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Review

Heart Rate Monitoring for Physiological Workload in Forestry Work: A Scoping Review

by
Masayuki Okuda
1,2,*,
Yutaka Kawamoto
3,
Hiroyuki Tado
3 and
Yoshimasa Fujita
2
1
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube 755-8505, Japan
2
Yamaguchi Occupational Health Support Center, Yamaguchi 753-0051, Japan
3
Yamaguchi Prefectural Agriculture and Forestry General Technology Center, Hofu 747-0004, Japan
*
Author to whom correspondence should be addressed.
Forests 2025, 16(3), 520; https://doi.org/10.3390/f16030520
Submission received: 12 February 2025 / Revised: 4 March 2025 / Accepted: 13 March 2025 / Published: 15 March 2025
(This article belongs to the Section Forest Operations and Engineering)
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Abstract

:
Physiological workload in daily forestry work is measured using heart rate (HR), but its standard method has not yet been established. This scoping review aimed to explore how HR had been measured in forestry fields and reported. Five databases and three Japanese journals were searched to find published journal articles reporting HR during the daily shift in silviculture and harvest operations as eligibility criteria. Working HR, resting HR, HR index, and HR reserve percentage (%HRR) were extracted, and working conditions and measurement methods were also collected. The tasks were classified into silviculture, harvest, and machine operations. HR variables were examined in terms of operations and their relationships with resting HR. Out of 232 articles searched, 22 were eligible. Only two studies reported all the items of working conditions and measurement methods. Furthermore, 17 studies, which included 371 participants and assessed 22 tasks, reported resting HR. Working HR, HR index, and %HRR showed significant differences among the operations (p < 0.001, p = 0.007, and p = 0.03, respectively). The HR index and %HRR negatively correlated with resting HR (r = −0.620 and −0.411, respectively). The working conditions and the measurement methods of HR, especially resting HR, have not been comprehensively reported, thereby possibly influencing the reported workload. This insufficiency should be addressed before future research.

1. Introduction

Forestry is one of the many industries requiring high physiological workload [1,2]. Workload measurement expressed as energy expenditure is useful not only for knowing the capacity of workers and keeping the balance with energy intake to preserve their own health but also for managing work under warm ambience [2] and assessing the efficiency of machinery introduction [3,4,5,6,7]. For these purposes, workload over an entire day, including usual operations consisting of complex tasks, should be assessed.
In developed countries, human tasks have changed since the introduction of high-performance machines in the last twentieth century [8]. The proportion of accidents during skidding and yarding operations has decreased in Japan [9]. However, even today, large machines are hardly available in steep terrains, as often seen in forestry worksites of many countries, such as those in Asia, Europe, and Oceania [10,11,12]. Manual and motor-manual labor is still needed today.
Heart rate (HR) monitoring is a practical method to assess cardiovascular workloads in fields [13,14,15]. Extra energy used during work activities generally comes from muscle work; however, monitoring muscle activity is inappropriate for measuring energy expenditure [16]. Modern technologies enable continuous HR monitoring through the entire day with a low burden on workers. Given that workload indicators can be obtained from continuous HR monitoring, the heart rate index (HRI), which is transformable into the metabolic equivalent of tasks [17,18], and the percentage of heart rate reserve (%HRR) equivalent to maximal oxygen uptake [19,20] have been proposed. Calculating the two indicators requires both the resting heart rate (HRrest) and the working heart rate (HRwork). However, measuring HRrest in forestry fields is difficult and cumbersome before starting the routine work. Daily operations involve multiple procedures that are distributed over time during a shift, rather than experimental uniform procedures. Little is known about the conditions under which HR has been monitored in forestry work literature and how accurate the measurement of HRrest was in the fields. A standard and simple measuring method to assess the daily physiological workload in the fields has never been proposed in forestry.
This study aimed to explore how cardiovascular workloads are measured through HR monitoring in forestry and reported, in terms of the factors of forestry work and the influence of HRrest on HR indicators. Forestry works such as silviculture and harvest operations on forest stands performed by forestry workers were included in this study according to the Japanese Occupational Classification [21]. Herein, a scoping review was conducted, collecting previous studies that measured workloads for one daily shift in forestry work on forest stands. A scoping review is evidence mapping of the extent and range of research fields to identify study gaps for future research [22,23]. A scoping review includes narrative commentary but is distinct from a narrative literature review [24]. It addresses broader topics through systematic literature research without combining quantitative and qualitative studies, performing quality assessments, or being limited to a specific research question [25]. It can identify gaps in existing research necessary for summarizing and sharing research findings [26].

