Abstract
Purposes
To evaluate whether a relationship exists between patient-based scoring systems and the activity level of patients with low back pain (LBP) by using wearable activity trackers, and to determine whether activity level was affected by patient factors.
Methods
The subjects were 66 patients with LBP. The physical activity of participants was objectively evaluated using the Micro-Motion logger (Actigraph). The activity level was analyzed with the mean active count of the proportional-integrating mode (PMAC) and zero-crossing mode. Clinical symptoms were evaluated using the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ), Roland–Morris Disability Questionnaire, the Oswestry Disability Index, and visual analog scale (VAS). The relationships between each item of the patient-based questionnaire and activity level, and the influence of individual factors (age, sex, body mass index [BMI], low back pain, and muscle mass) on the activity level were evaluated.
Results
In each domain of the JOABPEQ, lumbar spine dysfunction and social life dysfunction were correlated with PMAC (r = 0.327 and 0.321, respectively). The low back pain VAS scores were correlated with PMAC (r = − 0.246). Multiple regression analysis shows that individual factors affecting the activity level of patients with LBP were sex, BMI, low back pain, and muscle mass in PMAC (p < 0.01).
Conclusions
Some domains of the questionnaires were correlated with activity level, but others were not. Additionally, the activity level of patients with LBP was affected by sex, BMI, LBP, and skeletal muscle mass index.
Graphic abstract
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References
Manchikanti L, Singh V, Falco F, Benyamin RM, Hirsch JA (2014) Epidemiology of low back pain in adults. Neuromodulation 17:3–10. https://doi.org/10.1111/ner.12018
Konno S, Hayashino Y, Fukuhara S, Kikuchi S, Kaneda K, Seichi A, Chiba K, Satomi K, Nagata K, Kawai S (2007) Development of a clinical diagnosis support tool to identify patients with lumbar spinal stenosis. Eur Spine J 16:1951–1957. https://doi.org/10.1007/s00586-007-0402-2
Ohtori S, Sekiguchi M, Yonemoto K, Kakuma T, Takahashi K, Konno S (2014) Awareness and use of diagnostic support tools for lumbar spinal stenosis in Japan. J Orthop Sci 19:412–417. https://doi.org/10.1007/s00776-014-0551-1
Fujiwara A, Kobayashi N, Saiki K, Kitagawa T, Tamai K, Saotome K (2003) Association of the Japanese Orthopaedic Association score with the Oswestry Disability Index, Roland-Morris Disability Questionnaire, and short-form 36. Spine 28:1601–1607
Fukui M, Chiba K, Kawakami M, Kikuchi S, Konno S, Miyamoto M, Seichi A, Shimamura T, Shirado O, Taguchi T, Takahashi K, Takeshita K, Tani T, Toyama Y, Yonenobu K, Wada E, Tanaka T, Hirota Y (2009) JOA back pain evaluation questionnaire (JOABPEQ)/JOA cervical myelopathy evaluation questionnaire (JOACMEQ). The report on the development of revised versions. April 16, 2007. J Orthop Sci 14:348–365. https://doi.org/10.1007/s00776-009-1337-8
Hallal PC, Anderson LB, Bull FC, Guthold R, Haskell W, Ekelund U (2012) Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 380:247–257. https://doi.org/10.1016/S0140-6736(12)60646-1
Hedner J, Piller G, Pittman SD, Zou D, Grote L, White DP (2004) A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep 27:1560–1566
Montoye AH, Mudd LM, Biswa S, Pfeiffer KA (2015) Energy expenditure prediction using raw accelerometer data in simulated free living. Med Sci Sports Exerc 47:1735–1746. https://doi.org/10.1249/MSS.0000000000000597
Zhang JH, MacFarlane DJ, Sobko T (2016) Feasibility of a chest-worn accelerometer for physical activity measurement. J Sci Med Sport 19:1015–1019. https://doi.org/10.1016/j.jsams.2016.03.004
Smuck M, Muaremi A, Zheng P, Norden J, Sinha A, Hu R, Tomkins-Lane C (2018) Objective measurement of function following lumbar spinal stenosis decompression reveals improved functional capacity with stagnant real-life physical activity. Spine J 18:15–21. https://doi.org/10.1016/j.spinee.2017.08.262
Girardin JL, Kripke DF, Cole RJ, Assmus JD, Langer RD (2001) Sleep detection with an accelerometer actigraph: comparisons with polysomnography. Physiol Behav 72:21–28
Hjorth MF, Chaput JP, Damsgaard CT, Dalskov SM, Michaelsen KF, Tetens I, Sjödin A (2012) Measure of sleep and physical activity by a single accelerometer: can a waist-worn Actigraph adequately measure sleep in children? Sleep Biol Rhythms 10:328–335. https://doi.org/10.1111/j.1479-8425.2012.00578.x
