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Wearable technology mediated biofeedback to modulate spine motor control: a scoping review

BMC Musculoskeletal Disorders volume 25, Article number: 770 (2024) Cite this article

Abstract

Background

Lower back pain (LBP) is a disability that affects a large proportion of the population and treatment for this condition has been shifting towards a more individualized, patient-centered approach. There has been a recent uptake in the utilization and implementation of wearable sensors that can administer biofeedback in various industrial, clinical, and performance-based settings. Despite this, there is a strong need to investigate how wearable sensors can be used in a sensorimotor (re)training approach, including how sensory biofeedback from wearable sensors can be used to improve measures of spinal motor control and proprioception.

Research question

The purpose of this scoping review was to examine the wide range of wearable sensor-mediated biofeedback frameworks currently being utilized to enhance spine posture and motor function.

Methods

A comprehensive scoping review was conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines extension for Scoping Reviews (PRISMA-ScR) across the following databases: Embase, PubMed, Scopus, Cochrane, and IEEEXplore. Articles related to wearable biofeedback and spine movement were reviewed dated from 1980 - 2020. Extracted data was collected as per a predetermined checklist including the type, timing, trigger, location, and magnitude of sensory feedback being applied to the body.

Results

A total of 23 articles were reviewed and analysed. The most used wearable sensor to inform biofeedback were inertial measurement units (IMUs). Haptic (vibrotactile) feedback was the most common sensory stimulus. Most studies used an instantaneous online trigger to initiate sensory feedback derived from information pertaining to gross lumbar angles or the absolute orientations of the thorax or pelvis.

Conclusions

This is the first study to review wearable sensor-derived sensory biofeedback to modulate spine motor control. Although the type of wearable sensor and feedback were common, this study highlights the lack of consensus regarding the timing and structure of sensory feedback, suggesting the need to optimize any sensory feedback to a specific use case. The findings from this study help to improve the understanding surrounding the ecological utility of wearable sensor-mediated biofeedback in industrial, clinical, and performance settings to enhance the sensorimotor control of the lumbar spine.

Peer Review reports

Background

Low back pain (LBP) is the leading cause of lived-with disability and is the most common musculoskeletal dysfunction globally [14, 41]. Further, LBP is the leading cause of activity limitation [41], dramatically limiting one’s quality of life. Despite the major global public issue LBP continues to pose, there remains limited understanding about the underlying pathologies that may contribute to the development and persistence of LBP. Non-specific LBP (NSLBP) does not have any underlying structural basis, or prevailing treatment option. NSLBP is thought to be multifaceted in nature, with different underlying mechanisms that can be psychosocial and mechanical [25, 27]. In general, a biopsychosocial model of chronic LBP is widely accepted, with a specific emphasis on the implementation of the biomechanics of the disorder [9], however, there still lacks evidence for specific motor impairments leading to spine and trunk related motor dysfunction. Previous research surrounding LBP has focused on the neuromuscular deficiencies in individuals suffering from LBP, and suggested the presence of varying typologies (i.e., maladaptations) implemented by the motor control system in response to chronic LBP states [37]. These hypothetical typologies (i.e., subgroups) include a broad range of motor control phenotypes. For example, in one hypothetical scenario, some individuals may have a tendency to co-activate their trunk musculature as a pain avoidance mechanism to enhance spinal stiffness (i.e., mechanical stability). In contrast, others may adapt altered motor control strategies in response to sensory deficits (i.e., reduced spine/trunk proprioception) without adopting a spine stiffening strategy [37]. LBP patients demonstrate reduced spatial tactile acuity [18], a decreased ability to detect changes in trunk position and significantly higher trunk flexion repositioning error (i.e., both passive and active repositioning) compared to healthy individuals [17, 24]. Additionally, balance deficits have been exhibited in the LBP population compared to healthy controls, especially when asked to perform balance tasks with their eyes closed, further indicating impaired proprioception and increased reliance on visual feedback [10, 19, 20, 22]. Given the impaired motor control present with those experiencing LBP [13], emphasis on therapies leveraging sensorimotor learning principles have begun to emerge. Such approaches rely on the delivery of targeted sensory feedback to enhance motor learning and proprioceptive awareness.

