Neurotrophic biomarker change after physical activity and mindfulness interventions
- Authors: England, Gina
- Date: 2017
- Type: Text , Thesis , Masters
- Full Text:
- Description: BACKGROUND AND AIM AND HYPOTHESIS: BDNF, FGF2 and NGF are neurotrophins associated with neuroplasticity, nervous system development and psychiatric disorder in the literature. BDNF in particular is suggested as a useful biomarker of mood disorder. Both mindfulness and physical activity are shown to improve mood, reduce stress and are widely used as part of a multi-component treatment approach, reducing distressing symptoms of mood and affect disorders. The utility of protein level as a biomarker has been controversial in the literature following issues concerning the assessment of peripheral levels as a proxy for central levels. The principal aim of this study was to investigate the gene expression of three neurotrophins BDNF, FGF2 and NGF as potential biomarkers of mood disorder, at an early stage of these disorders, which are now widely recognised as having pathogenesis related to dysregulation in the neuro-immuno-endocrine axis. In addition, the study will explore the effect of both physical activity and mindfulness on neurotrophin expression clarifying the associations between the success of these interventions seen in the literature and their effect on the change of neurotrophin expression. Current literature reports increased levels of BDNF protein both centrally and peripherally following mood disorder treatment and participation in both physical exercise and mindfulness activities. Based on similarity of structure and function amongst the three neurotrophins, this thesis will hypothesize an increase in BDNF and potentially FGF2 and NGF mRNA following participation in the two interventional modules designed to improve wellbeing in clinical and non-clinical communities. METHOD: In this independent measures design, 28 non-clinical volunteers were randomly allocated to an 8 week intervention, comprising digital health tracking modules and participation in an unstructured increase in Physical Activity or Mindfulness program, to assess the effect of these interventions on levels of mRNA expression. RTqPCR was used to compare relative mRNA abundance in peripheral blood at baseline and 8 week time interval. The control group were allocated to a waitlist for the period of the 8 week study, followed by access to the program of their choice. Change in emotional state was measured using the DASS. RESULT: BDNF expression is shown significantly increased (p 0.01, n=5) in the Physical Activity group, and significantly decreased in the Mindfulness group (p 0.01, n=11). FGF2 and waitlisted controls showed no significant change. In the case of NGF no expression was seen in human peripheral blood either before or after the intervention. DASS scores revealed a significant decrease in negative affect in the Mindfulness group p = 0.03. CONCLUSION: This study revealed a significant positive association between physical activity and BDNF mRNA, although no significant reduction in distressing mood symptoms was shown. This was potentially due to the small group size. Mindfulness was significantly associated with decreasing negative affect, despite an unexpected decrease in BDNF mRNA consistent with pathophysiology of depression, likely related to neuro-immunoendocrine axis disturbance as suggested in the published literature. It is suggested decreasing mRNA levels reflect lower numbers of immune activated leucocytes present in the blood following mood improvement, albeit not verified in the study. This study suggests even in a small non-clinical sample there may be potential benefits to well-being by increasing levels of physical activity or becoming mindful, and that BDNF has potential as a biomarker of emotional state.
- Description: Masters by Research
- Authors: England, Gina
- Date: 2017
- Type: Text , Thesis , Masters
- Full Text:
- Description: BACKGROUND AND AIM AND HYPOTHESIS: BDNF, FGF2 and NGF are neurotrophins associated with neuroplasticity, nervous system development and psychiatric disorder in the literature. BDNF in particular is suggested as a useful biomarker of mood disorder. Both mindfulness and physical activity are shown to improve mood, reduce stress and are widely used as part of a multi-component treatment approach, reducing distressing symptoms of mood and affect disorders. The utility of protein level as a biomarker has been controversial in the literature following issues concerning the assessment of peripheral levels as a proxy for central levels. The principal aim of this study was to investigate the gene expression of three neurotrophins BDNF, FGF2 and NGF as potential biomarkers of mood disorder, at an early stage of these disorders, which are now widely recognised as having pathogenesis related to dysregulation in the neuro-immuno-endocrine axis. In addition, the study will explore the effect of both physical activity and mindfulness on neurotrophin expression clarifying the associations between the success of these interventions seen in the literature and their effect on the change of neurotrophin expression. Current literature reports increased levels of BDNF protein both centrally and peripherally following mood disorder treatment and participation in both physical exercise and mindfulness activities. Based on similarity of structure and function amongst the three neurotrophins, this thesis will hypothesize an increase in BDNF and potentially FGF2 and NGF mRNA following participation in the two interventional modules designed to improve wellbeing in clinical and non-clinical communities. METHOD: In this independent measures design, 28 non-clinical volunteers were randomly allocated to an 8 week intervention, comprising digital health tracking modules and participation in an unstructured increase in Physical Activity or Mindfulness program, to assess the effect of these interventions on levels of mRNA expression. RTqPCR was used to compare relative mRNA abundance in peripheral blood at baseline and 8 week time interval. The control group were allocated to a waitlist for the period of the 8 week study, followed by access to the program of their choice. Change in emotional state was measured using the DASS. RESULT: BDNF expression is shown significantly increased (p 0.01, n=5) in the Physical Activity group, and significantly decreased in the Mindfulness group (p 0.01, n=11). FGF2 and waitlisted controls showed no significant change. In the case of NGF no expression was seen in human peripheral blood either before or after the intervention. DASS scores revealed a significant decrease in negative affect in the Mindfulness group p = 0.03. CONCLUSION: This study revealed a significant positive association between physical activity and BDNF mRNA, although no significant reduction in distressing mood symptoms was shown. This was potentially due to the small group size. Mindfulness was significantly associated with decreasing negative affect, despite an unexpected decrease in BDNF mRNA consistent with pathophysiology of depression, likely related to neuro-immunoendocrine axis disturbance as suggested in the published literature. It is suggested decreasing mRNA levels reflect lower numbers of immune activated leucocytes present in the blood following mood improvement, albeit not verified in the study. This study suggests even in a small non-clinical sample there may be potential benefits to well-being by increasing levels of physical activity or becoming mindful, and that BDNF has potential as a biomarker of emotional state.
