令和４年度
産業保健調査研究報告書〈調査期間：令和４年４月～令和５年３月〉

林業従事者の傾斜地作業負荷
測定方法開発のためのパイロット研究

研究体制
研究代表者 ：

山口産業保健総合支援センター 産業保健相談員 奥田 昌之

研究分担者 ： 山口県農林総合技術センター

林業研究室室長

田戸 裕之

山口県農林総合技術センター

専門研究員

川元 裕

山口産業保健総合支援センター

労働衛生専門職

藤田 善政

令和５年３月

独立行政法人労働者健康安全機構
山口産業保健総合支援センター

Table of Contents
1. 背景 ........................................................................................................................................................................................ 1
2. 調査 1 .................................................................................................................................................................................... 2
2.1. 目的 ............................................................................................................................................................................... 2
2.2. 方法 ............................................................................................................................................................................... 2
2.3. 結果 ............................................................................................................................................................................... 2
2.4. 考察 ............................................................................................................................................................................... 4
3. 調査 2

調査票の集計 .................................................................................................................................................. 5

3.1. 目的 ............................................................................................................................................................................... 5
3.2 方法 ................................................................................................................................................................................ 5
3.3. 結果 ............................................................................................................................................................................... 5
問 1 作業内容 ............................................................................................................................................................... 5
問 2 1 週間あたりの就業日数 .............................................................................................................................. 6
問 3 最近の平均的な林業に従事する時間 ..................................................................................................... 6
問 4 休憩回数と時間 ................................................................................................................................................. 7
問 5 通勤 ......................................................................................................................................................................... 7
問 6, 7 交代勤務・夜勤 ............................................................................................................................................ 8
問 8, 9 収入・副業...................................................................................................................................................... 8
問 10 林業経験年数 ................................................................................................................................................... 9
問 11 事故 ...................................................................................................................................................................... 9
問 12 現在の健康状態 ............................................................................................................................................ 10
問 13 最近 1 か月の睡眠....................................................................................................................................... 10
問 14 住まいの状況 ................................................................................................................................................. 10
問 15 性別 .................................................................................................................................................................... 11
問 16 年齢 .................................................................................................................................................................... 11
3.4. 考察 ............................................................................................................................................................................. 12

4. まとめと将来展望......................................................................................................................................................... 13
5. 付録 調査票別添......................................................................................................................................................... 14

1. 背景林業は重大な労働災害の発生率の高い業種である。労働災害の発生の高い時間帯があり、疲労に関連すると考えられている。疲労は作業負荷と回復（休憩、就業外時間、休日）とのバランスである。
森林作業員の作業負荷のうち、主な身体的作業負荷に重い道具の運搬と傾斜地の移動がある。森
林作業員の勤務状況や作業環境は国・地域によって違う。広大な森林のある地域では現地に寝泊まりし交代勤務で行うが山口県のような森林では現場に毎日通って作業をしているといわれている。
また、傾斜の程度や機械化の程度も異なる。林業従事者の作業負荷を測定した報告は国際的に乏しい。身体的作業負荷は農業のような体幹・四肢の屈伸運動よりも、重量物を運搬しながらの傾斜
地移動による身体活動（エネルギー消費）での負担が高いと仮定する。この作業負荷を簡易に測定することができ、客観的に評価することができれば、休憩・休日の取得などの作業方法や自動化機
械導入の成果の評価が可能となる。屋外での身体活動の評価は、心拍数計、加速度計が用いられているが、様々な要因の影響を受けることや傾斜地と平地の区別が難しい欠点がある。実際の森林作業員の作業負荷を把握するために、本調査研究では、ウエアラブル測定機器を装着して、傾斜地を上り下りし、身体活動量を簡易測定することが可能であるか確認することをこのパイロット研究の第一の目的とした。身体活動量から作業の身体活動強度がわかる。また、作業負荷の実態を評価するために、林業従事者の業務を把握する必要があるが、これまで日本林業従事者の医学領域での報告がほとんどなく、森林作業員の作業種類の分布を明らかにすることをパイロット研究の第二の目的とした。
本調査研究の計画は、独立行政法人労働者健康安全機構本部医学系研究倫理審査委員会で承認を受けた。調査研究費用は独立行政法人労働者健康安全機構の交付金であった。研究者は当センターの産業保健相談員等で、それ以外の報告すべき利益相反はなかった。

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2. 調査 1
2.1. 目的
日本の森林は傾斜地にあること、湿潤な気候のため下草が育ちやすいことが特徴で、森林作業員の作業負荷が高くなる。森林作業として、荷物を負った歩行のエネルギー消費量を推定できるウエア
ラブル測定機器に測定方法を明らかにすることが目的である。

2.2. 方法
参加者に 6 人の 37-63 歳の男性をリクルートした。測定は森林総合研究所敷地内の森林を模した築山で行った。対象者の食後 2 時間で、森林作業をする服装・装具、20kg の背負子、さらに測定器具を装着してもらい、平地、15 度、30 度の傾斜地を歩行してもらった。平地は 270m、築山
は 8m の高さで、15 度と 30 度の傾斜値をそれぞれ 3 往復してもらった。それぞれ歩行の間には、心拍数が落ち着くように休憩を 5 分間程度とった。歩行は 50ｍ/分程度を目標に自由歩行してもらった。
エネルギー消費量は、携帯型呼吸代謝測定装置 K5（コスメッド, イタリア）で、基準となる酸素消費量を測定した。心拍数は Verity Sense（ポラール、フィンランド）を非利き手の前腕に装着してもらい計測した。身体動作加速度から身体活動量を推定できる。動作測定に加速度計 wGT3X-BT（アクチグラフ、米国）を右腰につけてもらった。垂直移動距離がわかれば仕事量を求めることができる。衛星測位システム GNSS を用いた Trimble TDC150 (ニコン・トリンブル、日本)を背負子に固定して水平移動、垂直移動を記録した。すべての装置は、時刻を同期し、パソコンにデータをダ
ウンロードでして、解析を行った。 身長、体重はそれぞれの参加者の歩行開始前に測定した。
酸素消費量から、活動強度として metabolic equivalent of task (MET)を計算した。心拍数
は、安静時の心拍数との比である heart rate index を用いた。また heart rate index(HRI)から エネルギー代謝率 MET を Wicks らの提案した式$ METs=HRI -5 $で推定した。加速度計のデータは、3 軸の加速度( カウント）を合成した vector magnitude（VM）を用いた。GNSS
データからは垂直方向の移動距離を用いた。心拍数から得られたエネルギー代謝率との相関を調べた。解析に R（R core team, オーストリア）を用いた。

