MSD&CTD

Describe how to calculate the incidence rate for musculoskeletal disorders (MSDs) and cumulative trauma disorders (CTDs) in a workplace.

What do you believe is the best approach you are familiar with for reducing the risk of MSDs and CTDs in a workplace? Explain your rationale.

International Journal of

Environmental Research

and Public Health

Article

Quantitative Models for Prediction of Cumulative Trauma
Disorders Applied to the Maquiladora Industry

Melissa Airem Cázares-Manríquez 1 , Claudia Camargo-Wilson 1, Ricardo Vardasca 2,3,4 ,
Jorge Luis García-Alcaraz 5,* , Jesús Everardo Olguín-Tiznado 1 , Juan Andrés López-Barreras 6

and Blanca Rosa García-Rivera 7

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Citation: Cázares-Manríquez, M.A.;

Camargo-Wilson, C.; Vardasca, R.;

García-Alcaraz, J.L.; Olguín-Tiznado,

J.E.; López-Barreras, J.A.;

García-Rivera, B.R. Quantitative

Models for Prediction of Cumulative

Trauma Disorders Applied to the

Maquiladora Industry. Int. J. Environ.

Res. Public Health 2021, 18, 3830.

https://doi.org/10.3390/ijerph18073830

Academic Editor: Wing-Keung Wong

Received: 23 January 2021

Accepted: 1 April 2021

Published: 6 April 2021

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Attribution (CC BY) license (https://

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4.0/).

1 Faculty of Engineering, Arquitecture and Design, Autonomous University of Baja California,
Ensenada BC 22860, Mexico; airem.cazares@uabc.edu.mx (M.A.C.-M.); ccamargo@uabc.edu.mx (C.C.-W.);
jeol79@uabc.edu.mx (J.E.O.-T.)

2 Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal; ricardo.vardasca@fe.up.pt or
ricardo.vardasca@islasantarem.pt

3 INEGI, Universidade do Porto, 4200-465 Porto, Portugal
4 ISLA Santarém, 2000-241 Santarém, Portugal
5 Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juarez,

Ciudad Juárez CHIH 32310, Mexico
6 Faculty of Chemical Sciences and Engineering, Autonomous University of Baja California,

Tijuana BC 22390, Mexico; jalopez@uabc.edu.mx
7 Faculty of Administrative and Social Sciences, Autonomous University of Baja California,

Tijuana BC 22390, Mexico; blanca_garcia@uabc.edu.mx
* Correspondence: jorge.garcia@uacj.mx

Abstract: Temperature gradient changes on the surface of the skin or in the middle of the body are
signs of a disease. The aim of this study is to develop quantitative models for the prediction of
cumulative trauma disorders (CTDs) arising from highly repetitive activities, considering risk factors,
such as age, gender, body mass index (BMI), blood pressure (BP), respiratory rate (RR), and heart
rate, to prevent injuries in manufacturing factory operators. This research involved 19 individuals
from the area of sanding and 14 individuals from the area of tolex in manufacturing factories who
had their vital signs and somatometry taken, as well as thermal images of their hands in the dorsal
and palmar areas; an evaluation by the OCRA method was also applied. Factors such as BP and
heart rate were determined to significantly influence the injuries, but no strong association with BMI
was found. Quadratic regression models were developed, the estimates of which were adequately
adjusted to the variable (R2 and R2 adjusted > 0.70). When integrating the factors of the OCRA
method to the generated models, a better fit was obtained (R2 and adjusted R2 > 0.80). In conclusion,
the participants who present levels out of the normal range in at least one of the factors have high
probabilities of developing injuries in their wrists.

Keywords: age; blood pressure; body mass index; carpal tunnel syndrome; cumulative trauma
disorder; heart rate; infrared thermography; respiratory rate; gender

1. Introduction

Musculoskeletal system disorders (MSDs) refer to health problems in the locomotive
system; that is, muscles, tendons, skeleton, cartilage, joints, ligaments, blood vessels, and
tendons [1,2]. MSDs are a set of symptoms and injuries (inflammatory or degenerative)
of the musculoskeletal system, and are related to the neck, back, and upper and lower
extremities of the body [3]. MSDs are the most common occupational diseases in industry.
They derive from various causes, and are divided into two categories: those caused by
acute trauma, such as slips or falls, and those due to repetitive exposure to a type of
physical activity, known as cumulative trauma disorders (CTDs), meaning that these
injuries develop over time, which can be as long as weeks, months, or even years of

Int. J. Environ. Res. Public Health 2021, 18, 3830. https://doi.org/10.3390/ijerph18073830 https://www.mdpi.com/journal/ijerph

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Int. J. Environ. Res. Public Health 2021, 18, 3830 2 of 19

propensity to repetitive stress, so that they are not due to a single temporary event, as is
the case of the first category, but to various micro-traumas [1,2].

When MSDs are caused by work-related issues, they are called work-related mus-
culoskeletal disorders (WMSDs) [4]. For years, studies have focused on ways to reduce
WMSDs. General knowledge of the mechanisms and factors that cause the given ailments,
among others, has enabled the development of a series of methods for risk occurrence
and identification. WMSDs can be prevented through ergonomic interventions, including
optimization of posture and working conditions, muscle and movement training, periodic
work breaks, and load-dependent work management, through which the load on the
musculoskeletal system can be reduced, thus providing workers with a longer working
life [5].

Workers who suffer from this disorder may experience severe pain, which is reflected
in a decrease in productivity and quality of work, and can even cause disability, which
causes absenteeism from work and leads to increased costs for businesses and for the public
health system [2]. Pain caused by musculoskeletal disorders is the second leading cause of
disability [6], and, according to the International Labour Organization (ILO), it is estimated
that occupational accidents and diseases cause the loss of 4% of the gross domestic product
(GDP), or about $2.8 billion in direct and indirect costs.

MSDs occur in different areas of the body, caused by a variety of different types of
tasks. In the upper extremities, such as the fingers, hands, wrists, arms, elbows, shoulders,
and neck, MSDs can originate from repetitive or lasting static force, leading to tendinitis or
nerve entrapment, such as carpal tunnel syndrome (CTS) [2,7–9]. CTS is due to compression
of the median nerve inside the carpal tunnel, while flexor tendinitis causes compression of
the median nerve by increased pressure in the carpal tunnel due to edema.

Currently, there are several methods that allow us to detect MSDs. For example, CTS
is detected by means of palpation tests, such as the Phalen’s and Tinel’s tests, and elec-
tromyography. However, the use of thermal imaging may improve medical diagnosis [10].

Temperature gradient changes (decrease and increase) on the skin surface or in the mid-
dle of the body are indicators of disease, allowing the evaluation of changes in metabolism
and blood flow, especially in a superficial layer of the skin [10–15]. Several studies indicate
that the symmetry of the extremities and torso will not have a temperature difference on
the two sides along a dermatome or thermatome of more than 0.30 ◦C, and of no more
than 0.90 ◦C on the forearms [16]. The diagnosis of neuromuscular pathology by infrared
thermography (IT) is based on the existence of thermal symmetry and asymmetry between
normal and abnormal sites [17,18]. IT works by measuring the temperature distribution of
a surface, which offers several advantages, because it is non-invasive, non-contact, non-
radioactive, and painless, and the results are easy to reproduce (thermal imaging); it also
has a low operating cost [10,19–21]. A broad range of research has proven the effectiveness
of IT in diagnosing CTS [20,21].

