Yamaguchi et al.
Journal of Physiological Anthropology(2024) 43:1
https://doi.org/10.1186/s40101-023-00349-w

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open AccessUtility of salivary cortisol profile
as a predictive biomarker in nurses’ turnover
risk: a preliminary study
Shinya Yamaguchi1,2 , Tomoko Fujita3, Shintaro Kato3, Yuichi Yoshimitsu3, Yoichi M. Ito4 and Rika Yano5*   

Abstract
Background Predicting nurse turnover risk is crucial due to the global nursing shortage; however, existing predictors,
such as fatigue and burnout, lack objectivity. Salivary cortisol is a non-invasive marker of stress and fatigue, but its utility in predicting nurse turnover risk is unknown. We examined whether salivary cortisol profiles across three different
day shifts in a month are predictors of the extent of nurses’ reluctance to stay in their current jobs.
Methods This preliminary longitudinal study followed forty female nurses who engaged in shift work at a university
hospital for 3 months. Data at enrollment were collected including demographics, working conditions, chronic fatigue(the Japanese version of the Occupational Fatigue/Exhaustion Recovery Scale), and burnout (Japanese Burnout
scale). Salivary cortisol was measured before the three different day shifts (after awakening) during the first month,and the means of these measurements were used as the cortisol profile. The extent of reluctance to stay was assessedusing the numerical rating scale at 3 months.
Results Among the forty female nurses (mean [SD] age, 28.3 [5.1]), all completed follow-up and were includedin the analysis. The cortisol profile was associated with the extent of reluctance to stay (P = 0.017), and this association was significant despite adjustments for chronic fatigue and burnout (P = 0.005). A multiple regression modelwith chronic fatigue, burnout, and job tenure explained 41.5% of the variation in reluctance to stay. When the cortisol
profile was added to this model, the association of the cortisol profile was significant (P = 0.006) with an R2 of 0.529(ΔR2 = 0.114).
Conclusions This preliminary study conducted in an actual clinical setting indicated the potential of the salivary cortisol profile across three different day shifts in a month to predict nurses’ reluctance to stay in their current jobs. The
combination of subjective indicators and the cortisol profile would be useful in predicting nurses’ turnover risk.
Keywords Personnel turnover, Nurses, Saliva, Cortisol, Fatigue, Burnout*Correspondence:
Rika Yanor-yano@med.hokudai.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Yamaguchi et al. Journal of Physiological Anthropology(2024) 43:1

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BackgroundThe nursing profession accounts for more than 50% of
the global shortage of healthcare workers [1]. This situation affects optimal staffing and the workload of nurses,
resulting in decreased quality of care, increased patientmortality, and the deterioration of nurses’ occupationalsafety [2–5]. One solution to this nursing shortage is theprevention of turnover [6, 7], which requires detecting
and addressing the early signs of nurse turnover.
Turnover and turnover intention are variables commonly measured in research related to nursing turnover [8–10]. These variables include employee-initiatedturnover, resulting from personal decisions that nursing
managers are unable to anticipate, and employer-initiated turnover, resulting from dismissals for employee
performance-related issues. Hence, different situations
require that nursing managers consider different countermeasures to mitigate the effects of turnover. Homet al. [11] considered employees’ motivational states
for staying or quitting, and posited that these mindsets can be explained by a combination of two aspects:
desired employment status and perceived volitional control (whether quit or stay decisions are completely up tothem or partially under external regulation). According
to this theory, reluctant stayers are those who would prefer to leave but feel they cannot and they include those
who can plan to leave their jobs following the expansionof their job search. Reluctant stayers may also have a low
person-organization fit, which results in their expressing
work avoidance behaviors (e.g., absences) or counterproductive workplace behaviors (e.g., sabotage) [11]. Nursing managers can identify at-risk nurses by recognizing
reluctant stayers, and early signs of turnover, and provide
interventions for turnover prevention.
Therefore, it is essential to identify predictors of nurses’
reluctance to stay in their current jobs. Chronic fatigue
and burnout, as determined by individual perceptions,can predict nurses’ reluctance to stay, because thesefactors—along with an excessive workload and an inadequate work environment—are associated with turnoveramong nurses [12–14]. Chronic fatigue causes a reduction in nurses’ interest, motivation, and doubts overtheir ability to maintain their current work patterns [15].
Burnout is a result of prolonged work stress [16, 17],
which induces negative emotions about the job [18, 19],and can increase reluctance to stay among nurses.
However, using chronic fatigue and burnout alone aspredictors has limitations. The cycles of stress, fatigue,
and burnout are insidious [20], and if individuals are notaware of them, the risk may remain undetected. Furthermore, the accuracy of subjective indicators is limited
because they include uncontrollable biases, such as recall
and confirmation bias.

