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• Research article
• Open access
• Published: 12 June 2019

The impact of skin care products on skin chemistry and microbiome dynamics

• Amina Bouslimani 1   na1 ,
• Ricardo da Silva 1   na1 ,
• Tomasz Kosciolek 2 ,
• Stefan Janssen 2 , 3 ,
• Chris Callewaert 2 , 4 ,
• Amnon Amir 2 ,
• Kathleen Dorrestein 1 ,
• Alexey V. Melnik 1 ,
• Livia S. Zaramela 2 ,
• Ji-Nu Kim 2 ,
• Gregory Humphrey 2 ,
• Tara Schwartz 2 ,
• Karenina Sanders 2 ,
• Caitriona Brennan 2 ,
• Tal Luzzatto-Knaan 1 ,
• Gail Ackermann 2 ,
• Daniel McDonald 2 ,
• Karsten Zengler 2 , 5 , 6 ,
• Rob Knight 2 , 5 , 6 , 7 &
• Pieter C. Dorrestein 1 , 2 , 5 , 8  

BMC Biology volume  17 , Article number:  47 ( 2019 ) Cite this article

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Use of skin personal care products on a regular basis is nearly ubiquitous, but their effects on molecular and microbial diversity of the skin are unknown. We evaluated the impact of four beauty products (a facial lotion, a moisturizer, a foot powder, and a deodorant) on 11 volunteers over 9 weeks.

Mass spectrometry and 16S rRNA inventories of the skin revealed decreases in chemical as well as in bacterial and archaeal diversity on halting deodorant use. Specific compounds from beauty products used before the study remain detectable with half-lives of 0.5–1.9 weeks. The deodorant and foot powder increased molecular, bacterial, and archaeal diversity, while arm and face lotions had little effect on bacterial and archaeal but increased chemical diversity. Personal care product effects last for weeks and produce highly individualized responses, including alterations in steroid and pheromone levels and in bacterial and archaeal ecosystem structure and dynamics.

Conclusions

These findings may lead to next-generation precision beauty products and therapies for skin disorders.

The human skin is the most exposed organ to the external environment and represents the first line of defense against external chemical and microbial threats. It harbors a microbial habitat that is person-specific and varies considerably across the body surface [ 1 , 2 , 3 , 4 ]. Recent findings suggested an association between the use of antiperspirants or make-up and skin microbiota composition [ 5 , 6 , 7 ]. However, these studies were performed for a short period (7–10 days) and/or without washing out the volunteers original personal care products, leading to incomplete evaluation of microbial alterations because the process of skin turnover takes 21–28 days [ 5 , 6 , 7 , 8 , 9 ]. It is well-established that without intervention, most adult human microbiomes, skin or other microbiomes, remain stable compared to the differences between individuals [ 3 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].

Although the skin microbiome is stable for years [ 10 ], little is known about the molecules that reside on the skin surface or how skin care products influence this chemistry [ 17 , 18 ]. Mass spectrometry can be used to detect host molecules, personalized lifestyles including diet, medications, and personal care products [ 18 , 19 ]. However, although the impact of short-term dietary interventions on the gut microbiome has been assessed [ 20 , 21 ], no study has yet tested how susceptible the skin chemistry and Microbiome are to alterations in the subjects’ personal care product routine.

In our recent metabolomic/microbiome 3D cartography study [ 18 ], we observed altered microbial communities where specific skin care products were present. Therefore, we hypothesized that these products might shape specific skin microbial communities by changing their chemical environment. Some beauty product ingredients likely promote or inhibit the growth of specific bacteria: for example, lipid components of moisturizers could provide nutrients and promote the growth of lipophilic bacteria such as Staphylococcus and Propionibacterium [ 18 , 22 , 23 ]. Understanding both temporal variations of the skin microbiome and chemistry is crucial for testing whether alterations in personal habits can influence the human skin ecosystem and, perhaps, host health. To evaluate these variations, we used a multi-omics approach integrating metabolomics and microbiome data from skin samples of 11 healthy human individuals. Here, we show that many compounds from beauty products persist on the skin for weeks following their use, suggesting a long-term contribution to the chemical environment where skin microbes live. Metabolomics analysis reveals temporal trends correlated to discontinuing and resuming the use of beauty products and characteristic of variations in molecular composition of the skin. Although highly personalized, as seen with the microbiome, the chemistry, including hormones and pheromones such as androstenone and androsterone, were dramatically altered. Similarly, by experimentally manipulating the personal care regime of participants, bacterial and molecular diversity and structure are altered, particularly for the armpits and feet. Interestingly, a high person-to-person molecular and bacterial variability is maintained over time even though personal care regimes were modified in exactly the same way for all participants.

Skin care and hygiene products persist on the skin

Systematic strategies to influence both the skin chemistry and microbiome have not yet been investigated. The outermost layer of the skin turns over every 3 to 4 weeks [ 8 , 9 ]. How the microbiome and chemistry are influenced by altering personal care and how long the chemicals of personal care products persist on the skin are essentially uncharacterized. In this study, we collected samples from skin of 12 healthy individuals—six males and six females—over 9 weeks. One female volunteer had withdrawn due to skin irritations that developed, and therefore, we describe the remaining 11 volunteers. Samples were collected from each arm, armpit, foot, and face, including both the right and left sides of the body (Fig.  1 a). All participants were asked to adhere to the same daily personal care routine during the first 6 weeks of this study (Fig.  1 b). The volunteers were asked to refrain from using any personal care product for weeks 1–3 except a mild body wash (Fig.  1 b). During weeks 4–6, in addition to the body wash, participants were asked to apply selected commercial skin care products at specific body parts: a moisturizer on the arm, a sunscreen on the face, an antiperspirant on the armpits, and a soothing powder on the foot (Fig.  1 b). To monitor adherence of participants to the study protocol, molecular features found in the antiperspirant, facial lotion, moisturizer, and foot powder were directly tracked with mass spectrometry from the skin samples. For all participants, the mass spectrometry data revealed the accumulation of specific beauty product ingredients during weeks 4–6 (Additional file  1 : Figure S1A-I, Fig.  2 a orange arrows). Examples of compounds that were highly abundant during T4–T6 in skin samples are avobenzone (Additional file  1 : Figure S1A), dexpanthenol (Additional file  1 : Figure S1B), and benzalkonium chloride (Additional file  1 : Figure S1C) from the facial sunscreen; trehalose 6-phosphate (Additional file  1 : Figure S1D) and glycerol stearate (Additional file  1 : Figure S1E) from the moisturizer applied on arms; indolin (Additional file  1 : Figure S1F) and an unannotated compound ( m/z 233.9, rt 183.29 s) (Additional file  1 : Figure S1G) from the foot powder; and decapropylene glycol (Additional file  1 : Figure S1H) and nonapropylene glycol (Additional file  1 : Figure S1I) from the antiperspirant. These results suggest that there is likely a compliance of all individuals to study requirements and even if all participants confirmed using each product every day, the amount of product applied by each individual may vary. Finally, for weeks 7–9, the participants were asked to return to their normal routine by using the same personal care products they used prior to the study. In total, excluding all blanks and personal care products themselves, we analyzed 2192 skin samples for both metabolomics and microbiome analyses.

Study design and representation of changes in personal care regime over the course of 9 weeks. a Six males and six females were recruited and sampled using swabs on two locations from each body part (face, armpits, front forearms, and between toes) on the right and left side. The locations sampled were the face—upper cheek bone and lower jaw, armpit—upper and lower area, arm—front of elbow (antecubitis) and forearm (antebrachium), and feet—in between the first and second toe and third and fourth toe. Volunteers were asked to follow specific instructions for the use of skin care products. b Following the use of their personal skin care products (brown circles), all volunteers used only the same head to toe shampoo during the first 3 weeks (week 1–week 3) and no other beauty product was applied (solid blue circle). The following 3 weeks (week 4–week 6), four selected commercial beauty products were applied daily by all volunteers on the specific body part (deodorant antiperspirant for the armpits, soothing foot powder for the feet between toes, sunscreen for the face, and moisturizer for the front forearm) (triangles) and continued to use the same shampoo. During the last 3 weeks (week 7–week 9), all volunteers went back to their normal routine and used their personal beauty products (circles). Samples were collected once a week (from day 0 to day 68—10 timepoints from T0 to T9) for volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, and 12, and on day 0 and day 6 for volunteer 8, who withdraw from the study after day 6. For 3 individuals (volunteers 4, 9, 10), samples were collected twice a week (19 timepoints total). Samples collected for 11 volunteers during 10 timepoints: 11 volunteers × 10 timepoints × 4 samples × 4 body sites = 1760. Samples collected from 3 selected volunteers during 9 additional timepoints: 3 volunteers × 9 timepoints × 4 samples × 4 body sites = 432. See also the “ Subject recruitment and sample collection ” section in the “ Methods ” section

Monitoring the persistence of personal care product ingredients in the armpits over a 9-week period. a Heatmap representation of the most abundant molecular features detected in the armpits of all individuals during the four phases (0: initial, 1–3: no beauty products, 4–6: common products, and 7–9: personal products). Green color in the heatmap represents the highest molecular abundance and blue color the lowest one. Orange boxes with plain lines represent enlargement of cluster of molecules that persist on the armpits of volunteer 1 ( b ) and volunteer 3 ( c , d ). Orange clusters with dotted lines represent same clusters of molecules found on the armpits of other volunteers. Orange arrows represent the cluster of compounds characteristic of the antiperspirant used during T4–T6. b Polyethylene glycol (PEG) molecular clusters that persist on the armpits of individual 1. The molecular subnetwork, representing molecular families [ 24 ], is part of a molecular network ( http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec5791d5ad ) generated from MS/MS data collected from the armpits of volunteer 1 (T0–T3) MSV000081582 and MS/MS data collected from the deodorant used by volunteer 1 before the study started (T0) MSV000081580. c , d Polypropylene glycol (PPG) molecular families that persist on the armpits of individual 3, along with the corresponding molecular subnetwork that is part of the molecular network accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 . Subnetworks were generated from MS/MS data collected from the armpits of volunteer 3 (T0–T3) MSV000081582 and MS/MS data collected from the deodorant used by volunteer 3 at T0 MSV000081580. The network nodes were annotated with colors. Nodes represent MS/MS spectra found in armpit samples of individual 1 collected during T0, T1, T2, and T3 and in personal deodorant used by individual 1 (orange nodes); armpit samples of individual 1 collected during T0, T2, and T3 and personal deodorant used by individual 1 (green nodes); armpit samples of individual 3 collected during T0, T1, T2, and T3 and in personal deodorant used by individual 3 (red nodes); armpit samples of individual 3 collected during T0 and in personal deodorant used by individual 3 (blue nodes); and armpit samples of individual 3 collected during T0 and T2 and in personal deodorant used by individual 3 (purple nodes). Gray nodes represent everything else. Error bars represent standard error of the mean calculated at each timepoint from four armpit samples collected from the right and left side of each individual separately. See also Additional file  1 : Figure S1

To understand how long beauty products persist on the skin, we monitored compounds found in deodorants used by two volunteers—female 1 and female 3—before the study (T0), over the first 3 weeks (T1–T3) (Fig.  1 b). During this phase, all participants used exclusively the same body wash during showering, making it easier to track ingredients of their personal care products. The data in the first 3 weeks (T1–T3) revealed that many ingredients of deodorants used on armpits (Fig.  2 a) persist on the skin during this time and were still detected during the first 3 weeks or at least during the first week following the last day of use. Each of the compounds detected in the armpits of individuals exhibited its own unique half-life. For example, the polyethylene glycol (PEG)-derived compounds m/z 344.227, rt 143 s (Fig.  2 b, S1J); m/z 432.279, rt 158 s (Fig.  2 b, S1K); and m/z 388.253, rt 151 s (Fig.  2 b, S1L) detected on armpits of volunteer 1 have a calculated half-life of 0.5 weeks (Additional file  1 : Figure S1J-L, all p values < 1.81e−07), while polypropylene glycol (PPG)-derived molecules m/z 481.87, rt 501 s (Fig.  2 c, S1M); m/z 560.420, rt 538 s (Fig.  2 c, S1N); m/z 788.608, rt 459 s (Fig.  2 d, S1O); m/z 846.650, rt 473 s (Fig.  2 d, S1P); and m/z 444.338, rt 486 s (Fig.  2 d, S1Q) found on armpits of volunteers 3 and 1 (Fig.  2 a) have a calculated half-life ranging from 0.7 to 1.9 weeks (Additional file  1 : Figure S1M-Q, all p values < 0.02), even though they originate from the same deodorant used by each individual. For some ingredients of deodorant used by volunteer 3 on time 0 (Additional file  1 : Figure S1M, N), a decline was observed during the first week, then little to no traces of these ingredients were detected during weeks 4–6 (T4–T6), then finally these ingredients reappear again during the last 3 weeks of personal product use (T7–T9). This suggests that these ingredients are present exclusively in the personal deodorant used by volunteer 3 before the study. Because a similar deodorant (Additional file  1 : Figure S1O-Q) and a face lotion (Additional file  1 : Figure S1R) was used by volunteer 3 and volunteer 2, respectively, prior to the study, there was no decline or absence of their ingredients during weeks 4–6 (T4–T6).

Polyethylene glycol compounds (Additional file  1 : Figure S1J-L) wash out faster from the skin than polypropylene glycol (Additional file  1 : Figure S1M-Q)(HL ~ 0.5 weeks vs ~ 1.9 weeks) and faster than fatty acids used in lotions (HL ~ 1.2 weeks) (Additional file  1 : Figure S1R), consistent with their hydrophilic (PEG) and hydrophobic properties (PPG and fatty acids) [ 25 , 26 ]. This difference in hydrophobicity is also reflected in the retention time as detected by mass spectrometry. Following the linear decrease of two PPG compounds from T0 to T1, they accumulated noticeably during weeks 2 and 3 (Additional file  1 : Figure S1M, N). This accumulation might be due to other sources of PPG such as the body wash used during this period or the clothes worn by person 3. Although PPG compounds were not listed in the ingredient list of the shampoo, we manually inspected the LC-MS data collected from this product and confirmed the absence of PPG compounds in the shampoo. The data suggest that this trend is characteristic of accumulation of PPG from additional sources. These could be clothes, beds, or sheets, in agreement with the observation of these molecules found in human habitats [ 27 ] but also in the public GNPS mass spectrometry dataset MSV000079274 that investigated the chemicals from dust collected from 1053 mattresses of children.

Temporal molecular and bacterial diversity in response to personal care use

To assess the effect of discontinuing and resuming the use of skin care products on molecular and microbiota dynamics, we first evaluated their temporal diversity. Skin sites varied markedly in their initial level (T0) of molecular and bacterial diversity, with higher molecular diversity at all sites for female participants compared to males (Fig.  3 a, b, Wilcoxon rank-sum-WR test, p values ranging from 0.01 to 0.0001, from foot to arm) and higher bacterial diversity in face (WR test, p  = 0.0009) and armpits (WR test, p  = 0.002) for females (Fig.  3 c, d). Temporal diversity was similar across the right and left sides of each body site of all individuals (WR test, molecular diversity: all p values > 0.05; bacterial diversity: all p values > 0.20). The data show that refraining from using beauty products (T1–T3) leads to a significant decrease in molecular diversity at all sites (Fig.  3 a, b, WR test, face: p  = 8.29e−07, arm: p  = 7.08e−09, armpit: p  = 1.13e−05, foot: p  = 0.002) and bacterial diversity mainly in armpits (WR test, p  = 0.03) and feet (WR test, p  = 0.04) (Fig.  3 c, d). While molecular diversity declined (Fig.  3 a, b) for arms and face, bacterial diversity (Fig.  3 c, d) was less affected in the face and arms when participants did not use skin care products (T1–T3). The molecular diversity remained stable in the arms and face of female participants during common beauty products use (T4–T6) to immediately increase as soon as the volunteers went back to their normal routines (T7–T9) (WR test, p  = 0.006 for the arms and face)(Fig.  3 a, b). A higher molecular (Additional file  1 : Figure S2A) and community (Additional file  1 : Figure S2B) diversity was observed for armpits and feet of all individuals during the use of antiperspirant and foot powder (T4–T6) (WR test, molecular diversity: armpit p  = 8.9e−33, foot p  = 1.03e−11; bacterial diversity: armpit p  = 2.14e−28, foot p  = 1.26e−11), followed by a molecular and bacterial diversity decrease in the armpits when their regular personal beauty product use was resumed (T7–T9) (bacterial diversity: WR test, p  = 4.780e−21, molecular diversity: WR test, p  = 2.159e−21). Overall, our data show that refraining from using beauty products leads to lower molecular and bacterial diversity, while resuming the use increases their diversity. Distinct variations between male and female molecular and community richness were perceived at distinct body parts (Fig.  3 a–d). Although the chemical diversity of personal beauty products does not explain these variations (Additional file  1 : Figure S2C), differences observed between males and females may be attributed to many environmental and lifestyle factors including different original skin care and different frequency of use of beauty products (Additional file  2 : Table S1), washing routines, and diet.

Molecular and bacterial diversity over a 9-week period, comparing samples based on their molecular (UPLC-Q-TOF-MS) or bacterial (16S rRNA amplicon) profiles. Molecular and bacterial diversity using the Shannon index was calculated from samples collected from each body part at each timepoint, separately for female ( n  = 5) and male ( n  = 6) individuals. Error bars represent standard error of the mean calculated at each timepoint, from up to four samples collected from the right and left side of each body part, of females ( n  = 5) and males ( n  = 6) separately. a , b Molecular alpha diversity measured using the Shannon index from five females (left panel) and six males (right panel), over 9 weeks, from four distinct body parts (armpits, face, arms, feet). c , d Bacterial alpha diversity measured using the Shannon index, from skin samples collected from five female (left panel) and six male individuals (right panel), over 9 weeks, from four distinct body parts (armpits, face, arms, feet). See also Additional file  1 : Figure S2

Longitudinal variation of skin metabolomics signatures

To gain insights into temporal metabolomics variation associated with beauty product use, chemical inventories collected over 9 weeks were subjected to multivariate analysis using the widely used Bray–Curtis dissimilarity metric (Fig.  4 a–c, S3A). Throughout the 9-week period, distinct molecular signatures were associated to each specific body site: arm, armpit, face, and foot (Additional file  1 : Figure S3A, Adonis test, p  < 0.001, R 2 0.12391). Mass spectrometric signatures displayed distinct individual trends at each specific body site (arm, armpit, face, and foot) over time, supported by their distinct locations in PCoA (principal coordinate analysis) space (Fig.  4 a, b) and based on the Bray–Curtis distances between molecular profiles (Additional file  1 : Figure S3B, WR test, all p values < 0.0001 from T0 through T9). This suggests a high molecular inter-individual variability over time despite similar changes in personal care routines. Significant differences in molecular patterns associated to ceasing (T1–T3) (Fig.  4 b, Additional file 1 : Figure S3C, WR test, T0 vs T1–T3 p  < 0.001) and resuming the use of common beauty products (T4–T6) (Additional file  1 : Figure S3C) were observed in the arm, face, and foot (Fig.  4 b), although the armpit exhibited the most pronounced changes (Fig.  4 b, Additional file 1 : Figure S3D, E, random forest highlighting that 100% of samples from each phase were correctly predicted). Therefore, we focused our analysis on this region. Molecular changes were noticeable starting the first week (T1) of discontinuing beauty product use. As shown for armpits in Fig.  4 c, these changes at the chemical level are specific to each individual, possibly due to the extremely personalized lifestyles before the study and match their original use of deodorant. Based on the initial use of underarm products (T0) (Additional file  2 : Table S1), two groups of participants can be distinguished: a group of five volunteers who used stick deodorant as evidenced by the mass spectrometry data and another group of volunteers where we found few or no traces suggesting they never or infrequently used stick deodorants (Additional file  2 : Table S1). Based on this criterion, the chemical trends shown in Fig.  4 c highlight that individuals who used stick deodorant before the beginning of the study (volunteers 1, 2, 3, 9, and 12) displayed a more pronounced shift in their armpits’ chemistries as soon as they stopped using deodorant (T1–T3), compared to individuals who had low detectable levels of stick deodorant use (volunteers 4, 6, 7, and 10), or “rarely-to-never” (volunteers 5 and 11) use stick deodorants as confirmed by the volunteers (Additional file  1 : Figure S3F, WR test, T0 vs T1–T3 all p values < 0.0001, with greater distance for the group of volunteers 1, 2, 3, 9, and 12, compared to volunteers 4, 5, 6, 7, 10, and 11). The most drastic shift in chemical profiles was observed during the transition period, when all participants applied the common antiperspirant on a daily basis (T4–T6) (Additional file  1 : Figure S3D, E). Finally, the molecular profiles became gradually more similar to those collected before the experiment (T0) as soon as the participants resumed using their personal beauty products (T7–T9) (Additional file  1 : Figure S3C), although traces of skin care products did last through the entire T7–T9 period in people who do not routinely apply these products (Fig.  4 c).

