INFANT Research Centre - Doctoral Theses
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- ItemMachine learning techniques for identifying early-life biomarkers in perinatal & child health(University College Cork, 2022) O'Boyle, Daragh; English, Jane A.; Murray, Deirdre M.; Mooney, Catherine; Lopez, Lorna; Science Foundation Ireland; Health Research BoardArtificial Intelligence (AI), and more specifically machine learning (ML), has been used in the investigation of biomarkers for many clinical conditions, reducing the need for specialist diagnosis, reducing waiting times and increasing access to reliable diagnostics. There are numerous areas yet to benefit from its application, particularly in the fields of perinatal and paediatric research. Two such brain related conditions, which will be the focus of this thesis are Hypoxic Ischemic Encephalopathy (HIE) and Autism Spectrum Disorder (ASD). HIE is a major cause of neurological disability globally and results from a lack of oxygen to the brain during and immediately after birth. The mainstay of treatment is therapeutic hypothermia, which, to be effective it must be applied within 6 hours of birth. This thesis aims to improve the early identification of infants eligible for therapeutic hypothermia using AI with clinical and metabolomic HIE biomarkers. Autism constitutes a group of neurodevelopmental disorders characterized by behavioural and cognitive symptoms. The underlying aetiology behind autism remains unclear and reliable predictive biomarkers are lacking. Intervention from an early age has been shown to reduce symptoms but diagnosis frequently occurs outside the window for effective treatment. Despite proven benefits in patient outcomes with intervention within the first two years of life, diagnosis often doesn’t occur until an individual is 3 or 4 years old, and in many cases much older. There is a pressing need for new methods to identify neonates most at risk to provide adequate treatment which improves long term outcomes. In the first experimental section, chapters 2-5, we have applied ML to first identity the optimum predictive clinical and metabolomic biomarkers for the identification of those with HIE. We initially assessed clinical variables using ML feature ranking and modelling. This study identified markers of a newborn’s condition at birth; Apgar scores, need for resuscitation, first measurements of pH and base deficit, as the most predictive. These models achieved an area under the receiver operator characteristic curve (AUROC) of 0.89 when distinguishing between those with perinatal asphyxia, who do not require treatment, and those with HIE. Furthermore, we then assessed a panel of promising metabolite markers for their predictive capabilities with and without clinical markers. ML identified metabolites alanine and lactate as the most predictive of HIE development and when combined with Apgar scores a measure of a newborn’s condition, at 1 and 5 minutes after birth, achieved a predictive AUROC of 0.96. These studies have successfully identified alanine as a candidate metabolite for cotside HIE risk assessment as well as displaying that ML models can improve on our current ability to identify those in need of therapeutic hypothermia. To validate these findings two studies were undertaken. The first independently compared alanine levels in cord blood of those with HIE and controls. This study has displayed elevated levels of alanine at birth and up to 6 hours after, successfully validating alanine as an early life marker for those with HIE in need of neuroprotective therapy. The second validation study successfully validated our algorithm for the prediction of HIE in a large diverse cohort comprised of infants with a range of differing conditions, compared to a set of HIE cases and controls previously assessed. Here 243 infants were assessed, using our model, to determine risk of HIE and an accuracy of 85% was maintained. This study has successfully validated our model's ability to retain performance when applied to a diverse, real-world cohort. In this section, we have successfully displayed the use of ML for improving HIE diagnostics and validated these findings. Further, larger validation studies are currently underway with the end goal of clinical use for determination of those in need of treatment for HIE. In the second experimental section, chapters 6-8, we aimed to apply ML methods to identify early life biomarkers for autism spectrum disorder. We first conducted a systematic review of all blood-based autism biomarkers. This study successfully catalogued all reported biomarkers and recorded the direction of change of theses markers in those with autism compared to neurotypical controls. This study also applied Genome Wide Association Studies (GWAS) and pathway analysis to test for biological processes which may be implicated at the level of the genome in autism. In chapter 7, ML analysis was applied to metabolomics data from cord