2. Materials and Methods

2.1. Protocol and Registration

The protocol of this scoping review was drafted using the Preferred Reporting Items for Systematic Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [23] and according to the method of Levac et al. [22]. It was registered in the Open Science Framework on 4 September 2024 (https://doi.org/10.17605/OSF.IO/PGCUX, accessed on 4 September 2024).

2.2. Eligibility Criteria

Eligible papers were journal articles that measured and reported HR during entire daily forestry work (or a whole shift) and showed daily mean values regardless of excluding breaks and recess. Forestry works included silviculture and harvesting operations, excluding operations not being conducted on usual forest stands, such as seedling nursery, mushroom cultivation, charcoal grilling, forest road construction, and forestry management and administration. Articles that reported only part of daily work, experimental trials of predetermined efforts, or non-routine work repeating single behaviors, as well as gray papers, were excluded. Additionally, articles were limited to those written in English; however, to understand the Japanese situation, we concurrently screened articles written in Japanese. We specifically searched for articles published up to June 2024, although the beginning of the target period was not determined (Figure 1).

2.3. Database and Screening

The bibliographic databases were MEDLINE on PubMed, Scopus, Google Scholar, and CINAHL on EBSCO (Figure 1). Given the lack of an exclusive database for forestry and forestry ergonomics, we used relatively comprehensive databases. We also searched for Japanese articles from the database ICHUSHI. The tables of contents from the Journal of the Japanese Forestry Society, Journal of the Japanese Forest Society (written in Japanese), and Journal of Forest Research were hand-searched. Regarding search strategy, we used the keywords “forest”, “forestry”, “silviculture”, “harvest”, “workers”, “heart rate”, “heart pulse”, and “cardiovascular workload”; for example, “heart” AND (“rate” OR “beat” OR “physiological workload” OR “cardiovascular workload”) AND (“forestry” OR “silviculture” OR “harvest”) AND “workers” on PubMed. When adding other detail keywords, such as “planting”, “nursery”, “brush-cutting”, “felling”, “trimming”, “delimbing”, “thinning”, “bucking”, “logging”, “cross-cutting”, “winching”, “yarding”, and “skidding”, selected articles were the same. For other databases, a similar strategy was used. Duplicates were removed using EndNote 21 (Clarivate Plc, Philadelphia, PA, USA).
Two authors (M.O. and Y.K.) independently selected potentially relevant articles by evaluating the title and abstract without blinding. After the full texts of relevant articles were obtained, final selection and data extraction were conducted by two authors (M.O. and Y.K.) independently. At this phase, relevant publications were included from the screened articles’ references. The data extraction form with the xlsx format was developed and tested beforehand with the agreement of all researchers with preliminary searching. Extracted items were the following: study purpose; country; survey period (year, month, and season); environment (temperature, humidity, wet-bulb globe temperature, and terrain); tasks (tree kind, operation and labor, and wage type); sample demographics (sample size, sex, age, and experience); sample anthropometrics (height, weight, and body mass index); measurement methods (HR monitoring device, measurement duration, measurement at rest, work breaks, and break exclusion/inclusion); and measured variables (HRrest, maximal heart rate [HRmax] at age, mean HRwork, HRI, %HRR, and other HR variables). For the numerical variables, the means, standard deviations, and ranges were extracted as applicable. Any disagreements on screening and extraction were resolved at each phase through discussion between all authors.

