Storti, K. L., et al. Gait speed and step-count monitor accuracy in community-dwelling older adults. Med Sci Sports Exerc. 2008; 40(1): 59-64. PMID: 18091020.
ABSTRACT: Accurate assessment of physical activity (PA) is necessary to identify the sedentary older individual who is in need of activity intervention. Activity monitors are quite popular, although it has been suggested that they are less accurate at slow gait speeds. PURPOSE: To examine the accuracy of the three activity monitors in older individuals who walk at various gait speeds. METHODS: Participants were 34 community-dwelling older men and women (mean age 79.2) who were asked to simultaneously wear three activity monitors: the Yamax DigiWalker (DW) pedometer (hip), the Actigraph (AG) accelerometer (hip), and the StepWatch activity monitor (SAM) (ankle). Monitor accuracy was evaluated against observed steps taken during a 100-step walking test. Percent error of the monitors was calculated as [(monitor steps – observed steps)/observed steps] x 100. Participants were categorized into three groups ( 1.0 m x s(-1)) according to gait speed, which was determined by a timed 4-m walk. RESULTS: Overall, the DW and AG failed to detect 16% and 7% of observed steps, respectively, and the SAM overestimated by 5.5%. When stratified by gait speed, all three monitors faired well at the gait speeds > 1.0 m x s(-1). For gait speeds between 0.80 and 1.0 m x s(-1), the SAM overestimated steps by 6.6%, and the AG and DW underestimated steps by 5.7% and 12.7%, respectively. However, at gait speeds
Wood, W. A., et al. Emerging uses of patient generated health data in clinical research. Mol Oncol. 2014; 9(5):1018-1024. PMID: 25248998.
ABSTRACT: Recent advancements in consumer directed personal computing technology have led to the generation of biomedically-relevant data streams with potential health applications. This has catalyzed international interest in Patient Generated Health Data (PGHD), defined as “health-related data – including health history, symptoms, biometric data, treatment history, lifestyle choices, and other information-created, recorded, gathered, or inferred by or from patients or their designees (i.e. care partners or those who assist them) to help address a health concern.”(Shapiro et al., 2012) PGHD offers several opportunities to improve the efficiency and output of clinical trials, particularly within oncology. These range from using PGHD to understand mechanisms of action of therapeutic strategies, to understanding and predicting treatment-related toxicity, to designing interventions to improve adherence and clinical outcomes. To facilitate the optimal use of PGHD, methodological research around considerations related to feasibility, validation, measure selection, and modeling of PGHD streams is needed. With successful integration, PGHD can catalyze the application of “big data” to cancer clinical research, creating both “n of 1” and population-level observations, and generating new insights into the nature of health and disease.
Bennett, AV, et al. Evaluation of pedometry as a patient-centered outcome in patients undergoing hematopoietic cell transplant (HCT): a comparison of pedometry and patient reports of symptoms, health, and quality of life. Quality of Life Research. 2016; 25(3): 535-546. PMID: 26577763.
ABSTRACT: AIMS: We evaluated pedometry as a novel patient-centered outcome because it enables passive continuous assessment of activity and may provide information about the consequences of symptomatic toxicity complementary to self-report. METHODS: Adult patients undergoing hematopoietic cell transplant (HCT) wore pedometers and completed PRO assessments during transplant hospitalization (4 weeks) and 4 weeks post-discharge. Patient reports of symptomatic treatment toxicities (single items from PRO-CTCAE, http://healthcaredelivery.cancer.gov/pro-ctcae ) and symptoms, physical health, mental health, and quality of life (PROMIS(®) Global-10, http://nih.promis.org ), assessed weekly with 7-day recall on Likert scales, were compared individually with pedometry data, summarized as average daily steps per week, using linear mixed models. RESULTS: Thirty-two patients [mean age 55 (SD = 14), 63 % male, 84 % white, 56 % autologous, 43 % allogeneic] completed a mean 4.6 (SD = 1.5, range 1-8) evaluable assessments. Regression model coefficients (β) indicated within-person decrements in average daily steps were associated with increases in pain (β = -852; 852 fewer steps per unit increase in pain score, p