2.3. 結果
GNSS データの垂直移動測定は基線がずれて移動距離を正確に測ることができなかった。
GNSSS データを除いて、検討した。歩行時間に限った解析では心拍数から求める HRI と加速度計から VM を酸素消費量から求めた METs との Pearson 相関は、それぞれ 0.932、0.354 であった。心拍数や酸素消費量は安静時の状態に回復するまでのタイムラグを考慮にいれた場合の相関は、それぞれ 0.843、0.506 であった。いずれも心拍数から求めた HRI の方が相関が高かった。HRI から推定した MET と酸素消費量から計算した MET とを比較すると、差は 0.134
METs で、その 95%信頼区間は-0.543 METs と 0.811 METs で１MET 以内であった。

2

(a)

(b)

(c)

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Figure １. (a) 心拍数から計算した HRI と酸素消費量から計算した MET の関係、(b)加速度計から計算した VM と酸素消費量から計算した MET の関係、(c)心拍数 HRI から推定した METと心拍数から計算した MET の Bland-Altman プロット

2.4. 考察
傾斜値の移動を測定するには、HRI の妥当性が最も高かった。森林作業には移動だけではなく、チェーンソ－を使った伐採（主伐、間伐）、下刈りなど上半身を使う作業もある。加速度右傾や GNSS
装置と違い、心拍数計ならばこれらの活動でもエネルギー代謝率を推定ができると考えられる。また用いた心拍数計は軽量（17ｇ）で作業者にも測定の負担は軽いと考えられる。実際に森林作業で心拍数モニターを行ってみることができると考える。

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3. 調査 2

調査票の集計3.1. 目的
森林作業には、樹木の生育によっていくつかの種類がある。作業の負荷は、森林作業による疲労や事故リスクとも関連すると考えられる。森林作業種類ごとの作業負荷程度を携帯型デバイスを用いて調べることができる。このような調査を行うために、事前に林業作業の種類、作業時間を把握しておき、調査方法を検討する必要がある。作業種類と従事時間に関する情報があまり公開されていな
い。山口県内に 6 か所の団体、森林作業員は 300 人程度であるという伝聞情報があり、今回山口
県全体で森林作業員を対象に森林作業について調べることとした。目標は、従事する作業の偏り、作業の時間、それに関係する要因を明らかにすることであった。

3.2 方法山口県内に 6 か所の森林組合あるいは民間事業体があり、それぞれの 5-10 人で構成される班で作業を行っていた。それぞれの森林組合あるいは民間事業体の一つの班の作業員に調査に回答いただくように依頼を行い、47 人の対象者が選ばれた。森林組合あるいは民間事業体を通して調査票を配布し、郵送で回答を得た。調査は、2022 年 6-7 月に行った。質問票は別添えの通りである。
質問票は 40 人から回収でき、それぞれの質問ごとに選択肢の集計あるいは代表値を求めた。

3.3. 結果
問 1 作業内容
地拵え、植栽、下刈り、除伐、間伐の作業には半分以上の森林作業員が従事し、枝打、主伐、搬出には 30%以上が従事していたが、製材・市場の作業に従事している森林作業員は少なかった。調査
時期が 6－7 月の回答では、最近 1 か月の作業として下刈り、間伐、搬出が多かった。

図 1‐1. １年間の作業種類
5図 1‐2. １か月間の作業種類
問 2 1 週間あたりの就業日数
週５、６日の就業であった。

図 2. １週間当たりの就業日数
問 3 最近の平均的な林業に従事する時間
多くが 1 日 8 時間勤務で、平均も 8.0 時間であった。

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表 3. 林業従事時間
最小値 第 1 四分位 中央値 平均値 第 3 四分位 最大値
１年間

2

8

8

8.03

8.62

10

１か月

2

8

8

7.96

8.62

10

問 4 休憩回数と時間
長い休憩は、回答のあった 31 人でほとんど 12 時から 1 時間であった。2 名は 12 時前から 1 時
間超の休憩で、1 名は 60 分未満の昼休みであった。
短い休憩には 33 名回答し、1 日に 1 回から 4 回の休憩をとっていた。2 名は休憩なしで、1 名240 回と回答した。10、15 時ぐらいから休憩をとる人が多く、休憩時間は 10～30 分間で、15 分
間が多かった。
問 5 通勤通勤は自宅からで、ほぼ 1 時間以内であった。

図 5‐1. 通勤経路7

図 5‐2. 通勤時間
問 6, 7 交代勤務・夜勤
交代勤務、夜勤があるという回答はなかった。
問 8, 9 収入・副業
収入は日給が 32.5%、月給が 57.5%、個人事業主が 10.0%であった。副業をしている人はいなかった。

図 8. 収入8

問 10 林業経験年数
経験年数の範囲は、0.5 から 45 年で、平均 13.96 年だった。

表 10. 林業経験年数
最小値 第 1 四分位 中央値 平均値 第 3 四分位 最大値
0.5

4.5

14

13.96

21.25

45

問 11 事故
事故の経験者は 47.5%、事故になりそうだった人は 42.5%、事故の経験がない人は 10.0%であった。

表 11. 事故経験9

問 12 現在の健康状態
治療中の人が 10.0%、健診で指摘されているが未治療中の人が 2.5%であった。

図 12. 健康状態
問 13 最近 1 か月の睡眠重複回答があった。よく眠れると回答したのは 67.5%であった。昼間眠たくなるのは 15%であった。
ただし眠れると重複回答があった。

図 13. 睡眠問 14 住まいの状況
結婚して同居している人は 62.5%、別居している人が 2.5%、結婚していない人が 35.0%であった。

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図 14. 世帯

問 15 性別
97.5%が男性、2.5%が女性であった。

図 15. 性別

問 16 年齢回答した 36 人の平均年齢は 44.25 歳で、範囲は 21 歳から 68 歳であった。

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表 16. 年齢
最小値 第 1 四分位 中央値 平均値 第 3 四分位 最大値 空白人数
21