blood samples from the Cork BASELINE birth cohort. Discovery and targeted metabolomics were completed on this data. In chapter 8, we applied ML to assessed clinical predictors for autism in the Danish National Birth Cohort, which included data from 500 autism cases and matched controls. We identified markers of maternal health and wellbeing as being important for autism prediction and achieved a prediction accuracy of 0.68 AUROC. Overall, this thesis successfully addressed its’ aims to apply ML methods for the identification of biomarkers and development of prediction models for HIE and autism. We have validated previously identified biomarkers and identified novel clinical and blood-based markers as well as created robust HIE predication models with the ability to improve clinical decision making. Possible future steps this research can follow to further add to the field are outlined within. Overall, this research has added to the growing body of evidence displaying the ability of ML to offer improvements to healthcare and specifically to perinatal and child health. Artificial Intelligence (AI), and more specifically machine learning (ML), has been used in the investigation of biomarkers for many clinical conditions, reducing the need for specialist diagnosis, reducing waiting times and increasing access to reliable diagnostics. There are numerous areas yet to benefit from its application, particularly in the fields of perinatal and paediatric research. Two such brain related conditions, which will be the focus of this thesis are Hypoxic Ischemic Encephalopathy (HIE) and Autism Spectrum Disorder (ASD). HIE is a major cause of neurological disability globally and results from a lack of oxygen to the brain during and immediately after birth. The mainstay of treatment is therapeutic hypothermia, which, to be effective it must be applied within 6 hours of birth. This thesis aims to improve the early identification of infants eligible for therapeutic hypothermia using AI with clinical and metabolomic HIE biomarkers. Autism constitutes a group of neurodevelopmental disorders characterized by behavioural and cognitive symptoms. The underlying aetiology behind autism remains unclear and reliable predictive biomarkers are lacking. Intervention from an early age has been shown to reduce symptoms but diagnosis frequently occurs outside the window for effective treatment. Despite proven benefits in patient outcomes with intervention within the first two years of life, diagnosis often doesn’t occur until an individual is 3 or 4 years old, and in many cases much older. There is a pressing need for new methods to identify neonates most at risk to provide adequate treatment which improves long term outcomes. In the first experimental section, chapters 2-5, we have applied ML to first identity the optimum predictive clinical and metabolomic biomarkers for the identification of those with HIE. We initially assessed clinical variables using ML feature ranking and modelling. This study identified markers of a newborn’s condition at birth; Apgar scores, need for resuscitation, first measurements of pH and base deficit, as the most predictive. These models achieved an area under the receiver operator characteristic curve (AUROC) of 0.89 when distinguishing between those with perinatal asphyxia, who do not require treatment, and those with HIE. Furthermore, we then assessed a panel of promising metabolite markers for their predictive capabilities with and without clinical markers. ML identified metabolites alanine and lactate as the most predictive of HIE development and when combined with Apgar scores a measure of a newborn’s condition, at 1 and 5 minutes after birth, achieved a predictive AUROC of 0.96. These studies have successfully identified alanine as a candidate metabolite for cotside HIE risk assessment as well as displaying that ML models can improve on our current ability to identify those in need of therapeutic hypothermia. To validate these findings two studies were undertaken. The first independently compared alanine levels in cord blood of those with HIE and controls. This study has displayed elevated levels of alanine at birth and up to 6 hours after, successfully validating alanine as an early life marker for those with HIE in need of neuroprotective therapy. The second validation study successfully validated our algorithm for the prediction of HIE in a large diverse cohort comprised of infants with a range of differing conditions, compared to a set of HIE cases and controls previously assessed. Here 243 infants were assessed, using our model, to determine risk of HIE and an accuracy of 85% was maintained. This study has successfully validated our model's ability to retain performance when applied to a diverse, real-world cohort. In this section, we have successfully displayed the use of ML for improving HIE diagnostics and validated these findings. Further, larger validation studies are currently underway with the end