RESEARCH Open Access
How long is enough? Identification of
product dry-time as a primary driver of
alcohol-based hand rub efficacy
Miranda Suchomel1, Rachel A. Leslie2, Albert E. Parker3,4 and David R. Macinga2*
Abstract
Background: The World Health Organization has called for the development of improved methodologies to evaluate
alcohol-based handrub (ABHR) efficacy, including evaluation at “short application times and volumes that reflect actual
use in healthcare facilities”. The objective of this study was to investigate variables influencing ABHR efficacy, under test
conditions reflective of clinical use.
Methods: The test product (60% V/V 2-propanol) was evaluated according to a modified EN 1500 methodology,
where application volumes of 1 mL, 2 mL, and 3 mL were rubbed until dry. Statistical analyses were performed to
investigate the relative influences of product volume, hand size, and product dry-time on efficacy, and hand size
and hand contamination on product dry-time.
Results: Mean log10 reduction factors (SD) were 1.99 (0.66), 2.96 (0.84) and 3.28 (0.96); and mean dry-times (SD) were
24 s (7 s), 50 s (14 s), and 67 s (20 s) at application volumes of 1 mL, 2 mL, and 3 mL, respectively (p≤ 0.030). When
data were examined at the individual volunteer level, there was a statistically significant correlation between dry-time
and log reduction factor (p < 0.0001), independent of application volume. There was also a statistically significant
correlation between hand surface area and dry-times (p = 0.047), but no correlation between hand surface area
and efficacy (p = 0.698).
Conclusions: When keeping other variables such as alcohol type and concentration constant, product dry-time
appears to be the primary driver of ABHR efficacy suggesting that dosing should be customized to each individual and
focus on achieving a product dry-time delivering adequate efficacy.
Keywords: Hand hygiene, Hygienic handrub, EN 1500, ABHR, Application volume, Dose
Background
Despite the universal acceptance of alcohol-based hand
rubs for routine hand antisepsis in healthcare settings,
guidance regarding appropriate application volume has
been vague and somewhat conflicting. The World
Health Organization (WHO) Hand Hygiene Guidelines
recommend a “palmful” of alcohol-based hand rub
whereas the US Centers for Disease Control and Preven-
tion (CDC) Hand Hygiene guidelines state that the ideal
volume of product to apply to the hands is not known
[1, 2]. Regarding product rub-in times (dry-times),
WHO guidelines state that product should take 20–30 s
to rub until dry whereas the CDC guidelines state that if
product is dry before 10 to 15 s, then an insufficient
amount was used [1, 2]. To make more accurate recom-
mendations, WHO and others have called for the devel-
opment of improved methodologies to evaluate efficacy
of alcohol-based handrubs (ABHR) to obtain results
reflective of clinical use [2, 3]. Recommendations include
evaluation at “short application times and volumes that
reflect actual use in healthcare facilities”.
Several groups have recently investigated the relation-
ships between key ABHR use variables such as product
volume, hand size, product dry-times, and log reduction
factors using standard EN 1500 methodology [4–6].
These studies suggest that hand size and product dry-
time are important variables, but the relative importance
* Correspondence: macingad@gojo.com
2GOJO Industries, Inc., One GOJO Plaza, Suite 500, Akron, OH 44311, USA
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 
https://doi.org/10.1186/s13756-018-0357-6
of each in determining ABHR efficacy remains unclear.
A limitation of these studies is that EN 1500 method-
ology requires the test product be rubbed for a specific
timeframe and then immediately sampled for bacterial
recovery [7]. Because the test product often remains wet
on the hands when sampled, the methodology does not
reflect product use in clinical settings or allow accurate
assessment of the influence of product-dry time on effi-
cacy [8–10]. Our group has developed a modified EN
1500 methodology, where the test product is rubbed
until dry as prescribed in ASTM E 2755 [10, 11]. Using
this method, we have demonstrated the importance of
product volume on mean log10 reduction factors (RFs).