Individualized influence of beauty product application on skin metabolomics profiles over time. a Multivariate statistical analysis (principal coordinate analysis (PCoA)) comparing mass spectrometry data collected over 9 weeks from the skin of 11 individuals, all body parts, combined (first plot from the left) and then displayed separately (arm, armpits, face, feet). Color scale represents volunteer ID. The PCoA was calculated on all samples together, and subsets of the data are shown in this shared space and the other panels. b The molecular profiles collected over 9 weeks from all body parts, combined then separately (arm, armpits, face, feet). c Representative molecular profiles collected over 9 weeks from armpits of 11 individuals (volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12). Color gradient in b and c represents timepoints (time 0 to time 9), ranging from the lightest orange color to the darkest one that represent the earliest (time 0) to the latest (time 9) timepoint, respectively. 0.5 timepoints represent additional timepoints where three selected volunteers were samples (volunteers 4, 9, and 10). PCoA plots were generated using the Bray–Curtis dissimilarity matrix and visualized in Emperor [ 28 ]. See also Additional file  1 : Figure S3

Comparing chemistries detected in armpits at the end timepoints—when no products were used (T3) and during product use (T6)—revealed distinct molecular signatures characteristic of each phase (random forest highlighting that 100% of samples from each group were correctly predicted, see Additional file  1 : Figure S3D, E). Because volunteers used the same antiperspirant during T4–T6, molecular profiles converged during that time despite individual patterns at T3 (Fig.  4 b, c, Additional file  1 : Figure S3D). These distinct chemical patterns reflect the significant impact of beauty products on skin molecular composition. Although these differences may in part be driven by beauty product ingredients detected on the skin (Additional file  1 : Figure S1), we anticipated that additional host- and microbe-derived molecules may also be involved in these molecular changes.

To characterize the chemistries that vary over time, we used molecular networking, a MS visualization approach that evaluates the relationship between MS/MS spectra and compares them to reference MS/MS spectral libraries of known compounds [ 29 , 30 ]. We recently showed that molecular networking can successfully organize large-scale mass spectrometry data collected from the human skin surface [ 18 , 19 ]. Briefly, molecular networking uses the MScluster algorithm [ 31 ] to merge all identical spectra and then compares and aligns all unique pairs of MS/MS spectra based on their similarities where 1.0 indicates a perfect match. Similarities between MS/MS spectra are calculated using a similarity score, and are interpreted as molecular families [ 19 , 24 , 32 , 33 , 34 ]. Here, we used this method to compare and characterize chemistries found in armpits, arms, face, and foot of 11 participants. Based on MS/MS spectral similarities, chemistries highlighted through molecular networking (Additional file  1 : Figure S4A) were associated with each body region with 8% of spectra found exclusively in the arms, 12% in the face, 14% in the armpits, and 2% in the foot, while 18% of the nodes were shared between all four body parts and the rest of spectra were shared between two body sites or more (Additional file  1 : Figure S4B). Greater spectral similarities were highlighted between armpits, face, and arm (12%) followed by the arm and face (9%) (Additional file  1 : Figure S4B).

Molecules were annotated with Global Natural Products Social Molecular Networking (GNPS) libraries [ 29 ], using accurate parent mass and MS/MS fragmentation patterns, according to level 2 or 3 of annotation defined by the 2007 metabolomics standards initiative [ 35 ]. Through annotations, molecular networking revealed that many compounds derived from steroids (Fig.  5 a–d), bile acids (Additional file  1 : Figure S5A-D), and acylcarnitines (Additional file  1 : Figure S5E-F) were exclusively detected in the armpits. Using authentic standards, the identity of some pheromones and bile acids were validated to a level 1 identification with matched retention times (Additional file  1 : Figure S6B, S7A, C, D). Other steroids and bile acids were either annotated using standards with identical MS/MS spectra but slightly different retention times (Additional file  1 : Figure S6A) or annotated with MS/MS spectra match with reference MS/MS library spectra (Additional file  1 : Figure S6C, D, S7B, S6E-G). These compounds were therefore classified as level 3 [ 35 ]. Acylcarnitines were annotated to a family of possible acylcarnitines (we therefore classify as level 3), as the positions of double bonds or cis vs trans configurations are unknown (Additional file  1 : Figure S8A, B).

Underarm steroids and their longitudinal abundance. a – d Steroid molecular families in the armpits and their relative abundance over a 9-week period. Molecular networking was applied to characterize chemistries from the skin of 11 healthy individuals. The full network is shown in Additional file  1 : Figure S4A, and networking parameters can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 for MS/MS datasets MSV000081582. Each node represents a consensus of a minimum of 3 identical MS/MS spectra. Yellow nodes represent MS/MS spectra detected in armpits samples. Hexagonal shape represents MS/MS spectra match between skin samples and chemical standards. Plots are representative of the relative abundance of each compound over time, calculated separately from LC-MS1 data collected from the armpits of each individual. Steroids detected in armpits are a , dehydroisoandrosterone sulfate ( m/z 369.190, rt 247 s), b androsterone sulfate ( m/z 371.189, rt 261 s), c 1-dehydroandrostenedione ( m/z 285.185, rt 273 s), and d dehydroandrosterone ( m/z 289.216, rt 303 s). Relative abundance over time of each steroid compound is represented. Error bars represent the standard error of the mean calculated at each timepoint from four armpit samples from the right and left side of each individual separately. See also Additional file  1 : Figures S4-S8

Among the steroid compounds, several molecular families were characterized: androsterone (Fig.  5 a, b, d), androstadienedione (Fig.  5 c), androstanedione (Additional file  1 : Figure S6E), androstanolone (Additional file  1 : Figure S6F), and androstenedione (Additional file  1 : Figure S6G). While some steroids were detected in the armpits of several individuals, such as dehydroisoandrosterone sulfate ( m/z 369.19, rt 247 s) (9 individuals) (Fig.  5 a, Additional file  1 : Figure S6A), androsterone sulfate ( m/z 371.189, rt 261 s) (9 individuals) (Fig.  5 b, Additional file  1 : Figure S6C), and 5-alpha-androstane-3,17-dione ( m/z 271.205, rt 249 s) (9 individuals) (Additional file  1 : Figure S6E), other steroids including 1-dehydroandrostenedione ( m/z 285.185, rt 273 s) (Fig.  5 c, Additional file  1 : Figure S6B), dehydroandrosterone ( m/z 289.216, rt 303 s) (Fig.  5 d, Additional file 1 : Figure S6D), and 5-alpha-androstan-17.beta-ol-3-one ( m/z 291.231, rt 318 s) (Additional file  1 : Figure S6F) were only found in the armpits of volunteer 11 and 4-androstene-3,17-dione ( m/z 287.200, rt 293 s) in the armpits of volunteer 11 and volunteer 5, both are male that never applied stick deodorants (Additional file  1 : Figure S6G). Each molecular species exhibited a unique pattern over the 9-week period. The abundance of dehydroisoandrosterone sulfate (Fig.  5 a, WR test, p  < 0.01 for 7 individuals) and dehydroandrosterone (Fig.  5 a, WR test, p  = 0.00025) significantly increased during the use of antiperspirant (T4–T6), while androsterone sulfate (Fig.  5 b) and 5-alpha-androstane-3,17-dione (Additional file  1 : Figure S6E) display little variation over time. Unlike dehydroisoandrosterone sulfate (Fig.  5 a) and dehydroandrosterone (Fig.  5 d), steroids including 1-dehydroandrostenedione (Fig.  5 c, WR test, p  = 0.00024) and 4-androstene-3,17-dione (Additional file  1 : Figure S6G, WR test, p  = 0.00012) decreased in abundance during the 3 weeks of antiperspirant application (T4–T6) in armpits of male 11, and their abundance increased again when resuming the use of his normal skin care routines (T7–T9). Interestingly, even within the same individual 11, steroids were differently impacted by antiperspirant use as seen for 1-dehydroandrostenedione that decreased in abundance during T4–T6 (Fig.  5 c, WR test, p  = 0.00024), while dehydroandrosterone increased in abundance (Fig.  5 d, WR test, p  = 0.00025), and this increase was maintained during the last 3 weeks of the study (T7–T9).

In addition to steroids, many bile acids (Additional file  1 : Figure S5A-D) and acylcarnitines (Additional file  1 : Figure S5E-F) were detected on the skin of several individuals through the 9-week period. Unlike taurocholic acid found only on the face (Additional file  1 : Figures S5A, S7A) and tauroursodeoxycholic acid detected in both armpits and arm samples (Additional file  1 : Figures S5B, S7B), other primary bile acids such as glycocholic (Additional file  1 : Figures S5C, S7C) and chenodeoxyglycocholic acid (Additional file  1 : Figures S5D, S7D) were exclusively detected in the armpits. Similarly, acylcarnitines were also found either exclusively in the armpits (hexadecanoyl carnitines) (Additional file  1 : Figures S5E, S8A) or in the armpits and face (tetradecenoyl carnitine) (Additional file  1 : Figures S5F, S8B) and, just like the bile acids, they were also stably detected during the whole 9-week period.

Bacterial communities and their variation over time

Having demonstrated the impact of beauty products on the chemical makeup of the skin, we next tested the extent to which skin microbes are affected by personal care products. We assessed temporal variation of bacterial communities detected on the skin of healthy individuals by evaluating dissimilarities of bacterial collections over time using unweighted UniFrac distance [ 36 ] and community variation at each body site in association to beauty product use [ 3 , 15 , 37 ]. Unweighted metrics are used for beta diversity calculations because we are primarily concerned with changes in community membership rather than relative abundance. The reason for this is that skin microbiomes can fluctuate dramatically in relative abundance on shorter timescales than that assessed here. Longitudinal variations were revealed for the armpits (Fig.  6 a) and feet microbiome by their overall trend in the PCoA plots (Fig.  6 b), while the arm (Fig.  6 c) and face (Fig.  6 d) displayed relatively stable bacterial profiles over time. As shown in Fig.  6 a–d, although the microbiome was site-specific, it varied more between individuals and this inter-individual variability was maintained over time despite same changes in personal care routine (WR test, all p values at all timepoints < 0.05, T5 p  = 0.07), in agreement with previous findings that individual differences in the microbiome are large and stable over time [ 3 , 4 , 10 , 37 ]. However, we show that shifts in the microbiome can be induced by changing hygiene routine and therefore skin chemistry. Changes associated with using beauty products (T4–T6) were more pronounced for the armpits (Fig.  6 a, WR test, p  = 1.61e−52) and feet (Fig.  6 b, WR test, p  = 6.15e−09), while little variations were observed for the face (Fig.  6 d, WR test, p  = 1.402.e−83) and none for the arms (Fig.  6 c, WR test, p  = 0.296).

Longitudinal variation of skin bacterial communities in association with beauty product use. a - d Bacterial profiles collected from skin samples of 11 individuals, over 9 weeks, from four distinct body parts a) armpits, b) feet, c) arms and d) face, using multivariate statistical analysis (Principal Coordinates Analysis PCoA) and unweighted Unifrac metric. Each color represents bacterial samples collected from an individual. PCoA were calculated separately for each body part. e , f Representative Gram-negative (Gram -) bacteria collected from arms, armpits, face and feet of e) female and f) male participants. See also Additional file  1 : Figure S9A, B showing Gram-negative bacterial communities represented at the genus level

A significant increase in abundance of Gram-negative bacteria including the phyla Proteobacteria and Bacteroidetes was noticeable for the armpits and feet of both females (Fig.  6 e; Mann–Whitney U , p  = 8.458e−07) and males (Fig.  6 f; Mann–Whitney U , p  = 0.0004) during the use of antiperspirant (T4–T6), while their abundance remained stable for the arms and face during that time (Fig.  6 e, f; female arm p  = 0.231; female face p value = 0.475; male arm p = 0.523;male face p  = 6.848751e−07). These Gram-negative bacteria include Acinetobacter and Paracoccus genera that increased in abundance in both armpits and feet of females (Additional file  1 : Figure S9A), while a decrease in abundance of Enhydrobacter was observed in the armpits of males (Additional file  1 : Figure S9B). Cyanobacteria, potentially originating from plant material (Additional file  1 : Figure S9C) also increased during beauty product use (T4–T6) especially in males, in the armpits and face of females (Fig.  6 e) and males (Fig.  6 f). Interestingly, although chloroplast sequences (which group phylogenetically within the cyanobacteria [ 38 ]) were only found in the facial cream (Additional file  1 : Figure S9D), they were detected in other locations as well (Fig.  6 e, f. S9E, F), highlighting that the application of a product in one region will likely affect other regions of the body. For example, when showering, a face lotion will drip down along the body and may be detected on the feet. Indeed, not only did the plant material from the cream reveal this but also the shampoo used for the study for which molecular signatures were readily detected on the feet as well (Additional file  1 : Figure S10A). Minimal average changes were observed for Gram-positive organisms (Additional file  1 : Figure S10B, C), although in some individuals the variation was greater than others (Additional file  1 : Figure S10D, E) as discussed for specific Gram-positive taxa below.

At T0, the armpit’s microflora was dominated by Staphylococcus (26.24%, 25.11% of sequencing reads for females and 27.36% for males) and Corynebacterium genera (26.06%, 17.89% for females and 34.22% for males) (Fig.  7 a—first plot from left and Additional file  1 : Figure S10D, E). They are generally known as the dominant armpit microbiota and make up to 80% of the armpit microbiome [ 39 , 40 ]. When no deodorants were used (T1–T3), an overall increase in relative abundance of Staphylococcus (37.71%, 46.78% for females and 30.47% for males) and Corynebacterium (31.88%, 16.50% for females and 44.15% for males) genera was noticeable (WR test, p  < 3.071e−05) (Fig.  7 a—first plot from left), while the genera Anaerococcus and Peptoniphilus decreased in relative abundance (WR test, p  < 0.03644) (Fig.  7 a—first plot from left and Additional file  1 : Figure S10D, E). When volunteers started using antiperspirants (T4–T6), the relative abundance of Staphylococcus (37.71%, 46.78% females and 30.47% males, to 21.71%, 25.02% females and 19.25% males) and Corynebacterium (31.88%, 16.50% females and 44.15% males, to 15.83%, 10.76% females and 19.60% males) decreased (WR test, p  < 3.071e−05) (Fig.  7 a, Additional file  1 : Figure S10D, E) and at the same time, the overall alpha diversity increased significantly (WR test, p  = 3.47e−11) (Fig.  3 c, d). The microbiota Anaerococcus (WR test, p  = 0.0006018) , Peptoniphilus (WR test, p  = 0.008639), and Micrococcus (WR test, p  = 0.0377) increased significantly in relative abundance, together with a lot of additional low-abundant species that lead to an increase in Shannon alpha diversity (Fig.  3 c, d). When participants went back to normal personal care products (T7–T9), the underarm microbiome resembled the original underarm community of T0 (WR test, p  = 0.7274) (Fig.  7 a). Because armpit bacterial communities are person-specific (inter-individual variability: WR test, all p values at all timepoints < 0.05, besides T5 p n.s), variation in bacterial abundance upon antiperspirant use (T4–T6) differ between individuals and during the whole 9-week period (Fig.  7a —taxonomic plots per individual). For example, the underarm microbiome of male 5 exhibited a unique pattern, where Corynebacterium abundance decreased drastically during the use of antiperspirant (82.74 to 11.71%, WR test, p  = 3.518e−05) while in the armpits of female 9 a huge decrease in Staphylococcus abundance was observed (Fig.  7 a) (65.19 to 14.85%, WR test, p  = 0.000113). Unlike other participants, during T0–T3, the armpits of individual 11 were uniquely characterized by the dominance of a sequence that matched most closely to the Enhydrobacter genera . The transition to antiperspirant use (T4–T6) induces the absence of Enhydrobacter (30.77 to 0.48%, WR test, p  = 0.01528) along with an increase of Corynebacterium abundance (26.87 to 49.74%, WR test, p  = 0.1123) (Fig.  7 a—male 11).

Person-to-person bacterial variabilities over time in the armpits and feet. a Armpit microbiome changes when stopping personal care product use, then resuming. Armpit bacterial composition of the 11 volunteers combined, then separately, (female 1, female 2, female 3, male 4, male 5, male 6, male 7, female 9, male 10, male 11, female 12) according to the four periods within the experiment. b Feet bacterial variation over time of the 12 volunteers combined, then separately (female 1, female 2, female 3, male 4, male 5, male 6, male 7, female 9, male 10, male 11, female 12) according to the four periods within the experiment. See also Additional file  1 : Figure S9-S13

In addition to the armpits, a decline in abundance of Staphylococcus and Corynebacterium was perceived during the use of the foot powder (46.93% and 17.36%, respectively) compared to when no beauty product was used (58.35% and 22.99%, respectively) (WR test, p  = 9.653e−06 and p  = 0.02032, respectively), while the abundance of low-abundant foot bacteria significantly increased such as Micrococcus (WR test, p  = 1.552e−08), Anaerococcus (WR test, p  = 3.522e−13), Streptococcus (WR test, p  = 1.463e−06), Brevibacterium (WR test, p  = 6.561e−05), Moraxellaceae (WR test, p  = 0.0006719), and Acinetobacter (WR test, p  = 0.001487), leading to a greater bacterial diversity compared to other phases of the study (Fig.  7 b first plot from left, Additional file  1 : Figure S10D, E, Fig.  3 c, d).