37.75

45.5 44.25

50.25

68

4

3.4. 考察作業の種類を知ることができた。1 年間ですべての作業に従事するわけではないが、地拵え、植栽、下刈り、除伐、間伐、枝打、主伐、搬出の作業に多くの森林作業員が従事していた。また季節によって取り組む作業の違いがあることが示唆された。海外のような宿泊所施設（宿泊する飯場）の利用はなく、通勤の負担も大きくなさそうであった。また、現在の健康状態について、治療の有無、睡眠についての質問からはとくに目立った大きな問題はなさそうで、作業内容自体に注目して作業負荷を評価すればよいだろう。
年齢も経験年数もさまざまであるが、多くの森林作業員が事故の経験、あるいは事故になりそうになった経験があった。自由記載にも危険な作業への自覚を促すコメントがあり、事故対策は必要である。今回の結果から 1 年間を通して複数の事業場（森林組合や民間団体）で調査を実施すれば山口県内の森林作業員の作業負荷を知ることができると推察した。

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4. まとめと将来展望
複雑な地形で行われる森林作業のエネルギー消費量を心拍数で測定できることが分かった。心拍数を安静時の心拍数との比で表す HRI を用いるとよい。また、森林作業員は 1 年を通して多くの作業に従事し、季節によって作業内容が変わっていることが分かった。とくに夏には下刈りや間伐が多くなっていた。
本調査研究で得られた知見から、森林作業員の作業種類を考慮した作業負荷を明らかにするメイン研究を次のように組み立てることができる。調査の対象は山口県内森林作業員とする。現在山口
県内に 6 か所の森林組合あるいは民間事業体がある。それぞれ 5～10 人で構成される班がいく
つかあり、班ごとに 1 年間を通して森林の育成（季節）に合わせた森林作業に従事している。作業日を単位として森林作業員の心拍数モニターを行う。測定日の 1 日の測定は、一つの班の全員（平均7 人、5～10 人）にお願いする。1 日を通して作業が行える天候の日に行う。調査研究全体の測定
日は、1 年を通して測定するため令和５年６月から令和６年５月までのそれぞれ 1 か月のうち 2 日とし、1 日の測定日には１か所の団体の測定日に作業を行う１班を割り当てる（平均 7 人、5～10 人）。
測定日に作業を行っている班で測定を行うため、一人の森林作業員には調査研究期間に２日以上測定日があたることがある。被検者には、身長、体重、性別、年齢、森林作業の経験年数、測定日の作業種類を尋ねる。研究者は測定日の天候、気温、作業場所の位置、地面の状態を記録する。また研究者は観察した作業の前後から作業内容も記録する。これらをもとに、主な作業内容に分類して作業負荷強度を明らかにする。測定した結果から、森林作業員、作業の管理者、林業関係者に作業負荷強度を知っていただき、作業管理、作業補助機機器の開発に活用してもらう。今回調査研究は将来の調査研究に役立つ知見を得ることができた。

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5. 付録

調査票別添

14

調査票 別添

令 和 ４ 年〇〇⽉

林業に従事される労働者の⽅へ
および 林業関係者の⽅へ
独⽴⾏政法⼈ 労働者健康安全機構
⼭⼝産業保健総合⽀援センター

林業従事者の作業に関する調査のお願い
平素は、当センターの活動にご理解ご⽀援を賜りましてまことに感謝しております。
当センターでは、事業場の規模に限らず労働者の健康管理に関する⽀援、助⾔、教育などを⾏い労働者の健康の向上を図っております。
このたび、「林業従事者の傾斜地作業負荷測定⽅法開発のためのパイロット研究」として林業従事者の作業負荷と疲労に関する調査を⾏っております。これまでの知⾒が少なく、まず労働実態を把握するための質問票を作成することとし、その質問票の調査可能性を確かめることとしました（パイロット研究といいます）。
林業事業所、森林組合等を通して、林業従事者にご回答をいただきたいと存じます。林業従事者個⼈を対象に、無記名で調査を⾏います。
調査対象者数はパイロット研究のため 30 ⼈を予定しております。ご回答いただくと調査にご協⼒同意いただいたものとします。ご回答は集計し、労働者の健康保持増進に貢献できるように努めてまいります。データをまとめた集計結果は公開し、関係機関での事業活動に活かせるようにしていきます。
記⼊された質問票は 〇〇⽉ 〇〇⽇まで同封した封筒でご返送ください。
なにとぞ趣旨をご理解の上、ご協⼒よろしくお願いします。

( 連絡先 )
〒753-0051 ⼭⼝市旭通り２丁⽬ 9-19 ⼭⼝建設ビル４階
独⽴⾏政法⼈労働者健康安全機構 ⼭⼝産業保健総合⽀援センター
研究代表者 奥⽥昌之
他の連絡先：藤⽥TEL 083-933-0105
FAX 083-933-0106

林業従事者の就業状況と疲労に関連する要因を調査するための質問票を作成するための予備調査です。回答に 10 分ぐらいかかります。最後までご協⼒
よろしくお願いします。
あてはまるところ☐にチェック（✔）を、かっこ（）には数字をご記⼊ください。
問番号

質問項⽬

問1

最近⾏った⼯程に当てはまるものはどれですか（当てはまるものすべて）。
1-1 最近 1 年間に⾏った⼯程 1-2最近 1 か⽉間に⾏った⼯程
a

問2

問3

地拵え

☐

☐

b 植栽

☐

☐

c

下刈

☐

☐

d 除伐

☐

☐

e

枝打

☐

☐

f

間伐

☐

☐

g

主伐

☐

☐

h 搬出

☐

☐

i 製材・市場

☐

☐

j その他

☐

☐

最近の 1 週間あたりの何⽇林業に従事しますか（ひとつ）。
2-1 最近 1 年間の平均的な
労働⽇数2-2 最近 1 か⽉間の平均的な
労働⽇数

1.

1 ⽇/週

☐

☐

2.

2 ⽇/週

☐

☐

3.

3 ⽇/週

☐

☐

4.

4 ⽇/週

☐

☐

5.

5 ⽇/週

☐

☐

6.

6 ⽇/週

☐

☐

7.

