Flexible polymeric materials and wearable biosensors for smart diabetes mellitus diagnostics and monitoring.

Flexible polymeric materials and wearable biosensors for smart diabetes mellitus diagnostics and monitoring - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice iScience . 2026 Mar 11;29(4):115306. doi: 10.1016/j.isci.2026.115306 Search in PMC Search in PubMed View in NLM Catalog Add to search Flexible polymeric materials and wearable biosensors for smart diabetes mellitus diagnostics and monitoring Valentine Saasa Valentine Saasa 1 Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Florida Science Campus, Roodepoort 1710, South Africa Find articles by Valentine Saasa 1, ∗ , Thandi Gumede Thandi Gumede 2 Department of Life Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9301, South Africa Find articles by Thandi Gumede 2 , Nkosikhona Theoren Msweli Nkosikhona Theoren Msweli 3 Department of Information Systems, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, Roodepoort 1710, South Africa Find articles by Nkosikhona Theoren Msweli 3 Author information Article notes Copyright and License information 1 Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Florida Science Campus, Roodepoort 1710, South Africa 2 Department of Life Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9301, South Africa 3 Department of Information Systems, College of Science, Engineering, and Technology, University of South Africa, Florida Science Campus, Roodepoort 1710, South Africa ∗ Corresponding author [email protected] Collection date 2026 Apr 17. © 2026 The Author(s) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13090661  PMID: 42006357 Summary Diabetes mellitus remains a major global health challenge that requires frequent and accurate glucose monitoring. Conventional finger-prick methods are invasive and often lead to poor patient compliance. In response, flexible and wearable biosensors have emerged as promising platforms for continuous and non-invasive glucose monitoring. This review provides a critical and framework-driven analysis of polymer-based material systems underpinning wearable glucose biosensors, including conductive polymers, biocompatible/biodegradable polymers, elastomers, hydrogels, and polymer-nanomaterial hybrids. We discuss how polymer properties govern electron transfer, mechanical compliance, biocompatibility, and long-term stability, highlighting trade-offs and limitations of each material class. Recent advances in hybrid and smart biosensing systems integrating microfluidics, self-powered operation, Internet of Things, and artificial intelligence-assisted analytics are systematically examined. Finally, we identify key challenges, such as material degradation, biofluid variability, and integration complexity, and propose targeted future directions for material design, device development, and clinical translation. Subject areas: health sciences, biological sciences, materials science Graphical abstract Open in a new tab Health sciences; biological sciences; materials science Introduction Diabetes mellitus remains a leading non-communicable disease worldwide. The disease disrupts individuals’ lives and healthcare systems. 1 According to the World Health Organization (WHO), about 431 million people suffer from diabetes globally. More than four million fatalities were reported in 2019. 2 The current management of the disease involves monitoring blood glucose (BG) levels, typically using the Accu-Chek glucometer and requires frequent BG checks, typically three times daily, in conjunction with a balanced diet and medication. The process of monitoring and managing BG can be unconformable due to lots of finger pricks which can lead to non-compliance. Abnormal BG regulation is a key factor in the development of serious complications. 3 These complications highlight the importance of developing continuous glucose monitoring (CGM) technologies that do not rely on invasive sampling. In particular, non-invasive monitoring technologies could significantly improve glucose management in individuals prone to hypoglycaemia. 4 Wearable biosensors integrate biochemical recognition, transduction, and signal processing into compact, user-friendly devices for continuous monitoring of biomarkers in sweat, saliva, tears, or interstitial fluid. However, the success of such systems depends critically on the properties of their constituent materials. Traditional rigid substrates and metallic electrodes often exhibit poor conformity with soft tissues, leading to discomfort and unreliable readings. In contrast, polymeric materials provide unique advantages including flexibility, biocompatibility, stretchability, and facile chemical tunability, making them ideal candidates for skin-mounted or implantable devices. Initially used as passive supports, polymers have evolved into active sensing components through advances in molecular design and nanofabrication. Conductive polymers (CPs), such as polyaniline (PANI), polypyrrole (PPy), and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) exhibit intrinsic electroactivity and enable efficient signal transduction in glucose sensing platforms. On the other hand, biocompatible and biodegradable polymers, such as polylactic acid (PLA), poly(lactic-co-glycolic acid) (PLGA), chitosan, and alginate enhance biosafety and support enzyme immobilization. Elastomeric polymers, such as polydimethylsiloxane (PDMS), polyurethane (PU), and ecoflex provide mechanical resilience and skin conformity, while hydrogels offer hydrated networks that can host glucose-responsive elements for selective biochemical recognition. These material classes form the foundation for next-generation flexible biosensors. Yet, polymers alone often suffer from limited conductivity, poor sensitivity, and mechanical degradation under physiological stress. To overcome these drawbacks, researchers have developed polymer/nanomaterial hybrid systems, where nanomaterials confer superior electronic and catalytic properties while polymers ensure flexibility and biocompatibility. Graphene/polymer composites, for example, achieve high surface area and conductivity for non-invasive glucose detection in sweat. Gold nanoparticle (AuNP)/polymer films enhance electrochemical signal amplification, while polymer/carbon nanotube (CNT) hydrogels have been integrated into microneedle patches for interstitial fluid glucose sensing. These hybrids demonstrate how the synergy between nanoscale materials and polymer matrices can yield sensors that are both highly sensitive and mechanically compliant. The latest frontier involves smart biosensors, which merge material innovation with digital intelligence. Polymer/nanomaterial hybrids are being integrated with Internet of Things (IoT) modules for wireless data transmission and microfluidic components for controlled analyte sampling. Coupled with artificial intelligence (AI) and smartphone-based analytics, these systems offer continuous feedback, predictive glucose trend modeling, and adaptive user alerts, advancing toward autonomous diabetes management. Therefore, from a materials perspective, the performance and reliability of wearable glucose biosensors are governed largely by the choice of polymer matrix. 1 , 2 , 3 , 4 , 5 Polymers enable mechanical compliance, chemical tunability, and biocompatible interfaces that are not achievable with rigid inorganic materials alone. In this review, polymeric materials are categorized into four interconnected classes: (1) CPs that facilitate efficient charge transfer, (2) biocompatible and biodegradable polymers that ensure safe biological interfacing, (3) elastomeric polymers that provide stretchability and skin conformity, and (4) hydrogel-based systems that offer hydrated, biomimetic environments for glucose recognition. Increasingly, these polymers are combined with nanomaterials to form hybrid sensing architectures that bridge material design with device-level and system-level functionality. Building on this materials classification, this review proposes a unified conceptual framework that organizes polymer-based wearable glucose biosensors according to the polymer strategy, the sensing mechanism and sampled biofluid, and the level of integration, spanning material design, wearable device implementation, and smart system connectivity through self-powered operation, IoT, and AI-driven analytics. This framework provides a coherent structure for critically comparing material choices, device architectures, and translational readiness across the field, thereby addressing limitations of prior reviews that primarily catalog individual studies. Polymer materials for glucose biosensing Polymer matrices provide both structural and functional foundations in biosensor design. It influences biorecognition, signal transduction, and device integration. Unlike traditional inorganic materials, polymers offer tunable physicochemical properties that allow fine control over electron mobility, flexibility, biodegradability, and biological compatibility. Several studies 2 , 3 , 4 , 5 , 6 highlight a shared challenge: designing polymer matrices that are simultaneously conductive, flexible, and biocompatible. The literature suggests that polymer blending and nanocomposite fabrication are central strategies for balancing these competing requirements. This review categorizes polymer matrices into four primary classes: (1) CPs for efficient charge transfer, (2) biocompatible and biodegradable polymers for safe interfacing with biological media, (3) elastomeric polymers enabling flexibility and wearable integration, and (4) hydrogel matrices offering biomimetic environments for selective and sensitive detection. Rather than presenting each group in isolation, this review highlights the cross-disciplinary interplay among them, where conductive hydrogels or biodegradable elastomers represent hybrid design directions. Conductive polymers as active transduction matrices CPs play an important role in wearable glucose biosensors by enabling efficient electron transport between biochemical recognition elements and external circuitry while maintaining mechanical flexibility and biocompatibility. Unlike rigid metallic electrodes, CPs can conform to soft biological tissues, reduce interfacial mismatch, and sustain electrochemical activity under repeated deformation. However, the choice of CP is highly application dependent and requires balancing electrical conductivity, electrochemical stability, mechanical compliance, and biological compatibility. 