MicroSweat: A wearable microfluidic patch for noninvasive and reliable sweat collection enables human stress monitoring
Open accessAbstract
Stress affects cognition, behavior, and physiology, leading to lasting physical and mental illness. The ability to detect and measure stress, however, is poor. Increased circulating cortisol during stress is mirrored by cortisol release from sweat glands, providing an opportunity to use it as an external biomarker for monitoring internal emotional state. Despite the attempts at using wearable sensors for monitoring sweat cortisol, there is a lack of reliable wearable sweat collection devices that preserve the concentration and integrity of sweat biomolecules corresponding to stress levels. Here, a flexible, self‐powered, evaporation‐free, bubble‐free, surfactant‐free, and scalable capillary microfluidic device, MicroSweat, is fabricated to reliably collect human sweat from different body locations. Cortisol levels are detected corresponding to severe stress ranging from 25 to 125 ng mL −1 averaged across multiple body regions and 100–1000 ng mL −1 from the axilla. A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS). Moreover, owing to the sweat variation in response to environmental effects and physiological differences, the longitudinal and personalized profile of sweat cortisol is acquired, for the first time, for various body locations. The obtained sweat cortisol data is crucial for analyzing human stress in personalized and clinical healthcare sectors.
AI snapshot
Written from the paper’s full text.
TL;DR
MicroSweat is a flexible, self-powered capillary microfluidic patch that reliably collects sweat from multiple body locations and enables ex-situ cortisol measurement for human stress monitoring. It reports body-location-specific sweat cortisol ranges that correlate nonlinearly with perceived stress scale scores, with armpit sweat showing the widest and most stress-compatible range.
Key findings
- The patch is about 230 µm thick (about 310 µm with skin adhesives), collects up to 120 µL sweat, and supports chrono sampling with filling intervals up to 2 min ± 20 s for larger fibers, 1.25 min ± 15 s for medium fibers, and up to 3 s for smaller fibers.
- Sweat cortisol ranges for severe/high stress were 25–125 ng mL⁻¹ averaged across multiple body regions and 100–1000 ng mL⁻¹ from the axilla; low and medium ranges were 1–5 and 5–25 ng mL⁻¹ for multiple locations, and 1–10 and 10–100 ng mL⁻¹ for armpits.
- Cortisol concentration showed a positive nonlinear/exponential relationship with PSS stress scores. Females had higher mean sweat cortisol than males (55 ng mL⁻¹ vs 13.7 ng mL⁻¹), with significant sex differences overall (p < 0.05) and in armpits (p ≤ 0.01); age groups differed significantly (p < 0.05), especially in armpits (p < 0.01) and hands (p < 0.05), while body side differences were not significant (p > 0.05).
- Average sweat collection times were 31.5 min for armpit (range 7–47 min), 33.1 min for lower back (10–55 min), 35 min for upper back (7–55 min), and 50 min for hand (15–60 min); the paper estimates future biosensor-integrated patches needing 5–10 µL could reduce testing to about 7–15 min.
Methods
- MicroSweat was fabricated by one-step laser cutting and assembly of flexible polymer sheets, pressure-sensitive adhesives, transparent polyester, cellulose wicking pads, and nitrocellulose or glass microfiber storage fibers; sweat flow was optimized using an electric circuit analogy model and ANSYS FLUENT VOF simulations.
- Human sweat was collected from 11 healthy volunteers (5 males, 6 females) during 30–60 min treadmill walking at low speed (level 5), with patches mounted on hands, armpits, forehead, and backs; PSS was assessed before exercise and one month after.
- Stored sweat was recovered by centrifuging fibers at 11,000 rpm for 5 min, frozen at −80 °C, and analyzed for cortisol by ELISA; statistical analysis used R v.4.1.1 with two-tailed t-tests and F-tests at α = 0.05.
Limitations
- The paper states that larger cohort studies are needed to address discrepancies and provide more robust support for claims about sex, age, and stress-level correlations, and notes that sweat cortisol reports are rare.
- Evident from the text: human trials used a small sample (11 volunteers; longitudinal monitoring n = 3), sweating was induced only by treadmill exercise, and cortisol was measured ex situ by ELISA rather than by an integrated real-time wearable sensor.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 43 cited sources checked · 0 supported · 0 flagged
- Findingown data
The mean difference in cortisol concentration between males and females is more highlighted in the armpit areas, showing a p-value ≤ 0.01.
“When comparing different body locations, the mean value difference in cortisol concentration is more highlighted in the armpit areas, showing a p‐value ≤ 0.01 among male and female groups (Figure 7b).”
Quote in the paperNot a substantial claim - Findingown data
MicroSweat can simultaneously collect human sweat from different body locations, including sweat produced by eccrine and apocrine glands.
“Figure 7a shows that the wearable MicroSweat patch developed in this study can simultaneously collect human sweat from different body locations, including the sweat produced by eccrine and apocrine glands.”
Quote in the paperSubstantial claim - Findingown data
MicroSweat preserves the concentration and integrity of sweat biomolecules.
“preserve the concentration and integrity of sweat biomolecules corresponding to stress levels”
- Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · boosted
“This fiber saturation confirms the preservation of sweat molecule concentrations in these fibers.”
Quote in the paperSubstantial claim - Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · boosted
- Findingown data
MicroSweat enables sequential storage of sweat generated at different time points (Chrono sampling).
“sequential storage of the sweat generated at different time points during the human test (Chrono sampling)”
- Also stated in: § A Chrono Sampling of Sweat · plain
“Chrono sampling of the human sweat by wearable MicroSweat.”
Quote in the paperSubstantial claim - Also stated in: § A Chrono Sampling of Sweat · plain
- Findingnone given
MicroSweat increases the volume of collected sweat up to 120 μL.
“increasing the volume of collected sweat up to 120 µL”
- Also stated in: § Intra‐Individual and Inter‐Individual Variations in Human Sweat Dynamics · plain
“The relatively long collection period is only applied to the tests aiming to collect high sweat volume (about 120 µL) for further biomarker discovery.”
Quote in the paperSubstantial claim - Also stated in: § Intra‐Individual and Inter‐Individual Variations in Human Sweat Dynamics · plain
- Findingown data
Cortisol levels detected corresponding to severe stress range from 25 to 125 ng mL−1 averaged across multiple body regions and 100-1000 ng mL−1 from the axilla.
“Cortisol levels are detected corresponding to severe stress ranging from 25 to 125 ng mL−1 averaged across multiple body regions and 100–1000 ng mL−1 from the axilla.”
Quote in the paperSubstantial claim - Findingnone given
MicroSweat reliably collects human sweat from different body locations.
“MicroSweat, is fabricated to reliably collect human sweat from different body locations.”
- Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · plain
“The wearable MicroSweat patch was developed for reliable sweat collection and storage and ease of fabrication using laser cutting, assembly, and bonding of multilayers of flexible polymer sheets and fibers.”
Quote in the paperSubstantial claim - Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · plain
- Findingown data
Sweat cortisol concentration is significantly different between male and female subjects (p-value < 0.05 in 95% confidence interval).
“The t‐test analysis (Section S7, Supporting Information) of cortisol detected in the sweat collected from all body parts shows a significant difference in cortisol concentration between male and female subjects according to the mean value of sweat cortisol data (p‐value < 0.05 in 95% confidence interval) (Figure 7a).”
Quote in the paperNot a substantial claim - Findingcitation
Except for the forehead, sweat cortisol concentration is higher in females versus males in both apocrine and eccrine sweat glands.
“Except for the forehead, in both apocrine and eccrine sweat glands, sweat cortisol concentration is higher in females versus males, which may be attributed to the larger pituitary gland volume in females as the primary regulatory organ of cortisol.[ 22 ]”
Quote in the paperNot a substantial claim - Findingnone given
A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS).
“A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS).”
Quote in the paperSubstantial claim - Findingreasoning
Longitudinal and personalized profiles of sweat cortisol are acquired for various body locations.
“Moreover, owing to the sweat variation in response to environmental effects and physiological differences, the longitudinal and personalized profile of sweat cortisol is acquired, for the first time, for various body locations.”
Quote in the paperSubstantial claim - Findingown data
Cortisol concentrations have similar distributions between males and females.
“Despite the mean value difference in cortisol concentration between males and females, the cortisol concentrations have similar distributions between males and females (Figure 7c).”
Quote in the paperNot a substantial claim - Findingown data
A saturation test confirms the preservation of sweat molecule concentrations in the storage fibers at their saturation points.
“Furthermore, a saturation test was performed to verify the preservation of sweat content stored in the storage fibers at their saturation points. For this test, microfluidic patches were fabricated using transparent PSAs (see Section S5, Supporting Information). This fiber saturation confirms the preservation of sweat molecule concentrations in these fibers.”
Quote in the paperSubstantial claim - Findingown data
A larger sweat cortisol distribution is observed for ages below 30 when comparing the density distribution plot of cortisol for the two age groups.
“However, a larger sweat cortisol distribution is observed for ages below 30 when comparing the density distribution plot of cortisol for the two age groups (Figure 7e).”
Quote in the paperNot a substantial claim - Findingown data
Delay valves embedded within each rectangular storage chamber's inlet control the sweat flow into each storage chamber.
“The trigger (delay) valves embedded within each rectangular storage chamber's inlet, control the sweat flow into each storage chamber (Figure 4a–c).”
Quote in the paperSubstantial claim - Findingown data
The numerical model and experimental data show that the shortest filling time is achieved for cases 1 and 2 as opposed to cases 3 and 4.
“The numerical model and experimental data show that the shortest filling time is achieved for cases 1 and 2 as opposed to cases 3 and 4 (Figure 3c,d).”
Quote in the paperSubstantial claim - Findingown data
The minimum achievable channel width was 125 ± 25 μm upon optimizing the laser parameters.
“The minimum achievable channels width was 125 ± 25 µm upon optimizing the laser parameters (see Figure S2b–g, Supporting Information).”
Quote in the paperNot a substantial claim - Findingown data
The sweat cortisol difference between males and females is more highlighted for apocrine sweat than eccrine sweat.
“Similar to the two studies above, the sweat cortisol difference between males and females is more highlighted for apocrine sweat than eccrine sweat.”
Quote in the paperNot a substantial claim - Findingreasoning
Capillary forces control the sweat flow rate within MicroSweat with no or minimal dependency on the sweat generation rate on the skin and its variations amongst people.
“The capillary forces control the sweat flow rate within the MicroSweat with no or minimal dependency on the sweat generation rate on the skin and its variations amongst people.”
Quote in the paperNot a substantial claim - Findingown data
An air bubble trapped between old and new sweat indicates complete removal of the old sweat where there was a delay between old and new sweat generation.
“Figure 4a–c shows that there is an air bubble trapped between the old and new sweat, indicating the complete removal of the old sweat where there was a delay between old and new sweat generation for a participant.”
Quote in the paperSubstantial claim - Findingown data
Sweat velocity at the outlet is almost zero at branches 11 and 12 (side channels) for both contact angles 90° and 120°.