We further evaluated the relationship between the two omics datasets by superimposing the principal coordinates calculated from metabolome and microbiome data (Procrustes analysis) (Additional file  1 : Figure S11) [ 34 , 41 , 42 ]. Metabolomics data were more correlated with patterns observed in microbiome data in individual 3 (Additional file  1 : Figure S11C, Mantel test, r  = 0.23, p  < 0.001), individual 5 (Additional file  1 : Figure S11E, r  = 0.42, p  < 0.001), individual 9 (Additional file  1 : Figure S11H, r  = 0.24, p  < 0.001), individual 10 (Additional file  1 : Figure S11I, r  = 0.38, p  < 0.001), and individual 11 (Additional file  1 : Figure S11J, r  = 0.35, p  < 0.001) when compared to other individuals 1, 2, 4, 6, 7, and 12 (Additional file  1 : Figure S11A, B, D, F, G, K, respectively) (Mantel test, all r  < 0.2, all p values < 0.002, for volunteer 2 p n.s). Furthermore, these correlations were individually affected by ceasing (T1–T3) or resuming the use of beauty products (T4–T6 and T7–T9) (Additional file  1 : Figure S11A-K).

Overall, metabolomics–microbiome correlations were consistent over time for the arms, face, and feet although alterations were observed in the arms of volunteers 7 (Additional file  1 : Figure S11G) and 10 (Additional file  1 : Figure S11I) and the face of volunteer 7 (Additional file  1 : Figure S11G) during product use (T4–T6). Molecular–bacterial correlations were mostly affected in the armpits during antiperspirant use (T4–T6), as seen for volunteers male 7 (Additional file  1 : Figure S11G) and 11 (Additional file  1 : Figure S11J) and females 2 (Additional file  1 : Figure S11B), 9 (Additional file  1 : Figure S11H), and 12 (Additional file  1 : Figure S11K). This perturbation either persisted during the last 3 weeks (Additional file  1 : Figure S11D, E, H, I, K) when individuals went back to their normal routine (T7–T9) or resembled the initial molecular–microbial correlation observed in T0 (Additional file  1 : Figure S11C, G, J). These alterations in molecular–bacterial correlation are driven by metabolomics changes during antiperspirant use as revealed by metabolomics shifts on the PCoA space (Additional file  1 : Figure S11), partially due to the deodorant’s chemicals (Additional file  1 : Figure S1J, K) but also to changes observed in steroid levels in the armpits (Fig.  5A, C, D , Additional file 1 : Figure S6G), suggesting metabolome-dependant changes of the skin microbiome. In agreement with previous findings that showed efficient biotransformation of steroids by Corynebacterium [ 43 , 44 ], our correlation analysis associates specific steroids that were affected by antiperspirant use in the armpits of volunteer 11 (Fig.  5 c, d, Additional file 1 : Figure S6G) with microbes that may produce or process them: 1-dehydroandrostenedione, androstenedione, and dehydrosterone with Corynebacterium ( r  = − 0.674, p  = 6e−05; r  = 0.671, p  = 7e−05; r  = 0.834, p  < 1e−05, respectively) (Additional file  1 : Figure S12A, B, C, respectively) and Enhydrobacter ( r  = 0.683, p  = 4e−05; r  = 0.581, p  = 0.00095; r  = 0.755, p  < 1e−05 respectively) (Additional file  1 : Figure S12D, E, F, respectively).

Despite the widespread use of skin care and hygiene products, their impact on the molecular and microbial composition of the skin is poorly studied. We established a workflow that examines individuals to systematically study the impact of such lifestyle characteristics on the skin by taking a broad look at temporal molecular and bacterial inventories and linking them to personal skin care product use. Our study reveals that when the hygiene routine is modified, the skin metabolome and microbiome can be altered, but that this alteration depends on product use and location on the body. We also show that like gut microbiome responses to dietary changes [ 20 , 21 ], the responses are individual-specific.

We recently reported that traces of our lifestyle molecules can be detected on the skin days and months after the original application [ 18 , 19 ]. Here, we show that many of the molecules associated with our personal skin and hygiene products had a half-life of 0.5 to 1.9 weeks even though the volunteers regularly showered, swam, or spent time in the ocean. Thus, a single application of some of these products has the potential to alter the microbiome and skin chemistry for extensive periods of time. Our data suggests that although host genetics and diet may play a role, a significant part of the resilience of the microbiome that has been reported [ 10 , 45 ] is due to the resilience of the skin chemistry associated with personal skin and hygiene routines, or perhaps even continuous re-exposure to chemicals from our personal care routines that are found on mattresses, furniture, and other personal objects [ 19 , 27 , 46 ] that are in constant contact. Consistent with this observation is that individuals in tribal regions and remote villages that are infrequently exposed to the types of products used in this study have very different skin microbial communities [ 47 , 48 ] and that the individuals in this study who rarely apply personal care products had a different starting metabolome. We observed that both the microbiome and skin chemistry of these individuals were most significantly affected by these products. This effect by the use of products at T4–T6 on the volunteers that infrequently used them lasted to the end phase of the study even though they went back to infrequent use of personal care products. What was notable and opposite to what the authors originally hypothesized is that the use of the foot powder and antiperspirant increased the diversity of microbes and that some of this diversity continued in the T7–T9 phase when people went back to their normal skin and hygiene routines. It is likely that this is due to the alteration in the nutrient availability such as fatty acids and moisture requirements, or alteration of microbes that control the colonization via secreted small molecules, including antibiotics made by microbes commonly found on the skin [ 49 , 50 ].

We detected specific molecules on the skin that originated from personal care products or from the host. One ingredient that lasts on the skin is propylene glycol, which is commonly used in deodorants and antiperspirants and added in relatively large amounts as a humectant to create a soft and sleek consistency [ 51 ]. As shown, daily use of personal care products is leading to high levels of exposure to these polymers. Such polymers cause contact dermatitis in a subset of the population [ 51 , 52 ]. Our data reveal a lasting accumulation of these compounds on the skin, suggesting that it may be possible to reduce their dose in deodorants or frequency of application and consequently decrease the degree of exposure to such compounds. Formulation design of personal care products may be influenced by performing detailed outcome studies. In addition, longer term impact studies are needed, perhaps in multiple year follow-up studies, to assess if the changes we observed are permanent or if they will recover to the original state.

Some of the host- and microbiome-modified molecules were also detected consistently, such as acylcarnitines, bile acids, and certain steroids. This means that a portion of the molecular composition of a person’s skin is not influenced by the beauty products applied to the skin, perhaps reflecting the level of exercise for acylcarnitines [ 53 , 54 ] or the liver (dominant location where they are made) or gallbladder (where they are stored) function for bile acids. The bile acid levels are not related to sex and do not change in amount during the course of this study. While bile acids are typically associated with the human gut microbiome [ 34 , 55 , 56 , 57 , 58 ], it is unclear what their role is on the skin and how they get there. One hypothesis is that they are present in the sweat that is excreted through the skin, as this is the case for several food-derived molecules such as caffeine or drugs and medications that have been previously reported on the human skin [ 19 ] or that microbes synthesize them de novo [ 55 ]. The only reports we could find on bile acids being associated with the skin describe cholestasis and pruritus diseases. Cholestasis and pruritus in hepatobiliary disease have symptoms of skin bile acid accumulation that are thought to be responsible for severe skin itching [ 59 , 60 ]. However, since bile acids were found in over 50% of the healthy volunteers, their detection on the skin is likely a common phenotype among the general population and not only reflective of disease, consistent with recent reports challenging these molecules as biomarkers of disease [ 59 ]. Other molecules that were detected consistently came from personal care products.

Aside from molecules that are person-specific and those that do not vary, there are others that can be modified via personal care routines. Most striking is how the personal care routines influenced changes in hormones and pheromones in a personalized manner. This suggests that there may be personalized recipes that make it possible to make someone more or less attractive to others via adjustments of hormonal and pheromonal levels through alterations in skin care.

Here, we describe the utilization of an approach that combines metabolomics and microbiome analysis to assess the effect of modifying personal care regime on skin chemistry and microbes. The key findings are as follows: (1) Compounds from beauty products last on the skin for weeks after their first use despite daily showering. (2) Beauty products alter molecular and bacterial diversity as well as the dynamic and structure of molecules and bacteria on the skin. (3) Molecular and bacterial temporal variability is product-, site-, and person-specific, and changes are observed starting the first week of beauty product use. This study provides a framework for future investigations to understand how lifestyle characteristics such as diet, outdoor activities, exercise, and medications shape the molecular and microbial composition of the skin. These factors have been studied far more in their impact on the gut microbiome and chemistry than in the skin. Revealing how such factors can affect skin microbes and their associated metabolites may be essential to define long-term skin health by restoring the appropriate microbes particularly in the context of skin aging [ 61 ] and skin diseases [ 49 ] as has shown to be necessary for amphibian health [ 62 , 63 ], or perhaps even create a precision skin care approach that utilizes the proper care ingredients based on the microbial and chemical signatures that could act as key players in host defense [ 49 , 64 , 65 ].

Subject recruitment and sample collection

Twelve individuals between 25 and 40 years old were recruited to participate in this study, six females and six males. Female volunteer 8 dropped out of the study as she developed a skin irritation during the T1–T3 phase. All volunteers signed a written informed consent in accordance with the sampling procedure approved by the UCSD Institutional Review Board (Approval Number 161730). Volunteers were required to follow specific instructions during 9 weeks. They were asked to bring in samples of their personal care products they used prior to T0 so they could be sampled as well. Following the initial timepoint time 0 and during the first 3 weeks (week 1–week 3), volunteers were asked not to use any beauty products (Fig.  1 b). During the next 3 weeks (week 4–week 6), four selected commercial beauty products provided to all volunteers were applied once a day at specific body part (deodorant for the armpits, soothing foot powder between the toes, sunscreen for the face, and moisturizer for front forearms) (Fig.  1 b, Additional file  3 : Table S2 Ingredient list of beauty products). During the first 6 weeks, volunteers were asked to shower with a head to toe shampoo. During the last 3 weeks (week 7–week 9), all volunteers went back to their normal routine and used the personal care products used before the beginning of the study (Fig.  1 b). Volunteers were asked not to shower the day before sampling. Samples were collected by the same three researchers to ensure consistency in sampling and the area sampled. Researchers examined every subject together and collected metabolomics and microbiome samples from each location together. Samples were collected once a week (from day 0 to day 68—10 timepoints total) for volunteers 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, and 12, and on day 0 and day 6 for volunteer 8. For individuals 4, 9, and 10, samples were collected twice a week. Samples collected for 11 volunteers during 10 timepoints: 11 volunteers × 10 timepoints × 4 samples × 4 body sites = 1760. Samples collected from 3 selected volunteers during 9 additional timepoints: 3 volunteers × 9 timepoints × 4 samples × 4 body sites = 432. All samples were collected following the same protocol described in [ 18 ]. Briefly, samples were collected over an area of 2 × 2 cm, using pre-moistened swabs in 50:50 ethanol/water solution for metabolomics analysis or in Tris-EDTA buffer for 16S rRNA sequencing. Four samples were collected from each body part right and left side. The locations sampled were the face—upper cheek bone and lower jaw, armpit—upper and lower area, arm—front of the elbow (antecubitis) and forearm (antebrachium), and feet—in between the first and second toe and third and fourth toe. Including personal care product references, a total of 2275 samples were collected over 9 weeks and were submitted to both metabolomics and microbial inventories.

Metabolite extraction and UPLC-Q-TOF mass spectrometry analysis

Skin swabs were extracted and analyzed using a previously validated workflow described in [ 18 , 19 ]. All samples were extracted in 200 μl of 50:50 ethanol/water solution for 2 h on ice then overnight at − 20 °C. Swab sample extractions were dried down in a centrifugal evaporator then resuspended by vortexing and sonication in a 100 μl 50:50 ethanol/water solution containing two internal standards (fluconazole 1 μM and amitriptyline 1 μM). The ethanol/water extracts were then analyzed using a previously validated UPLC-MS/MS method [ 18 , 19 ]. We used a ThermoScientific UltiMate 3000 UPLC system for liquid chromatography and a Maxis Q-TOF (Quadrupole-Time-of-Flight) mass spectrometer (Bruker Daltonics), controlled by the Otof Control and Hystar software packages (Bruker Daltonics) and equipped with ESI source. UPLC conditions of analysis are 1.7 μm C18 (50 × 2.1 mm) UHPLC Column (Phenomenex), column temperature 40 °C, flow rate 0.5 ml/min, mobile phase A 98% water/2% acetonitrile/0.1% formic acid ( v / v ), mobile phase B 98% acetonitrile/2% water/0.1% formic acid ( v / v ). A linear gradient was used for the chromatographic separation: 0–2 min 0–20% B, 2–8 min 20–99% B, 8–9 min 99–99% B, 9–10 min 0% B. Full-scan MS spectra ( m/z 80–2000) were acquired in a data-dependant positive ion mode. Instrument parameters were set as follows: nebulizer gas (nitrogen) pressure 2 Bar, capillary voltage 4500 V, ion source temperature 180 °C, dry gas flow 9 l/min, and spectra rate acquisition 10 spectra/s. MS/MS fragmentation of 10 most intense selected ions per spectrum was performed using ramped collision induced dissociation energy, ranged from 10 to 50 eV to get diverse fragmentation patterns. MS/MS active exclusion was set after 4 spectra and released after 30 s.

Mass spectrometry data collected from the skin of 12 individuals can be found here MSV000081582.

LC-MS data processing

LC-MS raw data files were converted to mzXML format using Compass Data analysis software (Bruker Daltonics). MS1 features were selected for all LC-MS datasets collected from the skin of 12 individuals and blank samples (total 2275) using the open-source software MZmine [ 66 ]—see Additional file  4 : Table S3 for parameters. Subsequent blank filtering, total ion current, and internal standard normalization were performed (Additional file  5 : Table S4) for representation of relative abundance of molecular features (Fig.  2 , Additional file  1 : Figure S1), principal coordinate analysis (PCoA) (Fig.  4 ). For steroid compounds in Fig.  5 a–d, bile acids (Additional file  1 : Figure S5A-D), and acylcarnitines (Additional file  1 : Figure S5E, F) compounds, crop filtering feature available in MZmine [ 66 ] was used to identify each feature separately in all LC-MS data collected from the skin of 12 individuals (see Additional file  4 : Table S3 for crop filtering parameters and feature finding in Additional file  6 : Table S5).

Heatmap in Fig.  2 was constructed from the bucket table generated from LC-MS1 features (Additional file  7 : Table S6) and associated metadata (Additional file  8 : Table S7) using the Calour command line available here: https://github.com/biocore/calour . Calour parameters were as follows: normalized read per sample 5000 and cluster feature minimum reads 50. Procrustes and Pearson correlation analyses in Additional file  1 : Figures S10 and S11 were performed using the feature table in Additional file  9 : Table S8, normalized using the probabilistic quotient normalization method [ 67 ].

16S rRNA amplicon sequencing

16S rRNA sequencing was performed following the Earth Microbiome Project protocols [ 68 , 69 ], as described before [ 18 ]. Briefly, DNA was extracted using MoBio PowerMag Soil DNA Isolation Kit and the V4 region of the 16S rRNA gene was amplified using barcoded primers [ 70 ]. PCR was performed in triplicate for each sample, and V4 paired-end sequencing [ 70 ] was performed using Illumina HiSeq (La Jolla, CA). Raw sequence reads were demultiplexed and quality controlled using the defaults, as provided by QIIME 1.9.1 [ 71 ]. The primary OTU table was generated using Qiita ( https://qiita.ucsd.edu/ ), using UCLUST ( https://academic.oup.com/bioinformatics/article/26/19/2460/230188 ) closed-reference OTU picking method against GreenGenes 13.5 database [ 72 ]. Sequences can be found in EBI under accession number EBI: ERP104625 or in Qiita ( qiita.ucsd.edu ) under Study ID 10370. Resulting OTU tables were then rarefied to 10,000 sequences/sample for downstream analyses (Additional file  10 Table S9). See Additional file  11 : Table S10 for read count per sample and Additional file  1 : Figure S13 representing the samples that fall out with rarefaction at 10,000 threshold. The dataset includes 35 blank swab controls and 699 empty controls. The blank samples can be accessed through Qiita ( qiita.ucsd.edu ) as study ID 10370 and in EBI with accession number EBI: ERP104625. Blank samples can be found under the metadata category “sample_type” with the name “empty control” and “Swabblank.” These samples fell below the rarefaction threshold at 10,000 (Additional file  11 : Table S10).

To rule out the possibility that personal care products themselves contained the microbes that induced the changes in the armpit and foot microbiomes that were observed in this study (Fig.  7 ), we subjected the common personal care products that were used in this study during T4–T6 also to 16S rRNA sequencing. The data revealed that within the limit of detectability of the current experiment, few 16S signatures were detected. One notable exception was the most dominant plant-originated bacteria chloroplast detected in the sunscreen lotion applied on the face (Additional file  1 : Figure S9D), that was also detected on the face of individuals and at a lower level on their arms, sites where stable microbial communities were observed over time (Additional file  1 : Figure S9E, F). This finding is in agreement with our previous data from the 3D cartographical skin maps that revealed the presence of co-localized chloroplast and lotion molecules [ 18 ]. Other low-abundant microbial signatures found in the sunscreen lotion include additional plant-associated bacteria: mitochondria [ 73 ], Bacillaceae [ 74 , 75 ], Planococcaceae [ 76 ], and Ruminococcaceae family [ 77 ], but all these bacteria are not responsible for microbial changes associated to beauty product use, as they were poorly detected in the armpits and feet (Fig.  7 ).

To assess the origin of Cyanobacteria detected in skin samples, each Greengenes [ 72 ] 13_8 97% OTU table (per lane; obtained from Qiita [ 78 ] study 10,370) was filtered to only features with a p__Cyanobacteria phylum. The OTU maps for these tables—which relate each raw sequence to an OTU ID—were then filtered to only those observed p__Cyanobacteria OTU IDs. The filtered OTU map was used to extract the raw sequences into a single file. Separately, the unaligned Greengenes 13_8 99% representative sequences were filtered into two sets, first the set of representatives associated with c__Chloroplast (our interest database), and second the set of sequences associated with p__Cyanobacteria without the c__Chloroplast sequences (our background database). Platypus Conquistador [ 79 ] was then used to determine what reads were observed exclusively in the interest database and not in the background database. Of the 4,926,465 raw sequences associated with a p__Cyanobacteria classification (out of 318,686,615 total sequences), at the 95% sequence identity level with 100% alignment, 4,860,258 sequences exclusively recruit to full-length chloroplast 16S by BLAST [ 80 ] with the bulk recruiting to streptophytes (with Chlorophyta and Stramenopiles to a lesser extent). These sequences do not recruit non-chloroplast Cyanobacteria full length 16S.