7 , 8 , 9 , 10 Among the available CPs, PANI, PPy, and PEDOT:PSS remain the most widely adopted in wearable glucose biosensors due to their established synthesis routes, tunable redox behavior, and compatibility with enzyme-based sensing strategies. Although emerging CPs, such as polycarbazoles and redox-active polymer networks show promise, their integration into wearable glucose biosensors remains comparatively limited, especially in long-term on-body applications. Furthermore, CPs serve as passive charge carriers and also active bioelectronic interfaces, where polymer chemistry regulates electron transfer pathways, enzyme orientation, and interfacial biocompatibility. The following subsections analyze these polymers from a mechanistic and application-driven perspective. Structure-property relationships PANI, PPy, and PEDOT:PSS each exhibit distinct advantages. The protonic doping of PANI allows tunable conductivity through pH control, while the electrochemical stability of PPy ensures reproducible oxidation-reduction cycling. PEDOT:PSS, with its sulfonate counterions, combines high conductivity and transparency with mechanical flexibility. 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 Comparative studies 7 , 8 , 9 , 10 show that PEDOT:PSS maintains superior conductivity under physiological conditions, positioning it as a preferred choice for wearable and implantable biosensors. Table 1 compares conductivity, flexibility, and structure-property relationships of these. 2 , 3 , 4 , 5 , 6 , 8 , 9 , 10 PEDOT:PSS achieves an optimal balance of electrical, mechanical, and chemical performance, making it highly suitable for dynamic or conformal interfaces. In contrast, PANI and PPy, while exhibiting moderate conductivity, provide complementary advantages: PANI allows fine-tuning of redox properties and PPy ensures stable electrochemical cycling. Polymer selection should therefore be guided by a combination of electronic, mechanical, and operational factors, rather than single-property optimization. Table 1. Comparative properties of conductive polymers Polymer Conductivity (S/cm) Flexibility Best-suited application Strength Key limitation References PANI 1–10 moderate pH-sensitive enzymatic sensors tunable redox behavior environmental instability Mao and Wang 2 ; Moulahoum et al. 3 ; Tseghai et al. 4 ; Khairul et al. 5 ; Zhang et al. 6 ; Bhat et al. 7 ; Shahid et al. 8 ; Li et al. 9 ; Yang et al. 10 PPy 10–50 low-moderate stable electrochemical interfaces stable electrochemistry brittle under strain Tseghai et al. 4 ; Khairul et al. 5 ; Bhat et al. 7 PEDOT:PSS 100–500 high epidermal and implantable wearables flexible and water stable swelling induced drift Tseghai et al. 4 ; Bhat et al. 7 ; Shahid et al. 8 ; Yang et al. 10 Open in a new tab The morphology of PANI/PPy composites with graphene nanoparticles (GNPs) has been examined using scanning electron microscopy (SEM) ( Figure 1 ). The images reveal a nanofibrous polymer network with some agglomerated grains, indicative of the polymer matrix, while the GNPs appear to be homogeneously distributed throughout the composite. The observed features correspond to micrometer-scale aggregates, likely composed of smaller nanoparticles. This structural arrangement, with well-dispersed graphene within the polymer matrix, is expected to enhance electron mobility and provide more active sites for redox reactions. Such morphology supports the observed electrochemical performance of the composites and is consistent with the conductivity trends discussed previously. 6 Figure 1. Open in a new tab Morphology of nanomaterials and hybrid nanocomposites Scanning Electron Micrographs images (A–I) of the GNP, PANI-PPy, and hybrid nanocomposites. Adapted with permission from Khairul et al. 5 Electron transfer in polymer-based glucose biosensors occurs primarily through mediated electron transfer (MET) and, in fewer cases, direct electron transfer (DET). CPs facilitate MET by providing redox-active sites and percolation pathways that shuttle electrons from the enzymatic reaction (typically glucose oxidase) to the electrode surface. For example, the protonation state of PANI governs its redox potential and charge carrier density, enabling tunable electrochemical response under physiological pH conditions. 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 PEDOT:PSS exhibits superior electron mobility due to its delocalized π-conjugated backbone and ionic coupling between PEDOT chains and sulfonate groups. This structure supports stable charge transport even under mechanical deformation, making PEDOT:PSS particularly suitable for epidermal and implantable glucose biosensors. 4 , 7 , 8 , 10 In contrast, PPy offers robust electrochemical cycling stability but lower intrinsic conductivity, which can limit sensitivity unless reinforced with nanomaterials. 4 , 5 , 7 Beyond conductivity, polymer chemistry directly influences biocompatibility and enzyme stability. Hydrophilic functional groups and hydrated polymer domains reduce nonspecific protein adsorption and minimize inflammatory responses. PEDOT:PSS and chitosan-modified CPs, for instance, form hydrated interfacial layers that preserve enzyme conformation and activity. 4 , 7 , 8 , 10 However, excessive swelling or ion migration can induce signal drift, showing the need for crosslinking and hybridization strategies. These mechanistic considerations highlight that polymer selection must be guided by sensing mechanism, target biofluid, and mechanical environment rather than conductivity alone. These differences show that CP selection must be tailored to the sensing mechanism, deformation environment, and targeted biofluid. Synthesis and processing strategies for wearable applications Beyond material composition, fabrication and synthesis strategies play a decisive role in determining the performance, scalability, and wearability of CP-based biosensors. Although electropolymerization remains a widely used technique due to its simplicity and ability to entrap enzymes during polymer growth, it represents only one class of synthesis approaches currently shaping the field. Electropolymerization enables precise control over film thickness, morphology, and redox activity, making it suitable for enzymatic glucose biosensors. By immobilizing glucose oxidase within the growing polymer matrix, this method minimizes mass-transport limitations and enhances direct or MET. However, its scalability and patterning resolution are limited, especially for large-area or multiplexed wearable devices. 3 , 6 , 8 To address these limitations, printing-based techniques, including screen printing, inkjet printing, and aerosol jet printing, have gained significant attention. These methods allow scalable deposition of CP-nanomaterial inks onto flexible substrates, such as polyethylene terephthalate (PET), PDMS, and textile fabrics. Printed PEDOT:PSS- and PANI-based electrodes enable low-cost, high-throughput fabrication and are compatible with wearable and disposable sensor formats. 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 More recently, additive manufacturing (3D printing) has emerged as a powerful approach for fabricating architecturally complex biosensors. CP composites and hydrogels can be printed into microstructured or porous designs that improve analyte diffusion, mechanical resilience, and skin conformity. Although still at an early stage for glucose biosensing, 3D printing offers unique opportunities for customized, patient-specific wearable sensors. Functional stability and limitations Despite the high conductivity of CPs, their long-term stability in aqueous and enzymatic environments remains a major challenge. Comparative studies show that PEDOT:PSS exhibits superior oxidative stability relative to PANI and PPy. 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 However, its intrinsic hydrophilicity can lead to swelling-induced signal drift, which compromises measurement reliability over time. To mitigate these limitations, researchers have explored crosslinking strategies using ionic liquids, which improve structural integrity and reduce water-induced expansion. 9 Additionally, the integration of PEDOT:PSS with biodegradable polymers such as PLA or chitosan has emerged as a promising approach. These hybrid matrices preserve conductivity and enhance biocompatibility and mechanical stability, creating scaffolds suitable for implantable or wearable biosensors. In general, no single CP meets all requirements for wearable glucose biosensing. Although PEDOT:PSS provides high conductivity and mechanical compliance, it can suffer from swelling-induced drift. 4 , 7 , 8 , 10 PANI offers tunable redox activity but limited stability 2 , 3 , 4 , 5 , 6 , 8 , 9 , 10 ; PPy demonstrates cycling stability yet lower stretchability. 