The effects of muscle biofeedback versus posture biofeedback on pain and functional outcome in smartphone users with chronic neck pain

doi:10.21203/rs.3.rs-7469275/v1

Background: Smartphones have become an essential part of our daily lives, and prolonged use in static neck flexion posture could contribute to chronic neck pain. Objectives: This study compared the effects of two different forms of biofeedback on neck pain, habitual posture adoption and muscle activity during smartphone texting.

Methods: This was a randomised controlled trial with 3 groups. Group 1 involved muscle biofeedback and Group 2 postural biofeedback. Each participant used the biofeedback device for 2 hours daily for 6 weeks. Control group performed stretching exercises at home for 6 weeks. Outcome measures included neck pain score (0-10) and Neck Disability Index (NDI) scores. Muscle activity and neck postural angle during smartphone texting task were also evaluated during pre- and post-intervention.

Results: Both the postural biofeedback and muscle biofeedback groups showed significant reduction in pain and NDI scores in pre-post intervention comparisons (p<0.05), but not in control group. In smartphone texting task, bilateral cervical erector spinae muscle activity and average neck flexion angle showed a trend for greater reduction in Group 1 compared to Group 2. Control group showed no change at all in muscle activity and neck postural angle.

Conclusion: This trial demonstrated the effectiveness of both postural and muscle biofeedback training producing significant reduction in neck pain and comparable extent of change in neck flexion posture and muscle activity during smartphone texting. These results suggest that biofeedback with either device can be a useful tool for self-management of neck pain related to intensive smartphone use.

Neck pain is one of the most prevalent musculoskeletal conditions in modern society [1]. In particular, the increasing problem of chronic neck pain is associated with the constant use of electronic devices such as smartphones and tablet computers both at work and at home. This has been linked to the issue of static posture and increased muscle tension contributing to increased loading in the spine and upper limb regions [2–3]. Maintaining a forward head posture may further increase the mechanical stress on the cervical spinal column and increased tension in the cervical postural muscles such as the cervical erector spinae and trapezius muscles [4–5].

Past research has demonstrated that mal-adaptive postural habits and altered muscle control are key factors associated with chronic neck pain [3, 6–8]. A few studies have in fact demonstrated consistent patterns of increased muscle activity in upper trapezius and cervical erector spinae muscles among office workers with chronic neck pain when they typed on the computer keyboard [6–7]. Recent research also confirmed that this pattern of altered muscle activation is present in symptomatic individuals when they performed texting tasks with touchscreen smartphones [9–11]. Meanwhile, laboratory studies also reported altered neck postural angles among individuals with neck pain when they performed mobile phone or tablet computer tasks [10, 12–13].

Muscle re-education using biofeedback training has been proposed to be one approach to correct the altered muscle activation mechanisms in people with chronic neck pain [14]. Electromyographic (EMG) biofeedback has been reported to have beneficial effects in reducing over-activity of neck-shoulder muscles and decreasing pain [15–16]. It has been reported that computer workers with neck-shoulder pain who underwent EMG biofeedback training plus ergonomic counselling showed significantly decreased pain and disability, as well as significantly reduced long-term sick leave [17–19]. Ma et al. [14] conducted a randomised controlled trial that involved the comparison of the muscle biofeedback intervention against other interventions – active exercises, electrotherapy treatment and education pamphlet. The results showed that the biofeedback group had significantly reduced pain and functional disability compared to the other three intervention groups, withits positive effect well maintained over 6-month post-intervention reassessment.

In recent years, wearable sensors are available on the market that can be attached to the spine to remind the user about prolonged flexion posture, and a vibration signal can be activated when the flexion angle exceeds the threshold angle set for the individual. The present study aimed to compare the effectiveness of two different forms of biofeedback, one being an EMG-based muscle biofeedback and the other a form of postural biofeedback. These two forms of biofeedback interventions were compared to the control group that received standard neck mobility exercises only, for their impacts on the subjective rating of pain and functional status, as well as objective measurements of cervical kinematics and muscle activity during a simulated smartphone texting task.

