Using Smartphone-Based Accelerometer Data to Objectively Assess Outcomes in Spine Surgery

Smartphone-based activity tracking for spine patients: Current technology and future opportunities

Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients. Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology. Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors. There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

State-of-the-art Applications of Patient-Reported Outcome Measures in Spinal Care

Patient-reported outcome measures (PROMs) assign objective measures to patient's subjective experiences of health, pain, disability, function, and quality of life. PROMs can be useful for providers in shared decision making, outcome assessment, and indicating patients for surgery. In this article, we provide an overview of the legacy PROMs used in spinal care, recent advancements in patient-reported outcomes, and future directions in PROMs. Recent advances in patient-reported outcome assessments have included standardization of measurement tools, integration of data collection into workflow, and applications of outcome measures in predictive models and decision-making tools. Continual appraisal of instruments and incorporation into artificial intelligence and machine learning analytics will continue to augment the delivery of high-value spinal care.

Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables

Over the past generation, outcome measures in spine care have evolved from a reliance on clinician-reported assessment toward recognizing the importance of the patient's perspective and the wide incorporation of patient-reported outcomes (PROs). While patient-reported outcomes are now considered an integral component of outcomes assessments, they cannot wholly capture the state of a patient's functionality. There is a clear need for quantitative and objective patient-centered outcome measures. The pervasiveness of smartphones and wearable devices in modern society, which passively collect data related to health, has ushered in a new era of spine care outcome measurement. The patterns emerging from these data, so-called "digital biomarkers," can accurately describe characteristics of a patient's health, disease, or recovery state. Broadly, the spine care community has thus far concentrated on digital biomarkers related to mobility, although the researcher's toolkit is anticipated to expand in concert with advancements in technology. In this review of the nascent literature, we describe the evolution of spine care outcome measurements, outline how digital biomarkers can supplement current clinician-driven and patient-driven measures, appraise the present and future of the field in the modern era, as well as discuss present limitations and areas for further study, with a focus on smartphones (see Supplemental Digital Content , http://links.lww.com/NEU/D809 , for a similar appraisal of wearable devices).

Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery

Personalized medicine has made a tremendous impact on patient care. Although initially, it revolutionized pharmaceutical development and targeted therapies in oncology, it has also made an important impact in orthopaedic surgery. The field of spine surgery highlights the effect of personalized medicine because the improved understanding of spinal pathologies and technological innovations has made personalized medicine a key component of patient care. There is evidence for several of these advancements to support their usage in improving patient care. Proper understanding of normative spinal alignment and surgical planning software has enabled surgeons to predict postoperative alignment accurately. Furthermore, 3D printing technologies have demonstrated the ability to improve pedicle screw placement accuracy compared with free-hand techniques. Patient-specific, precontoured rods have shown improved biomechanical properties, which reduces the risk of postoperative rod fractures. Moreover, approaches such as multidisciplinary evaluations tailored to specific patient needs have demonstrated the ability to decrease complications. Personalized medicine has shown the ability to improve care in all phases of surgical management, and several of these approaches are now readily available to orthopaedic surgeons.

Spine patient care with wearable medical technology: state-of-the-art, opportunities, and challenges: a systematic review

BACKGROUND CONTEXT

Healthcare reforms that demand quantitative outcomes and technical innovations have emphasized the use of Disability and Functional Outcome Measurements (DFOMs) to spinal conditions and interventions. Virtual healthcare has become increasingly important following the COVID-19 pandemic and wearable medical devices have proven to be a useful adjunct. Thus, given the advancement of wearable technology, broad adoption of commercial devices (ie, smartwatches, phone applications, and wearable monitors) by the general public, and the growing demand from consumers to take control of their health, the medical industry is now primed to formally incorporate evidence-based wearable device-mediated telehealth into standards of care.

PURPOSE

To (1) identify all wearable devices in the peer-reviewed literature that were used to assess DFOMs in Spine, (2) analyze clinical studies implementing such devices in spine care, and (3) provide clinical commentary on how such devices might be integrated into standards of care.

STUDY DESIGN/SETTING

A systematic review.

METHODS

A comprehensive systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) across the following databases: PubMed; MEDLINE; EMBASE (Elsevier); and Scopus. Articles related to wearables systems in spine healthcare were selected. Extracted data was collected as per a predetermined checklist including wearable device type, study design, and clinical indices studied.

