Limiting racial disparities and bias for wearable devices in health science research
Open accessAbstract
Consumer wearables are devices used for tracking activity, sleep, and other health-related outcomes (e.g. Apple Watch, Fitbit, Samsung, Basis, Mio, PulseOn, Whoop). Wearables promise a myriad of health-related information, including low heart rate alerts, a personal electrocardiogram (ECG) monitor for detecting arrhythmia, sleep tracking (e.g. sleep architecture), and pulse pressure designed to promote healthy living and alert high-risk consumers based on real-time data. Their relative low cost, the collection of longitudinal data, and the ability to display/transmit information suggest a host of benefits if used in clinical practice and to advance remote research. Unfortunately, due to technological limitations of photoplethysmographic (PPG) green light signaling, these health constructs may only be accessible for a population of people with lighter skin tones. We note with deep concern that there is increasing evidence that these devices are not as accurate, and may not work at all, in people with darker skin tones [1, 2]. The reduced accuracy of wearable devices in people with darker skin tones seems to have been known for some time, yet this issue has garnered little attention from the medical community. Apart from limited media coverage [3], anecdotal reports, and minimal research publications [1, 2, 4, 5], the role of dark skin tones on wearable accuracy is a severely underreported phenomenon that requires direct attention and action from industry and the scientific community. Our concern is amplified as these devices are now transitioning from consumer goods into health-related research and their internal algorithms are becoming Food and Drug Administration (FDA) approved. As such, they stand to exacerbate existing structural health disparities of people with darker skin tones and may compound existing structural health disparities for Black Americans [5]. The Black Lives Matter movement is calling for everyone to unveil and dismantle systemic bias throughout society. We challenge the healthcare community to work towards this goal and to ensure that digital health solutions do not reinforce existing disparities in care and access as these devices are increasingly used in research and clinical practice. One of the issues with wearables and skin tone is a technical challenge, in that wearables use a PPG green light signal. Blood, due to its color, readily absorbs green light, where the greater the volume of blood present (i.e. when the heart pumps during systole), the higher the green light absorption and thus heart rate can be measured accordingly. This green light technology is cheap and generally robust, however, skin tone affects the absorption of light differently, which interferes with the algorithm output. Studies have shown that green light lacks precision and accuracy, and may not read at all, when measuring heart rate in darker skin types [2]. There are also several other factors that influence PPG accuracy that may cause an interaction with skin tone, including tattoos, presence of arm hair, sweat, ambient temperature, level of activity, thickness of skin epidermis [6], and body mass [7]. This potentially affects health-related outcomes that rely on PPG including a host of sleep-related indices such as sleep duration, architecture [8], diagnosis of sleep apnea [9], long-term sleep patterns, and other sleep disorders [10]. Despite this, green light technology remains the industry standard in wearables. A second problem with the current state of wearables research seems to be the lack of diversity in validation studies. The consumer wearable industry modus operandi is to bring to market a minimally viable product to the largest population as quickly as possible. Test marketing (there are too few true validation studies published) is often done with a local population that may lack a diversity of skin tones, and objective measurement of skin pigmentation is rarely reported. A lack of accurate information about how wearables work in diverse skin tones may cause unintended consequences by reinforcing existing healthcare disparities for those with darker skin tones. Inaccurate signal detection and validation in those with darker skin might lead to decreased utilization of novel wearable devices, or worse, provide false assurances about the effectiveness of monitoring. Thus, we urge both the industry and researchers to ensure that validation studies are performed in a diverse population. A confounding factor in accurately understanding the limitations of wearables on skin tone is the current standard of measuring skin tone: the Fitzpatrick Skin Type Scale (FST) [11]. Developed in 1975 by individuals with white skin for individuals with white skin [12], the FST is a subjective scale that classifies six skin type categories according to skin pigmentation and skin’s reaction to sun exposure. There is a substantive literature examining the racial biases and limitations of the FST [12–14]. The phototype
AI snapshot
Written from the paper’s full text.
TL;DR
This commentary argues that consumer wearables using green-light photoplethysmography (PPG) may be less accurate or fail in people with darker skin tones, and that validation research often lacks diverse skin-tone representation and relies on the biased Fitzpatrick Skin Type (FST) scale. It calls for objective skin-tone measurement, diverse validation studies, and action by industry and researchers to avoid reinforcing health disparities.
