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Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62769, first published .
Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis

Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis

Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis

Authors of this article:

Sanidhya Singh1 Author Orcid Image ;   Miles Romney Bennett2 Author Orcid Image ;   Chen Chen2 Author Orcid Image ;   Sooyoon Shin2 Author Orcid Image ;   Hamid Ghanbari1 Author Orcid Image ;   Benjamin W Nelson2, 3 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics

    Review

    1University of Michigan Medical School, Ann Arbor, MI, United States

    2Verily Life Sciences LLC, South San Francisco, CA, United States

    3Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States

    *these authors contributed equally

    Corresponding Author:

    Sanidhya Singh, BSE

    University of Michigan Medical School

    1301 Catherine Street

    Ann Arbor, MI, 48109

    United States

    Phone: 1 7349364000

    Email: sansin@med.umich.edu


    Background: Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities.

    Objective: This systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation.

    Methods: We identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or –5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI.

    Results: For SpO2, accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI –0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from –16.02 to 13.54, from –18.62 to 16.84, and from –33.69 to 32.54, and pooled mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively.

    Conclusions: SpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation.

    J Med Internet Res 2024;26:e62769

    doi:10.2196/62769

    Keywords

    photoplethysmography; pulse oximetry; arterial blood gas; skin tone; skin pigmentation; bias; digital technology 


    Photoplethysmography (PPG) technology has been used in medicine since the 1970s to assess pulse rate (PR) and blood oxygenation (SpO2). The accuracy of PPG-based SpO2 and PR is critical for medical practice, clinical decision-making, and patient outcomes [1].

    Technological advancements have led to rapid expansion of this technology into consumer devices [2]. This has enabled consumers to continuously and unobtrusively track health status, generating information that is becoming commonly used in health care settings and as a source of research end points [3,4].

    Researchers have identified several factors that influence the accuracy of PPG-based measurements, including skin temperature, sensor contact pressure, skin thickness, and hydration level [5,6]. Notably, darker skin pigmentation may influence PPG-based SpO2 and PR readings [7-10]. Patients with darker skin pigmentation are more likely than patients with lighter skin color to have overestimated SpO2 readings, leading to lower hospital admission; higher occult hypoxemia; and delayed or no access to dexamethasone, therapeutic oxygen, and COVID-19 therapies, resulting in increased hospital readmission, organ dysfunction, and mortality [11-18].

    Starting in 2013, a series of U.S. Food and Drug Administration (FDA) guidances, safety communications, and more recently, statements from attorneys general have called to address darker skin pigmentation bias in pulse oximeters [19]; however, these guidances lacked standards for assessing skin tone [20]. Recent pulse oximeter research has rekindled interest in skin pigmentation disparities in PPG sensor accuracy, leading to increased media and regulatory attention [21-24]. In November 2023, more than 24 attorneys general wrote a letter calling on the FDA to take urgent action to address pulse oximeter skin pigmentation disparities [25,26].

    More recent research has also highlighted the potential presence of these biases in research-grade and consumer PPG-based wearable devices [4,27-29], leading to calls to address inequity, bias, and discrimination in wearable health technology and clinical practice algorithms [30,31].

    Accuracy guidance for pulse oximeters and heart rate or PR devices has been delineated by regulatory bodies, industry, and medical standards. Overall accuracy of pulse oximeters can be assessed with accuracy root-mean-square (Arms), which combines mean bias and precision (SD of bias) into a single metric [32]. FDA guidance has a threshold of Arms≤3% [19] for transmittance devices and a threshold of Arms≤3.5% [19] for ear clip and reflectance devices, while international thresholds are set at Arms≤4% [32]. For heart rate or PR devices, overall accuracy can be assessed with 95% limits of agreement (LoAs). The American National Standards Institute (ANSI), Association for the Advancement of Medical Instrumentation (AAMI), and International Electrotechnical Commission (IEC) have set the recommendation that electrocardiography (ECG) devices have mean bias of +5 or –5 beats per minute (bpm) or mean absolute percentage error≤10%, whichever is greater [33,34]. Devices with accuracy measures breaching these accuracy thresholds can produce questionable results.

    It is, therefore, critical to generate evidence to inform the design and calibration of these devices to reduce algorithmic bias and improve accuracy, so that PPG-based technology can generalize to all segments of the population, mitigating racial disparities in health outcomes. There have been systematic reviews on PPG skin pigmentation bias, but these have been limited to either consumer device PR [35] or pulse oximeter SpO2 [36-38]. Only 1 meta-analysis focused on pulse oximetry for this topic is available [36], and an additional 7 studies have been published recently, providing an additional 50,980 participants and 182,369 paired observations. Therefore, performing a comprehensive meta-analysis to examine PPG accuracy, potential bias, and clinical relevance for SpO2 and PR by skin pigmentation is timely.


    Search Strategy and Selection Criteria

    We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [39] and developed a MEDLINE systematic search. Searches were performed between April 2022 and June 2023, and a final ad hoc search was conducted in June 2023. No additional studies were included after this date.

    We included studies that (1) investigated PPG-derived SpO2 or PR test devices per definition [40]; (2) used arterial blood gas (SaO2) or ECG as the reference device; and (3) reported mean bias and SD, SE, 95% LoA, or 95% CI by race, ethnicity, or skin tone. Inclusion disagreements were resolved by consensus.