7 ⽇/週

☐

☐

最近の平均的に林業に従事する労働時間（休憩時間を含む）
3-1 最近 1 年間の林業従事⽇ 3-2 最近 1 か⽉間の林業従事の平均的な 1 ⽇労働時間
⽇の平均的な 1 ⽇労働時間
休憩時間を含めて（）時間/⽇

休憩時間を含めて
（
）時間/⽇

問4

最近の林業に従事する平均的な⽇の休憩回数と時間
4-1 最近 1 年間の林業従事 4-2 最近 1 か⽉間の林業従⽇の平均的な休憩
事⽇の平均的な休憩

60 分
以上

60 分
未満

問5

60 分以上の休憩
a 回数

（

1 回⽬
b 開始時刻
c 時間

1 回⽬
（
（

2 回⽬
d 開始時刻
e 時間

2 回⽬
（
（

60 分未満の休憩
a 回数

（

1 回⽬
b 開始時刻
c 時間

1 回⽬
（
（

2 回⽬
d 開始時刻
e 時間

2 回⽬
（
（

3 回⽬
f 開始時刻
g 時間

3 回⽬
（
（

4 回⽬
h 開始時刻
i 時間

4 回⽬
（
（

）回/⽇
時

時

時

時

時

）回/⽇

分）
）時間/回

1 回⽬
（
（

時

分）
）時間/回

分）
）時間/回

2 回⽬
（
（

時

分）
）時間/回

）回/⽇
時

（

（

）回/⽇

分）
）時間/回

1 回⽬
（
（

時

分）
）時間/回

分）
）時間/回

2 回⽬
（
（

時

分）
）時間/回

分）
）時間/回

3 回⽬
（
（

時

分）
）時間/回

分）
）時間/回

4 回⽬
（
（

時

分）
）時間/回

通勤時間はどのくらいですか。経路と時間でそれぞれ⼀つ
5-1 最近 1 年間の平 5-2 最近 1 か⽉間の平
均的な通勤
均的な通勤
通勤の経路
1. ⾃宅から現場☐☐

2. ⾃宅から事業所☐☐

3. 現場近くに宿泊（宿泊所から現場）

☐

☐

☐

☐

☐

☐

通勤時間
1. 30 分未満
2.

30 分

〜

1 時間未満3.

1 時間 〜 1 時間 30 分未満

☐

☐

4.

1 時間 30 分 〜 2 時間未満

☐

☐

5.

2 時間以上☐☐問6

交代勤務がある
1. ☐

問7

はい

2. ☐

時給

2. ☐

はい

週給

4. ☐

⽉給

5. ☐ 個⼈事業主

2. ☐

いいえ

）年
ない
2. ☐
事故にあった

事故になりそうだった（ニアミス）

現在の健康状態
1. ☐
3. ☐

問 13

3. ☒

林業従事中に事故にあったことや、事故になりそうになった経験がありますか。
1. ☐
3. ☐

問 12

⽇給

林業従事の経験年数
（

問 11

いいえ

林業以外にも仕事をされていますか。
1. ☐

問 10

いいえ

給与の⽀払い⽅法の基準は次のどれですか。
（ひとつ。個⼈事業主）
1. ☐

問9

2. ☐

夜勤（午後 10 時から午前 5 時までの間に働く時間）がある
1. ☐

問8

はい

治療中
健康

2. ☐

治療はしていないが健診で指摘されている

最近 1 か⽉間の睡眠の様⼦はどうですか。
（当てはまるものすべて）
a ☐ よく眠れるb ☐ 寝付けないc ☐ 朝早く⽬覚める
d ☐ 夜間⽬が覚める e ☐ 朝眠りが⾜りない d ☐ 昼に眠たくなる

問 14

現在 1 か⽉間のお住まいの状況
1. ☐
3. ☐

問 15

2. ☐

結婚しているが別居

性別
1. ☐

問 16

結婚している（同居）
結婚していない
男性

2. ☐

⼥性

3. ☐

答えたくない☐答えたくない

年齢
（

）歳

問 17

これまでの質問について、コメントがありましたらご記⼊ください。
答えにくい質問がある、林業従事者には当てはまらない、林業従事者の業務には必要ない項⽬がある など 問の番号のご記⼊とあわせてお答えください。

問 18

回答記⼊⽇

（

）⽉

（

）⽇

最後までお答えいただき、ご協⼒ありがとうございました。
返信⽤封筒に⼊れて投函してください。
差出⼈のお名前は不要です。

Brief Report

Energy Expenditure Estimation for Forestry Workers Moving on
Flat and Inclined Ground
Masayuki Okuda 1,2, * , Yutaka Kawamoto 3 , Hiroyuki Tado 3 , Yoshimasa Fujita 1 and Yuta Inomata 4
1

2

3

4

*

Yamaguchi Occupational Health Support Center, Asahi-Dori 2-9-19, Yamaguchi 753-0051, Japan;
fujita@yamaguchis.johas.go.jp
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Minami-Kogushi 111,
Ube 755-8505, Japan
Yamaguchi Prefectural Agriculture and Forestry General Technology Center, Mure 10318,
Hofu 747-0004, Japan; kawamoto.yutaka@pref.yamaguchi.lg.jp (Y.K.);
tado.hiroyuki@pref.yamaguchi.lg.jp (H.T.)
Forestry and Forest Products Research Institute, Matsunosato 1, Tsukuba 305-8687, Japan;
y_inomata@ffpri.affrc.go.jp
Correspondence: okuda@yamaguchi-u.ac.jp; Tel.: +81-836-22-2231

Abstract: Forestry workers endure highly physical workloads. Japanese forestry workers experience
additional up-and-down movements due to geographical features. Fatigue is a common cause of
injury. This pilot study aimed to determine an appropriate method for estimating energy expenditure
while moving across inclined ground to simulate a Japanese forest. Six participants wore a portable.
indirect calorimeter (VO2 ), heart rate (HR) monitor (17 g), accelerometer (20 g; vector magnitude;VM), and a global navigation satellite system (GNSS) device. They walked shouldering 20 kg of.
weight on flat, 15◦ - and 30◦ -slopes. The time course of HR was similar to that of VO2 , but that.
of VM and the vertical movement varied from that of VO2 . GNSS cannot correctly detect verticalmovements. The HR index (HRI), indicating the ratio of activity HR to resting HR, was significantly.
correlated with the metabolic equivalent of the task (MET) calculated from VO2 (r = 0.932, p < 0.0001),
which fit the previously proposed formula for METs (METs = HRI × 6 − 5). However, VM was notcorrelated with VM (r = 0.354, p = 0.150). We can use HRI to measure the workload of Japanese
forestry workers with a small burden in the field.
Citation: Okuda, M.; Kawamoto, Y.;
Tado, H.; Fujita, Y.; Inomata, Y.
Energy Expenditure Estimation for

Keywords: accelerometer; energy expenditure; forestry workers; global navigation satellite
positioning system; heart rate index; oxygen consumption; simulated forestry slope

Forestry Workers Moving on Flat andInclined Ground. Forests 2023, 14,1038. https://doi.org/10.3390/
f14051038

1. Introduction

Academic Editor: Raffaele Spinelli

Forestry is one of the most hazardous industrial sectors in most countries [1]. According
to Japanese statistics, the incidence of absences from work lasting at least four days, including
death, was 9.1 times higher in forestry than in all other industries combined in 2021 [2].
The physically and mentally demanding workload in forestry is related to workers’ fatigue,
which is considered to be a contributing factor to accidents and injuries [3–5]. Japanese
forests have different land characteristics from forests in other countries [6]; 37.2% of Japanese
forest land has a slope >20◦ , which is the average among Asian countries, but higher thanthat in other regions [7]. Steep slopes hamper machinery operation and increase the risk ofmachine handling [1]. Workers must transfer forestry equipment and tools up and down
forest land, in addition to engaging in high-intensity activities. It has been suggested thatthe physical properties of land establish unique operational systems based on experienceand intuition [1,7,8]. Determining the workload of Japanese forestry workers is challenging,particularly when performing a broad range of forestry procedures. A descriptive study tomeasure the workload in the forestry industry in a temperate climate throughout the year isrequired to take measures to reduce fatigue and injury.

