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
The biopsychosocial impact of Autism on families and the contribution of solar irradiance to its aetiology
- Authors: Syed, Somayya
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Autism Spectrum Disorder (ASD) is a lifelong disorder of unknown aetiology. A recent hypothesis is that a lack of Vitamin D is implicated in either the aetiology or maintenance of ASD. The human body synthesises Vitamin D from Ultraviolet-B (UVB) radiation found in sunlight. It follows that greater exposure to sunlight hours may be related to decreased rates of ASD. There are no interventions that target the causes of ASD rather therapies address either its symptoms or its comorbidities. ASD not only affects individuals, it also has an impact on their families. Family members have experienced social, occupational and personal costs associated with their child’s ASD which can result in parental separation or divorce. While researchers have established some of the factors which contribute to the impact on families, this research has not addressed families living in regional areas nor have empirical studies used domain-specific scales. The aims in this thesis were: Study 1) to determine whether the prevalence rates of ASD vary as a function of exposure to sunlight by reviewing reported prevalence rates by latitude where, the greater the distance from the equator, the higher the expected prevalence rates; Study 2) to conduct interviews with parents and caregivers of children with ASD who live in a regional area to determine the factors which affect them and those which might protect them; and Study 3) use the interview data to develop domain specific measures and test a model of living with a child with ASD. The results of Study 1 revealed that there is an increase in the prevalence of ASD as distance from the equator increases lending some support to the hypothesis that Vitamin D is implicated in ASD. The 16 interviews conducted in Study 2 revealed seven themes: impact on finances; family life; child’s health and behaviour, and schooling; child’s future; limited support, and regional living. In Study 3, with 178 participants, domain-specific scales were developed to test a model of the impact of living with a child with ASD. Resilience manifested by social support and coping strategies, explained 54% of the variance in impact of living with a child with ASD which, was operationalised by financial and relationship costs, social impact and feelings. Family life as assessed in this thesis, is significantly impacted by living with a child with ASD. The implications of these findings are discussed, especially around the need for greater exposure to outdoor activities and hence sunlight for children with ASD, more regional facilities and assistance for families, the importance of educational interventions for the public as well as enhancing levels of family resilience, as operationalised by support and coping strategies. Limitations of the studies and future research are discussed.
- Description: Doctor of Philosophy
- Authors: Syed, Somayya
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: Autism Spectrum Disorder (ASD) is a lifelong disorder of unknown aetiology. A recent hypothesis is that a lack of Vitamin D is implicated in either the aetiology or maintenance of ASD. The human body synthesises Vitamin D from Ultraviolet-B (UVB) radiation found in sunlight. It follows that greater exposure to sunlight hours may be related to decreased rates of ASD. There are no interventions that target the causes of ASD rather therapies address either its symptoms or its comorbidities. ASD not only affects individuals, it also has an impact on their families. Family members have experienced social, occupational and personal costs associated with their child’s ASD which can result in parental separation or divorce. While researchers have established some of the factors which contribute to the impact on families, this research has not addressed families living in regional areas nor have empirical studies used domain-specific scales. The aims in this thesis were: Study 1) to determine whether the prevalence rates of ASD vary as a function of exposure to sunlight by reviewing reported prevalence rates by latitude where, the greater the distance from the equator, the higher the expected prevalence rates; Study 2) to conduct interviews with parents and caregivers of children with ASD who live in a regional area to determine the factors which affect them and those which might protect them; and Study 3) use the interview data to develop domain specific measures and test a model of living with a child with ASD. The results of Study 1 revealed that there is an increase in the prevalence of ASD as distance from the equator increases lending some support to the hypothesis that Vitamin D is implicated in ASD. The 16 interviews conducted in Study 2 revealed seven themes: impact on finances; family life; child’s health and behaviour, and schooling; child’s future; limited support, and regional living. In Study 3, with 178 participants, domain-specific scales were developed to test a model of the impact of living with a child with ASD. Resilience manifested by social support and coping strategies, explained 54% of the variance in impact of living with a child with ASD which, was operationalised by financial and relationship costs, social impact and feelings. Family life as assessed in this thesis, is significantly impacted by living with a child with ASD. The implications of these findings are discussed, especially around the need for greater exposure to outdoor activities and hence sunlight for children with ASD, more regional facilities and assistance for families, the importance of educational interventions for the public as well as enhancing levels of family resilience, as operationalised by support and coping strategies. Limitations of the studies and future research are discussed.
- Description: Doctor of Philosophy
Modeling neurocognitive reaction time with gamma distribution
- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Aryal, Sunil, Klein, Britt
- Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ACSW'18 . Proceedings of the Australasian Computer Science Week Multiconference; Brisbane, QLD; January 2018; Article 28 p. 1-10
- Full Text: false
- Reviewed:
- Description: As a broader effort to build a holistic biopsychosocial health metric, reaction time data obtained from participants undertaking neurocognitive tests have been examined using Exploratory Data Analysis (EDA) for assessing its distribution. Many of the known existing methods assume, that the reaction time data follows a Gaussian distribution and thus commonly use statistical measures such as Analysis of Variance (ANOVA) for analysis. However, it is not mandatory for the reaction time data, to necessarily follow Gaussian distribution and in many instances, it can be better modeled by other representations such as Gamma distribution. Unlike Gaussian distribution which is defined using mean and variance, the Gamma distribution is defined using shape and scale parameters which also considers higher order moments of data such as skewness and kurtosis. Generalized Linear Models (GLM), based on the family exponential distributions such as Gamma distribution, which have been used to model reaction time in other domains, have not been fully explored for modeling reaction time data in psychology domain. While limited use of Gamma distribution have been reported [5, 17, 21], for analyzing response times, their application has been somewhat ad-hoc rather than systematic. For this proposed research, we use a real life biopsychosocial dataset, generated from the 'digital health' intervention programs conducted by the Faculty of Health, Federation University, Australia. The two digital intervention programs were the 'Mindfulness' program and 'Physical Activity' program. The neurocognitive tests were carried out as part of the 'Mindfulness' program. In this paper, we investigate the participants' reaction time distributions in neurocognitive tests such as the Psychology Experiment Building Language (PEBL) Go/No-Go test [19], which is a subset of the larger biopsychosocial data set. PEBL is an open source software system for designing and running psychological experiments. Analysis of participants' reaction time in the PEBL Go/No-Go test, shows that the reaction time data are more compatible with a Gamma distribution and clearly demonstrate that these can be better modeled by Gamma distribution.
