James L. Fozard

Persuasive GERONtechnology: reaping technology's coaching benefits at older age

The keynote speaker for this conference, Dr. B.J. Fogg, defines persuasive technology as, interactive computing systems designed to change peoples attitudes and behaviors. [1]. Such changes find their origin in changes in peoples motivation. The coaching possibilities of technology may be viewed as an embellishment of conditioning and behavior therapy. n nWith respect to aging, most people want to live a long life--indeed as long as possible--but they dont want to grow old. Literally dozens of formal and informal surveys about the ambitions and desired activities of old people have been performed [2]. The results highlight the desire of older persons to maintain their accustomed way of life, maintain and identify new social contacts; and identify and develop new recreational, educational and artistic activities, some that replace or modify earlier ones associated with family and work. n nWith this background in mind, we will discuss persuasive technology as coaching benefits in relation to the ambitions, activities and wisdom of people as they age.

Movement Path Tortuosity in Free Ambulation: Relationships to Age and Brain Disease

Ambulation is defined by duration, distance traversed, number and size of directional changes, and the interval separating successive movement episodes; more complex measures of ambulation can be created by aggregating these features. This review article of published findings defines random changes in direction during movement as “movement path tortuosity” and relates tortuosity to the understanding of cognitive impairments of persons of all ages. Path tortuosity is quantified by subjecting tracking data to fractal analysis, specifically Fractal Dimension (Fractal D), which ranges from a value of 1 when the movement path is perfectly straight to a value of 2 when the movement path is random, resembling the “drunkards walk.” The review elucidates the mathematical assumptions underlying Fractal D, its use in the analysis of movements of free ranging animals, and its application to the study of cognitive impairment and the prediction of falls in older adults. We conclude Fractal D offers a reliable, valid, sensitive, and easily interpreted real-time longitudinal measure of unrestricted movement path tortuosity unaffected by mobility aid use.

Linking Longitudinal Variability in Physiological and Behavioral Data to Disease Processes: Opportunities and Challenges

As stated by Shameer and colleagues (2017), “monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting” (p.105). Shameer et al. (2017) have reviewed translational bioinformatics methods, tools, and resources, and provide an excellent summary of health-monitoring devices and their application to individualized diagnostics, prognosis, and clinical or wellness interventions. The opportunities they envision are advances in real-time biomedical and healthcare analytics in the clinical setting that will be driven by technologies that monitor, store, remotely transmit, analyze, and display everyday physiological and behavioral data in both healthy and disease states. A broad goal of these technologies is to obtain longitudinal measures in natural settings. “Big Data” analytics applied to masses of streamed sensor data and electronic health records are important tools for effectively realizing the opportunities presented by new monitoring technologies. The applications discussed extend well beyond existing telemedicine functions, in which clinical interactions and physiological and behavioral data traditionally accrued in clinical settings are gathered instead at significant distances using remote sensing. The challenges to linking data elements gathered from the same individual during both healthy and diseased states fall into two broad categories: (1) those common to any analysis of longitudinal measures taken on the same person over time; and (2) those unique to contemporary efforts to link multiple data sources gathered across home and clinical settings. The ever-increasing possibilities for linking healthy and diseased states summarized by Shameer and colleagues (2017) necessarily inherit longstanding challenges to repeated collection of longitudinal data from the same individuals. The scope of contemporary efforts compounds the complexity of known challenges, while creating new issues deriving from the technologies themselves. The following sections delineate these research challenges.

Personalized fall risk assessment for long-term care services improvement

Fall is a devastating critical event with high incidence and mortality rate among older adults in the United States. Due to the stochastic nature of falls, accurate modeling of fall occurrences is important to reduce and prevent falls. Existing approaches mainly focused on population-level modeling and evaluating fall risk based on static and aggregate measures, such as average fall frequency during a certain time period. To account for individual heterogeneity of fall occurrences and provide instantaneous assessment of fall risk over time, this paper proposes a personalized fall risk assessment model. Bayesian estimation is performed to achieve simultaneous fall risk assessment of multiple individuals. Different performance indices are further presented to comprehensively evaluate fall characteristics of individuals over time. A real case study based on fall records collected from Florida assisted living facilities is provided to illustrate the proposed work and demonstrate its effectiveness. The proposed work will facilitate personalized and proactive interventions and managements for long-term care services improvement.