goal of clinical use for determination of those in need of treatment for HIE. In the second experimental section, chapters 6-8, we aimed to apply ML methods to identify early life biomarkers for autism spectrum disorder. We first conducted a systematic review of all blood-based autism biomarkers. This study successfully catalogued all reported biomarkers and recorded the direction of change of theses markers in those with autism compared to neurotypical controls. This study also applied Genome Wide Association Studies (GWAS) and pathway analysis to test for biological processes which may be implicated at the level of the genome in autism. In chapter 7, ML analysis was applied to metabolomics data from cord blood samples from the Cork BASELINE birth cohort. Discovery and targeted metabolomics were completed on this data. In chapter 8, we applied ML to assessed clinical predictors for autism in the Danish National Birth Cohort, which included data from 500 autism cases and matched controls. We identified markers of maternal health and wellbeing as being important for autism prediction and achieved a prediction accuracy of 0.68 AUROC. Overall, this thesis successfully addressed its’ aims to apply ML methods for the identification of biomarkers and development of prediction models for HIE and autism. We have validated previously identified biomarkers and identified novel clinical and blood-based markers as well as created robust HIE predication models with the ability to improve clinical decision making. Possible future steps this research can follow to further add to the field are outlined within. Overall, this research has added to the growing body of evidence displaying the ability of ML to offer improvements to healthcare and specifically to perinatal and child health.
- ItemRethinking stillbirth through behaviour change(University College Cork, 2022) Escañuela Sánchez, Tamara; O'Donoghue, Keelin; Matvienko-Sikar, Karen; Meaney, Sarah; Byrne, Molly; Science Foundation IrelandBackground Worldwide, two million babies are stillborn every year. While the majority of stillbirths occur in low and middle-income countries, stillbirth is still one of the most common adverse pregnancy outcomes in high-income countries. In Ireland, the latest National Perinatal Mortality Clinical Audit report states a stillbirth rate of 4.20 per 1000 births for the year 2020, showing an increase compared to previous years. The belief that reduced stillbirth rates in high-income countries cannot be achieved is refuted by differences in stillbirth rates across different countries. Although not all stillbirths are preventable, there has been a call made in high-income countries to focus on risk factors for stillbirth, in order to reduce stillbirth rates. These risk factors include sociodemographic factors, medical factors, obstetric history-related factors, placental and fetal-related factors as well as behavioural and lifestyle-related factors. Some of these factors are modifiable through medical management or through behaviour change modification. This Thesis focuses on risk factors that have the potential to be modified through maternal behaviour change interventions: substance use (smoking, alcohol, and illicit drug use), high BMI, sleep position, and attendance at antenatal care. Strategies have been successfully implemented internationally to reduce stillbirth rates by designing and implementing care bundles that, amongst other elements, take into consideration the modifiable/behavioural risk factors for stillbirth. However, in Ireland, no such initiatives have been developed, although recommendations have been made that support their development. For behaviour change interventions or public health initiatives to have the best possible success in reducing the rates of stillbirth, they need to be designed with a solid evidence base. Hence, the overall objective of this Thesis was to build the evidence base to enhance the understanding of the modifiable behavioural risk factors for stillbirth and pregnancy. Further, this evidence base is needed to inform the future development of a behaviour change intervention that could be part of a care bundle with the objective of reducing stillbirth rates in Ireland. Methodology To address the Thesis´s aims, both qualitative and quantitative methods were utilised. Applying multiple methods to explore a phenomenon provides flexibility to analyse different aspects of it in the different studies. Initially, a non-systematic review of the literature was conducted to identify the target behavioural risk factors that this project was going to focus on (Chapter 2). A website quantitative content analysis was conducted to assess the availability of information related to stillbirth and behavioural risk factors for stillbirth in Irish and UK websites (Chapter 3). For this study, descriptive and inferential statistics were utilised. Further, three systematic qualitative meta-synthesis were conducted to identify