Factors associated with high physical exertion during healthcare work: Cross-sectional study among healthcare workers

Abstract

BACKGROUND:

High physical exertion during healthcare work is a documented risk factor for musculoskeletal pain, long term sickness absence and disability pension. Understanding the underlying factors of physical exertion is important to make the necessary preventive efforts in the working environment.

OBJECTIVE:

This study investigates factors associated with high physical exertion during healthcare work.

METHODS:

A total of 2047 Danish health care workers replied to a questionnaire about work and health. Associations (odds ratios; OR) of physical exertion (outcome variable) with the number of patients and self-reliant patients, frequency and type of assistive device use, BMI, leisure time activity, smoking, and age were modelled using mutually adjusted binary logistic regression.

RESULTS:

Factors associated with high physical exertion (OR and 95% CI) were high frequency of daily patient transfers 1.35 (1.23 – 1.48), less self-reliant patients 0.74 (0.62 – 0.89), less frequent use of necessary assistive devices 1.82 (1.50 – 2.21), as well as more frequent use of sliding pieces 1.23 (1.04 – 1.46), wheelchairs 1.23 (1.02 – 1.49), bed adjustments 0.88 (0.77 – 1.00) and intelligent beds 0.83 (0.71 – 0.95) during patient transfer. Age and lifestyle factors (BMI, smoking, and leisure time physical activity) were not associated with high physical exertion.

CONCLUSIONS:

The character of patient transfer specific healthcare work is associated with increased odds for high physical exertion whereas life-style factors are not. Thus, proper use of specific assistive devices and avoiding uneven distribution of difficult patients through appropriate planning may be protective strategies for lowering physical exertion during healthcare work.

Keywords

Musculoskeletal pain ergonomics occupational nursing assistive devices

1 Introduction

Although the global shortage of the healthcare work force is caused by multiple factors [1], one of the driving forces seems to be related to the character of the work and the associated health-related consequences [2]. Healthcare work is physically strenuous and includes, for example, frequent exposure to sudden and high loadings of the spine while twisting and bending the back during patient transfer [3]. Prospective studies have shown that physical exertion during healthcare work increases the risk of musculoskeletal pain [4 –8], long term sickness absence [9, 10] and disability pension [11 –13].

Laboratory studies show that physical exertion is closely related to the severity of work demands when expressed relative to the individuals’ physical capacity [14 –16]. Moreover, we have previously observed, in full working day measurements, that performing lifting tasks with high muscle activity in the neck muscles increases the odds for experiencing high physical exertion by 18 fold among blue-collar workers [17]. Hence, physical exertion seems to reflect the balance between the specific physical demands of the work and the individuals’ capacity to perform and cope with the work tasks. Thus, physical exertion during healthcare work can, theoretically, be modulated by lowering the physical work demands; for example through increased use of assistive devices or by increasing the physical capacity (i.e. muscle strength, cardiovascular fitness) through workplace physical exercise initiatives. However, it may be speculated that factors such as age and lifestyle factors such as BMI (or body weight), smoking status and level of physical activity during leisure time may influence physical exertion and the associated balance between individual capacity and work demands.

We have previously shown that performing 20 minutes of physical exercise per week with colleagues at the workplace reduces physical exertion during patient transfer [18]. On the other hand, lowering the biomechanical load and the risk of back injury through increased use of assistive devices does also seem to be a promising strategy for healthcare workers, yet, the quality of evidence is somewhat low and conflicting [19, 20]. Nevertheless, little is known about which specific assistive devices provide the greatest reductions in physical exertion and the associated risk of musculoskeletal disorders.

Several factors may altogether influence the level of physical exertion during work. Identifying and understanding the underlying factors associated with high physical exertion during work is, therefore, of great importance for developing and implementing effective preventive strategies in the working environment. Hence, the aim of this study was to investigate which factors are associated with high physical exertion during healthcare work.

2 Methods

2.1 Study design and participants

This cross-sectional study includes questionnaire data from 2047healthcare workers from Danish hospitals. The overall aim of the survey was to investigate the influence of work environment factors on the prevalence of low back pain and back injury. Questionnaire surveys were distributed in the summer of 2017 and includes data from 2047 females working as nurses, nurses' aides and assistants, physio- or occupational therapists, radiographers or porters, who engage in daily patient transfer (inclusion criteria). There were no further exclusion criteria for the participants who met the inclusion criteria. From this population, we have previously reported associations between stress and sleep as well as between pain and sleep [21].