Study Design and participants

The study was a three-arm randomized controlled trial with three, and the procedures were

conducted according to the CONSORT guidelines. Figure 1 presents the flowchart showing

the exact procedures of the trial. A research assistant was assigned to handle the initial

baseline assessment and conduct the randomization for group allocation. The outcome assessor was blinded to the group allocation of the participants.

A total of 90 participants with a history of neck pain for more than 3 months were recruited by convenience sampling among the university staff and student community. As the study progressed, some participants did not complete the intervention for 6 weeks despite repeated reminders. The numbers of drop-outs were 5, 1 and 3 for Muscle Biofeedback Group, Posture Biofeedback Group, and Control Group respectively. Hence the final number of participants who underwent the post-intervention assessment was 81. A flowchart illustrating the study design is presented in Figure 1.

The inclusion criteria for the participants were: 1) daily smartphone user for more than 2 hours per day; 2) current neck pain (at least 2/10); 3) NDI scored > 8/100. The exclusion criteria were: 1) presence of recent trauma or history of cervical or thoracic surgery; 2) severe arthritis or joint disorders; 3) other chronic diseases affecting the musculoskeletal system.

Participants were also asked to declare whether they were currently receiving any medical treatment or any form of therapy prior to the trial. They were instructed to refrain from these other forms of treatment during the trial period.

[Insert Fig 1 here]

Interventions

Muscle Biofeedback Group

Participants in Muscle Biofeedback group were taught how to use the portable EMG electromyographic (EMG) biofeedback device, the Pathway system (Pathway® MR-20 EMG Systems) with 2 channels (see Figure 2). The bilateral cervical erector spinae (CES) were the main muscles for the biofeedback training, as they are the primary muscles for controlling the head-neck flexion posture against gravity. Surface EMG were attached to standardised positions on the neck, and the participants were trained to apply these electrodes correctly. Participants could move around and continue their normal daily activities. They were instructed to use the biofeedback machine for a total of 2 hours (cumulative) daily for 6 weeks.

The researcher would set the threshold amplitude of the muscle biofeedback training in the first session. The baseline threshold amplitude was determined from the average muscle activity of the CES during the 15-min texting task.

Posture Biofeedback Group

In the first session, participants were instructed on how to wear the sensor Upright Go 2 (UpRight Technologies Ltd.) on the headband (see Figure 2) and to activate the “App” saved on their smartphone. Then they were asked to flex the neck slowly to a certain degree of flexion to set the “threshold” angle. Whenever the user’s neck angle exceeded this threshold angle, a vibration would be emitted in the sensor. It would only be turned off when the user reduced the neck flexion angle below the threshold.

[Insert Fig 2 here]

Control Group

Participants in Control Group were instructed to carry out standard home neck and shoulders exercises on a daily basis for 5 days a week for 6 weeks. The participants in the two intervention groups were also instructed to perform the same exercises as the Control group. During the 6-week intervention period, the participants were advised to avoid any co-interventions for their neck pain.

Outcome Measures

The same outcome measures were evaluated at baseline, after the 6-week intervention and at 6 months’ follow-up for all three groups. Subjective pain scores were recorded using the Numeric Pain Rating Scale (NPRS) from 0 to 10.Neck Disability Index (NDI) in the Chinese version was used as the functional outcome measure [20-21]. Both the NPRS and NDI were evaluated before and after the 6-week intervention as well as 6-month follow-up.

For the biomechanical measurements, surface electromyography (EMG) electrodes and 3D motion sensors were placed on the participants’ neck and upper back regions for recording the data during a 15-min texting task using a standard touch-screen smartphone. This specific assessment was conducted before and after the 6-week intervention.