Received: 10 April 2023
Revised: 2 May 2023
Accepted: 9 May 2023
Published: 17 May 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Forests 2023, 14, 1038. https://doi.org/10.3390/f14051038

https://www.mdpi.com/journal/forests

Forests 2023, 14, 1038

2 of 10

Heart rate (HR) monitoring is widely used to estimate the workload. Several studies
have measured the workload of forestry workers using HR monitors in the field [9–14].
They reported a single task or operation, such as afforestation, pruning, felling, or cable
work, and used the crude or HR relative to the maximal or submaximal HR. Relative HR isreliable against inter-individual variability, but is inconvenient for the detection of maximal
or even submaximal HR in the field. A few reports on the workload of Japanese forestry
workers (walking, mowing, or felling) were also found in the database [15–17], in which HR
monitoring was conducted with a small sample size; oxygen consumption as an indicatorof energy expenditure was not assessed.
HR and O2 had a linear relationship, except at low levels of activity (below the flex
HR; HRflex ) [18,19]. The heart rate index (HRI; actual HR/resting HR) is the most reliablepredictor of energy expenditure, not only for healthy people, but also for patients withcardiovascular diseases [20]. The HRI method is applicable in real practice in the forestry
field because only the resting HR must be known, whereas the maximal or submaximal HR
is not required. To determine the latter, an exercise test using a treadmill or cycle ergometry
is needed [21]. Although the HRI method is applicable to forestry work, validation with O2
consumption has only been tested with workers wearing light clothing under laboratory
conditions [20,22,23], and is impractical in the field. Other methods have been proposed toestimate energy expenditure in the field. Geographical information is a possible predictorof energy expenditure during simulated mountain climbing. Hagiwara et al. predicted theenergy expenditure on a treadmill from the total weight, including carrying packs and theup–down vertical transfer velocity [24]. De Müllenheim et al. compared an accelerometer,
heart rate monitor, and global positioning system (GPS) and concluded that GPS was
the most accurate method for estimating the energy expenditure in outdoor uphill anddownhill walking activities [25].
Before measuring the workload of the various tasks undertaken throughout the year
by Japanese forestry operations, a simple, accurate, and applicable method for estimatingenergy expenditure in the field should be clarified. The aim of this pilot study was toinvestigate which device is most suitable for estimating the energy expenditure during
forestry work on inclined ground, as is common in Japan. To achieve this, we selected aphysically intensive task in cable operations as the target task, which requires Japanese
forestry workers to bring heavy tools and equipment up and down slopes.
2. Materials and Methods
2.1. Participants and Fields
Six male adults, aged 37–63 years, were recruited for this pilot study. They were
not forestry workers, but engaged in forestry research and management, and sometimes
worked in the field. The protocol was approved by the ethics board of the Japan Organization of Occupational Health and Safety (approval number, 2022-07) in accordance with
the Declaration of Helsinki. Written informed consent was obtained from all participants.
Field measurements were conducted in an unsurfaced test field at the Forestry and Forest
Products Research Institute in Tsukuba, Japan, from 17–20 October 2022. We used flatground and 15◦ - and 30◦ -slopes up to a height of 8 m.
The measurement procedure began two hours after breakfast or lunch to minimize thethermal effect of food on the measurement. First, the participants prepared forestry work
appurtenances such as work garments, boots, helmets, and gloves (Supplementary Figure
S1) and wore portable metabolic analyzers, accelerometers, and heart rate monitors. Whenthe measurements began, they rested on a chair in a large room for 30 min and then moved
to the test field at the bottom of the slope. After sitting on a chair until their heart rate.
and VO2 became steady, they shouldered a frame pack of 20 kg on their backs and started
walking on flat ground. The weight-simulated packs included devices and tools for cableyarding. The participants could walk at any speed, but in the first 10 s, the researcher letthem hear a metronome at a speed of 67/min, which taught them a speed of approximately
50 m/min. They turned at the pylon 185 m from the starting point, returned at the same

Forests 2023, 14, 1038

3 of 10speed as the starting point (a total of 370 m), unloaded the frame pack, and sat down.
on a chair. After resting until the monitored heart rate and VO2 became steady within
5 min, the participants shouldered the frame pack and started climbing on a 15◦ slope.
They reached their peaks, turned around, and descended. They sequentially repeatedthe up-and-down procedures three times, unloaded, and rested. They then moved to thebottom of the 30◦ slope and rested. Next, they climbed and descended the 30◦ slope threetimes shouldering the frame pack. After the heart rate and VO2 stabilized, all devices
were removed. The entire procedures took 1.5–2 h, which made it possible to accurately
measure the air composition within the battery capacity and maintain the internal device
temperature of the portable indirect calorimeter.
2.2. Measurements
2.2.1. Portable Indirect Calorimeter
The metabolic rate was measured using a portable indirect calorimeter, K5 (Cosmed,
Albano Laziale, Italy). The K5 sensors were calibrated using a canister syringe for the flow
volume and standard gas (16.09% O2 and 5.01% CO2 ) prior to each measurement. During
the measurements, the participants wore K5 on their front chest with a mask attached totheir face. Data were recorded breath-by-breath and monitored in real-time via Bluetoothin Omnia (Version 2.1, Cosmed, Albano Laziale, Italy) installed on a laptop computer. A
K5 with attachments weighed 0.9 kg, which was included in the weight of the frame packof 20 kg. Oxygen consumption standardized as “standard temperature and pressure, dry(STPD)” was recorded via the device.
2.2.2. Heart Rate Monitor
Heart rate was monitored using Verity Sense (Polar Electro Oy, Kempele, Finland),
which weighed 17 g. The device was fully charged, and the inner time was adjusted to theStandard Time daily. The participants attached the device to their non-dominant forearmwith an elastic band. Heart rate data for every second (expressed as beats/min) wererecorded in the device, which was downloaded to the computer using Polar FlowSync(Version 3.0.0.1337, Polar Electro Oy, Kempele, Finland) on each measurement day. The
real-time heart rate was monitored using Bluetooth and K5.
2.2.3. Accelerometer
The participants attached 20 g of the wGT3X-BT with three-axis accelerometric sensors(ActiGraph, Pensacola, FL, USA) to their right waist with an elastic band. The sampling rate
was 30 Hz, and we used 1 s epoch counts of acceleration to measure the physical activity.
The device was completely charged and adjusted daily. Memorized data were downloaded
after each measurement day using ActiLife (Version 6.13.4, ActiGraph, Penacola, FL, USA).
The three-axis counts were summarized as vector magnitudes (VM; counts/s).
VM =qc x 2 + cy 2 + cz 2