- Description: Masters by Research
Biopsychosocial Data Analytics and Modeling
- Authors: Santhanagopalan, Meena
- Date: 2021
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement. The growth of information and communication technology has led to the increased use of DHI programs. These programs use an observational methodology that helps researchers to study the everyday behaviour of participants during the course of the program by analysing data generated from digital tools such as wearables, online surveys and ecological momentary assessment (EMA). Combined with data reported from biological and psychological tests, this provides rich and unique biopsychosocial data. There is a strong need to review and apply novel approaches to combining biopsychosocial data from a methodological perspective. Although some studies have used data analytics in research on clinical trial data generated from digital interventions, data analytics on biopsychosocial data generated from DHI programs is limited. The study in this thesis develops and implements innovative approaches for analysing the existing unique and rich biopsychosocial data generated from the wellness study, a DHI program conducted by the School of Science, Psychology and Sport at Federation University. The characteristics of variety, value and veracity that usually describe big data are also relevant to the biopsychosocial data handled in this thesis. These historical, retrospective real-life biopsychosocial data provide fertile ground for research through the use of data analytics to discover patterns hidden in the data and to obtain new knowledge. This thesis presents the studies carried out on three aspects of biopsychosocial research. First, we present the salient traits of the three components - biological, psychological and social - of biopsychosocial research. Next, we investigate the challenges of pre-processing biopsychosocial data, placing special emphasis on the time-series data generated from wearable sensor devices. Finally, we present the application of statistical and machine learning (ML) tools to integrate variables from the biopsychosocial disciplines to build a predictive model. The first chapter presents the salient features of the biopsychosocial data for each discipline. The second chapter presents the challenges of pre-processing biopsychosocial data, focusing on the time-series data generated from wearable sensor devices. The third chapter uses statistical and ML tools to integrate variables from the biopsychosocial disciplines to build a predictive model. Among its other important analyses and results, the key contributions of the research described in this thesis include the following: 1. using gamma distribution to model neurocognitive reaction time data that presents interesting properties (skewness and kurtosis for the data distribution) 2. using novel ‘peak heart-rate’ count metric to quantify ‘biological’ stress 3. using the ML approach to evaluate DHIs 4. using a recurrent neural network (RNN) and long short-term memory (LSTM) data prediction model to predict Difficulties in Emotion Regulation Scale (DERS) and primary emotion (PE) using wearable sensor data.
- Description: Doctor of Philosophy
- Authors: Santhanagopalan, Meena
- Date: 2021
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement. The growth of information and communication technology has led to the increased use of DHI programs. These programs use an observational methodology that helps researchers to study the everyday behaviour of participants during the course of the program by analysing data generated from digital tools such as wearables, online surveys and ecological momentary assessment (EMA). Combined with data reported from biological and psychological tests, this provides rich and unique biopsychosocial data. There is a strong need to review and apply novel approaches to combining biopsychosocial data from a methodological perspective. Although some studies have used data analytics in research on clinical trial data generated from digital interventions, data analytics on biopsychosocial data generated from DHI programs is limited. The study in this thesis develops and implements innovative approaches for analysing the existing unique and rich biopsychosocial data generated from the wellness study, a DHI program conducted by the School of Science, Psychology and Sport at Federation University. The characteristics of variety, value and veracity that usually describe big data are also relevant to the biopsychosocial data handled in this thesis. These historical, retrospective real-life biopsychosocial data provide fertile ground for research through the use of data analytics to discover patterns hidden in the data and to obtain new knowledge. This thesis presents the studies carried out on three aspects of biopsychosocial research. First, we present the salient traits of the three components - biological, psychological and social - of biopsychosocial research. Next, we investigate the challenges of pre-processing biopsychosocial data, placing special emphasis on the time-series data generated from wearable sensor devices. Finally, we present the application of statistical and machine learning (ML) tools to integrate variables from the biopsychosocial disciplines to build a predictive model. The first chapter presents the salient features of the biopsychosocial data for each discipline. The second chapter presents the challenges of pre-processing biopsychosocial data, focusing on the time-series data generated from wearable sensor devices. The third chapter uses statistical and ML tools to integrate variables from the biopsychosocial disciplines to build a predictive model. Among its other important analyses and results, the key contributions of the research described in this thesis include the following: 1. using gamma distribution to model neurocognitive reaction time data that presents interesting properties (skewness and kurtosis for the data distribution) 2. using novel ‘peak heart-rate’ count metric to quantify ‘biological’ stress 3. using the ML approach to evaluate DHIs 4. using a recurrent neural network (RNN) and long short-term memory (LSTM) data prediction model to predict Difficulties in Emotion Regulation Scale (DERS) and primary emotion (PE) using wearable sensor data.
