A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

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Intent to engage in therapeutic lifestyle changes: Impact of an intervention, self-efficacy expectations, outcome expectations, and locus of control

Engagement in Therapeutic Lifestyle Changes (TLCs), such as time in nature, physical activity, nutrition, sleep, social interaction, religion/spirituality, stress management, and helping others, provides many mental health benefits (Walsh, 2011). Young adults could particularly benefit from the use of TLCs, as they are at greater risk of experiencing mental health concerns (APA, 2013). I devised an intervention to enhance TLC engagement in college students, and examined the impact this intervention (against a control intervention), had on self-efficacy expectations, outcome expectations, mental health locus of control, intent to increase TLC use, and TLC use at one-week follow-up. Participants were 459 undergraduates. Participants completed baseline TLC use and self-efficacy expectations measures, then were randomly assigned to either a Control or my TLC intervention. Participants then responded to items assessing: post-intervention self-efficacy expectations, outcome expectations, mental health locus of control, intent to increase TLC use, and TLC preferences. Additionally, 211 of these participants completed a one-week follow-up survey inquiring about increased TLC use. Results demonstrated significant changes in pre- to post-intervention self-efficacy expectations for participants in the TLC condition; however, that condition did not bring a significant change in TLC use at follow-up. Outcome expectations partially mediated the direct relation between post-intervention self-efficacy expectations and intent to increase TLC use. Mental health locus of control did not moderate either intent to, or follow-up change in, TLC use post-intervention, as hypothesized. Regression analyses demonstrated that self-efficacy and outcome expectations accounted for 43% of the variance in intent to increase TLC use, and self-efficacy expectations accounted for 11% of the variance in post-intervention TLC use at one-week follow-up. I offer discussion on the implications of my findings and directions for future research

An Examination of the Impact of the COVID-19 Health Threat, Stress, and Social Isolation on Lifestyle Habits as Analyzed through the Protection Motivation Theory

The COVID-19 emerged in China in 2019 and quickly spread to other countries, leading to mandated lockdowns and social isolation. This cross-sectional study examined the impact of the COVID-19-generated stress, health threat, and social isolation on dietary, physical activity, and self-care habits of adults in Florida, utilizing the PMT as a framework. Participants (n = 478) completed online surveys about demographics, perceived stress, and changes in lifestyle habits. Significant positive changes were reported in cooking at home (p \u3c .001) frequency, sweets (p \u3c .001), and breakfast (p = .009) consumption, outdoors physical activity (p = .005), self-care (p \u3c .001), relaxation (p \u3c .001), and rest (p \u3c .001) habits. Significant negative changes were reported in fast food (p = .004) and snack (p \u3c .001) consumption. A significant relationship existed between self-reported stress, perceived threat, (r = .33, p \u3c .001), and perceived efficacy, (r = -.15, p = .002). Perceived threat was the most important predictor of changes in dietary habits (R2 = .13); stress was the main predictor of physical activity (R2 = .60) and self-care (R2 = .18) changes. Perceived threat and stress predicted changes in dietary (ß = .255, p \u3c .001; ß = .253, p \u3c .001) and physical activity (ß = .177, p \u3c .001; ß = .152, p \u3c .001) scores, and both with perceived efficacy predicted changes in self-care (ß = .184, p \u3c .001, ß = .375, p \u3c .001, ß = .098, p \u3c .05) scores. Protection-motivation seems to influence behavior change in times of distress and may support effective interventions to promote lifestyle changes. To our knowledge, this is the first study to examine the impact of COVID-19 generated stress, health threat, and social isolation on lifestyle habits of adults in Florida utilizing PMT constructs

PARIS: Personalized Activity Recommendation for Improving Sleep Quality

The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.Comment: 18 pages, 7 figures, Submitted to UMUAI: Special Issue on Recommender Systems for Health and Wellbeing, 202

Combined Nutrition and Exercise Interventions in Community Groups

Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

Perceived Alzheimer\u27s Disease Threat as a Predictor of Behavior Change to Lower Disease Risk: The Gray Matters Study

Alzheimer’s disease is a growing public health concern with the current number afflicted of 5 million in the US expected to triple by 2050. Since there is currently no cure or preventive pharmacological treatment, AD prevention research is now recognized as an important enterprise, with a goal to identify modifiable lifestyle factors that can reduce AD risk or delay its onset. Among these, increased physical activity, healthier food choices, more cognitive stimulation, better sleep quality, stress management, and social engagement have been identified as reasonable targets for behavioral intervention. A smartphone application-based behavioral intervention targeting these six behavioral domains was recently developed and a six-month randomized controlled trial was conducted, both to determine feasibility and compliance with technology usage and to test its efficacy. This study, titled the Gray Matters Study, was conducted in Cache County, Utah, enrolling a sample of 146 middle-aged participants (aged 40 to 64 years) randomized to treatment or control condition. Under the Health Belief Model, individuals who perceive a greater susceptibility to a particular health condition are hypothesized to be more likely to engage in more positive behaviors to reduce disease risk. Following this model, perceived threat of AD (operationalized by fear of AD, family history of AD, and metacognitive concerns) was examined for prediction of behavioral change over the six-month Gray Matters intervention period in these same six behavioral domains. Persons with a moderate level of fear of AD made significantly greater improvements in physical activity than those with low or high levels of fear. Family history was not a significant predictor of health-related behavioral change. However, persons with a moderate level of metacognitive concerns made significantly greater improvements in both physical activity and food quality than those with low or high levels of concerns. This is the first study to examine these psychological constructs related to AD risk and the extent to which they predict health-related behavior change. Future studies should extend the length of follow-up to at least one full year, include a more diverse sample of participants to expand generalizability, and build upon these findings to personalize supportive behavioral change interventions in order to be sensitive to these psychological factors

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

Aerobic Exercise Exposure Targeting Anxiety Sensitivity: Effects on Associated Health Behaviors in Young Adults

Anxiety sensitivity (AS) is associated with health behaviors such as low rates of physical activity, overeating, alcohol use, and poor sleep; however, interventions targeting AS via exercise-based interoceptive exposure have not assessed these as outcomes. In addition, previous studies are limited by brief follow-up periods. This study aimed to replicate previous aerobic exercise interoceptive exposures with an extended (6-week) follow-up and measurement of health behaviors. Participants were 44 sedentary young adults with elevated AS randomized to intervention (6 20-minute sessions of moderate-intensity treadmill walking) or assessment-only control. Assessments took place at baseline, week 2 (post-treatment), week 4, and week 8 with measurements of AS (ASI-3), physical activity (7-Day PAR), sleep (ISI), binge eating, alcohol use, depression (PHQ-8), anxiety (GAD-7), and stress (PSS-4). The intervention condition demonstrated a marginally significant reduction in AS compared to control at week 4 which eroded by week 8. There were no significant between-group differences for health behavior change. The intervention condition demonstrated decreases in depression, general anxiety, and perceived stress compared to control, but these effects eroded by week 4. There was no difference in findings for participants with BMI\u3c25 vs. those with BMI\u3e=25. Findings indicate that a brief intervention might not be sufficient to produce lasting changes in AS without additional treatment. Intervention effects were not as strong in this study compared to previous reports, which may be due to the size and greater racial/ethnic diversity of the current sample. Future research should objectively measure physical activity and explore individual variability in treatment response