2.4. Synthesis of the Results

For the knowledge synthesis, we checked the missing items and summarized the extracted items. If the HR was reported for different tasks in one article, each task was identified. According to the participants’ main activity, tasks were classified into silviculture, harvest operation (manual or motor-manual), and machine operation related to the two previous operations. Additionally, tasks were classified into manual labor, motor-manual labor, and machine operation (labor). When HR indicators were not reported, they were calculated from the means of other variables if possible. HRI was calculated as:
HRwork (beats per minute [bpm])/HRrest (bpm),
and %HRR as:
(HRwork − HRrest)/(HRmax − HRrest) × 100,
where HRmax (bpm) = 220 − age (y). To elucidate characteristics of the HR variables, they were compared using the analysis of variance, and Tukey’s adjusted p-value was calculated from the means of the studies with weights of sample sizes. The weighted correlation coefficients and 95% confidence limits (95% CLs) were calculated using a 1000-replicated bootstrap method. Statistical software R 4.40 (R Core Team, Vienna, Austria) was used, and the significance level was set at a p-value of 0.05.

3. Results

3.1. Literature

We selected 22 studies published from 1984 to 2024 (Figure 2 and Table 1). Since 1990, these studies have been published at a roughly constant pace, three at an interval of 5 years. Of them, nine were from Europe, including Turkey [14,27,28,29,30,31,32,33,34]; four studies were from Oceania [35,36,37,38]; and three each were from Asia [39,40,41], America [42,43,44], and Africa [45,46,47]. No articles written in Japanese were selected.
Most studies (18 studies) were descriptive (reporting physiological workload) or exploratory (investigating workload-related factors; Supplementary Table S1) [14,27,28,29,30,31,32,33,34,36,37,38,39,40,41,42,45,47]. Others included the estimation of oxygen uptake from the HR (two studies) [44,46], and experiments about the modification of working conditions (two studies) [35,43]. Only two studies reported all of the following items [31,44]: country, survey period (year, month, or season), environment (climate and terrain), tasks, sample demographics (age and sex), sample anthropometrics, and measurement methods (HRrest and whether breaks were included).

3.2. Work Conditions

Fifteen studies reported temperature, wet-bulb globe temperature (WBGT), or the alternative (Oxford index), twelve of which reported <20 °C (Supplementary Table S1). The highest mean temperature was measured in Iran (29.67 °C) [41], followed by South Africa, Romania, and Poland in summer, ranging from 23 °C to 24.1 °C [31,34,47]. We also found two studies reporting forestry work on a snow-covered ground [27,34]. Furthermore, slopes of terrain were reported in 12 studies from flat to 44 °C [14,28,29,30,33,35,36,37,38,40,41,45].

3.3. Tasks

One study included both silviculture and harvest operations [29]. However, only the harvest operation was used for the summary because the silviculture operation involved seeding and nursing; the latter was then excluded. Workload was measured in silviculture operations in 5 studies [31,36,38,42,44], and harvest operations in 17 studies (Figure 3 and Supplementary Table S2) [14,27,28,29,30,32,33,34,35,37,39,40,41,43,45,46,47]. Regarding the main tasks of individual workers, manual labor was performed in 10 studies [28,31,36,37,38,42,44,45,46,47], motor-manual labor with chainsaws in 12 studies [14,27,28,29,30,33,34,35,39,41,43,47], or with brushcutters in 2 studies [32,44], and machine operations in 2 studies [31,40]. In these studies, the HR was reported for both manual workers and motor-manual workers in two studies [44], or machine drivers [31], and separately for manual and motor-manual workers in two studies [28,47]. Many papers lacked information on labor conditions, and the reported conditions varied.

3.4. Participants

In total, the 22 selected studies included 468 participants (Supplementary Table S3). Sample sizes ranged from 1 to 138. They were all males in 14 studies that reported sex [27,28,29,30,31,33,36,38,39,41,44,45,46,47], and females and males in 1 study [42], but the 7 remaining studies did not report participants’ sex. Ages were reported in all studies, and the mean ages were 22–51 years. Meanwhile, seven studies did not report height, weight, and body mass index [28,30,32,38,40,43,45].

