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Actigraphy as an assessment of performance status in patients with advanced lung cancer

Published online by Cambridge University Press:  11 February 2019

Daisuke Fujisawa Open the ORCID record for Daisuke Fujisawa [Opens in a new window] ,
Jennifer S. Temel ,
Joseph A. Greer ,
Areej El-Jawahri ,
Lara Traeger ,
Jamie M. Jacobs ,
Stacy Cutrono  and
William F. Pirl
Daisuke Fujisawa*
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA Keio University School of Medicine, Department of Neuropsychiatry and Palliative Care Center, Tokyo, Japan
Jennifer S. Temel
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA
Joseph A. Greer
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA
Areej El-Jawahri
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA
Lara Traeger
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA
Jamie M. Jacobs
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA
Stacy Cutrono
Affiliation:
Sylvester Comprehensive Cancer Center, Miami University, Miller School of Medicine, Miami, FL
William F. Pirl
Affiliation:
Massachusetts General Hospital, Cancer Center, Boston, MA Dana-Faber Cancer Institute, Psychosocial Oncology and Palliative Care, Boston, MA
*
Author for correspondence: Daisuke Fujisawa, Department of Neuropsychiatry and Palliative Care Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan. E-mail: [email protected]
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Abstract

Objective

Wearable devices such as a wrist actigraph may have a potential to objectively estimate patients’ functioning and may supplement performance status (PS). This proof-of-concept study aimed to evaluate whether actigraphy data are significantly associated with patients’ functioning and are predictive of their survival in patients with metastatic non-small cell lung cancer.

Method

We collected actigraphy data for a three-day period in ambulatory patients with stage IV non-small cell lung cancer. We computed correlations between actigraphy data (specifically, proportion of time spent immobile while awake) and clinician-rated PS, subjective report of physical activities, quality of life (the Functional Assessment of Cancer Therapy – Trial Outcome Index), and survival.

Result

Actigraphy data (the proportion of time awake spent immobile) were significantly correlated with Functional Assessment of Cancer Therapy – Trial Outcome Index (r = −0.53, p < 0.001) and with the Eastern Cooperative Oncology Group PS (ECOG PS) (r = 0.37, p < 0.001). The proportion of time awake spent immobile was significantly associated with worse survival. For each 10% increase in this measure, the hazard ratio (HR) was 1.48 (95% confidence interval [CI95%] = 1.06, 2.06) for overall mortality, and odds ratio was 2.99 (CI95% = 1.27, 7.05) for six-month mortality. ECOG PS was also associated with worse survival (HR = 2.80, CI95% = 1.34, 5.86). Among patients with ECOG PS 0-1, the percentage of time awake spent immobile was significantly associated with worse survival, HR = 1.93 (CI95% = 1.10, 3.42), whereas ECOG PS did not predict survival.

Significance of Results

Actigraphy may have potential to predict important clinical outcomes, such as quality of life and survival, and may serve to supplement PS. Further validation study is warranted.

Keywords

ActigraphCancerPerformance statusPhysical activitySurvival
Type
Original Article
Information
Palliative & Supportive Care , Volume 17 , Issue 5 , October 2019 , pp. 574 - 578
Copyright
Copyright © Cambridge University Press 2019 