2.2 Outcome variables

2.2.1 Physical exertion

Participants were asked to rate their physical exertion using the following question: “How would you rate your physical exertion while working with the patients?” Subjects replied on a scale with 7 exertion levels taken from the BORG CR10 scale (0–10): “very, very light” (RPE = 0.5), “very light” (RPE = 1), “light” (RPE = 2), “moderately strenuous” (RPE = 3), “strenuous” (RPE = 5), “very strenuous” (RPE = 7), and “very, very strenuous” (RPE = 9) [22]. The Borg RPE scale has been validated in many different contexts to measure physical exertion during manual handling tasks [17 , 23].

2.3 Control variables

2.3.1 Lifestyle factors

Physical activity during leisure time was rated by answering the following question: “Within the past year - which best describes your physical activity in your spare time?” with one of the following response options: 1) Read, watching television or other sedentary occupation, 2) Stroll, cycle or have other light exercise at least 4 hours per week (including Sunday trips, light gardening and cycling / walking to work), 3) Doing exercise or doing heavy gardening or the like at least 4 hours a week, 4) Exercising hard or practicing competitive sports regularly and several times a week. The four options were categorized as 1 = sedentary, 2 = light exercise, 3 = moderate exercise and 4 = intense exercise. The participants reported their smoking status by answering one of the following categories: “No, never”, “Ex-smoker” or “Yes”. Body mass index (BMI, kg/m²) was calculated as body mass in kg divided by the squared participant height in meters.

2.3.2 Patient transfer

The participants reported their frequency of daily patient transfer by answering the question:

“How many patients do you transfer per day? (if you transfer the same patient more than once per day, it counts as more patients)” with possible response options ranging from 1) None, 2) Less than one per day (e.g. 2-3 per week), 3) 1-2 per day, 4) 3-4 per day, 5) 5-6 per day, 6) 7-8 per day. 7) 9-10 per day to 8) More than 10 per day.

Frequency of patient transfer without the use of assistive devices was assessed by asking: “How often do you transfer a patient without the necessary assistive devices? (think of situations where you should use assistive devices)” with five possible response options ranging from 0/4 (almost never) to 4/4 (every time).

Self-reliance of the patients was evaluated with the following question: “How many of your patients are so self-reliant that it is not necessary to use assistive devices during transfers? using five response options ranging from 0/4 (virtually none) to 4/4 (all patients).

Frequency of patient transfer performed together with one or more colleagues was evaluated with the question “How often are you more than one healthcare worker to perform the transfer?” with five possible response options ranging from 0/4 (almost never) to 4/4 (every time).

Quantitative use of specific assistive device was assessed for 15 devices: 1) Belt, 2) Patient walking aids, 3) Sliding piece, 4) Floor hoist, 5) Wheelchair, 6) Ceiling hoist, 7) Transfer board, 8) Transfer platform, 9) Sling, 10) Regular bed, 11) Intelligent bed, 12) Standing hoist with sling, 13) Toilet chair with tip function without electricity, 14) Toilet chair with tip function with electricity, 15) Turn sheet with electricity and 16) Turn sheet without electricity using the following question with five possible response options ranging from 0/4 (almost never) to 4/4 (every time): “How often do you use the following assistive devices for patient transfer?”.

2.4 Statistics

Associations between physical exertion (outcome variable), lifestyle factors and patient transfer factors were modelled and mutually adjusted using binary logistic regression (Proc Genmod, SAS version 9.4). Values are presented as odds ratios (OR) and 95% confidence intervals (CI).

3 Results

This questionnaire survey was completed by 2047 healthcare workers with daily patient transfer of which 1789 had daily patient transfer and answered. Table 1 shows the total number of answers per question. Use of regular beds (2.82 times out of every 4 patient transfers) and sliding pieces (2.80 out of 4) were the most frequently used assistive devices during patient transfers, whereas belts (1.02 out of 4) and electrical turn sheets (1.04 out of 4) were the most seldom used (Table 1).