RESULTS

Of the 2,646 publications that were initially screened, 55 were extensively analyzed and selected for retrieval. Ultimately 39 publications were identified as being suitable for inclusion based on the relevance of their content to the core objectives of this systematic review. The most relevant studies were included, with a focus on wearables technologies that can be used in patients' home environments.

CONCLUSIONS

Wearable technologies mentioned in this paper have the potential to revolutionize spine healthcare through their ability to collect data continuously and in any environment. In this paper, the vast majority of wearable spine devices rely exclusively on accelerometers. Thus, these metrics provide information about general health rather than specific impairments caused by spinal conditions. As wearable technology becomes more prevalent in orthopedics, healthcare costs may be reduced and patient outcomes will improve. A combination of DFOMs gathered using a wearable device in conjunction with patient-reported outcomes and radiographic measurements will provide a comprehensive evaluation of a spine patient's health and assist the physician with patient-specific treatment decision-making. Establishing these ubiquitous diagnostic capabilities will allow improvement in patient monitoring and help us learn about postoperative recovery and the impact of our interventions.

Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis

An increasing number of digital health interventions (DHIs) for remote postoperative monitoring have been developed and evaluated. This systematic review identifies DHIs for postoperative monitoring and evaluates their readiness for implementation into routine health care. Studies were defined according to idea, development, exploration, assessment, and long-term follow-up (IDEAL) stages of innovation. A novel clinical innovation network analysis used coauthorship and citations to examine collaboration and progression within the field. 126 DHIs were identified, with 101 (80%) being early stage innovations (IDEAL stage 1 and 2a). None of the DHIs identified had large-scale routine implementation. There is little evidence of collaboration, and there are clear omissions in the evaluation of feasibility, accessibility, and the health-care impact. Use of DHIs for postoperative monitoring remains at an early stage of innovation, with promising but generally low-quality supporting evidence. Comprehensive evaluation within high-quality, large-scale trials and real-world data are required to definitively establish readiness for routine implementation.

Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates

BACKGROUND

Rapid growth in smartphone use has expanded opportunities to use mobile health (mHealth) technology to collect real-time patient-reported and objective biometric data. These data may have important implication for personalized treatments of degenerative spine disease. However, no large-scale study has examined the feasibility and acceptability of these methods in spine surgery patients.

OBJECTIVE

To evaluate the feasibility and acceptability of a multimodal preoperative mHealth assessment in patients with degenerative spine disease.

METHODS

Adults undergoing elective spine surgery were provided with Fitbit trackers and sent preoperative ecological momentary assessments (EMAs) assessing pain, disability, mood, and catastrophizing 5 times daily for 3 weeks. Objective adherence rates and a subjective acceptability survey were used to evaluate feasibility of these methods.

RESULTS

The 77 included participants completed an average of 82 EMAs each, with an average completion rate of 86%. Younger age and chronic pulmonary disease were significantly associated with lower EMA adherence. Seventy-two (93%) participants completed Fitbit monitoring and wore the Fitbits for an average of 247 hours each. On average, participants wore the Fitbits for at least 12 hours per day for 15 days. Only worse mood scores were independently associated with lower Fitbit adherence. Most participants endorsed positive experiences with the study protocol, including 91% who said they would be willing to complete EMAs to improve their preoperative surgical guidance.

CONCLUSION

Spine fusion candidates successfully completed a preoperative multimodal mHealth assessment with high acceptability. The intensive longitudinal data collected may provide new insights that improve patient selection and treatment guidance.

Utilizing Data from Wearable Technologies in the Era of Telemedicine to Assess Patient Function and Outcomes in Neurosurgery: Systematic Review and Time-Trend Analysis of the Literature

BACKGROUND

The COVID-19 pandemic has driven the increased use of telemedicine and the adoption of wearable technology in neurosurgery. We reviewed studies exploring the use of wearables on neurosurgical patients and analyzed wearables' scientific production trends.

METHODS

The review encompassed PubMed, EMBASE, Web of Science, and Cochrane Library. Bibliometric analysis was performed using citation data of the included studies through Elsevier's Scopus database. Linear regression was utilized to understand scientific production trends. All analyses were performed on R 4.1.2.

RESULTS

We identified 979 studies. After screening, 49 studies were included. Most studies evaluated wearable technology use for patients with spinal pathology (n = 31). The studies were published over a 24-year period (1998-2021). Forty-seven studies involved wearable device use relevant to telemedicine. Bibliometric analysis revealed a compounded annual growth rate of 7.3%, adjusted for inflation, in annual scientific production from 1998 to 2021 (coefficient=1.3; 95% Confidence Interval = [0.7, 1.9], P < 0.01). Scientific production steadily increased in 2014 (n = 1) and peaked from 2019 (n = 8) to 2021 (n = 13) in correlation with the COVID-19 pandemic. Publications spanned 34 journals, averaged 24.4 citations per article, 3.0 citations per year per article, and 8.3 authors per article.