Key findings
- Wearables using PPG green light may be inaccurate or not read at all in darker skin types; green light lacks precision/accuracy for heart rate in darker skin, and skin tone can interfere with algorithm output. This may affect sleep-related outcomes such as sleep duration, architecture, sleep apnea diagnosis, and long-term sleep patterns.
- Validation studies often lack diversity: test marketing may use local populations lacking diverse skin tones, and objective skin pigmentation is rarely reported. A small study (N = 53) found “no statistically significant difference in accuracy across skin tones,” but the author notes only n = 9 had the darkest skin tones and FST was used rather than objective measurement, calling the conclusion misleading.
- The FST is a subjective six-category scale developed in 1975 by individuals with white skin for individuals with white skin; it has weak correlation with skin color, large within-group variance, and administrator bias. Reflectance spectrometry is proposed as the objective gold standard.
- Scale of use: Fitbit has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov; the Apple Watch Heart Study recruited 419,927 people. Some companies acknowledge limitations: Fitbit increased green-light transmitter power, newer Apple Watch models add an infrared sensor, and Garmin and Everion disclose PPG may not be accurate in dark skin.
Methods
- Not stated in the paper (commentary/editorial; no empirical design, sample, or analysis is reported).
Limitations
- The paper states limitations of wearables: PPG accuracy can be affected by tattoos, arm hair, sweat, ambient temperature, activity level, epidermis thickness, and body mass, in addition to skin tone; green-light technology remains the industry standard despite these issues.
- The paper states limitations of current evidence: minimal publicly available data exist to understand the scope and magnitude of the problem or whether company fixes work; there are too few true validation studies, and objective skin pigmentation is rarely reported.
- Evident from the text: this is a commentary/editorial rather than an original study or systematic review, so it presents no primary data, formal methods, or quantitative synthesis and relies on cited literature and examples.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
0 of 23 cited sources checked · 0 supported · 0 flagged
- Interpretationreasoning§ Limiting racial disparities and bias for wearable devices in health science research
Due to technological limitations of photoplethysmographic (PPG) green light signaling, health constructs from wearables may only be accessible for people with lighter skin tones.
“Unfortunately, due to technological limitations of photoplethysmographic (PPG) green light signaling, these health constructs may only be accessible for a population of people with lighter skin tones.”
Quote in the paperSubstantial claim - Premisecitation§ Limiting racial disparities and bias for wearable devices in health science research
Garmin and Everion directly let consumers know their validation studies show PPG data may not be accurate in individuals with dark skin.
“Both Garmin [21] and Everion [22] are the most forthcoming by directly letting consumers know their validation studies show PPG data may not be accurate in individuals with dark skin.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [21] Garmin, 2020
Not checked in the free run.
- cites [22] Everion, 2020
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- Interpretationreasoning§ Limiting racial disparities and bias for wearable devices in health science research
A lack of accurate information about how wearables work in diverse skin tones may cause unintended consequences by reinforcing existing healthcare disparities for those with darker skin tones.
“A lack of accurate information about how wearables work in diverse skin tones may cause unintended consequences by reinforcing existing healthcare disparities for those with darker skin tones.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Limiting racial disparities and bias for wearable devices in health science research
The use of the FST may give false assurances about skin tone and wearable effectiveness.
“As such, the use of the FST may give false assurances about skin tone and wearable effectiveness.”
Quote in the paperSubstantial claim - Premisereasoning§ Limiting racial disparities and bias for wearable devices in health science research
Skin tone affects the absorption of light differently, which interferes with the algorithm output of wearables.
“This green light technology is cheap and generally robust, however, skin tone affects the absorption of light differently, which interferes with the algorithm output.”
Quote in the paperSubstantial claim
- Premisecitation§ Limiting racial disparities and bias for wearable devices in health science research
The FST and other subjective measures (e.g. Taylor Pigmentation Scale) have been shown to be inaccurate and biased based on the administrator.
“Further, the FST and other subjective measures (e.g. Taylor Pigmentation Scale) have been shown to be inaccurate and biased based on the administrator [17].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] Fider, 2019, Differences in color categorization manifested by males and females: a quantitative World Color Survey study, Palgrave Commun source
Not checked in the free run.