4 , 5 , 7 These trade-offs emphasize the need for hybrid materials and context-specific polymer selection based on the intended biofluid and wearable format. Biocompatible and biodegradable polymers for biointerfacing: PLA, PLGA, chitosan, and alginate Biocompatible and biodegradable polymers serve as critical interfacial layers that regulate enzyme stability, minimize inflammatory responses, and enable safe long-term contact with biological tissues. Biocompatible polymers are important for ensuring safe interfacing between biosensors and biological systems by minimizing immunogenicity and cytotoxicity. Biodegradable polymers further enhance sustainability and enable the development of transient devices that degrade safely after their functional lifespan. Synthetic polyesters: PLA and PLGA PLA and PLGA are widely used degradable backbones for implantable biosensors. Their degradation rates are influenced by polymer molecular weight and, in the case of PLGA, the lactic-to-glycolic acid ratio. Adjusting this ratio allows precise tuning of degradation from weeks to months, enabling controlled temporal functionality for temporary implants. 11 , 12 , 13 Hydrolysis converts PLA and PLGA into lactic and glycolic acids, which are naturally metabolized by the body, supporting safe biodegradation. Table 2 summarizes the main properties of these synthetic polyesters. Table 2. Biodegradable polyester properties Polymer Degradation rate Application Compatibility Advantages Limitations References PLA weeks-months disposable sensors good biocompatibility biocompatible, low-cost brittle, low flexibility Moulahoum et al. 3 ; Lv et al. 11 ; Wang et al. 13 PLGA tunable (by ratio) implantable sensors excellent biocompatibility (FDA-approved) controlled degradation acidic by-products Lv et al. 11 ; Ma et al. 12 ; Wang et al. 13 Open in a new tab Table 2 highlights that PLA is suitable for short-term disposable devices due to its relatively fast degradation rate, whereas PLGA is more appropriate for implantable sensors requiring tunable and controlled degradation. Both polymers are electrically insulating, which limits their direct application in electrochemical biosensing. Consequently, the literature reports the incorporation of conductive fillers such as PEDOT:PSS to form hybrid PLA-conductive polymer (PLA-CP) or PLGA-CP matrices, thereby combining biodegradability, biocompatibility, and electrical conductivity within a single sensor platform. 3 , 11 , 12 Natural polysaccharides: Chitosan and alginate Natural polymers such as chitosan and alginate provide bioactive surfaces for enzyme immobilization and cell encapsulation. 13 The amine groups (–NH 2 ) of chitosan enable covalent attachment of enzymes and facilitate proton conduction, enhancing sensor response. The carboxylate groups (–COOH) of alginate undergo ionic gelation with Ca 2+ ions, allowing gentle encapsulation of whole cells or enzymes without denaturation. 3 , 4 , 5 , 7 , 8 , 9 , 12 , 13 Table 3 shows that natural polymers excel in biocompatibility, but their low conductivity necessitates hybridization with CPs (e.g., PEDOT:PSS-chitosan) or nanomaterials (e.g., AuNP-alginate). These hybrid matrices combine bioactivity with enhanced electron transfer, forming the foundation for next generation bioelectronic interfaces. Table 3. Polysaccharide-based polymers for biosensors Polymer Functional Group Biosensor role Advantage Limitation References Chitosan –NH 2 enzyme immobilization adhesive, bioactive low conductivity Moulahoum et al. 3 ; Tseghai et al. 4 ; Zhang et al. 6 ; Bhat et al. 7 ; Shahid et al. 8 ; Li et al. 9 ; Ma et al. 12 ; Wang et al. 13 Alginate –COOH cell encapsulation mild crosslinking poor mechanical strength Moulahoum et al. 3 ; Bhat et al. 7 ; Li et al. 9 ; Ma et al. 12 ; Wang et al. 13 Open in a new tab Biocompatible and biodegradable polymers provide essential interfacial compatibility but often compromise electrical performance. Chitosan and alginate stabilize enzymes effectively, yet their low conductivity limits standalone use in transduction. 3 , 4 , 6 , 7 , 8 , 9 , 12 , 13 Thus, hybridization with CPs or nanomaterials is typically required to achieve both biocompatibility and reliable signal transduction. Elastomeric polymers for stretchable and skin-conformal wearables Elastomeric polymers provide mechanical compliance that allows biosensors to conform to skin or soft tissues while maintaining signal fidelity under deformation. Among these, PDMS, PU, and ecoflex are widely used as stretchable substrates for wearable and implantable devices. PDMS and surface functionalization PDMS is chemically inert, optically transparent, and biocompatible, making it a key substrate for flexible biosensors. Its intrinsic hydrophobicity, however, limits biomolecule adhesion. To overcome this, plasma or silane surface treatments are commonly utilized, enhancing wettability and enabling stable coating with conductive films such as PEDOT:PSS or silver nanowires (AgNWs). 11 , 12 , 14 , 15 , 16 Microstructuring PDMS, through wrinkling or porous designs, further improves mechanical compliance and strain-insensitive electrical performance, allowing devices to accommodate dynamic deformation without compromising signal integrity. PU and ecoflex as dynamic matrices PU offers tunable elasticity and chemical versatility, supporting stable incorporation of nanomaterials while maintaining mechanical robustness. 2 , 4 , 8 , 11 , 12 , 13 Ecoflex, with its extremely low elastic modulus, provides skin-like stretchability, allowing epidermal sensors to accommodate large deformations with minimal discomfort. 12 When these elastomers are combined with conductive fillers, they maintain percolation networks that ensure consistent signal transmission under mechanical stress. Table 4 highlights the trade-off between mechanical softness and structural stability. While softer elastomers improve comfort and skin conformity, they are intrinsically insulating. Incorporation of conductive fillers is required to establish percolation networks for electrical signal transmission. Hierarchical designs, embedding rigid conductive domains within soft matrices, optimize both mechanical compliance and signal fidelity, representing a strategic design principle for next-generation wearable biosensors. While elastomers ensure mechanical compliance, their electrical inertness necessitates integration with CPs or fillers, reinforcing the importance of hybrid material strategies. Elastomeric polymers enhance mechanical compliance and skin conformity, which are critical for continuous wear, but they are electrically inert and can impose design constraints when integrating electrodes. The balance between softness and device robustness remains a key challenge, particularly in long-term, high-strain applications. Table 4. Mechanical properties of elastomeric polymers Polymer Elastic modulus (MPa) Stretchability (%) Application Distinct feature References PDMS 0.5–2 120 substrate for flexible circuits chemically stable Lv et al., Ma et al., Mei et al., Wu et al., Sang et al. 11 , 12 , 14 , 15 , 16 PU 2–10 300 flexible electrodes tunable elasticity Tseghai et al., Zhang et al., Lv et al., Ma et al., Sang et al. 4 , 6 , 11 , 12 , 16 Ecoflex 0.1–0.3 900 epidermal sensors skin-like deformability Ma et al. 12 Open in a new tab Hydrogel-based polymer systems Hydrogels provide hydrated, tissue-mimicking environments that facilitate analyte diffusion and maintain enzyme activity, making them ideal matrices for glucose sensing. Their intrinsic swelling and stimuli-responsive properties allow conversion of chemical changes into measurable signals. Mechanistic diversity The literature identifies three primary hydrogel-based sensing mechanisms 7 , 8 , 10 , 11 , 12 , 13 , 14 , 15 , 17 , 18 • Enzymatic systems (GOx-based) rely on glucose oxidase-mediated H 2 O 2 production, which is typically measured amperometrically. These systems exhibit high sensitivity but limited operational lifetime due to enzyme degradation. • Affinity systems (PBA-based) utilize reversible glucose binding, enabling label-free optical or impedance readouts. These hydrogels offer stability and reversibility but may show lower sensitivity than enzymatic systems. • Stimuli-responsive systems exhibit swelling, volume change, or conductivity modulation in response to glucose, which is transduced into an electrical signal. Comparative performance Table 5 demonstrates that conductive hydrogels such as PEDOT:PSS hybrids combine the high sensitivity of CPs with the hydrated, biocompatible environment of hydrogels, enabling real-time glucose monitoring with mechanical and chemical resilience. 2 , 3 , 4 , 6 , 14 Limitations include long-term enzymatic stability, environmental sensitivity, and reproducibility, which require optimization. Despite their biomimetic advantages, hydrogels remain susceptible to dehydration and mechanical degradation, posing challenges for long-term wearable operation. 13 , 15 , 17 , 18 Hydrogels offer hydrated, biomimetic environments that improve enzyme activity and sampling of sweat or interstitial fluid. However, they are prone to dehydration and mechanical fatigue over extended wear. These limitations must be addressed through crosslinking, hybridization, or encapsulation strategies to ensure consistent sensor performance. Table 5. Hydrogel-based glucose sensors Hydrogel Mechanism Transduction Sensitivity (mM −1 cm −2 ) Key finding References GOx-alginate enzymatic amperometric 1.5 high sensitivity, limited lifetime Khairul et al. 5 ; Zhang et al. 9 ; Ma et al. 12 ; Mei et al. 14 ; Wu et al. 15 ; Liu et al. 17 ; Li et al. 18 ; Shen et al. 19 PBA-hydrogel affinity impedance 0.8 reversible and stable response Wang et al. 13 ; Wu et al. 15 ; Liu et al. 17 ; Li et al. 18 PEDOT:PSS-hydrogel conductive potentiometric 2.1 high flexibility and stability Tseghai et al. 4 ; Bhat et al. 7 ; Shahid et al. 8 ; Li et al. 9 ; Yang et al. 10 ; Shen et al. 19 Open in a new tab Flexible biosensors based on polymer-nanomaterial hybrids Flexible biosensors have recently attracted a lot of attention in research because of their role in developing affordable, efficient, miniaturized, and rapid devices that can monitor diseases, 20 environmental pollution, 21 food pathogens 22 etc. More importantly, they must simultaneously achieve mechanical compliance, stable biorecognition, and efficient signal transduction requirements that are often in direct competition. Hence, the polymer-nanomaterial hybrid architectures have emerged as a strategy to balance these trade-offs, yet their effectiveness depends strongly on material compatibility, interface engineering, and device configuration. Polymers such as PDMS, polyimide (PI) and PET have been mostly used as substrate for the immobilization of flexible biosensors as they are commercially affordable and they could suffer large deformation. 