Perry Skeath

The Nature of Life-Transforming Changes Among Cancer Survivors:

Some cancer survivors report positive subjective changes they describe as “life transforming.” We used a grounded theory approach to identify the content, underlying process, and identifying characteristics of self-defined “life-transforming” changes (LTCs) reported by 9 cancer survivors. To actualize their hopes for improvement, participants used a self-guided process centered on pragmatic action: researching options, gaining experience, and frankly evaluating results. Many participants discovered unanticipated personal abilities and resources, and those became highly useful in coping with other challenges apart from cancer. This made the increased personal abilities and resources “life transforming” rather than being substantially limited to reducing cancer-related problems. The action-oriented features and processes of LTCs seemed to be more fully described by experiential learning theory than by posttraumatic growth and coping. Supportive intervention to facilitate positive change processes could decrease suffering and enhance positive psychosocial and spiritual outcomes for cancer survivors.

Increasing the Effectiveness of Palliative Care through Integrative Modalities: Conceptualizing the Roles of Animal Companions and Animal-Assisted Interventions

This chapter discusses how palliative care is being extended to relieve the disease symptoms, side effects, and associated burdens that can beset a patient at many points along the course of an illness. Integrative modalities aimed at enriching a patients quality of life, such as animal-assisted interventions, have been particularly helpful in enabling palliative care to be very successfully applied well beyond its historical roots of masking pain during end-of-life care.

Feature Importance and Predictive Modeling for Multi-source Healthcare Data with Missing Values

With rapid development of sensor technologies and the internet of things, research in the area of connected health is increasing in importance and complexity with wide-reaching impacts for public health. As data sources such as mobile (wearable) sensors get cheaper, smaller, and smarter, important research questions can be answered by combining information from multiple data sources. However, integration of multiple heterogeneous data streams often results in a dataset with several empty cells or missing values. The challenge is to use such sparsely populated integrated datasets without compromising model performance. Naïve approaches for dataset modification such as discarding observations or ad-hoc replacement of missing values often lead to misleading results. In this paper, we discuss and evaluate current best-practices for modeling such data with missing values and then propose an ensemble-learning based sparse-data modeling framework. We develop a predictive model using this framework and compare it with existing models using a study in a healthcare setting. Instead of generating a single score on variable/feature importance, our framework enables the user to understand the importance of a variable based on the existing data values and their localized impact on the outcome.

New and Future Directions in Integrative Medicine Research Methods with a Focus on Aging Populations: A Review

This review discusses existing and developing state-of-the-art noninvasive methods for quantifying the effects of integrative medicine (IM) in aging populations. The medical conditions of elderly patients are often more complex than those of younger adults, making the multifaceted approach of IM particularly suitable for aging populations. However, because IM interventions are multidimensional, it has been difficult to examine their effectiveness and mechanisms of action. Optimal assessment of IM intervention effects in the elderly should include a multifaceted approach, utilizing advanced analytic methods to integrate psychological, behavioral, physiological, and biomolecular measures of a patients response to IM treatment. Research is presented describing methods for collecting and analyzing psychological data; wearable unobtrusive devices for monitoring heart rate variability, activity and other behavioral responses in real time; immunochemical methods for noninvasive molecular biomarker analysis, and considerations and analytical approaches for the integration of these measures. The combination of methods and devices presented in this review will provide new approaches for evaluating the effects of IM interventions in real-life ambulatory settings of older adults, and will extend the concept of mobile health to the domains of IM and healthy aging.

A Regularization Approach for Identifying Cumulative Lagged Effects in Smart Health Applications

Recent development of wearable sensor technologies have made it possible to capture concurrent data streams for ambient environment and instantaneous physiological stress response at a fine granularity. Characterizing the delay in physiological stress response time to each environment stimulus is as important as capturing the magnitude of the effect. In this paper, we discuss and evaluate a new regularization-based statistical method to determine the ideal lagged effect of five environmental factors-carbon dioxide, temperature, relative humidity, atmospheric pressure and noise levels on instantaneous stress response. Using this method, we infer that the first four environment variables have a cumulative lagged effect, of approximately 60 minutes, on stress response whereas noise level has an instantaneous effect on stress response. The proposed transformations to inputs result in models with better fit and predictive performance. This study not only informs the field of environment-wellbeing research about the cumulative lagged effects of the specified environmental factors, but also proposes a new method for determining optimal feature transformation in similar smart health studies.