(1)

Acceleration counts are used to predict METs and energy expenditure per body mass,
and we used a unit “counts” in this study.
2.2.4. Global Navigation Satellite System Device and Video Camera
A global navigation satellite system (GNSS) device, Trimble TDC150 (Nikon-Trimble
Co., Ltd., Tokyo, Japan), was used to trace the geographical movements of the participants.
Latitude, longitude, and height were recorded in seconds and downloaded from a CSV
file. This device, weighing 0.85 kg, was fixed on a frame pack, which weighed 20 kg in
total. Participants’ behaviors were recorded in the video camera memory to define the time
points of events, such as starts, turns, and ends of walking.

Forests 2023, 14, 1038

4 of 102.2.5. Body Height and Weight
Before wearing the devices, body height was measured without boots to 0.1 cm using
a stadiometer. Weight was measured with fieldwork clothing and boots (0.1 kg using anelectronic scale, AD6201 (A&D Co., Ltd., Tokyo, Japan)). Body weight was calculated bysubtracting 3 kg from the measured weight.
2.3. Statsitical Analysis
The intensity of the activity was compared between measurements. For standard
activity intensity, the metabolic equivalents of tasks (METs) were calculated from oxygen
consumption as follows:
O2 mL·min−1(2)body weight (kg) 3.5 mL·kg−1 ·min−1
The heart rate index (HRI) was calculated as the mean heart rate during walking
divided by the resting heart rate (HRrest ), which was defined as the lowest heart rate while
the preceding rest was on a chair. Heart rate and oxygen consumption did not reach a steadystate during short walking on slopes; they increased after the start and gradually decreased
during the following resting period. First, we calculated the cumulative amounts of oxygen
consumption and HR exclusively during each of the three walking types because the restingheart rates after walking were not consistent for each participant. Next, we calculated thecumulative heart rate index and oxygen consumption as those during walking plus thosein the recovery period after walking, minus the resting amounts. For example,
walk .

rest .

sec

sec

.

∑ VOwalk (mL/ sec) + ∑ VOrest (mL/ sec) − VOrest (mL/ sec) × Rest(sec).
walk

rest

sec

sec

∑ HRIwalk + ∑ HRIrest − HRIrest × Rest(sec).

(3)

(4)

The walking periods of the accelerometer data were clearly demarcated, differing only
by one or two seconds from the video camera recordings, and the VM during rest was zero.
The cumulative vector magnitudes divided by each walking period (counts/min) were.
compared with the METs calculated from VO2 .
Each participant received three data points for each activity intensity variable. HRI
and VM per minute were examined using Pearson’s correlation coefficients, with oxygen
consumption as the MET. The Deming regression method was also used for comparison
with METs because of possible measurement errors in the two variables [26]. METs estimated from HRI using a previously proposed formula [20] were compared with METs.
calculated from VO2 (Equation (5)) using a Bland–Altman plot [27]:
METs = HRI × 6 − 5(5)The GNSS data from the device were compared with the land formation in the test
field. The data were expected to obtain work in physics from the vertical transfer and bodyand appurtenant weights (frame pack, clothing, shoes, and other equipment). However,the vertical movement was not accurately detected; therefore, we did not analyze the GNSS
data further. Graphical and statistical analyses were conducted using R (Version 4.2.1, R
Core Team, Vienna, Austria) and RStudio (Version 2022.12.0, Posit Software, Boston, MA,
USA). The significance level was set at p = 0.05.
3. Results
The participant characteristics are presented in Supplementary Table S1. On the firstday, there was a small amount of precipitation, and the ground was a little dirty until theForests 2023, 14, 1038

during the measurement was 22–25 °C, and the relative humidity was 53–72%. We missed
the GNSS recordings for one participant and video recordings for another participant.
．
The oxygen consumption (VO2), heart rate (HR), vector magnitude (VM) of theaccelerometer, and vertical (height) movement from the GNSS data were visuallyof 10compared at the same timescale (Figure 1 and Supplementary Figure S2). The start5 and．
．
end of each activity and rest coincided with the inflection points of VO2 and HR. VO2 andHR during activity increased in the order of flat, 15° incline, and 30° incline, but VM wassecondday, whenthree outof sixparticipantsweremeasured.
Three participantswere
notsignificantly
different
amongthethree walking
types.
Additionally,
VM was stableatmeasuredinthemorning
and
the
other
three
in
the
afternoon.
The
ambient
temperaturezero during rest. The height movement
clearly
depicted
up
and
down
transfers,butthe◦ C, and the relative humidity was 53–72%. We missed
during themeasurement
22–25 inbaseline
wasunstable, as wasobserved
all participants. In the following analysis, we used
． GNSS recordings for one participant and video recordings for another participant.
theVO2, TheHR, oxygen
and VM,consumption
excluding the(VOheightfrom the GNSS vector
device.magnitude (VM) of the ac．
2 ), heart rate (HR),The activity
as METsmovement
calculatedfrom
fromthe
VOGNSS
2 was dataplottedagainstHRIcompared
and VMcelerometer,
and intensity
vertical (height)
werevisually(Figure2).
HRI
wassignificantly
correlated
with
METs
(r
=
0.932,
p<0.001),
andregression
at the same timescale (Figure 1 and Supplementary Figure S2). The start and end of each.
.
coeﬃcients
6.38 (95% withconfidence
limits, 4.71,
8.06)
for2 slope
andVO
−5.49
(−7.97, −3.02)
activity andwere
rest coincided
the inflection
pointsof VO
and HR.
2 and HR during◦◦forintercept.
In contrast,
VM of
did
not
with30METs(r =but
0.354,= 0.150).
The
activity
increasedin the orderflat,
15correlate
incline, andincline,
VMpwasnot significoefficientsof itsamong
regression
model
were notsignificant(0.0012(−0.0001,
0.0026)for duringslope,
cantly different
the threewalking
types.
Additionally,VM
wasstableatzero．
rest.0.402
The heightclearly depicted
and downtransfers,
but
theHR,
baseline
wasand(−4.258,movement
5.062) for intercept).
When upinvolvinglag time
for VO
2 and
HRI and.
VM
were assignificantlywith InMETs,
but werelower wethanthoseunstable,observed in correlated
all participants.
the following
analysis,usedVO2measured, HR, andexclusively
during(r = 0.843,
p < 0.001;
r = 0.506, p = 0.032, respectively).
VM, excluding
the activity
height fromthe GNSS
device.