Half-life calculation for metabolomics data

In order to estimate the biological half-life of molecules detected in the skin, the first four timepoints of the study (T0, T1, T2, T3) were considered for the calculation to allow the monitoring of personal beauty products used at T0. The IUPAC’s definition of biological half-life as the time required to a substance in a biological system to be reduced to half of its value, assuming an approximately exponential removal [ 81 ] was used. The exponential removal can be described as C ( t )  =  C 0 e − tλ where t represents the time in weeks, C 0 represents the initial concentration of the molecule, C ( t ) represents the concentration of the molecule at time t , and λ is the rate of removal [ http://onlinelibrary.wiley.com/doi/10.1002/9780470140451.ch2/summary ]. The parameter λ was estimated by a mixed linear effects model in order to account for the paired sample structure. The regression model tests the null hypothesis that λ is equal to zero and only the significant ( p value < 0.05) parameters were considered.

Principal coordinate analysis

We performed principal coordinate analysis (PCoA) on both metabolomics and microbiome data. For metabolomics, we used MS1 features (Additional file  5 : Table S4) and calculated Bray–Curtis dissimilarity metric using ClusterApp ( https://github.com/mwang87/q2_metabolomics ).

For microbiome data, we used rarefied OTU table (Additional file 10 : Table S9) and used unweighted UniFrac metric [ 36 ] to calculate beta diversity distance matrix using QIIME2 (https://qiime2.org). Results from both data sources were visualized using Emperor ( https://biocore.github.io/emperor/ ) [ 28 ].

Molecular networking

Molecular networking was generated from LC-MS/MS data collected from skin samples of 11 individuals MSV000081582, using the Global Natural Products Social Molecular Networking platform (GNPS) [ 29 ]. Molecular network parameters for MS/MS data collected from all body parts of 11 individuals during T0–T9 MSV000081582 are accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 . Molecular network parameters for MS/MS data collected from armpits T0–T3 MSV000081582 and deodorant used by individual 1 and 3 MSV000081580 can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec57915ad and here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 , respectively. Molecular networks were exported and visualized in Cytoscape 3.4.0. [ 82 ]. Molecular networking parameters were set as follows: parent mass tolerance 1 Da, MS/MS fragment ion tolerance 0.5 Da, and cosine threshold 0.65 or greater, and only MS/MS spectral pairs with at least 4 matched fragment ions were included. Each MS/MS spectrum was only allowed to connect to its top 10 scoring matches, resulting in a maximum of 10 connections per node. The maximum size of connected components allowed in the network was 600, and the minimum number of spectra required in a cluster was 3. Venn diagrams were generated from Cytoscape data http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 using Cytoscape [ 82 ] Venn diagram app available here http://apps.cytoscape.org/apps/all .

Shannon molecular and bacterial diversity

The diversity analysis was performed separately for 16S rRNA data and LC-MS data. For each sample in each feature table (LC-MS data and microbiome data), we calculated the value of the Shannon diversity index. For LC-MS data, we used the full MZmine feature table (Additional file  5 : Table S4). For microbiome data, we used the closed-reference BIOM table rarefied to 10,000 sequences/sample. For diversity changes between timepoints, we aggregated Shannon diversity values across groups of individuals (all, females, males) and calculated mean values and standard errors. All successfully processed samples (detected features in LC-MS or successful sequencing with 10,000 or more sequences/sample) were considered.

Beauty products and chemical standards

Samples (10 mg) from personal care products used during T0 and T7–T9 MSV000081580 (Additional file  2 : Table S1) and common beauty products used during T4–T6 MSV000081581 (Additional file  3 : Table S2) were extracted in 1 ml 50:50 ethanol/water. Sample extractions were subjected to the same UPLC-Q-TOF MS method used to analyze skin samples and described above in the section “ Metabolite extraction and UPLC-Q-TOF mass spectrometry analysis .” Authentic chemical standards MSV000081583 including 1-dehydroandrostenedion (5 μM), chenodeoxyglycocholic acid (5 μM), dehydroisoandrosterone sulfate (100 μM), glycocholic acid (5 μM), and taurocholic acid (5 μM) were analyzed using the same mass spectrometry workflow used to run skin and beauty product samples.

Monitoring beauty product ingredients in skin samples

In order to monitor beauty product ingredients used during T4–T6, we selected only molecular features present in each beauty product sample (antiperspirant, facial lotion, body moisturizer, soothing powder) and then filtered the aligned MZmine feature table (Additional file  5 : Table S4) for the specific feature in specific body part samples. After feature filtering, we selected all features that had a higher average intensity on beauty product phase (T4–T6) compared to non-beauty product phase (T1–T3). The selected features were annotated using GNPS dereplication output http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=69319caf219642a5a6748a3aba8914df , plotted using R package ggplot2 ( https://cran.r-project.org/web/packages/ggplot2/index.html ) and visually inspected for meaningful patterns.

Random forest analysis

Random forest analysis was performed in MetaboAnalyst 3.0 online platform http://www.metaboanalyst.ca/faces/home.xhtml . Using LC-MS1 features found in armpit samples collected on T3 and T6. Random forest parameters were set as follows: top 1000 most abundant features, number of predictors to try for each node 7, estimate of error rate (0.0%).

BugBase analysis

To determine the functional potential of microbial communities within our samples, we used BugBase [ 83 ]. Because we do not have direct access to all of the gene information due to the use of 16S rRNA marker gene sequencing, we can only rely on phylogenetic information inferred from OTUs. BugBase takes advantage of this information to predict microbial phenotypes by associating OTUs with gene content using PICRUSt [ 84 ]. Thus, using BugBase, we can predict such phenotypes as Gram staining, or oxidative stress tolerance at each timepoint or each phase. All statistical analyses in BugBase are performed using non-parametric differentiation tests (Mann–Whitney U ).

Taxonomic plots

Rarefied OTU counts were collapsed according to the OTU’s assigned family and genus name per sample, with a single exception for the class of chloroplasts. Relative abundances of each family-genus group are obtained by dividing by overall reads per sample, i.e., 10,000. Samples are grouped by volunteer, body site, and time/phase. Abundances are aggregated by taking the mean overall samples, and resulting abundances are again normalized to add up to 1. Low-abundant taxa are not listed in the legend and plotted in grayscale. Open-source code is available at https://github.com/sjanssen2/ggmap/blob/master/ggmap/snippets.py

Dissimilarity-based analysis

Pairwise dissimilarity matrices were generated for metabolomics and 16S metagenomics quantification tables, described above, using Bray–Curtis dissimilarity through QIIME 1.9.1 [ 71 ]. Those distance matrices were used to perform Procrustes analysis (QIIME 1.9.1), and Mantel test (scikit-bio version 0.5.1) to measure the correlation between the metabolome and microbiome over time. The metabolomics dissimilarities were used to perform the PERMANOVA test to assess the significance of body part grouping. The PCoA and Procrustes plots were visualized in EMPeror. The dissimilarity matrices were also used to perform distance tests, comparing the distances within and between individuals and distances from time 0 to times 1, 2, and 3 using Wilcoxon rank-sum tests (SciPy version 0.19.1) [ 19 ].

Statistical analysis for molecular and microbial data

Statistical analyses were performed in R and Python (R Core Team 2018). Monotonic relationships between two variables were tested using non-parametric Spearman correlation tests. The p values for correlation significance were subsequently corrected using Benjamini and Hochberg false discovery rate control method. The relationship between two groups was tested using non-parametric Wilcoxon rank-sum tests. The relationship between multiple groups was tested using non-parametric Kruskal–Wallis test. The significance level was set to 5%, unless otherwise mentioned, and all tests were performed as two-sided tests.

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Acknowledgements

We thank all volunteers who were recruited in this study for their participation and Carla Porto for discussions regarding beauty products selected in this study. We further acknowledge Bruker for the support of the shared instrumentation infrastructure that enabled this work.

This work was partially supported by US National Institutes of Health (NIH) Grant. P.C.D. acknowledges funding from the European Union’s Horizon 2020 Programme (Grant 634402). A.B was supported by the National Institute of Justice Award 2015-DN-BX-K047. C.C. was supported by a fellowship of the Belgian American Educational Foundation and the Research Foundation Flanders. L.Z., J.K, and K.Z. acknowledge funding from the US National Institutes of Health under Grant No. AR071731. TLK was supported by Vaadia-BARD Postdoctoral Fellowship Award No. FI-494-13.

Availability of data and materials

The mass spectrometry data have been deposited in the MassIVE database (MSV000081582, MSV000081580 and MSV000081581). Molecular network parameters for MS/MS data collected from all body parts of 11 individuals during T0-T9 MSV000081582 are accessible here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=284fc383e4c44c4db48912f01905f9c5 . Molecular network parameters for MS/MS data collected from armpits T0–T3 MSV000081582 and deodorant used by individual 1 and 3 MSV000081580 can be found here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5325c3b278a46b29e8860ec5791d5ad and here http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=aaa1af68099d4c1a87e9a09f398fe253 , respectively. OTU tables can be found in Qiita ( qiita.ucsd.edu ) as study ID 10370, and sequences can be found in EBI under accession number EBI: ERP104625.

Author information

Amina Bouslimani and Ricardo da Silva contributed equally to this work.

Authors and Affiliations

Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, San Diego, USA

Amina Bouslimani, Ricardo da Silva, Kathleen Dorrestein, Alexey V. Melnik, Tal Luzzatto-Knaan & Pieter C. Dorrestein

Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92037, USA

Tomasz Kosciolek, Stefan Janssen, Chris Callewaert, Amnon Amir, Livia S. Zaramela, Ji-Nu Kim, Gregory Humphrey, Tara Schwartz, Karenina Sanders, Caitriona Brennan, Gail Ackermann, Daniel McDonald, Karsten Zengler, Rob Knight & Pieter C. Dorrestein

Department for Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

Stefan Janssen

Center for Microbial Ecology and Technology, Ghent University, 9000, Ghent, Belgium

Chris Callewaert

Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92307, USA

Karsten Zengler, Rob Knight & Pieter C. Dorrestein

Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA

Karsten Zengler & Rob Knight

Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, 92093, USA

Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92037, USA

Pieter C. Dorrestein

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Contributions

AB and PCD contributed to the study and experimental design. AB, KD, and TLK contributed to the metabolite and microbial sample collection. AB contributed to the mass spectrometry data collection. AB, RS, and AVM contributed to the mass spectrometry data analysis. RS contributed to the metabolomics statistical analysis and microbial–molecular correlations. GH, TS, KS, and CB contributed to the 16S rRNA sequencing. AB and GA contributed to the metadata organization. TK, SJ, CC, AA, and DMD contributed to the microbial data analysis and statistics. LZ, JK, and KZ contributed to the additional data analysis. AB, PCD, and RK wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Rob Knight or Pieter C. Dorrestein .

Ethics declarations

Ethics approval and consent to participate.

All participants signed a written informed consent in accordance with the sampling procedure approved by the UCSD Institutional Review Board (Approval Number 161730).

Competing interests

Dorrestein is on the advisory board for SIRENAS, a company that aims to find therapeutics from ocean environments. There is no overlap between this research and the company. The other authors declare that they have no competing interests.

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Additional files

Additional file 1:.

Figure S1. Beauty products ingredients persist on skin of participants. Figure S2. Beauty product application impacts the molecular and bacterial diversity on skin of 11 individuals while the chemical diversity from personal beauty products used by males and females on T0 is similar. Figure S3. Longitudinal impact of ceasing and resuming the use of beauty products on the molecular composition of the skin over time. Figure S4. Molecular networking to highlight MS/MS spectra found in each body part. Figure S5. Longitudinal abundance of bile acids and acylcarnitines in skin samples. Figure S6. Characterization of steroids in armpits samples. Figure S7. Characterization of bile acids in armpit samples. Figure S8. Characterization of Acylcarnitine family members in skin samples. Figure S9. Beauty products applied at one body part might affect other areas of the body, while specific products determine stability versus variability of microflora at each body site. Figure S10. Representation of Gram-positive bacteria over time and the molecular features from the shampoo detected on feet. Figure S11. Procrustes analysis to correlate the skin microbiome and metabolome over time. Figure S12. Correlation between specific molecules and bacteria that change over time in armpits of individual 11. Figure S13. Representation of the number of samples that were removed (gray) and those retained (blue) after rarefaction at 10,000 threshold. (DOCX 1140 kb)

Additional file 2:

Table S1. List of personal (T0 and T7–9) beauty products and their frequency of use. (XLSX 30 kb)

Additional file 3:

Table S2. List of ingredients of common beauty products used during T4–T6. (PDF 207 kb)

Additional file 4:

Table S3. Mzmine feature finding and crop filtering parameters. (XLSX 4 kb)

Additional file 5:

Table S4. Feature table for statistical analysis with blank filtering and total ion current normalization. (CSV 150242 kb)

Additional file 6:

Table S5. Feature table for individual feature abundance in armpits. (XLSX 379 kb)

Additional file 7:

Table S6. Feature table for Calour analysis. (CSV 91651 kb)

Additional file 8:

Table S7. Metadata for Calour analysis. (TXT 129 kb)

Additional file 9:

Table S8. feature table with Probabilistic quotient normalization for molecular–microbial analysis. (ZIP 29557 kb)

Additional file 10:

Table S9. OTU table rarefied to 10,000 sequences per sample. (BIOM 9493 kb)

Additional file 11:

Table S10. 16S rRNA sequencing read counts per sample. (TSV 2949 kb)

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Bouslimani, A., da Silva, R., Kosciolek, T. et al. The impact of skin care products on skin chemistry and microbiome dynamics. BMC Biol 17 , 47 (2019). https://doi.org/10.1186/s12915-019-0660-6

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International Journal of Interdisciplinary Research

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• Published: 05 January 2022

Changes in consumers’ awareness and interest in cosmetic products during the pandemic

• Yeong-Hyeon Choi 1 ,
• Seong Eun Kim 2 &
• Kyu-Hye Lee   ORCID: orcid.org/0000-0002-7468-0681 1  

Fashion and Textiles volume  9 , Article number:  1 ( 2022 ) Cite this article

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This research investigates the impact of the COVID-19 pandemic on consumers’ perspectives of beauty and individual cosmetic products. Since the first confirmed case of COVID-19 was announced on December 31st, 2019, the search volumes of Google News have been updated and information on confirmed cases of the disease has been collected. This study used Python 3.7, NodeXL 1.0.1, and Smart PLS 3.0 to analyze consumer awareness of cosmetic products during the pandemic. The results reveal that consumers’ perspectives of beauty are impacted by a pandemic. Global consumers perceive skincare as an important aspect during the pandemic, while the importance of makeup fell after the outbreak. The awareness of skincare and makeup products has changed. The spread of the pandemic (SOP) has a positive impact on skincare products, but a negative impact on makeup products, except for eye makeup products, which was positive. Finally, the SOP was not significant in terms of consumers’ interest in masks. Fifth, interest in masks showed a positive relationship with interest in skincare products, such as cleansing products, while a negative relationship was observed with interest in makeup products. Overall, this study concludes that pandemics certainly have an impact on global consumers’ perspectives. As a pandemic spread, interest in skincare products increases, while interest in makeup products decreases. This study has academic significance in that it investigates the effects of consumption of cosmetic products during the stay-at-home rules. It can be used as standard information for setting marketing strategies in pandemic-like situations in the future.

Introduction

The coronavirus disease‑19 (COVID‑19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus‑2 (SARS-CoV‑2) (Babu, 2020 ). As the number of confirmed cases of COVID-19 increased, the World Health Organization (WHO) announced a pandemic on March 11th, 2020. In an attempt to slow the pandemic, the WHO announced personal hygiene guidelines. Governments all over the world have induced people to follow the guidelines, which include regularly washing hands, social distancing, and wearing a medical mask (Cartaud et al., 2020 ). As the period of social distancing has been prolonged and people are wearing personal protective equipment (PPE) to prevent themselves from being infected, the overall demand for cosmetics has declined (Wischhover, 2020 ).

Since consumers now have to stay in their homes longer, they are stressed over difficulties in terms of their appearance (Pikoos et al., 2020 ). This situation led to people feeling less need or opportunity to wear makeup; hence, the overall demand for makeup products has dropped (Biskanaki et al., 2020 ). However, not all signals for the cosmetic industry are negative. Due to the current situation being conducive to wearing masks for prolonged periods, consumers are suffering from acne, and they are focusing more on skincare products (Rubin, 2020 ; Schiffer, 2020 ). Although the interest in makeup products has decreased, that for skincare products has shown a different direction in terms of sales. Interestingly, this behavior is a global phenomenon. Given that the preference for makeup products, such as eyeshadows or lipstick, is different from beauty awareness in each country, the present skincare-focused phenomena follow similar reactions and patterns. This indicates that a pandemic-like situation can make consumer’s attitude toward cosmetic products somewhat different to what it was before such a situation.

This behavior raises another intriguing point. The motivation of applying makeup is to express one’s identity, gain confidence, or be seen as a well-mannered person (Karabulut et al., 2020 ). No matter how little makeup consumers wear due to the PPE and stay-home policy, they still engage in social interactions. If consumers perceive wearing PPE as a tool for hiding themselves, they would not have an intention to use certain products. On the other hand, if consumers are only focused on survival during the pandemic, the results of cosmetic behavior would be more complicated.

Our research goal is to determine consumers’ cosmetic behavior in the COVID-19 pandemic situation. To do so, we (1) compare the perceptions of cosmetic products before and after the outbreak of COVID-19 using social big data analysis. (2) We establish the causal relationship between COVID-19 variances and purchase behavior of cosmetic products using partial least squares structural equation modeling (PLS-SEM). From a macro perspective, this study reveals the impact of COVID-19 on consumers’ attitude toward cosmetic and skincare products. The multi-dimensional approaches used herein contribute to an in-depth understanding of consumer behavior in the COVID-19 situation.

Literature review

The social self is “a tug-of-war” between societal similarity and individual uniqueness (Brewer, 1991 ). It is a human need to conform with others while simultaneously maintaining their individual presence. There are various ways to reveal one’s social self to society. Of these, makeup is a method to reveal and express one’s social self to society (Lee & Oh, 2018 ). In addition, according to which shades and the degree of makeup (from bare face to full makeup) a person wears, their social appearance could be easily and instantly changed. Due to this salience of makeup, the degree of makeup status is not only someone’s style but a metaphor for their feelings or intentions. However, COVID-19 has forced everyone to wear masks, which is a critical obstacle in constructing a social self through makeup. Thus, the decision of whether to apply makeup is an important indicator of cosmetic behavior, perception of a pandemic situation, and maintenance of societal appearance.

Social self and maskne

In the past, makeup was considered the preserve of women (Cash & Cash, 1982 ; Miller & Cox, 1982 ). However, male consumers have recently begun to show increased interest in caring for their appearances, which has led to a growth in the male cosmetic products market (Iida, 2006 ). Generation Y men tend to express their self-esteem through fashion products and are concerned about their bodies and weight (Sung & Yan, 2020 ). This phenomenon could provide the reason why male consumers have an increased interest in makeup, which is commonly viewed as an aesthetic concept. In fact, men want to be seen as more charming and masculine, not feminine (Souiden & Diagne, 2009 ). Thus, the context of makeup has shifted from being seen as more feminine to being able to express oneself to society (Kim & Choi, 2020 ).

