Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients

Gillian Gresham, Andrew E Hendifar, Brennan Spiegel, Elad Neeman, Richard Tuli, B J Rimel, Robert A Figlin, Curtis L Meinert, Steven Piantadosi, Arvind M Shinde, Gillian Gresham, Andrew E Hendifar, Brennan Spiegel, Elad Neeman, Richard Tuli, B J Rimel, Robert A Figlin, Curtis L Meinert, Steven Piantadosi, Arvind M Shinde

Abstract

An objective evaluation of patient performance status (PS) is difficult because patients spend the majority of their time outside of the clinic, self-report to providers, and undergo dynamic changes throughout their treatment experience. Real-time, objective activity data may allow for a more accurate assessment of PS and physical function, while reducing the subjectivity and bias associated with current assessments. Consenting patients with advanced cancer wore a wearble activity monitor for three consecutive visits in a prospective, single-cohort clinical trial. Provider-assessed PS (ECOG/Karnofsky) and NIH PROMIS® patient-reported outcomes (PROs) were assessed at each visit. Associations between wearable activity monitor metrics (steps, distance, stairs) and PS, clinical outcomes (adverse events, hospitalizations, survival), and PROs were assessed using correlation statistics and in multivariable logistic regression models. Thirty-seven patients were evaluated (54% male, median 62 years). Patients averaged 3700 steps, 1.7 miles, and 3 flights of stairs per day. Highest correlations were observed between average daily steps and ECOG-PS and KPS (r = 0.63 and r = 0.69, respectively p < 0.01). Each 1000 steps/day increase was associated with reduced odds for adverse events (OR: 0.34, 95% CI 0.13, 0.94), hospitalizations (OR: 0.21 95% CI 0.56, 0.79), and hazard for death (HR: 0.48 95% CI 0.28-0.83). Significant correlations were also observed between activity metrics and PROs. Our trial demonstrates the feasibility of using wearable activity monitors to assess PS in advanced cancer patients and suggests their potential use to predict clinical and patient-reported outcomes. These findings should be validated in larger, randomized trials.

Keywords: Cancer; Outcomes research; Quality of life.

Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Fitbit Charge HR® activity metrics by a ECOG performance status and b Karnofsky performance status
Fig. 2
Fig. 2
Heat map of average activity intensity for each patient over a 24 h period, as measured using the wearable activity monitor and sorted by ECOG-PS categories
Fig. 3
Fig. 3
Kaplan–Meier survival curve by step categories

