Using natural language processing to determine predictors of healthy diet and physical activity behavior change in ovarian cancer survivors

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ABSTRACT Cancer survivors are a growing population in the United States; more than 16 million currently live in the US and by 2030 this number is expected to exceed 22 million. It is estimated that more than 50 percent of new cancer cases could be eliminated through a combination of healthy behaviors (e.g., physical activity and healthy diet); and cancer survivors are at high risk for developing new and recurrent cancer. Unfortunately, a significant percentage of cancer survivors are not attaining the cancer preventive guidelines of healthy diet and physical activity. In the past few decades, a variety of telephone-based lifestyle interventions have demonstrated effectiveness in helping survivors meet cancer preventive guidelines, however these trials are labor intensive and expensive to deliver, limiting their potential for broad dissemination. We propose to address this hurdle by taking advantage of recent advances in artificial intelligence to reduce the cost and maximize the impact of these much-needed interventions. Machine learning (ML) and Natural Language Processing (NLP) are analytical techniques that automatically learn from direct and indirect patterns in data. We propose to use machine learned algorithms to analyze speech to aid in predicting who may be at risk of poor adoption of healthy lifestyle behaviors. These speech data will come from the Lifestyle Intervention for Ovarian cancer Enhanced Survival (LIVES) study, a telephone-based lifestyle intervention testing whether a diet low in fat and high in vegetables, fruit, and fiber, coupled with increased physical activity will increase time to disease progression in 1200 ovarian cancer survivors who have recently completed treatment, as compared to an attention control. Intervention coaches employed motivational interviewing to elicit behavior change and all calls on the LIVES trial were recorded with repeat assessments of diet, physical activity, patient reported and clinical outcomes. We will use this existing and robust longitudinal data set, which pairs conversational speech data with explicit outcomes, to achieve the following objectives. 1) Develop a ML model to identify patterns in the interactions between coaches and their participants that signal a likelihood of optimal behavior change in diet and physical activity given the comprehensive LIVES data set, utilizing voice recorded calls, demographics, and clinical and patient reported outcomes collected at multiple time points. 2) Decompose the ML model in terms of “intervenable factors”, so that participant affect, coach adherence to the intervention protocol, and other important aspects of the interaction can be individually evaluated for their role in predicting behavior change, as well as adherence to intervention goals. This decomposition will directly enable early and targeted adjustments to intervention plans for individuals, reducing the cost and increasing the efficacy of intervention strategies. ML and NLP methods can produce models that listen to a coaching conversation and automatically predict whether it will result in positive change towards enactment of healthy lifestyle behaviors. Such predictive models would enable more efficient, effective, and individualized lifestyle interventions, the first step towards personalized behavioral medicine.