Associations between maternal psychological distress and mother-infant bonding: a systematic review and meta-analysis.

PURPOSE: Maternal psychological distress and mother-infant bonding problems each predict poorer offspring outcomes. They are also related to each other, yet the extensive literature reporting their association has not been meta-analysed. METHODS: We searched MEDLINE, PsycINFO, CINAHL, Embase, ProQuest DTG, and OATD for English-language peer-reviewed and grey literature reporting an association between mother-infant bonding, and multiple indicators of maternal psychological distress. RESULTS: We included 133 studies representing 118 samples; 99 samples (110,968 mothers) were eligible for meta-analysis. Results showed concurrent associations across a range of timepoints during the first year postpartum, between bonding problems and depression (r = .27 [95% CI 0.20, 0.35] to r = .47 [95% CI 0.41, 0.53]), anxiety (r = .27 [95% CI 0.24, 0.31] to r = .39 [95% CI 0.15, 0.59]), and stress (r = .46 [95% CI 0.40, 0.52]). Associations between antenatal distress and subsequent postpartum bonding problems were mostly weaker and with wider confidence intervals: depression (r = .20 [95% CI 0.14, 0.50] to r = .25 [95% CI 0.64, 0.85]), anxiety (r = .16 [95% CI 0.10, 0.22]), and stress (r = .15 [95% CI - 0.67, 0.80]). Pre-conception depression and anxiety were associated with postpartum bonding problems (r = - 0.17 [95% CI - 0.22, - 0.11]). CONCLUSION: Maternal psychological distress is associated with postpartum mother-infant bonding problems. Co-occurrence of psychological distress and bonding problems is common, but should not be assumed. There may be benefit in augmenting existing perinatal screening programs with well-validated mother-infant bonding measures.

Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review.

BACKGROUND: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. OBJECTIVE: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. METHODS: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. RESULTS: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. CONCLUSIONS: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters.

Critical measurement issues in the assessment of social media influence on body image.

Progress towards understanding how social media impacts body image hinges on the use of appropriate measurement tools and methodologies. This review provides an overview of common (qualitative, self-report survey, lab-based experiments) and emerging (momentary assessment, computational) methodological approaches to the exploration of the impact of social media on body image. The potential of these methodologies is detailed, with examples illustrating current use as well as opportunities for expansion. A key theme from our review is that each methodology has provided insights for the body image research field, yet is insufficient in isolation to fully capture the nuance and complexity of social media experiences. Thus, in consideration of gaps in methodology, we emphasise the need for big picture thinking that leverages and combines the strengths of each of these methodologies to yield a more comprehensive, nuanced, and robust picture of the positive and negative impacts of social media.

Parent and child mental health trajectories April 2020 to May 2021: Strict lockdown versus no lockdown in Australia.

OBJECTIVE: To control a second-wave COVID-19 outbreak, the state of Victoria in Australia experienced one of the world's first long and strict lockdowns over July-October 2020, while the rest of Australia experienced 'COVID-normal' with minimal restrictions. We (1) investigate trajectories of parent/child mental health outcomes in Victoria vs non-Victoria and (2) identify baseline demographic, individual and COVID-19-related factors associated with mental health trajectories. METHODS: Online community sample of 2004 Australian parents with rapid repeated assessment over 14 time-points over April 2020 to May 2021. Measures assessed parent mental health (Depression, Anxiety and Stress Scales-21), child depression symptoms (13-item Short Mood and Feelings Questionnaire) and child anxiety symptoms (four items from Brief Spence Children's Anxiety Scale). RESULTS: Mental health trajectories shadowed COVID-19 infection rates. Victorians reported a peak in mental health symptoms at the time of the second-wave lockdown compared to other states. Key baseline predictors, including parent and child loneliness (standardized regression coefficient [ß] = 0.09-0.46), parent/child diagnoses (ß = 0.07-0.21), couple conflict (ß = 0.07-0.18) and COVID-19 stressors, such as worry/concern about COVID-19, illness and loss of job (ß = 0.12-0.15), predicted elevated trajectories. Effects of predictors on parent and child mental health trajectories are illustrated in an online interactive app for readers (https://lingtax.shinyapps.io/CPAS_trend/). CONCLUSION: Our findings provide evidence of worse trajectories of parent and child mental health symptoms at a time coinciding with a second COVID-19 outbreak involving strict lockdown in Victoria, compared to non-locked states in Australia. We identified several baseline factors that may be useful in detecting high-risk families who are likely to require additional support early on in future lockdowns.