CONCLUSION

Wearables can provide clinicians with objective measurements to determine patient function and quality of life. The rise in articles related to wearables in neurosurgery demonstrates the increased adoption of wearable devices during the COVID-19 pandemic. Wearable devices appear to be a key component in this era of telemedicine and their positive utility and practicality are increasingly being realized in neurosurgery.

Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021

STUDY DESIGN

Narrative review.

OBJECTIVES

There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care.

METHODS

We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice.

RESULTS

A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations.

CONCLUSIONS

Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.

Data-driven phenotyping of preoperative functional decline patterns in patients undergoing lumbar decompression and lumbar fusion using smartphone accelerometry

OBJECTIVE

Treatment of degenerative lumbar spine pathologies typically escalates to surgical intervention when symptoms begin to significantly impair patients’ functional status. Currently, surgeons rely on subjective patient assessments through patient-reported outcome measures to estimate the decline in patient wellness and quality of life. In this analysis, the authors sought to use smartphone-based accelerometry data to provide an objective, continuous measurement of physical activity that might aid in effective characterization of preoperative functional decline in different lumbar spine surgical indications.

METHODS

Up to 1 year of preoperative activity data (steps taken per day) from 14 patients who underwent lumbar decompression and 15 patients who underwent endoscopic lumbar fusion were retrospectively extracted from patient smartphones. A data-driven algorithm was constructed based on 10,585 unique activity data points to identify and characterize the functional decline of patients preceding surgical intervention. Algorithmic estimation of functional decline onset was compared with reported symptom onset in clinical documentation across patients who presented acutely (≤ 5 months of symptoms) or chronically (> 5 months of symptoms).

RESULTS

The newly created algorithm identified a statistically significant decrease in physical activity during measured periods of functional decline (p = 0.0020). To account for the distinct clinical presentation phenotypes of patients requiring lumbar decompression (71.4% acute and 28.6% chronic) and those requiring lumbar fusion (6.7% acute and 93.3% chronic), a variable threshold for detecting clinically significant reduced physical activity was implemented. The algorithm characterized functional decline (i.e., acute or chronic presentation) in patients who underwent lumbar decompression with 100% accuracy (sensitivity 100% and specificity 100%), while characterization of patients who underwent lumbar fusion was less effective (accuracy 26.7%, sensitivity 21.4%, and specificity 100%). Adopting a less-permissive detection threshold in patients who underwent lumbar fusion, which rendered the algorithm robust to minor fluctuations above or below the chronically decreased level of preoperative activity in most of those patients, increased functional decline classification accuracy of patients who underwent lumbar fusion to 66.7% (sensitivity 64.3% and specificity 100%).

CONCLUSIONS

In this study, the authors found that smartphone-based accelerometer data successfully characterized functional decline in patients with degenerative lumbar spine pathologies. The accuracy and sensitivity of functional decline detection were much lower when using non–surgery-specific detection thresholds, indicating the effectiveness of smartphone-based mobility analysis in characterizing the unique physical activity fingerprints of different lumbar surgical indications. The results of this study highlight the potential of using activity data to detect symptom onset and functional decline in patients, enabling earlier diagnosis and improved prognostication.

Developing a Prediction Model for Identification of Distinct Perioperative Clinical Stages in Spine Surgery With Smartphone-Based Mobility Data

BACKGROUND

Spine surgery outcomes assessment currently relies on patient-reported outcome measures, which satisfy established reliability and validity criteria, but are limited by the inherently subjective and discrete nature of data collection. Physical activity measured from smartphones offers a new data source to assess postoperative functional outcomes in a more objective and continuous manner.

OBJECTIVE

To present a methodology to characterize preoperative mobility and gauge the impact of surgical intervention using objective activity data garnered from smartphone-based accelerometers.

METHODS

Smartphone mobility data from 14 patients who underwent elective lumbar decompressive surgery were obtained. A time series analysis was conducted on the number of steps per day across a 2-year perioperative period. Five distinct clinical stages were identified using a data-driven approach and were validated with clinical documentation.

