Neuroscience for Atopic Dermatitis

Neuroscience for Atopic Dermatitis: The Brain-Skin Connection Unveiled

Atopic Dermatitis (AD), commonly known as eczema, is not just a skin condition—it’s a complex interplay of immunological, environmental, genetic, and increasingly recognized neurological factors. While traditional approaches have focused on skin barrier dysfunction and immune response, emerging neuroscience reveals that the nervous system plays a pivotal role in the onset, severity, and persistence of this chronic inflammatory disease.

🧠 The Brain-Skin Axis

The skin and the brain are intimately connected through the neuro-immuno-cutaneous system (NICS). This system allows the brain to communicate with the skin via nerve fibers, neuropeptides, and inflammatory mediators. In AD patients, this axis becomes overactive, leading to increased itch sensation, inflammation, and skin hypersensitivity.

🔁 Itch-Scratch Cycle: A Neurological Loop

At the heart of AD’s torment is pruritus (itch)—a symptom driven more by the nervous system than by visible skin inflammation. When the skin barrier is compromised, sensory neurons release neuropeptides like Substance P and Calcitonin Gene-Related Peptide (CGRP), which intensify inflammation and signal the brain to initiate the urge to scratch. The result is the well-known and damaging itch-scratch cycle that worsens skin lesions and perpetuates the disease.

🧬 Neuroinflammation in AD

Neuroinflammation—immune response within the nervous system—has been increasingly identified in chronic AD. Elevated levels of nerve growth factor (NGF) in both serum and skin lesions of AD patients suggest heightened nerve activity. Moreover, skin nerves in AD often show hyperinnervation, contributing to heightened sensitivity and persistent itching.

💡 New Therapeutic Targets

This neuroscience insight is now reshaping treatment paradigms. Modern therapies are targeting neuromediators involved in itch and inflammation:

• JAK inhibitors (e.g., abrocitinib, upadacitinib) can suppress neural pathways linked with itch and cytokine release.
• Neurokinin-1 receptor antagonists and anti-NGF therapies are under investigation to interrupt pruritic signals.
• Topical cannabinoids are also gaining traction for their dual anti-inflammatory and neuromodulatory effects.

🌍 The Future: Integrating Neuroscience in Dermatology

Understanding the brain-skin dialogue offers hope for a more holistic management of AD. Integrating psychological and neurological perspectives—such as stress management, cognitive behavioral therapy (CBT), and biofeedback—may enhance clinical outcomes and patient quality of life.

🔬 In Summary

Atopic Dermatitis is not just skin-deep. The integration of neuroscience into dermatology is paving the way for novel, patient-centric approaches that address both the visible and invisible aspects of this complex condition. As science continues to evolve, so too does our ability to offer relief to the millions affected by AD around the world.

Dry Skin Fluorescence Imaging

When UVA light strikes the surface of the Stratum Corneum (SC), particularly when the SC is dry, the dry corneocytes exhibit strong fluorescence, resulting in the emission of visible light. This phenomenon of UV-induced visible light fluorescence forms the basis of the Visioscan VC 20plus camera (Courage and Khazaka, Cologne, Germany), which is commonly used for imaging dry skin. In addition to its use for dry skin, this technique has also been applied in the imaging of psoriasis, mosaic melanoderm patterns, and residual sunscreen on the skin.

By capturing images, the Visioscan not only visualizes the skin’s surface but also allows for analysis of dry skin. Dry areas, which fluoresce more intensely and appear white, can be quantitatively assessed to determine the extent and distribution of dryness. The tool is especially useful for tracking changes in skin dryness over time, offering a detailed view of how different skin conditions progress.

Normal Skin

Dry Skin

Very Dry Skin

CLINICAL HAIR GROWTH – EVALUATION METHODS – (NESTE, 2001)

Evaluating the effectiveness of hair-growth products and cosmetics on a broad scale heavily relies on subjective assessments and individual satisfaction. Particularly in cases where the benefits are primarily cosmetic, acknowledging the significant influence of the placebo effect and potential biases is crucial. Therefore, prior to these products reaching consumers, rigorous safety and efficacy testing must adhere to scientific principles, ethical standards, and the rules of good clinical practice and medical research. To be considered valuable, an evaluation method should furnish data on key variables such as hair density (measured as the number of hairs per unit area), linear hair growth rate (LHGR) in millimeters per day, percentage of anagen growth phase (%A), hair diameter in micrometers, and the duration required for hair regrowth after the telogen phase. However, many evaluation techniques lack detailed methodology descriptions and information regarding sensitivity and reproducibility, essential components of clinical investigative techniques. Efforts toward standardizing evaluation methods are crucial to facilitate comparisons across different methods or results obtained from various centers using the same method. These methods can be classified for ease of understanding as invasive, semi-invasive, and non-invasive.