Spine posture and movement have historically been monitored using optical motion capture systems that use physical kinematic markers which can be affixed to the skin, or rigid bodies to represent underlying bony landmarks. These optical kinematic systems can be cumbersome and have limited utility in a clinical setting due to factors such as the cost, time, and training required to ensure proper function. Additionally, the use of these optical systems typically requires extensive anatomical knowledge, thereby dramatically limiting the ecological utility of such systems to a broad spectrum of consumers. In part due to these limitations of lab-based motion capture, there has been a recent uptake in the use of wearable sensors to facilitate the tracking of human kinematic variables in real-world (i.e., clinical) settings. Simpson et al. [34] reported that wearable sensors have good accuracy for assessing spinal posture. Further, according to systematic reviews done by Simpson et al. [34] and Papi et al. [27], inertial measurement units (IMUs) are the most common type of wearable sensor used to monitor spine movement. In addition to monitoring spine movement, IMUs have been paired with mobile-based applications to provide systematic biofeedback to the user to allow them to adjust their posture and/or movement patterns [33], and therefore present as a potential means to enhance sensorimotor training paradigms.

Considerations during the administration of sensory feedback to enhance motor outcomes are the type, timing, trigger, location, and magnitude of sensory feedback being applied to an individual. There are different modalities of biofeedback that can be administered such as visual, auditory, and haptic (i.e., tactile). Further, the timing of any biofeedback can be presented to the user in real-time (concurrent/online) while the movement or posture is occurring, or later (terminal/offline) once a bout of activities is completed [33]. In addition, concurrent/online biofeedback can be implemented in an instantaneous, or continuous manner whereby feedback is triggered based on a pre-defined threshold (instantaneous) or throughout the entirety of a movement (continuous). The triggering of any instantaneous or continuous biofeedback can be referenced to a variety of underlying kinematic phenomena including absolute segment orientation or set target joint angles. Finally, the location and magnitude of any sensory cue, as well as recording equipment, can be varied to further optimize motor learning outcomes. Collectively, there is a lack of consensus on the administration of these variables to optimize motor learning related to spine posture and movement.

Due to the ease of acquisition with wearable sensors to monitor spine posture and movement, wearable sensor-mediated biofeedback can be easily introduced in both clinical and real-world settings for everyday use. Given the wide variety of biofeedback types and timing, there is a clear need to optimize biofeedback administered by a wearable sensor to allow users to refine motor strategies based on reliable kinematic data streams. This is particularly relevant given that the type and timing of any wearable sensor-mediated biofeedback may vary across different use cases (i.e., wearable sensor-mediated biofeedback used to alert users to sustained high-risk postures vs. biofeedback implemented in a training framework to enhance user proprioception). As such, the purpose of this scoping review was to examine the wide range of wearable sensor-mediated biofeedback frameworks currently being utilized to enhance spine posture and motor function. Specific biological outcome measures relating to how any wearable sensor-mediated biofeedback can improve or alter clinically relevant outcomes (e.g., spine posture or range of motion) were explored. The findings of this scoping review aim to synthesize data across multiple scientific domains (engineering, computer science, neuroscience, rehabilitation medicine) that are using wearable sensor-mediated biofeedback to improve spine motor function.

Methods

To facilitate this review, databases were queried between the months of August 2020 and September 2020.

Search strategy

Five databases were searched including Embase, PubMed, Scopus, Cochrane, and IEEEXplore. Relevant spelling variations, synonyms, and alternative terms were included and modified as deemed appropriate by the researchers for each database. Sensors, outcomes, biofeedback, and spine were used as general areas to identify a comprehensive list of articles that encompassed the scope of this review, and the specific search terms for each can be found in Table 1. The reference lists of relevant articles were screened for appropriate titles that may have been missed in the electronic searches. Search results from each database were exported in an ASCII format and compiled using Microsoft Excel for further review and removal of duplicates. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines were followed, and the review process is summarized in Fig. 1.