23 There has been a lot of attempts to develop biosensors based on flexible polymer substrates, including polyester (PET, PI, and PDMS). For example, Pradhan et al. 24 developed the flexible enzymatic glucose biosensor by depositing ZnO nanowires (ZnONWs) electrochemically directly onto the Au-coated PET substrate as seen in Figure 2 A. As shown in Figure 2 B, the flexible biosensor exhibits outstanding glucose-sensing performance. The excellent sensor response was influenced by (1) large surface area and (2) high isoelectric point of 9.5 with positive charges that enable the strong binding to the negatively charged GOx by electrostatic attraction. Another study by Chen et al., 25 fabricated a thrombin aptamer-protein biosensor by depositing Au nanowires directly on PET substrate. The Au nanowires showed highest sensitivity compared to the counterparts with Au nanocorals, nanothorns, and nanoslices. This could be attributed to one-dimensional nanowire structure providing more exposed surface area than other nanostructures. The second reason is that aptamer can be firmly conjugated on Au nanowire through the stable Au–thiol bond, which provided well-supported evidence for immobilizing enzyme on certain nanomaterials. Figure 2. Open in a new tab Glucose biosensor design and electrochemical response (A) A schematic diagram of GOx/ZnO-NWs/Au/PET bioelectrode used for glucose monitoring. (B) Amperometric response of GOx/ZnO-NWs/Au/PET bioelectrode at an applied potential of 0.8 V (vs. Ag/AgCl, 3MKCl reference electrode). Adapted with permission from Pradhan et al. 24 Furthermore, another interesting study conducted by Gunatilake et al. 26 developed a three-dimensional titanium dioxide nanotubes/alginate hydrogel scaffold for detection of sweat biomarkers, including lactate and glucose in an artificial sweat. Titanium oxide which was synthesized hydrothermally was introduced into the alginate polymeric matrix, thereafter, cross-linked the nanocomposite with dicationic calcium ions in fabricating the scaffold platform. Figure 3 shows the mechanism of lactate detection under LOX and HRP catalytic pathways. Lactate is oxidized to pyruvate in the LOX catalysis path and oxygen (O 2 ) is reduced to hydrogen peroxide (H 2 O 2 ), following H 2 O 2 is reduced to H 2 O, and TMB is oxidized to TMBOX changing the TNT/alginate scaffold to blue color in the HRP catalysis pathway. Figure 3. Open in a new tab Mechanism of lactate detection under LOX and HRP catalytic pathways (Ez-Fl-flavoenzyme) Both the lactate and glucose biosensors response showed good linearity, ranging from 0.1 to 1.0 mM for lactate and 0.1 and 0.8 mM for glucose, with a correlation factor of R 2 = 0.981 for lactate and 0.989 for glucose. Moreover, the limit of detection (LOD) was 0.069 mM with a limit of quantification (LOQ) = 0.23 mM for lactate. On the other hand, for glucose, LOD = 0.044 mM and LOQ = 0.15 mM. This biocompatible colorimetric biosensor scaffold is a promising platform to implement the real-time detection of sweat biomarkers in wearable devices. Flexible biosensors have proven to continuously monitor health particularly blood/sweat/saliva glucose at a convenient diagnosis in real time. This is achieved through various substrates, novel nanomaterials, and detection techniques to develop conductive flexible platforms that can be applied to create flexible electrochemical biosensors. While polymer-nanomaterials hybrid provides high sensitivity due to well-defined interfaces, they are however evaluated under static or short-term conditions, with limited emphasis on mechanical fatigue, biofouling, and user-induced variability, highlighting a key challenge for wearable deployment. Addressing these gaps is essential for translating hybrid flexible biosensors from proof-of-concept devices to clinically relevant platforms. Wearables platforms for glucose biosensing Centralized healthcare service such as clinics and hospitals is the primary choice for disease diagnosis currently. They are however inconvenient, always over-populated, short staffed, shortage of infrastructure and time/cost-consuming for most people especially in developing continents such as Africa. 27 In recent years, wearable devices were then developed to monitor health status in real time, which provides a method to compensate for some shortcomings of centralized healthcare services. Wearable biosensors can convert physiological signals to measurable electrical signals in real time, such as resistance, current and capacitance that reflect specific health information. 28 In contrast with the expensive, bulky, and complicated equipment in a centralized healthcare system, wearable biosensors provide convenient and rapid characteristics, such as portability, timeous display, and low cost. 29 Among many health applications, wearable biosensors have recently been applied in cardio activities, 23 temperature changes, 30 metabolites in body fluids, etc. Importantly, there has been a drive to develop the wearable biosensors for monitoring of the glucose especially for diabetes mellitus, as the current used and popular method for diabetes management is based on checking for the BG through finger pricking method at home, where patients are required to keep their BG level between 4.4 and 6.7 mm/L. 31 However, this method cannot indicate or warn of hypoglycemia occurrences and can lead to relapse in glucose monitoring for children who are diabetic, who fears needles. So, the recent development in wearable biosensors for blood, saliva, sweat, or tears monitoring is a game changer in the management of diabetes mellitus. They provide continuous BG levels which rely on the precision and accuracy of biosensors to translate BG concentrations correctly and efficiently in real-time, 24 h per day. It should be noted that wearable glucose biosensors must operate reliably under continuous mechanical deformation, variable biofluid composition, and prolonged skin contact, while maintaining analytical accuracy comparable to conventional blood-based measurements. Biosensors consist of three main components as shown in Figure 4 ; namely (1) the recognition element termed bioreceptor, (2) a transducer, and (3) signal displayer, this can be an electronic system. 32 , 33 The biosensing devices usually detect the biological analytes, and in this case, it will be glucose and then coverts the glucose molecules into a concentration. Figure 4. Open in a new tab A schematic representation of biosensing components A CGM is an example of a successful example of wearable biosensors in the market for monitoring chemical components in matrix samples such as intertidal fluids non-invasively or minimally invasive. Most of the CGMs uses the enzymatic method, where the glucose oxidase act as the biorecognition molecule that bind with glucose. Some of the latest developed wearable glucose biosensors are listed in Table 6 . Table 6. Trends on wearable glucose biosensors Wearable biosensor type Sample Sensitivity Linear range Response time References Amperometric patch interstitial fluid 1.62 μAmM −1 cm −2 36 mM 13 s Lee et al. 34 Mouthguard saliva 100 μmol/L 1.75–10,000 μmol/L 20 min Arakawa et al. 35 Prussian blue doped carbon ink serum 16.66 μAmM −1 cm −2 0 to 12 mM. – Cai et al. 36 Eyeglasses-based tear biosensor tear ∼40 mg/dL – 15 min Sempionatto et al. 37 Epidermal patch sweat – 10–200 μM 4–7 s Wiorek et al. 38 Open in a new tab As shown in Table 6 , the study by Lee et al. 34 developed a minimally invasive non-enzymatic biosensor device by employing the microneedles. This wearable patch device was then in vitro and in vivo . The amperometric sensor exhibited a high sensitivity of 1.62 μAmM −1 cm −2 , linear range of up to 36 mM, detection limit of 50 μM, and a response time of 13 s. Another non-invasive wearable biosensor in a form of a mouth guard was developed by Arakwa and team. 35 They coated the mouthguard with a cellulose acetate membrane, used to reflect any interferences from the saliva, including uric acid (UA) and ascorbic acid (AA) as shown in Figure 5 . This wearable biosensor exhibited high selectivity and sensitivity, a wide linear range of 1.75–10,000 μmol/L and removing the need for pre-treatment of the saliva. Figure 5. Open in a new tab Mouthguard glucose sensor: structure, calibration curve, and android app Adapted with permission from Arakawa et al. 35 A wearable device made of Prussian blue doped carbon ink was developed and tested on an artificial serum as a testing medium for the detection of glucose. This flexible biosensor was implanted in the dorsal cervical. It exhibited excellent sensitivity of 16.66 μAmM −1 cm −2 (correlation coefficient R 2 = 0.9962). The amperometric response current showed a linear variation from 0 to 12 mM. The production of this sensor relied on a single-step modification of nano-polyaniline (PANI) and GOD, therefore making it simple, low cost, and applicable to large scale production. 