How To Assess Skin Color On African-american

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Clin Nurs Res. Writer manuscript; available in PMC 2013 Nov i.

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PMCID: PMC3465481

NIHMSID: NIHMS389078

Making Sense of Skin Color in Clinical Care

Janine S. Everett

¹University of Pennsylvania School of Nursing, PA, Us

²Franklin and Marshall Higher, Lancaster, PA, Us

Mia Budescu

³Temple University, Philadelphia, PA, United states

Marilyn Due south. Sommers

¹University of Pennsylvania Schoolhouse of Nursing, PA, USA

Abstract

The groundwork of this article is that assessment and quantification of skin colour is important to health intendance; color is one indicator of overall health and is linked to oxygenation, tissue perfusion, nutritional status, and injury. The purpose is to describe how skin colour varies beyond racial/ethnic groups so that the data tin be practical to clinical practice. The method used is cross-sectional, descriptive pattern (n = 257). We recorded self-divers race/ethnicity and used a spectrophotometer to measure skin colour at two anatomic sites. Pare color variables included L* (calorie-free/dark), a* (cherry-red/green), and b* (yellow/blue). As regards results, nosotros found significant differences in 50*, a*, and b* values by site and race/ethnicity in White, Asian, and Biracial participants. L*: F(3, 233) = 139.04, p < .01 and F(3, 233) = 118.47, p < .01. Black participants had significantly lower mean L* values and wider ranges of L*, a*, and b* equally compared with other groups. In regard to awarding, these findings suggest that clinicians and researchers should plan and provide care based on pare color, rather than race/ethnicity.

Keywords: skin color, injury, protection, CIELAB, spectrophotometry

Pare colour matters in health care. Clinicians make decisions based on colour assessments multiple times each day as they judge tissue perfusion and assess for jaundice, pallor, cyanosis, and the blanch response. They evaluate pressure level points for early on signs of pare breakup and appraise existing wounds for colour changes that might indicate healing, worsening, or infection. Clinicians and researchers have published findings relating the importance of pare colour in wellness care. These include differences in the potential for injury when faced with forces associated with trauma (soft tissue injury), friction/shearing (pressure ulcers), and stretching (childbirth; Howard, Davies, DeLancey, & Small, 2000; Robinson, Norwitz, Cohen, McElrath, & Lieberman, 1999; Saladin & Krause, 2009; Sommers et al., 2009). Authentic evaluation of skin color is important in both clinical and enquiry settings (Bennett, 1995; Berardesca, Derigal, Leveque, & Maibach, 1991; Ha et al., 2009; Lindholm et al., 2008). Previously, investigators have stratified peel colour based on socially constructed racial or indigenous lines with the untested hypothesis that Black populations have "dark" skin, European populations have "low-cal" skin, and biracial, Asian, and Latino populations have pare tones in the middle ranges (Abbade, Lastoria, & de Almeida Rollo, 2011; Fitzpatrick, 1988). Rigorous methods are easily available to quantify skin color (Andersen & Bjerring, 1990a; Daniel, Heckman, Kloss, & Manne, 2009; Pershing et al., 2008; Pierard, 1998). Yet, clinicians and researchers ofttimes utilise limited and outdated scales and instruments that perpetuate this faulty stratification. Working from these faulty stratifications impedes nursing'southward progress toward the provision of evidence-based care that reflects an private's physiological attributes.

Purpose

Our primary purpose was to determine how peel color varies within and differs beyond populations. Nosotros describe empirical determination of pare color values and ranges across several self-divers racial and/or ethnic groups and discuss the practice implications of skin color variation. Additionally, in the "clinical implications" portion of this work, we hash out the use of digital cameras and digital paradigm analysis (DIA) as a feasible alternative to using specialized colour quantification scales and instruments in clinical settings.

Groundwork

Human pare is the main interface betwixt nurse and patient, and every bit such, information technology is a key surface area of focus for health intendance providers (Lott, 1998). During concrete assessments, nurses evaluate the color of the patient'south skin as a meaning mensurate of overall health condition. They also evaluate the pare for signs of breakdown or other loss of integrity and appraise wounds in various stages of formation or healing.