Invasive Methods:

Biopsy

Apart from the conventional vertical sectioning of skin biopsies, which enables the examination of longitudinal follicular sections, horizontal sectioning (parallel to the skin surface) of scalp biopsies presents additional diagnostic possibilities. Initially introduced by Headington, it has been demonstrated that horizontal sectioning may yield superior diagnostic insights compared to vertical sectioning. This technique allows for the examination of a larger number of follicular structures. Inflammatory infiltrates are more readily discernible, and their correlation with follicular structures becomes more apparent than with vertical sectioning. Moreover, fibrous tracts, often challenging to visualize in vertical sectioning, become more evident with horizontal sectioning. Additionally, it facilitates the differentiation between vellus and terminal hairs, enables the identification of various stages of hair growth in a single section, and facilitates the classification of follicles into anagen, telogen, or catagen stages.

Semi-Invasive Methods:

Trichogram

The concept of assessing hair growth changes by examining hair roots was pioneered by Van Scott et al. In order to accurately diagnose hair disorders, it is imperative to examine the status of hair roots, necessitating the plucking of at least 50 hairs to mitigate sampling errors. Subsequently, these roots are scrutinized under a low-power microscope. The stability of root morphology allows for the preservation of hairs in dry packaging for several weeks before analysis. However, due to the generation of relative values such as the telogen/anagen (T/A) ratio, this technique is considered a suboptimal indicator of disease activity and severity, particularly in androgen-dependent alopecia in women. Consequently, in our center, we have ceased utilizing this method due to its reliance on relative values, contrasting with the more robust method detailed in the subsequent section.

Unit Area Trichogram (UAT)

UAT is a method wherein all the hairs within a specified area, typically 60 mm², are plucked and affixed onto double-sided tape on a glass slide. Using optical microscopy, various hair parameters such as hair density, anagen percentage, hair length, and hair diameter are estimated. Before sampling, the scalp area is degreased with an acetone/isopropanol mixture and marked with a roller pen. Each hair within the delineated area is plucked individually, ensuring a uniform grasp above the scalp and rapid, single-action epilation in the direction of hair growth to minimize root trauma.

Interestingly, UAT stands as an exception to a common trend in trichology where methods are often introduced alongside new drugs or cosmetic efficacy evaluation programs. Unlike these methods, UAT has been independently evaluated for reproducibility and clinical relevance, making it suitable for comparative purposes. Most hair growth parameters estimated through UAT and phototrichogram are similar, but UAT holds an advantage as it can be reliably used even in individuals with minimal contrast between hair and skin color.

Noninvasive Methods

Global Methods

Scoring Classification Systems

Hamilton initially described the progressive patterns of scalp hair loss in male pattern baldness in 1951. Subsequently, in 1975, Norwood proposed a modification to Hamilton’s classification, including three patterns relevant to women. In 1977, Ludwig delineated the stages of female androgenetic alopecia, identifying three distinct patterns.

Global Photography Global photography captures all aspects of hairiness simultaneously and is suitable for evaluating the efficacy of drugs, provided that scalp preparation and hairstyle are adequately maintained throughout the study. It’s the most patient-friendly photographic method, commonly employed in clinics under standardized exposure conditions. Processing and rating must occur under controlled conditions, such as being blinded to treatment and/or time, to ensure reproducible data. Trained raters can reliably generate data using this method.

