation of frequently used measures is the ceiling effect, which may be present when a larger subset of patients in a study (15%-20%) have scores on a variable that are at or near the possible upper limit[7]. Additionally, a recent review identified several biases that can be introduced, including non-response bias, collection mode-related bias, fatigue bias, timing bias, language bias, proxy response bias, and recall bias[8]. A patient’s baseline pain and functional status, alongside their socioeconomic status, educational level, and psychosocial status, may impact their postoperative satisfaction[9,10].

A variety of new technologies have emerged in clinical practice to enhance measurement data and inform medical decision-making. Wearable medical technologies have introduced the benefit of objective, continuous, and real-world data that have the potential to change the orthopedic landscape[11,12]. They provide a novel way to capture data surrounding function and daily activity, effectively supplementing clinical measures and PROMs, providing a better understanding of patient recovery. Recent devices include smart watches, activity trackers, wearable sensors, and smart orthopedic braces that can be synced to a smartphone, computer, or tablet to transmit patient data securely and instantly to healthcare providers[12-14]. These wearable devices may offer the capability of measuring real-time function data during rehabilitation and recovery, eliminating the need for direct supervision.

The objective of this review is to provide a comprehensive overview of wearable technologies in orthopedics. Specifically, we summarize the types of wearable devices currently available, describe the key functional parameters they are capable of measuring, and examine their clinical applications across various subspecialties. In addition, we discuss the challenges associated with integrating these technologies into clinical practice and highlight future directions for their development and implementation.

LITERATURE REVIEW

This work was designed as a review to provide a focused synthesis of the role of wearable technologies in orthopedic functional outcome assessment. Relevant literature was identified through targeted searches of PubMed, MEDLINE, and EMBASE using combinations of terms including orthopedics, wearable devices, arthroplasty, trauma, spine surgery, sports medicine, PROMs, and functional recovery. Expert opinion was applied to guide the selection of clinically relevant studies and key publications. Both consumer-grade and medical-grade wearable technologies were considered, with particular attention to studies evaluating functional outcomes. This narrative approach was intended to summarize current knowledge, highlight emerging applications, and outline challenges and future directions, rather than to provide an exhaustive systematic review.

TYPES OF DEVICES

Several types of devices exist to track real-time metrics of activity (Figure 1). Consumer-grade devices such as the Apple Watch, Fitbit, and Garmin smart watch offer access to potentially clinically meaningful data through platforms that are already widespread, familiar to patients, and relatively affordable. Furthermore, the prevalence in the general population reduces the learning curve and may improve both the adoption and adherence to monitoring protocols. The ability to capture other clinically meaningful data, such as heart rate, respiratory rate, and temperature, offers additional value in monitoring the postoperative course, and the seamless integration with smartphones and health applications also facilitates easy data sharing. Several of these devices have been reported as reliable and valid for measuring gait speed, step length, cadence, vertical oscillation, and ground contact time[15,16]. Additionally, several studies have utilized these devices for monitoring patient physical activity and function following orthopedic surgery, and implementation of these devices has been found to improve adherence and exercise quality in rehabilitation programs[17-19]. However, intra- and inter-device reliability and accuracy require further testing[20,21]. Algorithms are often proprietary, access to raw sensor data is often restricted, and the devices are not optimized for precise medical measurements. Additionally, there is a need for a better understanding of both clinical benefit and validation before devices can be routinely implemented in postoperative care, as while they have demonstrated accuracy and reliability for healthy individuals, their performance in post-surgical patients with altered movement patterns is less well established[21].

Figure 1  Various types of devices are used to track real-time activity.

In contrast, medical-grade devices such as the Axivity accelerometers, APDM Opal sensors, Physilog systems, and ActiGraph monitors represent a significant step up in measurement precision. These devices are specifically designed for clinical and research applications and often are validated against gold-standard motion analysis systems[22,23]. Generally, these devices demonstrate good reliability and variability in physical activity conditions, with high sampling rates and access to rich, granular kinematic data[24-27]. However, these devices may prove less practical and comfortable for patients depending on the location worn, and are often single-purpose devices for pure movement data capture, thus making them less accessible and interactive for patients[28]. Finally, these devices can be markedly more expensive than the consumer-grade devices, which may limit their widespread implementation in both healthcare systems and individual patients. Unlike consumer devices, patients are unlikely to already own these devices, which may result in logistical challenges with device dispersal and long-term monitoring.

The sensor modality implemented in each of the wearable technologies also merits consideration, as it ultimately dictates the type and quality of data captured. Inertial measurement units (IMUs) combine accelerometers, gyroscopes, and magnetometers to measure angular velocity, linear acceleration, and orientation. Not only are these sensors often compact, but depending on the device, measurements can be obtained at a rate of up to 1600 Hz, allowing for high-resolution capture of dynamic movement patterns[29]. Beyond the technologies utilized in IMUs, a range of novel technologies is being explored to enrich physiological and biomechanical monitoring. Wearable electromyography systems can allow for real-time measurement of muscle activation patterns and may prove useful for monitoring neuromuscular retraining and post-injury rehabilitation[30-32]. Pressure-sensing shoe insoles have demonstrated utility for real-time gait analysis, not only in measuring temporal parameters but also in detecting discrete gait events such as heel-strike and toe-off[33]. These capabilities can provide meaningful insight into gait asymmetry, weight-bearing progression, and functional recovery after lower extremity surgery. Finally, smart textiles, which embed sensors directly into fabric, represent a novel but rapidly advancing class of wearable technology with promising healthcare applications. These platforms would allow for unobtrusive and continuous monitoring, with applications that extend beyond biomechanical analysis to include therapeutic applications such as thermo- or electrotherapy[34].