Skin protection and the prevalence of injury have been reported in the literature as varying across populations (Baker, Fargo, Shambley-Ebron, & Sommers, 2010; Shriver & Parra, 2000; Sommers et al., 2008). Investigators describe differences in injury patterns, types, severity, and number between people of dissimilar races and/or ethnicities with respect to sexual assault, childbirth-related injury, pressure level ulcer germination, and healing (Baumgarten et al., 2004; Fogerty et al., 2008; Saladin & Krause, 2009; Sommers, 2007; Sommers et al., 2008). For example, in a population of women studied after consensual sexual intercourse, Sommers et al. found that not simply was injury prevalence significantly higher in White every bit compared with Black/African American women, but the upshot of race/ethnicity became nonsignificant after adding skin color values to the model predicting occurrence of genital injury (Sommers et al., 2008, 2009). These findings indicated the spurious nature of the relationship between race/ethnicity and injury prevalence and demonstrated that skin color rather than race/ethnicity is likely the important variable in injury prevalence.

The importance of peel colour in health intendance research extends across its relationship with injury chance or prevalence. For case, the difference in melanin, a significant contributor to visible skin color among different groups of people with varying skin color measurements, may besides impact sunburn and pare cancer risk, every bit well as the synthesis of vitamin D, levels of which are associated with a number of positive and negative wellness-related implications (Domingo & Matsui, 2009; Osterwalder & Herzog, 2009; Schauber & Gallo, 2008; Weinstock & Moses, 2009). Because of the multiple implications for wellness intendance that are inherent in color assessment, researchers and clinicians require an accurate and reliable method to measure peel colour.

About Skin Color

We define skin colour as the perceived pare pigmentation resulting from the selective absorption and scattering of lite from the dermis of the body (Pierard, 1998). Skin color is the product of a combination of anatomical and physiological phenomena inside the uppermost layers of the pare. Four pigments contribute to pare color: melanin, carotene, oxygenated hemoglobin, and reduced hemoglobin. Of these, the particle size, shape, and location of melanin contribute most significantly to overall colour; the more most the surface melanin is clustered, the darker pare will appear. Carotene gives the skin a yellow hue, while oxygenated and reduced hemoglobin are red and purplish-bluish, respectively. In addition to melanin, all light absorbing molecules and particles in the skin, or chromophores, play a office in perceived skin color. Skin color may be described every bit constitutive or every bit facultative. Constitutive skin color represents an individual's baseline, or the color of skin that has not been altered by sunday or other types of ultraviolet (UV) exposure; ane example of a constitutive skin site in nearly people is the upper inner arm. Facultative skin colour represents skin that has increased melanin production and thus alteration from baseline status afterwards exposure to the sun or other UV sources; nearly pare sites may exist classified as facultative (Choe, Jang, Jo, Ahn, & Youn, 2006).

In research and clinical settings, race has traditionally been considered to exist a suitable proxy for peel color. Few researchers take published findings describing physiological phenomena based on a quantitative mensurate of skin color, and even fewer clinical exercise protocols address issues of peel colour as a variable of interest when planning care. Caldwell and Popenoe (1995) cautioned practitioners to avoid classifying and treating individuals based on racial descriptions, every bit entirely different physical or biological characteristics may exist establish in people grouped within those broad categories.

Conceptual Framework

The most widely accepted model for colour quantification was established by the Committee Internationale d'Eclairage (Baldelli & Paciella, 2008) in 1976 (Fairchild, 2005; Lee, 2005) and is described as a color space, or a iii dimensional geometric model that represents color numerically. We chose to use the CIE 50*a*b* (CIELAB) color space for several reasons: (a) information technology represents all colors visible to the human eye (Fairchild, 2005; Ha et al., 2009; Lee, 2005); (b) it represents colors relative to a white reference point; (c) the distances between any two colors are measured proportionally to their space geometrically in the color space; and (d) equal distances in the color infinite represent equal colour differences (Fairchild, 2005; Lee, 2005).

In the CIELAB color space, which is specially useful in situations where the closeness of colors must be quantified scientifically, the L*, a*, and b* values are plotted at right angles to one another to form a three-dimensional coordinate system (Fairchild, 2005; Ha et al., 2009). Value L* represents lightness/darkness and extends from 0 (blackness) to 100 (white). Value a* represents the redness/greenness axis; positive a* is cerise and negative a* is green. Value b* represents the yellowness/blueness axis; positive b* is xanthous and negative b* is blue. There are no specific numerical limits for a* and b* (Fairchild, 2005).