Moran DS, Heled Y, Gonzalez RR (2004) Metabolic rate monitoring and energy expenditure prediction using a novel actigraphy method. Med Sci Monit 10:MT117–MT120
Blackwell T, Redline S, Ancoli-Israel S, Schneider JL, Surovec S, Johnson NL, Cauley JA, Stone KL (2008) Comparison of sleep parameters from actigraphy and polysomnography in older women: the SOF study. Sleep 31:283–291
Asaka Y, Takada S (2010) Activity-based assessment of the sleep behaviors of VLBW preterm infants and full-term infants at around 12 months of age. Brain Dev 32:150–155. https://doi.org/10.1016/j.braindev.2008.12.006
Sekiguchi M, Wakita T, Otani K, Onishi Y, Fukuhara S, Kikuchi S, Konno S (2014) Lumbar spinal stenosis-specific symptom scale: validity and responsiveness. Spine 39:E1388–E1393. https://doi.org/10.1097/BRS.0000000000000583
Eguchi Y, Suzuki M, Yamanaka H, Tamai H, Kobayashi T, Orita S, Yamauchi K, Suzuki M, Inage K, Kanamoto H, Abe K, Aoki Y, Koda M, Furuya T, Takahashi K, Ohtori S (2017) Assessment of clinical symptoms in lumbar foraminal stenosis using the Japanese Orthopaedic Association back pain evaluation questionnaire. Korean J Spine 14:1–6. https://doi.org/10.14245/kjs.2017.14.1.1
Fujimoto K, Inage K, Eguchi Y, Orita S, Toyoguchi T, Yamauchi K, Suzuki M, Kubota G, Sainoh T, Sato J, Shiga Y, Abe K, Kanamoto H, Inoue M, Kinoshita H, Norimoto M, Umimura T, Koda M, Furuya T, Maki S, Akazawa T, Terakado A, Takahashi K, Ohtori S (2019) Dual-energy x-ray absorptiometry and bioelectrical impedance analysis are beneficial tools for measuring the trunk muscle mass of patients with low back pain. Spine Surg Relat Res. https://doi.org/10.22603/ssrr.2018-0040
Iizuka Y, Iizuka H, Mieda T, Tajika T, Yamamoto A, Ohsawa T, Sasaki T, Takagishi K (2015) Association between neck and shoulder pain, back pain, low back pain and body composition parameters among the Japanese general population. BMC Musculoskelet Disord 16:333. https://doi.org/10.1186/s12891-015-0759-z
Inoue M, Orita S, Inage K, Suzuki MI, Fujimoto K, Shiga Y, Kanamoto H, Abe K, Kinoshita H, Norimoto M, Umimura T, Sato T, Sato M, Suzuki MA, Enomoto K, Eguchi Y, Aoki Y, Akazawa T, Ohtori S (2019) Comparison of the activity level of the upper limbs and trunk in patients with low back pain evaluated using a wearable accelerometer: a validation study. Spine Surg Relat Res. https://doi.org/10.22603/ssrr.2018-0100
Liszka-Hackzell JJ, Martin DP (2004) An analysis of the relationship between activity and pain in chronic and acute low back pain. Anesth Analg 99:477–481. https://doi.org/10.1213/01.ANE.0000132696.15310.DD
Hashimoto Y, Matsudaira K, Sawada SS, Gando Y, Kawakami R, Sloan RA, Kinugawa C, Okamoto T, Tsukamoto K, Miyachi M, Naito H (2018) Association between objectively measured physical activity and body mass index with low back pain: a large-scale cross-sectional study of Japanese men. BMC Public Health 18(1):341. https://doi.org/10.1186/s12889-018-5253-8
Chiarotto A, Maxwell LJ, Terwee CB, Wells GA, Tugwell P, Ostelo RW (2016) Roland-Morris disability questionnaire and Oswestry Disability Index: which has better measurement properties for measuring physical functioning in nonspecific low back pain? Systematic review and meta-analysis. Phys Ther 96:1620–1637. https://doi.org/10.2522/ptj.20150420
Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM, Simonsick EM, Harris TB (2005) Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 60:324–333
Mikkola TM, von Bonsdorff MB, Salonen MK, Simonen M, Pohjolainen P, Osmond C, Perälä MM, Rantanen T, Kajantie E, Eriksson JG (2018) Body composition as a predictor of physical performance in older age: a ten-year follow-up of the Helsinki Birth Cohort Study. Arch Gerontol Geriatr 77:163–168. https://doi.org/10.1016/j.archger.2018.05.009
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This work was financially supported by the research grant funded by the Japanese Orthopaedic Association (#2016-1).
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Inoue, M., Orita, S., Inage, K. et al. Relationship between patient-based scoring systems and the activity level of patients measured by wearable activity trackers in lumbar spine disease. Eur Spine J 28, 1804–1810 (2019). https://doi.org/10.1007/s00586-019-06023-z
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DOI: https://doi.org/10.1007/s00586-019-06023-z