Surface EMG signals were recorded using Noraxon MyoMotion (Noraxon, USA Inc., USA) with a sampling frequency of 1000Hz. Bilateral cervical erector spinae (CES) at C4, upper trapezius (UT) and thoracic erector spinae (TES) at T10 were selected as the muscles for measurement. The EMG signals were recorded with a sampling frequency of 1000Hz, bandwidth of 10–500Hz and common mode rejection ratio of 85db. The EMG signals received from the transmitter had undergone a 12-bit analogue-to-digital (A/D) conversion at a sampling frequency of 1500Hz. Standard procedures in skin preparation and signal processing were performed as in previous research studies.

Maximum voluntary contraction (MVC) was performed to normalise the EMG signals in the smartphone texting tasks. A handheld dynamometer was used to perform the resistance against the muscle contractions and the force produced in each trial was recorded. For each muscle group, three trials of MVC were performed with a 1-min rest in between. The EMG signals recorded during the simulated smartphone texting task were normalised to the respective MVC value of each muscle.

Four inertial motion sensors (IMU) were placed on the occiput, C7, T7 and T12 to record the spinal movements in three planes in the neck, upper thoracic and lower thoracic segments. These measurements were synchronised with the EMG data during the 15-min smartphone texting task.

Data Processing and Statistical Analysis

Baseline demographic data were summarized in descriptive statistics such as means, standard deviations and percentages. The primary dependent variables in the present study consisted of neck pain score (NPRS) and NDI scores. These outcome measures were compared at three time points: pre-, post-intervention, and at 6 months follow-up (T0, T1, T2). Linear mixed model was used to compare these variables with time (x 3 levels), Group (x3 levels), and Groups effects across time (with control group and T0 as reference). All analysis was two-tailed with the alpha-value set at 0.05 for statistical analysis.

Kinematics and muscle activity data collected were synchronized using Noraxon MR3.10 software. For the surface EMG data of the bilateral CES, TES and UT muscles during the smartphone texting task, the data were normalized to the MVC and then processed for the amplitude probability distribution function (APDF). The 50^th% APDF of each muscle was considered the median or average muscle activity value. These were compared within each group as the pre-post outcome measure. For the spinal kinematics, the mean flexion angle of the 3 spinal regions during the texting task was also compared at pre-and post-intervention.

All data sets underwent Shapiro–Wilk test to determine if they were normally distributed. Repeated Measure ANOVA and independent sample t-tests were performed for data analysis in the study. Tests were conducted to ensure the residual is normally distributed after model fitted. The significant value of p<0.05 was used for all the statistical analyses.

Altogether 81 participants completed the study, with 25 in Muscle Biofeedback Group, 29 in Posture Biofeedback Group and 27 in Control Group.

Participants’ baseline characteristics

The baseline characteristics of the subjects involved in the present study are summarised in Table 1. There were no significant differences between the three groups in age, height, weight, and baseline neck pain scores. There was slight differences in the number of male versus female participants but these were not affecting the outcomes of the study.

[Insert Table 1 here]

Effects of Biofeedback interventions on pain and functional outcomes

All three groups generally reported between 3.0-4.0 out of 10 for their neck pain scores at pre-intervention, and significantly reduced pain scores were reported at post-intervention and at 6 months follow-up (FU). The mean neck pain (NP) scores in all 3 groups comparing the three time points (T0=baseline, T1=post-intervention, T2=6 months FU) are presented in Figure 3. It is apparent that the change in NP is quite similar between the two biofeedback groups, while the change appears smaller in the Control group, especially at T2.

When the NP scores were compared statistically with repeated measure ANOVA to examine within-group and between-group changes over the 3 time points, the full model showed a significantly reduced residual compared to the null model. The post hoc tests showed us that the pre-post differences was greater in Muscle Biofeedback Group compared to Posture Biofeedback Group. The pattern of change in the two biofeedback groups were quite similar with significant reduction between T0 and T1, but not between T1 and T2. This suggests that the reduction in NP score was being maintained at the 6-months follow-up. Control group had significant differences in NP scores between T0-T1, and T1-T2. This suggests that the Control Group had increased neck pain again at 6 months follow-up. Results of the statistical analysis are summarised in Table 2.

