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Journal articles on the topic 'Start back screening tool'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Traeger, Adrian, and James H. McAuley. "STarT Back Screening Tool." Journal of Physiotherapy 59, no. 2 (June 2013): 131. http://dx.doi.org/10.1016/s1836-9553(13)70170-x.

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2

Hasan, Md Israt, and Syed Mozaffar Ahmed. "Translation, cross-cultural adaptation and validation of English start back screening tool into Bangla for patients with low back pain." Bangabandhu Sheikh Mujib Medical University Journal 13, no. 3 (September 19, 2020): 73–78. http://dx.doi.org/10.3329/bsmmuj.v13i3.49265.

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This study was intended to translate and culturally adapt the STarT back screening tool to produce an equivalent Bangla version. Total 58 patients with low back pain completed the newly developed Bangla version of STarT back screening tool and Bangla version of Ronald Morris Disability Questionnaire seven days apart. Reliability was assessed by internal consistency (Chronbach’s alpha for overall score 0.81 and for spychosocial subscale was 0.76) and test-retest reliability (intraclass correlation coefficient for overall score was 0.78 and for spychosocial subscale was 0.71). Reliability of Bangla version of STarT back screening tool was very good. Pearson’s correlation coefficient was carried out on the Bangla version of STarT back screening tool and Bangla version of RMDQ to assess construct validity (overall score was 0.88 and spychosocial subscale score was 0.83) which indicate a strong correlation between them. This study shows that the Bangla version of STarT back screening tool is a reliable, valid and culturally adapted responsive screening tool for the patients with low back pain.
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Fuhro, Fernanda Ferreira, Felipe Ribeiro Cabral Fagundes, Ana Carolina Taccolini Manzoni, Leonardo Oliveira Pena Costa, and Cristina Maria Nunes Cabral. "Örebro Musculoskeletal Pain Screening Questionnaire Short-Form and STarT Back Screening Tool." SPINE 41, no. 15 (August 2016): E931—E936. http://dx.doi.org/10.1097/brs.0000000000001415.

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4

Pilz, Bruna, Rodrigo A. Vasconcelos, Paulo P. Teixeira, Wilson Mello, Freddy B. Marcondes, Jonathan C. Hill, and Débora B. Grossi. "Construct and discriminant validity of STarT Back Screening Tool – Brazilian version." Brazilian Journal of Physical Therapy 21, no. 1 (January 2017): 69–73. http://dx.doi.org/10.1016/j.bjpt.2016.12.006.

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Betten, Carola, Christofer Sandell, Jonathan C. Hill, and Annelie Gutke. "Cross-cultural adaptation and validation of the Swedish STarT Back Screening Tool." European Journal of Physiotherapy 17, no. 1 (January 2, 2015): 29–36. http://dx.doi.org/10.3109/21679169.2014.1003962.

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Aebischer, Bernhard, Jonathan C. Hill, Roger Hilfiker, and Sven Karstens. "German Translation and Cross-Cultural Adaptation of the STarT Back Screening Tool." PLOS ONE 10, no. 7 (July 10, 2015): e0132068. http://dx.doi.org/10.1371/journal.pone.0132068.

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7

Morsø, Lars, Hanne Albert, Peter Kent, Claus Manniche, and Jonathan Hill. "Translation and discriminative validation of the STarT Back Screening Tool into Danish." European Spine Journal 20, no. 12 (July 19, 2011): 2166–73. http://dx.doi.org/10.1007/s00586-011-1911-6.

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8

Butera, Katie A., Trevor A. Lentz, Jason M. Beneciuk, and Steven Z. George. "Preliminary Evaluation of a Modified STarT Back Screening Tool Across Different Musculoskeletal Pain Conditions." Physical Therapy 96, no. 8 (August 1, 2016): 1251–61. http://dx.doi.org/10.2522/ptj.20150377.