“The sweat velocity at the outlet is almost zero at branches 11 and 12 (side channels) for both contact angles 90o and 120o (Figure 4d).”
Quote in the paperSubstantial claim - Findingown data
There is a significant difference in sweat cortisol levels between age groups <30 years and ≥30 years (p-value < 0.05).
“Comparing the cortisol concentrations of two different age groups (< 30 years and ≥ 30 years), shown in Figure 7d, indicates a significant difference in their sweat cortisol levels (p‐value < 0.05; statistical analysis done using F‐test and t‐test; see Table S5, Supporting Information).”
Quote in the paperNot a substantial claim - Findingown data
Sweat velocity at the outlet is almost zero for all microchannel widths tested, except for the largest microchannel width of 500 μm where the delay valves open.
“Figure 4f,g presents that sweat velocity at the outlet is almost zero for all microchannels widths tested, except for the largest microchannel width of 500 µm where the delay valves open.”
Quote in the paperSubstantial claim - Findingreasoning
Decreasing microchannel size from above 450 μm to <200 μm in width reduces the filling time for the main channel by 15 ms.
“Decreasing microchannel size from above 450 µm to <200 µm in width reduces the filling time for the main channel by 15 ms.”
Quote in the paperNot a substantial claim - Findingown data
The effect of body side on sweat cortisol concentration is insignificant for left and right sides, including armpits and hands, and for upper and lower parts of the back (p > 0.05, 95% confidence).
“The effect of the body side on sweat cortisol concentration is insignificant for both left and right sides, including the armpits and hands (Figure 7h), and for the upper and lower parts of the body (back).”
- Also stated in: § Chemical Analysis of Sweat Cortisol in Human trials · plain
“with no significant variation between sides of the body”
Quote in the paperSubstantial claim - Also stated in: § Chemical Analysis of Sweat Cortisol in Human trials · plain
- Findingown data
Longitudinal measurement of cortisol concentration in healthy subjects (n = 3) shows reproducible patterns of sweat cortisol variation in some body locations over a month.
“Further measuring the cortisol concentration longitudinally in healthy subjects (n = 3) shows reproducible patterns of the sweat cortisol variation in some body locations obtained where it was monitored on random days over a month period (Figure 8c).”
Quote in the paperSubstantial claim - Findingown data
Daily variation in cortisol concentration from 9 AM to 3 PM and at three different times of day is observed individually for three human subjects measured in armpit areas.
“The daily variation in cortisol concentration from 9 AM to 3 PM and at three different times of day is observed individually for three human subjects measured in armpit areas (Figure 8a).”
Quote in the paperSubstantial claim - Findingown data
Sweat cortisol concentration versus stress level shows an exponential relationship between sweat cortisol values (both whole body and armpits) and stress scores.
“The results of sweat cortisol concentration versus stress level (Figure 9c,d) show an exponential relationship between the sweat cortisol values (both the whole body and armpits) and the stress scores.”
Quote in the paperSubstantial claim - Findingnone given
Sweat cortisol concentration is higher in apocrine areas versus eccrine regions.
“Sweat cortisol concentration was shown to be higher in apocrine areas versus eccrine regions”
Quote in the paperNot a substantial claim - Findingown data
Other subjects examined showed medium ranges of stress levels.
“Other subjects examined showed medium ranges of stress levels, although a larger cohort study is needed in future studies to address the discrepancies.”
Quote in the paperNot a substantial claim - Findingnone given
Sweat cortisol concentration has larger distribution with age below 30 than beyond 30.
“with larger distribution with the age of below 30 than beyond 30”
Quote in the paperNot a substantial claim - Findingown data
Sweat cortisol concentration is more elevated in females (mean 55 ng/mL) than males (mean 13.7 ng/mL).
“more elevated in females (mean value of 55 ng mL−1) than males (mean value of 13.7 ng mL−1)”
Quote in the paperNot a substantial claim - Premisecitation
Compared to other biofluids, sweat provides an advantage for continuous collection by wearable devices with minimal human intervention.
“Compared to other biofluids, sweat provides an advantage for continuous collection by wearable devices with minimal human intervention.[ 6 ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] a) H. Y. Y. Nyein, M. Bariya, B. Tran, C. H. Ahn, B. J. Brown, W. Ji, N. Davis, A. Javey, Nat. Commun. 2021, 12, 1823; 33758197 PMC7987967 b) S. Anastasova, B. Crewther, P. Bembnowicz, V. Curto, H. M. Ip, B. Rosa, G. Z. Yang, Biosens. Bioelectron. 2017, 93, 139; 27743863 c) M. Chung, G. Fortunato, N. Radacsi, J. R. Soc., Interface 2019, 16, 20190217; 31594525 PMC6833321 d) A. J. Bandodkar, S. P. Lee, I. Huang, W. Li, S. Wang, C. J. Su, W. J. Jeang, T. Hang, S. Mehta, N. Nyberg, P. Gutruf, J. Choi, J. Koo, J. T. Reeder, R. Tseng, R. Ghaffari, J. A. Rogers, Nat. Electron. 2020, 3, 554; e) P. Pea source
Not checked in the free run.
- Premisecitation
Non-invasive and accurate detection and quantification of human stress levels require direct measurement of stress biomarkers such as cortisol, troponin, neuropeptide Y, and epinephrine.
“The non‐invasive and accurate detection and quantification of human stress levels require direct measurement of stress biomarkers such as cortisol and troponin hormones, and neuropeptide Y (NPY), and epinephrine neurotransmitters.[ 4 ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] a) A. J. Steckl, P. Ray, ACS Sens. 2018, 3, 2025; 30264989 b) S. Klinghammer, T. Voitsekhivska, N. Licciardello, K. Kim, C.‐K. Baek, H. Cho, K.‐J. r. Wolter, C. Kirschbaum, L. Baraban, G. Cuniberti, ACS Sens. 2020, 5, 4081; 33270427 c) B. A. Cardinell, M. L. Spano, J. T. La Belle, CRC Crit. Rev. Bioeng. 2019, 47, 207. 31679256 source
Not checked in the free run.
- Premisereasoning
Lack of a mechanism to actively remove sweat from the sensor causes analytes from sweat and skin to diffuse throughout the measured volume, leading to inaccurate results.
“Furthermore, due to the lack of a mechanism to actively remove sweat from the sensor, analytes both from sweat and the skin diffuse throughout the measured volume, leading to inaccurate results.”
Quote in the paperSubstantial claim - Premisecitation
Silicone elastomer materials often have a high affinity to many vitamins, hormones, and other potential biomarkers, adversely affecting their reliability for intact sample collection and/or biomarker sensing.
“Also, these materials often have a high affinity to many vitamins, hormones, and other potential biomarkers, adversely affecting their reliability for intact sample collection and/or biomarker sensing.[ 12c ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [ 12c ]
Not checked in the free run.
- Premisecitation
Skin topography, skin temperature, and pH may interfere considerably with measurement results when sweat is sensed directly on the skin.
“In addition, since sweat is not sampled but sensed directly on the skin, the skin topography, skin temperature[ 10 ] and pH[ 11 ] may interfere considerably with the measurement results.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [10] W. Gao, S. Emaminejad, H. Y. Y. Nyein, S. Challa, K. Chen, A. Peck, H. M. Fahad, H. Ota, H. Shiraki, D. Kiriya, D. H. Lien, G. A. Brooks, R. W. Davis, A. Javey, Nature 2016, 529, 509. 26819044 PMC4996079 source
Not checked in the free run.
- cites [11] a) A. Kaushik, A. Vasudev, S. K. Arya, S. K. Pasha, S. Bhansali, Biosens. Bioelectron. 2014, 53, 499; 24212052 b) S. K. Arya, A. Dey, S. Bhansali, Biosens. Bioelectron. 2011, 28, 166; 21803565 c) A. Bhide, S. Cheeran, S. Muthukumar, S. Prasad, Biosensors (Basel) 2019, 9, 13. 30654428 PMC6468720 d) B. Jagannath, S. Muthukumar, S. Prasad, Anal. Chim. Acta 2018, 1016, 29; 29534802 e) H. J. Jang, T. Lee, J. Song, L. Russell, H. Li, J. Dailey, P. C. Searson, H. E. Katz, ACS Appl. Mater. Interfaces 2018, 10, 16233; 29701946 PMC6026499 f) K. H. Kim, S. H. Lee, S. E. Seo, J. Bae, S. J. Park, O. S. Kwo source
Not checked in the free run.
- Premisecitation
Average cortisol concentrations in the apocrine areas are larger than in the eccrine areas.
“This agrees with the findings by Tu. et al.[ 24 ] and Pearlmutter et al.[ 6e ] who concluded that the average cortisol concentrations in the apocrine areas are larger than in the eccrine areas.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [24] E. Tu, P. Pearlmutter, M. Tiangco, G. Derose, L. Begdache, A. Koh, ACS Omega 2020, 5, 8211. 32309731 PMC7161047 source
Not checked in the free run.
- cites [ 6e ]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
In smaller channel sizes, capillary pressure is superior to flow resistivity, causing a shorter filling time.
“In smaller channel sizes, the capillary pressure is superior to the flow resistivity, which causes a shorter filling time.”
Quote in the paperSubstantial claim - Premisecitation
Cortisol level undergoes a diurnal cycle modulated by the circadian rhythm, with higher amount in the morning, lower in the afternoon, and lowest at night.
“Cortisol level is known to undergo a diurnal cycle modulated by the circadian rhythm, with a higher amount in the morning, a lower amount in the afternoon, and the lowest amount at night.[ 8 , 29 ]”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [29] S. Upasham, S. Prasad, Lab Chip 2020, 20, 1947. 32323689 source
Not checked in the free run.
- Interpretationown data
Each individual may have a personalized signature of cortisol concentrations in various body parts.
“Comparing the data among individuals proposes that each individual may have a personalized signature of cortisol concentrations in various body parts.”
Quote in the paperNot a substantial claim - Premisecitation
The same temporal change of cortisol concentration is reported for all biofluids, such as saliva, serum, and sweat.
“The same temporal change of cortisol concentration was also reported for all biofluids, such as saliva, serum, and sweat.[ 8 , 30 ]”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30] J. Xu, Y. Fang, J. Chen, Biosensors 2021, 11, 245. 34436047 PMC8391966 source
Not checked in the free run.
- Premisecitation
The greater stress an individual experiences over a certain period, the higher the basal cortisol concentration generated in the body.
“The greater stress an individual experiences over a certain period, the higher the basal cortisol concentration generated in the body.[ 24 ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] E. Tu, P. Pearlmutter, M. Tiangco, G. Derose, L. Begdache, A. Koh, ACS Omega 2020, 5, 8211. 32309731 PMC7161047 source
Not checked in the free run.
- Study design and conductown data
The Bracke model was compared with experiments and validated.
“The Bracke model was compared with experiments and validated in Section S3, Supporting Information.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The sweat flow in the microchannels can be modeled as laminar, viscous, incompressible, fully developed flow, and end effects, surface roughness, and gravity can be ignored.
