Online Program

Comparing Survival Analysis Models for Assessing the Persistence of Use of Activity Trackers for Increasing Sedentary Adults Physical Activity

Monday, November 2, 2015

Ralph Turner, Ph.D., Life Sciences Research, HealthCore, Wilmington, DE
This study compares parametric and semiparametric survival models for analyzing persistence in using an activity tracker to increase daily activity levels. Most studies employ semiparametric Cox regression to avoid distributional assumptions. This overlooks the potential benefits of parametric estimation for situations where it is well suited such as randomized trials.

Participants were randomized to either Activity Tracker Only (ATO, N = 474) or Activity Tracker plus Coaching (AT+C, N = 473). Exercise Readiness for Change was a measured continuous covariate. Males composed 16% of the sample and average age was 43 (22 to 68). Average BMI was 36 and ranged from 28.1 to 68.75; average weight was 222.5 pounds.

The primary study outcome was the number of months continuously using the activity tracker during one year (recorded electronically). Comparisons among models were made using log-likelihood values along with survival curves and SE’s. Robust standard errors were estimated for all models. Total time at risk was 4,616.8 months.

Fully 25% of participants discontinued using the activity tracker within one week and 50% of participants discontinued by 6-months. AT+C participants remained active users 6-months on average compared to 4.1months for AT participants. Because of the rapid discontinuation, the accelerated failure time models performed best in terms of –LL values (Gamma regression = -1800.24; Log-logistic regression = -1820.84) with Weibull regression (-1851.25) and Exponential regression performing well (-2148.37). The Cox model performed relatively less well (-4566.64). In this context, the Exponential and Weibull models produced the smallest SE’s.

Learning Areas:

Administer health education strategies, interventions and programs
Biostatistics, economics
Occupational health and safety
Planning of health education strategies, interventions, and programs
Public health or related public policy
Public health or related research

Learning Objectives:
Identify the utility of parametric and semi-parametric survival analysis models in detecting treatment differences in randomized studies.

Keyword(s): Biostatistics, Treatment Adherence

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have health care research since 1980 and I have been the PI on multiple studies on improving health. I taught research design and statistics at Temple University School of Medicine andthe University of the Science in Philadelphia for 35 years. I am the PI of the randomized controlled evaluation of activity tracker versus activity tracker plus health coaching. My doctoral degrees are in clinical psychology and statistics.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.