The objectives of this study were to further evaluate the
impact of ABHR volume on efficacy, and to investigate
the relationships between hand size, dry-time, and
efficacy, using a modified EN 1500 methodology where
volunteers rubbed test product until hands were dry.
Methods
The test product (60% V/V 2-propanol; Merck, 1.09634,
Darmstadt, Germany) was evaluated at application vol-
umes of 1 mL, 2 mL, and 3 mL, respectively, according
to a modified EN 1500 methodology on artificially con-
taminated hands of volunteers.
Experiments were performed at the Institute of Hygiene
and Applied Immunology of the Medical University of
Vienna, Austria. The laboratory was accredited according
to EN ISO/IEC 17025:2005 and recognized by the national
accreditation body “Akkreditierung Austria”. All areas of
testing were approved and reported to the Federal Ministry
of Science, Research and Economy, Austria. This study was
performed in compliance with the World Medical Associ-
ation, Declaration of Helsinki - Ethical Principles for
Medical Research Involving Human Subjects. Ethics board
approval was not required based on the classification of
Escherichia coli K12 (NCTC 10538) as a Risk Group 1
non-pathogenic organism by the German Safety Ordinance
on Gene Technology. All participants gave informed
written consent.
Modified EN 1500 methodology
For artificial contamination, freshly washed hands were
immersed in a suspension of a specified apathogenic
strain of Escherichia coli K12 (NCTC 10538) up to the
mid-metacarpals for 5 s, and allowed to dry for 3 min.
Then, to determine pre-decontamination values finger-
tips from both hands were rubbed for 1 min in a separ-
ate petri dish containing tryptic soy broth (Merck, 1.
05459, Darmstadt, Germany). Thereafter, the same 15
persons used each volume (1 mL, 2 mL and 3 mL,
respectively) of the test alcohol on contaminated hands
in a Latin-square crossover design and rubbed in the
alcohol until hands were dry. All three volumes (1, 2,
and 3 ml) were tested concurrently in a total of three
individual runs. Five volunteers used each volume in each
run such that after the three runs, each volunteer had
used each volume. All EN 1500 tests were carried out with
all test volunteers on the same day. Post-decontamination
values were determined as described above for the pre-
decontamination values. Neutralizing agents were not
necessary in these tests because even dilution with the
pure broth without supplement was shown in previous
validation tests with the test organism E. coli K12 to
neutralize any antimicrobial effect of the tested alcohol ac-
cording to the method described in the former “Standard
methods for testing chemical disinfection processes” of
the German Society for Hygiene and Microbiology
(DGHM) (Status 01.09.2001). Finally, all sampling fluids
were diluted as necessary and cultivated on the surface of
Tryptic soy agar (Merck, 1.05458, Darmstadt, Germany)
complemented with 0.05% sodium-desoxycholate (Merck,
1.06504, Darmstadt, Germany) to inhibit the growth of
resident microbial skin flora. The plates were then incu-
bated for a total of 48 h and colony forming units were
counted and transformed to a decimal logarithm. The
log10 counts from the left and right hands of each
volunteer were averaged separately, for both pre- and
post-decontamination values. The arithmetic means of all
individual log10 reduction factors (RFs) were calculated.
Dry-time measurements and hand size calculations
The dry-time interval from when a person began to rub
to when the person indicated that her or his hands felt
dry was recorded during the efficacy evaluation and,
additionally, when product was applied onto uncontam-
inated hands by using a calibrated stop watch. Dry-times
for uncontaminated hands were collected on a separate
day from the efficacy evaluation to prevent interference
from the hand contamination event. Hand surface area
(cm2) was measured as described previously [12]. Using the
definitions of hand size presented in Pires et al., [5] hand
size of the study population was evenly distributed with five
volunteers having “small” hands (surface area ≤ 375 cm2),
five having “medium” hands (surface area 376–424 cm2),
and five having “large” hands (surface area ≥ 425 cm2).