- Study design and conductown data
Figure S11 shows no deviation between the ELISA standard curve and the calibration curve for spiked artificial sweat sample with pH = 6, confirming minimal effect of physiologically relevant sweat pH in measuring sweat cortisol concentration.
“Figure S11, Supporting Information, shows no deviation between the ELISA standard curve and the calibration curve for the spiked artificial sweat sample with pH = 6, confirming the minimal effect of physiologically relevant sweat pH in measuring sweat cortisol concentration.”
Quote in the paperSubstantial claim - Interpretationreasoning
The MicroSweat patch provides a key enabling feature for routine stress monitoring in sports physiology and digital health monitoring.
“It provides a key enabling feature for routine stress monitoring in sports physiology and digital health monitoring.”
Quote in the paperNot a substantial claim- Rests on an assumption: The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
- Premisecitation
Cortisol concentration responds to the induction of physical stress in the body.
“In addition, cortisol concentration responds to the induction of physical stress in the body.[ 35 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] D. R. Seshadri, R. T. Li, J. E. Voos, J. R. Rowbottom, C. M. Alfes, C. A. Zorman, C. K. Drummond, NPJ Digit Med. 2019, 2, 72. 31341957 PMC6646404 source
Not checked in the free run.
- Interpretationown data
The static increase in cortisol level is an indicator of stress level with severe stress.
“The static increase in the cortisol level was shown to be an indicator of the stress level with severe stress, corresponding to a mean cortisol concentration of 25–125 ng mL−1 for the multiple‐body parts and 100–1000 ng mL−1 for the armpits.”
Quote in the paperSubstantial claim- Rests on an assumption: The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
- Interpretationown data
For the first time, a correlation between sweat cortisol ranges and stress levels is found that is distinguished for regional body locations.
“In addition, for the first time, a correlation between sweat cortisol ranges and stress levels is found that is distinguished for regional body locations.”
Quote in the paperSubstantial claim - Premisecitation
There is a strong empirical correlation between serum and sweat cortisol.
“Since there is a strong empirical correlation between serum and sweat cortisol”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [37] S. Upasham, A. Tanak, B. Jagannath, S. Prasad, Sci. Rep. 2018, 8, 16745. 30425312 PMC6233171 source
Not checked in the free run.
- Interpretationown data
Cortisol secretion levels are correlated to psychological stress in humans.
“Therefore, cortisol secretion levels are correlated to psychological stress in humans.”
Quote in the paperSubstantial claim- Rests on an assumption: The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
- May combine two claims. 31
- Classified as probably not a substantial claim. 22
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataMicroSweat is mechanically robust and flexible, withstanding 500 bending-releasing cycles.
“MicroSweat is mechanically robust with a bendable structure that can be easily mounted and safely sealed on the skin surface of different body locations.”
- Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · plain
“Figure 1h–j shows the stability of MicroSweat during 500 bending‐releasing cycles.”
Quote in the paperSubstantial claim- Also stated in: § Thin, Soft Microfluidic Devices for Sweat Sampling · plain
May combine two claims.
FindingreasoningStorage fibers facilitate collection of sweat generated at different time intervals, with older sweat collected by earlier storage fibers and newer sweat stored in downstream storage fibers.
“Therefore, storage fibers facilitate the collection of sweat generated at different time intervals. The older sweat is collected by the earlier storage fibers while the newer sweat is stored in the downstream storage fibers enabling the separation of the old and new sweat.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataVariations of cortisol concentrations between the two age groups are highlighted in the armpit areas with a p-value < 0.01 and hand areas with a p-value < 0.05.
“The difference in regional body locations highlights variations of cortisol concentrations between the two age groups in the armpit areas with a p‐value < 0.01 and hand areas with a p‐value < 0.05 (Figure 7f; see Table S6, Supporting Information, for more details on t‐test analysis).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningAll bubbles flowing through the inlet channels into the main channel run out through the trigger valves and the storage chambers, enabling bubble-free sweat flow downstream of the main channel.
“Meantime, all bubbles flowing through the inlet channels into the main channel run out through the trigger valves and the storage chambers. This enables bubble‐free sweat flow downstream of the main channel where future biosensors would be embedded.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOnly the valves with lower hydrophobicity are opened, allowing the sweat to flow into the storage chambers.
“Only the valves with lower hydrophobicity are opened (Figure 4e), allowing the sweat to flow into the storage chambers.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFilling time intervals for Chrono sampling are up to 2 min ± 20 s for larger fibers, 1.25 min ± 15 s for medium fibers, and up to 3 s for smaller fibers.
“The interval time of up to 2 min ± 20 s for larger fibers, 1.25 min ± 15 s for medium fibers, and up to 3 s for the smaller fibers indicates the reliable and repeatable pattern for Chrono sampling of the sweat.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere is intra-individual variation in sweat generation and collection as well as inter-individual variation depending on the body location.
“The results of on‐human sweat collection tests show that there is an intra‐individual variation in sweat generation and collection as well as inter‐individual variation depending on the body location.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataMedian cortisol concentrations slightly increase with increasing age.
“The results show a slight increase in median cortisol concentrations with increasing age.”
Quote in the paperNot a substantial claimMay combine two claims. The quote only partly states this claim.
Findingown dataLaser parameters were optimized, and the variation between channel widths in Ch1 to Ch7 is <±25 μm, indicating high reproducibility of creating microchannels.
“Figure S1a‐d, Supporting Information, shows that the variation between the channel widths in Ch1 to Ch7 is <±25 µm, indicating high reproducibility of creating microchannels of the wearable MicroSweat patches.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe armpit body location has the highest rate of sweat generation, followed by LB and UB, with an almost two-minute time difference.
“When comparing the average time of sweat generation, the armpit body location has the highest rate of sweat generation, followed by LB and UB, with an almost two‐minute time difference.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSweat cortisol concentration averaged across multiple body locations ranges from 1 to 5 ng/mL for low stress, 5 to 25 ng/mL for medium stress, and 25 to 125 ng/mL for high stress.
“The resulting sweat cortisol concentration for multiple body locations ranges from 1 to 5 ng mL−1 corresponding to low‐stress levels, 5 to 25 ng mL−1 corresponding to the medium stress level, and 25 to 125 ng mL−1 for high‐stress levels.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataA relative increase in armpit sweat cortisol from Monday to Friday is seen for all participants and appeared profound compared to other body locations.
“A relative increase in the cortisol of the armpit sweat from Monday to Friday is seen for all participants and appeared to be profound compared to other body locations.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost body locations demonstrate daily variation, with mean cortisol response higher in the morning than in the afternoon.
“Most body locations also demonstrate this variation, with a mean value of cortisol response being higher in the morning than in the afternoon (Figure 8b).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor armpit-only mean sweat cortisol, the ranges are 1-10 ng/mL for low stress, 10-100 ng/mL for medium stress, and 100-1000 ng/mL for high stress, with better compatibility with PSS test results.
“These ranges, however, differ for the mean value of only armpits with wider ranges but better compatibility with the PSS test results, with 1–10 ng mL−1 for low‐stress levels, 10–100 ng mL−1 for medium‐stress levels, and 100–1000 ng mL−1 for high‐stress level.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningHuman participant #1 expressed anxiety issues over a long period, leading to a high basal cortisol concentration.
“This was detectable for human participant #1, who expressed anxiety issues over a long period, leading to a high basal cortisol concentration.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenThe lateral flow nitrocellulose absorbs low volume of sweat (1-5 μL) while microfiber glass absorbs high volume of sweat (20-120 μL), for fibers with a specific size in this study.
“The lateral flow nitrocellulose absorbs the low volume of sweat (1–5 µL) while the microfiber glass absorbs a high volume of sweat (20–120 µL), for fibers with a specific size in this study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone givenThe hydrophilicity of the TPE stems from the surfactant-free treatment, which makes the surface intact to various biomarkers.
“The hydrophilicity of the TPE stems from the surfactant‐free treatment, which makes the surface intact to various biomarkers.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenThe wearable MicroSweat patch is a self-operated and electronic-free microfluidic system fabricated using a scale-compatible manufacturing process.
“In summary, the wearable MicroSweat patch is a self‐operated and electronic‐free microfluidic system fabricated using a scale‐compatible manufacturing process.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe MicroSweat patch enabled measuring sweat cortisol levels in different body locations, with the most predictive cortisol concentration detectable in the armpits.
“The MicroSweat patch enabled measuring sweat cortisol levels in different body locations, with the most predictive cortisol concentration detectable in the armpits.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenIt takes about 10 ± 5 s to mount the wearable MicroSweat patch on the skin.
“It takes about 10 ± 5 s to mount the wearable MicroSwrat patch on the skin.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn humans, stress increases circulating cortisol, and this increase is reflected as increased cortisol release from the sweat glands in the skin.
“In humans, stress increases circulating levels of cortisol. This increase is also reflected as an increase in cortisol release from the sweat glands in the skin.[ 5 ]”
- Also stated in: § Abstract · plain
“Increased circulating cortisol during stress is mirrored by cortisol release from sweat glands, providing an opportunity to use it as an external biomarker for monitoring internal emotional state.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] O. Parlak, Sens Actuators Rep. 2021, 3, 100036.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § Abstract · plain
May combine two claims.
PremisecitationMost current wearable sensors that contain electrodes on a flexible substrate are directly in contact with the skin to measure the cortisol response, and their temporal resolution of measured cortisol is often poor.
“Most of the current wearable sensors that contained electrodes on a flexible substrate were directly in contact with the skin to measure the cortisol response.[ 8 ] Although they are simple structures that do not require a complicated fabrication process for creating sweat‐collection features on the device, the temporal resolution of their measured cortisol is often poor.[ 9 ]”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [9] E. J. M. Moonen, J. R. Haakma, E. Peri, E. Pelssers, M. Mischi, J. M. J. den Toonder, View 2020, 1, 20200077.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationMost wearable sweat-collecting microfluidic devices used silicone elastomers and lithography approaches for chip fabrication, but the hydrophilic property of these materials is primarily temporal.
“Most wearable sweat‐collecting microfluidic devices used silicone elastomers and lithography approaches for chip fabrication.[ 12 ] However, the hydrophilic property of these materials is primarily temporal.[ 13 ]”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [12] a) A. Martin, J. Kim, J. F. Kurniawan, J. R. Sempionatto, J. R. Moreto, G. Tang, A. S. Campbell, A. Shin, M. Y. Lee, X. Liu, J. Wang, ACS Sens. 2017, 2, 1860; 29152973 b) S. B. Kim, J. Koo, J. Yoon, A. Hourlier‐Fargette, B. Lee, S. Chen, S. Jo, J. Choi, Y. S. Oh, G. Lee, S. M. Won, A. J. Aranyosi, S. P. Lee, J. B. Model, P. V. Braun, R. Ghaffari, C. Park, J. A. Rogers, Lab Chip 2020, 20, 84; 31776526 c) J. Choi, D. Kang, S. Han, S. B. Kim, J. A. Rogers, Adv. Healthcare Mater. 2017, 6, 1601355. 28105745 d) J. T. Reeder, J. Choi, Y. Xue, P. Gutruf, J. Hanson, M. Liu, T. Ray, A. J. B. Bandodkar, source
Not checked in the free run.
