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Article

Sitting Posture during Prolonged Computer Typing with and without a Wearable Biofeedback Sensor

1
Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
2
Department of Orthopedics, National Cheng Kung University Hospital, Tainan 701, Taiwan
3
Department of Rehabilitation, Sengkang Community Hospital 1 Anchorvale Street, Singapore 544835, Singapore
4
Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(10), 5430; https://doi.org/10.3390/ijerph18105430
Submission received: 28 April 2021 / Revised: 18 May 2021 / Accepted: 18 May 2021 / Published: 19 May 2021

Abstract

:
Prolonged sitting combined with an awkward posture might contribute to the increased risks of developing spinal pain. Maintaining an upright sitting posture is thus often suggested, especially nowadays when people spend longer periods in the sitting posture for occupational or leisure activities. Many types of assistive devices are commercially available to help computer users maintain an upright sitting posture. As the technology advances, wearable sensors that use microelectromechanical technology are designed to provide real-time biofeedback and promote adjusting posture actively. However, whether such wearable biofeedback sensors could assist adjusting sitting posture in computer users during prolonged typing remains unknown. This study aimed to investigate the effects of a wearable biofeedback sensor on maintaining an upright sitting posture. Twenty-one healthy young adults were recruited and performed a 1-h computer typing task twice, with and without using the active biofeedback device. The sagittal spinal posture during computer typing was measured using a three-dimensional motion analysis system. Using the wearable biofeedback sensor significantly decreased the neck flexion (p < 0.001), thoracic kyphotic (p = 0.033), and pelvic plane (p = 0.021) angles compared with not using the sensor. Computer users and sedentary workers may benefit from using wearable biofeedback sensors to actively maintain an upright sitting posture during prolonged deskwork.

1. Introduction

In modern society, people spend a significant amount of time sitting for occupational or leisure activities [1,2]. Prolonged sitting combined with an awkward posture, such as a forward-leaning head and increased thoracic kyphosis, increases the demands of the spinal muscles and joints, which might contribute to the increased risks of developing spinal pain in sedentary workers [3,4]. Previous studies showed that 17.7–63.0% and 23–34% of office workers experienced neck pain and back pain during the last 12 months, respectively [4,5,6]. Extended computer use during daily learning activities and recreation also contributes to the high prevalence rates of neck pain and/or back pain among college students [7,8]. Chronic spinal pain can lead to both physical and psychological problems for individuals, including a reduced range of motions, muscle weakness, disability, depression, and reduced quality of life. Back pain and neck pain have been recognized as the leading global cause of disability in most countries [9]. It results in an enormous economic burden for both individuals and societies due to healthcare costs, decreased productivity, work absenteeism, lost wages, and work compensation [10,11]. The total cost of back pain and neck pain in 2016 was estimated to be 1345 billion USD, which was the highest healthcare expenditure among 154 health conditions in the United States [11].
Studies have demonstrated that a slump posture or forward head posture resulted in increased external loading and muscle activities on the spine, and these postural changes were also associated with spinal pain [12,13,14,15]. Therefore, to maintain an upright sitting posture is commonly suggested [16]. An upright postural alignment involves “a minimal amount of stress and strain and which is conductive to maximal efficiency in the use of the body” [17]; however, maintaining an upright sitting posture is not easy [18,19]. Falla et al. reported that participants without neck pain or disorders progressively slouched (increased thoracic kyphosis) during a computer task as short as 10 min [18]. Claus et al. found that healthy participants required visual and verbal feedback to reproduce an upright sitting posture [19]. Therefore, many types and designs of chairs and assistive devices have been developed to promote an upright sitting posture [20,21,22,23].
Annetts et al. [20] compared the effects of different types of chairs on spinal angles of the cervical and lumbopelvic regions. Grondin et al. [22] investigated the use of a lumbar support pillow in maintaining a thoracolumbar posture. These chairs and assistive devices are considered passive devices because their designs are aimed to passively support the user to maintain an upright sitting posture. However, previous studies demonstrated that different types of chairs had various effects on the postural alignment and did not consistently produce an upright posture across spinal regions. The effects of the passive devices on maintaining an upright posture were inconsistent. Ergonomically designed chairs also have the disadvantages of being large in size, not easy to carry, and have limited usage in certain locations. Those chairs and passive devices may not be suitable for individuals with different anthropometric characteristics.
In contrast to passive devices, active devices are developed to provide feedback and promote postural adjustment actively. Celenay et al. showed an improvement in thoracic posture after an 8-week training program wearing a harness with an electronic sensor placed between the scapulae and waist [24]. An auditory alarm was given once the individual slouched and the sound could only be stopped while returning to the previous posture. Furthermore, Yoo et al., investigating the effects of auditory feedback from a chair during a short period of computer work (15–20 min), observed immediate effects on the muscle activities and kinematics of the head and neck [25,26]. The effects of these active devices depend on whether the users actively adjust their posture when feedback is received. Although previous studies have reported acceptable outcomes with these active devices, their prototype designs are preliminary and bulky, with many wires, making them impractical for everyday use [24,26].
As the technology advances, wearable sensors that use microelectromechanical technology are designed to provide real-time biofeedback and promote an upright posture actively. Such active biofeedback sensors are wearable devices that can be worn discretely on clothing and thus have the advantages of a compacted size, easy-use, portable use, and without a location limitation. The wearable sensor can be set up to vibrate whenever the user slouches. As a result, the user can actively correct his or her posture.
The effectiveness of different types of ergonomic training, including the use of assistive devices, was evaluated in a recent overview of available systematic reviews [27]. While 21.6% of the systematic reviews included supported the implication of ergonomic training to reduce physical demand and musculoskeletal symptoms among workers, 78.4% of systematic reviews found disproof or insufficient evidence to suggest a benefit or harm of the intervention for clinical practice. The authors attribute inconsistent evidence to factors such as erroneous identification of ergonomic risk factors and lack of scientific knowledge about the appropriate combination of ergonomic training. Nevertheless, wearable sensors were not included in any of the systematic reviews. Whether wearable biofeedback sensors could promote an upright sitting posture remains unclear.
Computer users and sedentary workers often seek solutions to maintain an upright sitting posture. Therefore, this study aimed to investigate the effects of a wearable biofeedback sensor in actively modifying the spinal posture during a prolonged computer typing task. The results of this study would provide useful information for health professionals who face inquiry for the usefulness of wearable biofeedback sensors and for people who experience musculoskeletal discomfort during prolonged computer work.

