key: cord-0852436-ordof7g5
authors: Nicksic, Peter J; Karczewski, Alison M; Zhao, Qianqian; Garcia, Nicholas A; Michelotti, Brett F; Mahajan, Ashish Y; Poore, Samuel O
title: The Contribution of the Lower Third of the Face to Perceived Age: Do Masks Make You Appear Younger?
date: 2021-05-07
journal: Aesthet Surg J Open Forum
DOI: 10.1093/asjof/ojab017
sha: 55f56e5cc49ed193f43167882cc63e0ebe7a7a9c
doc_id: 852436
cord_uid: ordof7g5

BACKGROUND: Extrinsic factors like smoking, alcohol use, and sun exposure have been shown to accelerate facial aging. There is evidence that changes to the midface and lower third of the face in isolation contribute significantly to one’s perception of overall facial age. With data suggesting that facial coverings are effective against the spread of the respiratory virus COVID-19, mask wearing has become commonplace. To date, there have been no studies that explore how covering the lower third of the face impacts an observer’s perception of age. OBJECTIVES: We hypothesize that covering the lower third of the face with a mask will make a person appear younger. METHODS: One hundred consecutive plastic surgery patients were photographed in a standardized fashion, both masked and unmasked. A questionnaire for factors known to contribute to facial aging was administered. These photographs were randomized to 6 judges who estimated the patients’ age and also quantified facial rhytids with the validated Lemperle wrinkle assessment score6. Data were analyzed using PROC MIXED analysis. RESULTS: Masked patients on average appeared 6.17% younger (mean difference = 3.16 years, 95% CI = 2.26-4.06, P < 0.0001). Wrinkle assessment scores were 9.81% lower in the masked group (mean difference = 0.21, 95% CI = 0.10-0.32, P = 0.0003). All subgroups appeared younger in a mask except for patients aged 18-40 years chronological age (P = 0.0617) and patients BMI>35 (P = 0.5084). CONCLUSIONS: The mask group appeared younger and had lower overall and visible wrinkle assessment scores when compared to the unmasked group. This has implications for our understanding of the contributions of the lower third of the face to overall perceived facial age.

A c c e p t e d M a n u s c r i p t

In 2018, the cosmetic surgery market generated an estimated 16.5 billion dollars in the United States according to the American Society of Plastic Surgeons. 1 Much of this revenue is attributable to patients seeking to reduce or reverse facial aginga well-studied process that alters the appearance of facial features. 1 Over time, skin elasticity reduces, retaining ligament integrity lessens, and volume of soft tissue and fat compartment shift which together constitute the appearance of advancing age 2 .

Environmental factors have been identified to accelerate this process including smoking, alcohol use, and sun exposure. [3] [4] [5] [6] [7] [8] Interestingly, higher BMI has been shown to be a ‗normalizing' factor for age, making both younger and older people appear to be middle-aged. 3 Gender differences and genetic factors such as sex hormones, skeletal anatomy, and facial physiology also play a role in the perception of age. 9 For example, larger eyes, taller lips, and greater facial contrast are all associated with appearing younger. [10] [11] [12] In addition to these intrinsic and extrinsic factors, the contribution of specific subunits of the face have been shown to disproportionately affect our perception of age. The midface, including the central facial triangle, is shown to be an important determinant of perceived attractiveness and age. 13 Artificially aging the lower third of the face with computer-based photo editing significantly increases the perceived age of a person and is among the most determining of the facial subunits. 13 To date, there have been no studies that have explored how covering subunits of the face with a mask affect the perception of age.

Prior to 2020, use of face masks in the United States was largely limited to the healthcare setting. However, as the respiratory virus causing COVID-19 began to spread worldwide with increasing morbidity and mortality, the use of face masks in the public setting became more common as many state governments began issuing mask mandates. 14 Despite there being evidence to support the use of face masks in preventing disease transmission, it remains a controversial topic driven by political divide, individualism, and distrust of authority. [15] [16] [17] The purpose of our study is to better understand the effects of wearing a mask on the perception of facial age. We hypothesize that based on our own observation, and well documented A c c e p t e d M a n u s c r i p t and studied contribution of the lower third of the face to our perception of age, 13 face masks make individuals appear younger. The implications of appearing younger extend beyond physical attractiveness. Looking younger than chronological age is associated with increased trustworthiness, competence, and warmth, 12, 18 whereas looking older correlates with decreased optimism, relationship satisfaction, and mental health. 19 Even cognitive function has demonstrated a stronger correlation with perceived age than with chronological age. 20 This study may also provide more insight into how the morphological features of each subunit of the face contributes to the perception of age, and if mask wearing makes one appear more youthful, the results of this study may highlight a benefit of mask wearing.