Nowadays, CTS is a pathology of great interest in medical research, since it represents
one of the greatest occupational health problems of any upper limb disorder [22], and yet,
the etiology is not appropriately described [22,23].

Epidemiological studies have been undertaken to identify risk factors for CTS, and the
results are contradictory. However, the most consistent factors have been being female [22–29],
thirty years or older [22,23,25,26,28–36], having repetitive motor activity, and having a num-
ber of systemic diseases, such as diabetes mellitus [10], rheumatoid arthritis [37,38], and
hypothyroidism [37,39].

Campillo & De la Vega [40] developed a predictive model for CTDs by using sensory
thermography as the main tool. They sought to determine whether there is a relationship
between temperature variability and CTD diagnosis and, at the same time, whether there is
a gender difference regarding CTDs. However, the model does not explain the temperature
variation over time well. In turn, Márquez Gómez [41] used traditional methods, such as
RULA (Rapid Upper Limb Assessment) and OCRA (Occupational Repetitive Action), in
combination with statistical techniques for the selection of significant predictor variables

Int. J. Environ. Res. Public Health 2021, 18, 3830 3 of 19

for the development of predictive models. Grieco [42] reported a logarithmic conversion
of the relative exposure (OCRA) and injury indices, with which he constructed a simple
linear regression model for risk prediction of WRMSDs. In the same context, Álvarez-Tello
et al. [43] developed a predictive model using binary logistic regression and the items of
the strain index questionnaire as predictor variables. The aim of this study is to develop
quantitative predictive models that integrate risk factors for CTD, such as age, sex, BMI
(body mass index), blood pressure (BP), respiratory rate (RR), and heart rate.

2. Materials and Methods
2.1. Recruitment and Selection of Participants

At first, the target company was approached to explain the purposes of the research
and to request approval to apply the project by means of a document expressing the
objectives, procedures, and analyses to be carried out, as well as estimated times. Once
permission was obtained from the business authorities, the study was initiated based on
the clinical procedures established by the company’s occupational health department.

Two production areas were assigned, the sanding and tolex areas, which had the
highest records for wrist problems among the operators. In the sanding area, the activities
of the operators consist of the sanding process of the body, neck, and edges of the wood
product using orbital and edge sanders. On the other hand, in the tolex area, the cabinet
subassembly and lining process is performed, which includes the activities of vinyl and
fabric cutting, gluing, and stapling. Next, each of the areas was visited to learn about their
production processes and to determine the experimental space. Afterwards, a questionnaire
was given to each operator (a total of 39 persons), designed to select the participants of the
study, obtaining their socio-demographic information and health conditions. This phase
had an approximate duration of one month, due to the time restriction so as not to affect
the daily production goals of the company.

Twenty-three questionnaires were applied in the sanding area and 16 in the tolex area.
At the end of the recruitment process, 19 participants were selected from the sanding area
(four persons were not selected due to disabilities and diabetes). Sixteen questionnaires
were applied in the tolex area, and 14 participants were chosen (one person did not want
to participate and another one had epilepsy). One woman and eighteen men participated
in the sanding area (average age = 33 ± 9.7 years). Six women and eight men participated
in the tolex area (average age = 35 ± 7.45 years). The experimental sample included a total
of 33 people.

Then, the vital signs and somatometry of the chosen participants were recorded,
including weight, height, body mass index (BMI), blood pressure (BP), heart rate, and
respiratory rate (R.R). The sanding area showed an average BMI = 27 kg/m2, BP 78%
normal, 13% high, and 9% low; 96% of the participants were right-handed, had an average
heart rate of 77.43 beats per minute (BPM), and an RR of 17.63 breaths per minute. In the
tolex area, an average BMI of 28.4 kg/m2, BP 75% normal, 12.5% high, and 12.5% low was
recorded. All of the participants in this area were right-handed, and had an average heart
rate of 94.18 BPM and an RR of 18.5 breaths per minute.

The selected subjects did not take drugs for the peripheral nervous system (vasodilator,
antihypertensive) so as not to interrupt the sympathetic vasoconstrictive response and,
therefore, affect their body temperature. Furthermore, they were asked to meet certain
criteria in order to take the thermograms listed below. Data collection and thermograms at
the company began in February 2019 and ended in June 2019.

A diagram with the measurement methodology appears in Figure 1.

Int. J. Environ. Res. Public Health 2021, 18, 3830 4 of 19
Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 4 of 20

Figure 1. Measurement methodology flowchart

2.2. Statement of Ethics
This study was conducted in accordance with the written consent granted by the

company, which was provided verbally to all participants. The protocol was reviewed
and approved by the ethics and bioethics committee of the postgraduate department of
the Faculty of Engineering, Architecture, and Design of the Autonomous University of
Baja California, according to the NOM-035-STPS-2018 Standard.

2.3. Preliminary Restrictions
Prior to taking the thermograms and in order to eliminate uncertainties in tempera-

ture measurements, the following restrictions were imposed on participants, based on the
protocols of Glamorgan [44], Standard Procedures for Infrared Imaging in Medicine [45],

Thermography for the prediction of

injuries and study of influence factors

Selection of participants

Somatometry and vital signs

Preparation of the area and

surroundings

Preparation of measuring devices

Thermograph collection

Temperature measurements

Thermogram analysis

Development of predictive models

Evaluation, Interpretation and comparison

of results, and recommendations

Figure 1. Measurement methodology flowchart.

2.2. Statement of Ethics

This study was conducted in accordance with the written consent granted by the
company, which was provided verbally to all participants. The protocol was reviewed and
approved by the ethics and bioethics committee of the postgraduate department of the
Faculty of Engineering, Architecture, and Design of the Autonomous University of Baja
California, according to the NOM-035-STPS-2018 Standard.

2.3. Preliminary Restrictions

Prior to taking the thermograms and in order to eliminate uncertainties in temperature
measurements, the following restrictions were imposed on participants, based on the
protocols of Glamorgan [44], Standard Procedures for Infrared Imaging in Medicine [45],
and Design and Application of a Protocol for Acquiring and Processing Infrared Images
from the Hands [46].

Int. J. Environ. Res. Public Health 2021, 18, 3830 5 of 19

• Not to smoke in the hours prior to taking the images (12 hours).
• Not to drink alcoholic beverages in the hours prior to the exam (12 hours).
• Not to drink coffee or tea for several hours before the study (12 hours).
• Preferably, not to eat fatty foods before the analysis.

2.4. Experimental Protocol
2.4.1. Environmental Conditions for the Study

To avoid vasomotion phenomena, the controlled temperature of the rooms assigned by
each area was kept between 23–24 ◦C (+/−1 ◦C). Regarding humidity, its values oscillated
between 50–60%, depending on the weather conditions of the region. On days when
the humidity of the environment was high, a dehumidifier was required to reduce it to
adequate levels. Within the space allocated for the recordings, air drafts on the subjects’
hands and lamps or domes above them were avoided during the taking of the thermal
images. The participants were asked to uncover their forearm (if necessary), not to wear
bracelets, rings, or wristbands, and to remove earrings, glasses, and caps.