Daily Collection of Shed Hair

The natural cycle of hair growth involves a daily shedding process, where telogen hairs are shed to make way for new anagen hairs. On average, individuals without hair or scalp disorders shed between 40 to 180 hairs per day. In a study involving 404 females without hair or scalp issues, researchers collected shed hair daily over six weeks to compare the effects of two shampoos. The results indicated mean hair loss rates ranging from 28 to 35 hairs per day. Notably, there were no significant differences observed in the mean daily hair loss rates between the two-week baseline period and the subsequent four-week treatment period.In another study involving 234 women who reported hair loss, 89 had what appeared to be normal hair density. The study found that individuals with seemingly normal hair density but experiencing hair loss shed less than 50 hairs per day. This challenges the commonly referenced “magic number” of 100 hairs per day often mentioned in textbooks and the media. Shedding fewer than 50 hairs per day may be considered abnormal, especially in individuals who have already lost 50% of their hair.

Hair Weight and Hair Count

To assess the effectiveness of hair-growth–promoting treatments, researchers can compare the total hair mass (weight) and counts of grown hair within a small, meticulously maintained area of the scalp. A plastic sheet with a 1 cm² hole is positioned over the designated site. All hairs within this area are pulled through the hole and trimmed by hand to a length of 1 mm. This method offers the advantage of providing a comprehensive measurement of growth using a small sample size, facilitating the detection of drug effects and comparisons between treatment regimens (e.g., 2% vs. 5% minoxidil). However, technical proficiency is crucial to handle samples properly and prevent hair loss between the clinic and the laboratory. Like many techniques, this method lacks methodological comparisons and evaluations of reproducibility and sensitivity typically required for laboratory assessments, as it was primarily introduced for drug evaluation purposes. The main drawback is that it yields a global growth index, and individual components cannot be analyzed separately.

Hair Pull Test

The hair-pull test relies on the notion that gently pulling the hair induces the shedding of telogen hairs. However, it is a coarse method that shows challenging to standardize due to significant interindividual variation among investigators. From a physical standpoint, the pulling force is unevenly distributed across the hair bundle, resulting in variability in the force applied to each hair. Consequently, it appears to be beneficial primarily in acute and severe conditions rather than in chronically evolving conditions such as androgen-dependent alopecia.

Analytical Methods:

Phototrichogram

The phototrichogram (PTG) involves taking photographs of a scalp area where the hair is cut for better visualization, then repeating this process after a set period to assess hair growth. The method allows for evaluating hair density, anagen percentage, and calculating the rate of hair growth. Despite its noninvasive and patient-friendly nature, some patients may be hesitant due to hair cutting. However, PTG permits chronological follow-up of the same area, providing valuable information over time. Technical improvements, such as frontal window application, have enhanced clarity. Challenges include factors affecting hair visibility in photographs, technician experience, and technical photography issues. Scalp immersion proxigraphy (SIP) has been developed for improved light diffusion. Combining PTG with hair-micrometry offers a valid method for assessing global hair perception and analytical description of hair quality variables.

Variants of Phototrichogram

Video PTG:

In the Video Phototrichogram (PTG) technique, a video camera with specialized lenses replaces the traditional photographic camera. This method has been predominantly reported in studies involving Asian subjects, as the contrast between hair and scalp appears to be advantageous for this approach. Additionally, the observed lower hair density figures may potentially have racial origins. It is advisable to consider these factors to minimize biological variation. The recent availability of inexpensive CCD cameras is expected to drive advancements in this area.

Traction PTG

This test relies on the observation that hairs easily pulled from the scalp are in the telogen phase, while those resisting pull are in the anagen phase. It involves performing the test on a small surface area of 0.25 cm². Hairs within this area are gently grasped between the thumb and index fingers and pulled repeatedly. The number of hairs easily pulled is counted as telogen hairs, while those resisting pull are clipped and counted as anagen hairs. This method allows for calculating hair density per unit area and anagen percentage.

However, it’s crucial to critically evaluate this semi-invasive method and standardize the pulling technique to ensure reproducibility. Comparative studies are necessary to determine the sensitivity and specificity of this method, which currently has several limitations such as its small surface area and lack of control over traction forces

BIBLIOGRAPHY

Neste, G. S. (2001). Hair. In M. P. Andre O. Barel, Handbook of Cosmetic Science and Technology (pp. 40-45). New York: Marcel Dekker, Inc.

Formulation Challenges – Cosmetics Industry

Scientific Advisor

The use of cosmeceutical ingredients has experienced substantial growth over time, and there is a continuous pursuit of new active agents within the cosmetics industry. Although many of these ingredients demonstrate promising outcomes in laboratory experiments (in-vitro data), there is often a lack of clinical data to substantiate their claims. Additionally, formulators commonly integrate these actives into pre-existing formulations instead of employing an optimized formulation strategy.