For the NDI results, the mean values of the three groups in pre- and post-intervention as well as 6 months FU are presented in Figure 3 which showed that the Muscle Biofeedback group had more apparent reduction in NDI score compared with the Posture Biofeedback group, and Control Group had the least amount of change at post-intervention.

When examined statistically, NDI scores showed statistically significant reduction within all 3 groups for pre-post comparison. However, when comparing the post-intervention to 6 months’ FU data, only the 2 biofeedback groups had significant differences but not the Control group.

[ Insert Table 2 & Figure 3 here]

Effects of Biofeedback interventions on spinal muscle activity and kinematics

Participants in all three groups were asked to perform a smartphone texting task at pre- and post-intervention, in order to evaluate the changes in the spinal muscle activity and kinematics. The three muscle groups selected were important muscles for controlling the cervical and thoracic spine posture. In the CES muscles, it appeared that the Posture Biofeedback Group produced more apparent changes in both the left and right CES muscles compared to pre-intervention. In contrast the change was small in the Muscle Biofeedback group and the Control group (Figure 4).

The UT muscles generally showed a lower level of muscle activity, and there was also more apparent change in the Postural Biofeedback group compared to the other 2 groups. The TES muscles showed the opposite trend with increased muscle activity at post-intervention in all 3 groups.

Statistical analysis with Repeated Measure ANOVA was performed to examine the within group differences in pre-post comparison of each muscle (50^th%APDF values) and the between-group differences (Table 3). The results indicated only the left and right CES muscles showed significant change in the Posture Biofeedback group with p<0.05. The left TES muscle showed significant differences in the Muscle Biofeedback group and the Control group with increased activity at post-intervention. In terms of the group factor in general, there was more variation in the right UT muscle activity on the whole producing a significant difference in the group factor (p=0.023).

For the spinal flexion angles, only the cervical flexion showed a significant change in the Muscle Biofeedback group. The other groups did not show any clear pattern of change at post-intervention.

[Insert Figure 4, and Tables 3, 4 here]

This study was designed to compare the effects of two different forms of biofeedback in influencing the chronic neck pain related to smartphone use. The study also aimed at correcting the posture and muscle activity of the neck when using mobile phones. These interventions were conducted for 6 weeks together with a standard home exercise programme, and these were compared with doing neck exercises alone. The results will be discussed in terms of their clinical implications in the following sections.

Effects of muscle biofeedback

The current study applied the muscle biofeedback on the CES muscles as these muscles have an important role in controlling neck flexion posture in smartphone users. Previous research has generally applied the biofeedback intervention on upper trapezius (UT) muscles, and the present study is one of the first to apply the biofeedback on the cervical erector spinae muscles. The present results demonstrated a significant effect in reducing neck pain and improving neck function (NDI) as well as an improvement in neck postural awareness and motor control at post-intervention. The present results are consistent with those of previous research on applying muscle biofeedback training in office workers.

In the present study, the Pathway MR-20 device was used for the muscle biofeedback as this device allows the selection of muscle activity level to be set as the threshold of feedback signal. This function may be important to accommodate individual differences in habitual muscle activity levels for motor control. Earlier research on muscle biofeedback adopted a fixed threshold method [22] and this may not be so effective in addressing individual variations in muscle activity during functional tasks. In the study by Ma et al. [14], the participants had significant reduction of neck pain after the 6 weeks’ biofeedback training on the bilateral upper trapezii muscles using the same Pathway MR-20 device. To our knowledge, the present study is one of the first to perform biofeedback to “down-train” the bilateral cervical erector spinae muscles, with the aim to induce muscle relaxation in these muscles and improve neck posture in smartphone users.

Effects of postural biofeedback

A previous study by Lin and Peper [9] demonstrated a positive effect of using the “first-generation” Upright GO for 4 weeks to improve quality of life measures. The present results support the findings of Lin and Peper [9], that the use of this postural correction device is effective for improving the individual’s self-awareness of static neck posture, and contributes to long-term improvement in posture and motor control in smartphone users.