2.4.2. Thermographic Infrared Camera Implementation

The IT camera used in this study was a FLIR ThermaCAMTM E25 model, fabricated
by FLIR Systems at Boston, MA, USA, with a resolution of 160 × 120 pixels, an accuracy of
±2 ◦C/±3.6 ◦C for ±2% of reading, and a spectral range of 7.5–13 µm. The camera was
mounted on a tripod for better handling, with an emissivity of 0.98, as this is the average
emissivity of human skin, and thus avoids errors in temperature measurement. Each time
the infrared camera was used, the emissivity was set to this value. The chosen region
(Figure 2) was taken for all participants. Before each shot, the camera was kept turned on
for 15 minutes to maintain thermal equilibrium with its surroundings. The camera was
placed perpendicular to the subject’s hand at a minimum distance of 0.601 m [46]. For this
study, a distance of two meters was considered. It is worth mentioning that a black surface
was placed as a background for the image, contributing to the improvement of the reading
of the thermograms and reducing the surrounding noise.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 5 of 20

and Design and Application of a Protocol for Acquiring and Processing Infrared Images
from the Hands [46].
• Not to smoke in the hours prior to taking the images (12 hours).
• Not to drink alcoholic beverages in the hours prior to the exam (12 hours).
• Not to drink coffee or tea for several hours before the study (12 hours).
• Preferably, not to eat fatty foods before the analysis.

2.4. Experimental Protocol
2.4.1. Environmental Conditions for the Study

To avoid vasomotion phenomena, the controlled temperature of the rooms assigned
by each area was kept between 23–24 °C (+/−1 °C). Regarding humidity, its values oscil-
lated between 50–60%, depending on the weather conditions of the region. On days when
the humidity of the environment was high, a dehumidifier was required to reduce it to
adequate levels. Within the space allocated for the recordings, air drafts on the subjects’
hands and lamps or domes above them were avoided during the taking of the thermal
images. The participants were asked to uncover their forearm (if necessary), not to wear
bracelets, rings, or wristbands, and to remove earrings, glasses, and caps.

2.4.2. Thermographic Infrared Camera Implementation
The IT camera used in this study was a FLIR ThermaCAMTM E25 model, fabricated

by FLIR Systems at Boston, MA, USA, with a resolution of 160 × 120 pixels, an accuracy of
±2 °C/±3.6 °C for ±2% of reading, and a spectral range of 7.5–13 μm. The camera was
mounted on a tripod for better handling, with an emissivity of 0.98, as this is the average
emissivity of human skin, and thus avoids errors in temperature measurement. Each time
the infrared camera was used, the emissivity was set to this value. The chosen region (Fig-
ure 2) was taken for all participants. Before each shot, the camera was kept turned on for
15 minutes to maintain thermal equilibrium with its surroundings. The camera was placed
perpendicular to the subject’s hand at a minimum distance of 0.601 m [46]. For this study,
a distance of two meters was considered. It is worth mentioning that a black surface was
placed as a background for the image, contributing to the improvement of the reading of
the thermograms and reducing the surrounding noise.

Figure 2. ROI (region of interest) taken for temperature analysis on the palms and back of the
hand of the study subjects.

2.4.3. Handling of the Participants
Prior to the start of the test, each participant was checked for compliance with the

requirements, that is, no caffeine, alcohol, vasodilator drugs, or smoking, to continue with

Figure 2. ROI (region of interest) taken for temperature analysis on the palms and back of the hand
of the study subjects.

2.4.3. Handling of the Participants

Prior to the start of the test, each participant was checked for compliance with the
requirements, that is, no caffeine, alcohol, vasodilator drugs, or smoking, to continue with
the tests. For this purpose, they were given a reminder the day before the tests were to take

Int. J. Environ. Res. Public Health 2021, 18, 3830 6 of 19

place. The female menstrual cycle was considered. Nevertheless, none of the participants
had their menstrual period during the intakes.

Afterwards, a black board formed of foil wood with plastic laminate was placed on
the chair, on which the shots were taken of the palms and backs of the hands. This board
had tape markers, which worked as guides to provide precise and reproducible positioning
of the hands. Each participant was instructed not to touch the board directly to prevent
hand heat from being retained on the board and causing noise on the thermograms. The
participant was asked to position him/herself behind the chair and bend down a little until
his/her fingers were positioned over the marks. Then, a sequence of infrared images was
taken, spaced every 5 min at times 5, 10, 15, and 20 (based on Vardasca, R., E. Francis, J.
Ring, P. Plassmann, C.D. Jones, and J. Gabriel [47], and García, A. [48]) for each participant
(Figure 3). After each thermal image of the palms and back of the hands was taken, the
participant waited seated in another chair, while five minutes remained to continue with
the next shot, until the four moments were completed. Thermal imaging sessions were
held Monday through Friday from 3:30 to 4:30 pm (hours established by the company),
with three participants per day.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 6 of 20

the tests. For this purpose, they were given a reminder the day before the tests were to
take place. The female menstrual cycle was considered. Nevertheless, none of the partici-
pants had their menstrual period during the intakes.

Afterwards, a black board formed of foil wood with plastic laminate was placed on
the chair, on which the shots were taken of the palms and backs of the hands. This board
had tape markers, which worked as guides to provide precise and reproducible position-
ing of the hands. Each participant was instructed not to touch the board directly to prevent
hand heat from being retained on the board and causing noise on the thermograms. The
participant was asked to position him/herself behind the chair and bend down a little until
his/her fingers were positioned over the marks. Then, a sequence of infrared images was
taken, spaced every 5 min at times 5, 10, 15, and 20 (based on Vardasca, R., E. Francis, J.
Ring, P. Plassmann, C.D. Jones, and J. Gabriel [47], and García, A. [48]) for each participant
(Figure 3). After each thermal image of the palms and back of the hands was taken, the
participant waited seated in another chair, while five minutes remained to continue with
the next shot, until the four moments were completed. Thermal imaging sessions were
held Monday through Friday from 3:30 to 4:30 pm (hours established by the company),
with three participants per day.

Figure 3. Experimental setup diagram.

The thermal images were then downloaded and analyzed through the ThermaCAM
Researcher Pro 2.10 software, from FLIR Systems company, located at Boston, MA, USA,
with which a total of 264 images were reviewed. When analyzing each IT image, the color
palette was configured in the rain option. The emissivity (0.98) was already adjusted dur-
ing the shots. The IR image was delimited according to the ROI in order to measure the
temperature in that area. Then, the

Results

option was activated to display the tempera-
ture values of maximum, minimum, max-min, average, and standard deviation (Figure
4).

Next, the data were exported to Excel to organize and group according to the times
in which the temperatures were recorded (5′, 10′, 15′, and 20′). Afterwards, the tempera-
ture differences were calculated for the minimum and maximum values of the tempera-
ture captured by the thermographic camera. Thereafter, the thermal asymmetries that
could represent a possible injury were identified and classified in their levels of alarm and
severity, as established by Marins et al. [49], and as shown in Table 1.

Figure 3. Experimental setup diagram.

The thermal images were then downloaded and analyzed through the ThermaCAM
Researcher Pro 2.10 software, from FLIR Systems company, located at Boston, MA, USA,
with which a total of 264 images were reviewed. When analyzing each IT image, the
color palette was configured in the rain option. The emissivity (0.98) was already adjusted
during the shots. The IR image was delimited according to the ROI in order to measure the
temperature in that area. Then, the Results option was activated to display the temperature
values of maximum, minimum, max-min, average, and standard deviation (Figure 4).

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 7 of 20

Figure 4. ThermaCAM Researcher Pro 2.10 software display.