- cites [13] a) S. B. Kim, Y. Zhang, S. M. Won, A. J. Bandodkar, Y. Sekine, Y. Xue, J. Koo, S. W. Harshman, J. A. Martin, J. M. Park, T. R. Ray, K. E. Crawford, K. T. Lee, J. Choi, R. L. Pitsch, C. C. Grigsby, A. J. Strang, Y. Y. Chen, S. Xu, J. Kim, A. Koh, J. S. Ha, Y. Huang, S. W. Kim, J. A. Rogers, Small 2018, 14, 1703334; b) J. D. Wang, N. J. Douville, S. Takayama, M. ElSayed, Ann. Biomed. Eng. 2012, 40, 1862. 22484830
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe higher sweat cortisol concentration in females versus males may be attributed to the larger pituitary gland volume in females as the primary regulatory organ of cortisol.
“Except for the forehead, in both apocrine and eccrine sweat glands, sweat cortisol concentration is higher in females versus males, which may be attributed to the larger pituitary gland volume in females as the primary regulatory organ of cortisol.[ 22 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] a) F. P. MacMaster, M. Keshavan, Y. Mirza, N. Carrey, A. R. Upadhyaya, R. El‐Sheikh, C. J. Buhagiar, S. P. Taormina, C. Boyd, M. Lynch, M. Rose, J. Ivey, G. J. Moore, D. R. Rosenberg, Life Sci. 2007, 80, 940; 17174342 PMC1853319 b) A. Levine, O. Zagoory‐Sharon, R. Feldman, J. G. Lewis, A. Weller, Physiol. Behav. 2007, 90, 43. 17055006 source
Not checked in the free run.
May combine two claims.
PremisecitationThe poor temporal resolution of direct-contact cortisol sensors is due to environmental artifacts such as evaporation and contamination, discontinuous sweat flow causing bubble reading signals, mixing of new and old sweat, and loss of dynamic monitoring of sweat composition.
“This is due to environmental artifacts such as evaporations and contamination issues, and discontinuous sweat flow, which causes bubble reading signals, mixing of new and old sweat, and losing dynamic monitoring of sweat for temporal variation in its composition.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] E. J. M. Moonen, J. R. Haakma, E. Peri, E. Pelssers, M. Mischi, J. M. J. den Toonder, View 2020, 1, 20200077.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAdenylate cyclase, a second messenger released by the pituitary gland, promotes different levels of sweat secretion in women versus men.
“Moreover, adenylate cyclase, a second messenger released by the pituitary gland, promotes different levels of sweat secretion in women versus men.[ 23 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] S. Sasaki, J. Watanabe, H. Ohtaki, M. Matsumoto, N. Murai, T. Nakamachi, J. Hannibal, J. Fahrenkrug, H. Hashimoto, H. Watanabe, H. Sueki, K. Honda, A. Miyazaki, S. Shioda, Br. J. Dermatol. 2017, 176, 413. 27453364 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDetection of cortisol as a stress biomarker in sweat is at the forefront of wearable stress detection devices currently in development.
“Detection of cortisol as a stress biomarker in sweat is at the forefront of wearable stress detection devices currently in development.[ 20 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] C. Samson, A. Koh, Front. Bioeng. Biotechnol. 2020, 8, 1037. 32984293 PMC7492543 source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe insulating adhesive layer separates the skin from the microfluidic layers, eliminating or minimizing the effect of skin temperature on the collected sweat constituents.
“In addition, the insulating adhesive layer separates the skin from the microfluidic layers, eliminating or minimizing the effect of skin temperature on the collected sweat constituents.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningMultiple inlet reservoirs help faster sweat collection due to the plurality effects of several inlet reservoirs.
“Having multiple inlet reservoirs, besides having the benefit of collecting sweat from different locations on the skin, can help with faster sweat collection due to the plurality effects of several inlet reservoirs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe wearable MicroSweat patch was fabricated in a process compatible with scale production of microfluidic devices.
“Notably, the wearable MicroSweat patch was fabricated in a process compatible with the scale production of microfluidic devices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Study design and conductnone givenThe numerical models are reliable, tested and verified for several microfluidic geometries and constitutions.
“Given the reliability of the numerical models, tested, and verified for several microfluidic geometries and constitutions, numerical modeling was performed to study the effect of hydrophobic surfaces (two different contact angles 90o and 120o) on the function of delay valves.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationVents located on top of the storage chambers that are <300 μm in diameter prevent sweat evaporation and help preserve the molecular contents of the sweat in the stored fibers.
“The vents located on top of the storage chambers are <300 µm in diameter to prevent sweat evaporation (based on the Langmuir's free evaporation equation in vacuum[ 11 , 18 ] (see Section S4, Supporting Information) and help preserve the molecular contents of the sweat in the stored fibers.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [11] a) A. Kaushik, A. Vasudev, S. K. Arya, S. K. Pasha, S. Bhansali, Biosens. Bioelectron. 2014, 53, 499; 24212052 b) S. K. Arya, A. Dey, S. Bhansali, Biosens. Bioelectron. 2011, 28, 166; 21803565 c) A. Bhide, S. Cheeran, S. Muthukumar, S. Prasad, Biosensors (Basel) 2019, 9, 13. 30654428 PMC6468720 d) B. Jagannath, S. Muthukumar, S. Prasad, Anal. Chim. Acta 2018, 1016, 29; 29534802 e) H. J. Jang, T. Lee, J. Song, L. Russell, H. Li, J. Dailey, P. C. Searson, H. E. Katz, ACS Appl. Mater. Interfaces 2018, 10, 16233; 29701946 PMC6026499 f) K. H. Kim, S. H. Lee, S. E. Seo, J. Bae, S. J. Park, O. S. Kwo source
Not checked in the free run.
- cites [18] a) M. A. Khoshooei, J. Therm. Anal. Calorim. 2021, 147, 6015; b) S. F. Wright, D. Dollimore, J. G. Dunn, K. Alexander, Thermochim. Acta 2004, 421, 25; c) K. Chatterjee, D. Dollimore, K. Alexander, Instrum. Sci. Technol. 2001, 29, 133.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSalivary cortisol concentration increase is more prominent in aged women (>60) than young women (<30).
“There is no other study comparing sweat cortisol levels in different ages, but the cortisol concentration analysis in salivary biofluid in the literature shows that the increase in the salivary cortisol concentration is more prominent in aged women (>60) than young women (<30).[ 25 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25] M. Bakke, A. Tuxen, C. E. Thomsen, A. Bardow, T. Alkjaer, B. R. Jensen, Gerontology 2004, 50, 383. 15477699 source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationCortisol plays a significant role in organizing the body's response to physiological and psychological stressors.
“Cortisol plays a significant role in organizing the body's response to physiological and psychological stressors.[ 22b ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [ 22b ]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFew studies have correlated cortisol concentrations and stress levels.
“There are physiological and psychological methods for stress detection,[ 31 ] but few studies correlated the cortisol concentrations and stress levels (see Table S9, Supporting Information).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] N. Skoluda, J. Strahler, W. Schlotz, L. Niederberger, S. Marques, S. Fischer, M. V. Thoma, C. Spoerri, U. Ehlert, U. M. Nater, Psychoneuroendocrinology 2015, 51, 227. 25462896 source
Not checked in the free run.
May combine two claims.
PremisecitationSweat biomarker information from both apocrine and eccrine glands facilitates the creation of accurate sweat-based wearable diagnostic systems and could provide reliable sweat cortisol concentrations.
“Sweat biomarker information from both apocrine and eccrine glands[ 21 ] facilitates the creation of accurate sweat‐based wearable diagnostic systems and could provide reliable sweat cortisol concentrations.[ 6e ]”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [21] T. Kaya, G. Liu, J. Ho, K. Yelamarthi, K. Miller, J. Edwards, A. Stannard, Electroanalysis 2019, 31, 411.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [ 6e ]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationNicolson et al. found no difference in salivary cortisol levels between males and females at all ages except for higher cortisol levels in old women than in old men.
“In addition, they found no difference in salivary cortisol levels between males and females at all ages except for higher cortisol levels in old women than in old men.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] N. Nicolson, C. Storms, R. Ponds, J. Sulon, J. Gerontol.: Med. Sci. 1997, 52A, M68. 9060972 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA physiological method previously subjected workers' stress levels to different stress levels equivalent to 0.5 to 4 ng/mL of salivary cortisol.
“For instance, the result of a physiological method for the salivary cortisol previously subjected workers' stress levels to different stress levels equivalent to 0.5 to 4 ng mL−1 of salivary cortisol.[ 32 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] H. Jebelli, B. Choi, S. Lee, J. Constr. Eng. Manag. 2019, 145, 04019079.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningArmpits can be one of the most reliable body locations for future real-time monitoring of stress levels in different populations because of considerable change in cortisol levels compared to other body locations and better compatibility with general stress scale tests.
“Therefore, armpits can be one of the most reliable body locations for future real‐time monitoring of stress levels in different populations because of i) a considerable change in cortisol levels compared to other body locations and ii) better compatibility with the general stress scale tests.”
Quote in the paperSubstantial claim- Rests on an assumption: The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
May combine two claims.
PremisecitationThe number of sweat glands and sweat rate might differ at each side of the body, but they seem to mostly affect sweat ions (excluding ions like Na+) with minimal effect on other biomarkers.
“Although the number of sweat glands and sweat rate might differ at each side of the body, they seem to mostly affect the sweat ions (excluding ions like Na+)[ 28 ] with a minimal effect on other biomarkers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] a) S. W. Harshman, R. L. Pitsch, Z. K. Smith, M. L. O'Connor, B. A. Geier, A. V. Qualley, N. M. Schaeublin, M. V. Fischer, J. J. Eckerle, A. J. Strang, J. A. Martin, PLoS One 2018, 13, e0203133; 30383773 PMC6211630 b) L. B. Baker, P. J. D. De Chavez, C. T. Ungaro, B. C. Sopena, R. P. Nuccio, A. J. Reimel, K. A. Barnes, Eur. J. Appl. Physiol. 2019, 119, 361. 30523403 PMC6373370 source
Not checked in the free run.
May combine two claims.
PremisecitationApocrine areas such as axillary areas are responsive to emotional sweating, and apocrine glands are activated by adrenergic stimulation and strongly respond to emotions such as stress.
“The apocrine areas, such as the axillary areas are reported to be responsive to emotional sweating and the apocrine glands are activated by adrenergic stimulation and strongly respond to emotions such as stress.[ 33 ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] A. Zamkah, T. Hui, S. Andrews, N. Dey, F. Shi, R. S. Sherratt, Biosensors (Basel) 2020, 10, 40. 32316280 PMC7235866 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNicolson et al. showed no change in hypothalamic-pituitary-adrenal (HPA) activity and the subsequent release of cortisol from the adrenal gland until ages over 70.