2. Materials and Methods

2.1. Study Design

This was a one-group quasi-experimental study. A single group of participants was measured twice, with and without using a wearable biofeedback device, while performing a computer typing task. The independent variables were the condition and time. The dependent variables were spinal angles. Data collection was conducted at the laboratory in the university.

2.2. Participants

Healthy young adults were recruited from the university campus. The inclusion criteria were as follows: (1) age between 20–25 years; and (2) willing and able to give informed consent for participation in the study. All participants were free from spinal pain and had no previous injury or surgery in the spine and abdominal regions. This study was approved by the investigators’ institutional review board. Written informed consent was obtained from all participants.
For the a priori power analysis, we used the G*power 3.1 software [28] to determine the sample size for this study. The minimum sample size was estimated based on the reported difference of sitting posture after 8-week spinal postural training using a biofeedback posture-correction device [24]. The required sample size for a statistical power of 0.8 and a two-tailed α of 0.05 for two dependent means was 6 and 16 for thoracic and lumbar angles, respectively. Considering that this study aimed to investigate the effects of a wearable biofeedback sensor during a single session of a computer typing task, the effect size of this study may be smaller than the calculated values from the literature. Therefore, we enrolled a convenient sample of 21 participants in this study.

2.3. Instrumentation

Lumo Lift (Lumo Bodytech Inc., Palo Alto, CA, USA) was used in this study, which includes an accelerometer-based sensor (approximately 4.4 cm long × 2.6 cm wide × 1.3 cm high), a magnetic clasp (approximately 1.5 cm long × 1.5 cm wide × 0.3 cm high), and an universal serial bus charging dock (Figure 1). This wearable biofeedback sensor can be worn with the sensor portion underneath the clothes, directly below the clavicle, and midway between the sternal notch and the acromion process. It provides vibratory biofeedback by comparing the acceleration due to gravity with the user’s downward acceleration. Based on our pilot data, this wearable biofeedback sensor measures about 5° of angular change from the preset posture in the sagittal plane.
A three-dimensional motion analysis system with six infrared cameras (Vicon Motion Systems Ltd., Oxford, UK) was used to measure the spinal posture at a sampling rate of 100 Hz during the computer typing tasks. Reflective markers were attached onto the bilateral canthus and tragus; suprasternal notch and spinal processes of T1, T3, T9, T11, L1, L2, L4, and L5; bilateral anterior superior iliac spines; and posterior superior iliac spines. The Vicon motion analysis system is a reliable instrument for postural measurement [29].