Table 1. Scale of the level of attention given according to differences of temperatures between the
body ROI (body region of interest) against laterals or between two shots of the same ROI.

Temperature Differences Level of Attention
≤ 0.4 °C Normal

0.5–0.7 °C Follow-up
0.8–1 °C Prevention

1.1–1.5 °C Warning
≥ 1.6 °C Seriousness

Source: Marins, J. C. B., I. Fernández, J. Arnaiz, A. A. Fernandes and M. Sillero [49].

Subsequently, an OCRA evaluation was performed in the two study areas to com-
pare the results with the proposed model.

2.4.4. Statistical Analysis
For the analysis process of the acquired data, the study factors were grouped to-

gether. Age was grouped in young age (<40 years of age) and mature age (≥40 years old), and for gender, value 1 was assigned to male and 0 to female; BMI was classified as non- obese (<30 kg/m2) and obese (≥30 kg/m2). BP was classified in normal, high, and low, with normal and high heart rate, and RR in normal and high. Value 1 was assigned to subjects who had an injury or discomfort, and 0 to participants with no injuries.

The statistical analyses were carried out in IBM SPSS Statistics v.25® software, from
IBM Corp at Armonk, NY, USA, with which the data normality tests were performed us-
ing the Shapiro–Wilk test. Once the normal and non-normal data were identified, non-
parametric tests were performed (Mann–Whitney), which were applied to all data due to
the small sample size [50]. The relationship of the influence of the factors with the tem-
perature differences was determined. Mixed-design analysis of variance was used to iden-
tify significant factors. Regression models and response surfaces were generated by
Minitab 17. Three types of regression models were constructed using (a) study factors
(age, gender, BMI, BP, RR, and heart rate), (b) relevant risk factors in OCRA (recovery
factor, strength factor, posture and movement factor, and frequency factor), and (c) all of
the above. The differences obtained with p < 0.05 were considered statistically significant with a 95% confidence interval.

The following flow chart summarizes the procedure carried out to obtain thermal
images, up to the generation of the prediction models.

Figure 4. ThermaCAM Researcher Pro 2.10 software display.

Int. J. Environ. Res. Public Health 2021, 18, 3830 7 of 19

Next, the data were exported to Excel to organize and group according to the times in
which the temperatures were recorded (5′, 10′, 15′, and 20′). Afterwards, the temperature
differences were calculated for the minimum and maximum values of the temperature
captured by the thermographic camera. Thereafter, the thermal asymmetries that could
represent a possible injury were identified and classified in their levels of alarm and severity,
as established by Marins et al. [49], and as shown in Table 1.

Table 1. Scale of the level of attention given according to differences of temperatures between the
body ROI (body region of interest) against laterals or between two shots of the same ROI.

Temperature Differences Level of Attention

≤0.4 ◦C Normal
0.5–0.7 ◦C Follow-up
0.8–1 ◦C Prevention

1.1–1.5 ◦C Warning
≥1.6 ◦C Seriousness

Source: Marins, J. C. B., I. Fernández, J. Arnaiz, A. A. Fernandes and M. Sillero [49].

Subsequently, an OCRA evaluation was performed in the two study areas to compare
the results with the proposed model.

2.4.4. Statistical Analysis

For the analysis process of the acquired data, the study factors were grouped together.
Age was grouped in young age (<40 years of age) and mature age (≥40 years old), and for gender, value 1 was assigned to male and 0 to female; BMI was classified as non-obese (<30 kg/m2) and obese (≥30 kg/m2). BP was classified in normal, high, and low, with normal and high heart rate, and RR in normal and high. Value 1 was assigned to subjects who had an injury or discomfort, and 0 to participants with no injuries.

The statistical analyses were carried out in IBM SPSS Statistics v.25® software, from
IBM Corp at Armonk, NY, USA, with which the data normality tests were performed
using the Shapiro–Wilk test. Once the normal and non-normal data were identified, non-
parametric tests were performed (Mann–Whitney), which were applied to all data due
to the small sample size [50]. The relationship of the influence of the factors with the
temperature differences was determined. Mixed-design analysis of variance was used to
identify significant factors. Regression models and response surfaces were generated by
Minitab 17. Three types of regression models were constructed using (a) study factors (age,
gender, BMI, BP, RR, and heart rate), (b) relevant risk factors in OCRA (recovery factor,
strength factor, posture and movement factor, and frequency factor), and (c) all of the above.
The differences obtained with p < 0.05 were considered statistically significant with a 95% confidence interval.

The following flow chart summarizes the procedure carried out to obtain thermal
images, up to the generation of the prediction models.

3. Results
3.1. Demographic Characteristics of the Participants

Tables 2 and 3 show the series of data obtained by taking vital signs and somatometry
pertaining to each of the participants in the sanding and tolex production areas, respectively.

3.2. Thermal Imaging

A total of 264 thermal images from the palmar and dorsal regions of the hand were
analyzed, nineteen from the sanding area and fourteen from the tolex area. Figures 5 and 6
show some examples of the images captured from the palmar and dorsal regions of the
hand, respectively. The thermograms were from moments 5′, 10′, 15′, and 20′ after they
had performed their tasks for approximately 8.5 h, with two breaks (30 min for the first
break and 15 min for the second).

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Table 2. Demographic characteristics from the sanding area.

Attribute Category QTY Percentage

Gender Male 18 95%
Female 1 5%

Age Average = 33 ± 9.7 years
Rank = 18–52 years

BMI No obese 14 74%
Obese 5 26%

Average = 27 kg/m2

BP Normal 16 84%
Low 1 5%
High 2 11%

Heart rate Normal 17 89%
High 2 11%

RR Normal 11 58%
High 8 42%

Injury Eight (men)
Dominant hand One left-handed person

QTY (quantity), BMI (body mass index), BP (blood pressure), RR (respiratory rate).

Table 3. Demographic characteristics from the tolex area.

Attribute Category QTY Percentage

Gender Male 8 57%
Female 6 43%

Age Average = 35 ± 7.45 years
Rank = 26–48 years

BMI No obese 9 64%
Obese 5 36%

Average = 28.4 kg/m2

BP Normal 11 79%
Low 1 7%
High 2 14%

Heart rate Normal 10 71%
High 4 29%

RR Normal 10 71%
High 4 29%

Injury One (woman)
Dominant hand 100% right-handed

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 9 of 20

had performed their tasks for approximately 8.5 h, with two breaks (30 min for the first
break and 15 min for the second).

The first thermogram was taken after 5 min of rest, and after the participant’s highly
repetitive activity (in this case, sanding). The sequence of thermograms corresponds to a
single participant. This volunteer presented the maximum asymmetry. In the images of
Figures 5 and 6, in particular, the fingers and wrist of the right hand show the highest
temperature values compared to the left hand. However, this volunteer did not report
having any injury or discomfort in his hands.

Figure 5. Example of thermogram taken from the front area of the palm at the times 5′, 10′, 15′, and 20′.

Figure 6. Thermogram from the back of the hand of one of the participants at moments 5′, 10′, 15′, and 20′.

Table 4 shows the minimum and maximum temperature differences of the palmar
and dorsal areas between the right and left wrists calculated for each period.

Table 4. Values of the minimum and maximum temperature differences from the back of the hand
obtained from the participants by time.