facilitators and barriers to modify identified behavioural risk factors according to the pregnant women’s experience (Chapters 4-6). A meta-ethnographic approach as described by Noblit and Hare was adopted to conduct these qualitative meta-syntheses. Reflexive Thematic Analysis as described by Braun and Clarke, with a constructivist approach, was used to conduct a qualitative semi-structured interview study with postpartum women about their experiences of stillbirth information provision and behaviour change during their antenatal care (Chapter 7). Finally, a systematic review of interventions designed in the context of stillbirth prevention that targeted behavioural risk factors was conducted (Chapter 8). This systematic review had the objective of identifying which behaviour change techniques (BCTs) have been used to date. Results The findings of the literature review (Chapter 2) showed that the modifiable behavioural risk factors with the strongest evidence of associations with stillbirth were substance use, smoking, heavy drinking and illicit drug use, lack of attendance and compliance with antenatal care, weight-related risks, and sleep position. The quantitative content analysis of websites (Chapter 3) revealed that information about stillbirth and behavioural risk factors for stillbirths was scarce on websites directed at the pregnant population, with only one website containing all the information sought. Five main areas of concern were identified across the three meta-synthesis of qualitative research of facilitators and barriers influencing women’s prenatal health behaviours (Chapters 4-6), regardless of the behaviour explored: 1) health literacy, awareness of risks and benefits; 2) insufficient and overwhelming sources of information; 3) lack of opportunities and healthcare professionals attitudes interfering with communication & discussion; 4) social influence of environment, and 5) social judgement, stigmatisation of women and silence around stillbirth. Further, the qualitative study with postpartum women (Chapter 7) revealed that women perceived behaviour change during pregnancy as easy and natural, as they were focused on obtaining the best outcomes for their babies. Although women had high levels of awareness regarding health advice, their awareness about stillbirth was very limited. Women reported a lack of discussion about stillbirth and behavioural risk factors during their antenatal care; however, most women showed a positive disposition towards receiving this information because “knowledge is key”, as long as it is done in a “sensible manner”. The systematic review of interventions designed in the context of stillbirth prevention identified nine relevant interventions. From the BCT coding, it was established that the most common BCT used was “information about health consequences”, followed by “adding objects to the environment” (Chapter 8). Conclusion This research makes a valuable contribution to the understanding of the maternal behaviours associated with an increased risk of stillbirth, and it provides a necessary evidence-base to inform future prevention strategies to reduce rates of stillbirth in Ireland and in similar healthcare settings. This research sought to incorporate women’s voices and use research methods to produce high-quality results that meet the research objectives. The findings from the studies in this Thesis support four overarching topics and highlight issues related to 1) health literacy and sources of information, 2) relationships with healthcare professionals (HCPs), 3) healthcare systems and structural barriers, and 4) interpersonal, social and structural factors. In response to the research findings, several recommendations are made in relation to policy, practice and research which are grounded on women’s experiences during pregnancy. Regarding policy, these recommendations include improving education and information sources for women and HCPs, providing pregnancy-specific supports, utilising community services to support women with behaviour change, and developing a care bundle to tackle the behavioural risk factors for stillbirth. Furthermore, the work practice recommendations made include developing clinical guidelines to support HCPs in providing care to pregnant women, and prioritising health promotion during antenatal care. These priorities might also serve to help funders and researchers to design and conduct policy-relevant research. The key future research areas identified by this Thesis are in relation to the involvement of PPI representatives, the assessment of the quality of the available sources of information and the further exploration of potential facilitators and barriers to modifying pregnant women’s sleeping position from a qualitative perspective. In addition, this Thesis proposes a detailed process to continue building on the work set out in the different studies to develop a pregnancy-specific behaviour change intervention for the modifiable behavioural risk factors for stillbirth in the future.