Colour Measurement

Skin color can be evaluated in several unlike ways (to exist discussed in what follows) with varying degrees of accuracy. Colour assessment methods may be categorized as either subjective or objective. Subjective cess is based on an individual'south interpretation of stimuli, while objective measurements are based strictly on the miracle of interest and not field of study to judgment or bias. In the by, most skin color assessment has been performed subjectively through the use of visual inspection, comparative color tiles or laminated colour cards, written pare-typing guidelines for comparative classification, or by fashion of qualitative visual estimation by individuals who have received varying levels of training (Roberts, 2009; Taylor, Arsonnaud, & Czernielewski, 2005). The accuracy of these subjective measures for the assessment of skin colour could be considered suspect at best, and the repeatability is dependent on a combination of factors, including ease of scale use, degree of rater preparation, and concrete influences such as ambience lighting during the assessment process. UV low-cal and polarized light photography were introduced as a ways of assessing the skin for lesions or other pathology, just have not been considered a reasonable means of assessing peel colour (Taylor, Westerhof, Im, & Lim, 2006). In 1986, the six-point Fitzpatrick scale was introduced as a means for classification of different peel types based on their response to ultraviolet or lord's day exposure (Fitzpatrick, 1986). Though not originally intended to serve this purpose, the Fitzpatrick calibration has been and continues to exist normally used as a ways of describing peel color.

A noninvasive method, diffuse reflectance spectroscopy (DRS), is considered to exist the gold standard for objective quantitative measurement of skin colour (Andersen & Bjerring, 1990a; Parra, 2007). The investigator uses the musical instrument to straight lights of specific wavelengths toward the area of interest and then the wavelength(s) and intensity of light reflected dorsum from the peel back to the instrument'south sensors are measured. DRS records reflectance values from colored surfaces and provides instrument operators with the unabridged reflectance spectrum also as specific color measurements within a number of colour models or spaces, as programmed by the user. Spectrophotometers vary in price from approximately Usa$5,000 to Usa$15,000 per unit, depending on model capabilities, with further costs depending on the software, accompaniment hardware, or warranty/service plan options selected (K. Corcoran, personal advice, September 10, 2010).

Decisions as to location for skin color measurement depend entirely on the goal of that measurement. For those evaluating wound progression, the choice is obvious: measurement should occur at and/or about the site of the wound. When assessing skin colour equally one means to evaluate peripheral tissue perfusion, the measured sites could include the extremities of concern as well as nonaffected sites for the purpose of comparing. If sequential colour measurements are called for, then clinicians should accept care to assess the same site with the same or similar conditions, particularly as related to ambient light. When researchers or clinicians perform pare color measurements for enquiry or clinical comparative purposes, consideration should be given to the inclusion of constitutive (unexposed or untanned, close to baseline) versus facultative (exposed and tanned) anatomical sites in clinical or research information collection equally each will provide different information to investigators.

In the main text of this article, we will discuss the portion of our report that addresses our primary aim: to determine how peel color varies inside and differs across populations. Later on reporting and commenting on our results, we talk over our secondary aim in the "Clinical Implications" section. In the "Clinical Implications" section, we will draw our piece of work applying digital paradigm analysis techniques as a viable proxy for spectrophotometry in clinical settings.

Method

We conducted this prospective, descriptive, cross-sectional report at a large university in an urban surroundings. We recruited a healthy community convenience sample past posting flyers on a university campus and at women'due south health clinics. We collected data from 237 women during 2 prospective studies (Study one: n = 152; Study two: n = 85) using the same procedures, variables, instruments, and skin scientific discipline measurements to minimize fault. The Institutional Review Board of the affiliated university approved both studies. Nosotros used the same inclusion and exclusion criteria for each study with respect to fundamental attributes including: self-identification every bit female, abeyance of the use of all topical products on the skin for 24 hr; absence of scars and rashes on the volar forearm and upper inner arm; ability to read, speak, and sympathize English language, and willingness to give informed consent for noninvasive skin testing and for collection of demographic data. Nosotros included participants based on self-identification as female every bit part of a parent written report that involved peel mechanics and injury to the pare in women higher up 21 years of historic period. Samples across both studies were demographically like; see Table i for total participant demographic data.

Table 1

Participant Demographic and Peel Color (L*, a*, and b*) Data in Females (Northward = 237)

Self-defined race (north)	Age Mean (SD)	Volar forearm Mean L* (SD)	Upper inner arm Mean 50* (SD)	Volar forearm Mean a* (SD)	Upper inner arm Hateful a* (SD)	Volar forearm Mean b* (SD)	Upper inner arm Mean b* (SD)
Asian (xiii)	23.seven (iv.4)	59.47 (vii.33)	59.30 (8.37)	8.55 (1.53)	9.17 (one.56)	xix.60 (2.17)	xix.41 (ii.99)
Blackness/African American (101)	33.viii (10.3)	47.26 (7.66)	46.49 (8.07)	nine.44 (1.12)	ix.84 (1.xiii)	20.07 (2.07)	20.13 (2.31)
White (118)	29.0 (9.v)	65.00 (iv.45)	65.22 (iv.88)	7.fourteen (1.63)	7.67 (1.58)	17.37 (ii.55)	17.18 (2.96)
Biracial/more one race (5)	29.1 (seven.nine)	59.73 (12.45)	59.28 (12.19)	7.79 (1.68)	viii.25 (2.20)	xviii.09 (1.52)	18.62 (1.60)