Table 1
Participant demographics, lifestyle- and patient transfer factors as well as use of specific assistive devices. The scale for “x out of 4” is from 0 to 4 with intervals of 1

Mean Std Dev

Age and lifestyle Age (years) 45.54 11.03

BMI (kg/m^∧2) 25.05 4.79

Smoking status (1 = yes, 2 = no) 1.91 0.28

Physical activity (1 = sedentary, 2 = light, 3 = moderate, 4 = intense) 2.28 0.61

Patient transfer Physical exertion during patient transfer (CR10 scale) 6.10 2.66

Amount of daily patient transfer (N) 3.99 2.10

Frequency of patient transfer without assistive devices (x out of 4) 1.89 0.86

Frequency of patient transfer involving several healthcare workers (x out of 4) 3.27 1.23

Frequency of self-reliant patients (x out of 4) 2.84 1.25

Use of assistive devices Belt (x out of 4) 1.02 0.17

Patient walking aids (x out of 4) 2.15 1.24

Sliding piece (x out of 4) 2.80 1.25

Floor hoist (x out of 4) 1.34 0.72

Wheelchair (x out of 4) 2.12 1.05

Ceiling hoist (x out of 4) 1.88 1.11

Transfer board (x out of 4) 1.85 1.13

Transfer platform (x out of 4) 1.30 0.67

Sling (x out of 4) 1.80 1.08

Regular bed (x out of 4) 2.82 1.47

Intelligent bed (x out of 4) 1.70 1.27

Standing hoist with sling (x out of 4) 1.16 0.44

Toilet chair with tip function without electricity (x out of 4) 1.34 0.80

Toilet chair with tip function with electricity (x out of 4) 1.13 0.50

Turn sheet with electricity (x out of 4) 1.04 0.30

Turn sheet without electricity (x out of 4) 1.11 0.45

		Mean	Std Dev
Age and lifestyle	Age (years)	45.54	11.03
	BMI (kg/m^∧2)	25.05	4.79
	Smoking status (1 = yes, 2 = no)	1.91	0.28
	Physical activity (1 = sedentary, 2 = light, 3 = moderate, 4 = intense)	2.28	0.61
Patient transfer	Physical exertion during patient transfer (CR10 scale)	6.10	2.66
	Amount of daily patient transfer (N)	3.99	2.10
	Frequency of patient transfer without assistive devices (x out of 4)	1.89	0.86
	Frequency of patient transfer involving several healthcare workers (x out of 4)	3.27	1.23
	Frequency of self-reliant patients (x out of 4)	2.84	1.25
Use of assistive devices	Belt (x out of 4)	1.02	0.17
	Patient walking aids (x out of 4)	2.15	1.24
	Sliding piece (x out of 4)	2.80	1.25
	Floor hoist (x out of 4)	1.34	0.72
	Wheelchair (x out of 4)	2.12	1.05
	Ceiling hoist (x out of 4)	1.88	1.11
	Transfer board (x out of 4)	1.85	1.13
	Transfer platform (x out of 4)	1.30	0.67
	Sling (x out of 4)	1.80	1.08
	Regular bed (x out of 4)	2.82	1.47
	Intelligent bed (x out of 4)	1.70	1.27
	Standing hoist with sling (x out of 4)	1.16	0.44
	Toilet chair with tip function without electricity (x out of 4)	1.34	0.80
	Toilet chair with tip function with electricity (x out of 4)	1.13	0.50
	Turn sheet with electricity (x out of 4)	1.04	0.30
	Turn sheet without electricity (x out of 4)	1.11	0.45

Table 2 shows the association (odds ratios) between age and lifestyle factors, patient transfer factors and assistive device used and high physical exertion (Borg > 4). In general, patient transfer factors and specific type of assistive device use were associated with high physical exertion, whereas age and lifestyle factors were not.

Table 2

Odds ratio (OR) for high physical exertion. CI; confidence interval. For the categorical variables it is the OR for a change of 1 point on the scale, e.g. from 2 to 3 on a 4-point scale