Table 1 Search terms used
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Fig. 1
figure 1

PRISMA chart outlining the review process

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Inclusion and exclusion criteria

Articles were included if they were published in English, assessed the spine/trunk, used wearable (wireless) technologies, implemented sensory biofeedback, were peer-reviewed, involved an adult population (> 18 y/o), presented original data, and were published on or after 1980. Articles were excluded if they were a review, pilot or case-study (i.e., insufficient sample size where n < 5), book or book chapter, used non-wearable devices, described potential technologies not validated on human subjects, did not assess motion of the spine/trunk, or featured wearable technologies that are classified as robotic or exoskeletons.

Selection process

Duplicate studies were removed and a primary screening of titles retrieved from each database was completed by one reviewer (AB). Following the primary screening, abstracts of potential articles were assessed by one reviewer (AB) with secondary assessment by a second reviewer if necessary (JPN). Following the title and abstract triage, two reviewers (AB and JPN) reviewed the full-text of potential articles against the eligibility criteria to ensure the articles satisfied the requirements for this scoping review.

Quality appraisal and data extraction

The overall quality of the articles was rated using adapted quality appraisal criterion derived from Papi et al. [27] and Ratcliffe et al. [29]. Each form was used to assess the quality of articles based on items such as external validity, potential outcome biases, and protocol reporting, specifically evaluating outcome evaluation and use of the technologies. The quality appraisal checklist has 20 items (Table 2); each item is rated as zero (no detail or comment), one (limited detail) or two (good detail). Articles were evaluated and scored based on the combined quality appraisal checklist by two reviewers (AB and JPN), and any discrepancies on scores (> 1 point separation between reviewers) were settled by a third reviewer (SMB). Mean results are reported throughout the paper, unless otherwise noted.

Table 2 Quality appraisal questions
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Further to the quality appraisal, a customized data extraction form was developed to identify relevant points for each full-text included for review. These key relevant points included study aims and design, sample size and population, wearable sensor type and instrumentation, kinematic data obtained, biofeedback type, biofeedback thresholds and triggers, conclusions, and limitations. Data extraction was completed by two researchers (AB and HM) in consultation with a third reviewer (SMB). Relevant data were summarized in a tabular format, and further interpreted to identify any systematic trends present throughout the reviewed literature.

Results

Article selection

The search identified 4651 potentially relevant articles, with 17 articles identified from the references of related articles. 2274 articles remained for consideration after duplicates were removed. Following the screening of titles and abstracts for inclusion and exclusion criteria, 29 articles were retrieved for further full-text review. Six additional full-text articles were then excluded due to the lack of association with the spine (n = 2), or the lack of wearable sensor data (n = 3), and inadequate sample size (n = 1). The final number of articles included for full review was 23. The article selection process and justifications for full-text exclusion are shown in Fig. 1.

Quality of reviewed articles

Of the 23 articles selected for full review, one paper was rated at low quality, 13 were rated at medium quality, and nine were rated at high quality. Itemized scores for each paper are presented in Table 3. Across most papers, sample sizes were poorly justified, as demonstrated by low scoring for a majority (16/23) of papers on item six. Further, 10/23 studies included demonstrated average or below average reporting on both standardization of spine/trunk movement instructions, and signal handling and processing (14/23). In contrast, most studies were rated as high quality on reporting the main objectives of the research (14/23), main findings of the research (21/23) and appropriate statistical tests used (18/23). Perhaps most important to the objectives of the current scoping review, almost all studies reported high detail regarding the type (20/23) and implementation of biofeedback (16/23).

Table 3 Quality assessments of included articles
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Descriptive aspects of reviewed articles

Of the articles reviewed, 10/23 examined a healthy participant population, free of any neuromuscular or musculoskeletal disorders related to spine/trunk. In contrast, 9/23 articles reported a clinical population, including patients with LBP, Parkinson’s disease, or other vestibular deficits. Two articles [1, 42] assessed both a healthy and a clinical population and two [7, 35] did not report any specific participant demographics, instead referring to them as “users” (Table 4).

Table 4 Data extraction table
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The majority (21/23) of the articles reviewed utilized inertial sensors (i.e., IMUs, accelerometers, gyroscopes), with one study [15] also incorporating EMG data. One article [26] assessed posture through a strain-based sensor affixed to the trunk dorsum. Finally, one article reported use of a wearable postural stabilizer [39], but failed to provide adequate information regarding the sensor type and function.