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Abstract Background The STarT Back Screening Tool is a validated multidimensional screening measure and risk stratification tool for people with low back pain. Objective The study objective was to compare relationships between a modified STarT Back Screening Tool (mSBT) and clinical and psychological measures in people with low back, neck, shoulder, and knee pain. The hypothesis was that the relationships between mSBT scores and clinical and psychological measure scores would be similar across the included musculoskeletal pain conditions. Design A cross-sectional, secondary analysis was done in this study. Methods Participants with low back (n=118), neck (n=92), shoulder (n=106), or knee (n=111) pain were recruited, and an mSBT was developed for use across the pain conditions. Separate hierarchical linear regression models were developed, with clinical (health status, pain intensity, and disability) and psychological (kinesiophobia, catastrophizing, fear avoidance, anxiety, depressive symptoms, and self-efficacy) measures as dependent variables. Demographic and pain region variables were entered in the first step, mSBT scores were entered in the second step, and pain region × mSBT interactions were entered in the last step. Results In the final models, no interactions were identified, suggesting that dependent measure scores did not differ by pain region. The strongest contributor for all dependent variables was mSBT scores (β=|0.32|–|0.68|); higher mSBT scores were associated with poorer health status and self-efficacy and with higher levels of pain intensity, disability, kinesiophobia, catastrophizing, fear avoidance, anxiety, and depressive symptoms. Limitations Generalizability was restricted to physical therapy outpatients with the included pain conditions. The mSBT used in this study is not ready for clinical implementation. Conclusions The results of this study support the feasibility of using a single measure for concise risk assessment across different musculoskeletal pain conditions. Further longitudinal studies are needed to better direct the clinical use of an mSBT in people with low back, neck, shoulder, and knee pain.
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Billis, Evdokia, Konstantinos Fousekis, Maria Tsekoura, Sofia Lampropoulou, Charalampos Matzaroglou, John Gliatis, Christos Sinopidis, Jonathan Hill, and Nikolaos Strimpakos. "Cross-cultural validation of the start back screening tool in a Greek low back pain sample." Musculoskeletal Science and Practice 53 (June 2021): 102352. http://dx.doi.org/10.1016/j.msksp.2021.102352.

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Gusi, Narcís, Borja del Pozo-Cruz, Pedro R. Olivares, Miguel Hernández-Mocholi, and Jonathan C. Hill. "The Spanish version of the “STarT Back Screening Tool” (SBST) in different subgroups." Atención Primaria 43, no. 7 (July 2011): 356–61. http://dx.doi.org/10.1016/j.aprim.2010.05.019.

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11

Piironen, Susanna, Markus Paananen, Marianne Haapea, Markku Hupli, Paavo Zitting, Katja Ryynänen, Esa-Pekka Takala, et al. "Transcultural adaption and psychometric properties of the STarT Back Screening Tool among Finnish low back pain patients." European Spine Journal 25, no. 1 (February 12, 2015): 287–95. http://dx.doi.org/10.1007/s00586-015-3804-6.

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Pilz, Bruna, Rodrigo A. Vasconcelos, Freddy B. Marcondes, Samuel S. Lodovichi, Wilson Mello, and Débora B. Grossi. "The Brazilian version of STarT Back Screening Tool - translation, cross-cultural adaptation and reliability." Brazilian Journal of Physical Therapy 18, no. 5 (October 2014): 453–61. http://dx.doi.org/10.1590/bjpt-rbf.2014.0028.

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Pauli, Jena, Angela Starkweather, and Jo Lynne Robins. "Screening Tools to Predict the Development of Chronic Low Back Pain: An Integrative Review of the Literature." Pain Medicine 20, no. 9 (October 10, 2018): 1651–77. http://dx.doi.org/10.1093/pm/pny178.

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AbstractObjectiveTo identify and describe available instruments that can be used to screen patients with acute or subacute low back pain for a chronic low back pain trajectory.DesignIntegrative literature review.MethodsAn electronic search of PubMed/MEDLINE, CINAHL, Cochrane Database of Systematic Reviews, and PsychINFO databases took place from May through July of 2014 using systematic search strategies to identify screening instruments developed to identify people at risk of chronic low back pain. After screening for inclusion and exclusion criteria as well as quality indicators, the identified studies were categorized based on whether the instrument measured psychological, clinical, or functional measures to predict chronic low back pain.ResultsInitial searches identified 2,274 potential articles. After assessing for duplicates, title, and abstract content, there were 129 remaining articles. Articles were further excluded after analysis of the text, for a total of 42 studies reviewed. Most instruments reviewed were unable to provide evidence of predictive power for developing chronic low back pain.ConclusionsThis review identified numerous instruments developed to assess the likelihood of chronic low back pain in acute and subacute low back pain populations. Of the instruments reviewed, the STarT Back Screening Tool and the Örebro Musculoskeletal Pain Questionnaire demonstrated superior predictive power compared with other instruments. Both screening tools offer evidence of validation, translation into different languages and international application, and usage in various health care settings and provide data on predictive power.
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Hill, Jonathan C., Kate M. Dunn, Chris J. Main, and Elaine M. Hay. "Subgrouping low back pain: A comparison of the STarT Back Tool with the Örebro Musculoskeletal Pain Screening Questionnaire." European Journal of Pain 14, no. 1 (January 2010): 83–89. http://dx.doi.org/10.1016/j.ejpain.2009.01.003.