Instruments

For skin color measurements, we used a hand-held ColorTec® spectrophotometer that was calibrated to CIELAB standards to mensurate colour via reflectance. The instrument is designed to obtain reflectance values from colored surfaces and render the entire reflectance spectrum as well as Fifty*, a*, and b* values. This type of spectrophotometer is recognized as the golden standard for peel color measurements and has been tested extensively in the cosmetics industry likewise equally by scientists investigating ultraviolet ray exposure to the skin (Takiwaki, 1998; Weatherall & Coombs, 1992). The technique has been found authentic and reliable in both in vitro studies with standardized color charts (Clarys, Alewaeters, Lambrecht, & Barel, 2000) and in vivo studies in experimentally induced ultraviolet erythema (Andersen & Bjerring, 1990a, 1990b). We performed external calibrations every vii to 14 days during the study period with standardized white and blackness tiles provided by the manufacturer. The results were within calibration standards (L* value error < 5%) during each instance, requiring no further actions or manufacturer recalibration.

We controlled for ambient temperature to explain the variation in surface blood catamenia amid participants. Skin color is direct afflicted by surface blood period changes that occur every bit functions in the peel's role of systemic thermoregulation; vasoconstriction routes blood away from the pare's surface in ambient common cold, and vasodilation increases surface blood flow equally a cooling mechanism in warmer environments. To monitor ambience temperatures, we used an Electro-Tech Systems, Inc.® (ETS) Model 5640 humidity and temperature meter to record temperature and humidity within the imaging laboratory. The Humidity/Temperature/Dew Point Meter is a handheld meter with a stock-still probe that measures temperature in Fahrenheit or Celsius with an accurateness of ±0.9°F and ±0.5°C. Relative humidity of 0% to 99% is also measured with an accurateness of ±iii% (ElectroTech, 2008). The ETS Model 5640 is International System for Standards (ISO) certified for accuracy (ElectroTech, 2008).

Procedures

Following recruitment and informed consent procedures, we completed information drove for all participants within a controlled environment, with temperatures ranging betwixt 68°F and 76°F; humidity was monitored just non controlled. Written report staff measured and recorded laboratory temperature using the ETS Model 5640 humidity and temperature meter prior to data collection for each participant. We maintained control over ambient lite in our skin imaging laboratory and data drove location by the use of low-cal-blocking shades, portable room partitions, and a consistent location for measurements. After obtaining informed consent, trained study staff collected demographic and other study-related data; staff were asked to follow a scripted dialogue and to avoid prompting participants on what might be a "meliorate" response for them. Thus all responses from all participants were self-reported demographics, characteristics, health histories, and behaviors.

After participants rested for ten min to acclimate to the environmental temperature and humidity, we obtained skin color measurements by way of spectrophotometry. Using a plastic template for consistency, we used soft-tipped, washable colored markers to define a 2″ × 2″ square measurement expanse one″ distal to the antecubital space on the volar forearm (a facultative, or exposed, site) and 2″ distal to the axilla on the upper inner arm (a constitutive, or unexposed, site). Taking care to avert the colored markings or any obvious aberrations in the participant's "typical" skin colour such as tattoos or moles, nosotros obtained three spectrophotometric measurements at each anatomical location and recorded the 50*, a*, and b* values for each reading.

Data Management and Assay Programme

All data were de-identified for participant confidentiality and then double entered into an encrypted database for security and stored on secured and encrypted inquiry drives. We completed data cleaning using EpiInfo^™ software version 3.5.1 to compare databases for discrepancies, which were then reviewed and corrected. Original physical (paper) data collection forms were used to resolve errors; these were stored in locked cabinets in a locked part, with access limited to written report principal investigators (PIs). Missing data were evaluated on a case-by-case basis, with every attempt to include each participant unless nosotros were missing critical data points. The PI for the parent study (Sommers) and for the nested study (Everett) retained concluding authorisation regarding error correction, data handling, and data management issues.

We used descriptive statistics such as measures of key tendency, dispersion and ranges to characterize the study variables and the characteristics of the sample. We used i-manner analyses of variance (ANOVAs) to examine differences in L*, a*, and b* values among the four self-identified racial groups included in the written report (Asian, Black/African American, White, and Biracial). Nosotros completed mail service hoc tests using Tukey's To the lowest degree Significant Departure criteria to assess where specific differences existed between the groups. We conducted all statistical analyses with SPSS® software version xviii.