Statistical methods
Log10 RFs were assessed via a mixed effects linear model of
dry-time and volume, including the interaction, with ran-
dom effects for run and order of testing (i.e., cross-over
period) that accounted for the experimental design
(described above), and a random effect for volunteer
nested in run that accounted for the repeated measures
from each of the 15 volunteers. Likelihood ratio tests and
the Akaike Information Criterion were used to assess the
effect of volume on log10 RFs after accounting for drying
time. The effect of hand size on efficacy was assessed by
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 Page 2 of 6
adding to the model above a covariate for hand size. The
effect of hand size on dry-times was assessed with a similar
model. The mean log10 RFs were compared across the 3
different volumes using ANOVA with volume as a fixed ef-
fect and volunteer as a random effect followed by Tukey’s
multiple comparison procedure. For each volume, dry-
times for contaminated and uncontaminated hands were
compared using one-tailed paired t-tests; pooled across all
volumes, dry-times were compared using ANOVA with
volume as a fixed effect and volunteer as a random effect.
A “conditional R2” was reported that includes the effect of
all mixed effects in the model [13]. All calculations were
performed using R v3.0.2 [14], packages nlme [15],
multcomp [16], and MuMIn [17]. Individual value,
residual, and normal probability plots were used to assess
model assumptions and check for outliers. All statistically
significant results are reported with respect to a
significance level of 5%.
Results
Log10 RFs and dry-times of 60% V/V 2-propanol were
evaluated at multiple application volumes using a modi-
fication of EN 1500 where products were rubbed into
the hands until dry (Table 1). Mean log10 RFs were
greater when larger application volumes were used
(p < 0.0001), however the mean log10 RFs between the
2 mL and 3 mL applications were not significantly differ-
ent (p = 0.08). Mean dry-times were also greater when
larger application volumes were used (p < 0.0001). When
individual log10 RFs were plotted versus dry-time (Fig. 1),
there was a statistically significant linear relationship be-
tween dry-time and log reduction factor (p < 0.0001),
while volume did not have a statistically significant effect
in addition to dry-times (p = 0.172). Regardless of volume,
there was an average increase of 0.29 in the log10 RF for
every 10 s increase of drying time.
The relationship between hand surface area and dry-
time is illustrated for each application volume in Fig. 2.
There was a weak negative relationship that reached
statistical significance only at the 2-mL application
volume (Fig. 2b). Overall by itself, hand size did not
correlate significantly to dry-time (R2 = 8%, p = 0.0703).
After accounting for the effect of differing volumes, (i.e.,
after considering the relationship between dry-times and
hand size for each volume as in Fig. 2a-c), there was a
statistically significant correlation between hand surface
area and dry times (p = 0.047). However, there was not a
statistically significant relationship between hand size
and log10 RF (p = 0.698), even after accounting for dry-
times and volumes (R2 = 77%, p = 0.403) (Fig. 3).
Dry-times for test product applied to hands con-
taminated according to EN 1500 were statistically sig-
nificantly longer at each individual volume than those
when test product was applied to uncontaminated
hands (p ≤ 0.030) (Table 1). Overall, it took an average
7.6 s (SD 2.7 s) longer for contaminated hands to dry
(p = 0.004). At the 3-mL application volume, mean
product dry-times were substantially longer than 30 s,
regardless of whether product was applied to contam-
inated hands (67 s) or uncontaminated hands (59 s).
Discussion
By modifying EN 1500 methodology to better simulate
ABHR product application in clinical practice (i.e.
rubbing test products until dry) our results provide a
better understanding of the relative influence of product
volume, hand size, and dry time on ABHR efficacy.