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Oka, Hiroyuki, Ko Matsudaira, Tomoko Fujii, Norimasa Kikuchi, Yuri Haga, Takayuki Sawada, Junji Katsuhira, et al. "Estimated risk for chronic pain determined using the generic STarT Back 5-item screening tool." Journal of Pain Research Volume 10 (February 2017): 461–67. http://dx.doi.org/10.2147/jpr.s129585.

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16

Kenny, D., J. Ball, C. Bloxham, G. Cashmore, F. Dick, P. Kannan, D. Baxter, and R. Mani. "An evaluation of the psychometric properties of the STarT Back Screening Tool—a systematic review." Physiotherapy 101 (May 2015): e948-e949. http://dx.doi.org/10.1016/j.physio.2015.03.1799.

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17

Morsø, Lars, Peter Kent, Claus Manniche, and Hanne B. Albert. "The predictive ability of the STarT Back Screening Tool in a Danish secondary care setting." European Spine Journal 23, no. 1 (June 21, 2013): 120–28. http://dx.doi.org/10.1007/s00586-013-2861-y.

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Lheureux, Alexis, and Anne Berquin. "Comparison between the STarT Back Screening Tool and the Örebro Musculoskeletal Pain Screening Questionnaire: Which tool for what purpose? A semi-systematic review." Annals of Physical and Rehabilitation Medicine 62, no. 3 (May 2019): 178–88. http://dx.doi.org/10.1016/j.rehab.2018.09.007.

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Medeiros, Flávia Cordeiro, Leonardo Oliveira Pena Costa, Marco Aurélio Nemitalla Added, Evelyn Cassia Salomão, and Lucíola da Cunha Menezes Costa. "Longitudinal Monitoring of Patients With Chronic Low Back Pain During Physical Therapy Treatment Using the STarT Back Screening Tool." Journal of Orthopaedic & Sports Physical Therapy 47, no. 5 (May 2017): 314–23. http://dx.doi.org/10.2519/jospt.2017.7199.

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20

Beneciuk, Jason M., Michael E. Robinson, and Steven Z. George. "Subgrouping for Patients With Low Back Pain: A Multidimensional Approach Incorporating Cluster Analysis and the STarT Back Screening Tool." Journal of Pain 16, no. 1 (January 2015): 19–30. http://dx.doi.org/10.1016/j.jpain.2014.10.004.

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21

Kendell, M., D. Beales, A. Smith, P. O'Sullivan, and M. Rabey. "The predictive ability of the start back screening tool was limited in a cohort with chronic low back pain." Manual Therapy 25 (September 2016): e37-e38. http://dx.doi.org/10.1016/j.math.2016.05.036.

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Bruyère, Olivier, Maryline Demoulin, Charlotte Beaudart, Jonathan C. Hill, Didier Maquet, Stéphane Genevay, Geneviève Mahieu, Jean-Yves Reginster, Jean-Michel Crielaard, and Christophe Demoulin. "Validity and Reliability of the French Version of the STarT Back Screening Tool for Patients With Low Back Pain." Spine 39, no. 2 (January 2014): E123—E128. http://dx.doi.org/10.1097/brs.0000000000000062.

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23

Nitsch, Kristian P., Katharine Davis, and Kelsey Stipp. "Psychometric Measurement Properties and Clinical Utility of the Keele STarT Back Screening Tool in People With Low Back Pain." Archives of Physical Medicine and Rehabilitation 97, no. 12 (December 2016): 2233–34. http://dx.doi.org/10.1016/j.apmr.2016.06.016.

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24

Woods, Emma, and Lynne Gaskell. "The STarT Back Tool: Physiotherapist Experience and Perceptions of its Clinical Utility." European Journal for Person Centered Healthcare 2, no. 4 (October 13, 2014): 497. http://dx.doi.org/10.5750/ejpch.v2i4.836.

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i) Rationale:The STarT Back Tool (SBT) has been recently validated for use as a prognostic screening tool with Lower Back Pain (LBP) patients in the primary care setting (1). A recent study by Hill et al (2) concluded that LBP outcomes and cost effectiveness could be improved from using the SBT to guide decision making and providing a stratified approach to care. The SBT was introduced to musculoskeletal physiotherapists working in the community setting for Bolton NHS Foundation Trust.This study aims to explore the perceptions and experiences of physiotherapists using the SBT in clinical practice. No study has yet explored thisii) Method:A qualitative study was conducted. A Nominal Group Technique (NGT) was undertaken to develop an appropriate set of questions for use in a single Focus Group (FG). The FG comprised of eight musculoskeletal physiotherapists. The FG was audio taped and the data obtained was analysed through Grounded Theory Methodology.iii) Results:Physiotherapists felt the SBT acted in a confirmatory manner. Although deemed easy to use, a number of barriers led to only a small impact on decision making. These being, perceived oversimplification of the decision making process, impact on professional reputations and professional development, risks associated with single treatment sessions, patient satisfaction and threats to patient centred care. iv) Conclusion:A number of barriers reduced Physiotherapist confidence in using the SBT to guide decision making and led to physiotherapists questioning whether a stratified approach to LBP management represented the ‘best care’ for their patients. The study highlighted the importance of a robust training prior to and during the implementation of the SBT and a stratified approach to care within physiotherapy departments.
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Al Zoubi, Fadi M., Owis Eilayyan, Nancy E. Mayo, and André E. Bussières. "Evaluation of Cross-Cultural Adaptation and Measurement Properties of STarT Back Screening Tool: A Systematic Review." Journal of Manipulative and Physiological Therapeutics 40, no. 8 (October 2017): 558–72. http://dx.doi.org/10.1016/j.jmpt.2017.07.005.