36 Futhermore, another study using tear fluid for glucose monitoring presents an innovative form of a wearable in the form of eyeglasses. This device was used to detect vitamins, alcohol, and glucose, but for the sake of this review, we will report on its glucose sensing capabilities. The eyeglasses were made of the wireless technology which is placed outside the eye for collecting stimulated tears by an external miniaturized flow detector. The biosensor showed a great correlation between the tear glucose current response Δi (black curve) and the measured BG levels (red curve) as shown in Figure 6 . 38 Figure 6. Open in a new tab Non-invasive tear glucose sensing using wearable biosensor (A) Scheme of the procedure used for on-body glucose sensing experiments: Tears are stimulated ∼15 min after a meal to measure the glucose levels using the GOx-modified PB detector using chronoamperometry. (B) Using (a) GOx sensor having meal (M), (b) GOx sensor without a meal, and (c) Prussian blue sensor without enzyme following a meal (M). Adapted with permission from Sempionatto et al. 37 Finally, Wiorek et al. 38 developed a wearable device made of GOD and chitosan composites, embedded on a wearable skin patch built as a microfluidic cell including a perspiration collection zone linked to a fluidic channel. This wearable device consists of glucose biosensor, temperature sensor, and a pH potentiometric electrode as shown in Figure 7 I. The biosensor was tested on sweat, which showed that the response time is more rapid at 4–7 s, but the linear range still resides in the lower glucose concentration range at 10–200 μM. Moreover, Figure 7 II shows the glucose concentration measured in sweat and blood. As can be seen, the glucose amount in blood is higher than in sweat, presenting molar ratios between 7 and 27 (×103) in the initial measurements. However, there was no clear correlation found between sweat and BG concentration further than the same qualitative trend. Figure 7. Open in a new tab Sweat-based glucose monitoring mechanisms and correlations (I) (a–f) Schematics and mechanism of sweat-based CGMs. (II) (a) Glucose concentration in blood measured with the glucometer at different times over T1–T9. (b) Sweat/BG concentration ratio observed at different times over T1–T9. (c) Correlation between blood and sweat concentrations ( n = 26). (d) Correlation between blood and sweat concentrations observed at time 0 min for every subject ( n = 6) and for only one subject over time ( n = 5). Notably, the samples corresponding to time 0 min were collected by iontophoresis. Adapted with permission from Wiorek et al. 38 Despite significant progress, most wearable glucose biosensing platforms remain at the proof-of-concept stage, with limited validation under realistic daily-use conditions. Future efforts must prioritize long-term wear studies, standardized mechanical testing protocols, and improved biofluid/blood correlation models to bridge the gap between laboratory performance and clinical utility. Although sweat, saliva, and tears glucose has attracted significant attention as a non-invasive alternative to BG monitoring, their direct correlation with BG remains a subject of ongoing debate. For example, Moyer et al. 39 have demonstrated in subjects with diabetes that sweet glucose (SG) and BG are highly correlated over a broad range of sweat rates and BG values. Although the device described here is not commercially viable, a substantially simpler device has recently been disclosed. Additionally, Nyini et al. 40 used regional and correlative sweat analysis using high-throughput microfluidic sensing patches toward decoding sweat. The patch is demonstrated for investigating regional sweat composition, predicting whole-body fluid/electrolyte loss during exercise, uncovering relationships between sweat metrics, and tracking glucose dynamics to explore sweat-to-blood correlations in healthy and diabetic individuals. Reported discrepancies arise from physiological delays between blood and sweat glucose transport, low analyte concentrations in sweat, and strong dependence on sweat rate, skin condition, and individual metabolism. 41 Furthermore, sweat glucose may originate from both transdermal diffusion and local skin metabolism, complicating quantitative interpretation. To address these challenges, glucose wearable platforms should employ various calibration strategies, including subject-specific calibration against capillary BG, incorporation of multi-parametric sensing (e.g., sweat rate, temperature, and pH), and algorithm-based signal correction using machine-learning (ML) models. While these approaches improve correlation under controlled conditions, long-term stability and inter-individual variability remain key limitations, highlighting the need for standardized calibration protocols and large-scale clinical validation. Smart biosensors Smart biosensors, particularly those integrating IoT-enabled polymer-nanomaterial glucose sensors, self-powered devices, microfluidics, and AI-driven analytics represent a momentous advancement in continuous health monitoring. These technologies leverage the unique properties of nanomaterials and polymers to improve sensitivity, stability, and real-time data processing capabilities. The integration of self-powered mechanisms, such as nanogenerators and biofuel cells, further encourage the autonomy and sustainability of these devices. IoT-enabled polymer-nanomaterial glucose sensors Wearable and flexible glucose sensors progressively combine polymers and nanomaterials to improve sensitivity, mechanical compliance, and biocompatibility while targeting IoT deployment for remote monitoring. Literature shows both enzymatic electrochemical and nonenzymatic electrocatalytic approaches using carbon, metal nanoparticles, hydrogels, and molecularly imprinted polymers (MIPs) as core sensing layers. 42 These systems often incorporate specialized materials, such as carbon-based substrates, metal nanoparticles (Au, Ni), MWCNTs, Prussian blue, conductive inks, and hydrogels, as polymer matrices. 43 The underlying sensing mechanisms include enzymatic amperometry based on glucose oxidase, non-enzymatic electrocatalytic oxidation facilitated by metal nanoparticle catalysts operating in locally alkaline microenvironments, and MIP-based affinity detection coupled with triboelectric or optical readouts. Additionally, printed bioelectrodes have been developed to enable accurate on-body glucose measurements. 44 , 45 , 46 While IoT enablement is highly envisioned via low-power wireless modules and smartphone interfacing, the full end-to-end IoT deployments with polymer-nanomaterial sensor stacks remain at prototype and research level. 47 Enzymatic glucose sensors offer well-established selectivity but continue to face limitations related to enzyme lifetime and environmental stability. 42 In contrast, non-enzymatic approaches have shown potential in improving stability and lower the LOD, particularly in neutral environments, by employing localized electrochemical conditioning and polymer reinforcement layers to improve robustness. 43 Furthermore, the use of printed and ink-based polymer nanocomposites enables scalable fabrication of flexible sensors that are well suited for wearable applications and can be seamlessly integrated with wireless communication modules. 48 Table 7 provides comparative examples together with performance metrics. Table 7. Comparative examples and performance metrics Device example and application Materials and polymer components Sensing mechanism Representative performance data References Continuous non-enzymatic wearable glucose sensor carbon fiber microelectrode reinforced with quince seed mucilage (polymer) and Au/Ni nanoparticles non-enzymatic electrocatalytic oxidation with pulsed potential conditioning sensitivity 13.8 μA·mM−1mm −2 ; LOD 11.3 μM in neutral pH Asdaq et al. 43 Epidermal sweat glucose sensor (stretchable) soft PDMS microfluidic layer with enzymatic electrodes enzymatic biofuel cell sensing and microfluidic sweat capture glucose sensitivity 0.11 mV/μM; determination coefficient R 2 = 0.96 for sweat measurements Bhide et al. 45 Screen-printed flexible glucose BFC sensor (wearable) screen printable polymer nanocomposite inks: MWCNT + naphthoquinone (anode); Prussian blue/MWCNT (cathode) enzymatic biofuel cell that both harvests energy and senses glucose open circuit voltage 0.45 V and max power density 266 μWcm −2 ; detects glucose up to 10 mM in artificial sweat Sengupta and Kottapalli 48 Open in a new tab Self-powered devices (nanogenerators and biofuel cells) using polymer-nanomaterial hybrids Recent publication shows the integration of polymer-nanomaterial hybrids with energy harvesters and enzymatic biofuel cells to support battery free or self-charging sensing platforms for continuous monitoring. Studies also report several energy generation strategies and concrete power/output metrics for wearable glucose sensors. 40 , 46 , 48 Enzymatic biofuel cells (BFCs), for example, convert biochemical fuels such as sweat glucose or lactate into electricity; screen-printed single-enzyme BFCs have achieved open-circuit voltages of 0.45 V and maximum power densities of 266 μWcm −2 while detecting glucose concentrations up to 10 mM. 48 Although biofuel cells enable continuous power generation under physiological conditions, their practical deployment is constrained by enzyme instability, limited power density, biofouling, and challenges in long-term operation under variable biological environments. Consequently, improving energy efficiency, durability, and integration with sensing elements remains critical for the translation of self-powered wearable biosensors into real-world applications. Similarly, piezoelectric nanogenerators embedded within hybrid films, comprising piezoelectric PVDF nanowires and polymer microgels, have been used to create self-charging sensors that transduce mechanical deformation into electrical signals linked to glucose concentration, demonstrating highly linear sensing responses (R 2 ≈ 0.9956) and measurable voltage outputs. 