While the results in Table 1 confirm previous findings
that mean log10 RFs increase with product application
volume (p < 0.0001) [10, 18, 19]; Fig. 1 demonstrates
Table 1 Influence of Product Application Volume on Antibacterial Efficacy and Dry-Times
Application
Volume
Mean Log10
Reduction Factor (SD)
Mean Dry-time p-value
EN 1500 Testing (SD) Uncontaminated
Hands (SD)
Comparison of
Dry-timesa
Overall Comparison of
Dry-times at All Volumesb
1 mL 1.99 (0.66) 24 s (7 s) 20 s (5 s) 0.006 0.004
2 mL 2.96 (0.84) 50 s (14 s) 39 s (12 s) 0.005
3 mL 3.28 (0.96) 67 s (20 s) 59 s (15 s) 0.030
N = 15. All data produced with the same participants
aPaired t-test, one-tailed
bRepeated measures linear model, one-tailed
Fig. 1 Linear Relationship Between Log10 Reduction Factor and
Product Dry-Time. The p-value indicates a test of correlation
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 Page 3 of 6
that for the individual volunteer, product dry-time is the
primary driver of product efficacy, independent of product
application volume (p < 0.0001). These results are not sur-
prising since antimicrobial action is a function of active
concentration and contact time [20, 21]. As Fig. 1
illustrates, a 2-mL application took longer to dry for some
volunteers than a 3-mL application did for other volun-
teers; and those longer dry-times produced greater log10
RFs. From this observation it becomes apparent that the
“adequate” or “efficacious” dose is unique for each
Fig. 2 Relationship Between Hand Size and Product Dry-Time. a),
1-mL application volume; b, 2-mL application volume; c, 3-mL
application volume. Dashed vertical lines delineate hand size classification
(S, small; M, medium; L, large) as presented in Pires et al. [5] The p-values
indicate a test of correlation at each volume separately
Fig. 3 Relationship Between Hand Size and Log10 Reduction
Factor. a), 1-mL application volume; b, 2-mL application volume; c,
3-mL application volume. Dashed vertical lines delineate hand size
classification (S, small; M, medium; L, large) as presented in Pires et al.
[5] The p-values indicate a test of correlation at each volume separately
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 Page 4 of 6
individual. This concept of individualized dosing is further
supported by the large inter-subject variability observed in
ABHR efficacy studies [22]. For example, dry-times at the
3-mL application volume, ranged from 29 s to 97 s and
log10 RFs ranged from 1.54 to 5.04. Since dry-time is the
primary driver of efficacy, it is clear that recommending a
fixed volume dose will not result in equal (or adequate) effi-
cacy for all individuals. Recommendations for ABHR usage
should therefore focus on achieving a specific dry-time (i.e.
contact time) as opposed to a prescribed volume.
A negative correlation was observed between hand
surface area and dry-time. In other words, the greater
the hand surface area, the shorter the dry-time. Even
after accounting for all of the observable factors in our
study (volumes and hand sizes), the unexplained vari-
ability in dry-times was still substantial (100%-R2 = 23%)
suggesting that other factors beyond hand size influence
product dry-time. While more data is needed, we
hypothesize that skin moisture content, skin barrier in-
tegrity, and amount of hair on the hands may each play
a role. Consistent with the findings of two previous stud-
ies, we did not find a correlation between hand surface
area and log10 RF values [5, 6]. These findings do not
support the recommendation by Bellisimo-Rodrigues
et al. that ABHR use should be customized to healthcare
workers’ hand size [4]. These data do suggest that when
evaluating ABHR efficacy, inclusion of participants with
a broad range of hand sizes may ensure a more repre-
sentative spread in product dry-times.
Our study has several limitations. First, we used a rela-
tively small sample size which may have limited the abil-
ity to detect significant correlations between variables.