Maternal bonding, negative affect, and infant social-emotional development: A prospective cohort study.

BACKGROUND: Theoretical perspectives and empirical evidence suggest that maternal bonding and negative affect play a role in supporting infant social-emotional development (Branjerdporn et al., 2017; Kingston et al., 2012; O'Donnell et al., 2014; Van den Bergh et al., 2017). However, the complex pathways likely to exist between these constructs remain unclear, with limited research examining the temporal and potentially bi-directional associations between maternal bonding and negative affect across pregnancy and infancy. METHODS: The interrelationships between maternal bonding, negative affect, and infant social-emotional development were examined using multi-wave perinatal data from an Australian cohort study (N = 1,579). Self-reported bonding and negative affect were assessed at each trimester, and 8 weeks and 12 months postpartum. The Bayley-III social-emotional scale was administered at age 12 months. RESULTS: Results revealed strong continuities in bonding and negative affect across pregnancy and postpartum. Small associations (ß = -.10 to -.20) existed between maternal negative affect during pregnancy and poor early bonding. Higher postnatal maternal bonding predicted infant social-emotional development (ß = .17). LIMITATIONS: Limitations include a somewhat advantaged and predominantly Anglo-Saxon sample of families, and the use of self-report measures (though with strong psychometric properties). These limitations should be considered when interpreting the study findings. CONCLUSIONS: Maternal bonding and negative affect are interrelated yet unique constructs, with suggested developmental interplay between mother-to-infant bonding and infant social-affective development.

Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach.

Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk" words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers.

Association Between Maternal Perinatal Depression and Anxiety and Child and Adolescent Development: A Meta-analysis.

Importance: There is widespread interest in associations between maternal perinatal depression and anxiety and offspring development; however, to date, there has been no systematic, meta-analytic review on the long-term developmental outcomes spanning infancy through adolescence. Objective: To provide a comprehensive systematic review and meta-analysis of the extant literature on associations between maternal perinatal depression and anxiety and social-emotional, cognitive, language, motor, and adaptability outcomes in offspring during the first 18 years of life. Data Sources: Six databases were searched (CINAHL Complete, Cochrane Library, Embase, Informit, MEDLINE Complete, and PsycInfo) for all extant studies reporting associations between perinatal maternal mental health problems and offspring development to March 1, 2020. Study Selection: Studies were included if they were published in English; had a human sample, quantitative data, a longitudinal design, and measures of perinatal depression and/or anxiety and social-emotional, cognitive, language, motor, and/or adaptability development in offspring; and investigated an association between perinatal depression or anxiety and childhood development. Data Extraction and Synthesis: Of 27 212 articles identified, 191 were eligible for meta-analysis. Data were extracted by multiple independent observers and pooled using a fixed- or a random-effects model. A series of meta-regressions were also conducted. Data were analyzed from January 1, 2019, to March 15, 2020. Main Outcomes and Measures: Primary outcomes included social-emotional, cognitive, language, motor, and adaptability development in offspring during the first 18 years of life. Results: After screening, 191 unique studies were eligible for meta-analysis, with a combined sample of 195 751 unique mother-child dyads. Maternal perinatal depression and anxiety were associated with poorer offspring social-emotional (antenatal period, r = 0.21 [95% CI, 0.16-0.27]; postnatal period, r = 0.24 [95% CI, 0.19-0.28]), cognitive (antenatal period, r = -0.12 [95% CI, -0.19 to -0.05]; postnatal period, r = -0.25 [95% CI, -0.39 to -0.09]), language (antenatal period, r = -0.11 [95% CI, -0.20 to 0.02]; postnatal period, r = -0.22 [95% CI, -0.40 to 0.03]), motor (antenatal period, r = -0.07 [95% CI, -0.18 to 0.03]; postnatal period, r = -0.07 [95% CI, -0.16 to 0.03]), and adaptive behavior (antenatal period, r = -0.26 [95% CI, -0.39 to -0.12]) development. Findings extended beyond infancy, into childhood and adolescence. Meta-regressions confirmed the robustness of the results. Conclusions and Relevance: Evidence suggests that perinatal depression and anxiety in mothers are adversely associated with offspring development and therefore are important targets for prevention and early intervention to support mothers transitioning into parenthood and the health and well-being of next-generation offspring.

schema: an open-source, distributed mobile platform for deploying mHealth research tools and interventions.

