Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking.

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

Dementia, dementia's risk factors and premorbid brain structure are concentrated in disadvantaged areas: National register and birth-cohort geographic analyses.

INTRODUCTION: Dementia risk may be elevated in socioeconomically disadvantaged neighborhoods. Reasons for this remain unclear, and this elevation has yet to be shown at a national population level. METHODS: We tested whether dementia was more prevalent in disadvantaged neighborhoods across the New Zealand population (N = 1.41 million analytic sample) over a 20-year observation. We then tested whether premorbid dementia risk factors and MRI-measured brain-structure antecedents were more prevalent among midlife residents of disadvantaged neighborhoods in a population-representative NZ-birth-cohort (N = 938 analytic sample). RESULTS: People residing in disadvantaged neighborhoods were at greater risk of dementia (HR per-quintile-disadvantage-increase = 1.09, 95% confidence interval [CI]:1.08-1.10) and, decades before clinical endpoints typically emerge, evidenced elevated dementia-risk scores (CAIDE, LIBRA, Lancet, ANU-ADRI, DunedinARB; ß's 0.31-0.39) and displayed dementia-associated brain structural deficits and cognitive difficulties/decline. DISCUSSION: Disadvantaged neighborhoods have more residents with dementia, and decades before dementia is diagnosed, residents have more dementia-risk factors and brain-structure antecedents. Whether or not neighborhoods causally influence risk, they may offer scalable opportunities for primary dementia prevention.

A blood biomarker of the pace of aging is associated with brain structure: replication across three cohorts.

Biological aging is the correlated decline of multi-organ system integrity central to the etiology of many age-related diseases. A novel epigenetic measure of biological aging, DunedinPACE, is associated with cognitive dysfunction, incident dementia, and mortality. Here, we tested for associations between DunedinPACE and structural MRI phenotypes in three datasets spanning midlife to advanced age: the Dunedin Study (age=45 years), the Framingham Heart Study Offspring Cohort (mean age=63 years), and the Alzheimer's Disease Neuroimaging Initiative (mean age=75 years). We also tested four additional epigenetic measures of aging: the Horvath clock, the Hannum clock, PhenoAge, and GrimAge. Across all datasets (total N observations=3380; total N individuals=2322), faster DunedinPACE was associated with lower total brain volume, lower hippocampal volume, greater burden of white matter microlesions, and thinner cortex. Across all measures, DunedinPACE and GrimAge had the strongest and most consistent associations with brain phenotypes. Our findings suggest that single timepoint measures of multi-organ decline such as DunedinPACE could be useful for gauging nervous system health.

A New Role for Chatbots: Automation of a Sleep-Dependent Memory Task.

Sleep is known to contribute to memory consolidation. Sleep-dependent memory is not often studied in patients with mild cognitive impairment (MCI), however, due to the need to attend sleep laboratories which are typically expensive, time-consuming and lacking in trained task administrators. We developed a conversation agent able to deliver a sleep-dependent memory task at home. Utility of the chatbot was confirmed through in-house testing and focus groups. The chatbot promises consistent task delivery and improved access for people with MCI.

Dolores: A Mobile Chatbot for People Living with Chronic Pain.

We provide an outline of the Dolores chatbot designed to gather data and provide information to people living with chronic pain. Dolores is equipped with selective language levels to provide language appropriate responses for all ages. A recent pilot study (N = 60) of adolescents, young-adults and adults was completed and the frequented topics that were accessed are summarised here.

Smoking Cessation Counseling in Practice: A Qualitative Analysis of Quitline Conversations in Queensland, Australia.