- ItemMulti-modal assessment of newborns at risk of neonatal hypoxic ischaemic encephalopathy – the MONItOr study(University College Cork, 2022) Garvey, Aisling A.; Dempsey, Eugene M.; Murray, Deirdre M.; Boylan, Geraldine B.; National Children’s Research Centre, Crumlin, IrelandBackground: Hypoxic ischaemic encephalopathy (HIE) is the leading cause of acquired brain injury in term infants. At present, therapeutic hypothermia (TH) is the only approved therapy for infants with moderate-severe HIE. However, it must be commenced before 6 hours of age resulting in a clinical challenge to resuscitate, stabilize, identify and stratify infants in this narrow timeframe. Furthermore, a significant proportion of infants with mild HIE will have neurodevelopmental impairment. Improved, timely identification of infants at risk of brain injury is required. The aim of this study was to improve our knowledge of the early physiology of infants with HIE by describing the evolution of electroencephalography (EEG), near-infrared spectroscopy (NIRS) and non-invasive cardiac output monitoring (NICOM) in infants with all grades of HIE and to determine whether these markers may be helpful in the identification of infants at risk of brain injury. Methods: This prospective observational study was set in a tertiary neonatal unit (November 2017-March 2020). Infants with all grades of HIE had multi-modal monitoring, including EEG, NIRS and NICOM, commenced after delivery and continued for up to 84 hours. All infants had an MRI performed in the first week of life. Healthy term controls were recruited after delivery and had NICOM monitoring at 6 and 24 hours of age. In this thesis, I also included infants recruited previously as part of four historic prospective cohorts that had early EEG monitoring. These infants were combined with infants with mild HIE from the current prospective cohort to examine the difference in EEG features between infants with mild HIE and healthy term controls. Results: Eighty-two infants were recruited in the prospective cohort (30 mild HIE, 25 moderate, 6 severe, 21 controls) and 60 infants were included from the historic cohorts. This study identified significant differences between EEG features of infants with mild HIE and controls in the first 6 hours after birth. Seventy-two percent of infants with mild HIE had some abnormal features on their continuous EEG and quantitative analysis revealed significant differences in spectral shape between the groups. In our cohort, cSO2 increased and FTOE decreased over the first 24 hours in all grades of HIE regardless of TH status. Compared to the moderate group, infants with mild HIE had significantly higher cSO2 at 6 hours (p=0.003), 9 hours (p=0.009) and 12 hours (p=0.032) and lower FTOE at 6 hours (p=0.016) and 9 hours (0.029). Beyond 18 hours, no differences were seen between the groups. NICOM was assessed in infants with HIE and compared with controls. Infants with mild HIE have a significantly higher heart rate at 6 hours of age compared with controls (p=0.034). Infants with moderate HIE undergoing TH have a significantly lower cardiac output compared with mild HIE (p=0.046) and control groups (p=0.040). Heart rate is significantly reduced (p<0.001) but stroke volume is maintained and gradually increases from 6-72 hours despite TH. Finally, we assessed the ability of EEG, NIRS and NICOM to predict short-term outcome (abnormal MRI +/- death in the first week of life). At 6 hours, none of the EEG, NIRS or NICOM measures predicted short-term outcome. At 12 hours of age, both qualitative and quantitative EEG features significantly predicted abnormal short-term outcome. Conclusion: Identification of infants at risk of brain injury immediately after birth is challenging. Objective, early biomarkers are required. This is the first study to combine EEG, NIRS and NICOM in infants with all grades of HIE. Multi-modal monitoring is feasible and this thesis provides novel insights into the underlying physiology and evolution of injury in infants with HIE. Furthermore, it reaffirms the importance of early continuous EEG in HIE.
- ItemThe impact of maternal chronic hypertension and chronic kidney disease on the risk of adverse pregnancy outcomes and long-term cardiovascular disease: a population-based epidemiology study(University College Cork, 2022-08-31) Al Khalaf, Sukainah; Khashan, Ali; McCarthy, Fergus; O'Reilly, Eilis; Ministry of Health – Kingdom of Saudi ArabiaBackground and aims: The prevalence of chronic hypertension (CH) and chronic kidney disease (CKD) have increased among pregnant women in recent decades. Given the improvement in antenatal care over the last few decades, it is still unclear whether the risk of adverse pregnancy outcomes (APOs) among women with CH and/or CKD has decreased. There is limited evidence on the association between antihypertensive treatment and APOs in women with CH. Although there