The primary outcome of 8/23 articles focused on joint angles (i.e., lumbar spine, hip, neck), while 14/23 articles reported on segment orientations (i.e., body tilt relating to the thorax and pelvis orientation relative to gravity, postural control). Further, 1/23 articles included outcomes related to muscle activation through EMG [15], and 1/23 articles’ primary outcome measure was static posturography following training with the wearable postural stabilizer [39].

In general, a wide range of sensory biofeedback subtypes were observed. Specifically, 10/23 articles reviewed provided only haptic/vibrotactile feedback, 5/23 provided only visual feedback, and 4/23 provided only auditory feedback. In addition to the previous studies assessing unimodal biofeedback, some research studies implemented multimodal biofeedback (including >1 types of sensory biofeedback). Specifically, 2/23 utilized both auditory and visual feedback [5, 38], 1/23 utilized both vibrotactile and visual feedback [16], and 1/23 used all three types of feedback [15].

A majority (16/23) of the articles reported biofeedback to be administered in an instantaneous, concurrent “online” manner (e.g., exceeding a predefined absolute or relative threshold of a trigger variable). In addition to this, 3/23 articles reported providing continuous, concurrent “online” biofeedback (i.e., continuous feedback during a movement) which was always coupled with visual biofeedback [21, 42, 36]. Additionally, 2/23 articles reported administering biofeedback in two different ways, with visual always being continuous concurrent “online” and the other (haptic/vibrotactile and auditory) being provided instantaneously concurrent “online” [5, 15]. Of the articles included for review, 2/23 articles did not report the biofeedback triggers and timing [35, 39]. Further, no articles utilized a delayed, terminal “offline” type of sensory biofeedback.

Almost all (21/23) of the articles deemed that the implementation of their biofeedback intervention was effective at improving their respective outcomes, and there was no apparent bias towards any one population, healthy or clinical, in terms of effectiveness. One article by Ribeiro et al. [30] found that their intervention was not effective, and one article [39] noted that their intervention was effective in improving their secondary clinical outcomes (e.g., reduced falls risk, increased quality of life), however, it was not the superior intervention for their primary objective clinical outcomes (i.e., posturographic changes). The biofeedback types employed by the two studies who did not observe changes were not the same (auditory and vibratory, respectively), so there is no discernible difference between effectiveness of biofeedback types.

Discussion

Wearable sensory biofeedback

The implementation of wearable sensor-mediated sensory biofeedback is becoming more broadly explored in the fields of clinical biomechanics and motor control as a means to assess spine posture and motor function. Given the recent uptake of wearable technologies, understanding the use of wearable sensor informed interventions to improve or alter clinically relevant motor outcomes is crucial. Given this, the aim of this study was to examine the types of wearable sensor-mediated biofeedback currently being employed to explore and optimize spine posture and motor function. It was expected that the types of biofeedback used throughout the literature would be varied. However, the findings of this scoping review aimed to synthesize data across multiple scientific domains that employ sensory biofeedback to identify potential gaps and areas for further study.

Quality of reviewed articles

In total, 23 studies were included for full-text review based on the inclusion criteria for this work. Following quality appraisal, some clear strengths of the reviewed literature emerged. First, the results of the quality appraisal suggest that many studies (20/23; 16/23) provided a proper description of biofeedback (i.e., type and implementation, respectively), as well as the objectives (14/23), findings of the research (21/23), and the full description of any statistical analyses (18/23). In addition to these strengths, the quality appraisal also identified some systematic shortcomings across the surveyed literature. Specifically, most studies failed to adequately justify sample size (16/23), properly describe movement instructions (10/23), and provide adequate detail regarding raw signal handling and processing (14/23). Despite these limitations, these results may stem from the diverse nature of the research articles included in the current review. Specifically, many of the papers included were written as proof-of-concept studies with some using commercial equipment with closed-source algorithms limiting the description of any raw signal processing or handling information. Interestingly, given the novelty of wearable sensor-mediated biofeedback, approximately half of the studies included assessed the utility of these technologies on a clinical population which suggests a strong desire to use wearable sensor-mediated biofeedback as a clinical tool to (re)train spine movement, and to enhance motor function. These data further contextualize those reported in a recent systematic review evaluating the use of wearable technology to assess spine kinematics whereby almost all studies reported research conducted in a research laboratory [27]. Taken together the results of the current work and those presented previously (i.e., [27]) suggest the need for future work to continue working with specialized populations in real-world environments including the workplace and other settings which promote the completion of activities of daily living or therapeutic-style spine and trunk movements.