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Abedi, Mohsen, Farshad Okhovatian, Maryam Heydarpour Meymeh, Seyed Javad Mousavi, and Alireza Akbarzadeh Baghban. "Construct validity and internal consistency of the modified Persian version of the STarT Back Screening Tool." Journal of Bodywork and Movement Therapies 25 (January 2021): 75–79. http://dx.doi.org/10.1016/j.jbmt.2020.11.010.

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27

Fritz, Julie M., Jason M. Beneciuk, and Steven Z. George. "Relationship Between Categorization With the STarT Back Screening Tool and Prognosis for People Receiving Physical Therapy for Low Back Pain." Physical Therapy 91, no. 5 (May 1, 2011): 722–32. http://dx.doi.org/10.2522/ptj.20100109.

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Toh, Irene, Hwei-Chi Chong, Jennifer Suet-Ching Liaw, and Yong-Hao Pua. "Evaluation of the STarT Back Screening Tool for Prediction of Low Back Pain Intensity in an Outpatient Physical Therapy Setting." Journal of Orthopaedic & Sports Physical Therapy 47, no. 4 (April 2017): 261–67. http://dx.doi.org/10.2519/jospt.2017.7284.

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Medeiros, Flávia Cordeiro, Leonardo Oliveira Pena Costa, Indiara Soares Oliveira, and Lucíola da Cunha Menezes Costa. "A Responsiveness Analysis of the Subgroups for Targeted Treatment (STarT) Back Screening Tool in Patients With Nonspecific Low Back Pain." Journal of Orthopaedic & Sports Physical Therapy 49, no. 10 (October 2019): 725–35. http://dx.doi.org/10.2519/jospt.2019.8776.

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Abedi, Mohsen, Farideh Dehghan Manshadi, Minoo Khalkhali, Seyed Javad Mousavi, Alireza Akbarzadeh Baghban, Ali Montazeri, and Mohamad Parnianpour. "Translation and validation of the Persian version of the STarT Back Screening Tool in patients with nonspecific low back pain." Manual Therapy 20, no. 6 (December 2015): 850–54. http://dx.doi.org/10.1016/j.math.2015.04.006.

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31

Katzan, Irene L., Nicolas R. Thompson, Steven Z. George, Sandi Passek, Frederick Frost, and Mary Stilphen. "The use of STarT back screening tool to predict functional disability outcomes in patients receiving physical therapy for low back pain." Spine Journal 19, no. 4 (April 2019): 645–54. http://dx.doi.org/10.1016/j.spinee.2018.10.002.

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32

Bae, Young-Hyeon, Soon Hyun Kwon, Se Hoon Park, Gwang Sik Park, Jang Hoon Shin, Hong Seon Shim, Seung Min Yoo, et al. "System Review on the STarT Back Screening Tool for Effective Care of Low Back Pain: Focus on Reliability, Validity and Effectiveness." Archives of Orthopedic and Sports Physical Therapy 14, no. 1 (June 30, 2018): 109–19. http://dx.doi.org/10.24332/aospt.2017.14.1.13.

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33

Lau, J., A. Yeung, and I. Wong. "Using STarT Back Screening Tool for managing chronic low back pain and treatment for subgroup with high risk of psychosocial factors." Hong Kong Physiotherapy Journal 33, no. 2 (December 2015): 95. http://dx.doi.org/10.1016/j.hkpj.2015.09.003.

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34

Luan, Shuo, Yu Min, Guoqi Li, Caina Lin, Xiao Li, Shaoling Wu, Chao Ma, and Jonathan C. Hill. "Cross-cultural Adaptation, Reliability, and Validity of the Chinese Version of the STarT Back Screening Tool in Patients With Low Back Pain." Spine 39, no. 16 (July 2014): E974—E979. http://dx.doi.org/10.1097/brs.0000000000000413.