		OR (95% CI)	p-value
Age and lifestyle	Age (years)	1.00 (0.99 – 1.02)	0.9816
	BMI (kg/m^∧2)	1.00 (0.97 – 1.03)	0.9804
	Smoking status (1 = yes, 2 = no)	1.44 (0.82 – 2.51)	0.2036
	Physical activity (1 = sedentary, 2 = light, 3 = moderate, 4 = intense)	0.81 (0.62 – 1.05)	0.1077
Patient transfer	Frequency of daily patient transfer (N)	1.35 (1.23 – 1.48)	< 0.0001
	Frequency of patient transfer without assistive devices (x out of 4)	1.82 (1.50 – 2.21)	< 0.0001
	Frequency of patient transfer involving several healthcare workers (x out of 4)	0.88 (0.74 – 1.04)	0.1395
	Frequency of self-reliant patients (x out of 4)	0.74 (0.62 – 0.89)	0.0013
Use of assistive devices	Belt (x out of 4)	1.69 (0.88 – 3.23)	0.1128
	Patient walking aids (x out of 4)	1.02 (0.87 – 1.21)	0.7893
	Sliding piece (x out of 4)	1.23 (1.04 – 1.46)	0.0169
	Floor hoist (x out of 4)	1.00 (0.80 – 1.24)	0.9971
	Wheelchair (x out of 4)	1.23 (1.02 – 1.49)	0.0350
	Ceiling hoist (x out of 4)	1.27 (0.98 – 1.63)	0.0680
	Transfer board (x out of 4)	1.12 (0.95 – 1.31)	0.1791
	Transfer platform (x out of 4)	0.88 (0.70 – 1.11)	0.2745
	Sling (x out of 4)	0.90 (0.70 – 1.16)	0.4161
	Regular bed (x out of 4)	0.88 (0.77 – 1.00)	0.0429
	Intelligent bed (x out of 4)	0.83 (0.71 – 0.95)	0.0094
	Standing hoist with sling (x out of 4)	0.92 (0.66 – 1.28)	0.6020
	Toilet chair with tip function without electricity (x out of 4)	1.17 (0.97 – 1.41)	0.0939
	Toilet chair with tip function with electricity (x out of 4)	1.01 (0.77 – 1.32)	0.9584
	Turn sheet with electricity (x out of 4)	0.91 (0.57 – 1.48)	0.7135
	Turn sheet without electricity (x out of 4)	0.82 (0.60 – 1.14)	0.2354

Frequency of daily patient transfer (OR 1.35 (CI 1.23 – 1.48)) and less frequent use of assistive devices (OR 1.82 (CI 1.50 – 2.21)) increased the odds for high physical exertion, whereas frequency of self-reliant patients (OR: 0.74 (CI 0.62 – 0.89)) lowered this risk.

Use of sliding pieces (OR 1.23 (CI 1.04 – 1.46)) and wheelchairs (OR 1.23 (CI 1.02 – 1.49)) for patient transfers were associated with increased risk of high physical exertion. However, use of the regular (OR 0.88 (CI 0.77 – 1.00)) and intelligent bed (OR 0.83 (CI 0.71 – 0.95)) lowered the risk of high physical exertion.

4 Discussion

The present study shows that high frequency of patient transfers, a lower number of self-reliant patients, increased amount and type of assistive devices used, is associated with increased odds for high physical exertion while life-style factors are not.

Not surprisingly, seldom use of assistive devices during patient transfers was associated with increased odds for high physical exertion. As high physical exertion during healthcare work is a risk factor for musculoskeletal pain [4 –8], long term sickness absence [9, 10] and disability pension [11 –13] the present findings support the importance of assistive device use as a strategy for preserving a healthy balance between work tasks and individual capacity among healthcare workers. Moreover, multiple studies have shown a reduction in injury rates after implementation of policies that aim to limit manual patient transfer and increase the use of assistive devices [24 –30]. Notably, several of these studies particularly promote the use of ceiling lifts as a preventive strategy [24 , 31–35]. However, we did not observe an association between the use of ceiling lifts for patient transfers and the odds for experiencing high physical exertion. On the other hand, more frequent use of regular and intelligent beds was shown to reduce the odds in the present study. The very frequent use of regular beds in more than 70 % of all patient transfers, highlights the overall protective mechanisms of proper implementation and use of beds for patient handling.

A somewhat surprising finding was that more frequent use of sliding pieces and wheelchairs increased the odds for high physical exertion. This finding does not necessarily imply that use of these assistive devices increases the risk of injuries and musculoskeletal disorders compared to manual patient transfers. If this was the case, it would indeed constitute a problem, as particularly sliding pieces were very frequently used in 70 % of patient transfers among the present healthcare workers. The increased physical exertion may rather reflect that use of sliding pieces and wheelchairs involves a degree of manual transfer that requires proper patient transfer techniques to minimize the high loadings on the lower back. Accordingly, several studies have observed that even though sliding pieces reduce the friction between the patient and the bed, the transfer technique adopted by the healthcare worker must be sufficient to reduce low back loading and risk of back injuries and musculoskeletal disorders [36 –38]. Similarly, for the wheelchair, which may assist when pushing the patient around, however, the patient needs to be transferred from the bed to the wheelchair and that may be physically exerting and promote large lower back compression forces [39]. Moreover, another explanation for the increased risk of high perceived exertion may be concerning a potential difference in the type of patients (i.e. degree of self-reliance), that are usually transferred using sliding pieces and wheelchairs. Although we adjusted the analysis for self-reliance in general, we did not adjust for patient self-reliance in regard to the specific type of transfer or assistive device used.