For example, the relationship between hand surface area
and dry-time reached statistical significance for only one
of 3 application volumes evaluated and was borderline
significant (p = 0.047) across the entire data set. And
while we did not detect a significant effect of volume on
the mean log reduction factor (p = 0.172), it is possible
that a larger study could detect an effect. Based on the
total number of hand rub efficacy measurements (45),
the study was conducted over three crossover periods on
a single day, which introduced another source of vari-
ability. Our statistical model included period and test
order as random effects (with subject nested in period as
a 3rd random effect). However, the variances associated
with period and test order were negligible and therefore
not included in our final statistical analysis. Finally, as
Table 1 illustrates, the EN 1500 hand contamination
procedure, which consists of immersion of hands up to
the mid-metacarpals in an E. coli broth culture, signifi-
cantly increased product dry-times compared to dry
hands (p = 0.004). This phenomenon is likely due to in-
creased moisture and soil introduced to the hands, and
has been previously shown to negatively impact ABHR
efficacy [19]. To achieve dry-times and efficacy measures
more reflective of those in clinical practice, modification
of the EN 1500 contamination procedure will be re-
quired. The “low-volume” contamination procedure
employed in ASTM E2755 has been previously demon-
strated to have negligible impact on product dry-times
and will be the focus of future experiments [19]. Such an
approach has also been proposed by Kampf and may
help to better define minimal dry-times needed to
achieve an effective hand disinfection [23].
Conclusions
When considering dosing recommendations for specific
ABHR formulations (constant alcohol type and concentra-
tion), product dry-time appears to be the primary driver of
product efficacy. These data suggest that ABHR dosing
should focus on product dry-time (i.e. contact time) to bet-
ter account for individual variability. The optimal dry-time
recommendation remains to be determined and should
balance efficacy with healthcare worker acceptance, while
create minimal disruption to clinical workflow. Achieving
this balance is complicated by the inability of product vol-
umes with short dry-times to meet current efficacy norms.
Further studies are needed to determine the clinical signifi-
cance of these findings (e.g. minimum log10 RF required to
prevent pathogen transmission) and to enable more mean-
ingful ABHR dosing recommendations.
Abbreviations
ABHR: Alcohol-Based Hand Rub; CDC: Centers for Disease Control and
Prevention; RF: Reduction Factor; WHO: World Health Organization
Funding
This study was funded by GOJO, Industries, Inc., Akron. OH, USA, a
manufacturer of hand hygiene products including alcohol-based handrubs.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
MS led study execution and was a major contributor to design, analysis and
writing of the publication. RAL contributed to the design, analysis and
writing of the manuscript. AEP executed the statistical analysis and
contributed to the writing of the manuscript. DRM led to the design, analysis
and writing of the manuscript. All authors read and approved the final
manuscript.
Ethics approval and consent to participate
Study protocol was approved by the institutional ethics committee of the
Medical University of Vienna. All participants gave informed written consent.
Competing interests
DRM and RAL are employed by GOJO, Industries, Inc.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1Institute of Hygiene and Applied Immunology, Medical University Vienna,
Vienna, Austria. 2GOJO Industries, Inc., One GOJO Plaza, Suite 500, Akron, OH
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 Page 5 of 6
44311, USA. 3Center for Biofilm Engineering at Montana State University,
Bozeman, MT 59717, USA. 4Department of Mathematical Sciences at
Montana State University, Bozeman, MT 59717, USA.
Received: 7 March 2018 Accepted: 7 May 2018
References
1. Boyce JM, Pittet D. Guideline for hand hygiene in health-care settings.
Recommendations of the healthcare infection control practices advisory
committee and the HICPAC/SHEA/APIC/IDSA hand hygiene task force.
Society for Healthcare Epidemiology of America/Association for
Professionals in infection control/Infectious Diseases Society of America.
MMWR Recomm Rep. 2002;51(RR-16):1–45. quiz
2. World Health Organization. WHO guidelines on hand hygiene in health
care: first global patient safety challenge clean care is safer care. Geneva:
World Health Organization; 2009. https://www.ncbi.nlm.nih.gov/books/
NBK144013/
3. Rotter M, Sattar S, Dharan S, Allegranzi B, Mathai E, Pittet D. Methods to
evaluate the microbicidal activities of hand-rub and hand-wash agents.
J Hosp Infect. 2009;73(3):191–9.
4. Bellissimo-Rodrigues F, Soule H, Gayet-Ageron A, Martin Y, Pittet D. Should
alcohol-based Handrub use be customized to healthcare Workers’ hand
size? Infect Control Hosp Epidemiol. 2016;37(2):219–21.
