The role of perseverative negative thinking in predicting depression, anxiety and quality of life in people with coronary heart disease.

Depression is common in people with coronary heart disease (CHD) and is associated with worse physical outcomes. The nature of the causal association between CHD and depression, and the mechanism underpinning the association of depression with worse physical outcomes, remains unclear. Perseverative negative thinking may contribute to the development of depression in people with CHD. The aim of this thesis was to investigate the prospective association of perseverative negative thinking with depression, anxiety and worse physical outcomes in people with CHD, and to explore factors that may mediate this association. First, a systematic review identified 30 studies, of which the majority found an association between measures of perseverative negative thinking and subsequent depression, anxiety or emotional distress in people with long term conditions. Studies that controlled for covariates showed more mixed results, though the majority (15 / 25) still supported a significant association, with effects being small in magnitude. Findings were limited mainly to the association of rumination and/or catastrophizing with subsequent depression, and study quality was limited. Next, in an observational prospective cohort study 169 inpatients and outpatients with recent acute coronary syndrome (ACS) completed self-report assessments of rumination (Ruminative Responses Scale brooding subscale), worry (Penn State Worry Questionnaire), depression (Patient Health Questionnaire-8), anxiety (Beck Anxiety Inventory), and health-related quality of life (EuroQol-5D health-related quality of life, Seattle Angina Questionnaire) after hospitalisation, and at 2 month and 6 month follow-up. Additionally, assessments of potential mechanistic factors (social support, problem solving, instrumental behaviours and negative cognitive biases) were made. Baseline brooding was a significant independent predictor of depression at 6 months after controlling for the effects of important confounding variables, accounting for 2% of the variance. Findings suggested that the association of brooding with depression may be explained by deficits in problem solving ability. Rumination and problem solving may provide useful targets for the development of evidence-based interventions to improve depression among people with CHD, although the findings presented here fall short of proving a causal relationship. Future trials could be used to investigate the causal nature of the association of rumination and problem solving with depression in people with ACS

Personalized functional health and fall risk prediction using electronic health records and in-home sensor data

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data

Incident Depression and Daily-life Mobility in Middle-aged and Older Adults

Depression is among the most prevalent mental disorders in middle-aged and older adults, with a global prevalence of up to 11%. Effective preventive measures for depression are often costly and labour-intensive and therefore require risk screenings to be practical. Recent studies suggested that clinically measured walking speed is a risk factor for depression, while little is known about whether other aspects of mobility are also predictive. To explore the temporal association between mobility, in particular daily-life mobility, and incident depression in older adults, one systematic review, one study on method development and validation, and three large-scale cohort studies were conducted. Significant findings include: • The Timed Up and Go Test, which incorporates multiple aspects of mobility (i.e., gait initiation, turning, and sit-to-stand time), is more predictive of depressive trajectories than the Six-Metre Walk Test and Five Times Sit to Stand Test. • Duration of the longest daily walking bout, measured with a waist-worn sensor, independently and significantly predicts incident depression over two years. • Daily-life walking speed, quality, quantity, and distribution can be reliably and validly measured with a wrist-worn sensor. • Daily-life gait quality and quantity, measured with a wrist-worn sensor, independently and significantly predict incident depression over nine years of follow-up. These findings add to the understanding of the association between human locomotion and depression. Gait quality and daily-life gait performances are independent and potentially modifiable predictors of depression. These measures, therefore, may have value for upcoming screening program development. Future research should investigate whether interventions addressing daily-life gait can play a role in preventing depression in middle-aged and older adults

Complementary and Integrative Health Services in a Low-resource Community: a Retrospective Examination

ABSTRACT COMPLEMENTARY AND INTEGRATIVE HEALTH SERVICES IN A LOW-RESOURCE COMMUNITY: A RETROSPECTIVE EXAMINATION by Barbara M. Wesson The University of Wisconsin – Milwaukee, 2017 Under the Supervision of Professor Ron Cisler Study Background and Significance: Complementary and Integrative Health (CIH) continues to be widely utilized despite a lack of consensus regarding its efficacy. Since 2001, CORE El Centro (CEC) has been providing acupuncture, massage, reiki, and mindful movement classes, charging sliding scale fees in a low-resource, primarily Latino community; underrepresented in the CIH literature. Purpose: This study examined the association between CIH use (time and treatment), subjective well-being assessments, biometrics records, as well as the association of reduced fees on service utilization in a low-resource community. Methods: CEC provided information from 1278 de-identified client records spanning 24-months. Within those records, 622 clients had received four or more treatments. Data preparation included reduction of 29 dependent variables (the subjective well-being questions) into four Health Factors (cognitive, emotional, physical, and medical). Additional dependent variables included total number of health conditions reported and a General Health response. Independent variables included length of engagement and types and frequencies of services used. Associations were examined between level of payment for services, utilization, and health conditions. Spaghetti and scatter plots were used to explore trends of change across time. Paired sample tests assessed significant change between assessments. Results: General Health, Total Health Conditions, and three Health Factor scores improved over time. Biometric health status indicators did not change significantly, but were with normal range at the first recording. Clients receiving four or more treatments averaged 12 treatments over eight months. A significant inverse association existed between payment level and Health Conditions. Clients in the low payment level group that reported more Health Conditions used more services and clients in the highest payment level group that reported fewer Health Conditions used more services. Conclusion: The cost of CIH may be one of the primary barriers to utilization, because when fees for service are scaled to income a low-resource community will use CIH. The Latino community demonstrated they will use CIH. Increased use by individuals with chronic conditions was supported. Subjective health and well-being improved. This study supports the rich history of research asserting the complexity in the study of CIH in a community-based center. Future Directions: There is a need to create community based research to further understand CIH as well as the socioeconomic and cultural influence inherent in CIH utilization. The next step is to create deeper partnerships between Universities and health care systems toward developing systems for tracking, monitoring, and analysis that are effective for both research and practitioners of CIH