“In contrast, studying the effect of age on salivatory cortisol concentration by Nicolson et al.[ 27 ] showed no change in the hypothalamic‐pituitary‐adrenal (HPA) activity and the subsequent release of the steroid hormone cortisol from the outer cortex of the adrenal gland until the ages of over 70.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] N. Nicolson, C. Storms, R. Ponds, J. Sulon, J. Gerontol.: Med. Sci. 1997, 52A, M68. 9060972 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDuring adolescence and beyond the age of 50, a gradual increase in salivary cortisol was observed for both males and females.
“Similarly, during adolescence and beyond the age of 50, a gradual increase in salivary cortisol was observed for both males and females, which was argued to be attributed to the onset of puberty.[ 26 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [26] R. Miller, T. Stalder, M. Jarczok, D. M. Almeida, E. Badrick, M. Bartels, D. I. Boomsma, C. L. Coe, M. C. Dekker, B. Donzella, J. E. Fischer, M. R. Gunnar, M. Kumari, F. Lederbogen, C. Power, C. D. Ryff, S. V. Subramanian, H. Tiemeier, S. E. Watamura, C. Kirschbaum, Psychoneuroendocrinology 2016, 73, 16. 27448524 PMC5108362 source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductcitationA urinary cortisol ELISA kit was selected due to its wide cortisol detection range of 0.47-200 ng/mL, meeting the needs of human skin cortisol concentration measurement, and previously used in other reports.
“A urinary cortisol ELISA kit was selected due to its wide cortisol detection range of 0.47–200 ng mL−1, meeting the needs of human skin cortisol concentration measurement, and previously used in other reports.[ 23 ]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] S. Sasaki, J. Watanabe, H. Ohtaki, M. Matsumoto, N. Murai, T. Nakamachi, J. Hannibal, J. Fahrenkrug, H. Hashimoto, H. Watanabe, H. Sueki, K. Honda, A. Miyazaki, S. Shioda, Br. J. Dermatol. 2017, 176, 413. 27453364 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationExciting opportunities are offered for non-invasive dynamic stress monitoring.
“exciting opportunities are offered for non‐invasive dynamic stress monitoring.[ 8 , 37 ]”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [37] S. Upasham, A. Tanak, B. Jagannath, S. Prasad, Sci. Rep. 2018, 8, 16745. 30425312 PMC6233171 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA nonlinear and differential relationship between human stress levels and salivary cortisol concentrations has been reported in another study.
“Such a nonlinear and differential relationship between human stress levels and salivary cortisol concentrations has also been reported in another study.[ 34 ]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] M. Castro, P. C. L. Elias, C. E. Martinelli Jr, S. R. R. Antonini, L. Santiago, A. C. Moreira, Braz. J. Med. Biol. Res. 2000, 33, 1171. 11004717 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataThese findings can be an essential basis for future detection of human stress levels using wearable microfluidic and sensor devices.
“These findings can be an essential basis for the future detection of human stress levels using wearable microfluidic and sensor devices.”
Quote in the paperNot a substantial claim- Rests on an assumption: The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
May combine two claims.
Interpretationown dataCompared to few flexible sensors and/or microfluidic devices in the literature for direct/indirect stress monitoring in sweat, this work presents a continuous sweat flow platform and comprehensively quantified sweat cortisol response regionally, individually including sex and age, and personally along with daily and longitudinally monitoring considerations.
“Table S9, Supporting Information, shows that compared to the few flexible sensors and/or microfluidic devices currently in the literature[ 8 , 36 ] for either direct/indirect stress monitoring in sweat, this work presents continuous sweat flow platform and comprehensively quantified sweat cortisol response regionally, individually including sex, and age, and personally along with daily and longit”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8] a) Y. J. Hong, H. Lee, J. Kim, M. Lee, H. J. Choi, T. Hyeon, D.‐H. Kim, Adv. Funct. Mater. 2018, 28, 1805754. b) Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2020, 38, 217; 31768044 c) H. B. Lee, M. Meeseepong, T. Q. Trung, B. Y. Kim, N. E. Lee, Biosens. Bioelectron. 2020, 156, 112133; 32174559 d) J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa‐Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737; 33589782 e) W. Tang, L. Yin, J. R
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [36] a) J. S. Nah, S. C. Barman, M. A. Zahed, M. Sharifuzzaman, H. Yoon, C. Park, S. Yoon, S. Zhang, J. Y. Park, Sens. Actuators, B 2021, 329, 129206; b) O. Parlak, S. T. Keene, A. Marais, V. F. Curto, A. Salleo, Sci. Adv. 2018, 4, eaar2904; 30035216 PMC6054510 c) B. Wang, C. Zhao, Z. Wang, K. A. Yang, X. Cheng, W. Liu, W. Yu, S. Lin, Y. Zhao, K. M. Cheung, H. Lin, H. Hojaiji, P. S. Weiss, M. N. Stojanović, A. J. Tomiyam, A. M. Andrews, S. Emaminejad, Sci. Adv. 2022, 8, eabk0967. 34985954 PMC8730602 source
Not checked in the free run.
The paper relies on this without support.
Wearable smartwatch physiological parameters are not direct stress signals; changes in them do not directly reflect internal state changes.
“Wearable smartwatches provide noninvasive monitoring of physiological parameters that may indicate changes in stress state. These parameters, however, are not direct stress signals; thus, changes do not directly reflect internal state changes.”
The paper relies on this without support.
Technologies for quantitative and longitudinal detection of stress biomarkers in sweat are in their infancy.
“The technologies for quantitative and longitudinal detection of stress biomarkers in sweat are in their infancy.”
The paper relies on this without support.
Material preparations and lithography processes require multistep processing, which is time-consuming and costly.
“In addition, the material preparations and lithography processes require multistep processing, which is time‐consuming and costly.”
The paper relies on this without support.
There is no other study comparing sweat cortisol levels in different ages.
“There is no other study comparing sweat cortisol levels in different ages, but the cortisol concentration analysis in salivary biofluid in the literature shows that the increase in the salivary cortisol concentration is more prominent in aged women (>60) than young women (<30).[ 25 ]”
The paper relies on this without support.
Reports on sweat cortisol levels are rare.
“Specifically, reports on sweat cortisol levels are rare.”
The authors state this as an assumption.
The sweat flow in the microchannels can be modeled as laminar, viscous, incompressible, fully developed flow, and end effects, surface roughness, and gravity can be ignored.
“To use an electric circuit analogy, the assumption of laminar, viscous, and incompressible fully developed fluid flow was considered for the sweat flow in the microchannels. Furthermore, the microchannels' end effects, surface roughness, and the effect of gravity were ignored.[ 39 ]”
The paper relies on this without support.
The exercise protocol used to induce sweating does not confound the relationship between sweat cortisol concentration and psychological stress measured by the PSS.
“In this study, sweating is induced using physical exercise via a 30–60 min treadmill walking at low speed (level 5) while wearing the MicroSweat patches on different body locations, including hands, armpits, foreheads, and backs.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Only a few studies have focused on the relationship between biofluid signals and human health parameters, such as cortisol sensing and stress, glucose sensing and diabetes, or physiological data related to Parkinson's disease.
“Only a few studies have focused on this, such as one recent work on cortisol sensing and its relation to human stress levels [129] and glucose sensing and its relation to diabetes levels in patients [252], or physiological data related to Parkinson’s disease [253].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [129]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 9 panels are assigned different measurements
MinorThe text assigns the multiple-body-location results to Figure 9a and the armpit results to Figure 9b. The caption instead assigns both 9a and 9b to the multiple-body-location results and 9c and 9d to the armpit results.
“the cortisol value averaged across multiple body locations (Figure 9a) and the armpits (Figure 9b), separately.”
“a) mean sweat cortisol measurement and their correlated stress score for different participants and b) the relation of sweat cortisol with a stress score of all body locations in the population level. c) Mean sweat cortisol measurement and their correlated stress score for different participants and d the relation of sweat cortisol with a stress score of armpit locations only in the population level.”
Figure 7 map is cited as panel i but caption labels it j
MinorThe discussion directs readers to Figure 7i for the map of individual cortisol concentrations, whereas the figure caption identifies that map as Figure 7j.
“A map of sweat cortisol concentrations measured from the sweat collected for different body locations of different individuals is shown in Figure 7i.”
“j) A map of sweat cortisol concentration between different human subjects and body locations (personalized sweat cortisol data).”
Figure 8a and 8b descriptions are reversed
MinorThe text identifies Figure 8a as the measurements at three times of day for individual participants and Figure 8b as the comparison across body locations. The caption assigns those descriptions to 8b and 8a, respectively.
“The daily variation in cortisol concentration from 9 AM to 3 PM and at three different times of day is observed individually for three human subjects measured in armpit areas (Figure 8a). Most body locations also demonstrate this variation, with a mean value of cortisol response being higher in the morning than in the afternoon (Figure 8b).”
“a) Statistical box plots of the sweat cortisol concentration and a comparison between morning and afternoon for different body locations and b) its daily variations for armpit locations of different healthy human participants (P1–P3) at 9 AM, 11 AM, and 3 PM.”
PSS score of 13 receives conflicting stress classifications
MinorThe Experimental Section defines low stress as scores below 13, while the Figure 9 caption includes 13 in the low-stress range.
“The stress scores range from 0 to 40, with scores < 13 corresponding to low‐stress levels, between 13 and 26 corresponding to the moderate stress level, and above 26 corresponding to the severe stress level.”
“From the PSS test, scores 0–13 refer to low‐stress levels, 14–26 refer to medium stress levels, and 27–40 refer to high‐stress levels.”