PARAMETERS COMMONLY MEASURED

With wearable technologies now increasingly incorporated into orthopedic care, a critical question is which functional domains they can quantify accurately and reproducibly to inform clinical decision-making. Current literature robustly demonstrates that wearable technologies, primarily IMUs, and to a lesser extent pressure insoles and stretch sensors, can objectively and accurately quantify key functional parameters such as spatiotemporal gait metrics, functional transitions, postural sway, joint-specific range of motion (ROM), and long-term activity patterns in orthopedic patient populations (Figure 2). These findings have been validated against gold-standard laboratory methods across diverse post-surgical and degenerative conditions[35-39].

Figure 2  Various ways to use wearable technology in orthopedic surgery.

Gait metrics represent the most thoroughly validated application of wearable sensors in orthopedic populations[40]. Devices ranging from single sensors to multi-segment IMU arrays have demonstrated high accuracy in measuring spatiotemporal parameters such as gait speed, step count, cadence, stride, and step length, and various measures of gait asymmetry and variability. These metrics have been validated in diverse cohorts, including post-total knee and hip arthroplasty, anterior cruciate ligament (ACL) reconstruction, spine surgery, and osteoarthritis populations, typically using optical motion capture or video analysis as the gold standard. Notably, several studies report strong correlation coefficients and reduced error for spatiotemporal gait variables, underscoring their reliability in both laboratory and real-world settings[35,36,41-44]. Importantly, gait asymmetry and variability metrics have emerged as sensitive indicators of pathology and recovery, particularly after ligament reconstruction and arthroplasty, often outperforming simple step counts in discriminating between disease states and monitoring longitudinal progress[45-47].

Direct measurement of sit-to-stand and similar functional transitions is feasible using IMUs and associated event-detection algorithms. These approaches have been validated against motion capture and demonstrate strong performance in identifying functional events in orthopedic patients, though such measures are less commonly reported compared to traditional gait parameters[35]. Quantitative assessment of balance and postural sway has been achieved with IMU-based platforms in spine surgery and myelopathy cohorts. While preliminary studies show promise, especially using single trunk-mounted sensors, this domain is less developed and less frequently validated than gait or joint-specific metrics[38,48].

Joint-specific ROM, particularly for the knee and hip, can be accurately measured using multi-IMU arrays or specialized sensor-embedded braces. These systems have demonstrated robust agreement with goniometer and optical motion capture measurements, with decreased mean error across activities such as walking, squats, and stair navigation[35,41-43,49,50]. Emerging sensor modalities, such as soft stretch sensors and smart braces, are extending the capacity for continuous, free-living ROM assessment, especially in post-surgical rehabilitation[39,51].

Wearable sensors reliably quantify activity patterns, distinguishing between ambulatory and sedentary behavior by measuring the proportion of time spent walking, standing, or sitting, and by tracking step and ROM metrics over days to weeks. These time-resolved assessments have been validated in large, post-arthroplasty and osteoarthritis cohorts, with cadence-based measures shown to provide enhanced sensitivity to recovery compared to simple step counts alone[37,52,53].

Despite the proliferation of consumer wearables capable of recording sleep and heart rate, there is a paucity of studies rigorously validating these metrics in orthopedic populations against polysomnography or electrocardiography. Most studies do not report sufficient accuracy or do not directly link sleep or heart rate variability to orthopedic functional outcomes. However, wearables have been used to estimate immobilization and inactivity, which may serve as proxies for sleep or rest in select contexts. Collectively, these validated functional domains form the basis of emerging clinical applications across orthopedics, which are explored in the following section.

APPLICATIONS IN JOINT ARTHROPLASTY

The incidence of total joint arthroplasty (TJA) continues to grow across the United States due to an aging population and patient satisfaction with pain relief and improved function postoperatively[54,55]. Wearable technologies offer a unique opportunity to measure this improvement in patient outcomes and satisfaction (Table 1)[56-59]. Traditional postoperative recovery assessments, such as the Timed Up and Go (TUG) test and the six-minute walk test, are widely regarded as gold-standard measures for evaluating mobility, balance, and walking ability[60,61]. However, these measurements are taken in person by healthcare providers and are not continuous measurements replicating daily activities. Wearable devices in TJA include accelerometers, which measure the acceleration of a limb or the whole body, gyroscopes, which capture orientation and angular velocity, and inertial measurements, which combine accelerometers, gyroscopes, and magnetometers to report patient position, orientation, and movement[62].

Table 1 Wearables in total joint arthroplasty.