Given these challenges, it is recommended the selection of physiological biomarkers for the evaluationof healthcare professionals [21]. Adaptation to stressinvolves a biological response (e.g., the hypothalamic–
pituitary–adrenal [HPA] axis), and allostatic load (ie,the cumulative burden of chronic stress), as an abnormality of this response, is associated with pathological responses [22]. Cortisol is an indicator of the HPA
axis and is suggested to be associated with work stress[23, 24], chronic fatigue [25, 26], and healthcare worker
burnout [27]. Considering its application to the work
setting, a convenient point-of-care device can be used
to quickly and noninvasively measure cortisol using
saliva samples [28]. A previous study of shift-working
nurses identified salivary cortisol profiles across several
different day shifts that were associated with chronic
fatigue. Among cortisol profiles observed over 1 month,
the profile of consistently low cortisol levels across two
different day shifts was associated with higher chronic
fatigue [29]. However, although the cortisol profiles aresuggested to be associated with chronic fatigue amongnurses [29], it remains unclear whether cortisol is apredictor of their reluctance to stay among nurses in
actual clinical settings.
Therefore, in this preliminary study, we examined
whether salivary the cortisol profile across three different
day shifts in a month is a predictor of nurses’ reluctanceto stay in their current jobs. Additionally, we exploredthe potential for better prediction of nurses’ reluctanceto stay by combining the cortisol profile and subjective
indicators. We hypothesized that the lower the cortisol
profile, the higher the reluctance to stay among nurses.
This study is the first step in examining the utility of thecortisol profile in the management of nursing turnover in
actual work situations. The findings will provide preliminary suggestions for applying a new objective indicator tothe management of turnover risk.

Methods
Study design and participantsThis longitudinal study was conducted at a university
hospital in Japan. The recruitment period for this study
was from October 1, 2021, to March 31, 2022, and eachparticipant was followed for 3 months. Participants
included 40 female nurses aged 23–41 years working in a
two-shift system (two 12-h shifts within 24 h) in a generalward. The gender and age of the participants were limited
as factors affecting cortisol levels [29]. The exclusion criteria were as follows:• New graduate nurses;• Administrators;

Yamaguchi et al. Journal of Physiological Anthropology(2024) 43:1

Page 3 of 10• Nurses who regularly used sleeping pills, antipsychotics, antidepressants, steroids, and oral contraceptives;• Pregnant nurses;• Nurses on leave; and• Nurses undergoing treatment for anemia, thyroid
disease, diabetes, menstrual irregularities, insomnia,
irregular heartbeat, insomnia, and autonomic nervous system disorders.

diurnal variation) [32, 33] and the other is an inter-day
trend [29, 34]. Our study considered inter-day trends toeliminate the burden of measurement and the influenceof work conditions. Additionally, salivary cortisol measured in the early morning is associated with work stressand fatigue among nurses [23, 29, 31]. Considering theinter-day trend due to differences in work patterns andother factors, three different day shifts (one-time point
more than in a previous study) were set as the measurement days [29].