49 These self-powered wearable biosensors aim to harvest energy directly from the user or surrounding environment, eliminating the need for external power sources. Among the emerging nanogenerators technologies, triboelectric and piezoelectric nanogenerators convert mechanical energy from human motion, pressure, or skin deformation into electrical signals through contact electrification or piezoelectric polarization mechanisms. Looking at other approaches, triboelectric sensors functionalized with molecularly imprinted poly (3-APBA) layers enable enzyme-free, self-powered glucose sensing by modulating surface charge upon glucose binding, achieving a reported sensitivity of 3.82 ± 0.13 mM −1 and sufficient power to illuminate LEDs without external energy sources. 46 , 47 Within these systems, polymer nanomaterial hybrids play crucial roles, such as conductive inks and nanocomposites containing MWCNTs, Prussian blue, and quinone mediators in polymer matrices serve as BFC electrodes and transducers, offering both high conductivity and mechanical flexibility, 48 while polymer microgels and MIPs contribute selective molecular recognition and swelling behaviors that couple effectively to electrical transduction in piezoelectric and triboelectric layers. 49 Limited stability and lifetime of enzymatic components and the difficulty of maintaining consistent power generation under variable physiological conditions are some of the challenges that hinder the implementation. Microfluidics with polymer-nanomaterial integration for continuous monitoring Microfluidic design and polymer integration are central to achieving continuous, minimally invasive glucose monitoring by enabling precise control of small biofluid samples and stabilizing sensing chemistries. Recent advances feature microneedles, PDMS microchannels, and hydrogel interfaces that integrate seamlessly with nanomaterial-based electrodes for on-body sampling and analyte transport. 50 Polymeric microneedles, often composed of swelling hydrogels or supported by iontophoresis-assisted extraction, allow low-pain interstitial fluid sampling and can be directly coupled to electrochemical sensors. 51 Similarly, ultra-thin PDMS microfluidics provide stretchable pathways for sweat capture and routing to enzymatic electrodes, maintaining stable sensor output even under 30% strain, while hydrogel-nanomaterial composites act as soft, conformal sensing layers that preserve enzyme activity, enhance analyte diffusion, and enable signal transduction through embedded catalysts or conductive fillers. Despite significant developments, the translation of polymer-nanomaterial-based smart glucose biosensors into real-world clinical and consumer applications remains constrained by several challenges. 50 For instance, long-term stability of sensing and self-powering components, especially enzyme degradation and nanomaterial aging, limits device lifetime and necessitates frequent recalibration. Simultaneously, variability in non-blood biofluids, arising from differences in sweat rate, composition, and environmental conditions, complicates accurate glucose inference and prompts the need for adaptive calibration and sensor fusion strategies. Power constraints further restrict continuous operation, as energy harvested from biofuel cells or nanogenerators must be efficiently managed to support sensing, computation, and wireless communication. 45 Smartphone and AI-driven analytics Smartphones are widely proposed as acquisition, processing, and UI hubs for portable glucose monitoring, while AI/ML is cited as necessary for calibration, artifact rejection, and predictive analytics. However, the reviewed studies contain limited implementation level details on deployed ML models and on device analytics. 47 , 51 The integration of smartphone highlights the functional roles, such as data acquisition, user interface, signal preprocessing, and wireless connectivity (Bluetooth/NFC) to electrochemical sensing modules. They also act as enablers of remote monitoring and potential closed-loop insulin delivery in future systems, emphasizing the need for robust sensor calibration and data pipelines. 47 These capabilities highlight the importance of powerful calibration strategies and reliable data pipelines to ensure clinical relevance and user safety. With regard to AI and ML, literature focus more on their role for predictive analytics, anomaly detection, and transforming continuous sensor streams into actionable insights. 45 , 51 Literature also references the use of established model classes, such as convolutional neural networks (CNNs) for feature extraction from multichannel sensor data and long short-term memory (LSTM) networks for time-series glucose prediction. 52 However, the reviewed studies provide limited implementation-level detail, with minimal reporting of specific algorithm architectures, training and validation datasets, performance metrics, or on-device inference benchmarks for polymer nanomaterial glucose wearables. Consequently, detailed claims regarding the deployment of ML models, their computational efficiency on smartphones, or their validated clinical predictive performance cannot be substantiated based on the current literature. 53 Challenges and future perspectives Literature highlights several emerging trends and persistent challenges in the development of wearable polymer nanomaterial glucose sensors. A dominant trend is the hybridization of polymers with nanomaterials, such as CNTs, metal nanoparticles, Prussian blue, and conductive inks, which enables the combination of mechanical flexibility with high electrochemical activity. 44 , 46 , 48 Another major area of advancement is self-powering, achieved through enzymatic biofuel cells, piezoelectric, and triboelectric generators, allowing battery-free sensing and generating local power densities sufficient for low-power electronics, for instance, up to 266 μWcm −2 has been reported for a screen-printed biofuel cell. 48 Enzymatic biofuel cells support the generation of electrical power via enzyme-catalyzed redox reactions, using biofluids such as sweat or interstitial glucose as fuel, thereby offering simultaneous sensing and energy harvesting capabilities. Additionally, microfluidic sampling and microneedle technologies are maturing as practical methods for continuous collection of sweat and interstitial fluid, facilitating seamless integration with polymer nanomaterial sensors. 45 , 50 Smartphone connectivity appears as a common feature in conceptual frameworks and prototype systems, offering data readout and user interaction; however, fully realized IoT ecosystems with validated analytics remain scarce in experimental reports. 47 Despite these promising trends, several critical challenges remain. Long-term stability and enzyme degradation in enzymatic sensors and biofuel cells continue to limit operational lifespan and necessitate frequent recalibration. 52 While enzymatic sensing and biofuel cells remain attractive due to their high selectivity and inherent self-powering capability, their sustained reliance raises fundamental concerns regarding long-term deployability in real-world wearable applications. As a result, there is a need for a strategic shift toward more durable, enzyme-free transduction mechanisms, such as MIPs and non-enzymatic electrocatalytic approaches, which may offer improved robustness for continuous and long-term glucose monitoring. Moreover, variations in biological samples, arising from individual differences, sweat rates, and environmental conditions, pose significant difficulties for accurate glucose quantification from non-blood biofluids. AI-enabled biosensing systems can mitigate these limitations by enabling adaptive, data-driven correction, and personalization mechanisms. ML models can learn individual-specific baselines and dynamically recalibrate sensor outputs by incorporating contextual variables, such as temperature, humidity, activity level, and sweat rate. Time-series models can further distinguish physiological glucose trends from motion- or dilution-induced artifacts, while sensor-fusion approaches combine signals from multiple modalities to improve robustness. Power intermittency and energy management issues also persist, as nanogenerator-based systems require efficient energy conditioning and storage solutions to ensure reliable wireless telemetry. It is also worth noting that the limited reporting of AI implementations and the lack of openly shared datasets hinder reproducibility and the validation of ML-enabled analytics in polymer-nanomaterial wearable systems. 45 , 47 For future research, literature recommends standardized performance reporting and clinical benchmarking for wearable polymer nanomaterial glucose sensors to enable comparison and regulatory pathways 44 . There is also a strong call for integrated system demonstrations that combine microfluidic sampling, robust polymer nanomaterial sensing, self-powering, and smartphone/cloud analytics in real world scenarios. 45 , 48 Strategies aimed at improving stability are necessary, including enzyme stabilization chemistries, enzyme free transduction (MIP, nonenzymatic catalysis), and protective hydrogel matrices to extend the durability of wearables. 