    Exclusion criteria included (1) literature reviews, systematic reviews, commentary, and meta-analyses; (2) non-English manuscripts; (3) irretrievable full source texts; (4) studies on remote PPG; and (5) those that lack SaO2 or ECG as the reference device. We chose to not exclude papers based on measurement hardware or underlying algorithms given that all measurement devices relied on contact-based PPG to measure the same end points of interest—SpO2 and PR.

    Data Analysis

    Data Extraction

    Data were independently extracted from published manuscripts (BWN and SS). A third and fourth reviewer (MRB and HG) adjudicated any differences between the initial 2 reviewers and resolved any disagreements by checking manuscripts. The first author verified the quality of manuscripts. Standardized data extraction was developed to extract the study characteristics (see the Materials—Data Extraction section in Multimedia Appendix 1). For 1 paper, we used participant-level data from the supplementary materials to calculate mean bias and SE by skin pigmentation category [41]. This resulted in 27 studies in our final analysis (Figure 1 and Table 1).

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram. ECG: electrocardiography; PPG: photoplethysmography; SpO2: blood oxygenation.
    Table 1. Characteristics of included studies.
    Device type and study (author, year)Sample size, nTest device evaluatedReference deviceSaO2a (%) rangeParticipant populationResearch settingSkin pigmentation method
    Pulse Oximetry

    		Abrams et al [42], 2003200
    • Nellcor N-200
    		Radiometer ABL-520Not reportedMedical—adult patients with cirrhosisInpatientRace—Black and White

    		Adler et al [43], 1998298
    • Nellcor D-25
    		4-wavelength spectro-photometer, or co-oximeter (Radiometer OSM3)50-99Medical—adult emergency department patientsInpatientSkin tone—Munsell color system categorized into light, medium, or dark

    		Andrist et al [44], 20221061
    • Not reported
    		Not reportedNot reportedMedical—pediatricInpatientRace

    		Barker and Wilson [45], 202375
    • Masimo SETb pulse oximeters with RD-SET sensors
    		Radiometer ABL-835 Flex CO-Oximeter70-100Mixed—healthy and mild systemic diseaseResearch — laboratoryRace

    		Bickler et al [10], 200523
    • Nellcor N-595 with Nellcor OxiMax A finger probe
    • Novametrix 513s models (2 types)
    • Nonin Onyx models (2 types)
    		Radiometer OSM360-100Healthy—nonsmokingResearch — laboratoryEthnicity—light (Northern European) and dark (African American)

    		Bothma et al [46], 1996100
    • Simed S100e
    • Nihon Koden
    • Ohmeda 3740
    		IL482 Co-oximeter System87.8-99.2Medical—critically ill adult patientsInpatientSkin tone—EELc reflectance spectrophotometer; all participants had dark skin tone

    		Burnett et al [47], 202246,253
    • Unspecified Nellcor and Masimo devices
    		GEMStat Premier 3000Not reportedMedical—patients receiving anesthesiaInpatientRace and ethnicity

    		Crooks et al [48], 20222997
    • Not reported
    		Not reportedNot reportedMedical—COVID-19InpatientRace

    		Ebmeier et al [49], 2018394
    • Marquette Rac-4A monitors with Masimo sensors and Philips IntelliVue
    • MP70 monitors with Philips
    • Adult Reusable SpO2d sensors
    		Radiometer ABL 800 FLEX SaO2 analyzerNot reportedMedical—multiple conditionsInpatientEthnicity

    		Fawzy et al [17], 20221216
    • Not reported
    		ABL825, ABL827, or ABL90 blood gas analyzersNot reportedMedical—COVID-19InpatientRace and ethnicity

    		Feiner et al [8], 200736
    • Nellcor N-595 (OxiMax A adhesive probe)
    • Nellcor N-595 (a clip-type probe)
    • Masimo Radical (clip probe)
    • Masimo Radical (adhesive disposable probe)
    • Nonin 9700 (clip-type probe)
    • Nonin 9700 (disposable adhesive probe)
    		Radiometer OSM3 multiwavelength oximeter60-100Healthy—nonsmokingResearch — laboratoryEthnicity—light (Caucasian), intermediate (Hispanic, Indian, Filipino, or Vietnamese), and dark (African American) categories

    		Foglia et al [50], 201736
    • Nellcor Oximax (Covidien)
    • Masimo Rainbow SET Radical 7
    		Siemens Rapidlab 126560-92Medical—infants with cyanotic congenital heart disease and oxygen saturation <90%InpatientSkin tone—Munsell color system

    		Jubran and Tobin [51], 199054
    • Nellcor pulse oximeter with disposable or reusable probes
    • Ohmeda-Biox3700 pulse oximeter with reusable probe
    		CO-oximetryNot reportedMedical—critically ill, ventilator-dependent patientsInpatientRace—Black and White

    		McGovern et al [52], 19968
    • Ohmeda 3700
    		IL 482 Co-oximeterNot reportedMedical—adults with stable condition with severe COPDfResearch — laboratoryRace—all White

    		Muñoz et al [53], 2008846
    • Minolta Pulsox-7
    		IL 682 co-oximeterNot reportedMedical—adults under assessment for long-term home oxygen therapyOutpatientRace—all White