2.4. Procedure

The participants completed a baseline survey, which included sex, side of the dominant hand, age, height, mass, and duration of daily sitting time and computer use. After completing the baseline survey, the investigator attached reflective markers to specific anatomical landmarks of participants while standing. The investigator also directly attached the sensor to the skin, below the clavicle, and midway between the sternal notch and the acromion process. Then, all the participants performed a 1-h computer typing task twice, with and without the sensor in a random order.
For the computer typing tasks, the participants sat on a backless, armless, and height-adjustable wooden chair with their hips and knees flexed to 90°. The computer workstation consisted of a standard computer desk (75-cm height), a standard keyboard, a mouse, and a 20-inch monitor. The monitor was placed in front of the participants, with the upper edge of the screen adjusted to the eye level. The participants were instructed to copy and type from an electronic book. After completing the first typing task, the participants were allowed to rest as long as they wished until they were ready for the second typing task.
Before commencing the computer typing task, the participants were verbally and manually guided by the investigator to sit with their scapulae slightly retracted and the thoracolumbar spine extended. This upright sitting posture was used to preset the wearable biofeedback sensor as the target posture. The participants were then instructed to actively adjust and resume the upright sitting posture whenever they received vibratory biofeedback from the sensor during the computer typing task.

2.5. Data Analysis

The kinematic data were digitized using Vicon Nexus 1.8.5 (Vicon Motion Systems Ltd.) and then filtered by a low-pass fourth-order Butterworth filter of 4 Hz using MATLAB software (The MathWorks, Inc., Natick, MA, USA). Spinal posture was evaluated on the basis of the joint angles or segment inclination angles, including head tilt, neck flexion, upper cervical, lower cervical, thoracic, lumbar, and pelvic plane angles (Figure 2) [30,31].
(a)
Head tilt angle: the segment of the mid-point of canthus and the mid-point of tragus relative to the horizontal plane.
(b)
Neck flexion angle: the segment formed by the mid-point of tragus and spinous process of T1 relative to the frontal plane.
(c)
Upper cervical angle: the angle between the mid-point of canthus, the mid-point of tragus and the T1 spinous process.
(d)
Lower cervical angle: the angle between the mid-point of tragus, the T1 spinous process and the suprasternal notch.
(e)
Thoracic angle: the angle between the segments of T1–T3 and T9–T11.
(f)
Lumbar angle: the angle between the segments of L1–L2 and L4–L5.
(g)
Pelvic plane angle: the angle between the segment of the mid-point of anterior superior iliac spines and the mid-point of posterior superior iliac spines relative to the horizontal plane.
Decreasing head tilt, upper cervical, and lower cervical angles indicate flexion, and decreasing neck flexion, thoracic, and lumbar angles indicate extension. A negative pelvic plane angle indicates an anterior tilt of the pelvis. The neck flexion angle is commonly used to quantify the forward head posture [32,33], with a greater angle suggesting a more forward head posture. A greater thoracic angle indicates increased thoracic kyphosis. The last 30-s angle data of the 5th, 15th, 25th, 35th, 45th, and 55th minute of 1-h typing were averaged for statistical analysis.
Descriptive statistics using means and standard deviations for continuous data and frequency for nominal data were used to describe the study sample. For each spinal angle, separate two-way analysis of variance with repeated measures was used to examine the statistically significant differences between the two conditions (with and without the sensor) at six time points. Significant main effects were followed up using Bonferroni’s correction post-hoc analyses. Effect sizes (r) based on the F-values were calculated [34], and classified as small (r = 0.2), medium (r = 0.3), and large changes (r ≥ 0.5) [35]. All data were analyzed using SPSS version 20 (IBM corp., Armonk, NY, USA) with the level of significance set as p < 0.05.

3. Results

Twenty-one participants (12 women, 9 men; age 23.33 ± 2.9 years; weight 61.4 ± 10.0 kg; height 167.0 ± 9.0 cm) enrolled in this study. Participants spent an average of 9.7 ± 3.2 h/day sitting and 7.0 ± 2.9 h/day using computer. Spinal angles during the computer typing tasks with and without using the wearable biofeedback sensor across different time points are shown in Table 1. Significant condition effects were observed for the neck flexion, upper cervical, lower cervical, thoracic, and pelvic plane angles (p < 0.05, Table 1). On average, the neck flexion, upper cervical, lower cervical, thoracic, and pelvic plane angles were significantly smaller with the wearable biofeedback sensor than without. The mean differences in the neck flexion, upper cervical, lower cervical, thoracic, and pelvic plane angles between the two conditions were 2.8° (95% confidence interval (CI): 1.4°–4.2°, r = 0.68), 2.8° (95% CI: 1.0°–4.6°, r = 0.59), 1.2° (95% CI: 0.2°–2.0°, r = 0.53), 1.9° (95% CI: 0.2°–3.6°, r = 0.46), and 2.2° (95% CI: 0.4°–4.1°, r = 0.49), respectively. No significant difference was observed in the other spinal angles (p > 0.05, Table 1). In addition, no significant time or interaction effects were identified (all p > 0.05, Table 1).

