# Associations Between Smartphone-Based Finger Tapping and Cognitive Performance in Older Adults: Observational Study

**Authors:** Huitong Ding, Taylor A Orwig, Jian Rong, Heaven Y Tatere, Chunyu Liu, Eric Schramm, Chathurangi H Pathiravasan, Joanne M Murabito, Honghuang Lin

PMC · DOI: 10.2196/82463 · Journal of Medical Internet Research · 2026-02-18

## TL;DR

This study shows that smartphone-based finger tapping tasks can reveal important information about cognitive performance in older adults.

## Contribution

The study demonstrates that smartphone-based finger tapping features are strongly and consistently associated with cognitive function in older adults.

## Key findings

- Eleven tapping features, including temporal variability and ITI slope, were significantly associated with global cognitive function.
- Finger tapping features showed the strongest associations with executive function, particularly with mean intertap interval.
- Composite scores from tapping features correlated with cognitive test scores, suggesting potential for scalable cognitive assessment.

## Abstract

Finger tapping tasks assess fine motor control and have been proposed as potential markers of cognitive function. With smartphones widely available, these tasks can be easily administered at home or in other nonclinical settings. However, the relationship between smartphone-based finger tapping measures and cognitive performance is still not well understood.

This study aimed to examine the association between smartphone digital finger tapping features and cognitive performance in an aging population.

Participants were enrolled in the study as part of the electronic Framingham Heart Study. They were instructed to perform a 2-finger tapping task every 2 months over a 1-year period. In each session, they tapped with 2 fingers of the left hand for 10 seconds and then with 2 fingers of the right hand for 10 seconds, for a total of 20 seconds. Global cognition performance and 4 cognitive domains, including memory, executive function, language, and visuospatial function, were assessed using a standardized neuropsychological battery. Fifteen tapping features were extracted to capture aspects of motor performance, including the mean, SD, skewness, and slope of the intertap interval (ITI). Associations between tapping-derived features and cognitive performance were assessed using linear regression models, adjusting for age, sex, handedness, education, cohort, and the time interval between cognitive and tapping assessments.

A total of 302 participants (mean age 74.7, SD 6.3 years; n=169, 56% female participants, and n=40, 13.2% non-White participants) completed the digital finger tapping tasks. Eleven tapping features such as basic temporal properties (eg, number of taps and mean ITI), temporal variability (eg, SD and coefficient of variation of ITI, ITI range, and Microfluctuation Index), and fatigue/temporal drift (ITI slope) were significantly associated with global cognitive function (all P<.001). Each 1 SD increase in the number of taps was associated with a 0.14-unit higher global cognitive function score (β=.14, 95% CI 0.07-0.21). Furthermore, finger tapping features were significantly associated with multiple cognitive domains, with the greatest number of associations observed for executive function (11 significant features), including a strong association with mean ITI (β=−0.27, 95% CI −0.33 to −0.20). Stratified analyses by hand showed consistent effect directions across both hands. The aggregated composite scores derived from finger tapping features demonstrated significant associations with their respective cognitive test scores, with partial correlation coefficients ranging from 0.21 for memory function to 0.41 for global cognitive function.

Digital finger tapping features, particularly those reflecting ITI variability, are significantly associated with cognitive performance. These findings suggest that finger tapping tasks may serve as noninvasive, scalable tools for assessing cognitive performance. Further research is warranted to validate their use for monitoring cognitive health in aging populations.

## Full-text entities

- **Diseases:** MCI (MESH:D060825), Mental Disorders (MESH:D001523), HD (MESH:D006816), arthritis (MESH:D001168), neurodegenerative disorders (MESH:D019636), HL (MESH:C538324), PD (MESH:D010300), cognitive decline (MESH:D003072), impairments in memory (MESH:D008569), depression (MESH:D003866), motor slowing (MESH:D012897), Fatigue (MESH:D005221), function (MESH:D003291), FHS (MESH:D006331), Dementia (MESH:D003704)
- **Chemicals:** FTT (-), Android (MESH:D008777)
- **Species:** Mus musculus (house mouse, species) [taxon 10090], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

36 references — full list in the complete paper: https://tomesphere.com/paper/PMC12916091/full.md

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Source: https://tomesphere.com/paper/PMC12916091