Findings

Of the 237 women who participated in the report, 52% self-identified as White (n = 118), 44% cocky-identified as Black/African American (due north = 101), 6% cocky-identified as Asian (north = xiii), and 2% self-identified as Biracial/More than one race (n = 5). Hateful L*, a*, and b* values and standard deviations for each anatomical site (volar forearm vs. upper inner arm) stratified past race (Blackness, White, Asian, and Biracial) may exist found in Table 1. We conducted a serial of ane-way ANOVAs to determine whether significant racial differences existed in L* values. The ANOVA for volar forearm 50* values was significant, F(iii, 233) =118.47, p < .01, with respect to race. Post hoc tests revealed that Black participants had significantly lower L* values (One thousand = 47.24, SD = 7.66) than Asian (Chiliad = 59.47, SD = 7.33), White (M = 64.67, SD = 5.71), and Biracial participants (One thousand = 56.85, SD = 10.82). Nosotros found similar results when conducting the series of one-way ANOVAs with upper inner arm 50* value data, F(3, 233) = 139.04, p < .01. Postal service hoc tests again revealed that Black participants had significantly lower L* values (M = 46.49, SD = 8.07) than Asian (Thou = 59.30, SD = eight.37), White (Thou = 65.22, SD = 4.88) and Biracial participants (M = 59.28, SD = 12.19).

One-style ANOVAs indicated significant racial differences in a* values for both volar forearm, F(3, 233) = 47.43, p < .01 and upper inner arm measurements, F(3, 233) = 42.99, p < .01. Mail hoc tests revealed that for both volar forearm and upper inner arm measurements, Black participants had significantly higher a* values than Asian, White, and Biracial participants (see Table 1). For b* values, 1-way ANOVAs also revealed meaning racial differences on both volar forearm, F(three, 233) = 25.49, p < .01 and upper inner arm measurements, F(2,233) = 22.39, p = .01. For both sites, post hoc analysis indicated that Black participants had significantly college b* values than only White participants, but no meaning differences existed between Blackness participants and those identifying as Asian or Biracial. Additionally, Asian participants had significantly college b* values (at both anatomical locations) than White participants but not significantly higher than those identifying as White or Biracial.

Word

In our sample, nosotros institute that those participants cocky-identifying as Black/African American had overall darker and more than variable skin lightness/darkness (L*) values than whatever other self-divers group. Those identifying as Asian had darker skin equally compared with White, simply lighter than Black/African American participants. Participants identifying as White had overall lighter skin values and far less variability among the grouping than any other group of participants. These findings were consequent for both the constitutive skin site (upper inner arm) and the facultative skin site (the volar forearm). The other colour variables, a* and b*, varied less dramatically between and amidst racially stratified groups. This lack of variation was likely due to the tendency for human skin to grouping in the same full general ranges for a* and b* colour values in the yellow and red hues. In contrast, some variation exists amid those with either increased superficial melanin or decreased carotenoids, which typically leads to lower b* values, indicating a trend toward more blueish and away from more yellowish coloring (Stamatas, Zmudzka, Kollias, & Beer, 2004; Stephen, Law Smith, Stirrat, & Perrett, 2009). Every bit observed in our sample, standard deviations within each group for those values were nearly identical beyond groups. Our findings replicated those of Shriver and Parra (2000), although it should be noted that our sample size was larger, included a greater representation of those identifying as Black/African American, and included those of Hispanic/Latina origin, whose data were incorporated within the coidentified groups (Blackness, White, Asian, or Bi/Multiracial) during analysis.

For those interested in pare color quantification, simply observe that spectrophotometry is not a feasible option considering of toll, implementation of digital cameras and digital image analysis offer a more readily accessible and typically less-expensive option for skin color assay (see "Clinical Implications"). Regardless of objective color measurement technique employed (spectrophotometry or digital image analysis), our information equally well every bit those of Shriver and Parra (2000) indicated that there are distinct differences in skin color among groups of individuals. With the widest range of 50* values associated with those people identifying as Black/African American (meet Figure i), measurement strategies should allow for the about variation in skin color for African Americans every bit compared to other populations.

Density plot of L* values by race and anatomical site in females (N = 237)

Society and health care personnel tend to ascribe characteristics based on the socially synthetic categorizations of race, rather than on an individual's physiology and phenotype. Recently, Torres and Kittles, (2007), Santos et al. (2009), Parra, Kittles, and Shriver (2004), and Shields et al. (2005) implored researchers and clinicians to expect toward physiology rather than ascribing characteristics to individuals based on racial or ethnic identification.