3.5. HRs

Measurement devices and time were presented in Supplementary Table S4. Most studies used Polar devices. Measurement time was 4–8.5 h, but six studies did not report it. Nine studies had missing data on HRI, %HRR, and HRrest, which were calculated from complementary mean values (Table 2, Tables S4 and S5) [29,30,31,32,39,41,42,43,44]. Finally, HRrest was measured in 17 studies with a total of 371 participants; it was defined as the minimum HR during rest in 1 study [33], the minimum HR during both rest and working in 5 studies [30,31,32,33,44], HR at the end of rest in 3 studies [36,38,45], and the mean HR during rest in 2 studies [28,37]; 6 studies did not provide definitions [14,29,40,41,42,43]. All studies published after 2009 reported HRrest (Figure 2). Ten studies included breaks in the working time for the measurement [27,30,31,32,33,34,36,38,39,41], whereas four excluded breaks from the working time [28,35,43,44]; the eight remaining studies did not define whether breaks were involved or not (Figure 4) [14,29,37,40,42,45,46,47].
Four studies included different tasks, which were divided into ten, along with their mean HR [28,34,40,47]. Figure 4 and Supplementary Figure S1 plot the HR of 28 tasks of 22 studies in Supplementary Table S5. The following comparisons were conducted in 17 studies reporting HRrest in 22 tasks (N = 371). One study with manual and machinery labor [30] was included in the silviculture operation after researcher discussion, as it is not exclusively a machinery operation. HRwork, HRI, and %HRR differed among the silviculture, harvest, and machine operations (p < 0.001, p = 0.007, and p = 0.003, respectively; Table 2). In pair-wise comparisons, HRwork (Figure 4A) and %HRR (Supplementary Figure S1B) in silviculture were higher than those in machine operation (adjusted p < 0.004 and p = 0.005, respectively). Pair-wise comparisons of HRI were not significant (Supplementary Figure S1A). HRrest was not different among the three operations (Figure 4A). When tasks were compared based on labor, excluding two additional studies with different kinds of labor (N = 320), motor-manual labor showed the highest %HRR (Table 2), but did not significantly differ from manual labor in HRwork and HRI (Table 2). Additionally, HRrest negatively correlated with HRI (r = −0.620; 95% CLs, −0.678 and −0.553; Figure 4B) and %HRR (r = −0.411; 95% CLs, −0.768 and 0.116; Figure 4C). HRrest measured as the minimum or at the end of rest was relatively low but cannot be distinguished from others.

3.6. Other Parametes

Ages had a significantly positive association with %HRR (r = 0.588; 95% CLs, 0.086 and 0.845; N = 371), but the association with HRI was not significant (r = 0.427; 95% CLs, −0.129 and 0.841). Due to the limited availability of mean values in experience, temperature, and slopes, we could not calculate 95% CLs of correlation coefficients. Without HR indicators, ages were positively associated with experiences (r = 0.714; 95% CLs, 0.070, and 0.911; N = 130) in 10 articles. Body mass index was negatively associated with HR indicators in nine articles, but weighted correlations were not significant (r = −0.412; 95% CLs, −0.889, and 0.418 for HRI, and r = −0.462; 95% CLs, −0.883, and 0.216 for %HRR).