Posters outlining this study were distributed by theadministrator to nurses in the target wards to recruit participants. This study was approved by the Ethics Review
Committee of the Faculty of Health Sciences, Hokkaido
University (reference No. 21-43) and was conducted inaccordance with the Declaration of Helsinki. All participants provided written informed consent. Participants
were offered a gratuity (Quo card) worth 12,000 yen.
Study procedures

Participants completed questionnaires regarding theirdemographics, working conditions, fatigue, and burnout.
During the first month, they were asked to collect saliva
samples upon awakening during three different day shifts.
After 3 months, they reported the extent of their fatigue,
burnout, and reluctance to stay (Fig. 1). In our study, thetime interval between assessing predictors and the outcome evaluation was three months, which corresponds
to a quarter term. This relatively short-term assessmentof turnover risk has the following advantages in terms ofnursing shortage: (1) early detection of turnover risk andprevention of problems becoming more severe, and (2)increased opportunity to identify challenges and dissatisfaction among nurses.
Considering the several influencing factors [30], themeasurement of salivary cortisol at a single time point is
not appropriate [29, 31] Two methods are used for multiple measurements: one is taking measurements multiple times on the same day (e.g., awakening response,

Fig. 1 Study procedure. Black circles indicate measurements

Reluctance to stay in the current job

Reluctance to stay was measured at three months using
the question “How reluctant do you feel about continuing on your current job?” Participants responded to a rating scale ranging from 0 (not at all) to 10 (extremely). In
related studies, nurses’ intention to leave is often assessedusing a single item [14, 35, 36]. Additionally, our primary
focus was to examine whether the cortisol profile is apredictor of reluctance to stay. In practice, the detailed
background of nurses’ intention to leave can be ascertained through subsequent interviews by administrators
after risk screening. Based on the above, we assessed only
the extent of reluctance to stay using the aforementioned
single question.
Salivary cortisol

Participants gargled upon awakening, rested for 5 min,put on disposable gloves, and collected saliva samples
with an oral fluid collector (OFC) swab (SOMA bioscience, Oxfordshire, United Kingdom). The OFC swab
containing the saliva was placed into a 3-mL buffer solution. The buffer bottle containing the OFC swab was
mixed for 2 min, then the OFC swab was removed fromthe bottle. Participants were asked in advance to avoid
heavy exercise and alcohol consumption the day before
saliva collection and to avoid eating, drinking, and brushing their teeth until the saliva was collected. Participantsalso reported their waking time, physical symptoms, andYamaguchi et al. Journal of Physiological Anthropology(2024) 43:1

mood (e.g., irritability, depression) on the day of saliva
collection, none of which were significantly correlatedwith each cortisol level. Saliva sample bottles were sealedin light-shielded bags, maintained at 37 °C or below, andsubmitted to the researcher upon arrival at work.
The saliva sample bottles were collected from the participants were kept frozen (up to 2 months) until analysis
according to the instrument’s manual in order to maintain sample stability. The buffer solution containing saliva
was placed in a cortisol lateral flow device (LFD) withthree drops. A SOMA CUBE Reader (SOMA bioscience,
Oxfordshire, United Kingdom) was placed on the LFD tomeasure cortisol concentrations. Cortisol levels measured using the SOMA CUBE reader [28, 37] and applying
the same principle of measurement [38, 39] have revealed
a positive correlation with the enzyme-linked immunosorbent assay (ELISA) method.
Each participant received an individual orientation session (lasting approximately 1 h) during which the detailed
method of saliva sampling was explained. In this orientation, they received a manual which indicated the process
and fully explained that saliva should be collected following 5 min of rest after awakening. The participants agreed
to report any failure to adhere to these instructions to theresearchers and understood that sampling dates would
need to be rescheduled in such cases. No participant
reported any violations of the sampling protocol, confirming their adherence to the procedure.
Burnout

We used the verified and reliable Japanese Burnout Scale[40], developed in accordance with the Maslach Burnout
Inventory to evaluate the participants’ burnout acrossthree dimensions: emotional exhaustion, depersonalization, and decline in personal accomplishment. The inventory consists of 17 items on a five-point rating scale,ranging from 1 (never) to 5 (always). The average of theitem scores included in each factor was used as the factor
score. In each factor, a higher score indicates a stronger
state of the condition.
Chronic fatigue

We used the Japanese version of the 15-item Occupational Fatigue/Exhaustion Recovery Scale [41], and only
5 items of chronic fatigue were used in our analysis. The
items are constructed on a seven-point Likert scale ranging from 0 (strongly disagree) to 6 (strongly agree). The
standardized score of chronic fatigue (range 0–100) was
calculated using the following formula: sum of the scoresof the five items applicable to chronic fatigue divided by
30 and multiplied by 100. A higher standardized scoreindicates a stronger extent of chronic fatigue.