- Premisecitation§ Limiting racial disparities and bias for wearable devices in health science research
Technological advancements are muted if their inherent biases continue historical structural health disparities.
“Technological advancements are muted if their inherent biases continue historical structural health disparities [24].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [24] Morris, 2020
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- Premisecitation§ Limiting racial disparities and bias for wearable devices in health science research
Disparities in access to health care exacerbate the harm that social structures and policies cause to the health of Black Americans.
“These disparities in access to health care exacerbate the harm that social structures and policies cause to the health of Black Americans [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Feiner, 2007, Dark skin decreases the accuracy of pulse oximeters at low oxygen saturation: the effects of oximeter probe type and gender, Anesth Analg. source
Not checked in the free run.
- Premisereasoning§ Limiting racial disparities and bias for wearable devices in health science research
There is a lack of diversity in validation studies of wearables.
“A second problem with the current state of wearables research seems to be the lack of diversity in validation studies.”
Quote in the paperSubstantial claim- Rests on an assumption: The consumer wearable industry's modus operandi is to bring to market a minimally viable product to the largest population as quickly as possible.
- Rests on an assumption: Test marketing of wearables is often done with a local population that may lack a diversity of skin tones, and objective measurement of skin pigmentation is rarely reported.
- Premisecitation§ Limiting racial disparities and bias for wearable devices in health science research
Reflectance spectrometry accurately identifies skin color/tone using multiple color wavelengths for classification.
“reflectance spectrometry which accurately identifies skin color/tone using multiple color wavelengths for classification [18]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Pershing, 2008, Reflectance spectrophotometer: the dermatologists’ sphygmomanometer for skin phototyping?, J Invest Dermatol. source
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- May combine two claims. 11
- Classified as probably not a substantial claim. 2
May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchWearable devices are not as accurate, and may not work at all, in people with darker skin tones.
“We note with deep concern that there is increasing evidence that these devices are not as accurate, and may not work at all, in people with darker skin tones [1, 2].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1] Shcherbina, 2017, Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort, J Pers Med source
Not checked in the free run.
- cites [2] Fallow, 2013, Influence of skin type and wavelength on light wave reflectance, J Clin Monit Comput. source
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May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchGreen light lacks precision and accuracy, and may not read at all, when measuring heart rate in darker skin types.
“Studies have shown that green light lacks precision and accuracy, and may not read at all, when measuring heart rate in darker skin types [2].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] Fallow, 2013, Influence of skin type and wavelength on light wave reflectance, J Clin Monit Comput. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchFitbit has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov, and the Apple Watch Heart Study recruited 419,927 people to track irregular heart rhythms.
“For example, Fitbit alone has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov [4] and the Apple Watch Heart Study recruited 419 927 people to track irregular heart rhythms [20].”
Quote in the paperNot a substantial claim0 of 2 sources checkedClassified as probably not a substantial claim.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchThe Fitzpatrick Skin Type Scale (FST) is a subjective scale that classifies six skin type categories according to skin pigmentation and skin's reaction to sun exposure.
“the FST is a subjective scale that classifies six skin type categories according to skin pigmentation and skin’s reaction to sun exposure.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Fitzpatrick, 1975, Sun and skin, Journal de Medecine Esthetique
Not checked in the free run.
May combine two claims.
Interpretationreasoning§ Limiting racial disparities and bias for wearable devices in health science researchThe conclusions of that study are misleading because too few people with the darkest skin tones were included (n = 9), as assessed by the FST rather than objective measurement.
“We fear that these conclusions are misleading because too few people with the darkest skin tones were included (n = 9), as assessed by the FST rather than objective measurement.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationreasoning§ Limiting racial disparities and bias for wearable devices in health science researchInaccurate signal detection and validation in those with darker skin might lead to decreased utilization of novel wearable devices, or provide false assurances about the effectiveness of monitoring.
“Inaccurate signal detection and validation in those with darker skin might lead to decreased utilization of novel wearable devices, or worse, provide false assurances about the effectiveness of monitoring.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchThe FST phototype designation with six categories has only a weak correlation with skin color, with large within-group variance of skin tone.