4. Discussion

This study reviewed 22 journal articles published from 1984 to 2022. Five studies did not measure HRrest for HRI and %HRR calculation. The definition of working time varied in terms of whether breaks were included in the HR measurement or not. The variety and lack of information about working procedures and labor conditions did not allow for detailed classification of operations and labor. The HRwork and %HRR of the machine operators were lower than those of the silviculture and harvesting operators, but the latter two operators did not demonstrate a difference. Moreover, the variation in HR indicators, especially HRI, depended on HRrest, which measurement was not uniformly defined in the literature.
Workloads may vary depending on not only the type, height, and diameter of trees but also working procedures specific to countries and areas. While chainsaw operations were dominant in the reviewed literature, other forms of manual labor, such as using axes, scissors, and hoes [31,36,48] were reported. Additionally, varying numbers of assistants (one or two) engaged in felling [28,34,43], and manual skidding and stacking as a whole task or a part of a daily task were reported [34,43,46,47]. Agricultural tractors were used in a small-scale forestry workplace instead of large-sized high-performance machines [34,41]. Tractors were versatile and allowed workers to perform intermediate tasks between manual labor and machinery [3,4,41,49,50]. Work conditions may have been changing during the four decades since the first published article in the literature. Working periods and wages varied among the studies, potentially influencing workloads, especially piece-rate wages [51,52]. However, information about procedures, working conditions, and employment remains insufficiently reported.
The ambient temperature may influence workers’ HR. At a lower temperature, the HR was higher than that at a higher temperature [34]. However, the relationships between temperature and HR were inconsistent, given that other articles reported no difference between different seasons and temperatures [27,37] or revealed an insignificant inverse relationship [53]. In warm ambient conditions, high body temperature caused by thermogenesis increases the HR [54] and delays HR recovery [55]. In studies that compared two temperatures, the highest temperature or WBGT was 23 °C [34]. The insight into how the HR is influenced near or above 30 °C remains unknown. In a Japanese study that measured the HR only during the experimental tasks, no difference was noted between summer (27.8–35.2 °C) and winter (5.3–14.1 °C) [56]. However, several articles did not report the ambient temperature or WBGT, and thus we had insufficient evidence to explore the influence of ambient conditions, as well as terrain conditions, on workload.
Physiological workload intensity is calculated as [17]:
METs = 6 × HRI − 5.0.
In estimating energy expenditure, 1 MET is equivalent to a 3.5 mL/kg/min uptake of oxygen, and 1 L of oxygen is equivalent to 5 kcal. HRI linearly correlates with oxygen consumption, even in conditions such as β-blockade usage, physical training, smoking, altitude, or a wide range of ages [17,18]. It is assumed that 30%–40% of HRR is the upper limit of 8 h continuous work, including breaks [57,58], and 1 h of work with 50% of aerobic capacity is practicable without any exhaustion sign [59]. Including breaks in the measurement seems reasonable, considering recovery during breaks in daily work. Both indicators require HRrest calculation, but methods to measure HRrest differ among studies. The inclusion of breaks was also inconsistent.
Aerobic capacity decreases with age [60] and is lower in females than in males [1,57]. In the reviewed literature, only one article included female participants [42], but it did not differentiate the results between sexes. In the articles focusing on ages, older workers worked at a slow pace, with a lower work rate than that of the youths [14,61]. However, a series of articles in this review showed positive associations between age and HR indicators, especially %HRR. The associations may be explained by the experiences of workers, which were linked to their ages. Experiences were not always reported alongside ages in the literature. Other than age and sex, training alters aerobic capacity [1,39]. In modern labor settings, workers can adjust the work rate according to their individual capacity, except for work performed by teams [57]. Forestry workers are likely to adjust their own work efficiency according to fatigue, environment, and age [45,62,63,64]. Although felling operations were conducted by a crew consisting of two or three workers in several studies, most of the tasks seemed to be performed at their own pace. The usual forestry workload is difficult to determine without the characteristic information of forestry workers. Personal factors, excluding age, were insufficiently reported in the previous articles to examine the influence of age on workload.
A limitation of this review is the lack of a universal definition of HRrest measurement. The accuracy of the HRrest measurement could not be predetermined. Samitto et al. recommended that the measurement should be performed at least 30 s after at least 5 min of rest in a sitting position without smoking, eating, caffeine, or physical exertion 30 min before the measurement [65]. The HR gradually decreases and settles during the rest. The mean HR during the whole rest may be higher than the actual HRrest. Meanwhile, the lowest HR may be slightly lower because of HR variation even at rest. However, in HR measurement at actual worksites, measuring HRrest is challenging when influencing factors are controlled in addition to impatience evoked by the assigned quota and piece-rate wage. In our previous study of simulated transferring on a sloped terrain [18], HRI using the least HRrest can predict the oxygen uptake proposed by Wicks et al., who did not mention the method of measuring HRrest [17]. While the least HR or one percentile during the whole measurement is simple and could be adopted, it remains unevaluated.
There are other limitations for interpretation. First, the insight into whether enough sources of evidence were found remains uncertain. We did not use gray literature, and we could not access the non-English literature. Most studies were submitted from non-English-native countries. Although the databases used in this study were limited, Google Scholar and Scopus are popular, covering broad disciplines. Including studies with controlled experimental conditions might provide more information, but they would not be applicable to typical forestry work, which consists of many procedures. Second, work practices may vary by region. In addition to vegetation and climate, work methods such as devices used and postures adopted may influence energy expenditure [66,67,68]. Unique work practices are familiar to each region/author, who might not report the difference in detail.