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Demographic factors and working conditions

A self-administered questionnaire was used to assess participants’ demographic factors and work conditions. The
factors included age, job tenure, body mass index (kg/m2), marital status, childcare, family care role, alcohol
consumption (drink or not drink), smoking habits (yes orno), and leisure time activities [42]. Working conditions
included the number of night shifts worked per month;
the total overtime hours per month (< 10 h, < 20 h, ≥20 h); the experience of less than 11 h of rest between
shifts (a quick return) in the previous month; and perceived change in workload (i.e., decreased, unchanged, orincreased vs previous month).
Statistical analysis

The sample size was calculated using the G*Power version 3.1.9.7 (Universität Kiel). For hypothesis testing, weused a linear regression model with “reluctance to stay”
as the objective variable and cortisol profile as the predictor, effect size (f2) = 0.20 (medium) [29], significancelevel = 0.05, power = 0.80, and a sample size of at least 42
would be sufficient.
Data were summarized using means (standard deviations [SD]) or frequencies (percentages). To normalize
the distributions, all cortisol data were log-transformed
before analysis l­og10 (X). For ease of interpretation, theresults show the untransformed values. Correlations
between variables were evaluated by Pearson correlation
analysis. There was no consistent correlation between
cortisol levels on each day shift and reluctance to stay(day shift 1: r = − 0.450, day shift 2: r = − 0.256, day shift
3: r = − 0.368). Because consistent levels across two different day shifts are associated with chronic fatigue innurses [29], previous studies [43] have used the averagecortisol level over two days. In contrast, this study used
more time points for cortisol measurements, with anaverage of three measurements, to represent the cortisol
profile.
Four separate linear regression models were performed
with “reluctance to stay” as the objective variable. First,
we assessed the association between the cortisol profileand reluctance to stay by a univariate regression modelfor our hypothesis (Model 1). Next, we adjusted forchronic fatigue and burnout during the enrollment of thestudy and examined the association of the cortisol profile(Model 2). Following this, a multiple regression model
was performed with job tenure significantly associatedwith “reluctance to stay” (r = − 0.425, P = 0.006) and subjective indicators as explanatory variables (Model 3). The
subjective indicators in Model 3, selected based on thelowest Akaike information criterion using the backwardelimination method, were chronic fatigue and decline inYamaguchi et al. Journal of Physiological Anthropology(2024) 43:1

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personal accomplishment. Finally, Model 4, which added
the cortisol profile to the explanatory variables, evaluated the association of the cortisol profile. In all models,
we reported the coefficient of determination (R2) and theadjusted R2.
Statistical analysis was performed using JMP Pro software, version 16.1 (SAS Institute Inc., Cary, NC, USA),with P < 0.05 considered statistically significant.

Table 1 Participants’ demographics and working conditions atenrollment (n = 40)
Mean (SD)/ Range
number (%)
Age, mean (SD), years

Among the 40 participants, none dropped out, and all
were included in the analysis. Participants ages ranged
from 23 to 41 years, with a mean (SD) of 5.3 (4.0) yearsof job tenure (Table 1). None had a smoking habit and47.5% (19/40) had a drinking habit. Among the demographic and work conditions data, quick return in themonth prior to study enrollment was significantly associated with the cortisol profile (see Additional file 1).
Meanwhile, none of the data presented in Table 1, including quick return, were statistically significantly associatedwith the extent of reluctance to stay after three months.