The present study showed that using the wearable posture-tracking device for 2 hours daily for 6 weeks was able to reduce neck muscle activity as well as influencing the neck posture. The significant reduction in neck pain and neck disability scores were similar to that of the muscle biofeedback group. Together these results may suggest that the postural biofeedback intervention can be equally effective as the muscle biofeedback training.

Comparing the posture biofeedback and muscle biofeedback interventions

The results of the two groups in terms of the changes in muscle activity and spinal kinematics showed that both forms of biofeedback can have more widespread effects than what they are purported to do. For example, the posture biofeedback group produced some significant reduction in CES muscle activity, while the muscle biofeedback group also revealed a reduction in neck flexion angles during smartphone texting. These results may be accounted for by the close relationship between muscle activity and joint movements as they are intrinsically related. Hence it can be suggested that the two methods can be equally effective in influencing the motor control of individuals which has an ultimate effect on their pain-generating mechanism.

While the present study comparing the two methods of biofeedback has not demonstrated major differences in the outcome measures, there are some differences in their application methods. The Upright Go device is more user-friendly and economical, and it can be adopted as a form of self-management for postural correction. The participants also commented that the Upright Go device was easier to use, compared to the muscle biofeedback device. The muscle biofeedback device operates on measuring EMG signals which can be subject to noise issue, and it is less convenient to apply the electrodes on the neck region due to the presence of hair and skin folding in that region of the spine.

The present study compared the effects of two different biofeedback interventions against doing exercises alone on neck pain and motor control in symptomatic smartphone users. Both the postural biofeedback and muscle biofeedback interventions were able to produce significant reduction in neck pain and neck disability and these effects were maintained for up to 6-month follow-up. In contrast, doing exercises alone was found to produce smaller and more transient effects on pain reduction. Both forms of biofeedback were able to produce some changes in muscle activity and spinal kinematics during smartphone texting. In summary, posture biofeedback device is more user-friendly and economical. Muscle biofeedback devices are more designed for clinical application, and they are also effective in correcting posture and muscle activity. Further study on a larger scale with longer follow-up may be able to produce more evidence to substantiate these findings.

Supplementary Information

Acknowledgements:

The authors thank the participants who took part in the study, and all the research personnel especially Mr Leo Lai for his support to the data collection process, and students who participated in the data collection process.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: GS, ST: data collection:GS, RL, JD; data analysis and interpretation of results: GS, ST, RL, JD; draft manuscript: GS, ST, RL, JD. All authors reviewed the results and approved the final version of the manuscript.

Funding: This research project has been supported by the Hong Kong Health & Medical Research Grant (No: 18191031).

Data availability

All data, analysis code, and research materials are available upon request by the corresponding author.

Competing Interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval and consent to participate: Ethics approval was obtained from the Human Ethics Committee (REC2021086) at the local institution. This work was conducted in accordance with the ethical standards of the Helsinki Declaration. All participants received detailed information regarding the purpose and procedures of the study and provided written informed consent prior to participation.

Clinical Trial Number: This study was registered with the Hong Kong University Trial Registry: HKUCTR-2947

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Table 1: Baseline demographic characteristics of participants.

Demographic Factor

Muscle Biofeedback Group

(n=25 )

Posture Biofeedback Group

(n= 29 )

Control Group

(n=27)

Statistical analysis (comparison between Group 1, 2,3)

Male/female(n)

M: 12

F: 13

M: 14

F: 15

M: 11

F: 16

NIL

Age (yr)

Mean: 26.76

SD: 8.24

Mean: 27.41

SD: 11.34

Mean: 25.04

SD: 4.61

p>0.05

Height (cm)

Mean: 166.28

SD: 9.45

Mean: 167.05

SD: 9.88

Mean: 165.46

SD: 8.10

p>0.05

Weight (kg)

Mean: 61.94

SD: 13.10

Mean: 58.98

SD: 9.15

Mean: 58.23

SD: 7.51

p>0.05

Average hours smartphone use (hrs/day)