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Medeiros, Flávia Cordeiro, Leonardo Oliveira Pena Costa, Indiara Soares Oliveira, Renan Kendy Oshima, and Lucíola Cunha Menezes Costa. "The use of STarT BACK Screening Tool in emergency departments for patients with acute low back pain: a prospective inception cohort study." European Spine Journal 27, no. 11 (April 18, 2018): 2823–30. http://dx.doi.org/10.1007/s00586-018-5586-0.

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36

Unsgaard-Tøndel, Monica, Ottar Vasseljen, Tom Ivar Lund Nilsen, Gard Myhre, Hilde Stendal Robinson, and Ingebrigt Meisingset. "Prognostic ability of STarT Back Screening Tool combined with work-related factors in patients with low back pain in primary care: a prospective study." BMJ Open 11, no. 6 (June 2021): e046446. http://dx.doi.org/10.1136/bmjopen-2020-046446.

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ObjectivePrimary care screening tools for patients with low back pain may improve outcome by identifying modifiable obstacles for recovery. The STarT Back Screening Tool (SBST) consists of nine biological and psychological items, with less focus on work-related factors. We aimed at testing the prognostic ability of SBST and the effect of adding items for future and present work ability.MethodsProspective observational study in patients (n=158) attending primary care physical therapy for low back pain. The prognostic ability of SBST and the added prognostic value of two work items; expectation for future work ability and current work ability, were calculated for disability, pain and quality of life outcome at 3 months follow-up. The medium and high-risk group in the SBST were collapsed in the analyses due to few patients in the high-risk group. The prognostic ability was assessed using the explained variance (R2) of the outcomes from univariable and multivariable linear regression and beta values with 95% CIs were used to assess the prognostic value of individual items.ResultsThe SBST classified 107 (67.7%) patients as low risk and 51 (32.3%) patients as medium/high risk. SBST provided prognostic ability for disability (R2=0.35), pain (R2=0.25) and quality of life (R2=0.28). Expectation for return to work predicted outcome in univariable analyses but provided limited additional prognostic ability when added to the SBST. Present work ability provided additional prognostic ability for disability (β=−2.5; 95% CI=−3.6 to −1.4), pain (β=−0.2; 95% CI=−0.5 to −0.002) and quality of life (β=0.02; 95% CI=0.001 to 0.04) in the multivariable analyses. The explained variance (R2) when work ability was added to the SBST was 0.60, 0.49 and 0.47 for disability, pain and quality of life, respectively.ConclusionsAdding one work ability item to the SBST gives additional prognostic information across core outcomes.Clinical trial number:NCT03626389
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Azimi, Parisa, Sohrab Shahzadi, Shirzad Azhari, and Ali Montazeri. "A validation study of the Iranian version of STarT Back Screening Tool (SBST) in lumbar central canal stenosis patients." Journal of Orthopaedic Science 19, no. 2 (March 2014): 213–17. http://dx.doi.org/10.1007/s00776-013-0506-y.

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38

Abdollahi, Masoud, Sajad Ashouri, Mohsen Abedi, Nasibeh Azadeh-Fard, Mohamad Parnianpour, Kinda Khalaf, and Ehsan Rashedi. "Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach." Sensors 20, no. 12 (June 26, 2020): 3600. http://dx.doi.org/10.3390/s20123600.

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Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.
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Beneciuk, Jason M., Mark D. Bishop, Julie M. Fritz, Michael E. Robinson, Nabih R. Asal, Anne N. Nisenzon, and Steven Z. George. "The STarT Back Screening Tool and Individual Psychological Measures: Evaluation of Prognostic Capabilities for Low Back Pain Clinical Outcomes in Outpatient Physical Therapy Settings." Physical Therapy 93, no. 3 (March 1, 2013): 321–33. http://dx.doi.org/10.2522/ptj.20120207.