Figure 1.
1. Time-course
Time-course ofof measurement
measurement variables.
variables. From
From thethe top
top toto thethe bottom,
bottom, oxygen
oxygen consumption
consumption
Figure．
(VO
mL/min)ininbreath
breathby
bybreath,
breath,heart
heartrate
rate(beats/min,(beats/min,bpm)bpm)ininsecond,second,vector
vectormagnitude
magnitude(VM;(VM;(V
O22; ;mL/min)counts/sec) of the accelerometer in one second epoch, and height from the global navigation satellite
system device in meter for one participant. The participant walked on flat, 15◦ and 30◦ slope grounds
after resting on chair. Before walking on flat ground and a 30◦ slope, the participant moved to eachstarting point.
.

The activity intensity as METs calculated from VO2 was plotted against HRI and VM(Figure 2). HRI was significantly correlated with METs (r = 0.932, p < 0.001), and regression
coefficients were 6.38 (95% confidence limits, 4.71, 8.06) for slope and −5.49 (−7.97, −3.02)
for intercept. In contrast, VM did not correlate with METs (r = 0.354, p = 0.150). The
coefficients of its regression model were not significant (0.0012 (−0.0001, 0.0026) for slope,.
and 0.402 (−4.258, 5.062) for intercept). When involving lag time for VO2 and HR, HRI
and VM were significantly correlated with METs, but were lower than those measured
exclusively during activity (r = 0.843, p < 0.001; r = 0.506, p = 0.032, respectively).

counts/sec) of the accelerometer in one second epoch, and height from the global navigation satellite
system device in meter for one participant. The participant walked on flat, 15° and 30° slope grounds
after resting on chair. Before walking on flat ground and a 30° slope, the participant moved to eachstarting point.

Forests 2023, 14, 1038

6 of 10(a)(b)

Forests 2023, 14, x FOR PEER REVIEW

7 of 11

Figure 2. Deming regression models and Pearson’s correlation coefficients of activity intensity:．

Figure2. Deming
regression
modelsandPearson’sof activity
intensity:(a)(a) heartrate index(HRI)
metabolicequivalentsof tasks(METs)
calculated
from oxygen conTheactivity
intensities
asand
METs
calculatedcorrelationfrom the coefficients
Equation
(5)
and
from
VO2 wereheartrateusingindex the(HRI)
and metabolic
equivalentsof tasks(METs)
calculated
from theoxygenplottedBland–Altman
method(Figure3).
The
difference
betweentwo
sumption; and (b) vector magnitude (VM) of accelerometer and METs from oxygen consumption.
consumption; and (b) vector magnitude (VM) of accelerometer and METs from oxygen
methods was 0.134 METs with 95% confidence limits, −0.543, and 0.811 METs.
.
consumption.

The activity intensities as METs calculated from the Equation (5) and from VO2 wereplotted using the Bland–Altman method (Figure 3). The difference between the two
methods was 0.134 METs with 95% confidence limits, −0.543, and 0.811 METs.

Figure 3. Bland–Altman plot of METs calculated between oxygen consumption and HRI. A red
FigureBland–Altman
plot difference,of METs calculated
betweenoxygenlines
consumption
HRI. A red limits of difference.
dotted3.lineis the mean
and blackdottedare 95%andconfidence
dotted line is the mean difference, and black dotted lines are 95% confidence limits of difference.

4. Discussion
In this pilot study, the HRI method was found to be the best for estimating the energy
expenditure rate of forestry workers. We simulated the movement of forestry workers
carrying 20 kg on their backs on flat and inclined ground. The activity intensity in METs

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4. Discussion
In this pilot study, the HRI method was found to be the best for estimating the energy
expenditure rate of forestry workers. We simulated the movement of forestry workers carrying
20 kg on their backs on flat and inclined ground. The activity intensity in METs estimated from
HRI using the equation previously proposed by Wicks et al. [20] was significantly correlatedwith that from the measured oxygen consumption. In addition, most of the estimates were
within ±1.0 METs of measured intensity on the Bland–Altman plot.
Heart rate is used to estimate energy expenditure because of its well-known linear
relationship, but at a low level of activity, a linear association between heart rate and oxygen
uptake O2 breaks down. The heart rate above which the heart rate is a good predictorof energy expenditure and below which the resting metabolic rate is used as energy
expenditure is called the flex heart rate, HRflex . A study of 167 adults proposed that heart
rate monitoring is a useful method for assessing energy expenditure in epidemiologicalstudies [28]. Although most participants (70%) from five experimental studies revealed
that the total energy expenditure estimated using the flex heart rate method was within±10% of the standards [29], the flex heart rate and a linear relationship above the flex heart
rate are vulnerable to between-individual variation [19,30].
Wicks et al. evaluated the relationship between heart rate and oxygen consumption in
exercise training using three variables: absolute HR (HRabsolute ), HRabsolute minus HRrest(HRnet ), and the ratio to resting HR (HRI) [20]. They selected 60 articles involving healthypeople and cardiovascular patients whose ages ranged from 27 to 85 years. Among theregression models with three variables, the model with HRabsolute explained 76.9% of thevariation; however, it was the lowest among the three methods. The model using the HRI
had the highest variance explanation R2 value of 95.2%. The HRI method was validated
using treadmill tests for professional rugby and soccer football players [22,23]. HRI is apossible indicator of the intensity of physical activity patterns. The results of this study
revealed a high correlation between HRI and oxygen consumption during the simulated
walk of forestry workers. Unlike previous reports, in which the workload was applied
using a treadmill or cycle ergometry in a well-conditioned room [20,22,23], this study
simulated forestry work. The HRI method must be applicable to real forestry fields undervarious conditions.
Factors influencing heart rate should be considered, such as environmental and genetic
factors, and medication. The HRflex method was reviewed by Leonard, who evaluated it
under several conditions, including age, sex, ethnicity, season, and lifestyle [31]. Medical
condition, particularly using β-blockade, may create problems in field assessment. In aprevious report, however, pathophysiological condition and using β-blockade did notaffect the relationship between the HRI and the oxygen consumption [20].
At forestry work sites, individual resting HR may be difficult to obtain. Japanese
forestry workers usually move between their home and the site and take breaks outdoors,
but not in field offices. The HRflex method uses the highest HR for lying or sitting and thelowest HR for activity, and requires individual calibration [18,19]. In contrast, the lowest
HR at rest, used to calculate the HRI, may be relatively easy to obtain before work or on
breaks. The time course of the HR suggested a shorter resting period before measurement.
Calculating HRI, unlike the relative HR, does not require a submaximal heart rate, which
may be influenced by age, sex, and fitness.
An accelerometer-based approach for assessing physical motion cannot correctly
discriminate activity types in terms of the magnitude proportional to energy expenditure [32,33]. Several trials have been conducted to estimate the energy expenditure fromaccelerometer data [34]; however, these have not yet been used in the field. Energy expenditure in ascending and descending inclines is not mirror symmetry [24]; oxygen
consumption on a −8% descending incline is the least and increases on a more descending
incline. Global positioning systems [35], barometric sensors [36], and accelerometry are
expected to accurately estimate the energy expenditure. However, in this study, the GNSS
receiver could not accurately measure vertical movements of 8 m up and down. This device