4. Discussion

Maintaining an upright sitting posture is often suggested for preventing excessive external mechanical loading and muscle activations on the spine, especially nowadays, when people spend longer periods in the sitting posture for occupational or leisure activities. Young adults use computer extensively for learning activities and recreation, which greatly increases their risk of developing spinal pain [7,8]. In a prospective study with 8-year follow-up, experiencing low back pain in youth was found to correlate with low back pain in adulthood [36]; therefore, it is important to identify effective approaches for maintain an upright sitting posture in the younger population. This study investigated the immediate effect of a wearable biofeedback sensor on the spinal posture of young adults during a prolonged computer typing task.
Our findings showed that using the active biofeedback sensor resulted in significantly smaller neck flexion, upper cervical, lower cervical, thoracic, and pelvic plane angles. These angular changes suggest that participants had a less forward head, less thoracic kyphosis, and a less posterior tilted pelvis. In other words, the participants were able to maintain a more upright sitting posture during the 1-h computer typing task when the wearable biofeedback sensor was used. A forward-leaning head is the most commonly recognized postural fault that is related to neck pain in adults [15]. A concern is whether the small postural changes resulting from the use of the wearable biofeedback sensor (range 1.2°–2.8°) would have any clinically meaningful benefits although the calculated effect sizes are medium to large (range 0.46–0.68). Conversely, these small postural changes cumulated throughout a workday may amplify the effect of the active biofeedback sensors. Further investigation is required to determine the long-term effects of active biofeedback sensors.
Posture is controlled through the integration of sensory information and motor output. The sense of the position and movement of our body parts that is provided by the somatosensory system signals us when and how to respond to the environment [37]. However, proprioceptive sensation can be influenced by musculoskeletal disorders. Previous studies showed that people with spinal pain demonstrated impaired proprioception when assessed with postural repositioning or motion perception threshold tests [38,39]. The ability to maintain an upright sitting posture during prolonged computer work may be difficult if there is no sufficient and accurate intrinsic feedback through proprioceptive sensations. Ribeiro et al. [40] addressed the importance of extrinsic feedback to help execute or improve motor performance in situations where motor improvements are difficult to achieve. Extrinsic feedback is thought to enhance the somatosensory system and restore optimal motor control [41,42]. Our study provides preliminary evidence to support the use of the wearable biofeedback sensor to provide extrinsic feedback and facilitate active postural adjustment. Active postural adjustment might also enhance the conscious awareness of the body posture, which is the subjective phenomenological aspect of proprioception. Higher postural awareness is associated with reduced clinical symptoms in patients with chronic pain [43]. The wearable biofeedback sensor has the advantage of being miniaturized, more portable, and more suitable for everyday use over other active postural devices used in studies [25,26]. In addition, active postural adjustment through extrinsic feedback might train the muscles involved in the task and decrease unnecessary muscle activation [44], which subsequently may lower the risk of musculoskeletal symptoms related to a poor posture.
Spinal posture involves the alignment of multiple joints. Kuo et al. found a significant chain of correlations between sagittal spinal angles in the sitting posture [31]. Decreased forward leaning of the neck was associated with the downward tilt of the head. The results of this study did not support previous findings. The use of the wearable biofeedback sensor did not alter the head tilt despite significant changes in other spinal regions within the kinematic chain. The biomechanical link between the neck flexion and head tilt angles was possibly interrupted by the computer typing task. The participants were required to tilt their head downward and upward to gaze at the keyboard and computer screen while typing. Therefore, the participants might have tilted their head upward while maintaining a less forward lean of the neck posture with the use of the wearable biofeedback sensor.
This study has several limitations. First, only the immediate effect was investigated. Participants in a previous study reported that vibrotactile feedback was easily notable but disturbing compared with graphical and physical types of feedback [45]. Some participants in this study commented that they might turn off the vibrotactile feedback in a long-term study. The compliance factor would definitely influence the effectiveness of the wearable biofeedback sensor for everyday use. The long-term effects of wearable biofeedback sensors must be further determined with a well-designed randomized controlled study. Second, measurement of spinal angles using the motion analysis system required the participants to remove the T-shirt or tank top for skin marker attachment. The wearable biofeedback sensor used in this study was directly attached to participants’ skin below the clavicle with adhesive tapes instead of being attached under the clothes using a magnetic clasp. The wearable sensor may not accurately detect a slouched posture if it is worn under loose-fitting clothes. The effect of the wearable biofeedback sensor for everyday use may be compromised. In addition, in this study, we analyzed the effects of only one type of active biofeedback device. Therefore, the results of this study may not be inferred to all active devices available in the market.