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BackgroundPsychologically informed practice emphasizes routine identification of modifiable psychological risk factors being highlighted.ObjectiveThe purpose of this study was to test the predictive validity of the STarT Back Screening Tool (SBT) in comparison with single-construct psychological measures for 6-month clinical outcomes.DesignThis was an observational, prospective cohort study.MethodsPatients (n=146) receiving physical therapy for low back pain were administered the SBT and a battery of psychological measures (Fear-Avoidance Beliefs Questionnaire physical activity scale and work scale [FABQ-PA and FABQ-W, respectively], Pain Catastrophizing Scale [PCS], 11-item version of the Tampa Scale of Kinesiophobia [TSK-11], and 9-item Patient Health Questionnaire [PHQ-9]) at initial evaluation and 4 weeks later. Treatment was at the physical therapist's discretion. Clinical outcomes consisted of pain intensity and self-reported disability. Prediction of 6-month clinical outcomes was assessed for intake SBT and psychological measure scores using multiple regression models while controlling for other prognostic variables. In addition, the predictive capabilities of intake to 4-week changes in SBT and psychological measure scores for 6-month clinical outcomes were assessed.ResultsIntake pain intensity scores (β=.39 to .45) and disability scores (β=.47 to .60) were the strongest predictors in all final regression models, explaining 22% and 24% and 43% and 48% of the variance for the respective clinical outcome at 6 months. Neither SBT nor psychological measure scores improved prediction of 6-month pain intensity. The SBT overall scores (β=.22) and SBT psychosocial scores (β=.25) added to the prediction of disability at 6 months. Four-week changes in TSK-11 scores (β=−.18) were predictive of pain intensity at 6 months. Four-week changes in FABQ-PA scores (β=−.21), TSK-11 scores (β=−.20) and SBT overall scores (β=−.18) were predictive of disability at 6 months.LimitationsPhysical therapy treatment was not standardized or accounted for in the analysis.ConclusionsPrediction of clinical outcomes by psychology-based measures was dependent upon the clinical outcome domain of interest. Similar to studies from the primary care setting, initial screening with the SBT provided additional prognostic information for 6-month disability and changes in SBT overall scores may provide important clinical decision-making information for treatment monitoring.
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40

Beneciuk, J., and S. George. "Prediction and clinical outcome implications of sustained STarT back screening tool high-risk status following 4-weeks of physical therapy for low back pain." Journal of Pain 14, no. 4 (April 2013): S16. http://dx.doi.org/10.1016/j.jpain.2013.01.075.

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Mbada (PhD PT), Chidozie Emmanuel, Aanuoluwapo Deborah Afolabi (MSc PT), Olubusola Esther Johnson (PhD PT), Adesola Christianah Odole (PhD PT), Taofik Oluwasegun Afolabi (MSc PT), Odunayo Theresa Akinola (PhD PT), and Moses Oluwatosin Makindes (BMR PT). "Comparison of STarT Back Screening Tool and Simmonds Physical Performance Based Test Battery in Prediction of Disability Risks Among Patients with Chronic Low-Back Pain." Rehabilitacja Medyczna 22, no. 4 (March 8, 2019): 4–17. http://dx.doi.org/10.5604/01.3001.0013.0856.

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Objectives This study identified disability sub-groups of patients with chronic low back pain (LBP) using the Subgroup for Targeted Treatment (or STarT) Back Screening Tool (SBST) and Simmonds Physical Performance Tests Battery (SPPTB). In addition, the study investigated the divergent validity of SBST, and compared the predictive validity of SBST and SPPTB among the patients with the aim to enhance quick and accurate prediction of disability risks among patients with chronic LBP. Methods This exploratory cross-sectional study involved 70 (52.0% female and 47.1% male) consenting patients with chronic non-specific LBP attending out-patient physiotherapy and Orthopedic Clinics at the Obafemi Awolowo University Teaching Hospitals, Ile-Ife and Ladoke Akintola University of Technology Teaching Hospital, Osogbo, Nigeria. Disability risk subgrouping and prediction were carried out using the SBST and SPPTB (comprising six functional tasks of repeated trunk flexion, sit-to-stand, 360-degree rollover, Sorenson fatigue test, unloaded reach test, and 50 foot walk test). Pain intensity was assessed using the Quadruple Visual Analogue Scale. Data on age, sex, height, weight and BMI were also collected. Descriptive and inferential statistics were used to analyze data at p<0.05 Alpha level. Results The mean age, weight, height and body mass index of the participants were 51.4 ±8.78 years, 1.61 ±0.76 m and 26.6 ±3.18 kg/m2 respectively. The mean pain intensity and duration were 5.37 ±1.37 and 21.2 ±6.68 respectively. The divergent validity of SBST with percentage overall pain intensity was r = 0.732; p = 0.001. Under SBST sub-grouping the majority of participants were rated as having medium disability risk (76%), whilst SPPTB sub-grouped the majority as having high disability risk (71.4%). There was a significant difference in disability risk subgrouping between SBST and SPPTB (χ²=12.334; p=0.015). SBST had no floor and ceiling effects, as less than 15% of the participants reached the lowest (2.9%) or highest (1.4%) possible score. Conversely, SPPBT showed both floor and ceiling effects, as it was unable to detect ‘1’ and ‘9’, the lowest and highest obtainable scores. The ‘Area Under Curve’ for sensitivity (0.83) and specificity (0.23) of the SBST to predict ‘high-disability risk’ was 0.51. The estimated prevalence for ‘high-disability risk’ prediction of SBST was 0.76. The estimate for true positive, false positive, true negative and false negative for prediction of ‘high-disability risk’ for SBST were 0.77, 0.23, 0.31, and 0.69 respectively. Conclusion The Start Back Screening Tool is able to identify the proportion of patients with low back pain with moderate disability risks, while the Simmonds Physical Performance Tests Battery is better able to identify high disability risks. Thus, SBST as a self-report measure may not adequately substitute physical performance assessment based disability risks prediction. However, SBST has good divergent predictive validity with pain intensity. In contrast to SPBBT, SBST exhibited no floor or ceiling effects in our tests, and demonstrated high sensitivity but low specificity in predicting ‘high-disability risk’.
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Beneciuk, Jason M., Julie M. Fritz, and Steven Z. George. "The STarT Back Screening Tool for Prediction of 6-Month Clinical Outcomes: Relevance of Change Patterns in Outpatient Physical Therapy Settings." Journal of Orthopaedic & Sports Physical Therapy 44, no. 9 (September 2014): 656–64. http://dx.doi.org/10.2519/jospt.2014.5178.