is evidence that women with a history of APOs have an increased risk of cardiovascular disease (CVD), it remains unclear whether pre-pregnancy hypertension and the occurrence of APOs would influence this association. The aim of this PhD project was to investigate the impact of maternal CH and/or CKD and antihypertensive treatment on the risk of APOs and long-term CVD. Structure and methods: This thesis includes eight chapters: Introduction, Methods, two systematic review articles on the impact of CH and CKD on APOs, three original research articles, and Discussion. Data from the Swedish National Registers were analysed to examine the associations between CH/CKD and the risk of APOs over the last three decades. Data from the UK CALIBER platform were used to investigate: i) the association between CH and APOs, with a focus on the role of antihypertensive treatment and control of hypertension, and ii) the associations between pre-pregnancy hypertension and subsequent diagnosis of 12 different CVDs, considering the role of APOs on these associations. Adverse pregnancy outcomes were pre-eclampsia, preterm birth, stillbirth, Caesarean section and small for gestational age (SGA). The statistical methods were done using logistic regression models for the Swedish data, logistic regression models with propensity score matching for the antihypertensive treatment analyses, while the associations between pre-pregnancy hypertension and CVD were analysed using stratified Cox models. All statistical models were adjusted for several potential confounders. Results: Systematic reviews and meta-analyses: CH was associated with 5-fold increased odds of pre-eclampsia and approximately 2-fold increased odds of stillbirth, preterm birth, and SGA, compared to women without CH. Women with treated CH (compared to untreated normotensive women) had higher odds of APOs. However, the results were inconsistent when outcomes were compared between treated and untreated women with CH; no increased odds of superimposed pre-eclampsia or other APOs were observed, except for 86% increased odds of SGA. Findings from the meta-analysis suggested that women with CKD had higher odds of pre-eclampsia, Caesarean section, preterm birth, very preterm birth, and SGA. All causes of CKD were associated with increased odds of pre-eclampsia, preterm birth, and SGA, with stronger associations in women with diabetic CKD, particularly for preterm birth [adjusted odds ratio (aOR): 4.76, (95% confidence interval (CI), 3.65–6.21)] and SGA [aOR: 4.50, (95% CI, 2.92–6.94)]. The findings according to the severity of kidney disease showed that later stages of CKD were associated with a greater odds of APOs than earlier stages. Swedish National Registers: The overall findings from this study suggested that the odds of APOs remain high in women with CH and/or CKD, and the odds persisted independent of parity, maternal age, and body mass index, among other potential confounders. No association was found between CKD and stillbirth. All causes of CKD were associated with higher odds of pre-eclampsia, emergency Caesarean section, and medically indicated preterm birth, and the ORs were higher in women with diabetic CKD, renovascular disease, and congenital kidney disease than other CKD subtypes. CALIBER studies: The results suggested a higher odds of APOs in women with CH (treated and untreated) compared to untreated normotensive women. In women with CH, those requiring treatment (versus untreated) had 17%, 25%, and 80% increased odds of superimposed pre-eclampsia, preterm birth, and fetal growth restriction (FGR), respectively. However, these results were mainly attributable to the level of blood pressure (BP) control among the treated group; as similar results were found between the untreated and treated women with CH who achieved tight control (BP<135/85 mmHg) for all assessed outcomes except for a 59% decreased odds of superimposed pre-eclampsia and a 55% increased odds of FGR. Pregnant women with CH who were prescribed methyldopa (versus β-blockers) had 43%, 59%, and 44% increased odds of superimposed pre-eclampsia, preterm birth, and very preterm birth, but 66% lower odds of FGR. No differences in outcomes were found in women who were prescribed calcium-channel-blockers (versus β-blockers) except for 94% increased odds of preterm birth. The magnitude of the associations increased with increasing BP, and the strongest associations were observed in women with severe hypertension (BP≥ 160/90 mmHg). In treated women with CH, less-tight (BP≥135/85 mmHg) versus tight (BP<135/85 mmHg) control was associated with almost 2-fold higher odds of superimposed pre-eclampsia, very preterm birth, and a 3-fold higher odds of severe hypertension. During the 20-year study period, 16,499 CVD incident were observed, of which two-thirds (66%) had occurred in young women (under 40 years). Pre-pregnancy hypertension (versus no pre-pregnancy hypertension) was associated with a 2-fold higher risk of