    		Pilcher et al [54], 2020400
    • Carescape B450 monitor with Nellcor probe
    • GE Dash 3000
    • Masimo Radical 7
    • Masimo SET Quartz (unspecified)
    • Masimo SET Quartz Q400
    • Nonin 2120
    • Nonin 2140
    • Nonin Avant (unspecified)
    • Nonin Avant 4000
    • Nonin Avant 9700
    • Nonin Lifesense Medair
    • Novametrix Model 512
    • Ohmeda Biox 3700E with a GE TruSignal or Nellcor probe
    • Philips Intellivue MP70 with a GE TruSignal Nellcor or Philips probe
    • Welch Allyn with a Nellcor probe
    		Radiometer ABL80072-100Medical—hospitalized adult patientsInpatient and outpatientSkin Tone—Fitzpatrick Scale categorized into light, medium, and dark

    		Ruppel et al [55], 2023774
    • Not reported
    		Not reportedNot reportedMedical—cardiac catheterizationInpatientRace

    		Sudat et al [56], 20238735
    • Not reported
    		Not reportedNot reportedMedical—multiple conditionsInpatientRace

    		Thrush and Hodges [57], 199425
    • Critikon Dinamap Plus Model 8700
    • Critikon Oxyshuttle Ohmeda 3700 Catalyst Research MiniOx IV
    		IL482 co-oximeter80-100Healthy—nonsmoking adultsResearch — laboratoryRace—all White

    		Valbuena et al [12], 2022372
    • Not reported
    		Not reported, blood gas analysisNot reportedMedical—adult patients with respiratory failure or COVID-19InpatientRace and ethnicity—White, Black, Hispanic, and Asian

    		Vesoulis et al [58], 2021294
    • Nellcor SpO2 module with Neonatal-Adult MAX-N adhesive SpO2 sensor (Covidien) (used with either Philips IntelliVue MP70 or MX800 monitors)
    		Radiometer ABL800 FlexNot reportedMedical—preterm infants at neonatal intensive care unitInpatientRace—White and Black

    		Wiles et al [59], 2022194
    • Nellcor reusable SpO2 probes or Mindray disposable SpO2 probes (GE Healthcare B1x5 M/P monitor)
    		RAPIDpoint 500 analyser (Siemens Healthcare GmbH)Not reportedMedical—adult patients with COVID-19 pneumonitisInpatientRace—Asian, Black, White, and other

    		Zeballos and Weisman [9], 199133
    • Hewlett-Packard HP-47201A
    • Ohmeda Biox IIA
    		IL282 co-oximeterNot reportedHealthy—non-smoking volunteersResearch — laboratoryRace—all Black
    Pulse Rate

    		Bent et al [41], 202053
    • Empatica E4
    • Apple Watch
    • Fitbit Charge
    • Garmin Vivosmart 3
    • Xiaomi Miband
    • Biovotion Everion
    		BittiumFaros 180, Bittium Inc.N/AgHealthyResearch — laboratorySkin tone—Fitzpatrick Scale:
    • 1 (n=7)
    • 2 (n=8)
    • 3 (n=10)
    • 4 (n=9)
    • 5 (n=9)
    • 6 (n=10)

    		Nelson and Allen [60], 20191
    • Apple Watch 3
    • Fitbit Charge 2
    		Vrije Universiteit Ambulatory Monitoring SystemN/AHealthyResearch — real worldSkin tone—Fitzpatrick Scale:
    • 1 (n=1)

    		Sanudo et al [61], 201945
    • Apple Watch (version not reported)
    		Polar Chest StrapN/AHealthyResearch — laboratorySkin tone—Fitzpatrick Scale:
    • 2 (n=15)
    • 3 (n=15)
    • 4 (n=15)

    		Chow and Yang [62], 202040
    • Garmin Vivosmart HR+
    • Xiaomi Mi Band 2
    		Polar H7 Chest StrapN/AHealthyResearch — laboratoryEthnicity and skin tone—East Asian, Fitzpatrick Scale 3 and 4

    aSaO2: arterial blood gas.

    bSET: Signal Extraction Technology.

    cEEL: electron energy loss.

    dSpO2: blood oxygenation.

    fCOPD: chronic obstructive pulmonary disease.

    gN/A: not applicable.

    Quality Assessment of the Overall Evidence

    QUADAS-2 tool was used to evaluate risk of bias and applicability (Figure S1 in Multimedia Appendix 1). Funnel plots evaluated publication bias using the metafor package [63] (Figures S2 and S3 in Multimedia Appendix 1).

    Statistical Analysis

    Skin Tone Categorization

    We mapped skin tone, race, and ethnicity into 3 primary skin pigmentation groups of light, medium, and dark, following published methodology [36] (see Table S1 in Multimedia Appendix 1) and examined biases for SpO2 and PR by each skin pigmentation group (described below).

    We elected to use this same skin pigmentation categorization schema as has previously been used, with the goal of expanding upon the analysis of Shi et al [36].

    Statistical Analysis

    The objective of the study was to assess whether the devices were accurate in estimating SpO2 and PR when compared with a SaO2 and ECG reference device, respectively, for each skin pigmentation group. If these measures were found to be inaccurate, biases were quantified and their clinical relevance was assessed. The analytical approach to execute these research objectives was formulated based on methodologies used in prior meta-analyses within the discipline [36].