Cites
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087 · see citing works
A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat
Yang, Y., Song, Y., Bo, X., Min, J., Pak, O. S., Zhu, L., Wang, M., Tu, J., Kogan, A., Zhang, H., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0321-x · Cited by 1,270 · see citing works
The stress system in the human brain in depression and neurodegeneration
Swaab, D. F., Bao, A.-M., & Lucassen, P. J. (2005) · Ageing Research Reviews · doi:10.1016/j.arr.2005.03.003 · Cited by 984 · see citing works
Stress and Health: A Review of Psychobiological Processes
O'Connor, D. B., Thayer, J. F., & Vedhara, K. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-062520-122331 · Cited by 948 · see citing works
An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers
Sempionatto, J. R., Lin, M., Yin, L., De la paz, E., Pei, K., Sonsa-ard, T., de Loyola Silva, A. N., Khorshed, A. A., Zhang, F., Tostado, N., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00685-1 · Cited by 658 · see citing works
Parlak, O., Keene, S. T., Marais, A., Curto, V. F., & Salleo, A. (2018) · Science Advances · doi:10.1126/sciadv.aar2904 · Cited by 603 · see citing works
Integrated textile sensor patch for real-time and multiplex sweat analysis
He, W., Wang, C., Wang, H., Jian, M., Lu, W., Liang, X., Zhang, X., Yang, F., & Zhang, Y. (2019) · Science Advances · doi:10.1126/sciadv.aax0649 · Cited by 564 · see citing works
Wearable sensors for monitoring the physiological and biochemical profile of the athlete
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0150-9 · Cited by 537 · see citing works
Investigation of Cortisol Dynamics in Human Sweat Using a Graphene-Based Wireless mHealth System
Torrente-Rodríguez, R. M., Tu, J., Yang, Y., Min, J., Wang, M., Song, Y., Yu, Y., Xu, C., Ye, C., IsHak, W. W., et al. (2020) · Matter · doi:10.1016/j.matt.2020.01.021 · Cited by 479 · see citing works
Martín, A., Kim, J., Kurniawan, J. F., Sempionatto, J. R., Moreto, J. R., Tang, G., Campbell, A. S., Shin, A., Lee, M. Y., Liu, X., et al. (2017) · ACS Sensors · doi:10.1021/acssensors.7b00729 · Cited by 475 · see citing works
Measuring cortisol in human psychobiological studies
Levine, A., Zagoory‐Sharon, O., Feldman, R., Lewis, J. G., & Weller, A. (2006) · Physiology & Behavior · doi:10.1016/j.physbeh.2006.08.025 · Cited by 472 · see citing works
Capillary microfluidics in microchannels: from microfluidic networks to capillaric circuits
Olanrewaju, A. O., Beaugrand, M., Yafia, M., & Juncker, D. (2018) · Lab on a Chip · doi:10.1039/c8lc00458g · Cited by 455 · see citing works
A wearable multisensing patch for continuous sweat monitoring
Anastasova, S., Crewther, B., Bembnowicz, P., Curto, V., Ip, H. M., Rosa, B., & Yang, G.-Z. (2017) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2016.09.038 · Cited by 443 · see citing works
Skin-interfaced systems for sweat collection and analytics
Choi, J., Ghaffari, R., Baker, L. B., & Rogers, J. A. (2018) · Science Advances · doi:10.1126/sciadv.aar3921 · Cited by 433 · see citing works
A wearable patch for continuous analysis of thermoregulatory sweat at rest
Nyein, H. Y. Y., Bariya, M., Tran, B., Ahn, C. H., Brown, B. J., Ji, W., Davis, N., & Javey, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22109-z · Cited by 426 · see citing works
Wearable aptamer-field-effect transistor sensing system for noninvasive cortisol monitoring
Wang, B., Zhao, C., Wang, Z., Yang, K.-A., Cheng, X., Liu, W., Yu, W., Lin, S., Zhao, Y., Cheung, K. M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abk0967 · Cited by 411 · see citing works
Wearable flexible sweat sensors for healthcare monitoring: a review
Chung, M., Fortunato, G., & Radacsi, N. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0217 · Cited by 378 · see citing works
Sklar, L. S., & Anisman, H. (1981) · PubMed · Cited by 369 · see citing works
Stress Biomarkers in Biological Fluids and Their Point-of-Use Detection
Steckl, A. J., & Ray, P. (2018) · ACS Sensors · doi:10.1021/acssensors.8b00726 · Cited by 319 · see citing works
Choi, J., Kang, D., Han, S., Kim, S. B., & Rogers, J. A. (2017) · Advanced Healthcare Materials · doi:10.1002/adhm.201601355 · Cited by 317 · see citing works
Recent advances in cortisol sensing technologies for point-of-care application
Kaushik, A., Vasudev, A., Arya, S. K., Pasha, S. K., & Bhansali, S. (2014) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2013.09.060 · Cited by 316 · see citing works
Reeder, J. T., Choi, J., Xue, Y., Gutruf, P., Hanson, J., Liu, M., Ray, T., Bandodkar, A. J., Avila, R., Xia, W., et al. (2019) · Science Advances · doi:10.1126/sciadv.aau6356 · Cited by 308 · see citing works
Touch‐Based Stressless Cortisol Sensing
Tang, W., Yin, L., Sempionatto, J. R., Moon, J., Teymourian, H., & Wang, J. (2021) · Advanced Materials · doi:10.1002/adma.202008465 · Cited by 302 · see citing works
Sklar, L. S., & Anisman, H. (1981) · Psychological Bulletin · doi:10.1037/0033-2909.89.3.369 · Cited by 288 · see citing works
Quantitative Analysis of Molecular Absorption into PDMS Microfluidic Channels
Wang, J. D., Douville, N. J., Takayama, S., & ElSayed, M. E. H. (2012) · Annals of Biomedical Engineering · doi:10.1007/s10439-012-0562-z · Cited by 268 · see citing works
Zhang, Y., Guo, H., Kim, S. B., Wu, Y., Ostojich, D., Park, S. H., Wang, X. S., Weng, Z., Li, R., Bandodkar, A. J., et al. (2019) · Lab on a Chip · doi:10.1039/c9lc00103d · Cited by 249 · see citing works
Skoluda, N., Strahler, J., Schlotz, W., Niederberger, L., Marques, S., Fischer, S., Thoma, M. V., Spoerri, C., Ehlert, U., & Nater, U. M. (2015) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2014.10.002 · Cited by 246 · see citing works
Portable biosensor for monitoring cortisol in low-volume perspired human sweat
Kinnamon, D., Ghanta, R., Lin, K.-C., Muthukumar, S., & Prasad, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-13684-7 · Cited by 231 · see citing works
Hong, Y. J., Lee, H., Kim, J., Lee, M., Choi, H. J., Hyeon, T., & Kim, D. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201805754 · Cited by 229 · see citing works
Daviu, N., Füzesi, T., Rosenegger, D. G., Rasiah, N. P., Sterley, T.-L., Peringod, G., & Bains, J. S. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0591-0 · Cited by 217 · see citing works
Self-citationWearable Biosensors for Non-Invasive Sweat Diagnostics
Xu, J., Fang, Y., & Chen, J. (2021) · Biosensors · doi:10.3390/bios11080245 · Cited by 208 · see citing works
Miller, R., Stalder, T., Jarczok, M., Almeida, D. M., Badrick, E., Bartels, M., Boomsma, D. I., Coe, C. L., Dekker, M. C. J., Donzella, B., et al. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2016.07.201 · Cited by 203 · see citing works
The kinetics of wetting: the dynamic contact angle
Bracke, M., Voeght, F. D., & Joos, P. (2007) · Steinkopff eBooks · doi:10.1007/bfb0116200 · Cited by 185 · see citing works
Sweat-activated biocompatible batteries for epidermal electronic and microfluidic systems
Bandodkar, A. J., Lee, S. P., Huang, I. A., Li, W., Wang, S., Su, C.-J., Jeang, W. J., Hang, T., Mehta, S., Nyberg, N., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-0443-7 · Cited by 183 · see citing works
Nah, J. S., Barman, S. C., Zahed, M. A., Sharifuzzaman, M., Yoon, H., Park, C., Yoon, S., Zhang, S., & Park, J. Y. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.129206 · Cited by 172 · see citing works
Paulsen, J. S., Nance, M., Kim, J.-I., Carlozzi, N. E., Panegyres, P. K., Erwin, C., Goh, A., McCusker, E., & Williams, J. K. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.08.003 · Cited by 168 · see citing works
Kim, S. B., Zhang, Y., Won, S. M., Bandodkar, A. J., Sekine, Y., Xue, Y., Koo, J., Harshman, S. W., Martin, J. A., Park, J. M., et al. (2018) · Small · doi:10.1002/smll.201703334 · Cited by 167 · see citing works
Extended gate field-effect-transistor for sensing cortisol stress hormone
Sheibani, S., Capua, L., Kamaei, S., Akbari, S. S. A., Zhang, J., Guerin, H., & Ionescu, A. M. (2021) · Communications Materials · doi:10.1038/s43246-020-00114-x · Cited by 158 · see citing works
Lee, H.-B., Meeseepong, M., Trung, T. Q., Kim, B.-Y., & Lee, N.-E. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112133 · Cited by 153 · see citing works
Kim, S., Lee, B.-R., Reeder, J. T., Seo, S. H., Lee, S.-U., Hourlier‐Fargette, A., Shin, J., Sekine, Y., Jeong, H., Oh, Y. S., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2012700117 · Cited by 153 · see citing works
Application of Wearable Biosensors to Construction Sites. I: Assessing Workers’ Stress
Jebelli, H., Choi, B., & Lee, S. H. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001729 · Cited by 146 · see citing works
Salivary Cortisol Levels and Stress Reactivity in Human Aging
Nicolson, N., Storms, C., Ponds, R., & Sulon, J. (1997) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/52a.2.m68 · Cited by 139 · see citing works
Flexible nanoporous tunable electrical double layer biosensors for sweat diagnostics
Munje, R. D., Muthukumar, S., Panneer Selvam, A., & Prasad, S. (2015) · Scientific Reports · doi:10.1038/srep14586 · Cited by 138 · see citing works
Stress Monitoring and Recent Advancements in Wearable Biosensors
Samson, C., & Koh, A. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.01037 · Cited by 129 · see citing works
Sweat and saliva cortisol response to stress and nutrition factors
Pearlmutter, P., Derose, G., Samson, C., Linehan, N., Cen, Y., Begdache, L., Won, D., & Koh, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-75871-3 · Cited by 128 · see citing works
Paper-Based Microfluidics: Fabrication Technique and Dynamics of Capillary-Driven Surface Flow
Songok, J., Tuominen, M., Teisala, H., Haapanen, J., Mäkelä, J. M., Kuusipalo, J., & Toivakka, M. (2014) · ACS Applied Materials & Interfaces · doi:10.1021/am5055806 · Cited by 121 · see citing works
Arya, S. K., Dey, A., & Bhansali, S. (2011) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2011.07.015 · Cited by 118 · see citing works
Review—Towards Wearable Sensor Platforms for the Electrochemical Detection of Cortisol