With regard to individualized care, clinicians should consider many intendance issues related to pare color, including (but not limited to) gauging tissue perfusion; assessing for jaundice, pallor, cyanosis, and the blanch response; evaluating force per unit area points for early signs of peel breakdown; and assessment of existing wounds for color changes that might indicate healing, worsening, or infection. Although more research is needed, clinicians should note differences of early signs of pare breakdown among those with lighter versus darker peel. While patients with calorie-free skin may have the blanch response that demonstrates adequate tissue perfusion, nighttime-skinned patients rarely accept the same response to light pare pressure level. Thus it may be hard to determine when dark-skinned patients are at risk for pressure ulcers. For patients with dark skin, pressure ulcer cess includes applying light pressure level and looking for an area that is darker than the surrounding skin or that is taut, shiny, or indurated (Sommers, 2011).

With respect to protection from sunday and other UV exposures, light-skinned individuals accept been documented to have an increased hazard of skin cancer every bit compared with darker skinned individuals (Rees, 1999; Rees et al., 1999). Darker skinned individuals, on the other mitt, have an increased gamble of decreased vitamin D levels (Chen et al., 2007), which may affect risk for multiple chronic atmospheric condition including cancer, autoimmune diseases, and cardiovascular disease (Chen et al., 2007; Giovannucci, 2005; Holick, Chen, Lu, & Sauter, 2007; Lappe, Travers-Gustafson, Davies, Recker, & Heaney, 2007). Teaching regarding protection from dominicus and other UV exposure should vary based on the needs of each individual depending, at least in role, on pare color. Those with dark peel are non allowed to sun impairment, despite the fact that there is a non-uncommon belief that "Black skin doesn't burn down." Thus patient teaching is required to reinforce the demand for sunday protection in anybody, regardless of skin color (Berwick, Fine, & Bolognia, 1992; Robinson, Rademaker, Sylvester, & Cook, 1997). At the same time, clinicians should be circumspect to the increased potential for Vitamin D deficiency among those with dark skin so every bit to residual pare protection, sun exposure, and potential supplementation to address deficiencies (Dawson-Hughes, 2004; Finkelstein et al., 2002; Holick, 2007; Lee, O'Keefe, Bell, Hensrud, & Holick, 2008).

Limitations

We used a spectrophotometer to quantify skin color variables at constitutive and facultative anatomic locations of 237 participants in a academy setting within an urban location in the northeastern United states. Our findings cannot be generalized to populations from other geographical locations at other latitudes as typical sun exposure levels may vary. Future work should include participants from additional geographic locations.

Measurement fault occurs during any biophysiological measurement. Random mistake may take resulted from incorrect instrument placement over skin markings, wrong placement on the skin causing the blanch response, or transcription errors of skin measurements. Nonrandom error may take occurred because of wrong calibration procedures. Other limitations include the use of multiple individuals to collect demographic and spectrophotometric data. While grooming and quality assurance checks were performed regularly with all individuals involved in data collection, adherence to technique may accept varied during data collection. Finally, lower numbers of participants who identified within groups other than Blackness or White limited our ability to explicate the differences and ranges in other groups, particularly those identifying as Asian and those of Hispanic origin.

Conclusion

Color quantification in clinical settings tin provide both immediate and long-term benefits. Rather than exercising "color incomprehension" when assessing and treating patients, practitioners should exercise "color awareness" by adjusting interventions and assessment techniques using the patients' physiological characteristics rather than depending on racial or ethnic categorization to guide care (Sommers, 2011). Equally our attention as clinicians (and researchers) shifts toward this goal of color sensation, nosotros will be able to address gaps in knowledge regarding more specific needs of our patient populations and provide ameliorate individualize teaching, prevention, and care to improve outcomes for all patient populations.

Implementation in Clinical Intendance ("Clinical Implications")

While spectrophotometry can exist used for highly accurate and reliable information on homo skin color, in many clinical settings the apply of spectrophotometers may be impractical. Spectrophotometers are sensitive instruments that may be damaged in the day-to-twenty-four hour period rush of clinical care. They also represent a significant financial investment that may be prohibitive in many settings. Digital cameras are easily attainable, cheap to maintain, and may present an acceptable alternative. In this section, we discuss the potential of and methods for using digital epitome assay as a substitute for spectrophotometry for the measurement of skin color in clinical settings.