5. Conclusions

The wearable sensor using biofeedback is able to assist maintaining an upright sitting posture during a single session of prolonged computer typing. Using the wearable biofeedback sensor significantly decreased the neck flexion, thoracic kyphotic, and pelvic plane angles in healthy young adults compared with not using the sensor. Computer users and sedentary workers may benefit from the use of wearable biofeedback sensors to actively maintain an upright sitting posture during prolonged computer work; however, the long-term effects of wearable sensors on prevention and treatment of spinal pain requires further investigation.

Author Contributions

Conceptualization, Y.-L.K. and Y.-J.T.; methodology, Y.-L.K., K.-Y.H., C.-Y.K., and Y.-J.T.; formal analysis, Y.-L.K., C.-Y.K., and Y.-J.T.; investigation, Y.-L.K., C.-Y.K., and Y.-J.T.; writing—original draft preparation, Y.-L.K.; writing—review and editing, Y.-L.K. and Y.-J.T.; supervision, Y.-L.K. and Y.-J.T.; project administration, C.-Y.K.; funding acquisition, Y.-L.K. and K.-Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Technology of Taiwan (R.O.C.) (grant number MOST 106-2410-H-006-083-MY2) and the National Cheng Kung University Hospital, Tainan, Taiwan (R.O.C.) (grant numbers NCKUH-10506017).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of National Cheng Kung University Hospital (A-BR-105-067).