    Evaluation End Points and Criteria

    A summary of the evaluation criteria used can be found in Table 2.

    Table 2. Evaluation end points and criteria for blood oxygenation (SpO2) and pulse rate studies.
    Category and objectiveEvaluation end pointEvaluation criteria
    SpO2

    		Evaluate accuracyArmsaAccurate if Arms≤3%

    		Statistically significant biasMean bias (95% CI)Statistically significant bias if 95% CI does not contain 0

    		Clinically relevant biasMean bias (95% CI)Clinically relevant bias if either upper bound of 95% CI≤4% or lower bound of 95% CI>4%
    Pulse rate

    		Evaluate accuracy95% LoAbAccurate if 95% LoA is bounded by +5 or –5 bpmc

    		Statistically significant biasMean bias (95% CI)Statistically significant bias if 95% CI does not contain 0

    		Clinically relevant biasMean bias (95% CI)Clinically relevant bias if either upper bound of 95% CI ≤5 bpm or lower bound of 95% CI >5 bpm

    aArms: accuracy root-mean-square.

    bLoA: limits of agreement.

    cbpm: beats per minute.

    Accuracy

    To evaluate the accuracy of SpO2 , Arms was used to measure accuracy, because of its commonality in the regulatory space [19]. It was calculated as , where precision is the SD of bias [42,43]. Arms is useful in clinical settings because it provides a single metric that accounts for both the systematic error (bias) and the random error (precision) in measurements and it provides a comprehensive assessment of how close the measurements are to the true values. A lower Arms value indicates better accuracy, meaning that the measurement is closer to the true value, whereas a higher Arms value suggests lower accuracy, indicating that the measurement is further away from the true value.

    For SpO2, our study used the stricter Arms>3% threshold to define inaccuracy as per FDA guidance [19]. We used this threshold as 22 (96%) out of 23 of pulse oximeter studies used a transmittance device and only one used an ear clip, which would have set a more liberal threshold of 3.5%. To evaluate accuracy of PR, the 95% LoA of bias was used, and LoA was constructed as mean bias (SD 1.96). For PR, a pooled 95% LoA not bounded by +5 or –5 bpm was considered inaccurate per ANSI/AAMI/IEC standards [33,34].

    Bias

    To evaluate bias of both SpO2 and PR, mean bias and its 95% CI were used. For each data point that compares the test with the reference device, bias was constructed as test device – reference device. Based on the final results, a measure was considered to have statistically significant bias if the 95% CI of its mean bias did not contain 0, with the estimated mean bias over 0 indicating overestimation and under 0 indicating underestimation.

    Clinical Relevance of Estimated Bias

    To evaluate the clinical relevance of the estimated bias for SpO2, if the 95% CI of mean bias was out of the +4% to –4% range it was considered clinically relevant. This threshold is inferred from FDA guidance on Pulse Oximeters—Premarket Notification Submissions [510(k)s]: Guidance for Industry and Food and Drug Administration Staff [19]. To evaluate the clinical relevance of the estimated bias for PR, if the 95% CI of mean bias was out of the bound of +5 or –5 bpm, it was considered clinically relevant as per ANSI/AAMI/IEC standards [33,34].

    Statistical Meta-Analysis

    To obtain pooled results for above listed end points, we collected sample size, paired observations, mean bias, and SD from various studies. When not available, we either transformed relevant parameters, such as 95% LoA and 95% CI, into these statistics or obtained them by analyzing the raw data.

    Methods to Pool Measures of Bias

    Correlated and hierarchical effect (CHE) models were used to pool mean biases from various studies. Specifically, 3-level hierarchical models were constructed, with Level 1 representing individual data points collected in a study, Level 2 representing potentially multiple comparisons within a research study, and Level 3 representing various studies included in the meta-analysis. Within a study, there may be multiple devices compared using the same participants’ data, and these effect sizes are dependent. To account for this dependence, the CHE model used the robust variance estimation (RVE) method, which allowed us to combine data from single-measure design studies with multiple dependent estimates of effect size, even when the dependence is unknown [64]. To execute the CHE model with RVE, we needed to plug in assumed correlation coefficients for these dependent effect sizes. We used correlation coefficient 0.9 following published methods [36], and small (⍴=0.30) and moderate (⍴=0.60) correlation coefficients were also explored in sensitivity analyses. Additionally, one report [48] had substantially larger SE, so we conducted a separate analysis excluding its results as part of the sensitivity analysis. To evaluate the heterogeneity of the studies, we reported the overall I2 (percentage of variability in the effect sizes that is not caused by sampling error) and its breakdown of within-study (I2Level2) and between-study (I2Level3) portions. We conducted subgroup analyses to see if there were statistically significant differences in bias between skin pigmentation categories (Table S2 from Multimedia Appendix 1). Forest plots were provided to visualize the mean bias (Figures S2 and S3 from Multimedia Appendix 1).