23.0–41.0

5.3 (4.0)

1.0–18.0

Body mass index, mean (SD), kg/m2

19.6 (1.8)

16.0–25.0

Marital status, no. (%)

Results
Participants characteristics

28.3 (5.1)

Job tenure, mean (SD), years

Married or with a partner

7 (17.5%)

Not married

33 (82.5%)

Dependent, children, no. (%)
Yes

1 (2.5%)No

39 (97.5%)

Dependent, care role, no. (%)
Yes

1 (2.5%)No

39 (97.5%)

Drink alcohol, no. (%)
Yes

19 (47.5%)

No

21 (52.5%)

Smoke cigarettes, no. (%)
Yes

0 (0%)

No

40 (100.0%)

Exercise-oriented, no. (%)

Summary and correlations of key variables

Descriptive statistics for reluctance to stay, chronic
fatigue, burnout, and cortisol profile are illustrated inTable 2. The means for both chronic fatigue and burnout
were similar to previous studies based on large samples[41, 44]. Among the subjective indicators upon enrollment in the study, chronic fatigue (r = 0.565, P < 0.001),
and emotional exhaustion (r = 0.565, P < 0.001), depersonalization (r = 0.360, P = 0.022), and decline in personal accomplishment (r = 0.463, P = 0.003) correlated
moderately or more with reluctance to stay.
Association between the cortisol profile and nurses’
reluctance to stay

Table 3 shows the results of the multiple regression
model examining the association of cortisol profile withnurses’ reluctance to stay. In Model 1, the cortisol profile
was negatively associated with the extent of reluctance tostay at three months (b [unstandardized regression coefficient] = − 2.97; 95% CI, − 5.39 to − 0.56; P = 0.017).
When adjusted for subjective indicators (Model 2), thecortisol profile was also negatively associated with reluctance to stay (b = − 2.83; 95% CI, − 4.74 to − 0.93; P =0.005). In a stepwise multiple regression model that usedchronic fatigue and decline in personal accomplishment and job tenure (Model 3), the significant predictor was chronic fatigue (b = 0.05; 95% CI, 0.02–0.08; P= 0.020), with R2 = 0.415. Chronic fatigue (b = 0.05; 95%

Yes

11 (27.5%)

No

29 (72.5%)

Sleep-oriented, no. (%)
Yes

24 (60.0%)No

16 (40.0%)

Activity-oriented, no. (%)
Yes

10 (25.0%)

No

30 (75.0%)

Number of night shifts per month, mean (SD)

4.6 (0.9)

2.0–6.0

Overtime hours in the previous month, no. (%)< 10 h11 (27.5%)< 20 h17 (42.5%)≥ 20 h12 (30.0%)

Experience of quick return, no. (%), a
Yes

12 (30.0%)No

28 (70.0%)

Change in workload, no. (%)
Decreased

3 (7.5%)Not change

27 (67.5%)

Increased

10 (25.0%)

SD standard deviationa

Quick return refers to less than 11 h of rest between shifts

CI, 0.02–0.08; P = 0.020) and cortisol profile (b = − 2.70;
95% CI, − 4.59 to − 0.82; P = 0.006) were significant predictors in Model 4. In this model, the R2 was 0.529, which
increased from Model 3 (ΔR2 = 0.114).

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Table 2 Summary and Pearson correlations of key variables (n = 40)
Mean (SD)Range

1 Reluctance to stay (T1)

4.8 (2.0)

0-10

2 Chronic fatigue (T1)

52.8 (16.4)

0-100

1

2
0.663

< 0.001

3 Chronic fatigue (T0)49.5 (18.2)

0-100< 0.001< 0.001

4 BOS-EE (T1)

3.4 (0.9)

1-5< 0.001< 0.001

3

4

5

6

7

8

9

10

0.565

0.673

0.565

0.420

0.360

0.256

0.463

− 0.375

0.796

0.801

0.709

0.483

0.525

0.228

0.439

− 0.2500.711< 0.001

5 BOS-EE (T0)

3.4 (0.9)

1-5< 0.001< 0.001< 0.001< 0.001

6 BOS-DP (T1)

1.9 (0.7)

1-5

0.007

0.002

0.001

< 0.001

0.725

0.498

0.585

0.294

0.415

− 0.056

0.846

0.597

0.639

0.145

0.335

− 0.3410.640< 0.001

0.756

0.319

0.445

− 0.120

0.819

0.263

0.222

− 0.160

7 BOS-DP (T0)

1.9 (0.7)