Digital image capture is already used to document burns, and sequential imaging shows the progression of pressure ulcers (Korber, Cesko, & Dissemond, 2009; Nelson, Boyle, Taggart, & Watson, 2006; Parry, Walker, Niszczak, Palmieri, & Greenhalgh, 2010). Nosotros propose that digital image assay (DIA) may exist used clinically to provide information for nursing diagnoses and outcomes of nursing care and is especially useful to improve understand skin injury across the continuum of peel color. When used properly, digital epitome processing programs, such equally Adobe Photoshop®, provide detailed colour data in a relatively brusk period of time.

Images intended for color quantification should be obtained with a camera capable of recording images with minimal in-camera processing and with no color changes to files after the image is obtained. Experts cull cameras that allow them to save images in the RAW file format, which eliminates all in-camera color and light adjustments to images. Additionally, the ability to plan the camera for consistent exposure over multiple image captures helps the photographer to achieve consistency across images. Several midrange digital cameras currently available meet these guidelines, including (but non limited to) the Catechism Powershot® G12 or S100, the Olympus® XZ-1, and the Panasonic Lumix® DMC-LX5; every bit of Feb 2012, market prices for these cameras range from approximately US$400 to US$500. When obtaining images, i should preserve consistent, predetermined exposure settings, with whatsoever "colour correction" choices turned off. Utilize full-spectrum, bright, ambience calorie-free (avoiding utilise of the photographic camera'southward flash) to illuminate the subject of the paradigm and, keeping the lighting the same, take an image of a color carte du jour prior to prototype capture of skin or a wound.

An manufacture standard colour card, such every bit the Munsell® Color Mini Colorchecker (Munsell, 2007), is useful when quantifying skin color because it is referenced to known colour values, thereby allowing image color correction to "near-true" colour. Color correction is a process past which an image of the reference standard, in this example the color menu, is used to institute what, if any, color residuum and white residual adjustments are needed to bring an image closer to "true color." In the case of the Mini Colorchecker card, each of the 24 colors on the card are sampled and analyzed then that the photographer is able to adjust that epitome and subsequent images from the same session, with the same camera, and in the aforementioned lighting conditions through the use of software. Nosotros use a software plug-in (a small computer programme designed to do a very specific chore within a larger software program) inside Photoshop® to create a customized color profile for each photographic session, thus assuasive for color-correction of all images to "near-true" colour prior to sampling for L*, a*, and b* values within Photoshop®.

We recommend two sampling methods for DIA. In the first, a user-defined shape is selected within the image using Photoshop's® "lasso" tool; the number of pixels selected may vary greatly (up to the hundreds of thousands or more than pixels) depending on the size of the choice, merely generally we analyze areas of 120,000 to 150,000 pixels to obtain a consistent and representative area for skin colour analysis. The 2d method of obtaining colour data from digital images is to use a Photoshop'south® "eye-dropper color sampler" tool to select several smaller points within the paradigm if no large expanse is available for color measurement. This technique would be useful for areas that have big amounts of hair or many peel markings such as tattoos or moles. In this case, the number of pixels is clearly defined, and may be every bit small-scale as a single pixel up to a 101 × 101 (x,201 pixels) average. In both methods, colour values obtained inside the software'southward expanded histogram window stand for the ways based on the total number of selected pixels.

While these ii methods are not new to those experienced in color quantification, nosotros could find no published literature outlining how one method might exist dissimilar from the other, nor if they could be considered comparable for use in clinical or inquiry settings. To attempt to respond this question, we had three independent raters use these DIA techniques to collect color data from multiple representative pare images of the volar forearm and upper inner arm of women from the primary investigators' paradigm repository. All procedures were approved by the university's IRB. After preparation staff members on color correction and each of the DIA methods, three private raters each completed DIA on 200 skin images. Each rater was blinded to the findings of the other raters. For the "lasso" method, each rater used the following procedure: they (a) opened the color card prototype and created a color contour using the color menu associated with that image; (b) assigned that color contour to the image being analyzed (thus performing color correction, bringing the image to nearly true color); (c) changed the color way inside Photoshop® to CIELAB; and (d) used the lasso tool to select a large surface area (between 120,000 and 150,000 pixels) inside the defined anatomical location captured on the image. Raters avoided tattoos, scars, and obvious aberrations in the participant's "typical" pare color. Raters and then recorded the mean color value (L*, a*, and b*) displayed within the expanded histogram from Photoshop® along with the standard deviation and number of pixels. After all values were recorded, the rater repeated the process twice more, for a total of three sets of "lasso" colour values. For the same image, the rater then performed a like procedure, this fourth dimension using the "eye-dropper color sampler" technique set for a five × 5 (25 pixels) pick within the defined measurement area. This process was completed after the rater recorded the mean L*, a*, and b* values for each of nine of these 25 pixel selections. To summarize, for each of the 200 images, each rater obtained three sets of L*, a*, and b* "lasso" and nine sets of "center-dropper color sampler" values.