RESULTS

Preoperative presentation was correctly classified as either a chronic or acute mobility decline in 92% of patients, with a mean onset of acute decline of 11.8 ± 2.9 weeks before surgery. Postoperative recovery duration demonstrated wide variability, ranging from 5.6 to 29.4 weeks (mean: 20.6 ± 4.9 weeks). Seventy-nine percentage of patients ultimately achieved a full recovery, associated with an 80% ± 33% improvement in daily steps compared with each patient's preoperative baseline (P = .002). Two patients subsequently experienced a secondary decline in mobility, which was consistent with clinical history.

CONCLUSION

The perioperative clinical course of patients undergoing spine surgery was systematically classified using smartphone-based mobility data. Our findings highlight the potential utility of such data in a novel quantitative and longitudinal surgical outcome measure.

Smartphone-based real-life activity data for physical performance outcome in comparison to conventional subjective and objective outcome measures after degenerative lumbar spine surgery

Introduction

Outcome assessments after surgery for degenerative lumbar disorders (DLDs) rely on subjective patient-reported outcomes (PROMs). New objective functional capacity tests, like the smartphone-based 6-min walking test (6WT), have been introduced but presumably also do not reflect the patient's real-life functional performance.

Research question

Pilot study to analyze changes in smartphone-based real-life activity data for physical performance outcome in patients undergoing surgery for DLD.

Material and methods

Prospective observational study of DLD patients. Objective functional capacity and subjective outcomes were measured using 6WT and PROMs. Real-life physical performance data were acquired retrospectively using Apple iPhone Health data and compared against objective capacity and subjective outcomes.

Results

Eight patients (mean 46 years, 62% male) provided 286.858 smartphone mile counts. PROMs and physical capacity (6WT) significantly improved postoperatively. 6WT results increased from 352m pre-to 555/567m at 6/12 weeks postoperatively (p ​= ​0.03). For physical performance a linear mixed effect models showed an increase in daily distance in the first 4 months after surgery (slope +0.178; p ​< ​0.001). However, those increases reversed from 4 until 12 months postoperatively (negative slope estimate of −0.076; p ​< ​0.001). Smartphone-derived physical performance measures showed a positive correlation with corresponding physical capacity in the 6WT (R ​= ​0.57,p ​= ​0.004) and negative correlations with PROMs (COMI: R ​= ​−0.62p ​= ​0.001; ZCQ-Physical-Function: R ​= ​−0.68,p ​< ​0.001; ZCQ-Symptom-Severity: R ​= ​−0.52,p ​= ​0.009).

Discussion and conclusion

Smartphone-based real-life activity data allows for longitudinal physical performance assessment. Physical performance correlated with physical capacity and patient's subjective perception of disability. However, physical performance may be more resistant to postoperative longtime change which should consult a more cautious use as objective outcome measure.

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Smartphone-based activity data allows for continuous longitudinal physical performance assessment after lumbar spine surgery.

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It captures both, a preoperative deterioration, and a postoperative improvement of patients' physical performance.

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Although smartphone-based performance correlated with physical capacity/PROMs, daily activity decreased again postoperatively

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This discrepancy indicates that smartphone-based performance may provide a more in-depth assessment of function over time.

Accelerometry Data Delineates Phases of Recovery and Supplements Patient-Reported Outcome Measures Following Lumbar Laminectomy

BACKGROUND

Patient-reported outcome measures (PROMs) are traditionally used to track recovery of patients after spine surgery. Wearable accelerometers have adjunctive value because of the continuous, granular, and objective data they provide. We conducted a prospective study of lumbar laminectomy patients to determine if time-series data from wearable accelerometers could delineate phases of recovery and compare accelerometry data to PROMs during recovery tracking.

METHODS

Patients with lumbar stenosis for whom lumbar laminectomy was indicated were prospectively recruited. Subjects wore accelerometers that recorded their daily step counts from at least 1 week preoperatively to 6 months postoperatively. Subjects completed the Oswestry Disability Index and the 12-Item Short Form Health Survey preoperatively and at 2 weeks, 1 month, 3 months, and 6 months postoperatively. Daily aggregate median steps and individual visit-specific median steps were calculated. The Pruned Linear Exact Time method was used to segment aggregate median steps into distinct phases. Associations between visit-specific median steps and PROMs were identified using Spearman rank correlation.

RESULTS

Segmentation analysis revealed 3 distinct postoperative phases: step counts rapidly increased for the first 40 days postoperatively (acute healing), then gained more slowly for the next 90 days (recovery), and finally plateaued at preoperative levels (stabilization). Visit-specific median steps were significantly correlated with PROMs throughout the postoperative period. PROMs significantly exceeded baseline at 6 months postoperatively, while step counts did not (all P < 0.05).