Increased number of self-reliant patients was, in fact, shown to decrease the odds for high physical exertion. Thus, engaging the patient to assist in the transfer seems to be a protective action if possible. Also a general high number of daily patient transfers were associated with increased physical exertion, regardless of assistive device use.

Lifestyle factors such as BMI, smoking status and leisure time physical activity and age were not associated with increased risk for higher physical exertion. Thus, being older, having a higher body mass per height and the potential detrimental effects of smoking on physical capacity does not seem to constitute an excessive or additional burden on the present healthcare workers when performing patient transfers. It may, on the other hand, be speculated that older healthcare workers are more experienced and potentially perform patient transfers with superior technique compared to novice healthcare workers, consequently leading to lower physical exertion [31]. In any event, individual variations in transferring technique may compromise the investigated associations.

Adults with higher BMI are generally a bit stronger [40], which may outweigh the extra kilos of individual body weight they need to carry when performing patient transfers. In support of this, it was somewhat surprising to observe that leisure time activity did not seem to benefit the current balance between physical capacity and work demands (i.e. physical exertion during patient transfers). It should be noted that the present data shows a borderline tendency (p = 0.11, OR 0.88 [0.62–1.05]) for an association between reduced odds for high physical exertion and leisure time physical activity. One possible explanation for this lack of significant association may be linked to the type of exercises performed and whether they promote increased muscle strength or cardiovascular fitness. However, context may also be important for the benefits of exercise. Accordingly, our research group has previously shown, that performing strength training exercises together with colleagues at the workplace promoted greater muscle strength gains and lowered pain intensity and physical exertion during patient handling compared to home-based strength exercises (i.e. leisure time physical activity) among healthcare workers [18, 41]. Thus, focusing on multiple dimensions (i.e. increasing the worker capacity through workplace exercise and reducing exposure through improved use of assistive devices) may be a more powerful strategy for reducing low back pain and injuries compared with single strategies that only aim to increase assistive device use [19 , 42].

Lastly, having a high frequency of patient transfers per day, regardless of assistive devices used, increased the odds for high physical exertion. Thus, one potential protective organizational strategy is to avoid a redundant distribution in the number of difficult or less self-reliant patients per healthcare worker through proper planning. One possible solution for this is to score the level of difficulty for the entire department’s patients and provide an even distribution of the patients across the department’s healthcare staff based on their level of difficulty and demands. However, since frequency of patient transfers without assistive device use is 1.83 out of 4 times, identifying barriers and developing solutions for improving the use of assistive devices (i.e. through participatory workshops) [43, 44], seem to be a good place to start when aiming to reduce the risk of musculoskeletal disorders among healthcare workers.

4.1 Strengths and limitations

The recall-bias accompanied with questionnaire designs is an inherent limitation of the study. As eluded to earlier, an apparent study limitation is, furthermore, that frequency of self-reliant patients was assessed in general and not for each specific assistive device. This may compromise the ability to show whether the individual assistive device has a “protective potential” in lowering physical exertion during patient handling or not. Nevertheless, identifying the association for each specific assistive device is a clear strength of the study as it creates a more detailed view of assistive devices used rather than only focusing on assistive devices collectively. Cross-sectional studies in general have the weakness that they do not reveal anything about causality. However, as we were interested in underlying factors for physical exertion during patient handling, a cross-sectional design is necessary to test the associations.

5 Conclusion

This cross sectional analysis shows that transfer specific healthcare work, the number of patients and less self-reliant patients and amount and type of assistive devices used, is associated with increased odds for high physical exertion, whereas life-style factors such as smoking status, BMI and leisure time activities are not. These data not only highlight the importance of assistive device use in general, but also provides an overview of which devices assist the most in lowering physical exertion among healthcare workers.

Conflict of interest

None to report.

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