Informed Consent Statement

Written informed consent was obtained from all participants involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Mielke, G.I.; Burton, N.W.; Turrell, G.; Brown, W.J. Temporal trends in sitting time by domain in a cohort of mid-age Australian men and women. Maturitas 2018, 116, 108–115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Matthews, C.E.; Chen, K.Y.; Freedson, P.S.; Buchowski, M.S.; Beech, B.M.; Pate, R.R.; Troiano, R.P. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am. J. Epidemiol. 2008, 167, 875–881. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Lis, A.M.; Black, K.M.; Korn, H.; Nordin, M. Association between sitting and occupational LBP. Eur. Spine J. 2007, 16, 283–298. [Google Scholar] [CrossRef] [Green Version]
  4. Cote, P.; van der Velde, G.; Cassidy, J.D.; Carroll, L.J.; Hogg-Johnson, S.; Holm, L.W.; Carragee, E.J.; Haldeman, S.; Nordin, M.; Hurwitz, E.L.; et al. The burden and determinants of neck pain in workers: Results of the Bone and Joint Decade 2000–2010 Task Force on Neck Pain and Its Associated Disorders. Spine 2008, 33, S60–S74. [Google Scholar] [CrossRef] [PubMed]
  5. Janwantanakul, P.; Pensri, P.; Jiamjarasrangsri, V.; Sinsongsook, T. Prevalence of self-reported musculoskeletal symptoms among office workers. Occup. Med. 2008, 58, 436–438. [Google Scholar] [CrossRef] [Green Version]
  6. Juul-Kristensen, B.; Søgaard, K.; Strøyer, J.; Jensen, C. Computer users’ risk factors for developing shoulder, elbow and back symptoms. Scand J. Work Environ. Health 2004, 30, 390–398. [Google Scholar] [CrossRef] [Green Version]
  7. Bhardwaj, Y.; Mahajan, R. Prevalence of neck pain and disability in computer users. Int. J. Sci. Res. 2017, 6, 1288–1290. [Google Scholar]
  8. Hafeez, K.; Ahmed Memon, A.; Jawaid, M.; Usman, S.; Usman, S.; Haroon, S. Back Pain-Are Health Care Undergraduates At Risk? Iran. J. Public Health 2013, 42, 819–825. [Google Scholar]
  9. Hurwitz, E.L.; Randhawa, K.; Yu, H.; Côté, P.; Haldeman, S. The Global Spine Care Initiative: A summary of the global burden of low back and neck pain studies. Eur. Spine J. 2018, 27, 796–801. [Google Scholar] [CrossRef]
  10. March, L.; Smith, E.U.; Hoy, D.G.; Cross, M.J.; Sanchez-Riera, L.; Blyth, F.; Buchbinder, R.; Vos, T.; Woolf, A.D. Burden of disability due to musculoskeletal (MSK) disorders. Best Pract. Res. Clin. Rheumatol. 2014, 28, 353–366. [Google Scholar] [CrossRef]
  11. Dieleman, J.L.; Cao, J.; Chapin, A.; Chen, C.; Li, Z.; Liu, A.; Horst, C.; Kaldjian, A.; Matyasz, T.; Scott, K.W.; et al. US Health Care Spending by Payer and Health Condition, 1996–2016. JAMA 2020, 323, 863–884. [Google Scholar] [CrossRef] [PubMed]
  12. Kwon, Y.; Kim, J.-W.; Heo, J.-H.; Jeon, H.-M.; Choi, E.-B.; Eom, G.-M. The effect of sitting posture on the loads at cervico-thoracic and lumbosacral joints. Technol. Health Care 2018, 26, 409–418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Patwardhan, A.G.; Khayatzadeh, S.; Havey, R.M.; Voronov, L.I.; Smith, Z.A.; Kalmanson, O.; Ghanayem, A.J.; Sears, W. Cervical sagittal balance: A biomechanical perspective can help clinical practice. Eur. Spine J. 2018, 27, 25–38. [Google Scholar] [CrossRef] [PubMed]
  14. Florencio, L.L.; Ferracni, G.N.; Chaves, T.C.; Palacios-Ceña, M.; Ordás-Bandera, C.; Speciali, J.G.; Grossi, D.B.; Fernández-de-Las-Peñas, C. Analysis of Head Posture and Activation of the Cervical Neck Extensors During a Low-Load Task in Women with Chronic Migraine and Healthy Participants. J. Manip. Physiol. Ther. 2018, 41, 762–770. [Google Scholar] [CrossRef] [PubMed]
  15. Mahmoud, N.F.; Hassan, K.A.; Abdelmajeed, S.F.; Moustafa, I.M.; Silva, A.G. The Relationship Between Forward Head Posture and Neck Pain: A Systematic Review and Meta-Analysis. Curr. Rev. Musculoskelet Med. 2019, 12, 562–577. [Google Scholar] [CrossRef] [PubMed]
  16. Poitras, S.; Blais, R.; Swaine, B.; Rossignol, M. Management of work-related low back pain: A population-based survey of physical therapists. Phys. Ther. 2005, 85, 1168–1181. [Google Scholar] [CrossRef] [PubMed]
  17. Kendall, F.P.; McCreary, E.K.; Provance, P.G.; Rodgers, M.M.; Romani, W.A. Muscles Testing and Function with Posture and Pain; Lippincott Williams & Wilkins: Baltimore, MD, USA, 2005. [Google Scholar]
  18. Falla, D.; Jull, G.; Russell, T.; Vicenzino, B.; Hodges, P. Effect of neck exercise on sitting posture in patients with chronic neck pain. Phys. Ther. 2007, 87, 408–417. [Google Scholar] [CrossRef]
  19. Claus, A.P.; Hides, J.A.; Moseley, G.L.; Hodges, P.W. Is ‘ideal’ sitting posture real? Measurement of spinal curves in four sitting postures. Man. Ther. 2009, 14, 404–408. [Google Scholar] [CrossRef]