A fully automated self-help biopsychosocial transdiagnostic digital intervention to reduce anxiety and/or depression and improve emotional regulation and well-being: pre-follow-up single-arm feasibility trial
- Klein, Britt, Nguyen, Huy, McLaren, Suzanne, Andrews, Brooke, Shandley, Kerrie
- Authors: Klein, Britt , Nguyen, Huy , McLaren, Suzanne , Andrews, Brooke , Shandley, Kerrie
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Formative Research Vol. 7, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. Objective: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. Methods: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). Results: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR] < .001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=
- Authors: Klein, Britt , Nguyen, Huy , McLaren, Suzanne , Andrews, Brooke , Shandley, Kerrie
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Formative Research Vol. 7, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. Objective: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. Methods: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). Results: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR] < .001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=
Understanding fear after an anterior cruciate ligament injury : a qualitative thematic analysis using the common-sense model
- Little, Cameron, Lavender, Andrew, Starcevich, Cobie, Mesagno, Christopher, Mitchell, Tim, Whiteley, Rodney, Bakhshayesh, Hanieh, Beales, Darren
- Authors: Little, Cameron , Lavender, Andrew , Starcevich, Cobie , Mesagno, Christopher , Mitchell, Tim , Whiteley, Rodney , Bakhshayesh, Hanieh , Beales, Darren
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Environmental Research and Public Health Vol. 20, no. 4 (2023), p.
- Full Text:
- Reviewed:
- Description: Fear is a significant factor affecting successful return to sport following an anterior cruciate ligament (ACL) injury. However, there is a lack of understanding of the emotional drivers of fear and how fear beliefs are formed. This study qualitatively explored the contextual and emotional underpinnings of fear and how these beliefs were formed, with reference to the Common-Sense Model of Self-Regulation. Face-to-face online interviews were conducted with ACL-injured participants (n = 18, 72% female) with a mean age of 28 years (range 18–50 years). Participants were either 1 year post ACL reconstruction surgery (n = 16) or at least 1 year post injury without surgery (n = 2) and scored above average on a modified Tampa Scale of Kinesiophobia. Four participants were playing state-level sport or higher. Five themes emerged describing factors contributing to fear: ‘External messages’, ‘Difficulty of the ACL rehabilitation journey’, ‘Threat to identity and independence’, ‘Socioeconomic factors’, and ‘Ongoing psychological barriers’. A sixth theme, ‘Positive coping strategies’, provided insight into influences that could reduce fear and resolve negative behaviors. This study identified a broad range of contextual biopsychosocial factors which contribute to fear, supporting the notion that ACL injuries should not be treated through a purely physical lens. Furthermore, aligning the themes to the common-sense model provided a conceptual framework conveying the inter-related, emergent nature of the identified themes. The framework provides clinicians with a means to understanding fear after an ACL injury. This could guide assessment and patient education. © 2023 by the authors.
- Authors: Little, Cameron , Lavender, Andrew , Starcevich, Cobie , Mesagno, Christopher , Mitchell, Tim , Whiteley, Rodney , Bakhshayesh, Hanieh , Beales, Darren
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Environmental Research and Public Health Vol. 20, no. 4 (2023), p.
- Full Text:
- Reviewed:
- Description: Fear is a significant factor affecting successful return to sport following an anterior cruciate ligament (ACL) injury. However, there is a lack of understanding of the emotional drivers of fear and how fear beliefs are formed. This study qualitatively explored the contextual and emotional underpinnings of fear and how these beliefs were formed, with reference to the Common-Sense Model of Self-Regulation. Face-to-face online interviews were conducted with ACL-injured participants (n = 18, 72% female) with a mean age of 28 years (range 18–50 years). Participants were either 1 year post ACL reconstruction surgery (n = 16) or at least 1 year post injury without surgery (n = 2) and scored above average on a modified Tampa Scale of Kinesiophobia. Four participants were playing state-level sport or higher. Five themes emerged describing factors contributing to fear: ‘External messages’, ‘Difficulty of the ACL rehabilitation journey’, ‘Threat to identity and independence’, ‘Socioeconomic factors’, and ‘Ongoing psychological barriers’. A sixth theme, ‘Positive coping strategies’, provided insight into influences that could reduce fear and resolve negative behaviors. This study identified a broad range of contextual biopsychosocial factors which contribute to fear, supporting the notion that ACL injuries should not be treated through a purely physical lens. Furthermore, aligning the themes to the common-sense model provided a conceptual framework conveying the inter-related, emergent nature of the identified themes. The framework provides clinicians with a means to understanding fear after an ACL injury. This could guide assessment and patient education. © 2023 by the authors.
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