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had no barometric sensors, but the portable indirect calorimeter K5 had them. However,even the barometric altitude at K5 was unstable during each measurement.
A heart rate monitor is a light device that may place a slight burden on the participants.
The device used in this study is worn with an arm strap attached to the forearm or upperarm. It may be more applicable in forestry fields than chest-strapped ones because there
is no need to take off clothes, and it may not disturb the use of hand tools as much asa wrist-worn monitor. Regarding where the monitor is worn, the accuracy of various
forestry tasks is warranted in the future [37]. Some conditions that influence the HR should
be considered. First, although it can estimate high workloads in forestry activities, HRduring periods of low workload and recess may be influenced by non-work-related factors
when considering daily work. We need to determine the energy expenditure specific tohard forestry work. Knowledge of the workload regarding energy expenditure can help
us properly allocate jobs to workers, deploy workers for their fitness, and prevent heat
illness by predicting thermogenesis from the workload. Second, terrain surface conditions
influence the energy cost [38], but this factor may not influence the relationship between
HR and oxygen consumption. Third, in some studies, such as felling, pruning, and mowing,
upper limb activity is considered the main cause of energy expenditure. In contrast to
motion sensors and GNSS devices, HR monitoring, as an estimator of energy expenditure,
has advantages for forestry workers.
This study has some limitations regarding the interpretation and adoption of theresults in the field. First, the sample consisted of only male participants and the sample
size was small. However, male workers were dominant in Japanese forestry, and the ageof the participants varied widely. The results of the association between heart rate andmetabolic rate were in accordance with a previous formula [20]. Several studies havereported small sample sizes in actual forestry fields (5–13 participants) [10–13]. A low riskof a statistical type 2 error due to small sample size, which indicates a small measurement
error in correlation, may be a strength for implementing the main study. Second, thetest field was not the same as the forestry field. Forestry activities are a combination ofcomplicated motions, rather than walking. We did not have information on how and whenheart rate monitoring was interrupted during forestry work activities due to loss of contact
with the skin. For one participant, the heart rate decreased to zero when the weights were
pulled down, and the elastic band of the monitor was soon tightened.
5. Conclusions
The HRI method was the best estimator of METs, similar to the energy expenditurerate in this pilot study. Accelerometry was insufficient to discriminate between activity
types, and estimated energy expenditure less efficiently than the HRI method. The GNSS
device did not accurately trace the vertical (height) movements; therefore, the work in
physics could not be calculated. The HRI method uses a light device to monitor HR, which
imposes only a small burden on forestry workers in the field. The arm-worn HR monitor
used in this study is more convenient for forestry workers in the field because they do nothave to wear a chest strap under their shirt. Simultaneous and repeated measurements
using a low-cost device by a team of forestry workers will provide valuable information
about various forestry tasks throughout the year. This study bridges the gap between
laboratory settings and HRI. The HRI method can be used to estimate the workload inforestry work and can present the workload of each forestry task and activity as a physicalactivity level (MET) in future studies in the field. MET as a common unit will be practicalin the prevention of fatigue and injury, healthy behavior related to energy and nutrition
intake, and heat-related illness.
Supplementary Materials: The following supporting information can be downloaded from: https://www.mdpi.com/article/10.3390/f14051038/s1, Table S1: Characteristics of the participants; Figure
S1: Participants during the measurement; Figure S2: Time course of measurement variables ofother participants.

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Author Contributions: Conceptualization, M.O. and Y.K.; methodology, M.O., Y.K. and Y.I.; formal
analysis, M.O.; resources, Y.I.; data curation, M.O. and Y.K.; writing—original draft preparation, M.O.;
writing—review and editing, M.O., Y.K., H.T., Y.F. and Y.I.; visualization, M.O.; project administration,
M.O.; funding acquisition, M.O., Y.K., H.T. and Y.F. All authors have read and agreed to the publishedversion of the manuscript.
Funding: This research was funded by the Japan Organization of Occupational Health and Safety(no grant number).
Data Availability Statement: The data presented in this study are available upon request from thecorresponding author. Data are not publicly available because of the identifiability of the participants.
Acknowledgments: We appreciate all participants of this study. We also thank Katsuhiro Kishinoue
in the Yamaguchi Prefectural Center of Agriculture and Forestry Technology for data collection and
Shigeki Aoki in the TOYO MEDIC Co., Ltd., for instructions to use the portable indirect calorimeter.
Conflicts of Interest: The authors declare no conflict of interest. The funder had no role in the designof the study, collection, analyses, or interpretation of data, writing of the manuscript, or the decision
to publish the results.

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令和４年度 産業保健調査研究－林業従事者の傾斜地作業負荷測定方法開発のためのパイロット研究－
著

者

発行者

奥田 昌之（山口産業保健総合支援センター
独立行政法人

産業保健相談員）

労働者健康安全機構

山口産業保健総合支援センター
753-0051
発行

山口市旭通り二丁目９-19

令和６年３月山口建設ビル４階