Due to differences in the consignment of color values inside Adobe'due south Photoshop® software, each of the color values obtained by the "lasso" method was converted to match the official CIELAB color space. To transform Adobe's values to match those within CIELAB color infinite, the following mathematical transformations were made prior to statistical analysis: (a) (Photoshop®-obtained L* value hateful 100)/255 = mean Fifty* value; (b) Photoshop®-obtained a* value hateful – 128 = hateful a* value; and (c) Photoshop®-obtained b* value mean – 128 = hateful b* value. One time these values were converted, the results of all color quantification were gear up for the data analysis procedure (Rochester Institute of Technology [RIT], northward.d.).

We wanted to understand how each rater's values compared to those of other raters (intrarater reliability) for each method ("lasso" and "eyedropper"). Intraclass correlation (ICC; Molinari, Fato, De Leo, Riccardo, & Beltrame, 2005) coefficients for this phase captured each rater's consistency from one measurement to the next. All raters had high ICCs (ranging from 0.98 to one.00, with confidence intervals from 0.97 to 1.00), indicating a high level of consistency among all three independent raters for all colour values for both techniques. We as well wanted to compare the two methods ("lasso" vs. "centre-dropper color sampler") for each color variable. We found a high level of understanding betwixt the "lasso" and "middle-dropper color sampler" methods for all color values with ICC of 0.99 (CI [0.98, i.00]) and correlation r = .99 for each of the L*, a*, and b* values. We likewise constitute that after training, students reached a high level of agreement between the "lasso" and "eye-dropper color sampler" method for each rater across the L*, a*, and b* variables (intraclass correlation coefficients [ICC] = 0.99 for each method and rater).

Finally, we wanted to compare the two methods (lasso and eyedropper) to encounter if they could exist used interchangeably. Each of the final mean Fifty*, a*, and b* values in this phase represented the average of the multiple measurements across the 3 raters so that nosotros were left with one L*, a*, and b* value per method per image. ICCs were once more calculated under the supposition of a ii-way mixed model with consistency. The results of this phase indicate a loftier level of agreement betwixt the "lasso" and "middle-dropper color sampler" methods for all color values with ICC of .99 (CI [.98, one.00]) and correlation r = .99 for each of the L*, a*, and b* values. Effigy ii illustrates the linear human relationship between the two methods for each pare color value.

Scatterplots for the lasso and eye-dropper colour sampler methods: Fifty*, a*, and b* values in females (N = 200)

In summary, we learned that raters could be trained to complete DIA and their measurements had loftier levels of agreement. We as well learned that the two methods are interchangeable; small-scale areas of color obtained by the "eyedropper" arroyo can exist compared to larger areas of color obtained by the "lasso" arroyo. Both techniques are useful in images captured in clinical situations.

Acknowledgments

Funding

The authors disclosed receipt of the following fiscal support for the research, authorship, and/or publication of this commodity: The research was supported past the National Institute of Nursing Research (F31NR011106; Janine Everett, Main Investigator; 2R01NR005352; Marilyn Sommers, Principal Investigator; 1R01NR011 589; Marilyn Sommers, Principal Investigator; T32NR007100; Marilyn Sommers, Main Investigator), The American Nurses Foundation, The Emergency Nurses Association, The International Clan of Forensic Nurses, and the Lillian Due south. Brunner Professorship of Medical-Surgical Nursing, University of Pennsylvania.

Biographies

•

Janine S. Everett, PhD, RN was a Postdoctoral Research Fellow at the University of Pennsylvania School of Nursing (Philadelphia, PA) on submission of this manuscript. She is currently a Research Investigator at the University of Pennsylvania School of Nursing and an Adjunct Banana Professor of Biology and Public Health at Franklin and Marshall College, Department of Biology, 415 Harrisburg Ave, Lancaster, PA 17603. She can be reached at ude.mdnaf@tterevE.eninaJ.

•

Mia Budescu, PhDc, is a doctoral candidate at the Temple University, Philadelphia, PA, The states.

•

Marilyn Southward. Sommers, PhD, RN, FAAN, is the Lillian S. Brunner professor of medical surgical nursing at the University of Pennsylvania Schoolhouse of Nursing, Philadelphia, PA, USA.

Footnotes

Reprints and permission: sagepub.com/journalsPermissions.nav

Declaration of Conflicting Interests

The authors alleged no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465481/

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