Smartphone tests track early Parkinson’s motor decline over two years

A smartphone held in the palm of the hand has become one of the most powerful tools in the search for the earliest fingerprints of Parkinson’s disease. In a new study published in npj Parkinson’s Disease, a team of Czech researchers from the Czech Technical University in Prague and Charles University followed three groups of volunteers for two full years, asking them to perform a short battery of motor tasks on a phone every two weeks. The participants included people with isolated REM sleep behavior disorder, a condition widely regarded as one of the strongest warning signs of future Parkinson’s disease, along with patients in the early stages of Parkinson’s itself and a group of healthy controls. The goal was deceptively simple: to find out which of the phone-based measurements actually change as the disease progresses, and which merely reflect the fact that people get better at a task the more often they practice it.

Isolated REM sleep behavior disorder, or iRBD, is a sleep condition in which people physically act out their dreams, sometimes shouting, punching, or kicking during the night. The reason it attracts so much attention from neuroscientists is that a large proportion of people with iRBD eventually develop Parkinson’s disease or a related disorder, often years or even decades after the sleep problem first appears. That makes people with iRBD an ideal population in which to study the prodromal phase of Parkinson’s, the long silent stretch before the classic motor symptoms of tremor, stiffness, and slowness of movement become obvious enough for a clinical diagnosis. If researchers can detect subtle motor decline during this prodromal window, they could identify candidates for future preventive treatments and measure whether those treatments actually slow the disease.

The study enrolled twenty-six healthy controls, twenty-two patients with iRBD, and twenty-three patients with early-stage Parkinson’s disease. Every fourteen days, for twenty-four months, each participant completed a set of tasks on a smartphone covering four distinct motor domains: speech, psychomotor performance, hand tremor, and a U-turn task. The speech tasks included sustained phonation, reading aloud, and rapid repetition of syllables, a test known as oral diadochokinesis. The psychomotor component involved tapping exercises, while the tremor task asked participants to hold the phone in a way that allowed its motion sensors to record any shaking of the hand. The U-turn task captured information about turning speed, an aspect of movement that is known to be affected early in parkinsonism. All of this was done remotely, without clinic visits, which is precisely what makes the approach attractive for large-scale trials.

The results, reported by Vojtěch Illner and colleagues including corresponding author Jan Rusz, reveal a striking asymmetry between the two patient groups. In the iRBD group, disease progression showed up in two places: a measure called monopitch derived from reading aloud, which reflects a flattening of pitch variation in the voice, and a slowing of turn speed in the U-turn task. Both of these changes reached statistical significance at the p < 0.05 level over the two-year follow-up. In the early Parkinson’s disease group, the clearest longitudinal signal was a shortening of the time to the first voice break during sustained phonation, again significant at p < 0.05. In other words, the voice and turning tasks carried genuine information about disease trajectory, while other measures behaved differently.

That difference matters because many of the smartphone measures proved excellent at telling groups apart at a single point in time but failed to track change over the two years. Several speech, psychomotor, and U-turn measures showed adequate cross-sectional sensitivity, meaning they could reliably distinguish people with iRBD or early Parkinson’s disease from healthy controls when everyone was compared at the same moment. Cross-sectional discrimination and longitudinal sensitivity are not the same thing, however. A measure can separate a patient from a control on any given day yet remain stable within an individual, making it useless for monitoring progression. The study’s two-year design allowed the team to separate these two properties, something shorter studies often cannot do.

Perhaps the most instructive finding of the study concerns the learning effect. When people repeat the same task every two weeks, they improve at it, and that improvement can mask or mimic true disease change. The researchers found a significant learning effect during the first year of monitoring in measures derived from oral diadochokinesis, the rapid syllable repetition task, as well as from tapping and hand tremor. In practical terms, participants got faster or steadier simply through repetition, and only after roughly a year did this practice effect plateau. For anyone designing a future remote monitoring trial, this is a crucial piece of guidance: measures derived from these tasks need either a stabilization period, careful modeling of the learning curve, or exclusion from progression analyses, otherwise the data will reflect practice rather than biology.