There is now a dilemma regarding using makeup to reveal one’s social self. Due to COVID-19, wearing a mask is a normal state or mandatory regulation in some countries when people go out, meet someone, or are in a public space. This regulation obliges people to cover half of their facial area, which has induced consumers to practice a passive attitude toward cosmetics (Han et al., 2020 ). The fact that masks can cause flare-ups of acne is considered one of the main reasons for this attitude. As the inner temperature of the mask causes flare-up in the facial area, many consumers have suffered from acne due to prolonged periods of wearing masks (Park et al., 2020 ). Given the significantly positive correlation between cosmetics and acne (Perera et al., 2018 ), the sealing effect of the mask makes the wearer more susceptible to acne flare-ups. It is popularly known as “maskne or mask acne” (Kosasih, 2020 ).

The dilemma of makeup starts here. To some, applying makeup could be a really important ritual that helps them prepare to present their social self. However, due to the COVID-19 pandemic, there is an adverse synergy between makeup and wearing a mask for an extended period due to the aforementioned side effects, like acne or flushing (Yu, 2020 ). It presents an awkward situation in which wearing makeup causes bad skin conditions and going bare faced is somewhat embarrassing. If someone decides to wear makeup for social activity, they make it their priority to maintain their social self. On the other hand, if someone does not apply makeup when they are going out, they put more weight on caring for their skin conditions. The side effects of wearing a mask for long periods could be considered a minor problem when the benefit is maintaining our health. Given that makeup is one of the ways to present a good social self, a no-makeup situation may induce feelings of shame. In particular, consumers who are very aware of their surroundings and others’ evaluation may regard going bare faced as a shameful situation. It leads to degradation of their social self-esteem and also increases stress (Gruenewald et al., 2004 ).

In a pandemic-like situation, people experience death, anxiety, and depression (Lee et al., 2020 ). Anxiety regarding appearance management occurs. Pikoos et al. ( 2020 ) revealed that during COVID-19 restrictions, societal appearance pressure persisted in individuals with high dysmorphic concerns. Pikoos et al. ( 2020 ) found that a highly appearance-concerned group displayed feelings of pain and suffering due to the shutdown of beauty services, and the demand for cosmetics once restrictions are lifted has increased. This is the result of the anxiety that they cannot maintain their appearance and worry about it when they go out. This study regards pandemic anxiety variance as the number of confirmed cases. It means that as more confirmed cases occur and COVID-19 spreads, this leads to higher levels of anxiety (Elbay et al., 2020 ). Therefore, it is expected that if the spread of the pandemic (SOP) becomes more severe, anxiety will increase. As a result, the interest in all cosmetic product categories (skincare and makeup products) would show an incremental relationship.

Research question 1 (R1): Pandemics will change consumers’ awareness of cosmetic products (skincare and makeup).

Hypothesis 1 (H1): Spread of the pandemic (SOP) will positively influence interest in skincare products.

Hypothesis 2 (H2): Spread of the pandemic (SOP) will positively influence interest in makeup products.

As one of the preventive measures, wearing a mask has become a default option during outdoor activities. In the early phase of the pandemic, there was significant interest in wearing a mask, as it was not a default option. However, before the outbreak of COVID-19, it was considered unusual behavior. More than a year since the outbreak began, mask wearing is no longer considered abnormal or special behavior. In a pandemic situation, the period during which a disease remains contagious is long time, and the interest in preventive measures shows a declining tendency (Bults et al., 2011 ). Consumers concentrate on their discomfort. In particular, wearing a mask for prolonged durations causes maskne, which is one of the direct and immediate side effects of the activity. This study expects that the interest in masks leads to incremental interest in skincare products but not in makeup products.

Hypothesis 3 (H3): Spread of the pandemic (SOP) will not influence interest in face masks.

Hypothesis 4 (H4): Interest in face masks will positively influence interest in skincare products.

Hypothesis 5 (H5): Interest in face masks will negatively influence interest in makeup products.

The main variables and hypotheses of this study are illustrated in Fig.  1 .

Conceptual research framework

• Social network analysis

One of the characteristics of social network and big data analysis is being able to investigate the potentially key elements of a study. This is done by extracting essential data and analyzing it through the bottom-up approach. It is cost effective and consumes less time as it does not require surveys or interviews to be conducted and this method is now being used in various fields (Niyirora & Argones, 2019 ; Wu et al., 2017 ). Social network analysis, the most commonly used method of analyzing big data, deduces the characteristics of the network by determining occurrence and regularity through appearance frequency and relationships between words (Park & Leydesdorff, 2004 ). Centrality, a measure of the central location of a node within a network, is used in the interpretation of data (Hanneman & Riddle, 2005 ).

Ahmed et al. ( 2020 ) investigated the COVID-19 situation using social network analysis. The authors examined Twitter posts and investigated the motive of the 5G COVID-19 conspiracy theory. They confirmed that the most popular web sources shared by users are fake news websites. Based on Twitter posts, Yum ( 2020 ) analyzed the role of public and political administrators in COVID-19 pandemic situation using social network analysis. Choi and Lee ( 2020 ) used text mining and social network analysis to examine the changes in consumers’ perspectives of fashion products during the pandemic. Korean fashion consumers were mainly concerned with precautions, home life, digital and cosmetic products, online channels, and consumption. This indicates a stronger stance on precaution than fashion during COVID-19 than in previous pandemics (SARS, MERS).

Measurement: Tf-idf and centralities

Term frequency-inverse document frequency ( Tf-idf ) and central value were measured and then analyzed. In PLS-SEM, development of worldwide confirmed cases was used as an exogenous variable, and search volumes of individual cosmetic products was used as an endogenous variable. Python 3.7 was used to measure Tf-idf value, and NodeXL 1.0.1 was used in the network analysis stage to measure centrality. Tf-idf , a measuring standard that supplements the frequency, is normally used to judge the importance of a certain word in a corpus. Tf-idf weighting is term frequency (tf) \(\times\) inverse document frequency (idf) . Given a collection of terms t \(\in\) T that appear in a set of N documents d \(\in\) D , each of length n d , Tf-idf, weighting is computed as shown in Eq.  1 (Choi & Lee, 2021 ; Yahav et al., 2019 ).

Centrality analysis defines the meanings of each keyword in the context of a document (Choi et al., 2021 ), which are indicated in terms of degree, betweenness, closeness, and eigenvector centrality (Kwahk, 2014 ), as shown in Eqs. 2 – 5 . The centrality measure can be summarized by standardizing the equation as follows: (1) \({C}_{x}({N}_{i})\) is the centrality of actor i in each calculation. (2) Every g is the number of actor i in networks (Kwahk, 2014 ; Wasserman & Faust, 1994 ). (3) The standardized actor betweenness centrality is then divided by the maximum value (( g  − 1)( g  − 2)/2) of the theoretical betweenness centrality (Freeman, 1979; Wasserman & Faust, 1994 ). (4) The value of closeness centrality for the number of actors g is \({C}_{c}({N}_{i})\) =1/( g  − 1), and the standardized actor closeness centrality is multiplied by g  − 1 to consider the size of the entire network (Beauchamp, 1965 ; Freeman, 1978 ). (5) In eigenvector centrality, actor i is the i th element of unit eigenvector e , and e represents the largest eigenvalue of the adjacency matrix, with x as an element. X is an adjacency matrix with \({X}_{ij}\) as an element, and \(\lambda\) is an array of eigen values (Bonacich, 2007 ; Kwahk, 2014 ). To obtain eigenvector centrality, multiple steps of computation are performed. In the first step, the centrality value is calculated as the sum of the degree for g . The centrality value of each actor is then calculated as the sum of the first-stage centrality values of each actor. The final centrality values are calculated as the sum of the results of the second stage. This step-by-step computation process is repeated until the centrality value no longer changes.

Equation  1 Calculation of Tf-idf

Equation  2 Degree centrality of actor i

Equation  3 Betweenness centrality of actor i

Equation  4 Closeness centrality of actor i

Equation  5 Eigenvector centrality of actor i.

Preliminary investigation

The occurrence of the first recorded case of COVID-19 on December 31st, 2019 until September 30th 2020 was set as the standard period for collecting data. To compare consumers’ awareness, data between December 31st, 2018 and September 30th, 2019 were also collected. Preliminary investigation was conducted before selecting keywords to be investigated. We analyzed Google News, Twitter posts, and YouTube comments containing the words “corona” and “beauty” for nine months following the outbreak of COVID-19 (total: 8192).

As a result of preliminary investigation, according to appearance frequency, “mask” (687), “skincare” (490), “makeup” (459), and “haircare” (330) were identified as the beauty keywords related to COVID-19. In the network analysis, “skincare,” “makeup,” and “beauty” were set as the keywords. “Mask” showed a relationship with “corona,” but it also showed a strong relationship with “face” and “facial.” This suggests it may relate to medical masks and skin management masks, so it was excluded from the analysis. Google News was selected as the collecting channel as the data contained relatively less noise (Table 1 ).

Data collection and analysis for PLS-SEM

PLS-SEM analysis was used to identify the impact of the spread of infectious disease on consumers’ interest in cosmetic products. To determine the development of the spread, worldwide confirmed cases were collected from the conoraboard.kr website, which provides real-time information about COVID-19. The data collection period was from January 21st, 2020 to September 30th, 2020, the range provided by the website. Consumers’ interest in cosmetic products is based on data on search volumes for keywords in Google Trends. The data were collected globally and limited to the category of “beauty & fitness.” In contrast, previous studies use “mask” as the main beauty keyword (e.g. Choi & Lee, 2020 ). Therefore, we identified the search volume data of the keywords “mask” and “acne.” Sub products of the specific cosmetic products are set according to social big data analysis in the Tf-idf standard.

To verify the model, we used development of coronavirus confirmed cases and search volume of keywords in Google Trends. Spread of disease is defined as the development of coronavirus confirmed cases. The measurement tool is the number of daily confirmed cases from January 21st to September 30th, 2020 obtained from conoraboard.kr. Mask interest is defined as consumers’ interest in masks, measured by the search volume of “mask” in Google. Interest in skincare and makeup are defined as consumers’ interest in individual products, measured by the search volume of all relative keywords in Google. As per the results of social network analysis, keywords with high Tf-idf are selected as representative items. Data on representative keywords are also collected within the same amount of time, using the same methods. A total of 253 records were collected and 13 items were measured. Smart PLS 3.0 was used to determine the influencing relationship. In PLS-SEM, the sampling was conducted 5000 times via bootstrapping. After verifying path coefficients and significance, it was evaluated by identifying the R 2 , f 2 , and Q 2 values.

Results and discussion

Comparison of consumers’ beauty awareness in the pandemic, based on tf-idf value.

Since the announcement of the first confirmed case of coronavirus on December 31st, 2019, social data including the keyword “beauty” have been collected. Keywords were distilled and centrality score and Tf-idf , which can complement simple frequency, were measured. The top 50 keywords were used in the analysis and the top 30 keywords according to the Tf-idf are reported in Table 2 .

Before the COVID-19 pandemic, keywords such as “makeup” ( Tf-idf  = 369.68), “product” (315.68), “brand” (276.77), “busy” (215.22), “routine” (200.66), “industry” (142.08), “natural” (140.15), “fashion” (138.77), “trick” (133.73), and “skincare” (132.91) appeared in the upper ranks of consumers’ awareness. Although keywords such as “product” (410.57), “makeup” (389.54), “brand” (328.95), “routine” (221.11), and “skincare” (191.71) appeared in the upper ranks, keywords such as “coronavirus” (191.71) and “pandemic” (85.51) appeared in the infectious disease keywords. These results indicate that a pandemic impacts general consumers’ awareness of cosmetics. Makeup remains the most significant product that composes consumers’ cosmetic awareness, irrespective of the pandemic situation. In the case of skincare, there has been an increase in rank since the pandemic.

Analysis of network structure and centrality

We extracted the top 50 keywords based on the co-occurrence frequency and then clustered them using the Wakita-Tsurumi algorithm. Consumer awareness of  cosmetic behavior before COVID-19 outbreak  is illustrated in Fig.  2 . Figure  3 represents  the beauty behavior after the COVID-19 outbreak. In Fig.  3 , new keywords in relation to COVID-19 have emerged after the outbreak. The comparison of the network size is as follows. The maximum geodesic distance (diameter) of the networks for 2019 and 2020 is 3, and the overall networks were similar in size because both were equally limited to the top 50 keywords. In 2019, the total number of edges of the network was 590, the average geodesic distance was 1.76, and the graph density was 0.24. The total number of edges for 2020 was 706, the average geodesic distance was 1.72, and the graph density was 0.28. Compared to 2019, the significant increase in the number of edges in 2020 means that the correlation between keywords has increased within the context. As a result, we can see that the average geodesic distance of the graph has decreased, and the graph density has increased.

Consumers’ cosmetic awareness before COVID-19 (Wakita-Tsurumi algorithm)

Consumers’ cosmetic awareness after COVID-19 (Wakita-Tsurumi algorithm)

For centrality measurement, in the case of makeup, both degree and betweenness centrality declined in consumers’ cosmetic awareness after, rather than before, the pandemic. On the other hand, skincare showed an increase in all centrality items (C d , C b , and C e ) except closeness centrality after the outbreak. On this basis, while the influence of makeup products has decreased, that of skincare products has increased. Moreover, in the results of Tf-idf and centrality measurement, keywords of color cosmetics, such as “lipstick,” “foundation,” and “eyeshadow,” which appeared as representative keywords, did not appear in the upper ranks after the outbreak.

Before COVID-19, skincare was classified in the compact groups such as “salon,” “versatile,” and “home,” but after the outbreak, skincare joined larger groups comprising “global,” “digital,” “clean,” and “technology,” which account for 28% of the whole group. Furthermore, before the pandemic, makeup was classified into groups containing the words “celebrity,” “fashion,” “Fenty,” and “tutorial.” After the pandemic, it was reclassified into groups containing “pandemic,” “change,” “face,” “treatment,” “skin,” and so on. During the pandemic, skincare was considered as cleansing to prevent coronavirus infection, and makeup was considered in terms of skin problems due to the pandemic situation.

According to the network analysis, the appearance of keywords related to coronavirus determined that pandemics affect consumers’ cosmetic awareness. After the outbreak, global consumers recognized skincare as extremely important. Makeup was also regarded as being important; however, its importance importance showed a declining trend.

Consumers’ awareness of skincare and makeup

The results of preliminary investigation revealed that skincare and makeup appear as the main cosmetic behaviors and products. We therefore conducted a comparison of consumers’ awareness based on the keywords before and after pandemic (Table 3 ). After the outbreak, “coronavirus” appeared as an upper ranked Tf-idf keyword in every cosmetic product. This highlights the fact that the COVID-19 pandemic impacts consumers’ cosmetic awareness.

The results of comparing representative keywords of individual cosmetic products before and after the pandemic are shown below. First, in the case of skincare, “self” ( Tf-idf  = 55.17), “mask” (47.81), “prevention” (47.50), “maskne” (46.92), “acne” (38.39), and “lockdown” (36.75) appeared as the representative keywords. In terms of consumer’s cosmetic awareness, this indicates the appearance of skin problems (or acne) caused due to wearing masks to prevent infection and the phenomena of self-care at home due to lockdown. The main products are serum (56.61/31.43), sunscreen (41.37/45.55), cream (37.61/28.58), and cleansers (24.29/45.30). Additionally, keywords such as “mask” (47.81) and “Maskne” (46.92) appear as representative keywords in cosmetic awareness, include skincare products for “acne” (38.39).

Second, in terms of consumers’ awareness on makeup, keywords such as “mask” (43.98), “lockdown” (32.80), “home” (26.39), “COVID-19” (26.39), “distance” (26.39), “maskne” (25.05), and “washing” (25.05) appeared after the outbreak. As with skincare, skin problems caused by wearing masks and cleansing the face appeared as the main interest after the spread of the disease. In addition, words related to social distancing and lockdown phenomena related to makeup started to appear. The representative products are “lipstick” (48.85/60.95), “eyeliner” (38.86/ 53.80), and “foundation” (35.07/45.26).

The analysis of consumers’ cosmetic awareness of skincare and makeup products before and after the outbreak of the COVID-19 pandemic revealed keywords related to infectious disease (coronavirus, mask, lockdown). Therefore, it can be argued that consumers’ awareness regarding individual cosmetic products has been affected by the pandemic.

Spread of the pandemic and consumers’ interest in cosmetic products

Model evaluation.

Since the confirmed cases of COVID-19 and search volume of individual cosmetic products’ keywords are measured with a single measurement object, significance and suitability, such as convergent validity, multi-collinearity, outer weights, and outer loading do not need to be judged. The PLS-SEM model is assessed by R 2 , f 2 , and Q 2 values (Shin, 2018 ). In investigation of consumer behaviors, if the coefficient of determination for the endogenous variable is larger than R 2  = 0.20, it is judged to have a very high estimated suitability (Hair et al., 2017 ). The values of R 2 in the endogenous variable are skincare products ( R 2 serum  = 0.31, R 2 sunscreen  = 0.21, R 2 acne  = 0.32, R 2 cleanser  = 0.15, and R 2 cream  = 0.25), makeup products ( R 2 eyeliner  = 0.21, R 2 foundation  = 0.36, and R 2 lipstick  = 0.27), and mask ( R 2  = 0.20). The values of R 2 for acne, serum, and foundation are larger than R 2  = 0.30. The majority of the other endogenous variables were larger than R 2  = 0.20, indicating a high level of suitability.

If the effect size ( f 2 ) in PLS-SEM is greater than f 2  = 0.02, it indicates a small effect size for latent exogenous variables on latent endogenous variables. If it is greater than f 2  = 0.15, it indicates a medium effect size. If it is greater than f 2  = 0.35, it indicates a large effect size. The interest in cosmetic products due to the spread of disease showed foundation as ( f 2  = 0.53), which has an effect size larger than f 2  = 0.35 and it was identified as having the largest contribution. Serum ( f 2  = 0.17), acne ( f 2  = 0.25), lipstick ( f 2  = 0.28), and mask ( f 2  = 0.25) have medium effect sizes. Sunscreen ( f 2  = 0.09), cleanser ( f 2  = 0.07), and eyeliner ( f 2  = 0.04) have small effect sizes. Masks, cream ( f 2  = 0.21), foundation ( f 2  = 0.24), and lipstick ( f 2  = 0.26) showed medium effect sizes, and the others showed small effect sizes ( f 2 serum  = 0.08, f 2 sunscreen  = 0.05, f 2 acne  = 0.03, f 2 cleansing  = 0.03, and f 2 eyeliner  = 0.11). Next, by identifying the estimated suitability ( Q 2 ) through the blindfolding process, all Q 2 values of the latent endogenous variables were larger than 0, indicating the suitability of the entire constitutive model.

Hypothesis testing

Sop and interest in cosmetic products.

The impact of the spread of the COVID-19 pandemic on global consumers’ awareness regarding individual cosmetic products is as follows (Table 4 ; Fig.  4 ). The SOP had a positively influencing relationship with all skincare products: serum ( β  = 0.50, p  < 0.001), sunscreen ( β  = 0.40, p  < 0.001), cream ( β  = 0.33, p  < 0.001), cleanser ( β  = 0.34, p  < 0.001), and acne ( β  = 0.52, p  < 0.001). Therefore, it was clear that as the number of confirmed cases increased, global consumers’ interest in skincare products also increased. This result statistically proves news reports and interviews about changes in cosmetic behaviors due to the pandemic (Cerullo, 2020 ; Wallace & CNN Business, 2020 ). Therefore, H1 (H1a, H1b, H1c, H1d, H1e) was supported.