Contemporary consumers have elevated their expectations when it comes to cosmetic products, seeking not only functional efficacy but also the fulfilment of their specific desires. Failure to meet these expectations significantly diminishes the chances of long-term success in the fiercely competitive marketplace.

Looking ahead, there are several formulation challenges that need to be addressed:

• Determining the ideal emulsion system: It is crucial to find the optimal formulation that effectively delivers the desired ingredient to the viable epidermis by overcoming the main barrier for skin penetration, the stratum corneum. Factors such as partition coefficients and penetrant polarity play a significant role in this process.
• Identifying novel ingredients aligned with the clean beauty concept: There is a growing demand for ingredients that meet clean beauty standards, have a minimal environmental impact in terms of carbon footprint, and are genuinely natural. These ingredients should also be multifunctional and compatible with other ingredients in the formulation.
• Advancing knowledge of skin molecular biology: Continuous research and understanding of the molecular biology of the skin, particularly in the specific region where the product will be used, are essential. This knowledge will contribute to the development of more effective and targeted cosmetic products.

Addressing these formulation challenges will be crucial for the future of the cosmetics industry, ensuring the development of innovative products that meet consumer expectations while prioritizing sustainability and efficacy.

Unveiling The Science Behind Skin Care: Exploring The World Of Skin Care Clinical Studies

Introduction

Skincare is a multi-billion-dollar industry, with countless products promising transformative results. But how do we separate fact from fiction? The answer lies in skin care clinical studies. These scientific investigations serve as the gold standard for evaluating the safety, efficacy, and overall performance of skin care products. In this blog post, we will embark on a journey into the world of skincare clinical studies, unravelling the mysteries and uncovering the valuable insights they provide.

Importance Of Skincare Clinical Studies

Skincare clinical studies are essential for several reasons. Firstly, they provide scientific evidence to support product claims, allowing consumers to make informed decisions based on objective data. Secondly, these studies ensure product safety by identifying potential adverse effects and assessing their risk profiles. Finally, skincare clinical studies contribute to the development of innovative formulations and techniques, driving advancements in the field.

Designing A Skincare Clinical Study

A well-designed scientific data-driven skincare clinical study is the foundation for accurate and reliable results. Here are some key elements involved in designing such a study:

Research Objective:

Clearly defining the research objective is crucial. It could be evaluating the anti-ageing effects of a new cream, determining the efficacy of sunscreen, or assessing the impact of a skincare regimen on specific skin conditions, etc.

Study Population:

The selection of participants plays a vital role in the study’s validity. Researchers consider factors such as age, skin type, ethnicity, and any existing skin conditions to ensure a representative sample.

Methodology:

Researchers utilize various methods to evaluate skin care products. These may include visual assessments, measurements of skin parameters (such as hydration, elasticity, and pigmentation) using bio instrumentation, questionnaires, and subjective assessments by participants.

Control Groups:

Control groups are an essential component of skin care clinical studies. These groups receive either a placebo or a standard treatment for comparison purposes, enabling researchers to determine the true effects of the product being tested.

Study Duration:

The duration of a skin care clinical study depends on the specific research question and the expected time frame for observable effects. Short-term studies may last a few weeks, while long-term studies could span several months or even years.

Analyzing and Interpreting Results:

Once the data is collected, researchers analyze and interpret the results to draw meaningful conclusions. Statistical analysis techniques are applied to determine the significance of any observed effects. Peer review and independent validation of the study’s findings add further credibility.

Translating Research into Real-World Applications:

Skin care clinical studies provide valuable insights that contribute to product development and consumer well-being. Manufacturers leverage these studies to refine formulations, optimize ingredient concentrations, and enhance product performance. Furthermore, consumers can rely on the findings of skin care clinical studies to make informed choices and select products that align with their specific needs and skin concerns.

Conclusion:

Skin care clinical studies serve as the backbone of the industry, providing scientific rigor and credibility to the claims made by skin care products. By following rigorous research methodologies and analysis, these studies deliver objective insights into the safety, efficacy, and performance of skin care formulations. Armed with this knowledge, consumers can confidently navigate the vast world of skin care, making informed decisions that nurture and protect their skin. Skin care clinical studies are instrumental in driving advancements, ensuring product safety, and empowering individuals to achieve healthy, radiant skin.