    Methods to Pool Measures of Accuracy

    When it comes to providing pooled 95% LoA and Arms, no established methodologies exist, so we followed an analytical approach used in prior meta-analyses within the discipline [36]. Specifically, SD of bias across studies were pooled using CHE models similar to those used to pool mean biases. The pooled mean bias and pooled SD were then used to provide pooled estimates of 95% LoA and Arms. Specifically,

    • Overall accuracy Arms =
    • Overall 95% LoA = pooled mean bias ± 1.96 × pooled SD
    Other Methods

    Descriptive statistics were provided for study characteristics, the device intended use, mean bias based on different skin tones, and reference devices. All analyses were conducted using R (version 4.3.1; R Foundation for Statistical Computing) with statistical models based on R packages metafor [63] and clubSandwich [65].


    Study Selection and Characteristics

    Search strategy resulted in 8582 records. We selected 27 studies for full review (pulse oximetry: n=23, PR: n=4; Figure 1, Table 1, and Table 2). A total of 23 pulse oximetry studies involving 59,684 participants with 197,353 paired SpO2-SaO2 observations were included. Additionally, 4 PR studies with 176 participants and 140,771 paired PR-ECG observations were analyzed (Table 3).

    Table 3. Summary characteristics of included studiesa.
    Item and subitemPulse oximetry (n=23), n (%)Pulse rate (n=4), n (%)
    Participants (pulse oximetry: n=59,684; pulse rate: n=176)

    		Light skin pigmentation40,416 (67.72)31 (17.61)

    		Medium skin pigmentation9967 (16.70)129 (73.30)

    		Dark skin pigmentation9301 (15.58)16 (9.09)
    Paired observations(pulse oximetry: n=197,353; pulse rate: n=140,771)

    		Light skin pigmentation131,008 (66.38)43,116 (30.60)

    		Medium skin pigmentation32,095 (16.26)90,733 (64.50)

    		Dark skin pigmentation34,250 (17.35)6922 (4·92)
    Device type included in study

    		Medical23 (100)0 (0)

    		Nonregulated0 (0)4 (100)
    Sensor type

    		Transmittance22 (96)0 (0)

    		Reflectance1 (4)4 (100)
    Patient population

    		Healthy4 (17)3 (75)

    		Medical18 (78)0 (0)

    		Healthy and medical1 (4)0 (0)

    		Not reported0 (0)1 (25)
    Research setting

    		Medical inpatient15 (65)0 (0)

    		Medical outpatient1 (4)0 (0)

    		Medical combined1 (5)0 (0)

    		Research laboratory6 (26)3 (75)

    		Research in the real world0 (0)1 (25)
    Skin pigmentation method

    		Skin tone4 (17)4 (100)

    		Race13 (57)0 (0)

    		Ethnicity3 (13)0 (0)

    		Race and ethnicity3 (13)0 (0)

    aMedical device defined as devices that received regulatory clearance for either blood oxygenation (SpO2) or pulse rate (PR).

    Descriptive Statistics on Race, Ethnicity, and Skin Tone

    Pulse Oximetry

    Skin pigmentation was classified by race in 13 (57%) out of 23 studies, ethnicity in 3 (13%) studies, skin tone in 4 (17%) studies, and both race and ethnicity in 3 (13%) studies (Table 3). Of the 59,684 patients with 197,353 paired observations, there were a total of 40,416 (67.72%) patients with light skin pigmentation with 131,008 (66.38%) paired observations, 9967 (16.70%) patients with medium skin pigmentation with 32,095 (16.26%) paired observations, and 9301 (15.58%) patients with dark skin pigmentation with 34,250 (17.35%) paired observations.

    PR Results

    Skin pigmentation was classified by race or ethnicity in 0 (0%) out of 4 studies and skin tone in 4 (100%) studies (Table 3). Of the 176 patients with 140,771 paired observations, there were a total of 31 (17.61%) participants with light skin pigmentation with 43,116 (30.60%) paired observations, 129 (73.30%) participants with medium skin pigmentation with 90,733 (64.50%) paired observations, and 16 (9.09%) participants with dark skin pigmentation with 6922 (4.92%) paired observations.

    PPG Accuracy and Bias by Skin Pigmentation

    Pulse Oximetry

    The pooled Arms across different skin pigmentation groups was 3.6%, 3.96%, 4.71%, and 4.15% for combined, light, medium, and dark skin pigmentation, respectively (Table 4 and Figures 2-4 [8-11,17,42-51,54-56,58,59]). Of note, studies implementing multiple trial conditions or using multiple study devices were shown multiple times in Figures 2-4 to delineate different devices used within the same study. We observed a pooled mean percent bias of 0.82% (95% CI 0.29%-1.35%) across all skin pigmentation groups using the CHE model. Between-study heterogeneity (I2Level3) accounted for 14.53% of the total variation, while within-study heterogeneity (I2Level2) explained 84.02% of total variation. Delineating by skin pigmentation, the pooled mean percent bias from the CHE model was 0.70% (95% CI 0.17%-1.22%) for light skin, 0.27% (95% CI –0.64% to 1.19%) for medium skin, and 1.27% (95% CI 0.58%-1.95%) for dark skin (Tables S3 and S4 in Multimedia Appendix 1). Subgroup analyses found no statistically significant difference between light and medium skin pigmentation (estimate=0.113, SE 0.259; 95% CI –0.459 to 0.686), but there was a statistically significant difference between light and dark skin pigmentation (estimate=0.596, SE 0.240; 95% CI 0.069-1.123), such that pooled bias for those with darker skin was higher as compared to those with lighter skin (Table S2 in Multimedia Appendix 1).