Ref.	Type of surgery	Device used	Key findings
Kuiken et al[59], 2004	TKA	Custom Goniometer	Patients had higher mean total activity rates on days they were not receiving device feedback compared to days they did receive feedback during TKA recovery, measured by ROM and mean activity rate
Kwasnicki et al[63], 2015	TKA	e-AR accelerometer (Imperial College London)	A rate of 89% accuracy was achieved in classifying patients into preoperative, normal, and 24-week postoperative groups based on outcomes, measured by TUG time and ROM
Toogood et al[53], 2016	THA	Fitbit accelerometer (Fitbit LLC)	Mean compliance over 30 days was 26.7 days, or 89%
Chiang et al[66], 2017	TKA	APDM OPAL	A rate of 17% of patients felt uncomfortable with the sensor belt, measuring patient satisfaction
Van der Walt et al[65], 2018	TKA/THA	Garmin Vivofit 2 accelerometer (Garmin Ltd)	Patients receiving device feedback had significantly higher mean daily step counts than those who did not receive device feedback
Saporito et al[64], 2019	THA	Custom accelerometer and barometer	Remote TUG can be estimated in older adults using 3-day of ADLs from a wearable pendant

TKA: Total knee arthroplasty; THA: Total hip arthroplasty; ROM: Range of motion; TUG: Timed Up and Go; ADLs: Activities of daily living.

Several studies have assessed the correlation between wearable-derived metrics and established outcome measures such as ROM and the TUG test; however, different devices vary in accuracy[63,64]. Kwasnicki et al[63] evaluated 14 total knee arthroplasty (TKA) patients using an ear-worn e-AR accelerometer and found that perioperative sensor scores were correlated with TUG and ROM outcomes, though not significantly for all activities. Similarly, Saporito et al[64] utilized a pendant-worn sensor in 239 older adults living in a shared living community, and reported a strong correlation with standardized in-lab TUG assessments.

Devices also have varying impacts on activity levels. In a randomized control trial by Van der Walt et al[65] investigating the Garmin Vivofit 2 (Garmin LTd) accelerometer, they found that the mean daily stop counts receiving feedback were significantly higher than those not using the device at multiple timepoints: Week 1 (43% increase), week 2 (33%), week 6 (21%), and 6 months (17%). These findings reflect how step count, a key proxy for gait recovery, changes over time, illustrating the utility of wearables in monitoring gait normalization trajectories. Chiang et al[66] found that 83% of patients reported no discomfort with thigh- and calf-worn sensors, while Toogood et al[53] reported an 89% compliance rate with daily use of an ankle-worn Fitbit in total hip replacement patients, with most patients wearing devices continuously.

An important measure is comparing preoperative and postoperative activity alongside PROMs. In a prospective study including 128 THA and 139 TKA patients, Lyman et al[67] investigated the feasibility of using mobile technology to collect daily step data via their smartphone accelerometer and biweekly PROMs (HOOS JR and KOOS JR, respectively) to track recovery over 6 months. The most robust recovery occurred within the first 2 months, demonstrating that groups with higher preoperative steps were more likely to recover their maximum daily steps at an earlier time point. There were modest correlations between step counts and PROMS scores[67]. Bendich et al[68] studied 22 patients undergoing TJA and demonstrated that changes in Fitbit-measured daily step counts and minutes active were significantly associated with changes in VR-12 and HOOS/KOOS scores over a six-week postoperative period. Notably, high device comfort and compliance were observed. Several other groups have utilized mobile health for postoperative follow-up, often providing an added benefit of a reduction in cost, time, and hospital visitation requirements[69-72]. Additionally, this has been done in surgical specialties outside of orthopedic surgery[73-76]. Other research suggests that wearable devices have discriminatory potential for identifying frailty in older adults and may provide a convenient way to monitor changes in mobility in unsupervised environments[77-80].

APPLICATIONS IN ORTHOPEDIC TRAUMA

Trauma-related injuries can lead to prolonged impairment in mobility, function, and quality of life[81]. While PROMs have traditionally been used to assess recovery, wearable devices have emerged as valuable tools for objective, real-time monitoring of patient activity, function, and adherence to recovery recommendations[82-84] (Table 2). Key applications in clinical practice include ensuring patients follow weight-bearing precautions, tracking early postoperative mobility (especially after hip fractures), and stratifying fall risk in high-risk geriatric populations[82-85].

Table 2 Wearables in orthopedic trauma.

Ref.	Patient population	Wearable device(s)	Key findings
Taraldsen et al[91], 2014	Elderly patients post-hip fracture in RCT	Accelerometer-based activity monitor	Patients receiving comprehensive geriatric care spent more time upright and recovered function more quickly than controls
Kammerlander et al[89], 2018	Older hip fracture patients post-surgery	Pressure-sensing insoles	Patients frequently exceeded prescribed partial weight-bearing. Wearables objectively revealed poor adherence to weight-bearing restrictions
Marmor et al[88], 2022	Patients with orthopedic trauma (systematic review)	Multiple (accelerometers, pressure insoles, etc.)	Wearable monitors are increasingly used to assess recovery post-fracture. Most commonly studied in hip and fragility fractures
Lockhart et al[94], 2021	Community-dwelling older adults	Sternum-mounted IMU	Gait variability and complexity predicted future falls over 6 months with approximately 82% accuracy. Validated use of wearables in fall risk assessment

RCT: Randomized controlled trial.