The technical achievement behind these results should not be understated. Modern smartphones contain high-precision accelerometers, gyroscopes, and microphones that can capture movement and voice at sampling rates more than sufficient for detecting the small changes that characterize early neurodegeneration. Speech analysis can quantify pitch variability, timing, and stability of phonation with millisecond and hertz-level resolution. Motion sensors can decompose a turning maneuver into speed and smoothness. By administering these tasks every fourteen days, the study generated a dense longitudinal record for each participant, far richer than what annual clinic visits could provide. This density is what allowed the researchers to observe gradual drifts in voice and turning measures that would otherwise be lost in the noise of day-to-day variation.

For the field of digital biomarkers in Parkinson’s disease, the study functions as a roadmap rather than a final answer. The authors describe their work as guidance for future remote smartphone trials that integrate diverse motor domains to monitor prodromal and early parkinsonism. The message is twofold. First, speech measures, particularly those capturing pitch characteristics during reading and voice stability during phonation, together with turning speed, appear to carry genuine longitudinal signal in the populations most at risk. Second, the tasks that seem most intuitive, such as tapping speed and tremor measurement, are the ones most vulnerable to practice effects in the first year, and therefore require the most careful handling. Trials aimed at disease modification, where the endpoint is slowing of progression, will depend on measures that are both sensitive to change and robust to repetition.

The broader implications reach beyond Parkinson’s research. Remote smartphone monitoring could eventually allow neurologists to follow patients continuously at home, reducing the burden of travel and capturing the real-world variability of symptoms that clinic assessments miss. For people with iRBD, many of whom are keenly aware of their elevated risk, a regular two-minute phone battery could offer an objective, quantified view of their own trajectory, replacing anxiety with data. And for pharmaceutical companies developing therapies intended to be given before symptoms appear, validated digital endpoints collected at home could dramatically shrink and shorten the trials needed to prove that a drug works. The present study, with its modest sample sizes and two-year horizon, is an early step on that path, but a carefully designed one.

There remain caveats worth keeping in mind. The cohorts were relatively small, with twenty to twenty-six participants per group, and the findings will need replication in larger and more diverse populations. The study also monitored participants for two years, a period in which only a subset of people with iRBD can be expected to convert to overt Parkinson’s disease, so the full predictive power of the smartphone measures for identifying who will convert remains to be established. Still, the core conclusion stands on solid ground: a consumer device carried in a pocket, paired with well-chosen tasks and a smart analysis plan, can detect meaningful motor change in the earliest stages of Parkinson’s disease, provided researchers respect the learning effects that come with repetition. As remote monitoring matures, the smartphone may well become the stethoscope of neurology, and studies like this one are teaching clinicians exactly where to listen.

Subject of Research: Smartphone-based remote motor monitoring for detecting disease progression in isolated REM sleep behavior disorder and early Parkinson's disease

Article Title: Two-year smartphone motor monitoring in isolated REM sleep behavior disorder and Parkinson’s disease

Article References: Illner, V., Novotný, M., Kouba, T., Tykalová, T., Šimek, M., Šubert, M., Sovka, P., Švihlík, J., Růžička, E., Šonka, K., Dušek, P., & Rusz, J. (2026). Two-year smartphone motor monitoring in isolated REM sleep behavior disorder and Parkinson’s disease. npj Parkinson's Disease. https://doi.org/10.1038/s41531-026-01588-1

Image Credits: AI Generated

DOI: 10.1038/s41531-026-01588-1

Keywords: Parkinson's disease, isolated REM sleep behavior disorder, digital biomarkers, smartphone monitoring, speech analysis, motor progression, prodromal Parkinson's, remote assessment, hand tremor, U-turn task, learning effect, npj Parkinson's Disease