The review, conducted by Edwin Ho Yin Lui and Ralph Jasper Mobbs of the University of New South Wales and the NeuroSpine Surgery Research Group in Sydney, followed the PRISMA-ScR reporting guidelines and searched PubMed, Embase, and Scopus for peer-reviewed studies that used a smartphone’s internal inertial measurement unit, or IMU, to quantify mobility, gait, or postural transitions. From an initial yield of 1,063 records, 84 studies made the final cut. The researchers then charted each study across four domains: the clinical target and purpose, the methodological implementation, the technological infrastructure, and the extracted metrics and validation standards. The goal was not to pool results into a meta-analysis — the field is far too heterogeneous for that — but to map where the science actually stands and where it stalls on the road to the clinic.

The headline finding is that smartphone-based mobility analysis has spread far beyond the engineering lab. Neurology dominated the clinical landscape, accounting for 36.9 percent of studies, with Parkinson’s disease alone featured in 19 percent and multiple sclerosis in 6 percent. Geriatrics followed at 14.3 percent and orthopedics at 13.1 percent, with smaller contributions from rheumatology and oncology. Perhaps more striking is what researchers are trying to do with the data: only 10.7 percent of studies were primarily validation exercises, while the overwhelming majority — 89.3 percent — pursued clinically directed aims such as diagnosis (36.9 percent), prognostication (38.1 percent), or longitudinal monitoring (14.3 percent). In other words, the field has largely moved past asking whether phones can measure movement and is now asking whether those measurements mean something for patients.

The clinical logic is compelling. Walking speed and gait characteristics have been repeatedly linked to cognitive decline, mortality, neurodegenerative trajectories, and disease progression — leading some researchers to call gait the sixth vital sign. Yet routine assessment still relies heavily on patient-reported questionnaires and episodic clinic visits, which capture only a snapshot of function under artificial conditions. Laboratory gait analysis, with its three-dimensional optoelectronic motion capture and force plates, offers exquisite precision but is expensive, immobile, and utterly unsuited to continuous monitoring. Dedicated research wearables fare better but impose costs and sustained-use burdens of their own. Smartphones, by contrast, are already carried by billions of people every day, making them arguably the most scalable mobility-sensing platform ever deployed.

But the review’s detailed breakdown reveals a deep methodological disconnect between the promise and the practice. More than half of the studies — 54.8 percent — confined their assessments to controlled clinical or laboratory environments, and 67.9 percent required a clinician or researcher to supervise the test. A striking 73.8 percent depended on active testing protocols, in which participants deliberately performed structured tasks such as short walk tests, the timed up-and-go, or the six-minute walk test. Only 23.8 percent of studies captured passive, free-living mobility — the spontaneous, unscripted walking that happens in kitchens, sidewalks, and shopping malls. Similarly, 63.1 percent collected a single cross-sectional snapshot rather than continuous longitudinal data. The very feature that makes smartphones revolutionary — their unobtrusive presence in daily life — remains largely untapped.

The technological infrastructure tells a similar story of fragmentation. In 71.4 percent of studies, participants had to secure the phone to a fixed anatomical location, typically the waist or lower back, using belts, straps, or pouches — a constraint that partly recreates the burden of dedicated wearables and may be impractical for older adults or people with cognitive impairment. Software is even more fragmented: 82.1 percent of studies relied on custom-built applications or proprietary research platforms such as mPower, GaitTrack, and GaitMate, while only 7.1 percent integrated with native operating-system health platforms like Apple HealthKit or Google Fit. This patchwork of bespoke pipelines makes it difficult to compare results across studies, reproduce findings, or transport algorithms from one device model to another. Differences in accelerometer resolution, sampling frequency, timestamp accuracy, calibration, and sensor-fusion pipelines — not to mention operating-system updates and aggressive power-management policies that can silently interrupt background data collection — all conspire to undermine reproducibility.

What are researchers actually extracting from these sensors? The most common measures are the macro-mobility staples: gait speed appeared in 38.1 percent of studies, step count in 33.3 percent, cadence in 32.1 percent, and step length in 28.6 percent. Gait-quality metrics — stance-to-swing ratios, asymmetry indices, temporal variability, and nonlinear measures such as approximate entropy — were reported less often, despite their potential to reveal neurological and musculoskeletal dysfunction that simple step counts miss. Notably, nearly a third of studies skipped interpretable kinematic parameters altogether, feeding raw triaxial acceleration into machine-learning classifiers. Validation, where performed, typically involved comparison against three-dimensional motion capture, pressure mats, dedicated wearables, or clinical rating scales. The review’s authors are careful to distinguish what such comparisons prove: technical validity, meaning the measure is accurate and reliable; clinical validity, meaning it associates with or predicts a meaningful health state; and clinical utility, meaning it actually changes clinical decisions or improves patient outcomes. The mapped literature demonstrates the first two in progress — but routine clinical utility has not been established.

The ecological question looms largest. Free-living monitoring can capture fatigue, diurnal fluctuations, and long-term variability that a brief clinic visit cannot, and the review argues that passive and structured assessment should be treated as complementary rather than interchangeable. Yet real-world data come with real-world noise: walking surface, gradient, footwear, assistive devices, crowding, and whether the phone is in a pocket, hand, or bag can all shift the measured signal independently of any clinical change. Crucially, the authors argue, this contextual variation is not always noise to be eliminated — it may reveal how environmental demands shape functional performance. Emerging analytical strategies, including placement-robust models, automated detection and segmentation of walking bouts, steady-state filtering, and within-person longitudinal comparison, aim to separate context-related from health-related variation. The experience of the mPower study, which enrolled more than 12,000 participants remotely for Parkinson’s research but suffered substantial attrition, underscores that scalability does not guarantee sustained engagement — a burden that passive background architectures may help relieve.

Privacy and equity complete the translation checklist. Continuous passive sensing, even without GPS, can expose daily routines, periods of inactivity, and changes in health status, demanding transparent consent, data minimization, on-device processing where feasible, encryption, and predefined retention and deletion policies. Governance must also confront data ownership, linkage with health records, third-party access, and accountability for missed deterioration — plus equitable alternatives for patients who do not own or consistently carry compatible smartphones. The review’s bottom line is measured but optimistic: smartphones are genuinely promising candidate platforms for multidimensional mobility sensing, and the field’s shift from validation toward diagnosis and prognostication shows real momentum. What stands between promising digital measures and routine clinical practice is disciplined translation — standardized acquisition and reporting, cross-device and cross-platform validation, context-aware interpretation, privacy-preserving governance, and prospective trials proving that these pocket-sized sensors actually improve clinical decisions and patients’ lives.

Subject of Research: Smartphone-based mobility and gait analysis for clinical assessment

Article Title: Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review

Article References: Lui, E. H. Y., & Mobbs, R. J. (2026). Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review. Journal of Neurology, 273(10), Article 597. https://doi.org/10.1007/s00415-026-14131-2

Image Credits: AI Generated

DOI: 10.1007/s00415-026-14131-2

Keywords: smartphones, gait analysis, mobility, digital biomarkers, neurology, Parkinson's disease, wearable technology, inertial measurement units, mHealth, scoping review, clinical utility, passive monitoring