53 Transparent AI/ML pipelines are also recommended, with clear algorithm descriptions, training/validation datasets, and on device inference benchmarks to translate predictive analytics claims into deployable features, current literature lacks sufficient implementation details to validate specific algorithms. 45 , 47 , 51 To accelerate the translation of wearable glucose biosensors from laboratory prototypes to practical healthcare tools, future research must move beyond incremental sensor improvements. In this context, three specific and feasible research directions emerge as particularly important. Robust calibration frameworks Future research should focus on developing robust calibration frameworks that account for physiological variability inherent in non-invasive glucose sensing. Integrating multi-parametric inputs, such as sweat rate, temperature, pH, and electrolyte composition with adaptive algorithms or ML models may significantly improve the correlation between wearable sensor outputs and BG levels. Emphasis should be placed on minimizing user-specific calibration requirements to enhance usability in real-world settings. Large-scale, longitudinal validation studies will be essential to assess the robustness of these approaches across diverse populations. Durable and wearable material systems The performance and reliability of wearable glucose biosensors are strongly influenced by material selection and interfacial stability. Future efforts should prioritize biocompatible, stretchable, and antifouling materials that maintain electrochemical performance under prolonged mechanical deformation and continuous exposure to biofluids. In particular, polymer-nanomaterial composites with enhanced adhesion and resistance to biofouling may extend sensor lifetime and reduce signal drift. Advancing scalable and reproducible fabrication methods will further support translation from laboratory prototypes to wearable products. Fully integrated low-power or self-powered sensing platforms To enable continuous and maintenance-free glucose monitoring, future wearable systems must move beyond sensor optimization toward full system integration. This includes coupling ultra-low-power electronics with energy-efficient signal processing and, where feasible, self-powered or hybrid energy-harvesting modules. Addressing challenges related to power stability, energy storage, and seamless sensor-electronics integration will be critical for long-term deployment. Such system-level designs are expected to accelerate the transition of wearable glucose biosensors toward real-world and clinical applications. Concluding remarks Flexible and polymeric biosensors have emerged as a promising platform for diabetes mellitus monitoring, motivated by advances in wearable electronics and materials science. The properties such as biocompatibility, mechanical compliance, and chemical tunability of polymeric materials allow skin contact and continuous sensing, offering superior advantages over rigid, conventional glucose monitoring devices. Significant progress has been achieved in enhancing sensitivity, stretchability, and integration with wearable formats, particularly through polymer-nanomaterial hybrid architectures. Despite these significance advances in flexible and wearable biosensors, this review highlights several persistent challenges that limit clinical translation. These include issues such as signal instability brought by mechanical deformation, biofouling, etc. The ongoing debate with inconsistent correlation of non-invasive samples such as sweat, saliva, tears with BG raise questions regarding the accuracy and reliability of the wearable technologies. Future progress in this field will require a shift from isolated sensor optimization toward system level design. This includes the development of robust calibration strategies, durable and antifouling polymeric interfaces, and seamless integration with low-power or self-powered electronics. Equally important is the need for standardized testing protocols and large-scale validation studies to enable meaningful comparison between platforms and accelerate regulatory acceptance. In conclusion, flexible and polymeric-based biosensors represent a compelling pathway toward next-generation diabetes management; however, their successful translation will depend on interdisciplinary efforts that bridge materials engineering, data analytics, and clinical science. Addressing these challenges holistically will be crucial for transforming wearable biosensing technologies from experimental prototypes into reliable tools for personalized diabetes care. Acknowledgments The authors would like to acknowledge the following institutions for financial support: University of South Africa (College of Agriculture and Environment Science, Department of Life and Consumer Sciences, College of Science, Engineering and Technology, Department of Information Systems) and Central University of Technology (Department of Life Sciences, Faculty of Health and Environmental Sciences). Author contributions All the authors contributed equally on conceptualization, writing – original draft, writing – review and editing. Declaration of interests The authors declare that there is no conflicting of interest to report. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used Grammarly in order to improve grammar, spelling, punctuation, and overall language clarity. After using this tool or service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References 1. Lakard B. Electrochemical biosensors based on conducting polymers: A review. Appl. Sci. 2020;10:6614. doi: 10.3390/app10186614. [ DOI ] [ Google Scholar ] 2. Mao H.n., Wang X.g. Use of in-situ polymerization in the preparation of graphene/polymer nanocomposites. N. Carbon Mater. 2020;35:336–343. doi: 10.1016/S1872-5805(20)60493-0. [ DOI ] [ Google Scholar ] 3. Moulahoum H., Ghorbanizamani F., Guler Celik E., Timur S. Nano-scaled materials and polymer integration in biosensing tools. Biosensors. 2022;12:301. doi: 10.3390/bios12050301. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Tseghai G.B., Mengistie D.A., Malengier B., Fante K.A., Van Langenhove L. PEDOT: PSS-based conductive textiles and their applications. Sensors. 2020;20:1881. doi: 10.3390/s20071881. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Khairul S.I., Ansari M.N.M., Duraisamy E., Elumalai P., Thomas P. Graphene nanoparticle-embedded PANI-PPy hybrid nanocomposite for high-performance symmetric supercapacitor application. Mater. Today Proc. 2022;53:441–447. doi: 10.1016/j.matpr.2022.01.414. [ DOI ] [ Google Scholar ] 6. Zhang P., Zhu B., Du P., Travas-Sejdic J. Electrochemical and electrical biosensors for wearable and implantable electronics based on conducting polymers and carbon-based materials. Chem. Rev. 2024;124:722–767. doi: 10.1021/acs.chemrev.3c00392. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Bhat M.A., Rather R.A., Shalla A.H. PEDOT and PEDOT: PSS conducting polymeric hydrogels: A report on their emerging applications. Synth. Met. 2021;273 doi: 10.1016/j.synthmet.2021.116709. [ DOI ] [ Google Scholar ] 8. Shahid M.A., Rahman M.M., Hossain M.T., Hossain I., Sheikh M.S., Rahman M.S., Uddin N., Donne S.W., Hoque M.I.U. Advances in conductive polymer-based flexible electronics for multifunctional applications. J. Compos. Sci. 2025;9:42. doi: 10.3390/jcs9010042. [ DOI ] [ Google Scholar ] 9. Li Y., Tan S., Zhang X., Li Z., Cai J., Liu Y. Design strategies and emerging applications of conductive hydrogels in wearable sensing. Gels. 2025;11:258. doi: 10.3390/gels11040258. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Yang T., Xu C., Liu C., Ye Y., Sun Z., Wang B., Luo Z. Conductive polymer hydrogels crosslinked by electrostatic interaction with PEDOT: PSS dopant for bioelectronics application. Chem. Eng. J. 2022;429 doi: 10.1016/j.cej.2021.132430. [ DOI ] [ Google Scholar ] 11. Lv K., Tian G., Yan Y., Zhou H., Fan Q., Liang L., Liu N., Wang D., Song Z., Xu F., Zhao G. Stretchable carbon nanotube/Ecoflex conductive elastomer films toward multifunctional wearable electronics. Chem. Eng. J. 2024;500 doi: 10.1016/j.cej.2024.157534. [ DOI ] [ Google Scholar ] 12. Ma J., Feng J., Zhang X., Zou Z., Li Q., Lei L., Dong Z.Z., Lin Z. Beyond static scaffolds: glucose-responsive hydrogels as dynamic intelligent platform for tissue engineering. Mater. Des. 2025;258 doi: 10.1016/j.matdes.2025.114635. [ DOI ] [ Google Scholar ] 13. Wang Y., Liu H., Yang X., Shi Z., Li J., Xue L., Liu S., Lei Y. A responsive hydrogel-based microneedle system for minimally invasive glucose monitoring. Smart Mater. Med. 2023;4:69–77. doi: 10.1016/j.smaim.2022.07.006. [ DOI ] [ Google Scholar ] 14. Mei X., Ye D., Zhang F., Di C.A. Implantable application of polymer-based biosensors. J. Polym. Sci. 2022;60:328–347. doi: 10.1002/pol.20210543. [ DOI ] [ Google Scholar ] 15. Wu Q., Wang L., Yu H., Wang J., Chen Z. Organization of glucose-responsive systems and their properties. Chem. Rev. 2011;111:7855–7875. doi: 10.1021/cr200027j. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Sang W., Yang X., Li H., Liang X., Ding H. Mechanically robust and conductive gelatin/glucose hydrogels enabled by the hofmeister effect for flexible strain sensors. Gels. 2025;11:694. doi: 10.3390/gels11090694. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Liu Z., Mao J., Li W., Xu C., Lao A., Shin A., Wu J., Gu A., Zhang Z., Mao L., et al. Smart glucose-responsive hydrogel with ROS scavenging and homeostasis regulating properties for diabetic bone regeneration. Chem. Eng. J. 2024;497 doi: 10.1016/j.cej.2024.154433. [ DOI ] [ Google Scholar ] 18. Li M., Cheng J., Hu Y., You L., Xiong C., Wang S. Gelatin-Based Conductive Hydrogel Wearable Sensor with Breathability and Adhesion. ACS Appl. Polym. Mater. 