1-5

0.022

0.001

< 0.001< 0.001< 0.001< 0.001

8 BOS-PA (T1)

3.7 (0.6)

1-5

0.111

0.157

0.065

0.373

0.045

0.100

0.069

0.291

9 BOS-PA (T0)

3.7 (0.6)

1-5

0.003

0.005

0.008

0.035

0.004

0.168

0.125

< 0.001

10 Cortisol profile, nM

6.6 (4.3)a

1-40

0.017

0.120

0.732

0.031

0.460

0.323

0.276

0.065

0.246

− 0.177

0.775

0.295
0.019

0.905

Correlation coefficients appear on the top triangle, and P values of correlation coefficients appear on the bottom triangle
Abbreviation: T0 at the enrollment of the study, T1 after three months, BOS burnout, EE emotional exhaustion, DP depersonalization, PA decline in personal
accomplishment, SD standard deviationsaCortisol profile (nM) values represent those without log transformation

Discussion
The management of nursing turnover risk has challenges
related to a lack of objectivity. As the first step in overcoming this challenge, this study examined the cortisol
profile’s potential and utility in predicting turnover risk
among clinical nurses. Our study revealed that the cortisol profile in the first month was associated with theextent of nurses’ reluctance to stay after three months.
The association of the cortisol profile was also significant in multiple regression models adjusted for subjective indicators and job tenure. These results support our
hypothesis and extend the existing knowledge, such as
the association between chronic fatigue and the cortisol
profile across several day shifts among shift work nurses[29]. Therefore, the cortisol profile has potential applications in not only fatigue assessment but also in detecting
reluctance to stay and the possible risk of nurse turnover.
One mechanism that explains the association between
the cortisol profile and nurses’ reluctance to stay is thenegative feedback response of the HPA axis to persistent stress [45]. Stress stimulates the release of cortisol,and this response returns to normal after the stress event
has resolved; however, prolonged stress is considered toresult in abnormal (i.e., low) cortisol secretion [46–48].
Cortisol secretion regulates energy production, metabolism, and mood and plays a role in coping with stress [49,50]. Thus, low saliva cortisol levels over a period may be
associated with the ability to cope adequately with further challenges and may affect nurses’ reluctance to stay.
In multiple regression models, the combination ofsubjective indicators and the cortisol profile better
explained nurses’ reluctance to stay. These results highlight the importance of using both indicators, as relying
on only one may only partially capture turnover risk. In

particular, the cortisol profile has significance in predicting nurses’ turnover risk for which effective biomarkers
have not been discovered. Previous studies have shownthat burnout and other psychological factors only partially explain nurses’ turnover intentions (R2 = 39%) [13].
Additionally, the variation of reluctance to stay explainedby subjective indicators and job tenure was limited (R2 =42%). This limitation of sufficiently predicting turnoverrisk with only subjective indicators may be influenced
by nurses’ characteristics, such as their attitude towardsocial expectations and sense of duty as nurses [51].
Therefore, the cortisol profile can be a valuable objective
indicator for predicting turnover risk among nurses.
Our findings have several implications for detecting nurse turnover risk effectively. First, self-monitoring
using the cortisol profile can help prevent work stressfrom escalating into a turnover risk, such as reluctanceto stay [52]. Traditionally, salivary cortisol measurement
has only been possible using the ELISA method, which
requires specialized knowledge and facilities. However,the SOMA CUBE reader is superior because it is a portable device that can be used in any location and is simple
to operate. Given its easy, non-invasive, and immediate
nature, the cortisol profile can be a self-assessment toolfor nurses to assess their turnover risk. Based on our
results, nurses might be able to use salivary cortisol samples monthly on three different day shifts. Second, thecortisol profile predicted nurses’ reluctance to stay after
three months, providing a preparatory period for supportand early intervention for high-risk individuals. Third,
early signs of reluctance to stay must be screened withhigh sensitivity to address the turnover risk in the severe
nursing shortage. Our findings suggest that nurses withhigh levels of reluctance to stay exhibit the physiological− 0.25