After fracture reduction followed by either operative or nonoperative management, patients are often given partial or non-weight-bearing recommendations to allow for proper bone healing[86,87]. Wearable sensors, such as pressure-sensing insoles, allow for real-time monitoring of weight-bearing protocols[88]. Recent studies utilizing pedobarographic insoles have demonstrated the feasibility of dynamic load tracking technologies[85]. Many patients, however, inadvertently exceed their weight-bearing limits[89]. These results highlight the opportunity to identify patients at risk of overloading a limb after traumatic injury and could help to accelerate recovery. However, the choice of device is critical for accurate monitoring of weight-bearing protocols. In a prospective study, Keppler et al[82] evaluated the accuracy of wearable devices in older adults with lower-limb injury, and demonstrated that accelerometer-based wearables underestimated cumulative step counts by about -21.8% to -29.0% vs an error rate of 0%-4.6% in pressure-based StappOne insoles. Additionally, wrist devices frequently registered zero steps in patients using assistive devices or walking slowly, highlighting their limited utility in specific populations[82].

Wearable devices have additionally proven valuable for the detection of early return to function, as well as measuring inactivity in patients with hip fractures. Particularly within this population, restoring early mobility and function has been linked to improved outcomes in risk of falls, mortality, disability, and quality of life[84,90]. Metrics such as step count, walking speed, and upright time provide objective insight into functional recovery. In a randomized trial regarding hip fracture patients. Taraldsen et al[91] found that when treated with comprehensive geriatric care compared to those receiving standard orthopedic care after a hip fracture, older adult patients spent more time in upright positions and had better lower limb function in the early postoperative period. Conversely, wearable technologies can also be used to track sedentary behavior and reduce the risk of complications such as pain, delirium, or poor motivation[92,93]. Interestingly, objective measurements from wearable devices have helped to elucidate that partial weight-bearing restrictions may reduce overall mobility compared to full weight-bearing, as patients prescribed toe-touch or limited weight-bearing often have slower step counts and diminished gait metrics. Overall, wearable monitors can provide continuous objective data, detecting both positive and negative trends in activity after hip fractures.

An added benefit is the opportunity for fall risk stratification in geriatric patients recovering from a traumatic orthopedic injury or fall[92,94]. Older adults who have sustained fractures from falls are at high risk of recurrent falls. Recent studies have shown that gait metrics captured by wearables correlate strongly with fall propensity. In a prospective study, Lockhart et al[94] used a random forest classifier to achieve 81.6% accuracy, 86.7% sensitivity, and 80.3% specificity in predicting patients who would fall within the following 6 months based on a 10 m walking test that measured gait variability, complexity, and smoothness.

APPLICATIONS IN SPINE SURGERY

With increased demand for objective outcome measures in spine surgery, wearable technologies continue to emerge in both inpatient and outpatient settings[95-98] (Table 3). Specifically, IMUs are particularly promising for their ability to provide continuous, ecologically valid data on postural alignment, walking speed, gait symmetry, and movement quality[29,99,100].

Table 3 Wearables in spine surgery.

Ref.	Type of surgery	Device used	Key finding
Natarajan et al[109], 2022	Degenerative lumbar spine disease (observational study)	Chest-based inertial wearable sensor (MetaMotionC)	Distinct gait patterns were observed for lumbar disc herniation, spinal stenosis, and chronic mechanical low back pain. LSS showed gait asymmetry and variability; LDH showed reduced gait velocity and cadence
Sheeran et al[110], 2024	Persistent non-specific low back pain	IMUs	Significant variations in range of motion during flexion, extension, and lateral flexion
Boutaayamou et al[114], 2025	Gait analysis	IMU-based system	Intraclass correlation coefficients exceeded 0.90 for spatiotemporal gait parameters including stride length, cadence, and speed, indicating an accurate method for analysis using IMUs
Bienstock et al[115], 2022	Lumbar laminectomy	Accelerometry	Continuous data from accelerometers effectively delineates 3 distinct stages of postop recovery and supplemented patient-reported outcomes
Inoue et al[116], 2020	Lumbar spinal surgery	Wearable activity tracker	Activity decreased 1 month postop followed by gradual recovery within 3 months although patient-based outcomes already indicated improvement at 1 month
Smuck et al[117], 2018	Lumbar spinal stenosis decompression	Wearable activity monitors (e.g., accelerometers)	6 months after surgery participants demonstrated significant improvements in self-reported function and objectively measured physical capacity, but real-life physical activity remained stagnant
Schulte et al[120], 2010	Lumbar decompression surgery	Step activity monitor (accelerometer-based)	Objective step activity increased post-surgery, indicating improved functional mobility
Sakaguchi et al[38], 2024	Corrective spinal fusion surgery	Triaxial accelerometer	Gait sway and motor function improved significantly post-surgery, measurable via accelerometry

IMU: Inertial measurement unit; LSS: Lumbar spinal stenosis; LDH: Lumbar disc herniation.