Participant ΔT5
Min

ΔT5
Max

ΔT10
Min

ΔT10
Max

ΔT15
Min

ΔT15
Max

ΔT20
Min

ΔT20
Max

Subject 1 with an
injury

0.3 0.4 2.1 0.4 0.2 0.7 0.3 0.7

Subject 2 with an
injury

0.5 0.3 1.6 0.2 0.2 0.4 0.1 0

Healthy Subject 1 0.4 0 0.6 0.4 0.8 0.4 1.6 0.2
Healthy Subject 2 3.4 0.6 1 0.5 2.9 0.1 2.3 0.2
Healthy Subject 3 0 0.4 0.4 0.1 0.5 0.2 0.2 0.1
ΔT = The temperature (minimum or maximum) of the back of the right hand and the temperature
(minimum or maximum) of the back of the left hand.

Figure 5. Example of thermogram taken from the front area of the palm at the times 5′, 10′, 15′, and 20′.

The first thermogram was taken after 5 min of rest, and after the participant’s highly
repetitive activity (in this case, sanding). The sequence of thermograms corresponds to
a single participant. This volunteer presented the maximum asymmetry. In the images
of Figures 5 and 6, in particular, the fingers and wrist of the right hand show the highest
temperature values compared to the left hand. However, this volunteer did not report
having any injury or discomfort in his hands.

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Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 9 of 20

had performed their tasks for approximately 8.5 h, with two breaks (30 min for the first
break and 15 min for the second).

Figure 5. Example of thermogram taken from the front area of the palm at the times 5′, 10′, 15′, and 20′.

Figure 6. Thermogram from the back of the hand of one of the participants at moments 5′, 10′, 15′, and 20′.

Table 4 shows the minimum and maximum temperature differences of the palmar
and dorsal areas between the right and left wrists calculated for each period.

Table 4. Values of the minimum and maximum temperature differences from the back of the hand
obtained from the participants by time.

Participant ΔT5
Min

ΔT5
Max

ΔT10
Min

ΔT10
Max

ΔT15
Min

ΔT15
Max

ΔT20
Min

ΔT20
Max

Subject 1 with an
injury

0.3 0.4 2.1 0.4 0.2 0.7 0.3 0.7

Subject 2 with an
injury

0.5 0.3 1.6 0.2 0.2 0.4 0.1 0

Figure 6. Thermogram from the back of the hand of one of the participants at moments 5′, 10′, 15′, and 20′.

Table 4 shows the minimum and maximum temperature differences of the palmar
and dorsal areas between the right and left wrists calculated for each period.

Table 4. Values of the minimum and maximum temperature differences from the back of the hand obtained from the
participants by time.

Participant ∆T5 Min ∆T5 Max ∆T10 Min ∆T10 Max ∆T15 Min ∆T15 Max ∆T20 Min ∆T20 Max

Subject 1 with an injury 0.3 0.4 2.1 0.4 0.2 0.7 0.3 0.7
Subject 2 with an injury 0.5 0.3 1.6 0.2 0.2 0.4 0.1 0

Healthy Subject 1 0.4 0 0.6 0.4 0.8 0.4 1.6 0.2
Healthy Subject 2 3.4 0.6 1 0.5 2.9 0.1 2.3 0.2
Healthy Subject 3 0 0.4 0.4 0.1 0.5 0.2 0.2 0.1

∆T = The temperature (minimum or maximum) of the back of the right hand and the temperature (minimum or maximum) of the back of
the left hand.

By analyzing the behavior of the minimum and maximum temperature differences
of the participants’ wrists from their palmar and dorsal hand areas over time (5′, 10′,
15′, and 20′), the palmar zone showed higher temperature values than the dorsal region
in the minimum temperature differences. This is true for both study areas, as shown
in Figures 7 and 8. In other research carried out by García, A., C. Camargo, J. Olguín,
and J.A.L. Barreras [51], highly repetitive activities were analyzed by means of sensory
thermography, in which wrist temperatures were evaluated, and, in all cases, temperature
increases of more than 0.6 ◦C in 15 min were identified. In the same context, in the research
of Camargo, C., J. Ordorica, E.J. De la Vega, J.E. Olguín, O.R. López, and J.A. López [52],
where temperature changes in the wrists were measured by sensory thermography, when
performing highly repetitive movements, the maximum temperatures of 35.078 ◦C in the
right wrist and 34.663 ◦C in the left wrist were obtained.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 10 of 20

Barreras [51], highly repetitive activities were analyzed by means of sensory thermogra-
phy, in which wrist temperatures were evaluated, and, in all cases, temperature increases
of more than 0.6 °C in 15 min were identified. In the same context, in the research of
Camargo, C., J. Ordorica, E.J. De la Vega, J.E. Olguín, O.R. López, and J.A. López [52],
where temperature changes in the wrists were measured by sensory thermography, when
performing highly repetitive movements, the maximum temperatures of 35.078 °C in the
right wrist and 34.663 °C in the left wrist were obtained.

Figure 7 shows the temperature difference behavior of the participants from the
sanding area, and it was observed that four subjects had asymmetries (≥1.1 °C) on Figure
7a, representing the possibility of suffering from a CTD. Moreover, hands with asymmetry
showed a great deal of temperature variations over time, while hands with symmetry
(healthy hands) produced minimal temperature variations (Figure 7b).

Figure 7. Behavioral graph of minimum temperature differences for the palm of the hand in the subjects of the sanding
area. (a) corresponds to subjects with asymmetries. (b) represents subjects with symmetries.

Figure 8 shows the temperature difference behavior of the participants from the tolex
area, and it was observed that five participants had asymmetries (≥1.1 °C), representing
the possibility of suffering from a CTD. Subjects with asymmetry showed a great deal of
temperature variations over time (Figure 8a), with a tendency to decrease slightly in time
10′, then increase in time 15′, and finally decrease in time 20′. Healthy subjects, on the other
hand, had minimal variations over time (Figure 8b).

Figure 8. Graph of behavior of minimum temperature differences of the palm of the hand from the tolex area. (a) corre-
sponds to subjects with asymmetries. (b) represents subjects with symmetries.

Figure 7. Behavioral graph of minimum temperature differences for the palm of the hand in the subjects of the sanding area.
(a) corresponds to subjects with asymmetries. (b) represents subjects with symmetries.

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Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 10 of 20

Figure 8. Graph of behavior of minimum temperature differences of the palm of the hand from the tolex area. (a) corre-
sponds to subjects with asymmetries. (b) represents subjects with symmetries.

Figure 8. Graph of behavior of minimum temperature differences of the palm of the hand from the tolex area. (a) corresponds
to subjects with asymmetries. (b) represents subjects with symmetries.

Figure 7 shows the temperature difference behavior of the participants from the sand-
ing area, and it was observed that four subjects had asymmetries (≥1.1 ◦C) on Figure 7a,
representing the possibility of suffering from a CTD. Moreover, hands with asymmetry
showed a great deal of temperature variations over time, while hands with symmetry
(healthy hands) produced minimal temperature variations (Figure 7b).

Figure 8 shows the temperature difference behavior of the participants from the tolex
area, and it was observed that five participants had asymmetries (≥1.1 ◦C), representing
the possibility of suffering from a CTD. Subjects with asymmetry showed a great deal of
temperature variations over time (Figure 8a), with a tendency to decrease slightly in time
10′, then increase in time 15′, and finally decrease in time 20′. Healthy subjects, on the other
hand, had minimal variations over time (Figure 8b).

