Patients with chronic neck pain (CNP) often describe the presence of associated symptoms such as unsteadiness, dizziness, light-headedness, loss of standing balance and a sensation of falling, as well as a history of increased incidence of actual falls. Effectively assessing balance in these patients would assist in their differential diagnosis, on-going management and the determination of treatment intervention(s) leading to improved rehabilitation outcomes and, potentially, a reduction in the likelihood of fall-related injuries. Research has demonstrated the relationship between chronic neck pain and disturbances of balance by measuring alterations of postural sway (PS) with computerised force plates (CFP) and / or posturography. Whilst providing reliable measurements of postural sway, the equipment is not readily available and is expensive for use in the clinical practice environment. New generations of Smart Phones (SP) with inbuilt sensors are capable of measuring metabolic energy expenditure, cardiovascular responses and gait parameters. However, no study to date has shown if a Smart Phone, with an embedded accelerometer sensor and application (App) establishing a wearable accelerometer (WA),can measure postural sway in patients with chronic neck pain. The primary aim of the study was to determine if Smart Phones can provide valid measures of postural sway in chronic neck pain sufferers, relative to the reference standard computerised force plates. The secondary aims were to investigate the relationship between the self-rating of pain and disability and mechanical pain threshold test (MPTT) measures, and the relationship of both of these measures with postural sway in participants with and without chronic neck pain. To determine these aims control and chronic neck pain test groups, each with 25 participants, were simultaneously measured for postural sway during five clinical balance tests (three static and two dynamic / functional) with computerised force plate and Smart Phone. The participants were also assessed for demographic and related medical history information: completed physical (height, weight, foot size & width, body mass index (BMI)) and pressure algometric (symptomatic & asymptomatic locations) measurements: and answered five self-rating questionnaires (VAS Pain, VAS Stress, NDI, DASS – 21,SF – 36). Spectral analysis produced frequency-domain measures (filtered 0-12 Hz) of Raw Standard Deviation (RSD),Root Mean Square (RMS),and Fast Fourier Transform (FFT) Mean and Maximum Frequencies. Statistical analyses employed independent t- tests and bivariate Pearson’s r correlations, while inferential statistics utilized Cohen’s criteria (Cohen,1988). The result analyses for “all participant data combined” indicated the most valid frequency-domain measure for assessing postural sway using the Smart Phone was FFT Mean frequency. This measure recorded eleven significant positive Pearson’s r correlation coefficients between the computerised force plate and Smart Phone measures ranging from small to large effect sizes (r = .28 - .53; p <0.01). Similarly valid measures were the RSD, with four significant small to large positive Pearson’s r correlation coefficients (r = .29 - .79; p <0.01),and the FFT Maximum frequency, with four medium to large positive (r = .40 - .53; p <0.01) correlations. The least valid measure was the RMS, with only one medium significant Pearson’s r correlation coefficient (r = .33; p <0.05) when compared to the computerised force plate. Analysis of each of the three axial accelerometer directions (vertical, anterio-posterior (AP) and medio-lateral (ML) directions) for FFT Mean frequency data demonstrated significant positive Pearson’s r correlation coefficients in all three axial directions for the single leg stance (SLS) and tandem walking (TW) balance tests ranging from medium to large (r = .3 - .49; p <0.01). Although for both test groups the FFT Mean frequency measure produced a similar number of significant Pearson’s r correlation coefficients for both AP and ML directions, the AP direction produced a stronger correlation for all balance tests ranging from medium to large (r = .46 - .53; p <0.01). For the Control Group FFT Mean frequency again was demonstrated to be the most valid frequency-domain measure in all three axial directions with ten significant positive Pearson’s r correlation coefficients between the computerised force plate and Smart Phone measures ranging from medium to large effect sizes (r = .42 - .58; p <0.01). For the Chronic Neck Pain Group FFT Mean frequency was the most valid frequency-domain measure but only in the AP direction, with five significant Pearson’s r correlation coefficients between the computerised force plate and Smart Phone measures of medium effect size (r = .41 - .49; p <0.01). Independent samples t-tests for the FFT Mean frequency measure revealed no significant difference between the control and chronic neck pain groups for either the computerised force plate or Smart Phone techniques for assessing postural sway. The relationship between Smart Phone postural sway and self-ratings of pain and disability measures revealed a greater number of significant Pearson’s r correlation coefficients, ranging from medium to large (r = .4 - .58; p <0.05),than the computerised force plate. Similarly, the relationship between Smart Phone postural sway and mechanical pain threshold test measures showed the same trend (a greater number and stronger significant Pearson’s r correlation coefficients ranging from medium to large (r = .46 - .57; p <0.05) than the computerised force plate). Measures of the relationship between self-rating of pain and disability and mechanical pain threshold test demonstrated significant positive Pearson’s r correlation coefficients for the self-perceived pain and physical health disability measures ranging from medium to large (r = .4 - .53; p <0.05). The results of the study suggest that Smart Phone measures of postural sway in the general population are valid when compared to those of the computerised force plate. In chronic neck pain sufferers Smart Phone accelerometer measures are valid in the AP direction during the more challenging single leg stance and tandem walking balance tests. Potentially the most valid frequency-domain measure for assessing postural sway is the FFT Mean Frequency, again particularly in the AP direction during the more challenging balance tests of single leg stance and tandem walking . Relative to the computerised force plate, the Smart Phone better demonstrates the relationship between postural sway and self-rating pain and disability measures. This relationship is most consistent for the more challenging balance tests of single leg stance and tandem walking and is best assessed by the DASS-21 questionnaire, a self-rating of depression, anxiety and stress. Lastly, the relationship between the various self-rating of pain and disability scores and mechanical pain threshold test measures are only evident for the physical health summary component in chronic neck pain sufferers. In conclusion, the Smart Phone with appropriate software, used as a wearable accelerometer, has the potential to become an inexpensive and easily accessible technique for quantitatively measuring postural sway suitable for the clinical practice environment. Used in conjunction with carefully selected self-rating questionnaires and mechanical pain threshold test measures, the Smart Phone could allow earlier detection and potentially better on-going monitoring of changes in postural sway. This may enable immediate assessment of treatment interventions leading to improved rehabilitation outcomes and a reduction in the likelihood of falls-related injuries in chronic neck pain sufferers from balance disturbances. Potentially these benefits of Smart Phone technology could also be used with sufferers experiencing balance disturbances arising from other orthopaedic, sports, neurological, geriatric or paediatric conditions.
| Date of Award | 2014 |
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| Original language | English |
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| Supervisor | Gordon WADDINGTON (Supervisor) & Stuart Cathcart (Supervisor) |
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