    ‎
    Figure 2. Forest plot showing bias in SpO2 measurements in patients with light skin pigmentation. Multiple entries from the same study are included, each representing different devices evaluated. Squares denote study weight; center of squares denote observed study effect size; vertical lines denote study CIs; and diamond denotes pooled effect. CHE: correlated and hierarchical effect; SpO2: blood oxygenation.
    ‎
    Figure 3. Forest plot showing bias in SpO2 measurements in subjects with medium skin pigmentation. Multiple entries from the same study are included, each representing different devices evaluated. Note: squares denote study weight, center of squares denote observed study effect size, vertical lines denote study CIs, and diamond denotes pooled effect. CHE: correlated and hierarchical effect; SpO2: blood oxygenation.
    ‎
    Figure 4. Forest plot showing bias in SpO2 measurements in patients with dark skin pigmentation. Multiple entries from the same study are included, each representing different devices evaluated. Squares denote study weight; center of squares denote observed study effect size; vertical lines denote study CIs; and diamond denotes pooled effect. CHE: correlated and hierarchical effect; SpO2: blood oxygenation.
    Table 4. Pulse rate and pulse oximetry bias by skin pigmentationa.
    Device type and skin pigment categoryStudies (evaluations), nSample size (data pairs), nUnitPooled mean bias (95% CI)Pooled SD (SE)95% LoAbArmsc (%)Overall I2 (between- and within-study heterogeneity)
    Pulse oximetry

    		Light20 (44)40,416 (131,008)Percent0.70 (0.17 to 1.22)d3.90 (1.36)–6.94 to 8.343.96e97.45% (46.44% and 51.01%)

    		Medium9 (15)9,967 (32,095)Percent0.27 (–0.64 to 1.19)4.71 (1.71)–8.95 to 9.504.71e95.31% (81.16% and 14.14%)

    		Dark19 (36)9,301 (34,250)Percent1.27 (0.58 to 1.95)3.96 (1.30)–6.49 to 9.024.15e98.46% (0.00% and 98.46%)

    		Combined23 (95)59,684 (197,353)Percent0.82 (0.29 to 1.35)3.50 (1.28)–6.04 to 7.683.60e98.55% (14.53% and 84.02%)
    Pulse rate

    		Light3 (9)31 (43,116)bpmf–1.24 (–5.31 to 2.83)7.54 (2.13)–16.02g to 13.54g—h10.99% (0.00% and 10.99%)

    		Medium3 (9)129 (90,733)bpm–0.89 (–3.70 to 1.93)9.05 (1.75)–18.62g to 16.84g—25.01% (0.00% and 25.01%)

    		Dark1 (6)16 (6,922)bpm–0.57 (–9.44 to 8.29)16.89 (1.31)–33.69g to 32·54g—13.70% (N/Ai and 13.70%)

    		Combined4 (24)176 (140,771)bpm–0.29 (–3.87 to 3.29)8.64 (1.67)–17.23g to 16.65g—30.66% (26.76% and 3.90%)

    aρ=0.9 was used in correlated and hierarchical effect models to pool both mean bias and SD.

    bLoA: limits of agreement.

    cArms: accuracy root-mean-square

    dItalicization denotes statistical significance.

    eExceeds U.S. Food and Drug Administration guidance for pulse oximetry.

    fbpm: beats per minute.

    gExceeds American National Standards Institute standards for pulse rate.

    hNot applicable.

    iN/A: not available.

    PR Results

    Analysis using the CHE model revealed LoA values of –17.23 to 16.65 bpm and a mean bias of –0.29 (95% CI –3.87 to 3.29) bpm across all studies. Heterogeneity analysis demonstrated that 26.76% of the variation in bias (I2Level3) stemmed from between-study differences, while 73.34% (I2Level2) originated from within-study variation. Our analysis revealed 95% LoA of –16.02 to 13.54 bpm, –18.62 to 16.84 bpm, and –33.69 to 32.54 bpm for light, medium, and dark skin pigmentation groups, respectively. Mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for the corresponding groups (Table 4 and Figures 5-7 [41,60-62]). Detailed results are provided in Tables S4 and S5 in Multimedia Appendix 1. Subgroup analyses found no statistically significant difference between light and medium or between light and dark skin pigmentation pooled bias (P≥.05; Table S2 in Multimedia Appendix 1).

    ‎
    Figure 5. Forest plot showing pulse rate measurement bias in patients with light skin pigmentation. Squares denote study weight; center of squares denotes observed study effect size; vertical lines denote study CIs; and diamond denotes pooled effect. CHE: correlated and hierarchical effect.
    ‎
    Figure 6. Forest plot showing pulse rate measurement bias in patients with medium skin pigmentation. Squares denote study weight; center of squares denotes observed study effect size; vertical lines denote study CIs; and diamond denotes pooled effect. CHE: correlated and hierarchical effect.
    ‎
    Figure 7. Forest plot showing pulse rate measurement bias in patients with dark skin pigmentation. Squares denote study weight; center of squares denotes observed study effect size; vertical lines denote study CIs; and diamond denotes pooled effect. CHE: correlated and hierarchical effect.
    Sensitivity Analyses

    Sensitivity analyses were performed for pulse oximetry SpO2 by removing Crooks et al [48] as an outlier due to substantially large SE (Tables S6 and S7 in Multimedia Appendix 1). The pooled mean percent biases and 95% LoA were essentially unchanged, while the pooled Arms across all skin pigmentation groups was 3.00%, 2.91%, and 3.40% for light, medium, and dark skin pigmentation, respectively. Sensitivity analyses using CHE models with a range of correlation coefficients (including small and moderate values) yielded similar conclusions to those obtained with a high correlation coefficient (ρ=0.9), except for the PR analysis in the light skin pigmentation group. This analysis revealed a statistically significant bias when a low correlation coefficient (ρ=0.3) was used. Details are provided in Table S6 in Multimedia Appendix 1.