The minT R point represents the minimum temperature of the right wrist (palm side),
and the minT L point corresponds to the minimum temperature of the left wrist (palm
side). In Figure 9, there were fifteen cases with temperature difference between the minT
R and minT L points representing thermal asymmetries (≥1.1 ◦C) with subjects probably
suffering from a CTD.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 11 of 20

R and minT L points representing thermal asymmetries (≥1.1 °C) with subjects probably
suffering from a CTD.

The minT R point represents the minimum temperature of the right wrist (palm side),
and the minT L point corresponds to the minimum temperature of the left wrist (palm
side). Ten cases of thermal asymmetries were observed in the graph of Figure 10, which
represents subjects probably suffering from a CTD.

Regarding the diagnoses of injuries by means of an IT, Table 5 provides a summary
of the participants who reported having symptoms with suspected CTD, or with a con-
firmed diagnosis of tendinitis, as well as the asymmetries obtained and their levels of at-
tention and the diagnoses obtained by thermal imaging. It is worth pointing out that seven
out of nine subjects were successfully classified. Table 6 shows the results of the OCRA
evaluation to measure the risk of highly repetitive activities carried out in the study areas,
where all of the participants are identified as having an unacceptably high risk, which
coincides with the injury conditions of the participants shown.

Figure 9. Graphs of minimum temperature differences between minT R (right wrist minimum tem-
perature) and minT L (left wrist minimum temperature) points of subjects from the sanding area.

Figure 10. Graphs of minimum temperature differences between minT R (right wrist minimum
temperature) and minT L (left wrist minimum temperature) points of subjects from the tolex area.

Figure 9. Graphs of minimum temperature differences between minT R (right wrist minimum
temperature) and minT L (left wrist minimum temperature) points of subjects from the sanding area.

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Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 11 of 20

R and minT L points representing thermal asymmetries (≥1.1 °C) with subjects probably
suffering from a CTD.

Figure 9. Graphs of minimum temperature differences between minT R (right wrist minimum tem-
perature) and minT L (left wrist minimum temperature) points of subjects from the sanding area.

Figure 10. Graphs of minimum temperature differences between minT R (right wrist minimum
temperature) and minT L (left wrist minimum temperature) points of subjects from the tolex area.
Figure 10. Graphs of minimum temperature differences between minT R (right wrist minimum
temperature) and minT L (left wrist minimum temperature) points of subjects from the tolex area.

Regarding the diagnoses of injuries by means of an IT, Table 5 provides a summary of
the participants who reported having symptoms with suspected CTD, or with a confirmed
diagnosis of tendinitis, as well as the asymmetries obtained and their levels of attention
and the diagnoses obtained by thermal imaging. It is worth pointing out that seven out of
nine subjects were successfully classified. Table 6 shows the results of the OCRA evaluation
to measure the risk of highly repetitive activities carried out in the study areas, where all of
the participants are identified as having an unacceptably high risk, which coincides with
the injury conditions of the participants shown.

Table 5. Diagnostic results of asymmetric wrist injuries in the palmar region of the hand.

Subject with Injuries Area Maximum Asymmetry (◦C) Level of Attention Injury Diagnosis
(Warning/Seriousness)

1 Sanding 3.1 Serious Yes
2 Sanding 2.3 Serious Yes
3 Sanding 2.1 Serious Yes
4 Sanding 0.9 Prevention No
5 Sanding 1.8 Serious Yes
6 Sanding 2 Serious Yes
7 Sanding 0.4 Normal No
8 Sanding 1.7 Serious Yes
9 Tolex 4.9 Serious Yes

Table 6. Results of the OCRA assessment conducted in the study areas.

Subject with Injuries Area OCRA Checklist Index Risk Level Recommended Action Equivalent OCRA Index

1 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

2 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

3 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

4 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

5 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

6 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

7 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

8 Sanding 63 unacceptable high risk Job upgrading, medical
supervision, and training >9

9 Tolex 51.8 unacceptable high risk Job upgrading, medical
supervision, and training >9

Int. J. Environ. Res. Public Health 2021, 18, 3830 12 of 19

According to the response surfaces generated, corresponding to Figures 11 and 12, it
was determined that the maximum values for temperature differences had normal levels
in BP and heart rate.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 12 of 20

According to the response surfaces generated, corresponding to Figures 11 and 12, it
was determined that the maximum values for temperature differences had normal levels
in BP and heart rate.

Figure 11. Response surface produced for the tolex area from the palm of the hand for the mini-
mum temperature difference in time 15′ and the factors age and BP.

Figure 12. Response surface produced for the tolex area from the palm of the hand for the mini-
mum temperature difference in time 15′ and the BP and heart rate factors.

Table 5. Diagnostic results of asymmetric wrist injuries in the palmar region of the hand.

Subject with Inju-
ries

Area Maximum Asymmetry
(°C)

Level of Atten-
tion

Injury Diagnosis (Warning/Seri-
ousness)

Figure 11. Response surface produced for the tolex area from the palm of the hand for the minimum
temperature difference in time 15′ and the factors age and BP.

Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 12 of 20

According to the response surfaces generated, corresponding to Figures 11 and 12, it
was determined that the maximum values for temperature differences had normal levels
in BP and heart rate.

Figure 11. Response surface produced for the tolex area from the palm of the hand for the mini-
mum temperature difference in time 15′ and the factors age and BP.

Figure 12. Response surface produced for the tolex area from the palm of the hand for the mini-
mum temperature difference in time 15′ and the BP and heart rate factors.

Table 5. Diagnostic results of asymmetric wrist injuries in the palmar region of the hand.

Subject with Inju-
ries

Area Maximum Asymmetry
(°C)

Level of Atten-
tion

Injury Diagnosis (Warning/Seri-
ousness)

Figure 12. Response surface produced for the tolex area from the palm of the hand for the minimum
temperature difference in time 15′ and the BP and heart rate factors.

The data normality test using Shapiro–Wilk yielded both normal and non-normal
data. Since the two samples were small in size, the Mann–Whitney U test was performed
for all data [50]. Table 7 shows a summary where the factors BP (p = 0.036, p = 0.014), heart
rate (p = 0.047, p = 0.023), RR (p = 0.020, p = 0.036), and age (p = 0.010) were determined
to be statistically significant for temperature differences, according to the non-parametric
tests performed. The sanding area reported the factors BP, heart rate, and RR to be very
significant, that is, associated with CTD, while in the tolex area, only the RR and age
factors were determined as significantly linked with CTD. The sanding area showed
higher significant values than the tolex area, predominating the time 10′ and the minimum
temperature differences.

The mixed-design ANOVA results (see Table 8) confirmed a relationship between the
factor BP (p = 0.009) and heart rate (p = 0.040) in the back, and BP (p = 0.009) and heart
rate (p = 0.002) in the palm as significant regarding temperature difference in the sanding
area. None of the factors evaluated were significant for the tolex area. Table 9 briefly
shows the quadratic regression models generated according to the factors used. Firstly,
the results of the study factors (age, gender, BMI, BP, RR, and heart rate) are displayed,
with the determination coefficients, the estimates of which were adjusted appropriately to
the study variable. Then, the values of the coefficients corresponding to the factors of the
OCRA method are shown (recovery factor, strength factor, posture and movement factor,
and frequency factor), and finally, we used all of the factors mentioned.

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Table 7. Mann–Whitney U-test of study factors for temperature differences.