Statistical Aspects Of Claims Substantiation

(MAXIMO C. GACULA & SINGH, 1998)

Claims on overall performance, liking, preference, and efficacy can be challenged through the Advertising Standard Council of India (ASCI), a self-regulatory system for the industry. Stated claims are supported by data that are statistically evaluated to judge if the experimental results are due to a real effect or to random variation. Therefore, before one can be translated into a statistical research hypothesis. If a claim cannot be translated as a research hypothesis, it cannot be substantiated.

Claims may be disputed for one or more of the following reasons:

• The experiment is poorly designed to address the claim.
• The experiment is poorly executed, i.e., protocol not strictly followed, unqualified personnel, and faulty instrumentation.
• The claim has no technical merit in relation to the product. Can the stated claim be related to product ingredients?
• Poor choice of methods for gathering the data, i.e., instrumental methods, use of consumer and/or trained panel. Is the method used following established guidelines, such as that provided by the Bureau of Indian Standards, Colipa guidelines for cosmetics, American Society for Testing and Materials, ISO standards etc?
• The statistical analysis is faulty. i.e., wrong methodology used.
• The stated claim is misleading.

Null And Alternative Hypotheses

A statistical hypothesis is a statement about the quantifiable aspects of products, which can be estimated from experimental results but are not otherwise directly observed. In statistical terminology, a research hypothesis (Claim) is called the alternative hypothesis. A complementary statement to the alternative hypothesis is called the null hypothesis. Statistical tests of significance are rules for judging whether the experimental results support the claim formulated as an alternative hypothesis.

Types Of Errors And The Power Of Statistical Test

Through experimental designs, data are collected and relevant sample statistics are computed, such as the mean, the standard deviation, etc. Since these statistics are subject to sampling and experimental errors, the statistical tests may lead to an incorrect decision. Suppose the decision is made to reject the null hypothesis and accept the alternative hypothesis. This decision, if it turns out to be wrong, is said to result in a type I error. The probability of type I error is denoted by α and is known as the significance level of the statistical test. A probability of α = 0.05 indicates that the test is liable to wrongly reject the null hypothesis 5 times in 100 cases. Significance levels α = 0.05 and 0.01 are often used in scientific applications and are generally the accepted levels for claims substantiations. On the other hand, a type II error results if the decision is made not to reject the null hypothesis but in fact it is false. The probability of type II error is denoted by ß. In the planning of a claim substantiation study, both types of errors should be controlled.

Statistical Significance | Experimental Significance

Statistical analysis is probabilistic. A statistically significant result may not be of practical significance to the consumers. For example, the colour of a cosmetic product may have changed over time from its original colour. The change may be statistically significant, but not necessarily in the eyes of consumers. Thus, instead of merely having a statically significant change, one may need to determine the amount of change that will be perceived as significant by the consumers. This amount of change must be determined by correlating trained panel results with consumer test results.

Types Of Claims

Claims may be classified by two properties: style and competitive focus. Style refers to the statement being made about the advertised brand, the most common being a “distinction” claim, in which a brand claims to be preferred, more efficacious, safer, etc. Another style is a “similarity” claim, which conveys that the advertised product is like the competitor’s product in one or more attributes. All products in this category must be tested against the advertised product.

A competitive focus claim is a statement being made about the competition against one or more explicitly identified brands or implied brands. For example, the claim may be targeted against an implied brand, i.e., “preferred over the leading brand.” or more broadly against a brand set, i.e., “No leading oil is more absorbent.”

In both style and competitive focus, a claim statement can be monadic, making no comparison with other products, i.e., a statement of quality, an invitation to try the product or an untargeted claim. An untargeted claim is considered puffery and requires no formal substation.

Models For Analysis Of Product Performance Data

A useful model for the evaluation of a proposed claim must address the following aspects:

• Rationale,
• Objective evidence,
• Subjective evidence, and
• Safety

A model incorporating these aspects becomes increasingly important in disputed claims.

Rationale

Consumer products contain ingredients that affect the perception that they are desirable. By linking ingredients in a product to experimental results, one can provide a rationale for the claim. Experimental support from allied sciences, such as in-vitro studies and model systems, can also provide additional rationale for the claim.