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43

Lis, Angela, Tara Brennan, LiJin Joo, and Sherri Weiser. "The STarT Back Screening Tool in Secondary Care: Do Psychological Subscale Scores Differ among Patients with Acute, Subacute and Chronic Spine Pain." Spine Journal 16, no. 10 (October 2016): S335—S336. http://dx.doi.org/10.1016/j.spinee.2016.07.426.

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Luz, Aline Cristina, Márcio Galvão Oliveira, and Lúcia Noblat. "Cross-cultural adaptation and content validation of START." Sao Paulo Medical Journal 134, no. 1 (January 19, 2016): 20–27. http://dx.doi.org/10.1590/1516-3180.2014.00303101.

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CONTEXT AND OBJECTIVE: Non-treatment of diseases or clinical conditions has been considered to constitute omission of care in several countries. The aim of the present study was to develop a transcultural adaptation of the Screening Tool to Alert Doctors to the Right Treatment (START) to Brazilian Portuguese and to validate the tool's content. DESIGN AND SETTING: Cultural adaptation and validation of the START criteria using the Delphi consensus technique. METHOD: START was translated from its original language into Brazilian Portuguese, followed by back-translation and validation by means of the modified Delphi technique. For this, an electronic form was developed and sent to 20 experts, who were asked to use a Likert scale to assess the statements included in START, in relation to their pertinence to Brazilian realities. All of the statements that exhibited mean scores greater than 4.0 were considered to have attained consensus. The experts' identities were kept confidential throughout the validation process. RESULTS: In the first phase of the validation process, 63.6% (14/22) of the statements in START attained consensus. The remaining statements were returned to the experts so that they could have the opportunity to review their comments and statements and to assess them again, based on the Likert scale used earlier. In this phase, 100% of the START instrument attained consensus. CONCLUSION: The content of START was entirely validated for Brazil, with all of the original criteria maintained.
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Davoudi, Mehrdad, Seyyed Mohammadreza Shokouhyan, Mohsen Abedi, Narges Meftahi, Atefeh Rahimi, Ehsan Rashedi, Maryam Hoviattalab, Roya Narimani, Mohamad Parnianpour, and Kinda Khalaf. "A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings." Sensors 20, no. 10 (May 20, 2020): 2902. http://dx.doi.org/10.3390/s20102902.

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The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart “sensor-based” practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today’s smartphones, as objective tools for various health applications towards personalized precision medicine.
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Tsuge, Takahiro, Hiroshi Takasaki, and Michio Toda. "Does the Keele STarT Back Screening Tool Contribute to Effectiveness in Treatment and Cost and Loss of Follow-Up of the Mechanical Diagnosis and Therapy for Patients with Low Back Pain?" Diagnostics 10, no. 8 (July 30, 2020): 536. http://dx.doi.org/10.3390/diagnostics10080536.

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Background: Mechanical diagnosis and therapy (MDT) and the stratified approach using the Keele STarT Back Screening Tool (SBST) are examples of stratified low back pain (LBP) management. We investigated whether the medium–high risk in SBST can contribute to the time and sessions until discharge from MDT (Question 1) and to the loss of follow-up before identifying a promising management strategy (Question 2). Methods: A retrospective chart study was conducted. Multiple regression modeling was constructed using 10 independent variables, including whether the SBST was medium–high risk or not for Question 1, and the 9/10 independent variables for Question 2. Results: The data of 89 participants for Question 1 and 166 participants for Question 2 were analyzed. SBST was not a primary contributing factor for Question 1 (R2 = 0.17–0.19). The model for Question 2 included SBST as a primary contributing factor and the shortest distance from the patient address to the hospital as a secondary contributing factor (93.4% correct classification). Conclusion: SBST status was not a primary contributing factor for time and sessions until discharge from MDT, but was a critical factor for the loss of MDT follow-up before identifying a promising management strategy.
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Werneke, Mark W., Susan Edmond, Michelle Young, David Grigsby, Brian McClenahan, and Troy McGill. "Association between changes in function among patients with lumbar impairments classified according to the STarT Back Screening Tool and managed by McKenzie credentialed physiotherapists." Physiotherapy Theory and Practice 36, no. 5 (July 9, 2018): 589–97. http://dx.doi.org/10.1080/09593985.2018.1490839.