5. Pires D, Soule H, Bellissimo-Rodrigues F, Gayet-Ageron A, Pittet D. Hand
hygiene with alcohol-based hand rub: how long is long enough? Infect
Control Hosp Epidemiol. 2017;38(5):547–52.
6. Wilkinson MA, Ormandy K, Bradley CR, Fraise AP, Hines J. Dose
considerations for alcohol-based hand rubs. J Hosp Infect. 2017;95(2):
175–82.
7. European Committee For Standardization. European norm 1500:: chemical
disinfectants and antiseptics. Hygienic handrub. Test method and
requirements (phase 2, step 2). Brussels: European Committee For
Standardization; 1997.
8. Girard R, Aupee M, Erb M, Bettinger A, Jouve A. Hand rub dose needed for
a single disinfection varies according to product: a bias in benchmarking
using indirect hand hygiene indicator. J Epidemiol Glob Health. 2012;2(4):
193–8.
9. Kampf G, Marschall S, Eggerstedt S, Ostermeyer C. Efficacy of ethanol-based
hand foams using clinically relevant amounts: a cross-over controlled study
among healthy volunteers. BMC Infect Dis. 2010;10:78.
10. Macinga DR, Shumaker DJ, Werner HP, Edmonds SL, Leslie RA, Parker AE,
Arbogast JW. The relative influences of product volume, delivery format and
alcohol concentration on dry-time and efficacy of alcohol-based hand rubs.
BMC Infect Dis. 2014;14:511.
11. ASTM International. E-2755-15. Standard test method for determining the
bacteria-eliminating effectiveness of hand sanitizer formulations using
hands of adults. West Conshohocken: ATSM International; 2015.
12. Lee JY, Choi JW, Kim H. Determination of hand surface area by sex and
body shape using alginate. J Physiol Anthropol. 2007;26(4):475–83.
13. Nakagawa S, Schielzeth H. A general and simple method for obtaining R2
from generalized linear mixed-effects models. Methods Ecol Evol. 2012;4(2):
133–42.
14. R Development Core Team. R; A language and environment for statistical
computing. Vienna, Austria: R foundations for statistical computing; 2010.
15. Pinhiero J, Bates D, DebRoy SSD. The R development Core team. Nlme:
linear and nonlinear mixed effects models. In: R package version 3;
2011. p. 1–104.
16. Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric
models. Biom J. 2008;50(3):346–63.
17. MuMIn: Multi-Model Inference. R package version 1.40.0. [https://CRAN.R-project.
org/package=MuMIn]. Accessed 11 May 2018.
18. Goroncy-Bermes P, Koburger T, Meyer B. Impact of the amount of hand rub
applied in hygienic hand disinfection on the reduction of microbial counts
on hands. J Hosp Infect. 2010;74(3):212–8.
19. Macinga DR, Beausoleil CM, Campbell E, Mulberry G, Brady A, Edmonds SL,
Arbogast JW. Quest for a realistic in vivo test method for antimicrobial
hand-rub agents: introduction of a low-volume hand contamination
procedure. Appl Environ Microbiol. 2011;77(24):8588–94.
20. Weavers LK, Wickramanayake GB. Kinetics of the inactivation of
microorganisms. In: Block SS, editor. Disinfection, sterilization, and
preservation, fifth edition. Fifth ed. Philadelphia: Lippincott Williams &
Wilkins; 2001. p. 65–78.
21. Watson HE. A note in the variation of the rate of disinfection with change
in the concentration of the disinfectant. J Hyg. 1908;8:536–92.
22. Rotter M, Mittermayer H, Kundi M. Investigations into a model of the
artificially-contaminated hand – proposal for a test method. Zentralbl
Bakteriol Orig B. 1974;159(5–6):560–81.
23. Kampf G. The puzzle of volume, coverage, and application time in hand
disinfection. Infect Control Hosp Epidemiol. 2017:1–2. https://doi.org/10.
1017/ice.2017.85.
Suchomel et al. Antimicrobial Resistance and Infection Control  (2018) 7:65 Page 6 of 6