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References

Albain, KS, Crowley, JJ, LeBlanc, M, et al. (1991) Survival determinants in extensive-stage non-small-cell lung cancer: The Southwest Oncology Group experience. Journal of Clinical Oncology 9, 1618–26. doi: 10.1200/JCO.1991.9.9.1618Google Scholar
Ando, M, Ando, Y, Hasegawa, , et al. (2001) Prognostic value of performance status assessed by patients themselves, nurses, and oncologists in advanced non-small cell lung cancer. British Journal of Cancer 85, 1634–39. doi: 10.1054/bjoc.2001.2162Google Scholar
Bansal, A and Joshi, R (2018) Portable out-of-hospital electrocardiography: A review of current technologies. Journal of Arrhythmia 34, 129–38. doi: 10.1002/joa3.12035Google Scholar
Beg, MS, Gupta, A, Stewart, T, et al. (2017) Promise of wearable physical activity monitors in oncology practice. Journal of Oncology Practice 13, 82–89. doi: 10.1200/JOP.2016.016857Google Scholar
Blagden, SP, Charman, SC, Sharples, LD, et al. (2003) Performance status score: Do patients and their oncologists agree? British Journal of Cancer 89, 1022–27. doi: 10.1038/sj.bjc.6601231Google Scholar
Boyne, K, Sherry, DD, Gallagher, PR, et al. (2013) Accuracy of computer algorithms and the human eye in scoring actigraphy. Sleeping and Breathing 17, 411–17. doi: 10.1007/s11325-012-0709-zGoogle Scholar
Broderick, JM, Ryan, J, O'Donnell, DM, et al. (2014) A guide to assessing physical activity using accelerometry in cancer patients. Supportive Care in Cancer 22, 1121–30. doi: 10.1007/s00520-013-2102-2Google Scholar
Cella, DF, Bonomi, AE, Lloyd, SR, et al. (1995) Reliability and validity of the Functional Assessment of Cancer Therapy-Lung (FACT-L) quality of life instrument. Lung Cancer 12, 199–220.Google Scholar
Chang, WP, Smith, R, and Lin, CC (2018) Age and rest-activity rhythm as predictors of survival in patients with newly diagnosed lung cancer. Chronobiology International 35, 188–97. doi: 10.1080/07420528.2017.1391278Google Scholar
Chasan-Taber, S, Rimm, EB, Stampfer, MJ, et al. (1996) Reproducibility and validity of a self-administered physical activity questionnaire for male health professionals. Epidemiology 7, 81–86.Google Scholar
Chen, H, Cantor, A, Meyer, J, et al. (2003). Can older cancer patients tolerate chemotherapy? A prospective pilot study. Cancer 97, 1107–14. doi: 10.1002/cncr.11110Google Scholar
Colditz, GA, Feskanich, D, Chen, WY, et al. (2003) Physical activity and risk of breast cancer in premenopausal women. British Journal of Cancer 89, 847–51. doi: 10.1038/sj.bjc.6601175Google Scholar
Decoster, L, Van Puyvelde, K, Mohile, S, et al. (2015) Screening tools for multidimensional health problems warranting a geriatric assessment in older cancer patients: An update on SIOG recommendations. Annals of Oncology 26, 288–300. doi: 10.1093/annonc/mdu210Google Scholar
Gresham, G, Schrack, J, Gresham, LM, et al. (2018) Wearable activity monitors in oncology trials: Current use of an emerging technology. Contemporary Clinical Trials Communications 64, 13–21. doi: 10.1016/j.cct.2017.11.002Google Scholar
Innominato, PF, Giacchetti, S, Bjarnason, GA, et al. (2012) Prediction of overall survival through circadian rest-activity monitoring during chemotherapy for metastatic colorectal cancer. International Journal of Cancer 131, 2684–92. doi: 10.1002/ijc.27574Google Scholar
Jones, LW, Hornsby, WE, Goetzinger, A, et al. (2012) Prognostic significance of functional capacity and exercise behavior in patients with metastatic non-small cell lung cancer. Lung Cancer 76, 248–52. doi: 10.1016/j.lungcan.2011.10.009Google Scholar
Kenis, C, Heeren, P, Bron, D, et al. (2014) Multicenter implementation of geriatric assessment in Belgian patients with cancer: A survey on treating physicians' general experiences and expectations. Journal of Geriatric Oncology 5, 431–38. doi: 10.1016/j.jgo.2014.06.043Google Scholar
Levi, F, Dugue, PA, Innominato, P, et al. (2014) Wrist actimetry circadian rhythm as a robust predictor of colorectal cancer patients survival. Chronobiology International 31, 891–900. doi: 10.3109/07420528.2014.924523Google Scholar
Loprinzi, CL, Laurie, JA, Wieand, HS, et al. (1994) Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. Journal of Clinical Oncology 12, 601–07. doi: 10.1200/JCO.1994.12.3.601Google Scholar
Madsen, MT, Huang, C, and Gogenur, I (2015) Actigraphy for measurements of sleep in relation to oncological treatment of patients with cancer: A systematic review. Sleep Medicine Reviews 20, 73–83. doi: 10.1016/j.smrv.2014.07.002Google Scholar
Meyerhardt, JA, Giovannucci, EL, Ogino, S, et al. (2009) Physical activity and male colorectal cancer survival. Archives of Internal Medicine 169, 2102–08. doi: 10.1001/archinternmed.2009.412Google Scholar
Oken, MM, Creech, RH, Tormey, DC, et al. (1982) Toxicity and response criteria of the Eastern Cooperative Oncology Group. American Journal of Clinical Oncology 5, 649–55.Google Scholar
Orr, ST and Aisner, J (1986). Performance status assessment among oncology patients: A review. Cancer Treatment Report 70, 1423–29.Google Scholar
Paesmans, M, Sculier, JP, Libert, P, et al. (1995) Prognostic factors for survival in advanced non-small-cell lung cancer: Univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party. Journal of Clinical Oncology 13, 1221–30. doi: 10.1200/JCO.1995.13.5.1221Google Scholar
Philips Respironics Actiwatch 2. http://www.actigraphy.respironics.com/solutions/actiwatch/actiwatch2.html (last accessed 14 January 2019).Google Scholar
Philips Respironics Actiware Tutorials. http://www.actigraphy.respironics.com/solutions/actiware/tutorials.html (last accessed 14 January 2019).Google Scholar
Sengelov, L, Kamby, C, Geertsen, P, et al. (2000) Predictive factors of response to cisplatin-based chemotherapy and the relation of response to survival in patients with metastatic urothelial cancer. Cancer Chemotherapy and Pharmacology 46, 357–64. doi: 10.1007/s002800000176Google Scholar
Stotter, A, Reed, MW, Gray, LJ, et al. (2015) Comprehensive geriatric assessment and predicted 3-year survival in treatment planning for frail patients with early breast cancer. British Journal of Surgery 102, 525–33. doi: 10.1002/bjs.9755Google Scholar
Tedesco, S, Barton, J, and O'Flynn, B (2017) A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors (Basel) 17. doi: 10.3390/s17061277Google Scholar
Wang, DD and Hu, FB (2018) Precision nutrition for prevention and management of type 2 diabetes. Lancet. Diabetes and Endocrinology 6, 416–426. doi: 10.1016/S2213-8587(18)30037-8Google Scholar