Patients undergoing spine surgery for degenerative or deformity-related conditions often experience postural imbalance and modified spinal kinematics that can endure or recur despite successful mechanical correction[101,102]. Postural sway, trunk inclination, and sagittal alignment during standing and movement tasks are specific measurements that could be recorded with wearable sensors[103-106]. Lumbar-mounted IMUs have been used to calculate alterations in sagittal trunk position before and after lumbar decompression and fusion[105,107]. Patients with lumbar spinal stenosis and degenerative lumbar disc disease often exhibit persistent trunk flexion during gait as a compensatory strategy that increases spinal canal dimensions and reduces nerve compression[108]. This flexed posture results in significant functional limitations that have been analyzed by IMU-based assessments of altered hip extension angle, maximum hip flexion moment, and step length[108,109]. Additional work has looked at the use of IMUs as a quantitative tool for spine and pelvic kinematics. Sheeran et al[110] analyzed spine movements of 81 participants with non-specific low back pain as well as 26 controls. With IMUs placed on the sacrum, fourth, and second lumbar vertebrae, and seventh cervical vertebra, they found significant differences in ROM during flexion, extension, and lateral flexion[110]. These findings highlight the potential of IMUs for offering unique insight into spinal kinematics and functional postural control.

Walking speed and gait analysis are often observed when looking at functional recovery following spine surgery, as diminished gait speed can be indicative of troublesome long-term outcomes. IMUs through the form of accelerometers and gyroscopes allow for real-time, three-dimensional observation of speed, cadence, stride length, and gait variability, enabling a more comprehensive evaluation of gait[111]. These systems have been validated through high correlation and agreement with gold-standard optical motion capture and instrumented treadmill systems for gait analysis[112,113]. Boutaayamou et al[114] showed the accuracy and reliability of IMUs for this use with intraclass correlation coefficients exceeding 0.90 for spatiotemporal gait parameters, including stride length, cadence, and speed. Accelerometry data have also revealed distinct phases of post-spinal surgery, with an initial rapid improvement in physical activity that then leads into a slower recovery phase[115]. Recovery eventually reaches a stable state as the quality of the patient’s gait lags behind subjective pain relief and radiographic changes[116,117]. Gait analysis studies have further confirmed this spatiotemporal and stride improvement postoperatively, although they may not normalize until a year out and are not always directly correlated with pain scores[118-120].

The mechanical success that comes with neural decompression or spinal alignment in spine surgery does not always lead to immediate functional triumph. Neuromuscular adaptation and movement compensation strategies are of increased importance since these cannot always be seen on imaging. Several studies have revealed that even after sagittal alignment, changes in lower limb, spinal, and pelvic kinematics may persist during sit-to-stand and walking tasks[38,102,121]. Atypical compensatory movements may include altered hip and knee motions, trunk flexion, and pelvic tilt, which can result from incomplete neuromuscular restoration. Bailey et al[122] evaluated 15 spinal deformity post-surgical patients with improved static alignment and found that their lower limb compensatory strategies persisted and only partially normalized compared to controls. Severijns et al[121] and Saad et al[123] further demonstrated that aberrant spinopelvic and lower limb movement strategies remain during functional tasks, often different than what is seen as radiographic improvements. This discrepancy highlights the utility of wearable IMUs to fully understand movement strategies and recovery. Importantly, early characterization of persistent gait asymmetries can inform the need for focused physical therapy programs that target neuromuscular control and functional movement, beyond radiographic correction alone[124].

APPLICATIONS IN SPORTS MEDICINE AND LIGAMENT RECONSTRUCTION

In patients undergoing ligament or tendon repairs or reconstructions, advances in wearable sensor technologies may allow for more personalized return-to-sport (RTS) criteria that adequately capture functional recovery and can ultimately prevent reinjury[125,126] (Table 4). For example, readiness following ACL reconstruction is often assessed using the limb symmetry index, which compares performance benchmarks such as strength or hop distance between limbs[127]. RTS decisions are commonly made using a 90% threshold, but when the dominant limb is injured, patients tend to achieve higher limb symmetry index values[127,128]. These metrics may potentially lead to overestimation of recovery and premature RTS[128]. Wearable IMUs can help detect more nuanced movement asymmetries that happen with gait, hopping, and landing tasks[129-131]. These devices have been shown to reliably identify persistent inter-limb kinematic differences and offer further insight than what standard strength tests may examine, potentially aiding in postoperative rehabilitation needs[132,133].

Table 4 Wearables in sports medicine.

