JMIR FORMATIVE RESEARCH

Sato et al

Original Paper

Novel Methodology for Identifying the Occurrence of Ovulationby Estimating Core Body Temperature During Sleeping: Validity
and Effectiveness Study
Daisuke Sato, PhD; Koyuki Ikarashi, PhD; Fumiko Nakajima, MSc; Tomomi Fujimoto, PhD
Sports Physiology Laboratory, Department of Health and Sports, Niigata University of Health and Welfare, Niigata, Japan

Corresponding Author:
Daisuke Sato, PhD
Sports Physiology Laboratory
Department of Health and Sports
Niigata University of Health and Welfare
1398 Shimami-cho
Kita-ku
Niigata, 950-3198
Japan
Phone: 81 25 257 4624
Fax: 81 25 257 4624
Email: daisuke@nuhw.ac.jp

Abstract
Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However,issues related to its low accuracy are still under discussion.
Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual
cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift
discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation
method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts.
Methods: In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible
thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicularand luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations intemperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2
followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches(estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using
urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests,
and a temperature shift was identified using a specific criterion called the “three-over-six rule.” This rule and the nested designof the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity.
Results: The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep wasconsistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, thepattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements,suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation
method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements,
highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBTprovides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT
throughout the menstrual cycle.
Conclusions: This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during
sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than
traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understandingof menstrual cycle dynamics.

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Sato et al(JMIR Form Res 2024;8:e55834) doi: 10.2196/55834
KEYWORDS
menstrual cycle; ovulation; biphasic temperature shift; estimation method; women

Introduction
Understanding the menstrual cycle and fertility period isimportant for those who wish to conceive or use contraception.
The fertile period starts 5 days before the day of ovulation, witha higher probability of conception on the day before ovulation[1-4]. Although several biomarkers have been used to predictthe day of ovulation, measuring basal body temperature (BBT)
immediately after waking is a simple and noninvasive method[5]. Indeed, a cohort study reported that 21% of women use
BBT to track their fertility [6], so it is very important to improve
the accuracy of BBT in predicting the presence or absence ofovulation.
The nadir, the time of the lowest BBT in a menstrual cycle, isobserved 1 day before ovulation, with BBT rising approximately
2 days after the luteinizing hormone (LH) peak [7]. This biphasic
shift in BBT is, therefore, an effective indicator of the presenceor absence of ovulation. However, there are several limitations
in identifying ovulation using the BBT method. First, becauseof the large diurnal variation in BBT [8], it must be measured
at the same time each day. In addition, multiple missing values
during a cycle make it difficult to detect biphasic temperature
shifts [9]. Therefore, women need to wake up a few minutes
before getting out of bed to measure their BBT [10], and it has
been reported that 85% of women find the BBT method too
burdensome [11]. Therefore, there is an ongoing debate
regarding BBT methods that are more accurate and lessburdensome for women.
Although there are several limitations to understanding ovulationinduction using BBT, much attention has been paid to measuring
body temperature during sleep using wearable devices. Attempts
have been made to predict ovulation induction based on the skintemperature during sleep [12,13]. Interestingly, wrist skintemperature (WST) during sleep has been shown to reflectbiphasic temperature shifts measured using the BBT throughout
the menstrual cycle [14]. In addition, research is underway to
improve accuracy by simultaneously measuring 1 or 2
physiological parameters in addition to skin temperature [13].
However, the possibility that these skin temperatures may be
influenced by the ambient temperature cannot be excluded.
Therefore, a simple method for measuring core body temperature(CBT) that is less sensitive to ambient temperatures is desirable.
Based on the background described above, in this study, wetested a method of estimating the CBT by measuring the skintemperature of the breast and the ambient temperature
simultaneously and using an algorithm to minimize the effecthttps://formative.jmir.org/2024/1/e55834

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capturing biphasic shifts in estimated CBT would improve theaccuracy of determining the presence or absence of ovulation.