Telephone-based services are a practical and effective behavioral support for smoking cessation, yet no in-depth analyses of this counseling have been conducted. Understanding the general content of Quitline conversations can help to improve current practices and may inform future interventions. Therefore, we aimed to independently explore conversation themes, topics, and client questions during Quitline counseling sessions with Quitline clients in Queensland, Australia. A purposive sample of 30 recorded counseling sessions, completed between January and March 2019, were de-identified, transcribed, and thematically analyzed. Seven themes, encompassing 35 topics, were derived from 26 initial calls and four follow-up calls: (1) Client details and building rapport; (2) Client history and motivation to quit; (3) Pharmacotherapy; (4) Behavioral aspects of quitting and relationship with smoking; (5) Understanding nicotine dependence and other important considerations; (6) Additional support and smoking cessation resources; and (7) Planning, goal setting and follow-up. Three themes emerged from 18 client questions including (1) Pharmacotherapy safety and contraindications; (2) Pharmacotherapy instructions and mechanism of action; and (3) Physiology of nicotine dependence. This is the first qualitative analysis of the content of Quitline counseling sessions in Australia. Counselors collect and deliver a breadth of information to provide tailored, evidence-based health care, while building rapport and trust. Findings may be translatable into personalized self-help interventions that are more accessible or appealing to people reluctant to contact Quitline. Harnessing educational opportunities regarding pharmacotherapy adherence and misconceptions can improve client confidence in the product and smoking cessation outcomes. Further research will map conversations to motivational interviewing and behavior change techniques.

Conversational artificial intelligence interventions to support smoking cessation: A systematic review and meta-analysis.

Background: Conversational artificial intelligence (chatbots and dialogue systems) is an emerging tool for tobacco cessation that has the potential to emulate personalised human support and increase engagement. We aimed to determine the effect of conversational artificial intelligence interventions with or without standard tobacco cessation interventions on tobacco cessation outcomes among adults who smoke, compared to no intervention, placebo intervention or an active comparator. Methods: A comprehensive search of six databases was completed in June 2022. Eligible studies included randomised controlled trials published since 2005. The primary outcome was sustained tobacco abstinence, self-reported and/or biochemically validated, for at least 6 months. Secondary outcomes included point-prevalence abstinence and sustained abstinence of less than 6 months. Two authors independently extracted data on cessation outcomes and completed the risk of bias assessment. Random effects meta-analysis was conducted. Results: From 819 studies, five randomised controlled trials met inclusion criteria (combined sample size n = 58,796). All studies differed in setting, methodology, intervention, participants and end-points. Interventions included chatbots embedded in multi- and single-component smartphone apps (n = 3), a social media-based (n = 1) chatbot, and an internet-based avatar (n = 1). Random effects meta-analysis of three studies found participants in the conversational artificial intelligence enhanced intervention were significantly more likely to quit smoking at 6-month follow-up compared to control group participants (RR = 1.29, 95% CI (1.13, 1.46), p < 0.001). Loss to follow up was generally high. Risk of bias was high overall. Conclusion: We found limited but promising evidence on the effectiveness of conversational artificial intelligence interventions for tobacco cessation. Although all studies found benefits from conversational artificial intelligence interventions, results should be interpreted with caution due to high heterogeneity. Given the rapid evolution and potential of artificial intelligence interventions, further well-designed randomised controlled trials following standardised reporting guidelines are warranted in this emerging area.

Introducing Quin: The design and development of a prototype chatbot to support smoking cessation.

INTRODUCTION: Chatbots emulate human-like interactions and may usefully provide on-demand access to tailored smoking cessation support. We have developed a prototype smartphone application-based smoking cessation chatbot, named Quin, grounded in real-world, evidence- and theory-based smoking cessation counselling sessions. METHOD: Conversation topics and interactions in Quitline counselling sessions (N=30; 18 hours) were characterised using thematic, content, and proponent analyses of transcripts. Quin was created by programming this content using a chatbot framework which interacts with users via speech-to-text. Reiterative changes and additions were made to the conversation structure and dialogue following regular consultation with a multidisciplinary team from relevant fields, and from evidence-based resources. RESULTS: Chatbot conversations were encoded into initial and scheduled follow-up 'appointments'. Collection of demographic information, and smoking and quit history, informed tailored discussion about pharmacotherapy preferences, behavioural strategies, and social and professional support to form a quit plan. Follow-up appointments were programmed to check in on user progress, review elements of the quit plan, answer questions and solve issues. Quin was programmed to include teachable moments and educational content to enhance health literacy and informed decision-making. Personal agency is encouraged through exploration and self-reflection of users' personal behaviours, experiences, preferences and ideas. CONCLUSION: Quin's successful development represents a movement towards improving access to personalised smoking cessation support. Qualitative foundations of Quin provide greater insight into the smoking cessation counselling relationship and enhances the conversational ability of the technology. The prototype chatbot will be refined through beta-testing with end-users and stakeholders prior to evaluation in a clinical trial. IMPLICATIONS: Our novel study provides transparent description of the translation of qualitative evidence of real-world smoking cessation counselling sessions into the design and development of a prototype smoking cessation chatbot. The successful iterative development of Quin not only embodies the science and art of health promotion, but also a step-forward in expanding the reach of tailored, evidence based, in-pocket support for people who want to quit smoking.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient.