Area-Section Factor Time (Min) Temperature Differences p-Value

Sanding-Back BP 10 Maximum 0.036
Sanding-Back BP 15 Minimum 0.014
Sanding-Palm BP 10 Minimum 0.014
Sanding-Palm Heart rate 10 Minimum 0.047
Sanding-Palm Heart rate 15 Minimum 0.023
Sanding-Palm RR 20 Minimum 0.02

Tolex-Back RR 15 Maximum 0.036
Tolex- Back Age 10 Minimum 0.01

Table 8. Mixed-design analysis of variance of the study factors for the sanding area.

Area-Section Factor p-Value

Sanding-Back BP 0.009
Sanding-Back Heart rate 0.040
Sanding-Palm BP 0.009
Sanding-Palm Heart rate 0.002

Table 9. Summary of models by zone, hand section, and factors included.

Area Hand Section Model (Factors Included) R-sq R-sq (Adj)

Sanding Back Proposed factors 0.8836 0.7905
Sanding Palm Proposed factors 0.9737 0.9475

Tolex Palm Proposed factors 0.9667 0.9134
Sanding Back OCRA factors 0.2583 0.0993
Sanding Palm OCRA factors 0.5854 0.4965

Tolex Back OCRA factors 0.3455 0.1492
Tolex Palm OCRA factors 0.3481 0.1525

Sanding Back All factors 0.9078 0.6864
Sanding Palm All factors 0.9850 0.9488

Tolex Back All factors 0.9697 0.8031
Tolex Palm All factors 0.9871 0.9164

R-sq (coefficient of determination). R-sq (Adj) (Adjusted coefficient of determination).

The highest values for temperature differences are observed in a middle age range
(36 years) and with a normal level in BP.

4. Discussions

In this study, 75% of the cases that indicated injury or discomfort were detected by IT
in the sanding area, while in the tolex area, just one case was reported. In addition, Papež
et al. [53] and Papež, B., Jesenšek, M. Palfy, M. Mertik and Z. Turk [54] established that
IT allows the correct classification of 72.2% of the hands, healthy and pathological, based
on the dorsal part of the hand, while when seriously affected hands and healthy hands
are evaluated, the percentage rises to >80%. Palfy and Papez [55] used 44 thermograms of
healthy and pathological hands to determine the effectiveness of IT as a diagnostic method
for CTS. Using IT and intelligence systems, they were able to diagnose cases of CTS with a
success rate close to or above 80%.

Tkáčová et al. [20] recorded 14 thermal images to determine the level of effectiveness
of IT for diagnosing CTS. The success rates found in the classification of healthy and
pathological hands for were close to 80%. In the same context, Tchou et al. [56] recorded
122 thermograms, and obtained success rates for the classification of healthy hands with
CTS pathologies close to or higher than 80%.

Therefore, IT has been shown to be remarkably effective in detecting asymmetries, which
are known to be disease indicators, particularly CTS. However, our research detected asym-
metries in the dorsal and palmar surfaces of the hand, contrary to other studies [20,21,53–56],

Int. J. Environ. Res. Public Health 2021, 18, 3830 14 of 19

who concluded that the dorsal side of the hand provides more satisfactory results when
diagnosing CTS than the palmar area of the hand.

Ninety percent of the participants from the sanding area exhibited asymmetries with
alert or serious attention. Out of the 17 subjects with asymmetries, 76% exhibited levels
outside the normal range in at least one of the study factors (age, BMI, BP, heart rate, or
RR), while 79% of the participants from the tolex area exhibited asymmetries with alert or
serious levels. Out of the 11 subjects with asymmetries, 73% had levels outside the normal
range in at least one of the study factors.

As for the sanding area, 94% of the participants with asymmetries are men (with less
than one year working in the area), and the other 6% is the only female operator in the area.
Regarding the tolex area, 36% of the participants with asymmetries are women, while 64%
are men (the subjects have been working in the area for two to eight years.) It should be
inferred that the sanding area would have a higher percentage of injuries, due to the fact
that the production activities that are carried out demand more movement for the wrists
than for the tolex area.

Moreover, other hypotheses are based on the production rate that has been increasing
by 50%, leading to faster movements in the wrists, thus hurting them. Given that seven
out of eight people with injuries from the sanding area have been working there for such a
short time (less than a year), this is alarming. In fact, one operator who had been working
in the area for only six months was disabled due to tendinitis during the study; this was
contrary for the participants from the tolex area who have been working there for two to
eight years. Another participant was also diagnosed with tendinitis, although it was not
detected by IT.

By analyzing the behavior of the minimum and maximum temperature differences
by time, the highest value of 6.5 ◦C (temperature difference between the back of the right
and left hand) was identified for the sanding area in the minimum temperature values,
in a time of 15 min for the back of the hand section. For the tolex area, the minimum
temperature differences with the highest value on the back of the hand were shown in
time 10′ (2.6 ◦C). For the maximum temperature differences, the highest value (1.6 ◦C) was
recorded at time 15′. In the palms section, its maximum value in the sanding area was
7.3 ◦C for the minimum temperature difference between the right and left palm, recorded
in time 10′. In the case of the tolex area, for the palmar region of the hand, the highest value
was in time 15′with 5 ◦C for the minimum temperature difference. On the other hand, for
the maximum temperature difference, its highest value was 1.1 ◦C in time 20′.

The factors age, BMI, heart rate, and RR have data with a normal distribution, whereas
the factors gender, dominant hand, and BP exhibited abnormal distribution for the sanding
area. In the tolex area, normality tests identified age and BMI as data with normal distribu-
tion, and the factors gender, BP, injury, heart rate, and RR exhibited abnormal distribution.

When carrying out the non-parametric tests for the back of the hand, the influence
of the BP on the maximum temperature difference in time 10′ for the sanding area was
verified. The relationship between the minimum temperature differences and BP for the
palmar section of the hand was identified in time 10; heart rate in times 10′and 15′; and
finally, RR in the minimum temperature difference in time 20′. An influence relationship
between RR and the maximum temperature differences for the section of the back of the
hand was found in time 15′. As for the tolex area, an influence relationship between RR
and the maximum temperature differences for the back section of the hand was found in
time 15′. There was no influence relationship for the palmar region of the hand.

The factors that influence the minimum temperature differences on the back of the hand
for the sanding area are BP in time 15′, heart rate in times 10′ and 15′, and RR in time 20′. A
relationship with BP in time 10′ was identified for the maximum temperature differences.

Regarding the non-parametric tests in the tolex area, in the section of the back of the
hand, an influence relationship was found between RR and the maximum temperature
differences in time 15′. In the palmar section of the hand, an influence was identified
between age and the minimum temperature differences in time 10′. Concerning the factors

Int. J. Environ. Res. Public Health 2021, 18, 3830 15 of 19

that influence the minimum temperature differences in the palm of the hand for the sanding
area, these are BP in time 10′, heart rate in times 10′ and 15′, and RR in time 20′.

The results indicate that the interaction between the significant factors for the sanding
area and the main asymmetries had a low BP. On the other hand, in the tolex area, 80% of
the asymmetries had normal BP. With regard to heart rate, for the palm of the hand in the
sanding area, one out of three asymmetries were observed with a high heart rate (the highest
value) for time 10′, while in time 15′, two out of three asymmetries exhibited a high heart
rate. In the tolex area, 80% of the asymmetries included normal heart rhythms. For the RR,
both areas were found to have normal RR for the diagnosed asymmetries. Regarding age, in
the tolex area, 100% were young, both for time 10′. In contrast, other research [23,30,32,35]
established that CTS increases with age, while several studies establish that age is associated
with the prevalence of CTS [22,31,33,57].