Methods
Ethical Considerations
This study was conducted in accordance with the Declarationof Helsinki and was approved by the Ethical Committee of
Niigata University of Health and Welfare (18759-211126).
Informed consent was obtained from all participants prior totheir inclusion in this study. The data collected in this study
were anonymized to ensure that individual participants could
not be identified. All personal identifiers were removed, anddata were stored securely with access limited to the researchteam. Measures were taken to ensure the confidentiality andprivacy of the participants throughout the study. However, incase some participants dropped out during the experiment, acorrespondence table of individual names and participant
numbers was created and saved as a separate file. Participantsin experiments 1 and 2 received cash compensation of JP ¥6000
and JP ¥3000 (about US $37 and US $18), respectively. No
figures, tables, or supplementary materials in this manuscript
contain identifiable information about individual participants.

Experimental Design and Procedure
This study comprised 2 experiments. The first experimentexamined the validity of the methodology for estimating CBT(experiment 1). The second experiment examined the accuracyof the estimated CBT in discriminating biphasic temperature
shift by comparing it with the oral temperature measured upon
waking (experiment 2).
Figure 1 shows the flow of recruitment and participation in thisstudy. Participants were recruited from a university campus.
The recruitment process involved contacting potential
participants directly and posting on social media. Potential
participants were informed about the study through email
invitations. Information sessions were held to explain the
purpose of the study, the procedures involved, and the expected
time commitment. Through this process, 32 women who met
the following inclusion criteria were recruited: aged 18 to 40
years, not currently on hormonal therapy, willing to comply
with the study protocol for up to 6 cycles, and no planned
pregnancy within the following 6 months [15]. The eligibility
criteria included no restrictions on the regularity or length ofthe menstrual cycle [16].

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Figure 1. Flowchart of recruitment and participation.

After arranging their individual dates, the participants received
a detailed briefing on the experiment in a university laboratory.
Private information on age, weight, height, and time since
discontinuation of hormonal contraception was collected after
obtaining informed consent. All participants were instructed towear a special night bra (Toppan Edge Inc) with a thermal sensor
to bed to estimate CBT, as explained in detail in the following
section, and measure BBT every morning immediately after
waking using an oral thermometer (Citizen CTEB503L-E;
Citizen Watch Co Ltd) 2 months before the experiment toestimate their cycle length accurately. Additionally, to estimate
the presence or absence of ovulation, the participants were
instructed to use an ovulation test kit (Doctor’s Choice One
Step Ovulation Test Clear; Beauty and Health Research) fromthe day after the end of menstruation. We asked the participants
to report their BBT, ovulation test results, and onset and end of
menstruation using Google Forms.
After the length of each participant’s menstrual cycle wasconfirmed, experiment 1 was started. An ingestible sensor andwireless data recorder were used to measure CBT during sleep
each day in the follicular and midluteal phases (as explained indetail in the following section), in addition to the estimated CBTand BBT described above.
After experiment 1, all participants were instructed in thelaboratory on the methods to be used in experiment 2. The
techniques used to determine menstrual cycle length, ovulation,and daily monitoring in experiment 1 were the same as thoseused in experiment 2. One participant withdrew from the study
due to oral contraceptive use after completion of experiment 1.

Estimated CBTThe estimated CBT was calculated using the heat flux method,which is a known method for estimating CBT. According tohttps://formative.jmir.org/2024/1/e55834

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Fourier’s law, heat flux (q) is proportional to the temperature
gradient (δt/δx) and thermal conductivity (λ), as illustrated in
the following equation:q=λ(δt/δx)
Applying this to the body temperature measurement model
shown in Figure 2, the CBT is adjusted to the skin temperature(Ts), which is influenced by the body’s resistance (Rb).
Subsequently, skin temperature is adjusted to the ambient
temperature (Ta) under the resistance of the sensor (Rs). The
heat flux for both the body and sensor is represented by thefollowing 2 equations:q = λ1(CBT – Ts)q = λ2(Ts – Ta)
Using these 2 equations, the CBT was calculated with thefollowing equation:
CBT = Ts + K(Ts – Ta) ※K = λ2 / λ1
K represents a coefficient that includes the thermal conductivityof both the sensor and body, and it can also be derived from thevalues obtained through measurements.
At this time, we converted the formula for calculating the CBT
as follows:
Ts = (K / (1 + K))Ta + CBT(1 + K)
Substitution of the experimentally determined skin temperatureand the ambient temperature into the above equation gives theslope (α) of this linear function:
α = K(1 + K)
By converting this expression into the following, K can becalculated from the experimental data:
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Figure 2. The body temperature measurement model.