  20. Annetts, S.; Coales, P.; Colville, R.; Mistry, D.; Moles, K.; Thomas, B.; van Deursen, R. A pilot investigation into the effects of different office chairs on spinal angles. Eur. Spine J. 2012, 21, 165–170. [Google Scholar] [CrossRef] [Green Version]
  21. Baumgartner, D.; Zemp, R.; List, R.; Stoop, M.; Naxera, J.; Elsig, J.P.; Lorenzetti, S. The Spinal Curvature of Three Different Sitting Positions Analysed in an Open MRI Scanner. Sci. World J. 2012, 2012, 184016. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Grondin, D.E.; Triano, J.J.; Tran, S.; Soave, D. The effect of a lumbar support pillow on lumbar posture and comfort during a prolonged seated task. Chiropr. Man. Ther. 2013, 21, 21. [Google Scholar] [CrossRef] [Green Version]
  23. Ward, J.; Coats, J. Comparison of the BackJoy SitSmart Relief and Spine Buddy LT1 H/C Ergonomic Chair Supports on Short-Term Neck and Back Pain. J. Manip. Physiol. Ther. 2017, 40, 41–49. [Google Scholar] [CrossRef] [Green Version]
  24. Celenay, S.T.; Kaya, D.O.; Ozudogru, A. Spinal postural training: Comparison of the postural and mobility effects of electrotherapy, exercise, biofeedback trainer in addition to postural education in university students. J. Back Musculoskelet. Rehabil. 2015, 28, 135–144. [Google Scholar] [CrossRef]
  25. Yoo, W.G.; Park, S.Y. Effects of posture-related auditory cueing (PAC) program on muscles activities and kinematics of the neck and trunk during computer work. Work 2015, 50, 187–191. [Google Scholar] [CrossRef] [PubMed]
  26. Yoo, W.G.; Yi, C.H.; Kim, M.H. Effects of a proximity-sensing feedback chair on head, shoulder, and trunk postures when working at a visual display terminal. J. Occup. Rehabil. 2006, 16, 631–637. [Google Scholar] [CrossRef]
  27. Rodrigues Ferreira Faisting, A.L.; de Oliveira Sato, T. Effectiveness of ergonomic training to reduce physical demands and musculoskeletal symptoms-An overview of systematic reviews. Int. J. Ind. Ergon. 2019, 74, 102845. [Google Scholar] [CrossRef]
  28. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Brink, Y.; Louw, Q.; Grimmer, K.; Schreve, K.; van der Westhuizen, G.; Jordaan, E. Development of a cost effective three-dimensional posture analysis tool: Validity and reliability. BMC Musculoskelet. Disord. 2013, 14, 335. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Brink, Y.; Louw, Q.; Grimmer, K.; Jordaan, E. The spinal posture of computing adolescents in a real-life setting. BMC Musculoskelet Disord. 2014, 15, 212. [Google Scholar] [CrossRef] [Green Version]
  31. Kuo, Y.L.; Tully, E.A.; Galea, M.P. Video analysis of sagittal spinal posture in healthy young and older adults. J. Manip. Physiol. Ther. 2009, 32, 210–215. [Google Scholar] [CrossRef] [PubMed]
  32. Yip, C.H.; Chiu, T.T.; Poon, A.T. The relationship between head posture and severity and disability of patients with neck pain. Man. Ther. 2008, 13, 148–154. [Google Scholar] [CrossRef] [PubMed]
  33. Szeto, G.P.; Straker, L.; Raine, S. A field comparison of neck and shoulder postures in symptomatic and asymptomatic office workers. Appl. Ergon. 2002, 33, 75–84. [Google Scholar] [CrossRef]
  34. Field, A. Discovering Statistics Using IBM SPSS Statistics; SAGE: London, UK, 2013. [Google Scholar]
  35. Kim, H.-Y. Statistical notes for clinical researchers: Effect size. Restor. Dent. Endod. 2015, 40, 328–331. [Google Scholar] [CrossRef]
  36. Hestbaek, L.; Leboeuf-Yde, C.; Kyvik, K.O.; Manniche, C. The course of low back pain from adolescence to adulthood: Eight-year follow-up of 9600 twins. Spine 2006, 31, 468–472. [Google Scholar] [CrossRef] [PubMed]
  37. Proske, U.; Gandevia, S.C. The Proprioceptive Senses: Their Roles in Signaling Body Shape, Body Position and Movement, and Muscle Force. Physiol. Rev. 2012, 92, 1651–1697. [Google Scholar] [CrossRef] [PubMed]
  38. Lee, A.S.; Cholewicki, J.; Reeves, N.P.; Zazulak, B.T.; Mysliwiec, L.W. Comparison of trunk proprioception between patients with low back pain and healthy controls. Arch. Phys. Med. Rehabil. 2010, 91, 1327–1331. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Stanton, T.R.; Leake, H.B.; Chalmers, K.J.; Moseley, G.L. Evidence of Impaired Proprioception in Chronic, Idiopathic Neck Pain: Systematic Review and Meta-Analysis. Phys. Ther. 2016, 96, 876–887. [Google Scholar] [CrossRef] [Green Version]
  40. Ribeiro, D.C.; Sole, G.; Abbott, J.H.; Milosavljevic, S. Extrinsic feedback and management of low back pain: A critical review of the literature. Man. Ther. 2011, 16, 231–239. [Google Scholar] [CrossRef]