“The phototype designation with six categories has been shown to have only a weak correlation with skin color with large within-group variance of skin tone [13, 15, 16].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [13] Ware, 2020, Racial limitations of Fitzpatrick skin type, Cutis.
Not checked in the free run.
- cites [15] Yun, 2010, Skin color analysis using a spectrophotometer in Asians, Skin Res Technol.
Not checked in the free run.
- cites [16] Xiao, 2017, Characterising the variations in ethnic skin colours: a new calibrated database for human skin, Skin Res Technol. source
Not checked in the free run.
May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchConsumer wearable companies seem to be aware their devices lack accuracy in people with dark skin tones and a few companies are actively working with varying approaches to manage this deficiency.
“Consumer wearable companies seem to be aware their devices lack accuracy in people with dark skin tones and a few companies are actively working with varying approaches to manage this deficiency.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [21] Garmin, 2020
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- cites [22] Everion, 2020
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May combine two claims.
Interpretationcitation§ Limiting racial disparities and bias for wearable devices in health science researchWearables may exacerbate existing structural health disparities for people with darker skin tones and may compound existing structural health disparities for Black Americans.
“As such, they stand to exacerbate existing structural health disparities of people with darker skin tones and may compound existing structural health disparities for Black Americans [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Feiner, 2007, Dark skin decreases the accuracy of pulse oximeters at low oxygen saturation: the effects of oximeter probe type and gender, Anesth Analg. source
Not checked in the free run.
May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchA small study (N = 53) examined the accuracy of several wearables using the FST and found no statistically significant difference in accuracy across skin tones.
“For example, a small study (N = 53) examined the accuracy of several wearables using the FST and found, “no statistically significant difference in accuracy across skin tones.” [4]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Bent, 2020, Investigating sources of inaccuracy in wearable optical heart rate sensors, NPJ Digit Med. source
Not checked in the free run.
May combine two claims.
Premisecitation§ Limiting racial disparities and bias for wearable devices in health science researchThe FDA and NIH are calling for increased remote clinical outcomes to patient-generated measures, and the Covid-19 crisis is accelerating the appeal for remote monitoring in clinical trials.
“There is a wearable revolution in healthcare coming, with the FDA and National Institutes of Health (NIH) calling for increased remote clinical outcomes to patient-generated measures that do not require clinician oversight; the Covid-19 crisis is accelerating the appeal for remote monitoring in clinical trials [19].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19] Turner, 2020, New FDA guidance on general clinical trial conduct in the era of COVID-19, Ther Innov Regul Sci source
Not checked in the free run.
May combine two claims.
Interpretationcitation§ Limiting racial disparities and bias for wearable devices in health science researchPPG accuracy issues potentially affect health-related outcomes that rely on PPG, including sleep duration, sleep architecture, diagnosis of sleep apnea, long-term sleep patterns, and other sleep disorders.
“This potentially affects health-related outcomes that rely on PPG including a host of sleep-related indices such as sleep duration, architecture [8], diagnosis of sleep apnea [9], long-term sleep patterns, and other sleep disorders [10].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [8] Fonseca, 2017, Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults, Sleep source
Not checked in the free run.
- cites [9] Shokoueinejad, 2017, Sleep apnea: a review of diagnostic sensors, algorithms, and therapies, Physiol Meas. source
Not checked in the free run.
- cites [10] Boe, 2019, Automating sleep stage classification using wireless, wearable sensors, NPJ Digit Med. source
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The paper relies on this without support.
The reduced accuracy of wearable devices in people with darker skin tones has been known for some time, yet this issue has garnered little attention from the medical community.
“The reduced accuracy of wearable devices in people with darker skin tones seems to have been known for some time, yet this issue has garnered little attention from the medical community.”
The paper relies on this without support.
Green light technology remains the industry standard in wearables.
“Despite this, green light technology remains the industry standard in wearables.”
The paper relies on this without support.
The consumer wearable industry's modus operandi is to bring to market a minimally viable product to the largest population as quickly as possible.
“The consumer wearable industry modus operandi is to bring to market a minimally viable product to the largest population as quickly as possible.”
The paper relies on this without support.