any subsequent CVD. When the results were subclassified according to the presence of APOs, the strongest associations were found in women with pre-pregnancy hypertension and APOs across the 12 CVD; with almost a 3-fold increased risk to develop any subsequent CVD, an 8-fold increased risk of coronary heart disease, and a 10-fold increased risk of heart failure, compared to those who remained normotensive without APOs. Conclusions: This thesis indicated that CKD and CH were associated with a wide range of APOs than the general obstetric population. Therefore, multidisciplinary prenatal consultation and antenatal management should be provided for these women with close monitoring during pregnancy. If antihypertensive treatment is required, clinicians might consider tighter control during pregnancy as better outcomes were observed in women with tightly controlled hypertension. β-blockers might be superior in reducing APOs than methyldopa, with an exception for FGR, which was higher in the β-blockers group. Finally, the findings suggested strong associations between pre‐pregnancy hypertension with subsequent CVD, with a greater risk among women who had pre-pregnancy hypertension and APOs. Pre-pregnancy hypertension should be managed adequately during pregnancy to reduce the risk of APOs and subsequently reduce the risk of CVD, which emphasizes that a history of reproductive risk factors (including APOs) should be considered in screening tools for CVD beyond the postpartum period to optimize long-term cardiometabolic health in women.
- ItemAttachment based early interventions: an examination of the impact on the attachment related behaviour of parents and caregivers(University College Cork, 2022-05) O'Byrne, Emma; Mccusker, Chris; Murray, Deirdre M.There were two research articles included in this thesis with two separate abstracts. Systematic Review: “Attachment and Biobehavioural Catch-Up” (ABC) is a 10 session home visiting programme, grounded in attachment theory. It aims to improve child emotion regulation, attachment and behavioural outcomes through changing caregivers’ attachment related behaviours. There is increasing evidence with respect to the efficacy of ABC, but the interventions direct effect on parent behaviour remains unclear. This review examined ABC’s association with parent behaviour (the putative mechanism of change). The PubMed, EMBASE, PyscINFO and SCOPUS databases were searched for relevant studies in August 2021, and again in April 2022. The eligibility criteria for included studies were (1) infants aged 0-27 months at time of the ABC intervention, (2) “at-risk” parents, (3) controlled trials published in peer-reviewed journals, and (4) measure of attachment related parent behaviour included. Eleven eligible studies were included, nine of which were rated as having good methodological quality. The findings showed ABC had a significant small to medium effect on a variety of attachment-related parent behaviours amongst parents’ with multiple psychosocial risk factors. “Sensitivity” was measured most frequently, with small to medium main effect sizes recorded at follow-up compared to controls. Implications for the clinical effectiveness of the ABC programme in community settings are discussed. Future research should clarify whom ABC is most effective for, and how it compares to similar attachment based interventions. Major Research Project: Infant massage has been shown to positively influence maternal wellbeing and the mother-child attachment in clinical samples and up to a 1 year follow-up period. The present study examined, in a longitudinal randomised controlled trial (RCT), whether such benefits may be accrued in non-clinical, community samples and across a 4 year period. Participants were recruited from a maternity hospital in Ireland. They were mostly educated to third level (93%), in employment (88%) and identified as Irish (88%). At baseline participants were randomised to an infant massage or control condition (N=269). Qualitatively mothers from the intervention group recalled their experience of infant massage from almost 4 years earlier in surprising detail. Four main themes emerged describing the infant massage experience as a positive opportunity for bonding and relaxing with a newborn. Quantitative data pertaining to maternal wellbeing and dyads attachment were collected at baseline, 4-months, 18-months and 48-months post-intervention. Overall, analyses showed no significant difference between groups with respect to maternal mental health or parent-child relationship factors at 4, 18 or 48-months. We concluded that, in a non-clinical sample, infant massage is (a) subjectively experienced in a positive way with personal and infant relationship benefits, yet (b) this did not translate into objective benefits on clinical scales related to maternal or relational outcomes in the short or long-term. Clinical implications and suggestions for research adaptations in this area are outlined.