Exploring the heterogeneity of musculoskeletal pain

Musculoskeletal pain often includes pain in the back, neck, knee, and hip, and is associated with a substantial financial and personal burden. Eight chapters are included in this thesis that aims to improve the understanding of the heterogeneity in treatment effects and prognosis of musculoskeletal pain. Four issues were identified: i) people with different pain phenotypes (i.e. back pain with or without neurological deficit) or with distinct underlying health conditions (e.g. pregnancy-related back pain) may respond differently to treatment strategies; ii) people with chronic back pain and presenting different radiological phenotypes may experience different course of the disease; iii) different patterns of analgesic use over time may be associated with different long term health status; iv) different types and number of sites of musculoskeletal pain may be associated with different clinical prognoses

New Perspectives in Rehabilitation after Traumatic Brain Injury

There has been increased focus on evaluating the scientific knowledge base within the field of traumatic brain injury (TBI) rehabilitation. TBI rehabilitation comprises several phases, from acute medical care to post-acute care in rehabilitation facilities and chronic care in the community. Rehabilitation is a multidisciplinary effort that covers the full spectrum of medical neuroscience, cognitive neuroscience, pharmacology, brain imaging, and assistive and smart technology. A future challenge is to integrate these areas to guide TBI rehabilitation into extensive research and clinical practice. The use of smart technologies and improved brain imaging techniques has an important future in the rehabilitation of patients with cognitive difficulties and disabilities. There is also the need for broad international collaboration to establish large multinational clinical trials in order to define effective service provision and to reach a consensus on the best evidence-based practice of TBI rehabilitation. With this Special Issue, we hope to encourage submissions that discuss ongoing knowledge gaps and controversies, and focus on new perspectives regarding the rehabilitation and management of TBI

Cognitive-motor interference in people with multiple sclerosis: a kinematic approach to clarify the effect of cognitive load on walking performance

The simultaneous performance of cognitive tasks during locomotion (or cognitive-motor dual-task) is known to cause performance deficits in either one of, or both tasks. Furthermore, these performance decrements are exacerbated by the presence of motor impairments and cognitive dysfunctions characteristic of numerous neurological diseases, such as multiple sclerosis (MS). In this regard the assessment of walking while performing a cognitive task may represent a relevant outcome measure, because it allows measuring, in a laboratory setting, individual’s ability to cope with walking challenging situations similar to everyday living. The first aim of this thesis is to provide an experimental setup, based on the use of optoelectronic stereophotogrammetry, for obtaining quantitative evaluation of walking biomechanics and motor strategies during dual-task performance in both healthy adults and people with MS (pwMS). Then, this experimental methodology is tested as suitable method not only for detecting, measuring and characterizing disability, but also for testing intervention effectiveness in clinical practice. Specifically, the study is focused on the assessment of spatiotemporal parameters and lower limb kinematics during single- (normal pace walking) and dual-task (walking while performing a discrimination and decision-making). This thesis is composed of four experiments. The first two aimed to measure the effect of cognitive-motor interference on walking biomechanics in terms of spatiotemporal parameters and lower limb joint kinematics. In this regard, a sample of pwMS stratified by disability level (low disability, EDSS 1.0-2.5, n=37; mild to moderate disability, EDSS 3.0-6.0, n=44) and a sample of age- and gender-matched healthy adults (n=41) underwent a 3D kinematic evaluation of single- and dual-task performance using a motion capture system. Differences between conditions and groups were investigated using a two-way repeated ANOVA. The results reported that gait speed and stride length were sensitive motor variables in detecting differences from single- to dual-task condition in both pwMS and unaffected individuals, whereas spatiotemporal parameters closely related to balance control (e.g. step width, double support phase duration) were sensitive to changes only in pwMS with moderate disability. Moreover, those patients showed significant changes in the kinematics of distal joint (shank-foot) and proximal joint (hip), including a reduction in ankle plantarflexion and hip extension peak at the terminal stance phase. These observed changes in more impaired patients are compensatory mechanism to stabilize body posture and allow safe locomotion during complicate dual-task activities. Finally, the other two experiments were designed to provide a clinical application of this methodology, as a tool for quantitatively assessing biomechanics changes after an innovative therapeutic intervention. In this regard, a sample of pwMS (n=34) with mild to moderate disability participated in a bicentric clinical trial. As per protocol, pwMS completed an intervention consisting of either active or sham multiple sessions of transcranial direct current stimulation (tDCS) combined with physical activity, aimed to improve walking performance. Following repeated application of active tDCS, the results obtained from the quantitative gait analysis showed greater improvements in gait velocity, step length and walking endurance. This improvement measured in walking had corresponding effects on walking dual-task performance. In fact, the dual-task cost of gait parameters was significantly reduced after the active tDCS intervention. In conclusion, the quantitative assessment of walking impairments during the execution of functional task in pwMS can support a deep learning of both movement features and motor strategies, which should have implications for the design and validation of clinical intervention aimed at improving functional walking