BACKGROUND: Mobile applications for health, also known as 'mHealth apps', have experienced increasing popularity over the past ten years. However, most publicly available mHealth apps are not clinically validated, and many do not utilise evidence-based strategies. Health researchers wishing to develop and evaluate mHealth apps may be impeded by cost and technical skillset barriers. As traditionally lab-based methods are translated onto mobile platforms, robust and accessible tools are needed to enable the development of quality, evidence-based programs by clinical experts. RESULTS: This paper introduces schema, an open-source, distributed, app-based platform for researchers to deploy behavior monitoring and health interventions onto mobile devices. The architecture and design features of the platform are discussed, including flexible scheduling, randomisation, a wide variety of survey and media elements, and distributed storage of data. The platform supports a range of research designs, including cross-sectional surveys, ecological momentary assessment, randomised controlled trials, and micro-randomised just-in-time adaptive interventions. Use cases for both researchers and participants are considered to demonstrate the flexibility and usefulness of the platform for mHealth research. CONCLUSIONS: The paper concludes by considering the strengths and limitations of the platform, and a call for support from the research community in areas of technical development and evaluation. To get started with schema, please visit the GitHub repository: https://github.com/schema-app/schema.

Attachment and child behaviour and emotional problems in autism spectrum disorder with intellectual disability.

BACKGROUND: Behaviour and emotional problems are highly prevalent in children with autism spectrum disorder (ASD). In typically developing children, attachment quality acts as a risk/protective factor for behavioural outcomes and adjustment, warranting investigation in children with ASD. METHOD: We investigated the relationship between attachment and child behaviour and emotional problems in children with ASD and comorbid intellectual disability. Data were collected from parent-child dyads where children were diagnosed with ASD and ID (n = 28) or other developmental disabilities (n = 20). RESULTS: Children with ASD had higher levels of behaviour and emotional problems and more attachment difficulties than children with other developmental disabilities. Poorer attachment quality contributed uniquely to the variance in child behaviour and emotional problems. CONCLUSIONS: Interventions targeting behaviour and emotional problems in children with ASD may benefit from an attachment model which addresses the child's difficulty in using caregivers as a coregulatory agent of emotions.

Machine learning in mental health: a scoping review of methods and applications.

BACKGROUND: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. METHODS: We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. RESULTS: Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. CONCLUSIONS: Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Caregiver Mental Health, Parenting Practices, and Perceptions of Child Attachment in Children with Autism Spectrum Disorder.

This paper investigates the role of caregiver mental health and parenting practices as predictors of attachment in children with intellectual disability/developmental delay, comparing between children with ASD (n = 29) and children with other developmental disabilities (n = 20). Parents reported that children with ASD had high levels of anxiety and stress, and attachment insecurity in children (less closeness and more conflict in attachment relationships, and more inhibited attachment behaviours) compared with children with other developmental disabilities. Children's attachment quality was associated with parenting practices and the presence of an ASD diagnosis. These results highlight the bidirectional nature of the quality of caregiving environments and attachment in children with ASD, and also provide a strong rationale for targeting children's attachment quality in early interventions.

Exploring the Transition to Fatherhood: Feasibility Study Using Social Media and Machine Learning.

BACKGROUND: Fathers' experiences across the transition to parenthood are underreported in the literature. Social media offers the potential to capture fathers' experiences in real time and at scale while also removing the barriers that fathers typically face in participating in research and clinical care. OBJECTIVE: This study aimed to assess the feasibility of using social media data to map the discussion topics of fathers across the fatherhood transition. METHODS: Discussion threads from two Web-based parenting communities, r/Daddit and r/PreDaddit from the social media platform Reddit, were collected over a 2-week period, resulting in 1980 discussion threads contributed to by 5853 unique users. An unsupervised machine learning algorithm was then implemented to group discussion threads into topics within each community and across a combined collection of all discussion threads. RESULTS: Results demonstrated that men use Web-based communities to share the joys and challenges of the fatherhood experience. Minimal overlap in discussions was found between the 2 communities, indicating that distinct conversations are held on each forum. A range of social support techniques was demonstrated, with conversations characterized by encouragement, humor, and experience-based advice. CONCLUSIONS: This study demonstrates that rich data on fathers' experiences can be sourced from social media and analyzed rapidly using automated techniques, providing an additional tool for researchers exploring fatherhood.