As the structure of the sensor remained consistent, its thermal
conductivity was considered constant. Similarly, the body’s
thermal conductivity is viewed as stable because fat and muscle
mass do not change significantly over a brief period for thesame individual. Therefore, for the same individual, K can beconsidered a constant, whereas K varies for different individuals.
However, the accuracy of absolute temperature values is lesscritical in predicting the menstrual cycle. What matters morein this verification is the daily temperature fluctuation withineach person. Thus, rather than calculating the thermal
conductivity for each participant, a general coefficient of thermal
conductivity, K, was established based on the data from previous
participants using the sensor.
In this method, the estimated CBT was recorded every minute
while sleeping. The initial hour’s data after application of thesensor were deemed unreliable owing to unstable heat flux, so
we set the measurement period to the subsequent 4 hours. To
mitigate the effect of external heat, readings in which ambient
temperature was higher than skin temperature were excluded
from the analysis. The average temperature over these 4 hours
was taken as the representative temperature. Theoretically, if
the structure and individuals remain unchanged, the thermal
conductivity is considered to be constant. However, shifts inthermal conductivity can occur if the sensor is moved awayfrom the skin or if there is a rapid change in ambient
temperature, leading to significant variations in the estimated
CBT. Hence, any large fluctuations in the estimated CBT owing
to sensor displacement or swift changes in ambient temperature
were deemed anomalies and excluded from the analysis.

True CBT
True CBT was measured using an ingestible temperature sensor
and a wireless data recorder (e-Celsius; BodyCap). The
participants visited the laboratory the evening before the dayof the follicular and midluteal phase measurements and were
instructed to swallow a pill-shaped sensor with water
approximately 2 hours before bedtime. Data were sampled every
minute, which allowed a maximum of 33 hours of measurement,
and retrieved via Bluetooth when the participants revisited thelaboratory the following morning and averaged over the sleeping
period.

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LH TestParticipants underwent a home-based urine LH test using an
ovulation test kit (Doctor’s Choice One-Step Ovulation Test
Clear; Beauty and Health Research) for each cycle, following
the manufacturer’s instructions. Home-based LH tests areprevalent tools for detecting ovulation and determining thefertile window [17]. The participants were instructed to continue
with the daily LH test until they observed a positive result,signified by a distinct line on the device, or until the onset of
their next menstruation. A positive result signifies an LH surge,
typically preceding ovulation by 1 day [9,10]. The participants
logged the test results using Google Forms. A cycle registering
a positive LH test result was labelled an ovulatory cycle,
whereas one showing only negative results was deemed an
anovulatory cycle. The LH test was used as the benchmark toassess the diagnostic accuracy of the specified temperature
methods.