Ref.	Type of surgery	Device used	Key finding
Bell et al[143], 2017	ACL reconstruction	Accelerometer (Actigraph)	Only 24% of post-operation patients met the recommended steps guideline. Moderate-to-vigorous physical activity was also significantly lower in postop patient
Dwyer et al[144], 2025	ACL reconstruction	Accelerometer	Physical activity outcomes during the first year of recovery showed gradual improvement, but physical activity targets were still not met at 3 months, 6 months, or 12 months postop
Werner et al[145], 2025	ACL reconstruction	Waist-worn accelerometer (Actigraph)	Average daily steps did not change from 6 months to 18 months post-ACL reconstruction
Laurent et al[148], 2020	Achilles tendon rupture recovery	Wearable insole sensors	Plantar pressure distribution and activity demonstrated little improvement at 12 weeks
Tavakkoli Oskouei et al[149], 2022	Achilles tendinopathy	Wearable technology	Daily physical activity and biomechanical measures were evaluated and changes in pain were not found to correlate with activity levels
Boyer et al[150], 2025	Shoulder physiotherapy	Smartwatch	Higher physiotherapy participation rates led to significant improvements for partial-thickness tears
Burns et al[151], 2021	Rotator cuff physiotherapy	Smartwatch	Clinically significant dose response of physiotherapy on treatment outcomes in rotator cuff pathology
Burns et al[152], 2018	Shoulder physiotherapy	Smartwatch (Inertial signals)	Machine learning successfully recognized shoulder physiotherapy exercises from smartwatch inertial signals

ACL: Anterior cruciate ligament.

Neuromuscular training (NMT) is crucial in restoring postural stability and reducing post-surgical secondary injuries[134-136]. Even when joint-specific strength and power are restored, hip and trunk stability deficits may persist for some time after[137]. Individualized NMT programs have continued to show significant improvements in movement quality assessments, including hop symmetry and reduced knee valgus moments[136,138,139]. These NMT programs can be further supplemented with the incorporation of wearable technology that can provide real-time feedback on movement patterns[140-142].

In addition to improving motion analysis in community or outpatient settings, wearables allow for continuous monitoring of adherence to rehabilitation protocols. By tracking physical activity and exercise in real time, wearables can objectively measure compliance, a well-established predictor of postoperative outcomes. Wearable devices have shown that post-ACL reconstruction patients typically have decreased levels of moderate-to-vigorous physical activity and lower daily step counts when compared to healthy controls. In a prospective study, Bell et al[143] used ActiGraph accelerometers in 33 participants with a history of ACL reconstruction and found that only 24% of these patients met the guideline of 10000 steps per day, compared to 42% of matched controls[144]. This difference persisted despite patients reporting similar levels of physical activity on the Tegner and Marx activity scales. Objectively measured moderate-to-vigorous physical activity was also significantly lower in postop patients (79.4 ± 24.0 minute/day) than in controls (93.1 ± 23.9 minute/day)[143]. Another prospective cohort study by Dwyer et al[144] found that while overall activity levels improved over the first year of surgery, accelerometer-based outcomes were still not meeting recommended physical activity targets at 3 months, 6 months, or 12 months postop. Additionally, wearable devices continue to show that average daily steps remain unchanged, around 7500 steps per day, between 6 months to 18 months post ACL reconstruction[145]. This falls below the 10000-step benchmark recommended by public health guidelines, highlighting a persistent gap between actual and recommended activity levels[146]. However, a recent meta-analysis suggests that 7000 steps per day may be sufficient for patients to achieve a clinically meaningful improvement in health outcomes and could be a more realistic goal for patients[147].

Similar results have been seen in Achilles tendon rehabilitation with consistent use of insole sensors and IMUs, demonstrating that objective measures of daily activity, such as step count and plantar pressure distribution, remain relatively stable during the early rehab phase[148,149]. Laurent et al[148] evaluated the use of wearable insoles in 15 patients over 3 months and found that digital biomarkers effectively tracked recovery, and compliance was high for accurate function tracking. However, the plantar pressure distribution and patient self-reported activity demonstrated little improvement at 12 weeks, indicating overall physical activity was not increasing over time. Similarly, Tavakkoli et al[149] looked at patients with Achilles tendinopathy and found that continuous monitoring of proxies of daily load measures via IMUs did not fluctuate over a one-week period, and changes in pain did not consistently correlate with changes in activity levels. The use of IMUs for monitoring daily load may provide key information for potential load management strategies and improved Achilles tendinopathy management.

Wearable adherence monitoring has also been successfully explored in the rotator cuff rehabilitation space, where smartwatch data can play a key role in quantifying exercise adherence and its relationship to clinical outcomes. Smartwatches are capable of accurately detecting and classifying physiotherapy exercises, which allows for real-time monitoring of adherence and dose-response effects. In a prospective study, Boyer et al[150] evaluated 92 patients undergoing physiotherapy for symptomatic rotator cuff pathology and found that higher participation rates led to significant improvements in patients with partial-thickness tears but not for full-thickness tears. Similarly, Burns et al[151] found a clinically significant dose response of physiotherapy on treatment outcome in patients with rotator cuff pathology. Machine learning models trained on inertial data from smartwatches have demonstrated high accuracy in distinguishing exercise from non-exercise activity and in classifying specific shoulder exercises, supporting their utility for scalable, objective adherence monitoring in both clinical and home settings. This technology has the potential to address the current reliance on self-reported compliance and provides actionable data for the development of optimized rehabilitation protocols[150,152].