0.09

0.47

β

< 0.001

0.162

0.632

0.020

P value

Model 4

0.475

0.529

− 0.10 (− 0.27, 0.07)

0.48 (− 0.66, 1.63)

0.05 (0.02, 0.08)− 2.70 (− 4.59, − 0.82)

b (95% CI)− 0.200.15

0.44− 0.34β< 0.001

0.229

0.399

0.020

0.006

P value

Abbreviation: T0 at the enrollment of the study, BOS burnout, EE emotional exhaustion, DP depersonalization, PA decline in personal accomplishment, b unstandardized regression coefficient, CI confidence interval, βstandardized partial regression coefficient, R2 coefficient of determination

0.366

0.118

Adjusted R2
0.475− 0.13 (− 0.31, 0.05)

0.30 (− 0.95, 1.55)

0.05 (0.02, 0.08)

0.415< 0.001

Model 3b (95% CI)

0.141

0.542

0.086

0.268

0.130

0.070

0.005

P valueR2

0.24− 0.210.34

0.32− 0.36βJob tenure

Covariate− 0.64 (− 1.79, 0.51)
0.76 (− 0.11, 1.64)

BOS-DP

BOS-PA− 2.83 (− 4.74, − 0.93)

0.77 (− 0.24, 1.78)

0.017

0.017
0.04 (0, 0.08)− 0.38

BOS-EE− 2.97 (− 5.39, − 0.56)

Chronic fatigue

Scale score (T0)Cortisol profile

Model 2
P valueb (95% CI)

βModel 1b (95% CI)

Table 3 Association between reluctance to stay (outcome variable) and chronic fatigue, burnout, and cortisol profile (n = 40)

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signs characterized by the cortisol profile or the psychological signs such as burnout and chronic fatigue. As anadvantage of using both the cortisol profile and subjective indicators, concretely, even if the subjective indicators do not detect high-risk individuals, they may still bescreened with cortisol and vice versa.

also adjusting for their influences. The additional studies
involving a larger population of nurses are imperative inorder to comprehensively address the multitude of thesefactors. This would improve the utility of the cortisol profile and contribute to improved accuracy in screening fornursing turnover risk and reduced turnover rates.

LimitationsFirst, the sample was recruited from only one hospital in Japan, and participants’ characteristics were limited. As the study was conducted during the COVID-19
pandemic, nurses’ working conditions and the extent
of chronic fatigue and burnout could deviate from thenorm. However, since specific information, such as which
participants had treated COVID-19 patients, was not collected, the pandemic’s impact could not be controlled for.
Therefore, it is unclear whether our results can be applied
to nurses with other characteristics or to other professions in different situations.
Second, salivary cortisol secretion can be affected by circadian rhythm changes due to irregular shift patterns [53],menstrual cycles, and various other personal factors. However, it was difficult to comprehensively collect and control
for the numerous individual factors and diverse shift patterns in this clinical settings survey. Therefore, the influence of these factors on our results must be acknowledged.
In order to mitigate the inter-day variation of salivary
cortisol levels caused by the aforementioned factors, weemployed the approach of calculating the average cortisol
levels across three distinct daily shifts to create the cortisol
profile. This allowed for smoothing out the inter-day variations in cortisol and showed long-term trends. It would
also be somewhat robust to the effects of irregular shift
patterns, menstrual cycle, and circadian rhythm changes.
Third, we did not measure the outcome (reluctanceto stay) at study enrollment. We focused primarily on
predicting the degree of reluctance to stay after threemonths, and the significance of the change in reluctanceto stay was unclear. Additionally, there were some concerns about how other questions would be influenced
or biased by measuring reluctance to stay at enrollment.
Consequently, we could not examine the cross-sectional
association between cortisol profile and reluctance tostay, nor could we describe the characteristics of reluctance to stay at enrollment.
Future studies that include a larger number of nurses
and consider a variety of influencing factors could
strengthen our findings and provide a threshold for thecortisol profile. Based on our preliminary findings, we
contend that the practical implementation of this cortisol profile necessitates thorough validation through thecollection of menstrual cycles and shift patterns, while