Descriptive aspects of reviewed articles

The data extraction procedure employed within this research uncovered several interesting trends throughout the sampled literature. First, the most common type of biofeedback employed across the studies assessed was haptic/tactile feedback (12/23). Although the motive for this apparent bias towards tactile biofeedback is unclear, many studies noted that haptic biofeedback tends to be easy to administer for the researcher, and easy to understand and respond to for the participants. Future work regarding user preferences across a range of sensory modalities would be warranted to further support any unimodal sensory feedback paradigm in specific clinical groups. Despite the absence of user preference data, many of the studies reviewed in the current work note a general improvement in spine-related motor outcomes in the studies evaluated that implemented haptic/tactile feedback. These outcome measures included lumbopelvic control (i.e., rhythm), time spent in harmful positions, posture and balance awareness, and trunk stability. Despite this prevailing bias towards haptic feedback, there remains a lack of consensus about the most appropriate means of administering this type of biofeedback. For example, only about half of the research studies (7/12) administer haptic feedback to the lumbar region (i.e., skin of the trunk dorsum), with other studies administering the feedback at a secondary location (i.e., headband) despite the IMUs being fixed to the low back (e.g., [23]). There was also a wide variation in the timing and structure of any sensory feedback in addition to variation in the anatomical location of the sensory feedback. Some studies administered graded continuous vibrotactile feedback depending on various pre-determined absolute or relative threshold values (e.g., [13, 32]), and some adopted a simple instantaneous “on” or “off” approach (e.g., [2, 16, 26]).

The timing of the delivery of the feedback varied, with a mix of instantaneous “on” or “off” approaches and continuous delivery, with some studies implementing both approaches across one single or multiple sensory modalities. In general, the differences in timing of delivery are dependent on the sensory modality being targeted, but remained relatively consistent within types of biofeedback. For example, every instance of visual biofeedback reviewed in the present paper involved the continuous delivery of a target variable, and a majority (11/12) of vibrotactile feedback was instantaneous, informed based on pre-defined thresholds. However, auditory feedback was administered using both methods (including on/off stimuli, and those implemented in a continuous fashion with varying tones/pitches). Given the range of different modalities evaluated, the lack of consensus regarding the timing of delivery still presents a challenge as different modalities may be leveraged in contrasting ways to optimize varying clinical outcomes. Further work is required to optimize the anatomical location, trigger/threshold, and the waveform characteristics of any supplementary vibrotactile feedback, including a justification of these parameters to elicit an optimal motor response outcome.

There were approximately equal number of studies investigating healthy participants (10/23) and clinical populations (9/23); however, only two studies [1, 42] compared clinical and healthy participants. This divide in the research may be due to the high number of validation-type studies included within this scoping review, given the novelty and growing interest regarding wearable sensor-mediated biofeedback. Future work is needed to optimize the implementation of sensory feedback for those with and without motor impairments related to the spine, especially if any motor impairments are accompanied by apparent sensory deficits. There is agreement in the literature regarding inertial sensors as the most common sensor type used in this area of research (21/23). Furthermore, the results of the current work are in agreement with recent systematic reviews investigating wearable sensors in spine posture analysis [27, 34]. Specifically, IMU’s can be cost-effective to acquire and maintain, are user-friendly, and able to produce easily interpretable results for clinicians and researchers alike. Additionally, they have been found to be reliable at measuring spine posture and movements, and can be a valuable tool to provide real-time biofeedback [34].