References

    1. Karnofsky DA. The Clinical Evaluation of Chemotherapeutic Agents in Cancer in Evaluation of Chemotherapeutic Agents. New York: Columbia University Press; 1949.
    1. Zubrod CG, et al. Appraisal of methods for the study of chemotherapy of cancer in man: comparative therapeutic trial of nitrogen mustard and triethylene thiophosphoramide. J. Chronic Dis. 1960;11:7–33. doi: 10.1016/0021-9681(60)90137-5.
    1. Kelly CM, Shahrokni A. Moving beyond Karnofsky and ECOG performance status assessments with new technologies. J. Oncol. 2016;2016:6186543. doi: 10.1155/2016/6186543.
    1. Schrack JA, Gresham G, Wanigatunga AA. Understanding physical activity in cancer patients and survivors: new methodology, new challenges, and new opportunities. Cold Spring Harb. Mol. Case Stud. 2017;3:a001933. doi: 10.1101/mcs.a001933.
    1. Christakis NA, Smith JL, Parkes CM, Lamont EB. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study commentary: Why do doctors overestimate? Commentary: Prognoses should be based on proved indices not intuition. BMJ. 2000;320:469–473. doi: 10.1136/bmj.320.7233.469.
    1. Shinde AM, et al. J. Clin. Oncol. Abstr. 2016;34(Suppl. 228):7.
    1. Taylor AE, Olver IN, Sivanthan T, Chi M, Purnell C. Observer error in grading performance status in cancer patients. Support Care Cancer. 1999;7:332–335. doi: 10.1007/s005200050271.
    1. Blagden SP, Charman SC, Sharples LD, Magee LR, Gilligan D. Performance status score: do patients and their oncologists agree? Br. J. Cancer. 2003;89:1022. doi: 10.1038/sj.bjc.6601231.
    1. Basch E, et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA. 2017;318:197–198. doi: 10.1001/jama.2017.7156.
    1. Pakhomov S, Jacobsen SJ, Chute CG, Roger VL. Agreement between patient-reported symptoms and their documentation in the medical record. Am. J. Manag. Care. 2008;14:530–539.
    1. Dy SM, et al. Evaluating the quality of supportive oncology using patient-reported data. J. Oncol. Pract. 2014;10:223–230. doi: 10.1200/JOP.2013.001237.
    1. Cella D, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years. Med. Care. 2007;45(Suppl. 1):S3. doi: 10.1097/01.mlr.0000258615.42478.55.
    1. Tudor-Locke C, et al. How many steps/day are enough? For older adults and special populations. Int. J. Behav. Nutr. Phys. Act. 2011;8:80. doi: 10.1186/1479-5868-8-80.
    1. Speck RM, Courneya KS, Mâsse LC, Duval S, Schmitz KH. An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J. Cancer Surviv. 2010;4:87–100. doi: 10.1007/s11764-009-0110-5.
    1. Gresham G, et al. Wearable activity monitors in oncology trials: current use of an emerging technology. Contemp. Clin. Trials. 2018;64:13–21. doi: 10.1016/j.cct.2017.11.002.
    1. Fitbit Inc. Our technology. Accessed 30 Aug 2017 (2017).
    1. Lynch, S., Stockton, J., Weissman, N. Equity Research Fitbit, Inc. Wells Fargo Securities. July 12, 1988 (2016).
    1. Small Steps Labs LLC. Fitabase. Accessed 30 Aug 2017 (2015).
    1. Kelly CM, Shahrokni A. From shelf to bedside—wearable electronic activity monitoring technologies might assist oncologists in functional performance status assessment of older cancer patients. Clin. Colorectal Cancer. 2017;16:e115–e118. doi: 10.1016/j.clcc.2016.11.002.
    1. Fung C, et al. Feasibility of utilizing a novel mhealth platform to deliver an evidence-based exercise intervention among testicular cancer survivors. J. Clin. Oncol. Abstr. 2017;35(Suppl. e21608):15.
    1. Melisko ME, et al. Objective assessment of physical activity during chemotherapy for breast cancer. J. Clin. Oncol. Abstr. 2017;35(Suppl. TPS6626):15.
    1. Beg MS, et al. Feasibility of wearable physical activity monitors in cancer patients (PAMCaP) J. Clin. Oncol. Abstr. 2017;35(Suppl. 6577):15.
    1. Ohri N, et al. Continuous activity monitoring during concurrent chemoradiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 2017;97:1061–1065. doi: 10.1016/j.ijrobp.2016.12.030.
    1. Pirl WF, et al. Actigraphy as an objective measure of performance status in patients with advanced cancer. J. Clin. Oncol. Abstr. 2015;33(Suppl. 62):29.
    1. Van Remoortel H, et al. Validity of activity monitors in health and chronic disease: a systematic review. Int. J. Behav. Nutr. Phys. Act. 2012;9:1–23. doi: 10.1186/1479-5868-9-1.
    1. Vooijs M, et al. Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interact. J. Med. Res. 2014;3:1–14. doi: 10.2196/ijmr.3056.
    1. Adams SA, et al. The effect of social desirability and social approval on self-reports of physical activity. Am. J. Epidemiol. 2005;161:389–398. doi: 10.1093/aje/kwi054.
    1. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313:625–626. doi: 10.1001/jama.2014.17841.
    1. Hildebrand M, Van Hees VT, Hansen BH, Ekelund U. Age group comparability of raw accelerometer output from wrist-and hip-worn monitors. Med. Sci. Sports Exerc. 2014;46:1816–1824. doi: 10.1249/MSS.0000000000000289.
    1. Shcherbina A, et al. Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. J. Pers. Med. 2017;7:1–14. doi: 10.3390/jpm7020003.
    1. Vooijs M, et al. Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interact. J. Med. Res. 2014;3:e1–e14. doi: 10.2196/ijmr.3056.
    1. Lee JM, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med. Sci. Sports Exerc. 2014;46:1840–1848. doi: 10.1249/MSS.0000000000000287.
    1. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int. J. Behav. Nutr. Phys. Act. 2015;12:e1–e14. doi: 10.1186/s12966-015-0201-9.
    1. Bennett GG, Wolin KY, Puleo E, Emmons KM. Pedometer-determined physical activity among multiethnic low-income housing residents. Med. Sci. Sports Exerc. 2006;38:768–773. doi: 10.1249/01.mss.0000210200.87328.3f.

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