VALIDATION AGAINST EXISTING TOOLS

A critical step in the adoption of wearable technology is the validation of its data against established benchmarks in orthopedic assessment, including PROMs. Few studies have evaluated the correlation of wearable technology-obtained health data with PROMs, including the KOOS, JR and the HOOS, HR, and the correlations are often modest[45,153,154]. For example, in a cohort of patients undergoing knee arthroplasty, average daily step counts show weak, but statistically significant, positive correlations with KOOS JR scores both preoperatively and at 1-month postoperatively (r = 0.19 pre-operation, r = 0.17 post-operation; all P < 0.0001)[153]. Another study found that the correlation between PROMs (KOOS) and standardized functional tests (such as walk pace and chair stand) was low (r = 0.00-0.30)[155]. While this study did not use wearable technology-obtained data, it used similar metrics, indicating that future research is needed that further evaluates associations between wearable health technologies and PROMs.

The motion capture laboratory remains the gold standard for biomechanical analysis due to its high accuracy and detailed kinematic data, but its clinical and research use is limited by cost, space, and artificial testing environments[156]. Wearable health technologies have demonstrated comparable reliability and accuracy to marker-based motion capture for lower-limb kinematics during walking and other functional tasks, with root mean square errors typically in the range of 4-8 degrees for hip and knee joints[157-160]. In another study, the wearable-technology collected data showed higher accuracy than motion capture systems for specific measurements, including pelvis tilt (5.49 ± 2.22 deg compared to 4.28 ± 1.47 deg, P = 0.013), hip rotation (6.09 ± 1.74 deg compared to 4.82 ± 2.30 deg, P = 0.009), and hip adduction (6.10 ± 1.35 deg compared to 4.06 ± 0.78 deg, with P = 0.019)[159]. However, accuracy can be lower for more complex or dynamic movements, and consumer-grade devices (e.g., smartwatches) generally underperform compared to research-grade IMUs, especially for precise joint angle and torque measurements[159,161]. Despite these limitations, wearable technologies offer significant advantages: They are cost-effective, portable, and enable continuous, real-world data collection outside the laboratory, capturing patient function in natural environments over extended periods[162,163].

INTEGRATION INTO CLINICAL WORKFLOWS

In clinical practice, wearable health technologies, such as smartwatches and activity trackers, are increasingly incorporated into orthopedic clinical practice for remote monitoring, post-discharge care, telehealth, digital physical therapy, and early detection of recovery plateaus or complications[12,154,164-166]. One particular clinical advantage of wearable health technology is seen in its potential to engage with patients in real-time and alert providers to complications. For example, one smartphone-based technology provided immediate biofeedback via tactile and auditory cues to support gait correction in telemonitoring and virtual rehabilitation settings[166]. Additionally, it has been proposed that automated systems can be used to alert clinicians to plateaus or declines in function, prompting early assessment for complications such as poor joint loading, overloading, or non-adherence[12,154]. These alerts can enable proactive management and may reduce the risk of adverse events or delayed recovery. Future research is needed to directly evaluate outcomes for programs that provide automated alerts to physicians specifically for setbacks or complications in orthopedic patients, as measured by wearable health technologies.

PATIENT AND CLINICIAN PERSPECTIVES

The implementation of new technologies into healthcare will always face hurdles, and in the case of wearable technologies in orthopedic care, success is not solely dependent on device accuracy or technical capability. Patient acceptance and engagement represent the first and most immediate hurdle. Although many of the commercial-grade technologies are already widely adopted in broader society, other factors such as comfort, convenience, perceived value, and concerns about privacy are likely to significantly impact patient adherence[167]. Devices that are unobtrusive, familiar, seamlessly integrate into day-to-day activities, and potentially offer features and functions beyond motion data capture may be more likely to be accepted and consistently used[76,162].

As the United States population over the age of 65 is projected to increase by 47% from 2022 to 2050, devices need to be designed with simplicity and accessibility in mind, accommodating users across a wide range of age and technological literacy levels[168]. Importantly, patients and clinicians alike must have confidence in the accuracy and clinical relevance of the data captured; validation of devices in the orthopedic populations can help build this trust. When patients believe that the data is used meaningfully to inform and personalize their care, they may be more likely to engage[73,74]. For the clinician, data from reliable and validated devices that are integrated into electronic health records for ease of interpretation and implementation may lead to greater utilization.

Beyond acceptability, reimbursement models and payer interest will heavily influence the wider adoption of wearable technologies[169]. Clinicians and patients alike recognize the importance of measuring objective data outside of the clinic and view wearable sensors as an objective tool for data collection. Although some insurance plans offer programs or incentives to help offset the cost associated with obtaining these devices, standardized pathways for billing and reimbursement of wearable device monitoring and interpretation are limited, and thus, there are barriers to wider adoption[170,171].

Although wearable technologies offer promising and novel insights into patient recovery, they may come at the expense of increased clinician burden. The devices can generate large volumes of high-frequency data, which can be challenging to manage, interpret, and analyze. If implemented without careful design, wearables risk contributing to workflow disruption, excessive workload, and longer hours, all of which are associated with physician burnout[172]. Furthermore, concerns about the medico-legal implications of missed data signals and the lack of clinical thresholds may further challenge integration. To ensure that wearable devices enhance patient care, rather than complicate it, systems must incorporate intelligent data filtering, summarization tools, and evidence-based alerts, alongside seamless integration into the electronic health record, to minimize any additional cognitive burden placed upon the care team[173,174]. Ultimately, the successful integration of wearable technology into orthopedic care must balance patient engagement, clinical utility, and system-wide feasibility. Multidisciplinary collaboration between clinicians, patients, engineers, and payers will be essential to developing accurate, practical, and interpretable systems for real-world care delivery.