2025;7:15493–15506. doi: 10.1021/acsapm.5c03079. [ DOI ] [ Google Scholar ] 19. Shen D., Yu H., Wang L., Chen X., Feng J., Li C., Xiong W., Zhang Q. Glucose-responsive hydrogel-based microneedles containing phenylborate ester bonds and N-isopropylacrylamide moieties and their transdermal drug delivery properties. Eur. Polym. J. 2021;148 doi: 10.1016/j.eurpolymj.2021.110348. [ DOI ] [ Google Scholar ] 20. Saasa V., Chibagidi R., Ipileng K., Feleni U. Advances in cancer detection: A review on electrochemical biosensor technologies. Sens. Biosens. Res. 2025;49 doi: 10.1016/j.sbsr.2025.100826. [ DOI ] [ Google Scholar ] 21. Feleni U., Morare R., Masunga G.S., Magwaza N., Saasa V., Madito M.J., Managa M. Recent developments in waterborne pathogen detection technologies. Environ. Monit. Assess. 2025;197:233. doi: 10.1007/s10661-025-13644-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Ipeleng K.E., Feleni U., Saasa V. Recent Developments on Salmonella and Listeria monocytogenes Detection Technologies: A Focus on Electrochemical Biosensing Technologies. Foods. 2025;14:4139. doi: 10.3390/foods14234139. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Yang A., Yan f. Flexible electrochemical biosensors for health monitoring. ACS Appl. Electron. Mater. 2020;3:53–67. doi: 10.1021/acsaelm.0c00534. [ DOI ] [ Google Scholar ] 24. Pradhan D., Niroui F., Leung K.T. High-performance, flexible enzymatic glucose biosensor based on ZnO nanowires supported on a gold-coated polyester substrate. ACS Appl. Mater. Interfaces. 2010;2:2409–2412. doi: 10.1021/am100413u. [ DOI ] [ PubMed ] [ Google Scholar ] 25. Pal S., Rakshit T., Saha S., Jinagal D. Glucose-responsive materials for smart insulin delivery: from protein-based to protein-free design. ACS Mater. Au. 2025;5:239–252. doi: 10.1021/acsmaterialsau.4c00138. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Gunatilake U.B., Garcia-Rey S., Ojeda E., Basabe-Desmonts L., Benito-Lopez F. TiO2 nanotubes alginate hydrogel scaffold for rapid sensing of sweat biomarkers: lactate and glucose. ACS Appl. Mater. Interfaces. 2021;13:37734–37745. doi: 10.1021/acsami.1c11446. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Chen Y.L., Lee C.Y., Chiu H.T. Growth of gold nanowires on flexible substrate for highly sensitive biosensing: detection of thrombin as an example. J. Mater. Chem. B. 2013;1:186–193. doi: 10.1039/C2TB00010E. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Malakoane B., Heunis J.C., Chikobvu P., Kigozi N.G., Kruger W.H. Improving public health sector service delivery in the Free State, South Africa: development of a provincial intervention model. BMC Health Serv. Res. 2022;22:486. doi: 10.1186/s12913-022-07777-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Zhu P., Peng H., Rwei A.Y. Flexible, wearable biosensors for digital health. Med. Nov. Technol. Devices. 2022;14 doi: 10.1016/j.medntd.2022.100118. [ DOI ] [ Google Scholar ] 30. Hannan A.L., Harders M.P., Hing W., Climstein M., Coombes J.S., Furness J. Impact of wearable physical activity monitoring devices with exercise prescription or advice in the maintenance phase of cardiac rehabilitation: systematic review and meta-analysis. BMC Sports Sci. Med. Rehabil. 2019;11:14. doi: 10.1186/s13102-019-0126-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Li F., Xue H., Lin X., Zhao H., Zhang T. Wearable temperature sensor with high resolution for skin temperature monitoring. ACS Appl. Mater. Interfaces. 2022;14:43844–43852. doi: 10.1021/acsami.2c15687. [ DOI ] [ PubMed ] [ Google Scholar ] 32. Saasa V., Beukes M., Lemmer Y., Mwakikunga B. Blood ketone bodies and breath acetone analysis and their correlations in type 2 diabetes mellitus. Diagnostics. 2019;9:224. doi: 10.3390/diagnostics9040224. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Saasa V., Orasugh J.T., Mwakikunga B., Ray S.S. Electrospun rGO-PVDF/WO3 composite fibers for SO2 sensing. Mater. Sci. Semicond. Process. 2024;181 doi: 10.1016/j.mssp.2024.108631. [ DOI ] [ Google Scholar ] 34. Lee S.J., Yoon H.S., Xuan X., Park J.Y., Paik S.J., Allen M.G. A patch type non-enzymatic biosensor based on 3D SUS micro-needle electrode array for minimally invasive continuous glucose monitoring. Sensor. Actuator. B Chem. 2016;222:1144–1151. doi: 10.1016/j.snb.2015.08.013. [ DOI ] [ Google Scholar ] 35. Arakawa T., Tomoto K., Nitta H., Toma K., Takeuchi S., Sekita T., Minakuchi S., Mitsubayashi K. A wearable cellulose acetate-coated mouthguard biosensor for in vivo salivary glucose measurement. Anal. Chem. 2020;92:12201–12207. doi: 10.1021/acs.analchem.0c01201. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Cai Y., Liang B., Chen S., Zhu Q., Tu T., Wu K., Cao Q., Fang L., Liang X., Ye X. One-step modification of nano-polyaniline/glucose oxidase on double-side printed flexible electrode for continuous glucose monitoring: Characterization, cytotoxicity evaluation and in vivo experiment. Biosens. Bioelectron. 2020;165 doi: 10.1016/j.bios.2020.112408. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Sempionatto J.R., Brazaca L.C., García-Carmona L., Bolat G., Campbell A.S., Martin A., Tang G., Shah R., Mishra R.K., Kim J., et al. Eyeglasses-based tear biosensing system: Non-invasive detection of alcohol, vitamins and glucose. Biosens. Bioelectron. 2019;137:161–170. doi: 10.1016/j.bios.2019.04.058. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Wiorek A., Parrilla M., Cuartero M., Crespo G.A. Epidermal patch with glucose biosensor: pH and temperature correction toward more accurate sweat analysis during sport practice. Anal. Chem. 2020;92:10153–10161. doi: 10.1021/acs.analchem.0c02211. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Moyer J., Wilson D., Finkelshtein I., Wong B., Potts R. Correlation between sweat glucose and blood glucose in subjects with diabetes. Diabetes Technol. Ther. 2012;14:398–402. doi: 10.1089/dia.2011.0262. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Nyein H.Y.Y., Bariya M., Kivimäki L., Uusitalo S., Liaw T.S., Jansson E., Ahn C.H., Hangasky J.A., Zhao J., Lin Y., et al. Regional and correlative sweat analysis using high-throughput microfluidic sensing patches toward decoding sweat. Sci. Adv. 2019;5 doi: 10.1126/sciadv.aaw9906. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. La Count T.D., Jajack A., Heikenfeld J., Kasting G.B. Modeling glucose transport from systemic circulation to sweat. J. Pharm. Sci. 2019;108:364–371. doi: 10.1016/j.xphs.2018.09.026. [ DOI ] [ PubMed ] [ Google Scholar ] 42. Kim T.Y., De R., Choi I., Kim H., Hahn S.K. Multifunctional nanomaterials for smart wearable diabetic healthcare devices. Biomaterials. 2024;310 doi: 10.1016/j.biomaterials.2024.122630. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Asdaq S.M.B., Ikbal A.M.A., Sahu R.K., Bhattacharjee B., Paul T., Deka B., Fattepur S., Widyowati R., Vijaya J., Al Mohaini M., et al. Nanotechnology integration for SARS-CoV-2 diagnosis and treatment: an approach to preventing pandemic. Nanomaterials. 2021;11:1841. doi: 10.3390/nano11071841. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Kim H., Nam S., Durukan M.B., Unalan H.E., Lee H.J. Self-Charging Dual-Modal Sensor for Glucose Monitoring Based on Piezoelectric Nanowire/Microgel Hybrid Film. Adv. Funct. Mater. 2024;34 doi: 10.1002/adfm.202308086. [ DOI ] [ Google Scholar ] 45. Bhide A., Ganguly A., Parupudi T., Ramasamy M., Muthukumar S., Prasad S. Next-generation continuous metabolite sensing toward emerging sensor needs. ACS Omega. 2021;6:6031–6040. doi: 10.1021/acsomega.0c06209. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Xu J., Yan Z., Liu Q. Smartphone-based electrochemical systems for glucose monitoring in biofluids: a review. Sensors. 2022;22:5670. doi: 10.3390/s22155670. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Chang Y.H., Chang L.Y., Chang C.C., Chiu Y.S., Kanokpaka P., Ho K.C., Mizuguchi H., Yeh M.H. Enzyme-free triboelectric biosensor with glucose response molecularly imprinted polymer for self-powered biomedical monitoring. Nano Energy. 2024;120 doi: 10.1016/j.nanoen.2023.109114. [ DOI ] [ Google Scholar ] 48. Sengupta D., Kottapalli A.G.P. Nanomaterials-Based Bioinspired Next Generation Wearable Sensors: A State-of-the-Art Review. Adv. Electron. Mater. 2024;10 doi: 10.1002/aelm.202300436. [ DOI ] [ Google Scholar ] 49. Wang Y., Wu Y., Lei Y. Microneedle-based glucose monitoring: a review from sampling methods to wearable biosensors. Biomater. Sci. 2023;11:5727–5757. doi: 10.1039/d3bm00409k. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Veenuttranon K., Kaewpradub K., Jeerapan I. Screen-printable functional nanomaterials for flexible and wearable single-enzyme-based energy-harvesting and self-powered biosensing devices. Nano-Micro Lett. 2023;15:85. doi: 10.1007/s40820-023-01045-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Huang X., Li J., Liu Y., Wong T., Su J., Yao K., Yu X. Epidermal self-powered sweat sensors for glucose and lactate monitoring. Bio-Design Manuf. 2022;5:201–209. doi: 10.1007/s42242-021-00156-1. [ DOI ] [ Google Scholar ] 52. Baluta S., Suresh V., Chmielowska M., Smeesters L., Cabaj J. Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies. ACS Omega. 2026;11:82–99. doi: 10.1021/acsomega.5c06072. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Chenani H., Saeidi M., Rastkhiz M.A., Bolghanabadi N., Aghaii A.H., Orouji M., Hatamie A., Simchi A. Challenges and advances of hydrogel-based wearable electrochemical biosensors for real-time monitoring of biofluids: from lab to market. A review. Anal. Chem. 2024;96:8160–8183. doi: 10.1021/acs.analchem.3c03942. [ DOI ] [ PubMed ] [ Google Scholar ] Articles from iScience are provided here courtesy of Elsevier ACTIONS View on publisher site PDF (8.2 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top