After obtaining institutional review board approval at The University of Wisconsin, we collected 6 standardized photographs of 100 patients from the clinic of two senior authors, BFM and SOP, from October 2020 to February 2021. All patients were offered a standardized light blue, around-the-ear surgical mask. Photographs were all taken with the same background and included a right lateral, frontal, and left lateral. The same photographs were taken with and without a mask. Exclusion criteria were any patient being seen for facial pathology and those under 18 years of age. We also administered a questionnaire to obtain demographic information and a social history of factors known to contribute to facial aging (Appendix). 3 Questions were asked by members of the research team in a face-to-face manner while answers were written down on paper. Patients were assured their answers would remain anonymous. Data were collected during a single plastic surgery clinic visit so as to not further increase the patients' exposure to a healthcare setting amidst the COVID-19 pandemic.

Informed consent and photo release were also obtained at this time.

The photometric data were then reviewed independently by 6 board-certified plastic surgeons from our institution. The judges were blinded to the goal of the study and were not directly involved in patient care of any of the study subjects. Each judge estimated the age and severity of facial A c c e p t e d M a n u s c r i p t wrinkles according to the validated Lemperle wrinkle assessment scale 21 of either the masked or unmasked photographs for each patient. When a mask concealed a portion of the subject's face, the wrinkles that were not visible were not scored and excluded from analysis. This was the overall wrinkle assessment score. We also compared wrinkle assessment scores for wrinkles that were visible in all patients, including the forehead, glabella, periorbita, preauricular rhytids, and neck. This was the visible wrinkle assessment score. Each judge appraised each patient once, either masked or unmasked, but never saw the same patient masked and unmasked. The mask status of the patients was randomized to the judges with equal weight by a biostatistician from the Department of Biostatistics and Informatics biostatisticians using R software. Power analysis was performed with assistance from the Department of Biostatistics and Informatics biostatisticians using a paired T-test based on an anticipated effect size of 19.8% increase on perceived age attributable to artificially aging the lower third of the face from a prior study. 13, 22 A sample size of 199 data pairs achieves 80.0% power to reject the null hypothesis of zero effect size when the population effect size is 0.20 and the significance level () is 0.05 using a two-sided paired T-test, and 90 data pairs achieves 80.0% power to reject the null hypothesis of zero effect size when the population effect size is 0.30. As such, we collected data on 100 consecutive patients and performed the statistical analysis.

Statistical analyses were conducted using SAS software (SAS Institute Inc., Cary, NC, USA), version 9.4. Descriptive statistics such as mean and standard deviation were calculated for continuous variables, and count and frequency were generated for categorical variables. To determine if masked patients appeared younger than unmasked patients, the data were analyzed using ‗PROC MIXED' since each patient was reviewed multiple times by different surgeons. Both the visible and overall wrinkle assessment scores were compared between the masked and unmasked groups using the same method. Analysis was also performed to determine if the unmasked group was statistically different than the patients' actual age. Subgroup analyses were carried out based on gender, BMI, chronological age, smoking history, and alcohol intake. We divided the age groups prior to data collection to represent a young cohort (18-40 years of age), middle-aged cohort (41-65 years of age), and elderly cohort (66 and older). For the purpose of this study, greater than or equal to 2 standard A c c e p t e d M a n u s c r i p t drinks per week qualified a person as an alcohol consumer, and greater than or equal to 5 pack-years smoking history qualified a person as a smoker. Inter-rater reliability was assessed using Shrout-Fleiss' intraclass correlations (ICC), 23 and the judges all rated the quality and consistency of the photographs on a 5-point scale.

Our study included 100 consecutive patients seen in plastic surgery clinic from October 2020 to Twenty percent of patients used sunscreen daily, 53% used sunscreen for exposure, and 27% did not use sunscreen. Eleven percent of patients had Botox treatment in the past, and 3% had a history of facial filler. Two patients had a facelift in the past, and 3 patients had a brow lift. The demographic data is summarized on Table 1 .

Inter-rater reliability was assessed to be Shrout-Fleiss' ICC = 0.78 and ICC = 0.62 for estimated age and wrinkle scale score, respectively (ICC > 0.60 is considered substantial agreement, and ICC < 0.30 are considered problematic for comparison between judges.). The photograph quality was scored at an average 4.83 out of 5. Consistency between masked and unmasked patients was scored at 4.5 out of 5. A c c e p t e d M a n u s c r i p t 0.10-0.32, P = 0.0003). Visible wrinkle assessment scores for masked patients were 1.92, and the unmasked group was 2.06 (mean difference = 0.15, 95% CI = 0.04-0.25, P = 0.0082). The perceived age of the unmasked group was determined not to be statistically different from the patients' actual age (mean difference = 0.45, P = 0.4344). Results of perceived age and wrinkle assessment score data are summarized in Table 2 .