Results of partial least squares structural equation modeling (PLS-SEM)

The SOP had a negatively influencing relationship with lipstick ( β  =  − 0.23, p  < 0.001) and foundation ( β  =  − 0.41, p  < 0.001). This is statistical proof that the sales of makeup products have decreased (Cerullo, 2020 ; Halliday, 2020 ). This decrease is due to wearing masks or staying at home to prevent infection and spreading of the virus. Since people decide not to apply makeup to the area of the face covered by a mask does not need to have makeup applied, interest in makeup has naturally fallen. Meanwhile, eyeliner ( β  = 0.38, p  < 0.001) showed a positively influencing relationship with regards to interest in the product. Opposite to the case of lipsticks and foundations, the eyes are still visible when wearing masks, so consumers tend to pay more attention to eye makeup, leading to the increased interest in eyeliners (see Table 4 ).

“Maskne” behavior manifests in that consumers tend to focus their efforts on the exposed area of their face. This is also similar to Muslim females’ cosmetic behavior, in which they pay more attention to eye makeup as they cover the rest of their face due to their religious ideology. In each case, the social setting forces people to hide their facial area, which leads to the limited application of makeup, that is, eye makeup. Hill et al. ( 2012 ) argued cosmetics such as lipsticks are a decorative product. In a pandemic situation, since a mask covers the nose, lips, and chin, eye makeup products are considered a decorative product. Therefore, H2c was accepted, and H2a and H2b were rejected.

Interest in face mask and cosmetic products

The SOP and consumers’ interest in masks were β  = 0.11, p  > 0.05, indicating that it does not have a significantly influencing relationship; H3 is therefore rejected (Table 5 ). However, the result of dividing the confirmed cases of COVID-19 by the timing of the spread was interesting. In the early phase from January to April 2020, it presented a high coefficient ( β  = 0.80, p  < 0.001). In the next phase from May to July 2020, SOP also revealed a positive impact on masks ( β  = 0.57, p  < 0.001), but it was lower than the previous phase. However, since the pandemic has been prolonged, the interest in masks has fallen and it seems to have levelled off. Consequently, the increase of confirmed cases may have influenced the relationship with the interest in masks. However, as the pandemic continues, it does not have a significantly influencing relationship; H3 was therefore accepted.

Although the interest in face masks did not form significantly influencing relationships with serum, sunscreen, and cream, it showed a positively influencing relationship with acne ( β  = 0.15, p  < 0.001) and cleansing ( β  = 0.11, p  < 0.05). This is similar to the background of the appearance of the word “maskne,” which proves the interest in masks has a relationship with skin problems, such as acne, caused by wearing masks. On the other hand, cream ( β  =  − 0.12, p  < 0.05) had a significantly negative influencing relationship. Since “cream” can include moisturizing cream, BB cream, sun cream, and so on, it can be assumed that this affected the result.

Therefore, H4a, H4b, and H4c were rejected, and H4d and H4e were accepted. Meanwhile, interest in masks showed a negatively influencing relationship with lipstick ( β  =  − 0.46, p  < 0.001), foundation ( β  =  − 0.36, p  < 0.001), and eyeliner ( β  =  − 0.25, p  < 0.001). As consumers’ interest in masks increases, interest in makeup products for the entire face decreases. Therefore, H5 (H5a, H5b, H5c) was accepted.

Conclusions

This research explored the changes in consumers’ awareness of cosmetic products during the pandemic. The results suggest that epidemiological situations, such as COVID-19, affect global consumers’ awareness of cosmetics and each cosmetic category (skincare and makeup). The SOP has increased consumers’ interest in skincare products, while interest in makeup products (lipstick and foundation) has decreased except for eyeliner products. This suggests that anxiety regarding of severity of COVID-19 not only affects interest in skin conditions but also that consumers seek to maintain their social self-esteem by wearing makeup on the exposed areas of their face (Gruenewald et al., 2004 ). However, interest in makeup products was shown to have a negative influencing relationship with masks. Obligation to wear a mask for an extended period may cause discomfort experiences (Park et al., 2020 ). In terms of masks and makeup products, consumers have expressed complaints and discussed the side effects of makeup and wearing PPE in the online community.

The SOP did not have a significantly influencing relationship with consumers’ interest in face masks. In the early stages of increased confirmed cases, a positively influencing relationship with the interest in face masks was observed, but it did not form a significantly influencing relationship as the pandemic progressed. It is obvious that the prolonged pandemic situation makes the preventive measure ineffective compared to the early phase (Bults et al., 2011 ). The outbreak has lasted more than a year and mask wearing has become the norm. Therefore, regardless of whether the number of confirmed cases is on the rise, the interest in mask wearing would not follow a similar trend. A summary of the results of this study is shown in (Table 6 ).

In terms of practical contribution, this study analyzed global consumers’ macro awareness, allowing cosmetic companies to examine comprehensive opinions about consumers’ beauty habits during a pandemic. Therefore, if future pandemics have similar characteristics to the current COVID-19 outbreak, beauty companies have to pay more attention to the production of sanitary, cleanliness, and so-called trouble-care products. As consumers also consider their social self and social self-esteem as important as skincare despite the spread of pandemic, it is necessary to establish a niche makeup strategy to enable consumers to represent their own identity.

This study has the following limitations. We collected data in English, which is the global official language; therefore, it is difficult to apply this study to non-English speaking countries. Newspaper articles and industrial reports were used as references to best measure the social phenomena occurring in real time, and macro data were used to encompass all consumers. Future research should be conducted with much more elaborate theories and accurate measurements. From another viewpoint, demand for cosmetic products decreased in the early stage of the outbreak, and people created new forms of beauty behaviors such as “stay-home makeup” and “makeup for Zoom meetings.” Moreover, consumers’ preferences or behaviors regarding cosmetic products are subject to change, depending on the characteristics of infectious diseases. For example, if a new pandemic is not caused by a respiratory infection, the results derived from this study cannot be applied. Subsequent studies need to analyze new beauty behaviors after COVID-19.

Despite the aforementioned limitations, this study is considered important as it has statistically demonstrated that in a pandemic situation, interest in cosmetics may vary depending on their function and category. In addition, the motivations for wearing makeup could be construed as behavior of maintaining the social self. This provides the cosmetic industry some insights into the fact that consumers’ concerns about appearance in pandemic situations are diverse. Consumers are diverse and every case has customized needs for managing their societal appearance. Thus, the results have some managerial implications for the cosmetic industry in terms of which products they should focus on for production and establishing marketing strategies during pandemic situations. Moreover, as a new approach, this study expanded the generalizable scope by using macroscopic data such as social media text and volume of Google keyword searches.

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Yeong-Hyeon Choi, Ph.D. Candidate, Department of Clothing and Textiles, Hanyang University, Seoul, Korea. Seong Eun Kim, Doctoral Student, Department of Textiles and Apparel Management, University of Missouri, Columbia, Missouri, USA. Kyu-Hye Lee, Professor, Human-Tech Convergence Major, Dept of Clothing and Textiles, Hanyang University, Korea.

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The general research objectives were to investigate women's attitudes to skincare and to skincare products. 

About this collection: Peter Cooper (1936-2010) was co-founder of Cooper Research & Marketing, later CRAM International, with his wife Jackie French. Cooper studied Clinical Psychology at the University of Manchester where he became a Lecturer in the early 1960s. He became involved in conducting commercial Motivational Research and by 1968 opened Cooper Research & Marketing in Manchester. Cooper was one of the key pioneers of what we now know as Qualitative Research. CRAM opened its London office in Wimpole Street in 1970 and moved to 53 St Martins Lane, WC2N 4EA, in 1972 where it remained until Peter's passing in 2010. The company changed its name to CRAM International in around 1985/86, reflecting the increasingly international nature of its work. The CRAM/Peter Cooper Archive Collection, which includes commercial research reports and early academic papers, has been preserved by Peter's children, Diana, Helen and Jonathan, and his colleague Simon Patterson. The scanning of the Archive has been supported by ESOMAR, AMSR, Peter's colleague Dr Alan Branthwaite & family, the Cooper family, and QRi Consulting. The CRAM/Peter Cooper Archive Collection is managed by QRi Consulting. The CRAM logo and CRAM International name are Registered Trademarks and the property of QRi Consulting.

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The Effect of Hairdresser's Job Crafting on Customer Orientation and Work Performance

This study aims to understand the effect of salon selection factors on customer satisfaction and customer loyalty through SNS to provide marketing strategies to increase salon profits through SNS. In the SPSS 21.0 program, frequency analysis, factor analysis, reliability analysis, and multiple regression analysis were performed using data from the final 301 part of the questionnaire. The results of the survey are as follows. First of all, the characteristics of SNS were universal and tangible, divided into information, reliability, and beauty salon options. In addition, customer satisfaction and customer loyalty are divided into a single dimension. Second, it was reported that the more the latest information on SNS characteristics is recognized, the higher the customer satisfaction and customer loyalty are. Third, it was found that the more reliable objective information on the factors for selecting beauty salons through SNS was recognized, the higher customer satisfaction and customer loyalty were. Taken together, it is expected that a differentiated education system through SNS will be introduced, and sales will improve by positively affecting customer satisfaction and customer loyalty. In subsequent studies, more reliable studies are expected to be conducted through age diversification and segmentation of various variables.

Identification of Pathogenic Microbes in Tools of Beauty Salon in Jeddah City

Beauty salons may draw in customers with glamour; however, they could also be considered a major health issue. They can cause the spread of bacterial and fungal infections. The purpose of this research was to identify pathogenic microbes from beauty salon tools. Microorganisms from contaminated salon tools and cosmetic products were isolated using various selective media. Microbial isolates were identified based on their molecular and biochemical characteristics. The most common bacterial species isolated were Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus equorum, Microbacterium spp., Bacillus siamensis, Bacillus subtilis, Sphingomonas aeria, Macrococcus spp., Microbacterium oxydans, Brachybacterium spp., Micrococcus luteus, and Brachybacterium nesterenkovii. Fungal isolates included Penicillium spp., Aspergillus niger, Purpureocillium lilacium, and Aspergillus flavus. Overall, Staphylococcus spp. and A. niger were the most common organisms isolated from the samples. The presence of potential pathogens indicates that the tools used in salons have not been adequately sterilized and the high risk of diseases spread.

Transformação digital: um estudo de caso em um salão de beleza / Digital tranformation: study case in a beauty salon

Apesar do avanço da globalização, muitas empresas de pequeno porte ainda possuem certas dificuldades para adaptar-se às novas tecnologias, dessa forma, o objetivo deste artigo é fornecer metodologias para aprimorar a gestão empresarial de uma empresa de pequeno porte, por meio de um projeto de transformação digital. Para isso, foi escolhido um salão de beleza, localizado em Belém do Pará, onde foram feitos estudos nas áreas de gerência de projetos, gerência da qualidade e simulação de processos, e aplicadas ferramentas de cada uma delas. Na gerência de projetos, foram usadas ferramentas como Canvas Project, Estrutura Analítica de Projetos - para definir o escopo - e Cronograma. Para gerência da qualidade, foi elaborado indicadores com foco na satisfação do cliente no processo de atendimento. Por fim, na simulação de processos, o software ARENA foi usado para criar uma precisão do resultado das propostas apresentadas. Com isso, foi possível perceber que as ferramentas e softwares utilizados para viabilizar e gerenciar o projeto foram fundamentais para a sua execução, e o salão de beleza, o qual foi o objeto do estudo, foi beneficiado com a aplicação do sistema de gestão proposto.

Qualidade no atendimento e sua relação com a satisfação do cliente: o caso de um salão de beleza / Quality in customer service and its relation to customer satisfaction: the case of a beauty salon

Peran kapabilitas interaksi dan kreasi nilai bersama untuk meningkatkan kinerja pemasaran.

ABSTRACTThis study focuses on the role of Individual and Relational Interaction Capability in increasing Emotional and Social Value Co-Creation on Market Performance in beauty salon services. Value Co-Creation is one of the service organization's strategies in utilizing external resources from customers. This strategy is crucial because the performance of service organizations is mostly determined by the ability of the frontline staff who interact with customers during service meetings. This research belongs to explanatory research with a population of beauty salon customers in Central Java. The sample consisted of 203 customers, and it was determined using the purposive sampling technique. The author collects customer data through the distribution of offline and online questionnaires analyzed using SPSS 23.0-based regression. This study has proven that Emotional and Social Value Co-Creation can mediate between Individual and Relational Capability with Market Performance. Emotional and social values that are created together will be beneficial for the parties involved. For customers, interactions during the value co-creation process will increase their satisfaction because it can fulfill their needs and desires. This study has also shown the role of individual and relational capability in increasing customer willingness to engage in mutual value creation interactions.JEL  Codes: D20, L10, M20.Keywords : individual interaction capability, relational interaction capability, emotional value co-creation, social value co-creation, market performance. ABSTRAKStudi ini fokus pada peran Individual dan Relational Interaction Capability dalam meningkatkan Emotional dan Social Value Co-Creation menuju Market Performance pada jasa salon kecantikan. Value Co-Creation merupakan salah satu strategi organisasi jasa dalam memanfaatkan sumberdaya eksternal yang berasal dari pelanggan. Strategi ini sangat penting karena performa organisasi jasa sangat ditentukan oleh kemampuan frontline staff dalam berinteraksi dengan pelanggan pada saat pertemuan jasa. Jenis penelitian bersifat explanatory research dengan populasi pelanggan salon kecantikan di Jawa Tengah. Ukuran sampel yang digunakan sebanyak 203 pelanggan. Teknik pengambilan sampel menggunakan pPurposive sSampling. Peneliti mengumpulkan data pelanggan melalui penyebaran kuisioner secara offline dan online. Alat analisis data menggunakan regresi berbasis SPSS 23.0. Studi ini telah berhasil membuktikan bahwa Emotsional dan Social Value Co-Creation mampu memediasi antara Individuasl dan Relational Capability dengan Market Performance Nilai emosional dan soscial yang dikreasikan bersama akan bermanfaat bagi pihak-pihak yang terlibat. Bagi pelanggan, interaksi selama proses value co-creation akan meningkatan kepuasannya karena kebutuhan dan keinginannya dapat terpenuhi. Studi ini juga telah membuktikan peran individual dan relational capability dalam meningkatkan keinginan pelanggan terlibat dalam interaksi penciptaan nilai bersama.

Selected factors determining the choice of cosmetic masks by cosmetologists

A very important issue in the practice of cosmetologists is the selection of an appropriate cosmetic mask at the end of each care treatment. Due to their effects, cosmetic masks can contain various active substances in different concentrations that are tailored to the needs of a given skin type. The aim of the study was to investigate what are the guidelines of cosmetologists while choosing cosmetic masks in a beauty salon and home care.

The Influence of Beauty-salon Relational Benefits on Continuance Intention in the Young Elderly

Transformação digital: um estudo de caso em um salão de beleza / digital transformation: study case in a beauty salon, effects of awareness of the beauty master compositional system on job satisfaction and turnover intention of beauty workers.

As the beauty industry grows and the level of beauty technology increases, companies must deploy highly qualified workers, among whom beauty salons are certificates obtained by the best technology hairdressers in the beauty field, but they require high technology, time and cost, but have low acceptance and recognition. Therefore, in this study, we sought to investigate the effect of beauty salon perceptions on compositional systems, job satisfaction and turnover intentions for general hairdressers, and analyzed a total of 289 copies using SPSS21.0 as a self-entry survey. First, female managers in their 40s, who earn more than 5 million won with more than seven years of experience, and secondly, the necessity, adequacy, complement, job satisfaction, and turnover were derived in a single dimension. Third, due to the effect of recognition of beauty functional systems on the composition of beauty functional testing systems, the higher the propensity for recognition, the higher the propensity for appropriateness, and the higher the propensity for subsidiarity. Fourth, job satisfaction showed a fifth definition relationship with a higher propensity for awareness, and a higher tendency for turnover meant a definition relationship with a higher propensity for awareness. As such, it is very important to raise awareness of beauty salons in order to develop the beauty industry and improve advanced technology, and efforts should be made to establish a system to revitalize the beauty salons' system on-site.

The Effects of the Consumer's Consumption Tendency on the Consumption Behavior of Customers visiting Hair salons

As Korea's economic leeway has increased through rapid economic growth, the past simple consumption patterns for food, clothing, and shelter have changed from consumption patterns that invest for quality of life. This change in consumption has also affected the beauty industry, and competition in the beauty salon industry is getting fiercer day by day. Accordingly, it is judged that it is necessary to study the consumption propensity and behavior of customers visiting beauty salons in the current situation. Therefore, this study was conducted to find out whether consumption propensity affects consumption behavior by filling out a questionnaire targeting people in their 20s and 40s who have visited beauty salons. We want to provide the necessary basic data.

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• News & Analysis on Cosmetics Innovation

Skin science: Top 10 most-read stories on cosmetics science and research of 2021

17-Dec-2021 - Last updated on 17-Dec-2021 at 02:30 GMT

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1 – Sunscreen still needed: Protective surgical face masks ineffective against UV protection, says Kao ​

People still need to protect their skin from ultraviolet sun rays ​ with sunscreen even when wearing protective surgical face masks, according to new research from Kao.

For the first experiment, researchers from the firm’s Beauty Research and Creation Center (BRCC) conducted the experiment on a model with ‘skin’ that turns purple when it comes into contact with ultraviolet (UV) rays.

A cream sunscreen with SPF 50+ and a triple plus protection grade (PA) was applied to half of the model’s face and protected with a standard non-woven surgical face mask.

As hypothesised, the skin underneath the mask that was not protected by sunscreen turned purple, indicating exposure to UV rays.

2 – Power of sandalwood: Quintis to double down on cosmetics market on the back of antioxidant effectiveness ​

Australian sandalwood supplier Quintis is eyeing new opportunities in the cosmetic space after a peer-reviewed study showed that it is a more potent antioxidant than vitamin E. ​

Quintis Sandalwood is a supplier of Indian and Australian sandalwood raw materials, including oil, powder, logs and chips.

It supplies sandalwood materials to multiple industries for use in fragrance, cosmetics, as well as incense and religious carvings.

The firm owns and manages an Indian sandalwood plantation that spans over 12,000 hectares across northern Australia and is home to more than 5.5 million trees.

The fourth iteration of Shiseido’s best-selling Ultimune serum showcases the company’s research into the co-relation between blood circulation and skin health. ​

The latest serum feature’s Shiseido’s The Lifeblood technology as well as new ingredients Houttuynia cordata and fermented hibiscus Extract.

Shiseido’s latest dermatological discovery, dubbed the Lifeblood Research, dove into the significance of blood circulation and its relation to skin health and its appearance.

“The latest ground-breaking Lifeblood Research involves fundamentally improving the skin by constantly enhancing blood flow. It is different from conventional skincare technology, which only deals with individual skin concerns temporarily and only on the surface level,” ​said Ryota Yukisada, chief brand officer of Shiseido.