CONCLUSIONS

Continuous data from accelerometers allowed for identification of 3 distinct stages of postoperative recovery after lumbar laminectomy. PROMs remain necessary to capture subjective elements of recovery.

Objective Outcomes in Lateral Osteotomy Through Anterior-to-Psoas for Severe Adult Degenerative Spine Deformity Correction

Multilevel lateral interbody fusion is an acceptable surgical technique in patients with severe degenerative adult spinal deformity (ASD). The current standard-of-care in spine surgery includes the use of patient reported outcome measures (PROMs) to assess post-operative improvement. Objective activity data during the peri-operative period may provide supplementary information for patients recovering from ASD surgery. In this report, we use smartphone-based activity data as an objective outcome measure for a patient who underwent a two-stage operation for ASD corrective surgery: lateral osteotomy and lumbar interbody fusion with posterior column release. An 82-year-old male presented with intractable back pain secondary to severe thoracolumbar scoliotic deformity (Lenke 5BN). Pre-operative images demonstrated the presence of bridging osteophytes over the left lateral aspect of L2-5 disc spaces and over the apex of the lumbar curvature, with significant neuroforaminal stenosis. Surgical correction was completed in two stages: (1) left-sided lateral osteotomy using anterior-to-psoas approach (ATP) in a right lateral decubitus position, and (2) multilevel Ponte osteotomies and instrumented fusion from T10-pelvis. Post-operative radiography showed correction to scoliotic deformity and sagittal misalignment. The patient had developed seroma and wound dehiscence, which was evacuated on post-operative day 11. At 14-month follow-up, the patient reported significant improvement in pain symptoms, corroborated by patient reported outcome measures. To further quantify and assess patient recovery, smartphone-based patient activity data was collected and analyzed to serve as a proxy for the patient's functional improvement. The patient's walking steps-per-day was compared pre- and post-operatively. The patient's pre-operative baseline was 223 steps/day; the patient's activity during immediate post-operative recovery dropped to 179 steps/day; the patient returned to baseline activity levels approximately 3 months after surgery, reaching an average of 216 steps/day. In conclusion, we found that lateral osteotomy through an ATP approach is a powerful tool to restore normal spine alignment and can be successfully performed using anatomic landmarks. Additionally, smartphone-based mobility data can assess pre-operative activity level and allow for remote patient monitoring beyond routine follow-up schedule.

Using smartphone-based accelerometers to gauge postoperative outcomes in patients with NPH: Implications for ambulatory monitoring

Background

The surgical treatment of normal pressure hydrocephalus (NPH) with shunting remains controversial due to the difficulty in distinguishing such pathology from other neurological conditions that can present similarly. Thus, patients with suspected NPH should be carefully selected for surgical intervention. Historically, clinical improvement has been measured by the use of functional grades, alleviation of symptoms, and/or patient/family-member reported surveys. Such outcome analysis can be subjective, and there is difficulty in quantifying cognition. Thus, a push for a more quantifiable and objective investigation is warranted, especially for patients with idiopathic NPH (INPH), for which the final diagnosis is confirmed with postoperative clinical improvement. We aimed to use Apple Health (Apple Inc., Cupertino, CA) data to approximate physical activity levels before and after shunt placement for NPH as an objective outcome measurement. The patients were contacted and verbally consented to export Apple Health activity data. The patient's physical activity data were then analyzed. A chart review from the patient's EMR was performed to understand and better correlate recovery.

Case Description

Our first patient had short-term improvements in activity levels when compared to his preoperative activity. The patient's activity level subsequently decreased at 6 months and onward. This decline was simultaneous to new-onset lumbar pain. Our second patient experienced sustained improvements in activity levels for 12 months after his operation. His mobility data were in congruence with his subjectively reported improvement in clinical symptoms. He subsequently experienced a late-decline that began at 48-months. His late deterioration was likely confounded by exogenous factors such as further neurodegenerative diseases coupled with old age.

Conclusion

The use of objective activity data offers a number of key benefits in the analysis of shunted patients with NPH/INPH. In this distinctive patient population, detailed functional outcome analysis is imperative because the long-term prognosis can be affected by comorbid factors or life expectancy. The benefits from using smartphone-based accelerometers for objective outcome metrics are abundant and such an application can serve as a clinical aid to better optimize surgical and recovery care.