Sekar, M. K., Sriramprabha, R., Sekhar, P. K., Bhansali, S., Ponpandian, N., Manickam, P., & Viswanathan, C. (2020) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ab7e24 · Cited by 118 · see citing works
Baker, L. B., Chavez, P. J. D. D., Ungaro, C. T., Sopeña, B. C., Nuccio, R. P., Reimel, A. J., & Barnes, K. A. (2018) · European Journal of Applied Physiology · doi:10.1007/s00421-018-4048-z · Cited by 114 · see citing works
Zhang, Y., Chen, Y., Huang, J., Liu, Y., Peng, J., Chen, S., Song, K., Ouyang, X., Cheng, H., & Wang, X. (2020) · Lab on a Chip · doi:10.1039/d0lc00400f · Cited by 111 · see citing works
Zamkah, A., Hui, T., Andrews, S., Dey, N., Shi, F., & Sherratt, R. S. (2020) · Biosensors · doi:10.3390/bios10040040 · Cited by 110 · see citing works
ZnO Nanorod Integrated Flexible Carbon Fibers for Sweat Cortisol Detection
Madhu, S., Anthuuvan, A. J., Ramasamy, S., Manickam, P., Bhansali, S., Nagamony, P., & Chinnuswamy, V. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00730 · Cited by 102 · see citing works
Wright, S., Dollimore, D., Dunn, J. G., & Alexander, K. (2004) · Thermochimica Acta · doi:10.1016/j.tca.2004.02.021 · Cited by 100 · see citing works
Wearable Sweat Sensors: Background and Current Trends
Kaya, T., Liu, G., Ho, J. C.-T., Yelamarthi, K., Miller, K. C., Edwards, J. E., & Stannard, A. B. (2018) · Electroanalysis · doi:10.1002/elan.201800677 · Cited by 98 · see citing works
Kim, S. B., Koo, J., Yoon, J., Hourlier‐Fargette, A., Lee, B.-R., Chen, S., Jo, S., Choi, J., Oh, Y. S., Lee, G., et al. (2019) · Lab on a Chip · doi:10.1039/c9lc01045a · Cited by 93 · see citing works
Anwer, S., Manzar, M. D., Alghadir, A. H., Salahuddin, M., & Abdul Hameed, U. (2020) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s268582 · Cited by 92 · see citing works
Comparison of Colorimetric Analyses to Determine Cortisol in Human Sweat
Tu, E., Pearlmutter, P., Tiangco, M., Derose, G., Begdache, L., & Koh, A. (2020) · ACS Omega · doi:10.1021/acsomega.0c00498 · Cited by 90 · see citing works
Wearable sweat sensing for prolonged, semicontinuous, and nonobtrusive health monitoring
Moonen, E. J. M., Haakma, J. R., Peri, E., Pelssers, E., Mischi, M., & den Toonder, J. M. J. (2020) · VIEW · doi:10.1002/viw.20200077 · Cited by 86 · see citing works
Salivary cortisol as a tool for physiological studies and diagnostic strategies
Castro, M., Elias, P. C. L., Martinelli Jr., C. E., Antonini, S. R. R., Santiago, L., & Moreira, A. C. (2000) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2000001000006 · Cited by 83 · see citing works
Jang, H.-J., Lee, T., Song, J., Russell, L., Li, H., Dailey, J., Searson, P. C., & Katz, H. E. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b18855 · Cited by 80 · see citing works
Bakke, M., Tuxen, A., Thomsen, C. E., Bardow, A., Alkjær, T., & Jensen, B. R. (2004) · Gerontology · doi:10.1159/000080176 · Cited by 78 · see citing works
Flows in rectangular microchannels driven by capillary force and gravity
Jong, W., Kuo, T. H., Ho, S.-S., Chiu, H. H., & Peng, S. H. (2006) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2006.09.011 · Cited by 71 · see citing works
Paul, B., Demuru, S., Lafaye, C., Saubade, M., & Briand, D. (2021) · Advanced Materials Technologies · doi:10.1002/admt.202000910 · Cited by 71 · see citing works
Choi, J., Chen, S., Deng, Y., Xue, Y., Reeder, J. T., Franklin, D., Oh, Y. S., Model, J. B., Aranyosi, A. J., Lee, S. P., et al. (2021) · Advanced Healthcare Materials · doi:10.1002/adhm.202000722 · Cited by 67 · see citing works
Development and sexual dimorphism of the pituitary gland
MacMaster, F. P., Keshavan, M., Mirza, Y., Carrey, N., Upadhyaya, A. R., El-Sheikh, R., Buhagiar, C. J., Taormina, S. P., Boyd, C., Lynch, M., et al. (2007) · Life Sciences · doi:10.1016/j.lfs.2006.11.040 · Cited by 63 · see citing works
CortiWatch: Watch-Based Cortisol Tracker
Rice, P., Upasham, S., Jagannath, B., Manuel, R., Pali, M., & Prasad, S. (2019) · Future Science OA · doi:10.2144/fsoa-2019-0061 · Cited by 63 · see citing works
Klinghammer, S., Voitsekhivska, T., Licciardello, N., Kim, K., Baek, C.-K., Cho, H., Wolter, K.-J., Kirschbaum, C., Baraban, L., & Cuniberti, G. (2020) · ACS Sensors · doi:10.1021/acssensors.0c02267 · Cited by 62 · see citing works
Portable and wearable real-time stress monitoring: A critical review
Parlak, O. (2021) · Sensors and Actuators Reports · doi:10.1016/j.snr.2021.100036 · Cited by 60 · see citing works
Jagannath, B., Muthukumar, S., & Prasad, S. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.02.013 · Cited by 56 · see citing works
Harshman, S. W., Pitsch, R. L., Smith, Z. K., O’Connor, M. L., Geier, B. A., Qualley, A. V., Schaeublin, N. M., Fischer, M. V., Eckerle, J. J., Strang, A. J., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0203133 · Cited by 51 · see citing works
Sankhala, D., Muthukumar, S., & Prasad, S. (2018) · SLAS TECHNOLOGY · doi:10.1177/2472630318759257 · Cited by 50 · see citing works
Development of ultra-low volume, multi-bio fluid, cortisol sensing platform
Upasham, S., Tanak, A., Jagannath, B., & Prasad, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-35199-5 · Cited by 50 · see citing works
SLOCK (sensor for circadian clock): passive sweat-based chronobiology tracker
Upasham, S., & Prasad, S. (2020) · Lab on a Chip · doi:10.1039/d0lc00134a · Cited by 38 · see citing works
Flexible, low volume detection of chronobiology biomarkers from human sweat
Upasham, S., Thai, K., Muthyala, R., & Prasad, S. (2019) · The Analyst · doi:10.1039/c9an01968e · Cited by 34 · see citing works
Enzymatic Low Volume Passive Sweat Based Assays for Multi-Biomarker Detection
Bhide, A., Cheeran, S., Muthukumar, S., & Prasad, S. (2019) · Biosensors · doi:10.3390/bios9010013 · Cited by 30 · see citing works
Kim, K. H., Lee, S. H., Seo, S. E., Bae, J., Park, S. J., & Kwon, O. S. (2020) · Micromachines · doi:10.3390/mi11040439 · Cited by 26 · see citing works
Toward a Label-Free Electrochemical Impedance Immunosensor Design for Quantifying Cortisol in Tears
Cardinell, B. A., Spano, M. L., & Belle, J. T. L. (2019) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2019026109 · Cited by 25 · see citing works
CALCULATION OF VAPOR PRESSURE CURVES FOR ETHYL, PROPYL, AND BUTYL PARABENS USING THERMOGRAVIMETRY
Chatterjee, K., Dollimore, D., & Alexander, K. (2001) · Instrumentation Science & Technology · doi:10.1081/ci-100103461 · Cited by 23 · see citing works
Pituitary adenylate cyclase‐activating polypeptide promotes eccrine gland sweat secretion
Sasaki, S., Watanabe, J., Ohtaki, H., Matsumoto, M., Murai, N., Nakamachi, T., Hannibal, J., Fahrenkrug, J., Hashimoto, H., Watanabe, H., et al. (2017) · British Journal of Dermatology · doi:10.1111/bjd.14885 · Cited by 22 · see citing works
Modeling of capillary-driven microfluidic networks using electric circuit analogy
Mikaelian, D., & Jones, B. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-2088-6 · Cited by 22 · see citing works
Acute Depression, Extreme Anxiety, and Prolonged Stress among COVID-19 Frontline Healthcare Workers
(2020) · Psychosociological Issues in Human Resource Management · doi:10.22381/pihrm8120209 · Cited by 19 · see citing works
Investigation of Multiphase Flow in a Trifurcation Microchannel—A Benchmark Problem
Chiriac, E., Avram, M., & Bălan, C. (2022) · Micromachines · doi:10.3390/mi13060974 · Cited by 9 · see citing works
Thermal probe of vapor–liquid thermodynamic equilibrium
Khoshooei, M. A. (2021) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-021-10972-3 · Cited by 5 · see citing works
Wearable Physiological Stress Monitoring System—A Proof-of-Concept Study
Ong, S. W., Sundar, R., Bandla, A., & Kim, N. (2021) · IRC-SET 2020 · doi:10.1007/978-981-15-9472-4_54 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 85 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Cardinell B. A., 2019, CRC Crit. Rev. Bioeng., 47, 207
- Chiriac E., 2022, Micromachines (Basel), 13, 6
Cited by
Sorted by citations
The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535 · see citing works
Self-citationHaghayegh, F., Norouziazad, A., Haghani, E., Feygin, A. A., Rahimi, R. H., Ghavamabadi, H. A., Sadighbayan, D., Madhoun, F., Papagelis, M., Felfeli, T., et al. (2024) · Advanced Science · doi:10.1002/advs.202400595 · Cited by 125 · see citing works
Self-citationEpidermal wearable optical sensors for sweat monitoring
Wang, J., Luo, Y., Zhou, Z., Xiao, J., Xu, T., & Zhang, X. (2024) · Communications Materials · doi:10.1038/s43246-024-00518-z · Cited by 93 · see citing works
Flexible and Wearable Biosensors for Monitoring Health Conditions
Song, Z., Zhou, S., Qin, Y., Xia, X., Sun, Y., Han, G., Shu, T., Hu, L., & Zhang, Q. M. (2023) · Biosensors · doi:10.3390/bios13060630 · Cited by 90 · see citing works
Detection and monitoring of stress using wearables: a systematic review
Pinge, A., Gad, V. R., Jaisighani, D., Ghosh, S., & Sen, S. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1478851 · Cited by 104 · see citing works
Khosravi, S., Soltanian, S., Servati, A., Khademhosseini, A., Zhu, Y., & Servati, P. (2023) · Biosensors · doi:10.3390/bios13070684 · Cited by 76 · see citing works
Skin-interfaced colorimetric microfluidic devices for on-demand sweat analysis
Liu, W., Cheng, H., & Wang, X. (2023) · npj Flexible Electronics · doi:10.1038/s41528-023-00275-y · Cited by 71 · see citing works
High-precision flexible sweat self-collection sensor for mental stress evaluation
Wang, C., Wang, Z., Wei, W., Zhang, Z., Li, A. A., Huang, G., Li, X., Ge, S. S., Zhou, L., & Kong, H. (2024) · npj Flexible Electronics · doi:10.1038/s41528-024-00333-z · Cited by 55 · see citing works
Point-of-Care Testing: The Convergence of Innovation and Accessibility in Diagnostics
Augustine, S., Venkadesh, A., Kaushal, S., Lee, E., Ajaj, M., & Lee, N. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c07075 · Cited by 52 · see citing works
Khumngern, S., & Jeerapan, I. (2024) · Communications Materials · doi:10.1038/s43246-024-00557-6 · Cited by 50 · see citing works
E-skin – Based advanced wearable technology for Health Management