    Principal Findings

    The study revealed a paucity of studies properly assessing skin pigmentation, a tendency for uneven distribution across different skin pigmentation groups (overrepresentation of light skin for SpO2 and a single study with representation of dark skin for PR), and lack of consumer-device reporting despite growing use in clinical setting. Results suggest inaccurate SpO2 and PR measurements across all skin pigmentation groups as they breach FDA and ANSI/AAMI/IEC standards, respectively, with wearable accuracy varying considerably depending on the model, which may be due to date of model production or algorithm development. Pulse oximeters may also overestimate SpO2 significantly for light and dark skin pigmentation, but without clinically relevant bias. We did not find statistically significant or clinically relevant bias in wearable PR devices.

    Despite not meeting FDA guidance across all groups, pulse oximeter SpO2 was inaccurate only across medium and dark skin pigmentation groups when compared to the more liberal international thresholds. Additionally, in the sensitivity analyses without the outlier study of Crooks et al [48], all pooled Arms values dropped, resulting in inaccurate pulse oximeter SpO2 only for dark skin pigmentation and no group exceeding international thresholds.

    The results showing pulse oximeters significantly overestimating SpO2 were expected for dark pigmentation and supported by findings on patient outcomes [11-15,17], but overestimated values for light pigmentation were unexpected. Two possible reasons come to mind. First, less melanin in lighter skin could distort the PPG signal [18]. Second, devices calibrated on individuals with medium skin pigmentation (note that 48% of the US population is categorized as Fitzpatrick Skin Tone Scale III [66]) may lead to inaccurate readings for both lighter and darker skin pigmentation, since both may be suboptimally represented during algorithm training and testing.

    Lastly, it should be noted that both SpO2 and PR studies had lower between-study heterogeneity and higher within-study heterogeneity, indicating that the studies that were included in these analyses were largely consistent with one another and that most of the variation came from within studies potentially due to higher variability across devices used or participants enrolled within each study.

    Overall, these findings suggest that when pulse oximetry devices are deployed in their setting of intended use (ie, uncontrolled settings, such real-world medical settings and home environments with diverse patient populations), the performance observed in analytical validation studies may not generalize.

    Strengths and Limitations

    There were a few limitations, mostly from limitations inherent in prior studies, that should be noted. First, our skin pigmentation categorization approach has strengths but also limitations. It was an effort to overcome the reporting heterogeneity and the tendency in the published literature to conflate data collection on race, ethnicity, and skin tone. This is particularly problematic as skin tone is a physiological concept (determined by the melanin amount in the basal layer of the epidermis), while ethnicity and race are largely social constructs, with high underlying physiological heterogeneity [66]. To reduce heterogeneity, this meta-analysis reclassified race, ethnicity, and skin tone into a universal schema for skin pigmentation based on the system used by Shi et al [36]. This method, however, classifies most White people in the United States as light rather than medium skin pigmentation [66]. Second, there were only 4 prior PR studies that collected participant skin tone that also reported on device accuracy and only 1 that had participants with dark pigmentation. Third, our study used stated FDA and ANSI/AAMI/IEC standards as set thresholds to gauge device performance. It is possible that guidelines and thresholds cited in our study may change in the future, potentially limiting the applicability of the conclusions drawn in this paper. Fourth, our study chose to group papers using a variety of patient populations and testing methodologies with the goal of aggregating the largest pool of data possible on which to draw conclusions across multiple contexts. As described above, low between-study heterogeneity indicates that the studies used in this meta-analysis were largely consistent with one another. This reduces the likelihood of a moderating variable having a significant effect between studies. Instead, it suggests that most of the variation originated within studies, possibly due to higher variability across devices used or participants enrolled within each study. Despite this, we conducted subgroup analyses to examine the potential moderating variables of medical versus healthy populations for pulse oximeters (see Table S8 in Multimedia Appendix 1). We did not conduct these analyses for pulse rate because most studies did not specify whether subjects were healthy or part of medical populations. We did not examine the potential moderating effects of sensor type, transmittance versus reflectance sensors, wavelength of light used by sensors, and location of sensor placement given the core sensor technology studied was PPG across all included studies. The level of SpO2 was not examined given this study’s goal of gathering the largest sample upon which to study the effect of skin tone. Participant activity level was not evaluated as a moderator variable because many included studies did not delineate participant activity level explicitly. Future studies should evaluate the effect of these and other moderating variables on SpO2 and PR.