4 – Smart skin: Amorepacific to use wearable device to develop cosmetics for specific environmental needs ​

South Korean cosmetics firm Amorepacific said it intends to use the sweat-proof wearable skin measurement device ​ it developed with MIT to produce cosmetic products according to environmental needs.

Amorepacific announced in June that it had collaborated with Massachusetts Institute of Technology (MIT) to develop a wearable skin measuring device.

The film-like and stretchable patch is embedded with a flexible sensor. It can be stuck on the skin and used to measure the skin condition for a long period of time, withstanding sweat and remaining comfortable for the user.

“Efforts to measure the condition of human skin more precisely and stably have been carried out in various fields. However, the skin is naturally affected by various external environmental changes such as sweat, and thus, it was difficult to maintain measurement and observe changes without interruption,” ​ said Han Jiyeon, a scientist from the Amorepacific R&D centre clinical research lab.

5 – Personalisation and skin microbiome: S. Korea’s Cosmax to develop AI-powered platform ​

South Korean ODM giant Cosmax is set to develop an AI-powered platform ​ that will help both its customers and end-consumers get personalised information about the skin microbiome.

CosmeticsDesign-Asia ​ has learnt that the commercial platform would map out its specialised technology by factors such as efficacy to aid developers in producing skin microbiome products.

To build up the system, the company has collected skin microbiome information from over 1,000 people to date and analysed the statistical significance between them.

Furthermore, the company is planning to develop a consumer-based personal skin microbiome platform as well to provide  “high-quality value” ​ to consumers.

6 – Mask-proof lip gloss? Maquillage launches new transfer-resistant product featuring Shiseido’s latest tech ​

Shiseido-owned make-up brand Maquillage has launched a new lip product featuring new technology ​ that makes it transfer resistant despite its glossy finish.

Maquillage Dramatic Lip Tint debuted on October 21 with five shades that retail for JPY2,300 (U$21).

The newest product was developed in response to the increased use of protective face masks, which caused a devastating blow to lipstick sales in the past 18 months or so.

“Due to COVID-19, consumers' values and behaviours around the world are changing dramatically. Reflecting such changes there is increased awareness of health and skincare, while at the same time frequencies to use make-up like lipstick are decreasing as people go out less and wear masks,”  ​remarked Masahiko Uotani, CEO of Shiseido, last year.

7 – A good alternate? Aussie firm eyeing opportunities for Indian sandalwood as a CBD alternative following China ban ​

Australian sandalwood supplier Quintis is eyeing new opportunities in China’s cosmetic space after a new scientific review revealed Indian sandalwood oil has more scientifically proven benefits ​ than CBD oil.

Quintis Sandalwood is a supplier of Indian and Australian sandalwood raw materials, including oil, powder, logs and chips for multiple industries for use in fragrance, cosmetics, as well as incense and religious carvings.

Previously, the company told  CosmeticsDesign-Asia ​ that it has been placing more emphasis on the cosmetics side of the business, believing it could tap into the demand for natural products in the market.

Now, Quintis is looking to target the Chinese market on the back of a new review revealing that Indian sandalwood oil, or  Santalum album, ​ has more substantiated benefits than cannabidiol (CBD).

8 – Seaweed saviour: Marinova highlights ‘unmet needs’ in the market for skin microbiome-friendly atopic dermatitis treatment ​

There is a gap in the market for skin microbiome solutions to help treat atopic dermatitis ​, claims biotechnology company Marinova, for which it believes its brown seaweed extract could play a major role.

Atopic dermatitis is the most common type of eczema, a widespread condition characterised by dry and itchy skin.

While there are treatments for atopic dermatitis available in the market, Tasmania-based biotech firm Marinova believes there is a gap in the market targeting the skin microbiome.

“Broadly, atopic dermatitis is quite a complex illness where there's a multitude of factors that contribute to it… and there's an unmet need, particularly in the skin microbiome space,” ​said Dr Damien Stringer, operations manager, Marinova.

9 – Too complex: Chinese team questions ‘quality and value’ of Asian herb research on skin whitening ​

A team of researchers from two Chinese institutes have questioned the validity ​ of the existing research undertaken on Asian herbs for skin whitening applications, concluding that the ingredients were “too complex to obtain reliable results”. ​

Despite being fraught with potential hazards, skin care products with whitening claims continue to thrive in the Asian beauty market because fair skin is still considered the ideal of beauty.

According to a 2019 report by Grand View Research, the global market size of skin whitening in 2018 was $8.3bn. In the largest product segment – the cream category at 53% -- China, Japan, India, Indonesia and South Korea, emerged as the top five-ranked countries in terms of sales.

The high demand for skin whitening solutions coupled with the increasing concern for product safety, has led to a raft of research into traditional Asian herbs and potential skin whitening properties.

10 – Clean sweep: Clé De Peau’s new micellar cleansing water to feature Shiseido’s new make-up removing tech ​

Japanese beauty giant Shiseido has developed new technology ​ to enhance the effectiveness of micellar water that it will launch with Clé De Peau Beauté starting from June 2021.

The latest tech will be applied to Clé De Peau Beauté Micellar Cleansing Water will be rolled out in Japan in June and will be launched subsequently overseas from July.

Clé De Peau Beauté Micellar Cleansing Water will be the first product to be enhanced with the newly developed tech and Shiseido intends to develop more cleansing products with it in the future.

The impetus of the new launch was Shiseido’s development of a unique state of surfactant, the sponge phase.

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Home » 500+ Qualitative Research Titles and Topics

500+ Qualitative Research Titles and Topics

Table of Contents

Qualitative research is a methodological approach that involves gathering and analyzing non-numerical data to understand and interpret social phenomena. Unlike quantitative research , which emphasizes the collection of numerical data through surveys and experiments, qualitative research is concerned with exploring the subjective experiences, perspectives, and meanings of individuals and groups. As such, qualitative research topics can be diverse and encompass a wide range of social issues and phenomena. From exploring the impact of culture on identity formation to examining the experiences of marginalized communities, qualitative research offers a rich and nuanced perspective on complex social issues. In this post, we will explore some of the most compelling qualitative research topics and provide some tips on how to conduct effective qualitative research.

Qualitative Research Titles

Qualitative research titles often reflect the study’s focus on understanding the depth and complexity of human behavior, experiences, or social phenomena. Here are some examples across various fields:

• “Understanding the Impact of Project-Based Learning on Student Engagement in High School Classrooms: A Qualitative Study”
• “Navigating the Transition: Experiences of International Students in American Universities”
• “The Role of Parental Involvement in Early Childhood Education: Perspectives from Teachers and Parents”
• “Exploring the Effects of Teacher Feedback on Student Motivation and Self-Efficacy in Middle Schools”
• “Digital Literacy in the Classroom: Teacher Strategies for Integrating Technology in Elementary Education”
• “Culturally Responsive Teaching Practices: A Case Study in Diverse Urban Schools”
• “The Influence of Extracurricular Activities on Academic Achievement: Student Perspectives”
• “Barriers to Implementing Inclusive Education in Public Schools: A Qualitative Inquiry”
• “Teacher Professional Development and Its Impact on Classroom Practice: A Qualitative Exploration”
• “Student-Centered Learning Environments: A Qualitative Study of Classroom Dynamics and Outcomes”
• “The Experience of First-Year Teachers: Challenges, Support Systems, and Professional Growth”
• “Exploring the Role of School Leadership in Fostering a Positive School Culture”
• “Peer Relationships and Learning Outcomes in Cooperative Learning Settings: A Qualitative Analysis”
• “The Impact of Social Media on Student Learning and Engagement: Teacher and Student Perspectives”
• “Understanding Special Education Needs: Parent and Teacher Perceptions of Support Services in Schools

Health Science

• “Living with Chronic Pain: Patient Narratives and Coping Strategies in Managing Daily Life”
• “Healthcare Professionals’ Perspectives on the Challenges of Rural Healthcare Delivery”
• “Exploring the Mental Health Impacts of COVID-19 on Frontline Healthcare Workers: A Qualitative Study”
• “Patient and Family Experiences of Palliative Care: Understanding Needs and Preferences”
• “The Role of Community Health Workers in Improving Access to Maternal Healthcare in Rural Areas”
• “Barriers to Mental Health Services Among Ethnic Minorities: A Qualitative Exploration”
• “Understanding Patient Satisfaction in Telemedicine Services: A Qualitative Study of User Experiences”
• “The Impact of Cultural Competence Training on Healthcare Provider-Patient Communication”
• “Navigating the Transition to Adult Healthcare Services: Experiences of Adolescents with Chronic Conditions”
• “Exploring the Use of Alternative Medicine Among Patients with Chronic Diseases: A Qualitative Inquiry”
• “The Role of Social Support in the Rehabilitation Process of Stroke Survivors”
• “Healthcare Decision-Making Among Elderly Patients: A Qualitative Study of Preferences and Influences”
• “Nurse Perceptions of Patient Safety Culture in Hospital Settings: A Qualitative Analysis”
• “Experiences of Women with Postpartum Depression: Barriers to Seeking Help”
• “The Impact of Nutrition Education on Eating Behaviors Among College Students: A Qualitative Approach”
• “Understanding Resilience in Survivors of Childhood Trauma: A Narrative Inquiry”
• “The Role of Mindfulness in Managing Work-Related Stress Among Corporate Employees: A Qualitative Study”
• “Coping Mechanisms Among Parents of Children with Autism Spectrum Disorder”
• “Exploring the Psychological Impact of Social Isolation in the Elderly: A Phenomenological Study”
• “Identity Formation in Adolescence: The Influence of Social Media and Peer Groups”
• “The Experience of Forgiveness in Interpersonal Relationships: A Qualitative Exploration”
• “Perceptions of Happiness and Well-Being Among University Students: A Cultural Perspective”
• “The Impact of Art Therapy on Anxiety and Depression in Adult Cancer Patients”
• “Narratives of Recovery: A Qualitative Study on the Journey Through Addiction Rehabilitation”
• “Exploring the Psychological Effects of Long-Term Unemployment: A Grounded Theory Approach”
• “Attachment Styles and Their Influence on Adult Romantic Relationships: A Qualitative Analysis”
• “The Role of Personal Values in Career Decision-Making Among Young Adults”
• “Understanding the Stigma of Mental Illness in Rural Communities: A Qualitative Inquiry”
• “Exploring the Use of Digital Mental Health Interventions Among Adolescents: A Qualitative Study”
• “The Psychological Impact of Climate Change on Young Adults: An Exploration of Anxiety and Action”
• “Navigating Identity: The Role of Social Media in Shaping Youth Culture and Self-Perception”
• “Community Resilience in the Face of Urban Gentrification: A Case Study of Neighborhood Change”
• “The Dynamics of Intergenerational Relationships in Immigrant Families: A Qualitative Analysis”
• “Social Capital and Economic Mobility in Low-Income Neighborhoods: An Ethnographic Approach”
• “Gender Roles and Career Aspirations Among Young Adults in Conservative Societies”
• “The Stigma of Mental Health in the Workplace: Employee Narratives and Organizational Culture”
• “Exploring the Intersection of Race, Class, and Education in Urban School Systems”
• “The Impact of Digital Divide on Access to Healthcare Information in Rural Communities”
• “Social Movements and Political Engagement Among Millennials: A Qualitative Study”
• “Cultural Adaptation and Identity Among Second-Generation Immigrants: A Phenomenological Inquiry”
• “The Role of Religious Institutions in Providing Community Support and Social Services”
• “Negotiating Public Space: Experiences of LGBTQ+ Individuals in Urban Environments”
• “The Sociology of Food: Exploring Eating Habits and Food Practices Across Cultures”
• “Work-Life Balance Challenges Among Dual-Career Couples: A Qualitative Exploration”
• “The Influence of Peer Networks on Substance Use Among Adolescents: A Community Study”

Business and Management

• “Navigating Organizational Change: Employee Perceptions and Adaptation Strategies in Mergers and Acquisitions”
• “Corporate Social Responsibility: Consumer Perceptions and Brand Loyalty in the Retail Sector”
• “Leadership Styles and Organizational Culture: A Comparative Study of Tech Startups”
• “Workplace Diversity and Inclusion: Best Practices and Challenges in Multinational Corporations”
• “Consumer Trust in E-commerce: A Qualitative Study of Online Shopping Behaviors”
• “The Gig Economy and Worker Satisfaction: Exploring the Experiences of Freelance Professionals”
• “Entrepreneurial Resilience: Success Stories and Lessons Learned from Failed Startups”
• “Employee Engagement and Productivity in Remote Work Settings: A Post-Pandemic Analysis”
• “Brand Storytelling: How Narrative Strategies Influence Consumer Engagement”
• “Sustainable Business Practices: Stakeholder Perspectives in the Fashion Industry”
• “Cross-Cultural Communication Challenges in Global Teams: Strategies for Effective Collaboration”
• “Innovative Workspaces: The Impact of Office Design on Creativity and Collaboration”
• “Consumer Perceptions of Artificial Intelligence in Customer Service: A Qualitative Exploration”
• “The Role of Mentoring in Career Development: Insights from Women in Leadership Positions”
• “Agile Management Practices: Adoption and Impact in Traditional Industries”

Environmental Studies

• “Community-Based Conservation Efforts in Tropical Rainforests: A Qualitative Study of Local Perspectives and Practices”
• “Urban Sustainability Initiatives: Exploring Resident Participation and Impact in Green City Projects”
• “Perceptions of Climate Change Among Indigenous Populations: Insights from Traditional Ecological Knowledge”
• “Environmental Justice and Industrial Pollution: A Case Study of Community Advocacy and Response”
• “The Role of Eco-Tourism in Promoting Conservation Awareness: Perspectives from Tour Operators and Visitors”
• “Sustainable Agriculture Practices Among Smallholder Farmers: Challenges and Opportunities”
• “Youth Engagement in Climate Action Movements: Motivations, Perceptions, and Outcomes”
• “Corporate Environmental Responsibility: A Qualitative Analysis of Stakeholder Expectations and Company Practices”
• “The Impact of Plastic Pollution on Marine Ecosystems: Community Awareness and Behavioral Change”
• “Renewable Energy Adoption in Rural Communities: Barriers, Facilitators, and Social Implications”
• “Water Scarcity and Community Adaptation Strategies in Arid Regions: A Grounded Theory Approach”
• “Urban Green Spaces: Public Perceptions and Use Patterns in Megacities”
• “Environmental Education in Schools: Teachers’ Perspectives on Integrating Sustainability into Curricula”
• “The Influence of Environmental Activism on Policy Change: Case Studies of Grassroots Campaigns”
• “Cultural Practices and Natural Resource Management: A Qualitative Study of Indigenous Stewardship Models”

Anthropology

• “Kinship and Social Organization in Matrilineal Societies: An Ethnographic Study”
• “Rituals and Beliefs Surrounding Death and Mourning in Diverse Cultures: A Comparative Analysis”
• “The Impact of Globalization on Indigenous Languages and Cultural Identity”
• “Food Sovereignty and Traditional Agricultural Practices Among Indigenous Communities”
• “Navigating Modernity: The Integration of Traditional Healing Practices in Contemporary Healthcare Systems”
• “Gender Roles and Equality in Hunter-Gatherer Societies: An Anthropological Perspective”
• “Sacred Spaces and Religious Practices: An Ethnographic Study of Pilgrimage Sites”
• “Youth Subcultures and Resistance: An Exploration of Identity and Expression in Urban Environments”
• “Cultural Constructions of Disability and Inclusion: A Cross-Cultural Analysis”
• “Interethnic Marriages and Cultural Syncretism: Case Studies from Multicultural Societies”
• “The Role of Folklore and Storytelling in Preserving Cultural Heritage”
• “Economic Anthropology of Gift-Giving and Reciprocity in Tribal Communities”
• “Digital Anthropology: The Role of Social Media in Shaping Political Movements”
• “Migration and Diaspora: Maintaining Cultural Identity in Transnational Communities”
• “Cultural Adaptations to Climate Change Among Coastal Fishing Communities”

Communication Studies

• “The Dynamics of Family Communication in the Digital Age: A Qualitative Inquiry”
• “Narratives of Identity and Belonging in Diaspora Communities Through Social Media”
• “Organizational Communication and Employee Engagement: A Case Study in the Non-Profit Sector”
• “Cultural Influences on Communication Styles in Multinational Teams: An Ethnographic Approach”
• “Media Representation of Women in Politics: A Content Analysis and Audience Perception Study”
• “The Role of Communication in Building Sustainable Community Development Projects”
• “Interpersonal Communication in Online Dating: Strategies, Challenges, and Outcomes”
• “Public Health Messaging During Pandemics: A Qualitative Study of Community Responses”
• “The Impact of Mobile Technology on Parent-Child Communication in the Digital Era”
• “Crisis Communication Strategies in the Hospitality Industry: A Case Study of Reputation Management”
• “Narrative Analysis of Personal Stories Shared on Mental Health Blogs”
• “The Influence of Podcasts on Political Engagement Among Young Adults”
• “Visual Communication and Brand Identity: A Qualitative Study of Consumer Interpretations”
• “Communication Barriers in Cross-Cultural Healthcare Settings: Patient and Provider Perspectives”
• “The Role of Internal Communication in Managing Organizational Change: Employee Experiences”

Information Technology

• “User Experience Design in Augmented Reality Applications: A Qualitative Study of Best Practices”
• “The Human Factor in Cybersecurity: Understanding Employee Behaviors and Attitudes Towards Phishing”
• “Adoption of Cloud Computing in Small and Medium Enterprises: Challenges and Success Factors”
• “Blockchain Technology in Supply Chain Management: A Qualitative Exploration of Potential Impacts”
• “The Role of Artificial Intelligence in Personalizing User Experiences on E-commerce Platforms”
• “Digital Transformation in Traditional Industries: A Case Study of Technology Adoption Challenges”
• “Ethical Considerations in the Development of Smart Home Technologies: A Stakeholder Analysis”
• “The Impact of Social Media Algorithms on News Consumption and Public Opinion”
• “Collaborative Software Development: Practices and Challenges in Open Source Projects”
• “Understanding the Digital Divide: Access to Information Technology in Rural Communities”
• “Data Privacy Concerns and User Trust in Internet of Things (IoT) Devices”
• “The Effectiveness of Gamification in Educational Software: A Qualitative Study of Engagement and Motivation”
• “Virtual Teams and Remote Work: Communication Strategies and Tools for Effectiveness”
• “User-Centered Design in Mobile Health Applications: Evaluating Usability and Accessibility”
• “The Influence of Technology on Work-Life Balance: Perspectives from IT Professionals”

Tourism and Hospitality

• “Exploring the Authenticity of Cultural Heritage Tourism in Indigenous Communities”
• “Sustainable Tourism Practices: Perceptions and Implementations in Small Island Destinations”
• “The Impact of Social Media Influencers on Destination Choice Among Millennials”
• “Gastronomy Tourism: Exploring the Culinary Experiences of International Visitors in Rural Regions”
• “Eco-Tourism and Conservation: Stakeholder Perspectives on Balancing Tourism and Environmental Protection”
• “The Role of Hospitality in Enhancing the Cultural Exchange Experience of Exchange Students”
• “Dark Tourism: Visitor Motivations and Experiences at Historical Conflict Sites”
• “Customer Satisfaction in Luxury Hotels: A Qualitative Study of Service Excellence and Personalization”
• “Adventure Tourism: Understanding the Risk Perception and Safety Measures Among Thrill-Seekers”
• “The Influence of Local Communities on Tourist Experiences in Ecotourism Sites”
• “Event Tourism: Economic Impacts and Community Perspectives on Large-Scale Music Festivals”
• “Heritage Tourism and Identity: Exploring the Connections Between Historic Sites and National Identity”
• “Tourist Perceptions of Sustainable Accommodation Practices: A Study of Green Hotels”
• “The Role of Language in Shaping the Tourist Experience in Multilingual Destinations”
• “Health and Wellness Tourism: Motivations and Experiences of Visitors to Spa and Retreat Centers”