Objective

A claim becomes stronger if its usefulness can be objectively and subjectively determined. An objective measure of product performance is desirable. It can be obtained by clinical studies in real-life settings on humans and the targeted population per product. Responses from such clinical studies can be measured by bio instruments or obtained by trained or expert panels. Indeed, data from trained panels are recognized as objective measures. Descriptive analysis and the spectrum method, which used a trained panel, can provide objective measures of the sensory properties of personal care products.

A descriptive panel undergoes rigid training and validation/calibration as specified by each method.

Subjective

When properly carried out, subjective measures obtained from home use testes, among others, may provide useful and acceptable data for claims substantiation.

Safety

Obviously, cosmetics products must be safe and without adverse side effects. The model must address the safety aspects. Safety-related data can be obtained from research guidance panel tests, central location consumer tests, and the various types of laboratory model systems, i.e., in vitro and in vivo tests.

As indicated, a model incorporating these aspects provides a way to deal with conflicts, permits more efficient use of data for the development of truthful claims, and promotes effective communications between parties in disputed situations.

A conceptual model for assessing perception data measuring interdependent attributes is postulated, this model defines the following:

1. Whether the product performance claim is based on a product attribute or not i.e., the emphasis is on overall product performance with no focus on product attribute or benefit.
2. Whether the product performance claims focus on a specific attribute or on a set of attributes i.e., drag, stickiness, residue, spreadability.
3. Whether the claim is for a specific attribute or a set of attributes and whether it focuses on a feature or a benefit. For example, in antibacterial personal care products, the active ingredient provides a product dimension and the benefit of this dimension is cleanliness and safety to the users against bacterial infection.
4. Whether the claim is merely stating the presence of the attribute or also its benefit and whether it suggests a parity or superiority against a specific competitor or class of competitors.
5. Finally, whether the parity or superiority claim is restricted to an attribute and its advertised benefits or to an overall parity or superiority. For example, in consumer tests, an overall preference or overall liking when used as a claim suggests that all product attributes contribute, incorporating interdependence among sensory attributes during product evaluation by consumers.

The experimental designs suited for obtaining data for parity and superiority claims are discussed further on.

Superiority Claims

A superiority claim simply indicates that the product advertised is the best in the market. It is essential that direct product-to-product comparative testing be used for substantiating a superiority claim. An appropriate design for comparing two products at a time is known as the paired-comparison design. Which is discussed further on. An example of a superiority claim is “compared to the leading brands, tropical Isles is unsurpassed as a skin moisturizer and conditioner” As stated before, a claim must be translated into a statistical hypothesis. In order to do this, we must have a well-defined scale on which these products can be scored for comparison. Suppose product A is being compared with a leading brand for claim substantiation. If, on a scale for comparing such products, high scores correspond to superior products, we can formulate two statistical hypotheses such as the following:

H0: Average score of leading brand   average score of product A

H1: Average score of leading brand <average score of product A

To be able to claim superiority for product A, the null hypothesis must be rejected at, say, the 95% confidence level (5% significance level) in favour of the alternative hypothesis, which states that product A is superior to the leading brand.

Parity Claims

Parity claims are difficult to establish by means of hypothesis testing methodology because for parity claims the research hypothesis essentially states that the products are equivalent. Using a rating scale, the equivalency is translated as equality of two average scores, equality of average scores can only be stated as the null hypothesis. A statistical test will either reject the null hypothesis when there is sufficient evidence in support of the alternative or will not reject it. If the null hypothesis is not rejected, it should not be understood that the products are equivalent. Intentionally or otherwise, one can design an experiment to collect insufficient data, lacking information that leads to a decision not to reject H0. This decision only means that there is insufficient information to disown the parity claim. it does not mean that a parity claim is established with any degree of confidence.

In disputed parity claims, if a proper formulation of hypotheses and a sound design are not used, differences may arise that will be difficult to resolve among the parties involved. It is a waste of time to argue about the validity of a claim if the methodology and the design are not carefully employed. As stated above, one can design an experiment with an insufficient sample to mask significant differences between products because of the failure of the study to reject the null hypothesis. Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypothesis”.

Therefore, the formulation of hypotheses for a parity claim and their statistical testing must be done in such a way that the decision to reject the null hypothesis amounts to the parity of products.