Wearable devices are transitioning from consumer goods into health-related research and their internal algorithms are becoming FDA approved.
“Our concern is amplified as these devices are now transitioning from consumer goods into health-related research and their internal algorithms are becoming Food and Drug Administration (FDA) approved.”
The paper relies on this without support.
Test marketing of wearables is often done with a local population that may lack a diversity of skin tones, and objective measurement of skin pigmentation is rarely reported.
“Test marketing (there are too few true validation studies published) is often done with a local population that may lack a diversity of skin tones, and objective measurement of skin pigmentation is rarely reported.”
The paper relies on this without support.
There is minimal data publicly available to understand the scope and magnitude of the potential problem, and whether the solutions above actually work.
“At the current time, however, there is minimal data publically available to understand the scope and magnitude of the potential problem, and whether the solutions above actually work as the pace of technology advances faster than researchers can keep up [3].”
The paper relies on this without support.
Increasing numbers of studies are undertaken without information about skin tone and wearable performance.
“Action is needed as increasing numbers of studies are undertaken without information about skin tone and wearable performance.”
The paper relies on this without support.
Large US-based companies (e.g. IBM and Target) are starting to offer trackers as part of wellness programs.
“There are further opportunities for inequalities as large US-based companies (e.g. IBM and Target) are starting to offer trackers as part of wellness programs.”
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?
Darker skin tones are more susceptible to signal distortion and data loss compared to lighter skin.
“This makes darker skin tones more susceptible to signal distortion and data loss compared to lighter skin [2].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [2]
If there are significant errors by skin tone that are not specifically examined, it can limit accurate health-related information for individuals with darker skin tones, further exacerbating existing structural health disparities.
“if there are significant errors by skin tone that are not specifically examined it can limit accurate health-related information for individuals with darker skin tones, further exacerbating already existing structural health disparities13”
From Response To: Investigating sources of inaccuracy in wearable optical heart rate sensors (2021) · cites this paper as [13]
Other wearable healthcare products used to measure heart rate and arrhythmia have been shown to be limited by racial bias.
“Other wearable healthcare products used to measure heart rate and arrhythmia, for example, have also been shown to be limited by racial bias.144”
From Effect of skin color on optical properties and the implications for medical optical technologies: a review (2024) · cites this paper as [144]
These often-overlooked disparities with diversity (e.g., skin tone and obesity) are now becoming more documented in the literature.
“These often-overlooked disparities with diversity (e.g., skin tone and obesity) are now becoming more documented in the literature [34,35,36].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [34]
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Evaluation of stroke volume estimation during orthostatic stress: the utility of Modelflow
Lucci, V.-E. M., Parsons, I. T., Hockin, B. C. D., & Claydon, V. E. (2023) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000671 · Cited by 12 · see citing works
Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring
Asif, S., AlSaafeen, A., Nadar, S., Nambiar, S., Dannawi, J., Korrapati, H., & Wilkhoo, H. S. (2025) · Cureus · doi:10.7759/cureus.94074 · Cited by 9 · see citing works
Kostrna, J., Oparina, E., Palacios, C., Rodriguez, A. J., Pei, J., Ajmal, A., & Ramella-Roman, J. C. (2026) · Sensors · doi:10.3390/s26102922 · Cited by 2 · see citing works
- Authors
- Colvonen, P. J., DeYoung, P. N., Bosompra, N.-O. A., & Owens, R. L.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Colvonen, Peter J
- Venue
- Sleep
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-07
- Volume
- 43
- Issue
- 10
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- PMC
- PMC8477341
- OpenAlex
- W3083941446
- Semantic Scholar
- bc713da88f91693fee6a15d9e4e839e30aca972f
- Cited by
- 137
- References
- 20
- Citations by year
- 2026: 18 · 2025: 23 · 2024: 26 · 2023: 29 · 2022: 19 · 2021: 21 · 2020: 1
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- SLEEP (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Veterans Affairs (I01 RX003623, IK2 RX002120)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Keywords
- wearable devices, racial disparities, skin tone, photoplethysmography, green light absorption, heart rate measurement, darker skin tones, African Americans, algorithmic bias, digital health equity, remote patient monitoring, sleep monitoring, pulse rate monitoring, device accuracy, health equity policy
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 ›
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