Subgroup analyses revealed that females looked 3.41 years younger (95% CI = 2.36-4.46, P < 0.0001) when masked, and males looked 2.17 years younger when masked (95% CI = 0.59-3.76, P = 0.0077). Overall wrinkle scores were 0.24 lower when masked in the female subgroup (P = 0.0002), but not significant in the male subgroup (mean difference = 0.08, P = 0.4706). Perceived age (P = 0.6183) and wrinkle assessment (P = 0.3921) scores were not significant when compared on the basis of gender.

When subdivided by age groups, the difference in perceived age increased with actual age ( Figure 1 ). In the 18 to 40-year-old subgroup, the mean differences in perceived age was 1.75, which was not significant between the masked and unmasked subgroups (P = 0.0617). In the 41 to 65-yearold subgroup, the mean difference was 3.46 years (95% CI = 2.23-4.68, P < 0.0001). In the 66 years of age and older subgroup, the mean difference was 3.76 years (95% CI = 1.99-5.53, P < 0.0001).

Smokers appeared on average 5.25 years younger when masked (95% CI =3.22-7.29, P < 0.0001), whereas non-smokers demonstrated a mean difference of 2.68 years (95% CI =1.67-3.68, P < 0.0001). Female smokers appeared 5.72 years younger in the masked group (95% CI = 3.03-8.41, P < 0.0001). The average wrinkle scores for non-smokers were not statistically different when comparing masked and unmasked groups (P = 0.2678). Perceived age (P = 0.8803) and wrinkle assessment scores (P = 0.3735) were not significant for smokers versus non-smokers. Alcohol consumers were found to have a difference of perceived age of 2.72 years (95% CI =1. 25-4.19 , P = 0.0003), where judges determined that there was difference of 3.42 years in non-drinkers (95% CI =2.27-4.56, P < 0.0001). When comparing the differences in perceived age (P = 0.9874) and wrinkle assessment scores (P = 0.5623) between alcohol consumers and non-drinkers, this was not statistically significant. Obese patients demonstrated no difference in perceived age (P = 0.5084) or wrinkle assessment score (P = 0.2209) between the masked and unmasked group. Perceived age (P = 0.2368) and wrinkle assessment scores (P = 0.1590) were not significant when comparing obese versus nonobese patients.

When controlling for all effect-modifiersgender, age, smoking, alcohol consumption, and BMIthe mask group demonstrated a lower perceived age than the unmasked group (mean difference 3.17, 95% CI = 2.27-4.07, P < 0.0001) and lower overall wrinkle score (mean difference 0.2097, 95% CI = 0.10-0.32, P = 0.0002). Figure 2 and Figure 3 show the average difference in age and winkle score, respectively, for all patients and subgroups.

The ‗mask effect,' or the effect on perceived age attributable only to mask wearing, made patients appear on average 6.17% younger (mean difference = 3.16 years, 95% CI = 2.26-4.06, P < 0.0001).

This effect size is similar to other studies that examined the effect of aesthetic surgery on perceived age. A prospective study of 49 facial aesthetic surgery patients demonstrated a reduction in perceived age of 3.1 years from pre-to post-operative photographs. 24 Another study of 10 hyaluronic acid injection patients demonstrated a reduction in age of 6.1 to 7.3 years 2 to 4 weeks after the procedure. 25 While there is a relatively modest reduction in perceived age attributable to mask effect in this study, the reduction in perceived age is on the same order of magnitude of other popular facial rejuvenation strategies today.

One study on the topic of facial subunits and aging found that artificially aging the lower third of the face in isolation made a patient appear 19.8% older (P < 0.0001). 13 While it is logical to assume that obscuring the lower third would have less of an effect on perceived age than artificially aging it, the question still remains: why does wearing a mask affect our perception of age at all? One reason is that the aging lower third of the face contains vertical lip rhytids, nasolabial folds, and jowls, which have been demonstrated to contribute significantly to our perception of facial aging even in isolation. 3 A c c e p t e d M a n u s c r i p t While all subgroupsexcept patients whose BMI was greater than 35 and patients aged 18 to 40demonstrated a significant mask effect to variable degrees, there was no significant difference between subgroups for either perceived age or wrinkle scores. With a larger study powered to detect differences between subgroups, the variable mask effects may be significant. For example, females demonstrated a mask effect of 3.41 years (95% CI = 2.36-4.46, P < 0.0001), which was not significant when compared to the 2.17-year mask effect in males (95% CI = 0.59-3.76, P = 0.0077). Our study was not powered to determine the difference in mask effect based on gender. However, women invest more in ‗anti-aging' products like skin care products, hair coloring, and make-up. 26 Male-pattern baldness may also have been a tell of age that was not concealed by a mask (Figure 5 ), though there was not a correlation between degree of baldness and overestimation of age demonstrated in one photometric study. 27 So, it is possible that a larger study with equivalent numbers of males and females may detect a larger mask effect in females. There was also an insignificant effect for wrinkle assessment score in males (mean difference = 0.08, P = 0.4706). This may be due to the fact that subgroup analysis in the study was underpowered to detect this difference, or it may be due to facial hair obscuring lower third facial wrinkles in men.