Qualitative Research Topics

Qualitative Research Topics are as follows:

• Understanding the lived experiences of first-generation college students
• Exploring the impact of social media on self-esteem among adolescents
• Investigating the effects of mindfulness meditation on stress reduction
• Analyzing the perceptions of employees regarding organizational culture
• Examining the impact of parental involvement on academic achievement of elementary school students
• Investigating the role of music therapy in managing symptoms of depression
• Understanding the experience of women in male-dominated industries
• Exploring the factors that contribute to successful leadership in non-profit organizations
• Analyzing the effects of peer pressure on substance abuse among adolescents
• Investigating the experiences of individuals with disabilities in the workplace
• Understanding the factors that contribute to burnout among healthcare professionals
• Examining the impact of social support on mental health outcomes
• Analyzing the perceptions of parents regarding sex education in schools
• Investigating the experiences of immigrant families in the education system
• Understanding the impact of trauma on mental health outcomes
• Exploring the effectiveness of animal-assisted therapy for individuals with anxiety
• Analyzing the factors that contribute to successful intergenerational relationships
• Investigating the experiences of LGBTQ+ individuals in the workplace
• Understanding the impact of online gaming on social skills development among adolescents
• Examining the perceptions of teachers regarding technology integration in the classroom
• Analyzing the experiences of women in leadership positions
• Investigating the factors that contribute to successful marriage and long-term relationships
• Understanding the impact of social media on political participation
• Exploring the experiences of individuals with mental health disorders in the criminal justice system
• Analyzing the factors that contribute to successful community-based programs for youth development
• Investigating the experiences of veterans in accessing mental health services
• Understanding the impact of the COVID-19 pandemic on mental health outcomes
• Examining the perceptions of parents regarding childhood obesity prevention
• Analyzing the factors that contribute to successful multicultural education programs
• Investigating the experiences of individuals with chronic illnesses in the workplace
• Understanding the impact of poverty on academic achievement
• Exploring the experiences of individuals with autism spectrum disorder in the workplace
• Analyzing the factors that contribute to successful employee retention strategies
• Investigating the experiences of caregivers of individuals with Alzheimer’s disease
• Understanding the impact of parent-child communication on adolescent sexual behavior
• Examining the perceptions of college students regarding mental health services on campus
• Analyzing the factors that contribute to successful team building in the workplace
• Investigating the experiences of individuals with eating disorders in treatment programs
• Understanding the impact of mentorship on career success
• Exploring the experiences of individuals with physical disabilities in the workplace
• Analyzing the factors that contribute to successful community-based programs for mental health
• Investigating the experiences of individuals with substance use disorders in treatment programs
• Understanding the impact of social media on romantic relationships
• Examining the perceptions of parents regarding child discipline strategies
• Analyzing the factors that contribute to successful cross-cultural communication in the workplace
• Investigating the experiences of individuals with anxiety disorders in treatment programs
• Understanding the impact of cultural differences on healthcare delivery
• Exploring the experiences of individuals with hearing loss in the workplace
• Analyzing the factors that contribute to successful parent-teacher communication
• Investigating the experiences of individuals with depression in treatment programs
• Understanding the impact of childhood trauma on adult mental health outcomes
• Examining the perceptions of college students regarding alcohol and drug use on campus
• Analyzing the factors that contribute to successful mentor-mentee relationships
• Investigating the experiences of individuals with intellectual disabilities in the workplace
• Understanding the impact of work-family balance on employee satisfaction and well-being
• Exploring the experiences of individuals with autism spectrum disorder in vocational rehabilitation programs
• Analyzing the factors that contribute to successful project management in the construction industry
• Investigating the experiences of individuals with substance use disorders in peer support groups
• Understanding the impact of mindfulness meditation on stress reduction and mental health
• Examining the perceptions of parents regarding childhood nutrition
• Analyzing the factors that contribute to successful environmental sustainability initiatives in organizations
• Investigating the experiences of individuals with bipolar disorder in treatment programs
• Understanding the impact of job stress on employee burnout and turnover
• Exploring the experiences of individuals with physical disabilities in recreational activities
• Analyzing the factors that contribute to successful strategic planning in nonprofit organizations
• Investigating the experiences of individuals with hoarding disorder in treatment programs
• Understanding the impact of culture on leadership styles and effectiveness
• Examining the perceptions of college students regarding sexual health education on campus
• Analyzing the factors that contribute to successful supply chain management in the retail industry
• Investigating the experiences of individuals with personality disorders in treatment programs
• Understanding the impact of multiculturalism on group dynamics in the workplace
• Exploring the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Analyzing the factors that contribute to successful employee engagement strategies in organizations
• Investigating the experiences of individuals with internet addiction disorder in treatment programs
• Understanding the impact of social comparison on body dissatisfaction and self-esteem
• Examining the perceptions of parents regarding childhood sleep habits
• Analyzing the factors that contribute to successful diversity and inclusion initiatives in organizations
• Investigating the experiences of individuals with schizophrenia in treatment programs
• Understanding the impact of job crafting on employee motivation and job satisfaction
• Exploring the experiences of individuals with vision impairments in navigating public spaces
• Analyzing the factors that contribute to successful customer relationship management strategies in the service industry
• Investigating the experiences of individuals with dissociative amnesia in treatment programs
• Understanding the impact of cultural intelligence on intercultural communication and collaboration
• Examining the perceptions of college students regarding campus diversity and inclusion efforts
• Analyzing the factors that contribute to successful supply chain sustainability initiatives in organizations
• Investigating the experiences of individuals with obsessive-compulsive disorder in treatment programs
• Understanding the impact of transformational leadership on organizational performance and employee well-being
• Exploring the experiences of individuals with mobility impairments in public transportation
• Analyzing the factors that contribute to successful talent management strategies in organizations
• Investigating the experiences of individuals with substance use disorders in harm reduction programs
• Understanding the impact of gratitude practices on well-being and resilience
• Examining the perceptions of parents regarding childhood mental health and well-being
• Analyzing the factors that contribute to successful corporate social responsibility initiatives in organizations
• Investigating the experiences of individuals with borderline personality disorder in treatment programs
• Understanding the impact of emotional labor on job stress and burnout
• Exploring the experiences of individuals with hearing impairments in healthcare settings
• Analyzing the factors that contribute to successful customer experience strategies in the hospitality industry
• Investigating the experiences of individuals with gender dysphoria in gender-affirming healthcare
• Understanding the impact of cultural differences on cross-cultural negotiation in the global marketplace
• Examining the perceptions of college students regarding academic stress and mental health
• Analyzing the factors that contribute to successful supply chain agility in organizations
• Understanding the impact of music therapy on mental health and well-being
• Exploring the experiences of individuals with dyslexia in educational settings
• Analyzing the factors that contribute to successful leadership in nonprofit organizations
• Investigating the experiences of individuals with chronic illnesses in online support groups
• Understanding the impact of exercise on mental health and well-being
• Examining the perceptions of parents regarding childhood screen time
• Analyzing the factors that contribute to successful change management strategies in organizations
• Understanding the impact of cultural differences on international business negotiations
• Exploring the experiences of individuals with hearing impairments in the workplace
• Analyzing the factors that contribute to successful team building in corporate settings
• Understanding the impact of technology on communication in romantic relationships
• Analyzing the factors that contribute to successful community engagement strategies for local governments
• Investigating the experiences of individuals with attention deficit hyperactivity disorder (ADHD) in treatment programs
• Understanding the impact of financial stress on mental health and well-being
• Analyzing the factors that contribute to successful mentorship programs in organizations
• Investigating the experiences of individuals with gambling addictions in treatment programs
• Understanding the impact of social media on body image and self-esteem
• Examining the perceptions of parents regarding childhood education
• Analyzing the factors that contribute to successful virtual team management strategies
• Investigating the experiences of individuals with dissociative identity disorder in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in healthcare settings
• Exploring the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Analyzing the factors that contribute to successful community-building strategies in urban neighborhoods
• Investigating the experiences of individuals with alcohol use disorders in treatment programs
• Understanding the impact of personality traits on romantic relationships
• Examining the perceptions of college students regarding mental health stigma on campus
• Analyzing the factors that contribute to successful fundraising strategies for political campaigns
• Investigating the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Understanding the impact of social support on mental health and well-being among the elderly
• Exploring the experiences of individuals with chronic illnesses in medical treatment decision-making processes
• Analyzing the factors that contribute to successful innovation strategies in organizations
• Investigating the experiences of individuals with dissociative disorders in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in education settings
• Examining the perceptions of parents regarding childhood physical activity
• Analyzing the factors that contribute to successful conflict resolution in family relationships
• Investigating the experiences of individuals with opioid use disorders in treatment programs
• Understanding the impact of emotional intelligence on leadership effectiveness
• Exploring the experiences of individuals with learning disabilities in the workplace
• Analyzing the factors that contribute to successful change management in educational institutions
• Investigating the experiences of individuals with eating disorders in recovery support groups
• Understanding the impact of self-compassion on mental health and well-being
• Examining the perceptions of college students regarding campus safety and security measures
• Analyzing the factors that contribute to successful marketing strategies for nonprofit organizations
• Investigating the experiences of individuals with postpartum depression in treatment programs
• Understanding the impact of ageism in the workplace
• Exploring the experiences of individuals with dyslexia in the education system
• Investigating the experiences of individuals with anxiety disorders in cognitive-behavioral therapy programs
• Understanding the impact of socioeconomic status on access to healthcare
• Examining the perceptions of parents regarding childhood screen time usage
• Analyzing the factors that contribute to successful supply chain management strategies
• Understanding the impact of parenting styles on child development
• Exploring the experiences of individuals with addiction in harm reduction programs
• Analyzing the factors that contribute to successful crisis management strategies in organizations
• Investigating the experiences of individuals with trauma in trauma-focused therapy programs
• Examining the perceptions of healthcare providers regarding patient-centered care
• Analyzing the factors that contribute to successful product development strategies
• Investigating the experiences of individuals with autism spectrum disorder in employment programs
• Understanding the impact of cultural competence on healthcare outcomes
• Exploring the experiences of individuals with chronic illnesses in healthcare navigation
• Analyzing the factors that contribute to successful community engagement strategies for non-profit organizations
• Investigating the experiences of individuals with physical disabilities in the workplace
• Understanding the impact of childhood trauma on adult mental health
• Analyzing the factors that contribute to successful supply chain sustainability strategies
• Investigating the experiences of individuals with personality disorders in dialectical behavior therapy programs
• Understanding the impact of gender identity on mental health treatment seeking behaviors
• Exploring the experiences of individuals with schizophrenia in community-based treatment programs
• Analyzing the factors that contribute to successful project team management strategies
• Investigating the experiences of individuals with obsessive-compulsive disorder in exposure and response prevention therapy programs
• Understanding the impact of cultural competence on academic achievement and success
• Examining the perceptions of college students regarding academic integrity
• Analyzing the factors that contribute to successful social media marketing strategies
• Investigating the experiences of individuals with bipolar disorder in community-based treatment programs
• Understanding the impact of mindfulness on academic achievement and success
• Exploring the experiences of individuals with substance use disorders in medication-assisted treatment programs
• Investigating the experiences of individuals with anxiety disorders in exposure therapy programs
• Understanding the impact of healthcare disparities on health outcomes
• Analyzing the factors that contribute to successful supply chain optimization strategies
• Investigating the experiences of individuals with borderline personality disorder in schema therapy programs
• Understanding the impact of culture on perceptions of mental health stigma
• Exploring the experiences of individuals with trauma in art therapy programs
• Analyzing the factors that contribute to successful digital marketing strategies
• Investigating the experiences of individuals with eating disorders in online support groups
• Understanding the impact of workplace bullying on job satisfaction and performance
• Examining the perceptions of college students regarding mental health resources on campus
• Analyzing the factors that contribute to successful supply chain risk management strategies
• Investigating the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Understanding the impact of cognitive-behavioral therapy on social anxiety disorder
• Understanding the impact of COVID-19 on mental health and well-being
• Exploring the experiences of individuals with eating disorders in treatment programs
• Analyzing the factors that contribute to successful leadership in business organizations
• Investigating the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Understanding the impact of cultural differences on intercultural communication
• Examining the perceptions of teachers regarding inclusive education for students with disabilities
• Investigating the experiences of individuals with depression in therapy programs
• Understanding the impact of workplace culture on employee retention and turnover
• Exploring the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Analyzing the factors that contribute to successful crisis communication strategies in organizations
• Investigating the experiences of individuals with anxiety disorders in mindfulness-based interventions
• Investigating the experiences of individuals with chronic illnesses in healthcare settings
• Understanding the impact of technology on work-life balance
• Exploring the experiences of individuals with learning disabilities in academic settings
• Analyzing the factors that contribute to successful entrepreneurship in small businesses
• Understanding the impact of gender identity on mental health and well-being
• Examining the perceptions of individuals with disabilities regarding accessibility in public spaces
• Understanding the impact of religion on coping strategies for stress and anxiety
• Exploring the experiences of individuals with chronic illnesses in complementary and alternative medicine treatments
• Analyzing the factors that contribute to successful customer retention strategies in business organizations
• Investigating the experiences of individuals with postpartum depression in therapy programs
• Understanding the impact of ageism on older adults in healthcare settings
• Examining the perceptions of students regarding online learning during the COVID-19 pandemic
• Analyzing the factors that contribute to successful team building in virtual work environments
• Investigating the experiences of individuals with gambling disorders in treatment programs
• Exploring the experiences of individuals with chronic illnesses in peer support groups
• Analyzing the factors that contribute to successful social media marketing strategies for businesses
• Investigating the experiences of individuals with ADHD in treatment programs
• Understanding the impact of sleep on cognitive and emotional functioning
• Examining the perceptions of individuals with chronic illnesses regarding healthcare access and affordability
• Investigating the experiences of individuals with borderline personality disorder in dialectical behavior therapy programs
• Understanding the impact of social support on caregiver well-being
• Exploring the experiences of individuals with chronic illnesses in disability activism
• Analyzing the factors that contribute to successful cultural competency training programs in healthcare settings
• Understanding the impact of personality disorders on interpersonal relationships
• Examining the perceptions of healthcare providers regarding the use of telehealth services
• Investigating the experiences of individuals with dissociative disorders in therapy programs
• Understanding the impact of gender bias in hiring practices
• Exploring the experiences of individuals with visual impairments in the workplace
• Analyzing the factors that contribute to successful diversity and inclusion programs in the workplace
• Understanding the impact of online dating on romantic relationships
• Examining the perceptions of parents regarding childhood vaccination
• Analyzing the factors that contribute to successful communication in healthcare settings
• Understanding the impact of cultural stereotypes on academic achievement
• Exploring the experiences of individuals with substance use disorders in sober living programs
• Analyzing the factors that contribute to successful classroom management strategies
• Understanding the impact of social support on addiction recovery
• Examining the perceptions of college students regarding mental health stigma
• Analyzing the factors that contribute to successful conflict resolution in the workplace
• Understanding the impact of race and ethnicity on healthcare access and outcomes
• Exploring the experiences of individuals with post-traumatic stress disorder in treatment programs
• Analyzing the factors that contribute to successful project management strategies
• Understanding the impact of teacher-student relationships on academic achievement
• Analyzing the factors that contribute to successful customer service strategies
• Investigating the experiences of individuals with social anxiety disorder in treatment programs
• Understanding the impact of workplace stress on job satisfaction and performance
• Exploring the experiences of individuals with disabilities in sports and recreation
• Analyzing the factors that contribute to successful marketing strategies for small businesses
• Investigating the experiences of individuals with phobias in treatment programs
• Understanding the impact of culture on attitudes towards mental health and illness
• Examining the perceptions of college students regarding sexual assault prevention
• Analyzing the factors that contribute to successful time management strategies
• Investigating the experiences of individuals with addiction in recovery support groups
• Understanding the impact of mindfulness on emotional regulation and well-being
• Exploring the experiences of individuals with chronic pain in treatment programs
• Analyzing the factors that contribute to successful conflict resolution in romantic relationships
• Investigating the experiences of individuals with autism spectrum disorder in social skills training programs
• Understanding the impact of parent-child communication on adolescent substance use
• Examining the perceptions of parents regarding childhood mental health services
• Analyzing the factors that contribute to successful fundraising strategies for non-profit organizations
• Investigating the experiences of individuals with chronic illnesses in support groups
• Understanding the impact of personality traits on career success and satisfaction
• Exploring the experiences of individuals with disabilities in accessing public transportation
• Analyzing the factors that contribute to successful team building in sports teams
• Investigating the experiences of individuals with chronic pain in alternative medicine treatments
• Understanding the impact of stigma on mental health treatment seeking behaviors
• Examining the perceptions of college students regarding diversity and inclusion on campus.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Elektrostal

Elektrostal Localisation : Country Russia , Oblast Moscow Oblast . Available Information : Geographical coordinates , Population, Area, Altitude, Weather and Hotel . Nearby cities and villages : Noginsk , Pavlovsky Posad and Staraya Kupavna .

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Elektrostal Demography

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Elektrostal Geography

Geographic Information regarding City of Elektrostal .

Elektrostal Distance

Distance (in kilometers) between Elektrostal and the biggest cities of Russia.

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Geographic coordinates of Elektrostal, Moscow Oblast, Russia

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Coordinates of Elektrostal in decimal degrees

Coordinates of elektrostal in degrees and decimal minutes, utm coordinates of elektrostal, geographic coordinate systems.

WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).

Geographic coordinates (latitude and longitude) define a position on the Earth’s surface. Coordinates are angular units. The canonical form of latitude and longitude representation uses degrees (°), minutes (′), and seconds (″). GPS systems widely use coordinates in degrees and decimal minutes, or in decimal degrees.

Latitude varies from −90° to 90°. The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. N). Negative latitude values correspond to the geographic locations south of the Equator (abbrev. S).

Longitude is counted from the prime meridian ( IERS Reference Meridian for WGS 84) and varies from −180° to 180°. Positive longitude values correspond to the geographic locations east of the prime meridian (abbrev. E). Negative longitude values correspond to the geographic locations west of the prime meridian (abbrev. W).

UTM or Universal Transverse Mercator coordinate system divides the Earth’s surface into 60 longitudinal zones. The coordinates of a location within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone’s central meridian, and measured in meters.

Elevation above sea level is a measure of a geographic location’s height. We are using the global digital elevation model GTOPO30 .

Elektrostal , Moscow Oblast, Russia

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Gardeners & Lawn Care Companies in Elektrostal'

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Popular Locations

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• Elektrostal', Moscow Oblast, Russia

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