    Evidence Generation Guidelines for Future Analytical Validation Studies

    The 2013 FDA guidance may be insufficient to ensure accuracy in pulse oximeters across all skin pigmentation and settings of intended use [19]. But there are now multiple FDA guidances for digital health tools requiring fit-for-purpose evidence as well as growing concern/guidance on clinical research diversity [4,30,31,67-73]. The FDA currently categorizes consumer devices as low-risk wellness products, exempting them from stringent regulatory oversight. However, as these devices are integrated into clinical decision-making and used as tools in clinical research [3,4], it becomes crucial to understand and communicate their advantages and limitations. Increased reliance on consumer devices increases the demand for accurate devices whose performance features and potential impact on health outcomes are known with transparency. To generate fit-for-purpose evidence applicable to diverse population, here, we propose 5 recommendations based on FDA guidance and literature (Textbox 1) [4,30,31,67-73]:

    Textbox 1. Recommendations for future analytical validation studies.

    Recommendation 1

    It is vitally important for medical pulse oximetry devices as well as nonregulated research and consumer devices to incorporate the V3 framework [74] for sensor verification and analytical validation of derived SpO2 and PR values for regulatory submission before these metrics can be responsibly deployed in medical, consumer, and research settings.

    Recommendation 2

    When possible, analytically validate devices in settings of intended use [74], rather than relying on controlled laboratory settings where digits may be warmed prior to testing, and confirm device accuracy in all subgroups (sex, race, skin pigmentation, healthy vs medical populations).

    Recommendation 3

    Use objective measures of skin pigmentation, rather than relying on race and ethnicity, as this will reduce heterogeneity in studies and allow for a more accurate understanding of how skin pigmentation impacts device performance.

    Recommendation 4

    Industry should set a priori maximum allowable difference thresholds using FDA and ANSI guidelines, properly power each subgroup, and require that 95% LoA fit within these standards for each subgroup and in the setting of intended use before receiving regulatory approval, production, and deployment.

    Recommendation 5

    Future studies should report device and firmware versions, as firmware updates may include changes in underlying algorithms influencing accuracy of metric generation, as described previously in the literature [4].

    Conclusions

    PPG has been applied in clinical practice for decades, and its accuracy in patients with different skin pigmentation has long been in question. Whether this technology contributes to diagnostic biases and by how much is only more pressing for clinicians and patients with the advent of consumer wearable PPG sensors and the growing interest and incorporation of these devices into clinical practice and in clinical research. This systematic review and meta-analysis found that pulse oximeter SpO2 and wearable PR were inaccurate across all skin pigmentation groups as the resulting accuracy values breached FDA guidance and ANSI/AAMI/IEC standard thresholds, respectively, although pulse oximeter SpO2 was only found to be inaccurate for dark skin pigmentation in sensitivity analyses. In addition, despite not exceeding clinically relevant bias thresholds, pulse oximeters were found to significantly overestimate SpO2 for light and dark skin pigmentation. No systematic or clinically relevant bias was found in estimation of PR. The recommendations in this paper can help advise patients, study participants, care providers, device manufacturers, application developers, researchers, and legislators on best practices going forward.

    Data Availability

    Open code and data are available on Open Science Framework [75] (see the Materials—Open Code and Data section in Multimedia Appendix 1).

    Authors' Contributions

    SS contributed to study concept and design, data collection, and writing portions of the Methods section of the manuscript. HG contributed to study concept and design, data collection, and writing portions of the Methods and Discussion sections of the manuscript. BWN contributed to study concept and design; data collection; data analysis and interpretation; and writing portions of the Abstract, Introduction, Methods, Results, and Discussion sections of the manuscript. MRB contributed to data collection and writing portions of the Methods section of the manuscript. CC contributed to statistical analysis method determination, code review, and writing portions of the Methods section of the manuscript. All authors (SS, MRB, CC, SS, HG, and BWN) reviewed the final manuscript. HG and BWN contributed to this project as shared senior authors.

    Conflicts of Interest

    BWN, MRB, SS, and CC report past or current employment and/or equity ownership in Verily Life Sciences. HG serves or has served as a consultant for Verily Life Sciences, Boston Scientific, Huxley Medical, and Happitech.

    Multimedia Appendix 1

    Supplementary information.

    DOCX File , 619 KB
    Multimedia Appendix 2

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist.

    PDF File (Adobe PDF File), 89 KB
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    ‎
    AAMI: Association for the Advancement of Medical Instrumentation
    ANSI: American National Standards Institute
    Arms: accuracy root-mean-square
    bpm: beats per minute
    CHE: correlated and hierarchical effect
    ECG: electrocardiography
    FDA: Food and Drug Administration
    IEC: International Electrotechnical Commission
    LoA: limits of agreement
    PPG: photoplethysmography
    PR: pulse rate
    PRIMSA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    RVE: robust variance estimation
    SaO2: arterial blood gas
    SpO2: blood oxygenation

    Edited by G Eysenbach, A Mavragani; submitted 04.06.24; peer-reviewed by U Sinha, T Nagamine; comments to author 04.07.24; revised version received 25.07.24; accepted 16.08.24; published 10.10.24.

    Copyright

    ©Sanidhya Singh, Miles Romney Bennett, Chen Chen, Sooyoon Shin, Hamid Ghanbari, Benjamin W Nelson. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.10.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.