Mean: 6.24

SD: 2.49

Mean: 6.34

SD: 2.93

Mean: 6.52

SD: 2.41

p>0.05

Baseline Neck Pain score (0-10)

Mean: 3.56

SD: 2.16

Mean: 3.79

SD: 2.44

Mean: 2.78

SD: 1.40

p>0.05

*p<0.05 indicates statistical significance

Table 2: Statistical analysis of Neck Pain and NDI scores in 3 groups across 3 timepoints (T0, T1, T2)

Variable	Factor	F & p values	Group	Contrasts	p value
Neck Pain score	Time	F=31.430, p<0.001*	Muscle Biofeedback	T0 vs T1	<0.001*
	Time		Muscle Biofeedback	T1 vs T2	0.915
			Posture Biofeedback	T0 vs T1	<0.001*
			Posture Biofeedback	T1 vs T2	0.587
			Control	T0 vs T1	<0.001*
			Control	T1 vs T2	0.021*
	Group	F=0.436, p=0.648

NDI score	Time	F=113.05, p<0.001*	Muscle Biofeedback	T0 vs T1	<0.001*
	Time		Muscle Biofeedback	T1 vs T2	0.049*
			Posture Biofeedback	T0 vs T1	<0.001*
			Posture Biofeedback	T1 vs T2	0.032*
			Control	T0 vs T1	0.020*
			Control	T1 vs T2	0.251
	Group	F=0.555, p=0.577

*p<0.05 indicates statistical significance

Table 3: Statistical analysis of pre-post comparison of muscle activity in 3 groups

Muscle	Group	Pre-Post comparison F & p value	Group factor F & p value
LCES	Muscle Biofeedback	F=1.272, p=0.270	F=0.229, p=0.796
	Posture Biofeedback	F=7.250, p=0.011*
	Control	F=0.003, p=0.955
RCES	Muscle Biofeedback	F=0.008, p=0.413	F=0.141, p=0.869
	Posture Biofeedback	F=4.810, p=0.036*
	Control	F=0.781, p=0.384
LUT	Muscle Biofeedback	F=0.015, p=0.903	F=1.256, p=0.290
	Posture Biofeedback	F=0.316, p=0.578
	Control	F=2.859, p=0.102
RUT	Muscle Biofeedback	F=0.575, p=0.455	F=3.963, p=0.023*
	Posture Biofeedback	F=0.296, p=0.590
	Control	F=1.272, p=0.270
LTES	Muscle Biofeedback	F=7.964, p=0.009*	F=0.204, p=0.816
	Posture Biofeedback	F=0.000, p=0.989
	Control	F=4.811, p=0.037*
RTES	Muscle Biofeedback	F=1.276, p=0. 269	F=0.123, p=0.885
	Posture Biofeedback	F=3.360, p=0.077
	Control	F=1.276, p=0.269

*p<0.05 indicates statistical significance

Table 4 : Statistical analysis of spinal flexion angle in 3 groups for pre-post comparison

Spinal movement	Group	Pre-Post comparison F & p value	Group factor F & p value
Cervical Flexion	Muscle Biofeedback	F=6.679, p=0.016*	F=1.713, p=0.186
	Posture Biofeedback	F=0.034, p=0.855
	Control	F=2.406, p=0.132
Thoracic Flexion	Muscle Biofeedback	F=1.260, p=0.272	F=0.585, p=0.560
	Posture Biofeedback	F=0.879, p=0.356
	Control	F=0.049, p=0.827
Lumbar Flexion	Muscle Biofeedback	F=0.096, p=0.759	F=1.124, p=0.330
	Posture Biofeedback	F=0.222, p=0.641
	Control	F=1.928, p=0.176

*p<0.05 indicates statistical significance

No competing interests reported.

The effects of muscle biofeedback versus posture biofeedback on pain and functional outcome in smartphone users with chronic neck pain

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Effects of muscle biofeedback

Effects of postural biofeedback

Comparing the posture biofeedback and muscle biofeedback interventions

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1