A c c e p t e d M a n u s c r i p t Patients with known extrinsic risk factors for premature aging like smoking and alcohol consumption showed variable effects on perceived age. For smokers, the mask effect was responsible for making them appear 5.25 years younger (95% CI =3. 22-7.29 , P < 0.0001), while their nonsmoking counterparts demonstrated a mask effect of 2.68 years (95% CI =1.67-3.68, P < 0.0001).

When compared to non-smoker identical twins, smokers demonstrated disproportionate premature aging of the lower face, including vertical lip rhytids, jowls, and nasolabial folds. 8 We hypothesize that future studies would demonstrate an increased mask effect in these patients, and these signs of smoking-related facial aging are evident in our study ( Figure 6 ).

Interestingly, subgroup analysis for patients with BMI > 35 had insignificant changes to their perceived age and wrinkle assessment scores (P = 0.51, 0.22, respectively). In another twin study, a BMI 8 points higher than the respective twin was associated with a higher perceived age in patients under 40 years old and a younger perceived age in patients over 55 years old. 3 Elevated BMIs are associated with reduced wrinkling in all subunits of the face, 3 which we hypothesize would reduce the mask effect (Figure 7 ).

The subgroup with the largest mask effect was in women who smoked, who appeared 5.72 (10.75%) years younger with a mask (95% CI = 3.02-8.41, P < 0.0001). This finding indicates that individuals with a history of smoking will significantly benefit from rejuvenation procedures focused on the lower two-thirds of the face.

One interesting effect is that, overall, masked patients were shown to have a lesser visible wrinkle assessment score. The masked individuals demonstrated a 7.09% reduction in wrinkle score (mean difference = 0.1463, 95% CI = 0.03798-0.2547, P = 0.0082). One would assume that the visible wrinkle assessment scorescoring averaged only for forehead, glabellar, periorbital, preauricular, and neck wrinklesbetween the masked and unmasked group would be equivalent if the photography of the masked and unmasked patients were consistent. Therefore, it is possible that this effect may be A c c e p t e d M a n u s c r i p t due to a subtle flash artifact in masked patients, or it is possible that obscuring more significant lower facial wrinkles reduced the appearance of all wrinkles in the face. The near perfect average consistency score (4.5 out of 5) for the photographs of masked versus unmasked patients supports that wearing a mask affects one's perception of the entire face.

In order to minimize potential judgement bias, we made the decision to show judges a mix of mask and unmasked patients without showing a judge the same patient masked and unmasked. The judges were blinded to the goal of the study, but due to the subject matter of the study, it is possible that they inferred the goal, which introduced another potential source of judgement bias.

The power analysis was performed to compare the entire sample masked versus unmasked.

This increases our risk of a beta-type error in subgroup analysis. The majority of subgroupsexcept morbidly obese patients and patients under 40 years of agedemonstrated a reduction in perceived age when masked even with what is likely insufficient power for the anticipated effect.

Our study population consisted of 80% females. Each participant acted as their own control, so there were equivalent numbers of males and females in the masked versus unmasked groups.

Difference in mask effect of the possible effect modifiersobesity, smoking, and alcohol consumptionwere non-significant between subgroups. Therefore, despite there being a disproportionate number of females in the study, the results of the study can still be applied to both genders and does not affect the overall conclusion of the study. The average BMI of the study population was 29.4 and contained 12% smokers. These statistics are similar to the World Health Organization's estimates for the United States for average BMI (29.1) and prevalence of smokers (14%).

This study demonstrates that there is a significant mask effectthat those who wear a mask appear younger on average and have a lower overall and visible wrinkle score. This effect was seen to increase with age but was not significant between subgroups of patients with known risk factors for premature facial aging like smoking and consuming alcohol. These findings expand our understanding A c c e p t e d M a n u s c r i p t of the importance of the lower third of the face and its role in an observer's perception of age. When determining what facial subunit to address first in facial rejuvenation, the findings of this study suggest that treating the lower third with surgery or injectables will reduce the patient's perceived facial age.

Furthermore, the mask effect offers a positive incentive for mask wearing. Given the resistance nationally to mask wearing due to political views and distrust of authority 15 M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 

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PN, QZ, and SP designed the study. PN and AK collected the data and drafted the manuscript. QZ did the statistical analysis. NG, BM, AM, and SP edited the manuscript.

A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t