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#small language model Review Open access

Mechanical Contact Conditions in Wearable Reflectance Photoplethysmography: Scoping Review.

Chenxi Yang Jiahang Xie Zifei He Jianqing Li Chengyu Liu
Aug 2026 · JMIR mHealth and uHealth · Vol 14, pp. e99333 · 0 citations
Medicine

TL;DR

A multilevel conceptual pathway in wearable reflectance PPG is supported, in which mechanical conditions at the sensor-skin interface are associated with changes in PPG signal characteristics, derived features, and, in a smaller body of studies, downstream physiological estimation.

Abstract

Background

Cardiovascular diseases remain a major global health burden, highlighting the need for long-term physiological monitoring. Photoplethysmography (PPG) is widely used in wearable devices for noninvasive monitoring of heart rate (HR), rhythm, and oxygen saturation in mobile health (mHealth) apps. However, the reliability of wearable reflectance PPG depends on sensing conditions, including sensor-skin contact force and pressure.

Objective

This scoping review maps how contact force and contact pressure have been defined, controlled, measured, represented, and reported in wearable or wearable-relevant reflectance PPG studies; characterizes the reported signal-, waveform-, feature-, and task-level responses under different contact conditions; and identifies methodological and evidence gaps.

Methods

A comprehensive literature search was conducted in PubMed, IEEE Xplore, Scopus (Elsevier), and Web of Science Core Collection (Clarivate) from database inception to June 1, 2026. Studies were eligible if they addressed contact force or contact pressure in wearable or wearable-relevant reflectance PPG and reported measurement approaches, measurement sites and device configurations, force or pressure representation, or PPG responses across signal quality, waveform and feature characteristics, and downstream physiological estimation. Database filters were applied, where available, to restrict results to English-language publications. Search results were imported into EndNote for deduplication. Database searches were supplemented by reference-list screening. After screening, 53 reports were sought for retrieval; 1 was not retrieved, 52 were assessed at full text, and 21 studies met the inclusion criteria.

Results

The 21 included studies showed substantial heterogeneity, with sample sizes ranging from single-participant experiments to a wrist PPG dataset including 1142 participants. Most human studies recruited healthy volunteers, whereas some used public datasets or tissue-vessel phantoms, and 1 combined theoretical modeling with human-participant validation. The mapped evidence identified contact force and contact pressure as important measurement conditions in wearable reflectance PPG. Across the included studies, different contact conditions were associated with changes in alternating current/direct current components, amplitude- and morphology-related features, derivative-based indices, wavelength-dependent responses, and fiducial-point detection. Several studies also examined downstream physiological tasks, including HR, oxygen saturation, pulse transit or arrival time, blood pressure-related estimates, and HR variability.

Conclusions

The evidence mapped in this scoping review supports a multilevel conceptual pathway in wearable reflectance PPG, in which mechanical conditions at the sensor-skin interface are associated with changes in PPG signal characteristics, derived features, and, in a smaller body of studies, downstream physiological estimation. Evidence remains limited by small samples, short-term controlled protocols, and inconsistent reporting of mechanical parameters, including units, contact area, and probe geometry. Insufficient population diversity and limited free-living validation further restrict generalizability. Future research should standardize reporting of contact conditions and device geometry, incorporate real-world validation, and develop force-aware signal-quality assessment algorithms, adaptive attachment designs, and context-aware models to improve mHealth and cardiovascular monitoring.

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