5. Conclusions

This scoping review revealed how the cardiovascular workload of forestry workers has been reported and found that the literature did not provide enough data for a comprehensive analysis of the combined results due to insufficient measurement quality and a small sample size. The gap between the current literature and the requirement for future studies needs to be bridged. Reviewing 22 journal articles cannot depict forestry-operation-specific workloads in terms of HRI and %HRR because of various tasks, insufficient information, and small sample sizes. In addition to the mean HR during the daily work shift, the accurate measurement of HRrest is required to calculate workload indicators, and its measurement conditions should be reported. HRrest measurement varied among the reviewed studies, thereby influencing HRI and %HRR variation. After careful observation of HR fluctuations, the lowest HR at rest before work, but not the mean HR during rest, should be cautiously adopted. Accurate measurement of HRrest is needed to reliably assess workload. It is also essential to conduct studies that validate the reliability and stability of HRrest measurement criteria in practice for future research of combined evidence. Additionally, details of forestry workers’ tasks, work environment, work conditions, working and measuring periods, breaks, and personal factors are required to assess the workloads of diverse procedures implemented in forestry fields. The information could broaden the application of measurements and clarify the scope of application. If the information is missing, the reasons should also be described in reports. Accurate and detailed information on the measurement could improve the quality of physiological workload measurement for forestry work. The scoping review did not provide enough information to classify and differentiate the operations and labor adequately, despite some comparisons revealing significant differences. Research is needed to standardize the terminology related to forestry tasks and to classify them. Furthermore, research about each task with a large sample size needs to be carried out.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f16030520/s1, Table S1: Study purpose, country, survey period, and environment; Table S2: Tasks; Table S3: Sample, demographics, and anthropometrics; Table S4: Measurement methods of heart rate; Table S5: HR in selected studies; Figure S1: Heart rate index and the percentage of HR reserves in the literature.

Author Contributions

Conceptualization, M.O. and Y.K.; methodology, M.O.; formal analysis, M.O., Y.K., H.T. and Y.F.; data curation, M.O. and Y.K.; writing—original draft preparation, M.O.; writing—review and editing, M.O., Y.K., H.T. and Y.F.; visualization, M.O.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.

Funding

The Japan Organization of Occupational Health and Safety funded this study.

Data Availability Statement

All available data are presented in Supplementary Materials. When seeking additional information, please contact the corresponding author.

Acknowledgments

We appreciate the staff of the Yamaguchi Occupational Health Support Center for their assistance in searching the databases and preparing the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CLconfidence limit
HRheart rate
HRIheart rate index
%HRRthe percentage of heart rate reserve
HRrestheart rate at rest
HRworkheart rate at work