Experimental Designs For Claim Support

There are three important elements in the development of a strong product claim:

• A clearly stated claim,
• A good experimental design to address the claim, and
• A properly executed study following the experimental design. A critical part of the first element is the specification of the target population, because once this is done, the development maire development, sample size, test execution.

Target Population

A product is developed to meet either the needs of the general population or those of a specific user group in the population. Depending on the stated claim, the general population or a specific group defines the target population. In particular, the user of the product could be the purchaser and not necessarily the user. For instance, the wife is the purchaser of baby powder. on the other hand, the husband is the purchaser of after-shave skin conditioners. In the first case, wives would be the target population, and in the latter case, husbands. If the claim is for the general population, then the participants in the test would be a random sample of the population. Similarly, a random sample of a specific user group should be used in the study.

Questionnaire Design

In gathering consumer data for claim substantiation, it is important that the product attributes related to the claim be included in the questionnaire. For example, if “soft” and “smooth” are sensory attributes claimed for the product, then these attributes must be included in the questionnaire in the form of intensity and /or hedonic (like/dislike) questions.

How many attributes questions the questionnaire should include is often a difficult decision to make in questionnaire development. If a product has undergone a series of descriptive sensory analyses, this should provide the appropriate number of attributes for inclusion. Briefly, descriptive analysis is a sensory methodology that provides quantitative descriptions of products based on the perceptions of a group of qualified subjects. It is a total sensory description, taking into account all sensations perceived—visual, auditory, olfactory, kinesthetic, and so on – when the product is evaluated. In practice, the desirable number of attributes has ranged from 10 to 15.

Another aspect of questionnaire development is the choice of the rating scale (1=dislike extremely, 5=neither like nor dislike, 9=like extremely) developed in 1947 at the Quartermaster Food and Container Institute for the U.S. Armed Forces. This is the most extensively studied of rating scales and, as a result, is the most reliable one for acceptance/preference measurement. Information on questionnaire development is widely available.

Paired Comparison

The paired comparison is the most powerful design to support almost all types of product claims. The statistical analysis of paired-comparison design is simple and meets all the essential statistical assumptions; the test is simple to execute for both the experimenter and the panellist, and the evaluation of two products by a single panellist, and the evaluation of two products by a single panellists first nicely into the classic paired-comparison situation (i.e., right/left sides of biological materials.)

The general idea of the paired-comparison design is to form homogeneous pairs of like units so that comparisons between units of a pair measure differences due to treatments rather than units. This arrangement leads to dependency between observation on units of a pair measure difference due to treatments rather than units. This arrangement leads to dependency between observations on units of the same pair. This situation can be extended to sensory and consumer testing. The statistical assumption in the analysis is that the differences is independent and normally distributed; in most cases this assumption is satisfied in practice. Furthermore, the common problem of correlation of ratings among panelists becomes irrelevant, since one is now dealing with differences di.

Randomized Complete Block Design

For reasons of cost, time, and other business constraints, one must conduct a consumer test with more than two products for evaluation by panelists at the same time. In this situation, the randomized complete block design (RCBD) is used for claim substation. The statistical model for describing an observation is

yij=µ+Ai+Bj+eij

Where = the observed rating for the  product given by the  panelist; µ= the grand mean; = the effect of the  product; = the effect of the panelist; and = random errors assumed to be independently and normally distributed, with mean zero and variance . In this model, the effect of panelist-to-panelist variation is removed from the random errors  , making the test of significance more sensitive.

In most consumer testing claim studies, the statistical analysis from the RCBD or the single-factor repeated- measures design is sufficient. Also, the SAS code in table 5 can easily be expanded to include demographics, product usage information, and so on.

Concluding Remarks

We have covered the importance of statistical experimental design to consumer tests for supporting claim substantiation. In particular, the formulation of statistical research hypotheses is discussed and its importance in parity claims reviewed. The use of a paired-comparison design is recommended for claims substantiation. The importance of understanding the power of a statistical test and its relationship to sample size to provide a claim that can withstand rigorous scrutiny was emphasized.

Bibliography

Maximo C. Gacula, J., & Singh, J. (1998). Consumer Testing Statistics and Claims Substantiation. In L. B. Aust, Handbook of Cosmetic Claims Substnatiations (pp. 235-258). New York: Marcel Dekker, Inc.