Data Analysis
Experiment 1 examined the relationship between the estimated
CBT, calculated using the chest skin temperature and ambient
temperature, and the true CBT, assessed using ingestible sensors.
Kolmogorov-Smirnov tests and 2-tailed paired t tests were
performed using PASW Statistics in SPSS (version 27; IBM
Corp). The Kolmogorov-Smirnov test revealed normal
distributions for both sets of temperature data. A 2-tailed pairedt test was used to compare the true and estimated CBT in eachmenstrual phase and the between-phase temperature changes.
Additionally, correlations among true CBT, estimated CBT,
skin temperature, and ambient temperature at the follicular andluteal phases were examined using repeated measures correlation
with the R (R Project for Statistical Computing) package rmcorr.
The rmcorr correlation coefficient (rrm) determines the common
intraindividual relation for paired measurements assessed on 2
or more occasions for multiple individuals [18]. The significancelevel was set at 5%.
Experiment 2 used the results of the LH test as a benchmark totest the accuracy of the 2 temperature measurement methods(estimated CBT and oral BBT) in determining ovulatory cycles.
In this experiment, all cycles were included in the analysis,
regardless of the presence or absence of ovulation as assessedby the LH test. The final analysis excluded cases with missing
LH test results or missing temperature readings >30% for any
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device cycle. This resulted in the collection of 74 cycles of datafrom 26 women. A temperature shift was defined as occurring
when 3 consecutive temperature measurements were at least
0.2 °C higher than the highest of the previous 6 measurements,
or at least 4 of the previous 6 measurements if data were missing[5]. According to Freundl et al [19], 2 exceptions apply to thisrule: if the third temperature is less than 0.2 °C higher than thepreceding 6, a fourth measurement is required. This fourthtemperature must exceed the 6 previous lower temperatures,
although not necessarily by 0.2°C. If among these 3 higher
temperatures, 1 drops to or below the average of the preceding
6, it can be disregarded, provided the third higher temperature
is at least 0.2°C above this average. These exceptions were
mutually exclusive. To discern multiple temperature shifts
within a cycle, we focused on those occurring in the last 14
days, excluding shifts due to factors like illness [16]. A cycle
was classified as monophasic if it lacked a temperature shift.

The accuracy of these shifts was evaluated using an LH test as
a benchmark. The diagnostic accuracy measures included
sensitivity, specificity, and predictive value, considering thenested design of the study.

Results
Relationship Between True and Estimated CBT(Experiment 1)
Table 1 shows the true and estimated CBT in the follicular andmidluteal phases and the between-phase temperature changes.
There were significant differences between the true andestimated CBT in both the follicular (P<.001) and midluteal
phases (P<.001). However, the difference between the 2
menstrual phases did not differ between the true and estimated
CBT groups (P=.10).

Table 1. Mean true and estimated core body temperature (CBT) in follicular and midluteal phases. There was a significantly higher true CBT thanestimated CBT in both follicular and midluteal phases. However, between-phase temperature change did not differ between true and estimated CBT.
PhasesTrue CBT (ºC), mean (SD)

Estimated CBT (ºC), mean (SD)P value

Follicular

36.48 (0.22)

36.19 (0.27)<.001

Midluteal

36.91 (0.21)

36.55 (0.24)<.001

Between-phase temperature change

0.41 (0.20)

0.34 (0.24).10

Figure 3 shows a repeated measures correlation plot for data
for each second for the 2 temperatures. Positive correlations
were found between true and estimated CBT (rrm=0.642, 95%
CI 0.631-0.652; P<.001), true CBT and skin temperature(rrm=0.362, 95% CI 0.347-0.377; P<.001), and ambient
temperature and skin temperature (rrm=0.961, 95% CI

0.960-0.962; P<.001). Figure 4 shows a repeated measurescorrelation plot of averaged data for true and estimated CBT.
We found a positive correlation between true and estimated
CBT at the follicular and midluteal phases (rrm=0.866, 95% CI
0.739-0.934; P<.001).

Figure 3. Repeated measures correlation plot for averaged data of 2 temperatures at the follicular and luteal phases.

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Figure 4. Repeated measures correlation plot for averaged data of 2 temperatures at the follicular and luteal phases.

Diagnostic Accuracy of Ovulation Cycle (Experiment
2)
Table 2 shows the diagnostic accuracy of estimated CBT and
BBT. The χ2 test revealed no significant difference in thepercentage of the cycles with a biphasic shift in temperature
between estimated CBT and BBT (P=.11). Using the LH test
as the reference standard for ovulation, biphasic temperature
shifts observed in the estimated CBT were more accurate fordetecting the occurrence of ovulation in the menstrual cycle
than BBT, followed by the McNemar test (P=.01).