  41. Huang, H.; Wolf, S.L.; He, J. Recent developments in biofeedback for neuromotor rehabilitation. J. Neuroeng. Rehabil. 2006, 3, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Schmidt, R.; Wrisberg, C. Motor Learning and Performance: A Situation-Based Learning Approach, 4th ed.; Human Kinetics: Champaign, IL, USA, 2008. [Google Scholar]
  43. Cramer, H.; Mehling, W.E.; Saha, F.J.; Dobos, G.; Lauche, R. Postural awareness and its relation to pain: Validation of an innovative instrument measuring awareness of body posture in patients with chronic pain. BMC Musculoskelet. Disord. 2018, 19, 109. [Google Scholar] [CrossRef] [Green Version]
  44. McLean, L. The effect of postural correction on muscle activation amplitudes recorded from the cervicobrachial region. J. Electromyogr. Kinesiol. 2005, 15, 527–535. [Google Scholar] [CrossRef] [PubMed]
  45. Haller, M.; Richter, C.; Brandl, P.; Gross, S.; Schossleitner, G.; Schrempf, A.; Nii, H.; Sugimoto, M.; Inami, M. Finding the Right Way for Interrupting People Improving Their Sitting Posture; Human-Computer Interaction–INTERACT 2011; Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 1–17. [Google Scholar]
Figure 1. The wearable biofeedback sensor used in this study.
Figure 1. The wearable biofeedback sensor used in this study.
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Figure 2. Placement of reflective markers and angle definitions. Decreasing head tilt, upper cervical, and lower cervical angles indicate flexion. Decreasing neck flexion, thoracic, and lumbar angles indicate extension. A negative pelvic plane angle indicates an anterior tilt of the pelvis. MP: midpoint; ASIS: anterior superior iliac spine; PSIS: posterior superior iliac supine.
Figure 2. Placement of reflective markers and angle definitions. Decreasing head tilt, upper cervical, and lower cervical angles indicate flexion. Decreasing neck flexion, thoracic, and lumbar angles indicate extension. A negative pelvic plane angle indicates an anterior tilt of the pelvis. MP: midpoint; ASIS: anterior superior iliac spine; PSIS: posterior superior iliac supine.
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Table 1. Descriptive and inferential statistics of the spinal angles under two conditions across six time points (N = 21).
Table 1. Descriptive and inferential statistics of the spinal angles under two conditions across six time points (N = 21).
AnglesDeviceT5T15T25T35T45T55Condition
(C)
Time
(T)
Interaction
(C × T)
Head tiltWithout12.7 ± 7.411.7 ± 8.011.6 ± 8.811.4 ± 8.613.7 ± 9.511.3 ± 9.4p = 0.434p = 0.582p = 0.079
With12.2 ± 8.912.5 ± 9.713.1 ± 9.712.9 ± 8.712.3 ± 9.412.9 ± 10.2
Neck flexionWithout59.3 ± 5.360.2 ± 6.259.7 ± 6.559.8 ± 6.359.0 ± 6.660.3 ± 6.2p < 0.001 *p = 0.619p = 0.201
With57.2 ± 5.757.4 ± 5.756.8 ± 5.856.5 ± 5.857.1 ± 6.056.6 ± 5.9
Upper cervicalWithout155.1 ± 8.2154.9 ± 9.0154.1 ± 9.7153.9 ± 8.9155.7 ± 10.5154.4 ± 10.7p = 0.004 *p = 0.650p = 0.334
With151.6 ± 7.2152.3 ± 8.5152.3 ± 8.1151.7 ± 7.7151.6 ± 8.1151.6 ± 8.8
Lower cervicalWithout62.3 ± 8.661.8 ± 8.061.5 ± 7.961.4 ± 8.362.2 ± 9.561.4 ± 8.6p = 0.012 *p = 0.688p = 0.149
With60.3 ± 7.660.7 ± 7.560.9 ± 8.160.4 ± 7.760.5 ± 7.560.9 ± 8.1
ThoracicWithout28.9 ± 7.829.7 ± 8.028.8 ± 9.029.0 ± 8.329.3 ± 9.629.6 ± 8.0p = 0.033 *p = 0.816p = 0.613
With27.2 ± 8.127.6 ± 8.427.2 ± 8.227.5 ± 9.527.9 ± 9.126.6 ± 8.7
LumbarWithout8.6 ± 4.99.4 ± 4.99.3 ± 4.68.9 ± 5.28.9 ± 4.58.4 ± 5.2p = 0.217p = 0.094p = 0.516
With7.2 ± 4.89.0 ± 5.08.1 ± 5.47.1 ± 4.78.1 ± 5.08.5 ± 5.1
Pelvic planeWithout7.3 ± 6.67.0 ± 7.37.7 ± 6.86.8 ± 6.48.8 ± 8.86.7 ± 8.4p = 0.021 *p = 0.566p = 0.501
With5.7 ± 6.35.2 ± 7.15.8 ± 6.54.1 ± 5.64.8 ± 6.85.5 ± 5.5
Values are expressed as the mean ± standard deviation. * The post-hoc analysis indicates any statistically significant difference between the two conditions (without and without the sensor).
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Kuo, Y.-L.; Huang, K.-Y.; Kao, C.-Y.; Tsai, Y.-J. Sitting Posture during Prolonged Computer Typing with and without a Wearable Biofeedback Sensor. Int. J. Environ. Res. Public Health 2021, 18, 5430. https://doi.org/10.3390/ijerph18105430

AMA Style

Kuo Y-L, Huang K-Y, Kao C-Y, Tsai Y-J. Sitting Posture during Prolonged Computer Typing with and without a Wearable Biofeedback Sensor. International Journal of Environmental Research and Public Health. 2021; 18(10):5430. https://doi.org/10.3390/ijerph18105430

Chicago/Turabian Style

Kuo, Yi-Liang, Kuo-Yuan Huang, Chieh-Yu Kao, and Yi-Ju Tsai. 2021. "Sitting Posture during Prolonged Computer Typing with and without a Wearable Biofeedback Sensor" International Journal of Environmental Research and Public Health 18, no. 10: 5430. https://doi.org/10.3390/ijerph18105430

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