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., Mahmud, Rahardjo Sri, Mahendra Rama Iqbal, Wicaksana Anggi Lukman, and Hertanti Nuzul Sri. "The prevalence and risk factors of low back pain among the nurses at Sardjito Hospital, Yogyakarta, Indonesia." Anaesthesia, Pain & Intensive Care 25, no. 1 (January 20, 2021): 19–25. http://dx.doi.org/10.35975/apic.v25i1.1432.

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Background: The study aimed to identify the prevalence and risk factors of LBP among the nurses at Sardjito Hospital. Method: A cross-sectional study was conducted in Sardjito Hospital Yogyakarta. LBP was measured in nurses who had work for one year using the Keele Start Back Screening Tool (KSBT) and Orebro Musculoskeletal Pain Questionnaire (OMPQ). A simple and multiple logistic regression was used for analysis. Results: Of 800 valid subjects, 324 (40.5%) have the history of LBP complaint, 88.9% were suffering the pain during the data collection. The most significant variables are the history of previous spinal complaints (p = 0.00), slide board use for lateral transfer (p=0.010), gender (p=0.026) and current work place (p=0.019). Nine of the 288 patients of LBP have high risk in line with the KSBT stratification, while 22 of 288 patients have High Estimated Risk of Future Work Disability that agrees with the stratification of performance prognosis using OMPQ. Conclusion: The prevalence of LBP among the nurses at Sardjito Hospital reaches 40.5%, with 88.9% of whom still suffer from pain during the data collection. The risk factors of LBP among the nurses are previous spinal complaint, tools for lateral transfer, work place and gender. Key words: Risk factor; Lower back pain; Nurses; Pain; Prevalence Citation: Mahmud, Sri R, Iqbal MR, Lukman WA, Sri HN. The prevalence and risk factors of low back pain among the nurses at Sardjito Hospital, Yogyakarta, Indonesia. Anaesth. pain intensive care 2021;25(1):19–26; DOI 10.35975/apic.v25i1.1432 Received: 13 November 2020, Reviewed: 18 November 2020, Accepted: 16 December 2020
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Forsbrand, M., B. Grahn, J. C. Hill, I. I. Peterson, C. Post Sennehed, and K. Stigmar. "THU0631-HPR Validation of the Swedish Version of Start Back Tool Against the Short Version of The ÖRebro Musculoskeletal Pain Screening Questionnaire in Patients with Back and/or Neck Pain in Primary Health Care." Annals of the Rheumatic Diseases 74, Suppl 2 (June 2015): 1319.2–1319. http://dx.doi.org/10.1136/annrheumdis-2015-eular.4622.

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Brumitt, Jason, Kyle Nelson, Duane Duey, Matthew Jeppson, and Luke Hammer. "Preseason Y Balance Test Scores are not Associated with Noncontact Time-Loss Lower Quadrant Injury in Male Collegiate Basketball Players." Sports 7, no. 1 (December 24, 2018): 4. http://dx.doi.org/10.3390/sports7010004.

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The Y-Balance Test-Lower Quarter has shown promise as a screening tool for identifying athletes at risk of injury. Subsequent studies, utilizing heterogeneous populations or different operational definitions of injury, have presented equivocal findings. Therefore, studies evaluating the efficacy of the Y-Balance Test to discriminate injury risk in a homogeneous population is warranted. One-hundred sixty-nine male (mean age 19.9 ± 1.5 y) collegiate basketball players were recruited during 2 consecutive seasons (2016–2017/2017–2018). Athletes completed the Y-Balance testing protocol at the start of each preseason. Athletic trainers tracked noncontact time-loss lower quadrant injuries over the course of the season. Receiver operator characteristic curves failed to identify cutoff scores; therefore, previously reported cutoff scores were utilized when calculating relative risk. There was no association between preseason Y-Balance Test scores and noncontact time-loss lower back or lower extremity injury in a population of male collegiate basketball players. This study adds to a growing body of evidence that demonstrates no relationship between preseason Y-Balance Test scores and subsequent injury.
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