Table 2. Diagnostic accuracy of estimated core body temperature (CBT) and basal body temperature (BBT).
Variables

Estimated CBT

BBTCycles with biphasic shift (n=74), n (%)

52 (70)44 (60)

Cycles with no biphasic shift (n=74), n (%)

22 (30)30 (40)Diagnostic accuracy (urine luteinizing hormone tests as standard reference; n=60)
True positives, n (%)

51 (69)41 (55)

True negatives, n (%)

13 (18)11 (15)

False positives, n (%)

1 (1)

3 (4)

False negatives, n (%)

9 (12)

19 (26)

Sensitivity

85.00

68.33

Specificity

92.86

78.57

Negative predictive value

98.08

93.18

Positive predictive value

59.09

36.67

F1-score

0.91

0.79

There was no significant difference in the percentage of cycles
with a biphasic shift in temperature between estimated CBTand BBT. On the other hand, the McNemar test revealed thatthe biphasic temperature shift based on estimated CBT was
more accurate for detecting the occurrence of ovulation in the
menstrual cycle than BBT.

Discussion
This study evaluated a method for estimating CBT bysimultaneously monitoring skin temperature and ambient
temperature during sleep. It also tested the hypothesis that
detecting biphasic shifts in estimated CBT would improve theaccuracy of determining the presence or absence of ovulation.
The following 2 main findings were obtained: first, estimated
CBT derived from skin and ambient temperature during sleep
was significantly lower in both the follicular and midluteal
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phases compared to digestive temperatures (true CBT).
However, the differences between these 2 phases were similar
for the true and estimated CBT values. In addition, asignificantly repeated measures correlation was observed
between the true and estimated CBT. This suggests that although
the estimated CBT does not directly assess the true CBT, it
effectively reflects its fluctuations. Second, this method of
estimating the CBT showed greater sensitivity and specificity
in detecting ovulation than traditional BBT measurements taken
orally at awakening. These results suggest that the estimated
CBT approach is a valuable tool for reproductive healthmonitoring.
The results of experiment 1 indicate that the estimated CBTderived from skin temperature and ambient temperature is areliable marker for monitoring CBT. Previous research has
demonstrated the superiority of CBT over skin and oral
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measurements is critical for accurately delineating biphasic
temperature shifts associated with the menstrual cycle [22,23].
Recently, there has been an increase in research attempting tounderstand the biphasic shifts in body temperature using skintemperature measurements. However, these measurements havethe limitation of being affected by environmental conditions[24]. In this study, skin temperature was found to besignificantly lower than digestive temperature (a direct indicatorof true CBT) and estimated CBT and showed a strong
correlation with ambient temperature. Furthermore, theestimated CBT showed a more robust correlation with the true
CBT (rrm=0.642), while skin temperature maintained asignificant positive association with the true CBT (rrm=0.362).
Consequently, the method used in this study to estimate the
CBT may provide a feasible, noninvasive approach tomonitoring CBT.
In experiment 2, the estimated CBT calculated using skintemperature and ambient temperature during sleep showed
greater accuracy in detecting ovulation (the occurrence of an
LH surge) than the conventional method using oral temperature.
This advancement is significant for the physiological monitoringof women’s health. Traditional BBT, which uses oral
temperature measured immediately upon waking, requires
precise timing every day. Missing data from this method canhinder the detection of biphasic temperature shifts [9]. It also
requires women to wake up several minutes before getting out
of bed to measure their BBT, which 85% of women findinconvenient [11]. Therefore, new methods for detecting
ovulation that involve measuring the skin temperature at restand during sleep have been validated [12,16]. In particular,
measurements taken during stable sleep states eliminate
fluctuations caused by daytime activities, leading to a moreaccurate understanding of the menstrual cycle [16]. In this study,
we performed measurements during sleep to minimize theinfluence of variable factors on the measurement protocol. In
addition, our method for calculating the estimated CBT includes
ambient temperature in the equation to adjust for external
environmental effects, potentially improving the accuracy of
biphasic detection.
The results of this study suggest that CBT can be estimatedusing skin temperature and ambient temperature. Daily
monitoring of CBT is crucial for detecting biphasic shifts in