Conclusions
This preliminary study conducted in an actual clinical setting indicated the potential of the salivary cortisol profile across three different day shifts in a month topredict nurses’ reluctance to stay in their current jobs. It
found that the cortisol profile is an objective predictor
of nurses’ reluctance to stay independent of subjective
indicators such as chronic fatigue and burnout. In addition, the cortisol profile in combination with subjective
indicators may contribute to a better prediction of reluctance to stay. Therefore, combining self-monitoring of
the salivary cortisol profile and surveillance with subjective indicators can be a valuable tool for predicting nurse
turnover risk.
Abbreviations
BOS	Burnout
EE	Emotional exhaustion
DP	Depersonalization
PA	Decline in personal accomplishment
CI	Confidence interval
HPA	Hypothalamic–pituitary–adrenal
OFC	Oral fluid collector
LFD	Lateral flow device
SD	Standard deviations

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​023-​00349-w.
Additional file 1.
Acknowledgements
The authors express deep appreciation to the hospital for their cooperation in
the conduct of this study.
Authors’ contributions
Conceptualization: Shinya Yamaguchi, Tomoko Fujita, Shintaro Kato, Yuichi
Yoshimitsu, Rika Yano; Data Curation: Shinya Yamaguchi, Rika Yano; formal
analysis: Shinya Yamaguchi, Tomoko Fujita, Shintaro Kato, Yuichi Yoshimitsu,
Rika Yano; funding acquisition: Rika Yano; investigation: Shinya Yamaguchi,
Rika Yano; methodology: Shinya Yamaguchi, Tomoko Fujita, Shintaro Kato,
Yuichi Yoshimitsu, Rika Yano; project administration: Rika Yano; resources:
Tomoko Fujita, Shintaro Kato, Yuichi Yoshimitsu; supervision: Tomoko Fujita,
Shintaro Kato, Yuichi Yoshimitsu, Yoichi M. Ito, Rika Yano; validation: Shinya
Yamaguchi, Tomoko Fujita, Shintaro Kato, Yuichi Yoshimitsu, Yoichi M. Ito, Rika
Yano; visualization: Shinya Yamaguchi, Tomoko Fujita, Shintaro Kato, Yuichi
Yoshimitsu, Yoichi M. Ito, Rika Yano; writing—original draft preparation: Shinya
Yamaguchi; writing—review and editing: Tomoko Fujita, Shintaro Kato, Yuichi
Yoshimitsu, Yoichi M. Ito, Rika Yano. All authors read and approved the final
manuscript.

Yamaguchi et al. Journal of Physiological Anthropology(2024) 43:1

Page 9 of 10

Funding
This study was funded by NEC Solution Innovators, Ltd.
Availability of data and materials
The datasets generated and/or analyzed during the current study are notpublicly available due to ethical restrictions or confidentiality of participants
but are available from the corresponding author on reasonable request.

11.
12.

Declarations

13.

Ethics approval and consent to participate
This study was approved by the Ethics Review Committee of the Facultyof Health Sciences, Hokkaido University (reference No. 21-43) and was
conducted in accordance with the Declaration of Helsinki. All participants
provided written informed consent.

14.
15.

Consent for publication
We obtained consent for publication from our participants by oral explanation and documentation. Informed consent to participate in the study was
included in this process.

16.

Competing interests
The authors declare that they have no competing interests.

18.

Author details
1
Department of Nursing, Teine Keijinkai Hospital, Sapporo, Japan. 2 Graduate
School of Health Sciences, Hokkaido University, Sapporo, Japan. 3 NEC Solution
Innovators, Ltd., Tokyo, Japan. 4 Data Science Center, Promotion Unit, Instituteof Health Science Innovation for Medical Care, Hokkaido University Hospital,
Sapporo, Japan. 5 Faculty of Health Sciences, Hokkaido University, Sapporo,
Japan.

19.

Received: 5 August 2023 Accepted: 28 November 2023

17.

20.
21.

22.
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