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Forests 16 00520 g001
Figure 1. Flow diagram of the literature search.
Figure 1. Flow diagram of the literature search.
Forests 16 00520 g001
Forests 16 00520 g002
Figure 2. Publication year of studies on forestry workers’ heart rate. White parts indicate studies that did not report resting HR, while black parts indicate those that did.
Figure 2. Publication year of studies on forestry workers’ heart rate. White parts indicate studies that did not report resting HR, while black parts indicate those that did.
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Forests 16 00520 g003
Figure 3. Forestry operation and labor types. Ellipses indicate studies that reported two different labors, presenting the heart rate of workers in combination (connected with a double line) or separately (a dotted line).
Figure 3. Forestry operation and labor types. Ellipses indicate studies that reported two different labors, presenting the heart rate of workers in combination (connected with a double line) or separately (a dotted line).
Forests 16 00520 g003
Forests 16 00520 g004
Figure 4. Heart rate (HR) of forestry workers. Mean working and resting HRs (blue and orange, respectively) were grouped in silviculture, harvest, and machine operations (A). Closed and open circles indicate heart rates including and excluding breaks, respectively. Triangles depict heart rates; it is unknown whether breaks were included. Sample-size weighted bubble plots are drawn between the resting heart rate (HRrest) and the heart rate index [15,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] (B), or the percentage of the heart rate reserve (C), with weighted correlation coefficients (95% confidence limits). Closed and open orange bubbles indicate resting HRs measured as the minimum or one percentile and at the end of rest, respectively. Closed and open blue bubbles indicate resting HRs measured as means during rest and with undefined measurement, respectively.
Figure 4. Heart rate (HR) of forestry workers. Mean working and resting HRs (blue and orange, respectively) were grouped in silviculture, harvest, and machine operations (A). Closed and open circles indicate heart rates including and excluding breaks, respectively. Triangles depict heart rates; it is unknown whether breaks were included. Sample-size weighted bubble plots are drawn between the resting heart rate (HRrest) and the heart rate index [15,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] (B), or the percentage of the heart rate reserve (C), with weighted correlation coefficients (95% confidence limits). Closed and open orange bubbles indicate resting HRs measured as the minimum or one percentile and at the end of rest, respectively. Closed and open blue bubbles indicate resting HRs measured as means during rest and with undefined measurement, respectively.
Forests 16 00520 g004
Table 1. Selected studies.
Table 1. Selected studies.
AuthorCountryOperationsNGenderAge, yMeasurement at RestBreaks.HRrest, bpmHRwork, bpmHRI%HRR
Kukkonen-Harjula (1984) [27]FinlandHarvest6Male34 ± 3-in.-123 ± 4--
Kurumatani (1992) [39]JapanHarvest6Male51-in.-99.7--
Trites (1993) [42]CanadaSilviculture10Mixed26 ± 4--66.5 *116.5 ± 9.01.74 *39.2 ± 4.0
Kirk (1994) [35]New ZealandHarvest4-35 ± 2.8-ex.-117.5 ± 9.8 --
Seixas (1995) [43]BrazilHarvest2-34.5-ex.59114.61.94 *44.0 *
Inoue (1996) [40]JapanMachinery84-36.1--67.486.81.29 * 16.7 *
Kirk (1996) [36]New ZealandSilviculture6Male22 ± 5endin.78 ± 6112 ± 101.45 ± 0.129 ± 7
Sullman (2000) [38]New ZealandSilviculture6Male23endin.62 ± 3.0 134 ± 7.2 2.2 ± 0.252.6 ± 6.2
Kirk (2001) [37]New ZealandHarvest4-28.8 ± 10.1mean-58 ± 5.6106 ± 6.9 1.84 ± 0.1136.4 ± 3.1
Wästerlund (2004) [45]ZimbabweHarvest4Male28.0 ± 8.6end-65 ± 3.4126 ± 81.94 *48.1 ± 3.1