- Description: Doctor of Philosophy
An examination of physical exercise as an adjunct treatment for depressive symptoms in adults aged 65 years and older
- Authors: Miller, Kyle
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: In light of impending demographic shifts and projected strain on healthcare systems, this thesis set out to progress our putative understanding of the benefits of physical exercise on mental health in older adults aged 65 years and over. Herein, four studies of divergent research design interrogated the current knowledge base relating to the potential benefits of exercise in older adults with depressive symptomology. Study 1 set out to establish preliminary experimental evidence that four years of unsupervised aerobic exercise can improve cardiorespiratory function (determined by VO2max) and health-related quality of life (HRQL) in lifelong sedentary ageing men compared with lifelong exercising athletes. Results demonstrated preliminary proof of concept for exercise-induced benefits on cardiorespiratory function and HRQL in ageing men. Study 2 surveyed community-dwelling older adults (n = 586) to establish a hierarchy of exercise-associated factors to predict depressive symptomology. Contrary to expectation, exercise behaviour did not confer additional antidepressant effect, but was substantially predicted by exercise-induced mood, exercise self-efficacy, and social support (f2 = 0.993). Study 3 pooled evidence from randomised controlled trials (RCTs) to quantitatively compare the treatment effectiveness from aerobic, resistance and mind-body exercise training in older adults with pre-existing clinical depression, whereas Study 4 followed the same methodology in apparently health older adults without pre-existing clinical depression. Using network meta-analytical techniques, both clinical depressed (g = -0.41 to -1.38) and apparently healthy (g = -0.27 to -0.51) older adults demonstrated equivalent effectiveness for aerobic, resistance, and mind-body exercise interventions, with encouraging levels of study compliance. Taken together, these findings encourage personal exercise preference when prescribing either aerobic, resistance, or mind-body exercise as a treatment adjunct for clinical depression and older adults with symptoms thereof. The sum of works herein provide new knowledge to guide exercise prescription for stakeholders in mental health and older adults over 65 years.
- Description: Doctor of Philosophy
- Authors: Miller, Kyle
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: In light of impending demographic shifts and projected strain on healthcare systems, this thesis set out to progress our putative understanding of the benefits of physical exercise on mental health in older adults aged 65 years and over. Herein, four studies of divergent research design interrogated the current knowledge base relating to the potential benefits of exercise in older adults with depressive symptomology. Study 1 set out to establish preliminary experimental evidence that four years of unsupervised aerobic exercise can improve cardiorespiratory function (determined by VO2max) and health-related quality of life (HRQL) in lifelong sedentary ageing men compared with lifelong exercising athletes. Results demonstrated preliminary proof of concept for exercise-induced benefits on cardiorespiratory function and HRQL in ageing men. Study 2 surveyed community-dwelling older adults (n = 586) to establish a hierarchy of exercise-associated factors to predict depressive symptomology. Contrary to expectation, exercise behaviour did not confer additional antidepressant effect, but was substantially predicted by exercise-induced mood, exercise self-efficacy, and social support (f2 = 0.993). Study 3 pooled evidence from randomised controlled trials (RCTs) to quantitatively compare the treatment effectiveness from aerobic, resistance and mind-body exercise training in older adults with pre-existing clinical depression, whereas Study 4 followed the same methodology in apparently health older adults without pre-existing clinical depression. Using network meta-analytical techniques, both clinical depressed (g = -0.41 to -1.38) and apparently healthy (g = -0.27 to -0.51) older adults demonstrated equivalent effectiveness for aerobic, resistance, and mind-body exercise interventions, with encouraging levels of study compliance. Taken together, these findings encourage personal exercise preference when prescribing either aerobic, resistance, or mind-body exercise as a treatment adjunct for clinical depression and older adults with symptoms thereof. The sum of works herein provide new knowledge to guide exercise prescription for stakeholders in mental health and older adults over 65 years.
- Description: Doctor of Philosophy
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