EVIDENCE GAPS

While early studies support the feasibility of wearable monitoring, robust evidence demonstrating improved clinical outcomes remains scarce[17,18,175]. Most studies are small, single-center investigations with limited longitudinal follow-up[176]. Larger multicenter trials and registry-based efforts are needed to understand the true impact of wearable technology on recovery trajectories, complication rates, and patient satisfaction. Furthermore, studies specifically designed to evaluate cost-effectiveness, scalability, and health equity implications will be critical for broader implementation.

TECHNICAL LIMITATIONS

A fundamental challenge to the widespread adoption of wearable technology is the variability in sensor accuracy across different devices and at varying anatomic placement sites[177-180]. Due to this variability, intra-and inter-device reliability can fluctuate[181]. Consumer-grade devices often rely on proprietary algorithms, limiting transparency and access to raw sensor data, which complicates validation efforts[182-185]. Additionally, limitations in battery life and challenges in data synchronization across devices further hinder long-term monitoring in real-world settings[186].

CLINICAL AND METHODOLOGICAL CHALLENGES

From a clinical standpoint, there is currently no consensus on standardized benchmarks or thresholds to interpret wearable-derived metrics[179,187,188]. Unlike PROMs or traditional functional assessments, wearable data often lack normative values or clinically validated cutoffs, making it difficult for providers to determine what constitutes “normal” recovery or meaningful change[153,188,189]. Moreover, correlations between wearable-derived metrics and PROMs are often modest, raising questions about how best to integrate these disparate data streams into clinical decision-making[153,175,190].

PATIENT-RELATED BARRIERS

Patient adherence to device usage poses a practical challenge. Factors such as comfort[21], ease of use, battery charging requirements, and perceived value influence long-term engagement[21,186]. Additionally, patients with low digital literacy may struggle with operating devices or interpreting feedback[182]. Inconsistent use or improper wear can result in incomplete or inaccurate datasets, reducing the clinical utility of the information collected[182].

SYSTEM-LEVEL AND REGULATORY ISSUES

On a broader level, integration into existing clinical workflows and electronic health records remains limited[191]. Concerns about medico-legal responsibility for real-time monitoring and lack of established protocols for responding to wearable-generated alerts may further disincentivize adoption[187]. Additionally, issues of data privacy, ownership, and consent remain unresolved, especially as commercial entities increasingly enter the healthcare space[182,187].

FUTURE DIRECTIONS

Existing research on wearable technology in orthopedic surgery has demonstrated rapid growth and development throughout recent years, with the greatest prevalence of usage in TJA and trauma patient populations[17,18,175,192,193]. Wearable technologies are beneficial in that they may deliver real-time feedback to help patients adhere to appropriate rehabilitation exercise regimens; however, existing research on this is limited. Argent et al[194] reported on 15 TKA patients trialing a prototype exercise biofeedback system; however, the adherence rate was 79% as there were many technical issues with the biofeedback system. In addition, the rise of artificial intelligence and machine learning provides interesting avenues of research in the field of wearable technologies[195,196]. The use of artificial intelligence and machine learning may enhance the ability to deliver real-time feedback.

With the wide range of emerging wearable technologies, there exists a need for standardization protocols and validated outcome measures[193]. Braun et al[193] analyzed results of a cross-sectional expert opinion survey, noting one of the largest obstacles was the interpretation of the data from wearable technology, as there exists no objective patient outcome measurement tool[197]. Examples of potential standardization metrics include the digital 6-minute walk test or TUG[198,199]. The 6-minute walk test has been commonly used in patients with cardiopulmonary disease and TUG in geriatric medicine[198,199]. Lastly, existing pilot and prospective studies remain limited in cohort sizes, emphasizing the importance for registry data and multi-institutional research.

CONCLUSION

Objective functional assessment is redefining how outcomes are evaluated in orthopedic surgery. The integration of wearable technologies enables continuous monitoring of patient function in real-world environments, offering a more accurate reflection of recovery than traditional, clinic-based assessments. These tools provide quantitative insights into gait, mobility, and adherence to rehabilitation protocols, allowing earlier identification of deviations from expected recovery and more targeted clinical intervention. In addition to their clinical utility, wearable devices generate high-resolution data that can strengthen research methodologies and facilitate more objective comparisons across studies. As sensor technology and analytic methods continue to advance, wearable systems are likely to become standardized components of outcome assessment. Successful integration, however, will depend on robust validation, clear definitions of clinically meaningful thresholds, and seamless incorporation into existing health record systems. By combining continuous functional monitoring with traditional clinical evaluation, wearable technologies have the potential to personalize orthopedic care, improve the precision of outcome measurement, and enhance the overall quality of patient recovery.