In the area of sanding, it was not possible to evaluate the gender factor, since there
is only one woman in this section. However, various studies [22,24,29,30,35] found an
association between CTS and the female gender. However, there is also research that found
no relationship between gender and CTS [27]. In the present study, BMI was not determined
to be a significant risk factor, whereas other studies have found a strong association with
CTS [22,30,33,35,57–61].

When carrying out the mixed-design analysis of variance for the sanding area, BP
and heart rate turned out to be significant factors for the back and palm. Based on the
mixed-design analysis of variance, for the tolex area, factors were determined to be non-
significant. The regression models developed with the study factors obtained a coefficient
of determination of 0.9737 for the sanding palm area, and a coefficient of determination of
0.9667 for the tolex palm area. It is worth mentioning that, to attain these percentages, all
of the study factors are included in the equations, since omitting them would considerably
reduce the determination coefficients.

On the other hand, the models constructed using the OCRA method generated lower
coefficients of determination. For sanding palm, R2 = 0.5854 and for tolex palm, R2 = 0.3481.
However, when both groups of factors were integrated into a single regression model,
the best results were obtained with the following coefficients of determination: 0.9850
for sanding palm and 0.9871 for tolex palm. Meanwhile, research conducted by Márquez
Gómez, M. [41] achieved an accuracy of 83.91% and a kappa index of 63.14% in their
statistical model for the prediction of WRMSD-related (work-related musculoskeletal
disorders) discomfort in the hands/wrists, in which they considered six factors: postural
overload, repetitiveness of movements, gender, medical history related to MSD, frequency
of household chores, and job rotation. In another study, multiple regression models were
developed to predict the combined frequency and severity of the pain from WRMSDs,
achieving an R-squared of 32.9% [62]. In addition, Sasikumar, V. and S. Binoosh [63]
employed various machine learning algorithms in their predictive model for WRMSDs
among computer professionals, considering postural, physiological, and work-related
factors, with an accuracy of 81.25%.

5. Limitations

During the process of obtaining data in a maquiladora* company and acquiring the
thermograms, the humidity levels (45% to 60%) depended greatly on the climatic conditions.
For the application of questionnaires and the taking of vital signs and somatometry, as well
as the taking of thermal images, there was a restricted schedule in order not to affect the
daily production goals for the company.

6. Conclusions

This research proved the effectiveness of IT in the detection of suspected CTS injuries
by means of thermal asymmetries in the wrists. Three temperature prediction models were
developed based on the study factors, two for the sanding area, with which it is possible to
make predictions of the temperature differences for times of 10 and 15 min to determine

Int. J. Environ. Res. Public Health 2021, 18, 3830 16 of 19

whether or not the subject exhibits thermal asymmetries that could lead to injury, and
another for the tolex area, with predictions for times of 15 min. The maximum thermal
asymmetries were registered for the minimum temperature differences, both in the sections
of the back of the hand and in the palm of the hand in both study areas.

Ninety-four percent of the participants in the sanding area had thermal asymmetries
with a degree of alert or seriousness attention, while for the tolex area, only 73 percent had
them. In the tolex area, 36% of the participants with thermal asymmetries with a degree
of alert or seriousness attention are women, while 64% are men. The subjects have been
working in this area from two to eight years. In the area of sanding, 94% of the participants
with asymmetries (alert or seriousness level) are men, and have been working in the area
for less than one year.

Factors such as age, BP, heart rate, and RR were found to highly influence injuries
in participants. However, no strong association with BMI was found. It is inferred that
participants who exhibit levels out of the normal range for at least one of the factors, such as
advanced age (≥40 years), BMI, BP, heart rate, or RR, are highly likely to have wrist injuries
with a level of attention that is alarming or serious, so it is important to follow up with staff
who exhibit such characteristics and consider these results during the recruitment process.
In this study, in sanding area, the RR factor stands out, with a high level in personnel
with thermal asymmetries of alert or seriousness, while for the tolex area, advanced age
predominates.

Finally, these are preliminary results from a limited number of thermal images, so in
the next section proposals are made that include increasing the sample size.

7. Future Research

To continue with the development of this research, it is proposed to carry out future
studies of wrist temperature, comparing temperatures before starting activities with the
peak time of work activity. This will allow for a more accurate assessment of temperature
differences compared to time. It is suggested to analyze the values of average temperatures,
as well as the study of other ROIs in the hands. In addition, it is recommended to carry out
studies of temperature differences considering a large sample size, where there is a greater
number of participants of both genders, and, if possible, with dominant hand variation.
Only nine subjects with injuries were reported, and in one of the study areas, only one
person had a left dominant hand, while there were none in the other. For this reason, it
is advisable to extend the investigations to other production areas of the same company.
In turn, it is advisable to analyze other factors particular to each process, such as force,
vibrations, movement amplitude, postural overload, length of employment, overtime,
and rotation, or, in the case of tolex, carry out studies on the cold factor, since this area
works with low temperatures. Furthermore, it is proposed to apply the standardized
Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), used by Acquah et al. [64]
in their study of the prevalence and intensity of symptoms of musculoskeletal disorders
(MSDs) in workers of informal recycling of electrical and electronic waste. In addition,
it is suggested to consider demographic factors, such as smoking, MSD-related medical
history, exercise, household chores (studied by Márquez Gómez, M. [41]), physiological
factors, work-related factors [63], job fatigue, and strain [65]. Furthermore, we recommend
future research on the development of regression models with acceptable determination
coefficients, in which only the found influence factors are included. At the same time, we
propose to use the machine learning method for the development of predictive models and
to make comparisons between both results. Finally, it is suggested to extend the study to
other upper extremities, lower extremities, and the lower and upper back to analyze the
behavior of temperature differences and to make comparisons between these.

Int. J. Environ. Res. Public Health 2021, 18, 3830 17 of 19

Author Contributions: Conceptualization, M.A.C.-M.; Investigation, M.A.C.-M., C.C.-W., and R.V.;
Data organization, M.A.C.-M. and R.V.; Writing, original draft, M.A.C.-M. and C.C.-W.; Writing,
review & editing, J.L.G.-A.; Supervision, J.A.L.-B. and J.E.O.-T.; Project administration, B.R.G.-
R.; Funding acquisition, J.E.O.-T. All authors have read and agreed to the published version of
the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: The study was conducted according to the guidelines of
the Declaration of Helsinki, and approved by the Institutional Ethics and Bioethics Committee of
Autonomous University of Baja California Campus Ensenada according to protocol code 2020-023
and approved on 20 March 2020.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: Written informed consent has been obtained from the participant(s) to
publish this paper.

Acknowledgments: The authors appreciate the doctoral scholarship granted to Melissa Cazares
from the Mexican National Council for Science and Technology (CONACYT).

Conflicts of Interest: The authors state that they have no conflicts of interest to declare.

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Introduction
Materials and Methods

Recruitment and Selection of Participants

Statement of Ethics

Preliminary Restrictions

Experimental Protocol

Environmental Conditions for the Study

Thermographic Infrared Camera Implementation

Handling of the Participants

Statistical Analysis

Results

Demographic Characteristics of the Participants

Thermal Imaging

Discussions
Limitations
Conclusions
Future Research

References

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