Scott (2004) [46]South AfricaHarvest23Male35.5 ± 8.7---118 ± 13.3--
Christie (2008) [47]South AfricaHarvest29; 29Male35.8 ± 6.3; 36.1 ± 9.3---123.3 ± 10.8; 117.6 ± 13.0--
Çalışkan (2010) [15]TurkeyHarvest10-33.9--70.5122.81.7444.8
Melemez (2010) [28]TurkeyHarvest46; 92Male46; 30meanex.73; 72115 ± 7; 91 ± 81.58; 1.2642; 17
Eroglu (2015) [29]TurkeyHarvest31Male43.1--61.31081.7640.9
Dubé (2016) [44]CanadaSilviculture41Male46.3 ± 13.2 1%tileex.63 ± 7123 ± 19 1.95 *54 ± 13
Cheţa (2018) [30]RomaniaHarvest1Male42minin.70.0 *107.11.5334.4
Marogel-Popa (2019) [31]RomaniaSilviculture14Male46.4minin.71.9107.21.49 *36.8
Borz (2019) [32]RomaniaHarvest1-42minin.71108.41.53 *34.9
Arman (2021) [41]IranHarvest13Male44.61 ± 6.46undefinedin.70.5 ± 3.0116.1 ± 12.41.67 ± 0.0743.5 ± 3.5
Halilovic (2021) [33]Bosnia HerzegovinaHarvest2Male39.5minin.531162.19 *48.6
Grzywiński (2022) [34]PolandHarvest4-35.2 ± 10.6minin.win, 67; sum, 64.5win, 135.1; sum, 110.6win, 2.02; sum, 1.71win, 58.0; sum, 38.7
HRrest, resting heart rate; HRwork, working heart rate; HRI, heart rate index; %HRR, the percentage of heart rate reserve; win, winter; sum, summer; min, the minimum heart rate; end, heart rate at end; bpm, beats per minute. HRrest is expressed as the mean, minimum (min.), one-percentile (1%tile), or end heart rate (end). Breaks were either included (in.) in the measurement or excluded (ex.). The means are presented as ± standard deviation, if applicable. * Calculated from the means of other variables. Blank (-) is “not recorded” or “not applicable”.
Table 2. Weighted means and standard deviations of the heart rate variables among operations.
Table 2. Weighted means and standard deviations of the heart rate variables among operations.
HRrest, bpmHRwork, bpm HRI%HRR, %
Mean ± SDMean ± SDMean ± SDMean ± SD
Operations †
  Silviculture66.2 ± 4.9119.3 ± 7.5Forests 16 00520 i001p = 0.0041.82 ± 0.2346.9 ± 9.0Forests 16 00520 i002p = 0.005
  Harvest69.6 ± 4.7104.4 ± 12.61.51 ± 0.2431.6 ± 13.0
  Machine67.4 ± 1.186.8 ± 3.41.29 ± 0.0516.7 ± 2.9
    ANOVAp = 0.358p < 0.001 p = 0.007p = 0.003
Labors ‡
  Manual70.7 ± 3.9103.8 ± 14.2Forests 16 00520 i003p < 0.0011.40 ± 0.27Forests 16 00520 i004p < 0.00122.8 ± 11.0Forests 16 00520 i005p = 0.007
  Motor-manual68.2 ± 5.5115.8 ± 7.01.69 ± 0.1342.6 ± 3.5Forests 16 00520 i006p = 0.018
  Machine67.4 ± 1.186.8 ± 3.41.29 ± 0.0516.7 ± 2.9
    ANOVAp = 0.385p < 0.001 p = 0.006 p < 0.001
† N = 371, including 17 studies reporting resting HR (heart rate at rest, HRrest). ‡ N = 320, including 15 studies reporting HRrest. SD, standard deviation; bpm, beats per minute; HRwork, working heart rate; HRI, heart rate index; %HRR, the percentage of heart rate reserve. Analysis of weighted variance (ANOVA) and Tukey tests for pair-wise comparison were conducted, with significant p-values presented.
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Okuda, M.; Kawamoto, Y.; Tado, H.; Fujita, Y. Heart Rate Monitoring for Physiological Workload in Forestry Work: A Scoping Review. Forests 2025, 16, 520. https://doi.org/10.3390/f16030520

AMA Style

Okuda M, Kawamoto Y, Tado H, Fujita Y. Heart Rate Monitoring for Physiological Workload in Forestry Work: A Scoping Review. Forests. 2025; 16(3):520. https://doi.org/10.3390/f16030520

Chicago/Turabian Style

Okuda, Masayuki, Yutaka Kawamoto, Hiroyuki Tado, and Yoshimasa Fujita. 2025. "Heart Rate Monitoring for Physiological Workload in Forestry Work: A Scoping Review" Forests 16, no. 3: 520. https://doi.org/10.3390/f16030520

APA Style

Okuda, M., Kawamoto, Y., Tado, H., & Fujita, Y. (2025). Heart Rate Monitoring for Physiological Workload in Forestry Work: A Scoping Review. Forests, 16(3), 520. https://doi.org/10.3390/f16030520

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