Chugh, V., Basu, A., Kaushik, A. K., & Basu, A. K. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100129 · Cited by 46 · see citing works
Xiong, S., Wang, C., Zhu, C., Dong, P., & Wu, X. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c14962 · Cited by 37 · see citing works
Autonomous wearable sensing enabled by capillary microfluidics: a review
Kuruvinashetti, K., Komeili, A., & Sanati‐Nezhad, A. (2025) · Lab on a Chip · doi:10.1039/d5lc00536a · Cited by 39 · see citing works
Self-citationZhang, A., Zhou, L., Liang, Q., Wang, X., Hu, X., Jia, K., Chu, H., Luo, Y., Qiu, L., Peng, H., et al. (2023) · Science China Materials · doi:10.1007/s40843-023-2720-6 · Cited by 37 · see citing works
Hu, M., Zhu, K., Wei, J., Xu, Z., Yang, K., Wu, L., Zong, S., & Wang, Z. (2024) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2024.137152 · Cited by 36 · see citing works
Advances in wearable electrochemical antibody-based sensors for cortisol sensing
Khumngern, S., & Jeerapan, I. (2023) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-023-04577-y · Cited by 38 · see citing works
Advances in electrochemical biosensor design for the detection of the stress biomarker cortisol
Weber, C. J., Clay, O. M., Lycan, R. E., Anderson, G. K., & Simoska, O. (2023) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-023-05047-1 · Cited by 35 · see citing works
Microfluidic Sensing Textile for Continuous Monitoring of Sweat Glucose at Rest
Zhao, H., Zhang, L., Deng, T., & Li, C. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c01912 · Cited by 35 · see citing works
Cho, S., Shajari, S., Xiong, Y., Madsen, K. E., Lv, Z., Cho, S., Park, J., Li, M., Li, S., Feng, L., et al. (2026) · Nature Sensors · doi:10.1038/s44460-025-00005-z · Cited by 24 · see citing works
Self-citationBan, S., Yi, H., Park, J., Huang, Y., Yu, K. J., & Yeo, W. (2025) · Advanced Materials · doi:10.1002/adma.202416899 · Cited by 22 · see citing works
Customizable Microfluidic Devices: Progress, Constraints, and Future Advances
Aljabali, A. A. A., Obeid, M. A., Mishra, V. K., El‐Tanani, M., & Tambuwala, M. M. (2023) · Current Drug Delivery · doi:10.2174/0115672018264064231017113813 · Cited by 20 · see citing works
Babamiri, B., Farrokhnia, M., Mohammadi, M., & Sanati‐Nezhad, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-93025-1 · Cited by 19 · see citing works
Self-citationTan, Z., Zheng, Y. H., Shi, H., Xu, W., Jia, X., Dan, Z., Liao, J., Dai, Z., & Xu, C. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.155938 · Cited by 18 · see citing works
Inukonda, S. M., Parambil, A. M., & Panda, S. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.147066 · Cited by 17 · see citing works
Composite Nanofiber Membrane-Based Microfluidic Fluorescence Sensors for Sweat Analysis
Mei, X., Zhou, L., Zhu, L., & Wang, B. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c04616 · Cited by 16 · see citing works
Vertical textile microfluidics: advancing on-garment sweat sampling for real-time biosensing
Galliani, M., Ismailova, E., Azizian, P., Makhinia, A., & Cabot, J. M. (2025) · npj Flexible Electronics · doi:10.1038/s41528-025-00416-5 · Cited by 16 · see citing works
Dermal-fluid-enabled detection platforms for non-invasive ambulatory monitoring
Veronica, A., Li, Y., Li, Y., Hsing, I., & Nyein, H. Y. Y. (2023) · Sensors & Diagnostics · doi:10.1039/d3sd00165b · Cited by 15 · see citing works
AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis
Khalghollah, M., Zare, A., Shakeri, E., Far, B. H., & Sanati‐Nezhad, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-11508-7 · Cited by 12 · see citing works
Self-citationA miniaturized battery-free wireless epidermal platform for multiplexed sweat biomarker monitoring
Yang, Y., Lei, X., Zhang, D., Dong, F., & Liu, S. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.167787 · Cited by 10 · see citing works
Liu, Y., Luo, T., Ding, C., Xuan, L., Li, J., & Yang, R. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202400619 · Cited by 11 · see citing works
Challenges of Wearable Biosensors and Ways to Overcome Them
Tarasov, S. E., Plekhanova, Y. V., Reshetilov, A., Melenkov, S., & Saltanov, I. (2026) · Biosensors · doi:10.3390/bios16030159 · Cited by 10 · see citing works
ecSkin: Low-Cost Fabrication of Epidermal Electrochemical Sensors for Detecting Biomarkers in Sweat
Panigrahy, S. N., Lee, C. H., Nagoria, V., Janghorban, M., Pandey, R., & Nittala, A. S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642232 · Cited by 8 · see citing works
Bistable magnetic valves for selective sweat sampling in wearable microfluidics
Kim, C., Shin, C., Lee, A., Ha, J., & Choi, J. (2025) · Lab on a Chip · doi:10.1039/d5lc00576k · Cited by 8 · see citing works
Advances and Challenges in Wearable Sensors for Health Monitoring
Brazaca, L. C., Lozano, A. M., Mayol, B., Scheidt, D. T., Fazio, D. D., Kim, E., Maroli, G., Rosati, G., Ates, H. C., Wang, H., et al. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c04520 · Cited by 8 · see citing works
Recent advances in nanomaterials for wearable devices: classification, synthesis, and applications
Zhang, T., Li, L. H., Sun, X., Shi, Y., Cheng, W. M., & Pan, L. (2025) · Nanotechnology · doi:10.1088/1361-6528/add9a8 · Cited by 5 · see citing works
Patel, V., Bhatt, S., Daspoddar, A., Shirsekar, V., Kumari, R., Chandra, P., & Srivastava, R. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.111165 · Cited by 4 · see citing works
Liang, C., Zhang, H., Zhang, Z., Wang, L., Lin, X., Fu, Y., Zhu, X., Zhu, S., & Liu, J. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.114302 · Cited by 3 · see citing works
Jalali, P., & Sanati‐Nezhad, A. (2026) · Lab on a Chip · doi:10.1039/d5lc01172h · Cited by 3 · see citing works
Self-citationFlexible multilayered skin health sensing platform for eyewear applications
Park, B., Lee, S., Song, Y.-H., Yeon, P., & Kim, M. (2025) · Micro and Nano Systems Letters · doi:10.1186/s40486-025-00226-4 · Cited by 3 · see citing works
Cano, S. P., Franco, E., Apablaza, J., & García, M. J. (2023) · Preprints.org · doi:10.20944/preprints202308.1236.v1 · Cited by 3 · see citing works
Qi, S., Yu, Z., Guo, B., Li, S., Li, Z., Ma, H., Ho, T.-Y., Chakrabarty, K., & Huang, X. (2025) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3768629 · Cited by 2 · see citing works
De-Cortisensor: A Portable and Highly Sensitive Direct Enzyme-Based Cortisol Detection Platform
Aryal, K. P., Félix, E., Chowdhury, M.-U.-S., Tallur, S. G., & Pandey, R. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3619842 · Cited by 2 · see citing works
Lv, F., Zhao, R., Chen, Z., Wang, S., Huang, Y., & Wang, L. (2025) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2025.116245 · Cited by 1 · see citing works
Umapathi, K. (2026) · Analytical Methods · doi:10.1039/d6ay00459h · Cited by 1 · see citing works
Sample preparation using microfluidic technologies for non-invasive tests
Oksuz, C., Tarim, E. A., Ozcan, H. A., Koc, S., & Tekin, H. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15978-7.00038-2
Non Invasive Stress Detection in Pregnant Women: A Smart IOT based System
Kamlakar, M., Patil, L. V., & Patil, D. D. (2024) · IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · doi:10.1109/icbds61829.2024.10837466
Hassani, M., Zare, A., Soheili, F., Daneshvar, S. M. M. H., Biouki, M. A., Tahernezhad, Y., Sun, C., Glogauer, M., Ghafar‐Zadeh, E., & Sanati‐Nezhad, A. (2026) · Advanced Materials Technologies · doi:10.1002/admt.202502599
Self-citationFeng, A., Zhao, X., Gao, S., Qian, C. Z., Li, W., & Cheng, A. (2026) · Behavioral Sciences · doi:10.3390/bs16060934
Silk fibroin-based wearable bioelectronics for self-powered healthcare
Ansari, A. I., & Dekhtyar, Y. (2026) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-026-02669-4
Painless Biosensing for Non‐Invasive Health Monitoring: Advances, Platforms and Future Directions
Alam, A., Gull, F., Hameed, M., Iqbal, I., Naz, S., Noor, H., Aslam, A., Younis, S., Vit, V., Mehmood, M. A., et al. (2026) · Electrochemical Science Advances · doi:10.1002/elsa.70041
Feng, J., Yin, T., Wei, Z., Zhong, H., Zhang, B., Zhang, H., Yin, J., Wan, Y., Lv, J., Yang, X., et al. (2026) · Chemical Communications · doi:10.1039/d6cc04420d
Cortisol Dynamics at High Temporal Resolution For Wearable Biosensor Research
Ferdoushi, M., Billings, T., Cai, W., & Khan, Y. (2026) · Advanced Sensor Research · doi:10.1002/adsr.70212
The Digital Pillbox: Evaluating the Impact of Digital Health on Medication Compliance
Patil, P. Y. (2026) · Asian Journal of Pharmaceutical Research · doi:10.52711/2231-5691.2026.00071
Human Sweat Cortisol Versus Blood and Saliva: Quantitative Interpretation
Albanese, L. (2026) · Methods and Protocols · doi:10.3390/mps9060154
- Authors
- Shajari, S., Salahandish, R., Zare, A., Hassani, M., Moossavi, S., Munro, E., Rashid, R., Rosenegger, D., Bains, J. S., & Sanati Nezhad, A.
- Affiliations
- University of Calgary (CA)
- SenseSi Co. Calgary Alberta T2L 1Y8 Canada
- BioMEMS and Bioinspired Microfluidic Laboratory Department of Biomedical Engineering University of Calgary Calgary Alberta T2N 1N4 Canada
- Corresponding author
- Bains, Jaideep S.
- ORCID
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2022-12-03
- Volume
- 10
- Issue
- 7
- Pages
- e2204171-e2204171
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC9982588
- OpenAlex
- W4310641398
- Semantic Scholar
- 6bcc66437a69fbacc0e699b91d2337a7ef626e91
- Cited by
- 55
- References
- 85
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 15 · 2023: 12 · 2022: 1
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Calgary
- Alberta Innovates
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Capillary Electrophoresis Applications
- Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
- Keywords
- sweat collection, wearable sensors, cortisol, stress monitoring, non-invasive biomarkers, capillary microfluidics, self-powered devices, Perceived Stress Scale
- MeSH terms
- Wearable Electronic Devices, Humans, Hydrocortisone, Sweat, Sweat Glands, Microfluidics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review