Sato et al
BBT that occur during the menstrual cycle. However, traditional
methods for measuring CBT involve measuring esophageal orrectal temperatures, which are invasive. The noninvasive
technique for estimating CBT developed in this study isparticularly useful for identifying biphasic shifts in BBT because
it avoids the difficulties associated with regular invasive
measurements. In addition, recording temperature during sleep
helps reduce the variability caused by physical activity, resulting
in more consistent readings [14]. Moreover, the ability of thisestimation method to account for the effects of ambient
temperature is particularly important for understanding
menstrual cycles in regions with extreme weather conditions
or where it is difficult to control the ambient temperature.
The prediction of ovulation via BBT is vulnerable to disruption
from a multitude of factors, including fever, alcohol
consumption, emotional or physical stress, sleep disturbances,variations in room temperature, changes in physical activity,
climatic fluctuations, and the recent initiation or cessation ofcontraceptive pills or antipyretics [5]. Despite thesesusceptibilities, BBT is widely used for both contraceptive
purposes and the assessment of ovulatory function, as theestimated BBT provides a noninvasive, straightforward metricthat consistently delineates ovulation through discernible shifts
during the menstrual cycle. Thus, these findings offer valuable
practical insights, yet they must be approached with prudence.
Furthermore, a significant limitation of BBT is its inability toforecast ovulation proactively, thereby rendering it ineffective
for pinpointing the fertile window. Additionally, this researchused the detection of the LH surge to ascertain ovulation. Moreprecise methodologies, such as ultrasonography or the analysisof reproductive hormone concentrations in serum and saliva,
are required. It is also important to note that all the study
participants were Japanese. Future studies should include a morediverse range of physiological and ethnic backgrounds toconfirm the wider applicability of these findings.
This study demonstrated that the estimated CBT derived fromskin and ambient temperatures during sleep accurately captured
variations in the actual CBT. Furthermore, this estimation
method has proven to be more precise in determining theoccurrence or absence of ovulation than traditional methods,
which rely on oral temperature measurements taken immediately
upon waking.

Acknowledgments
This study was funded by Toppan Edge, Inc. We acknowledge this team for their data extraction guidance and support. ChatGPT
was used for English editing in writing the original manuscript. We would like to thank Editage for English language editing in
the completed manuscript.

Data Availability
The data are available from the corresponding author on reasonable request.

Authors' Contributions
DS, KI, and TF conceptualized the study; KI and TF carried out the experiments and analyses; FN carried out the additional
analysis in the revision; and DS wrote the initial draft. All authors reviewed and critically revised the final manuscript.

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Conflicts of Interest
This research was conducted as part of a collaborative project with Toppan Edge, Inc. Toppan Edge provided funding for the
study and some of the materials used. However, the integrity of the research process was maintained, with independence in the
study design, data collection and analysis, and manuscript preparation. Toppan Edge had no direct influence on the content of
this study. The corresponding author, DS, received research grants from Toppan Edge Inc. However, there is no evidence that
this has influenced the results of this study. The other coauthors have no financial relationships with Toppan Edge Inc.

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Abbreviations
BBT: basal body temperature
CBT: core body temperature
LH: luteinizing hormone
WST: wrist skin temperature

Edited by A Mavragani; submitted 27.12.23; peer-reviewed by R Meng, MA Rojo-Tirado; comments to author 23.04.24; revised
version received 04.05.24; accepted 15.06.24; published 05.07.24
Please cite as:
Sato D, Ikarashi K, Nakajima F, Fujimoto T
Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity andEffectiveness StudyJMIR Form Res 2024;8:e55834
URL: https://formative.jmir.org/2024/1/e55834
doi: 10.2196/55834
PMID: 38967967©Daisuke Sato, Koyuki Ikarashi, Fumiko Nakajima, Tomomi Fujimoto. Originally published in JMIR Formative Research(https://formative.jmir.org), 05.07.2024. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license
information must be included.

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