Clinical applicability

As noted, clinical presentations of low back disorders can be complex, and often non-specific. This apparent heterogeneity of individual responses to LBP suggests that the implementation of wearable sensor-mediated biofeedback into clinical settings also needs to be optimized for varying use cases. At present there is a lack of evidence for specific motor impairments associated with the development and chronicity of LBP; however, it is possible that through guided interventions using sensory feedback motor impairments may be reduced. The current scoping review presents a clear consensus towards implementing IMU hardware to extract biomechanically relevant spine and trunk kinematics. Further, a large majority of studies included in this review leverage haptic/vibrotactile feedback from IMU hardware. The lack of consensus is apparent in the timing and design of any IMU-derived vibrotactile feedback. Given this, it is likely that any sensory feedback parameters need to be optimized for individual clinical use cases. For example, future research could aim to investigate the most appropriate feedback administration (i.e., continuous or instantaneous) in individuals presenting with LBP and apparent sensory deficits. Further, it could be of interest to assess the use of tonal stimuli (i.e., varying in amplitude/frequency) with this population, and how it may be best applied to these users in an acute training framework to enhance the individual detection of specific spine/trunk postures given their sensory deficits. Such sensory feedback could be systematically reduced over time to encourage the dependence on natural stimuli to cue body postures and movements, and to improve the control of spine and trunk movement. Additionally, triggers could be re-evaluated over time to enhance individual lumbar range of motion (ROM) while concurrently limiting pain avoidance behaviours. In line with this, it should be noted that special consideration should be taken when deciding what type of biofeedback to utilize specific to various clinical populations and their deficits (e.g., sensory processing deficits).

Practicality & future directions

Although the study of wearable sensor-derived biofeedback is growing throughout the scientific literature, the practicality and scalability of such sensory feedback in a therapeutic context remain unknown. There is a strong theoretical basis for the use of sensory cues to evoke motor changes of the spine and trunk [4, 28], however, it is clear that future work is required to both (1) optimize the structure of any sensory feedback to individual use cases and (2) understand individual preferences and compliance to any sensorimotor training paradigm leveraging wearable technology. Given this, future work should aim to address the relative strengths of varying sensory feedback types, timing, triggers, locations, and magnitudes across a wide range of use cases, while also considering individual preferences to such feedback in day-to-day and clinical environments. This scoping review is limited as it is only up to date as of September 2020, so further up-to-date evidence in these areas would be beneficial as the field is rapidly growing.

Conclusions

The results presented here synthesize the literature aiming to provide wearable sensor-mediated sensory feedback to facilitate motor adaptations relating to movement of the spine. The general findings of this scoping review found overall positive effects of wearable sensor-mediated biofeedback training on clinically relevant outcomes (i.e., spine posture, ROM and/or balance). This evidence suggests that this technology can be used as a clinical modality to improve spine motor function and posture. Care needs to be taken to properly report any motor task, and raw signal processing. Further, future work is necessary to further optimize the use of vibrotactile feedback as a biofeedback modality to elicit motor learning. Specifically, future work is needed to optimize the anatomical location, trigger/threshold, and waveform characteristics of any supplementary vibrotactile feedback. Collectively the research papers evaluated suggest strong promise in the use of biofeedback to complement the current uptake of wearable sensors in spine posture and movement retraining.

Availability of data and materials

All processed data generated or analysed during this study are included in this published article. Raw data can be provided upon reasonable request to the corresponding author (SMB).

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Acknowledgements

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Funding

The work presented here is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada (RGPIN-2020–05195).

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Authors and Affiliations

  1. Department of Kinesiology, Faculty of Applied Health Sciences, Brock University, St. Catharines, ON, Canada

    Aurora Battis, Jarrett P. Norrie, Hannah McMaster & Shawn M. Beaudette

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AB and SMB were responsible for the conceptualization and development of the study and design and were major contributors in writing the manuscript. JN and AB performed all record identification (i.e., acquisition), assessment of texts for eligibility and quality assessment of articles. HM and AB were responsible for the data extraction (i.e., synthesis) from eligible articles. All authors read and approved the final manuscript.

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Correspondence to Shawn M. Beaudette.

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Battis, A., Norrie, J.P., McMaster, H. et al. Wearable technology mediated biofeedback to modulate spine motor control: a scoping review. BMC Musculoskelet Disord 25, 770 (2024). https://doi.org/10.1186/s12891-024-07867-3

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Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

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