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Acceptability of a Pain History Assessment and Education Chatbot (Dolores) Across Age Groups in Populations With Chronic Pain: Development and Pilot Testing.

BACKGROUND: The delivery of education on pain neuroscience and the evidence for different treatment approaches has become a key component of contemporary persistent pain management. Chatbots, or more formally conversation agents, are increasingly being used in health care settings due to their versatility in providing interactive and individualized approaches to both capture and deliver information. Research focused on the acceptability of diverse chatbot formats can assist in developing a better understanding of the educational needs of target populations. OBJECTIVE: This study aims to detail the development and initial pilot testing of a multimodality pain education chatbot (Dolores) that can be used across different age groups and investigate whether acceptability and feedback were comparable across age groups following pilot testing. METHODS: Following an initial design phase involving software engineers (n=2) and expert clinicians (n=6), a total of 60 individuals with chronic pain who attended an outpatient clinic at 1 of 2 pain centers in Australia were recruited for pilot testing. The 60 individuals consisted of 20 (33%) adolescents (aged 10-18 years), 20 (33%) young adults (aged 19-35 years), and 20 (33%) adults (aged >35 years) with persistent pain. Participants spent 20 to 30 minutes completing interactive chatbot activities that enabled the Dolores app to gather a pain history and provide education about pain and pain treatments. After the chatbot activities, participants completed a custom-made feedback questionnaire measuring the acceptability constructs pertaining to health education chatbots. To determine the effect of age group on the acceptability ratings and feedback provided, a series of binomial logistic regression models and cumulative odds ordinal logistic regression models with proportional odds were generated. RESULTS: Overall, acceptability was high for the following constructs: engagement, perceived value, usability, accuracy, responsiveness, adoption intention, esthetics, and overall quality. The effect of age group on all acceptability ratings was small and not statistically significant. An analysis of open-ended question responses revealed that major frustrations with the app were related to Dolores' speech, which was explored further through a comparative analysis. With respect to providing negative feedback about Dolores' speech, a logistic regression model showed that the effect of age group was statistically significant (χ22=11.7; P=.003) and explained 27.1% of the variance (Nagelkerke R2). Adults and young adults were less likely to comment on Dolores' speech compared with adolescent participants (odds ratio 0.20, 95% CI 0.05-0.84 and odds ratio 0.05, 95% CI 0.01-0.43, respectively). Comments were related to both speech rate (too slow) and quality (unpleasant and robotic). CONCLUSIONS: This study provides support for the acceptability of pain history and education chatbots across different age groups. Chatbot acceptability for adolescent cohorts may be improved by enabling the self-selection of speech characteristics such as rate and personable tone.

A blood biomarker of accelerated aging in the body associates with worse structural integrity in the brain: replication across three cohorts.

Biological aging is the correlated decline of multi-organ system integrity central to the etiology of many age-related diseases. A novel epigenetic measure of biological aging, DunedinPACE, is associated with cognitive dysfunction, incident dementia, and mortality. Here, we tested for associations between DunedinPACE and structural MRI phenotypes in three datasets spanning midlife to advanced age: the Dunedin Study (age=45 years), the Framingham Heart Study Offspring Cohort (mean age=63 years), and the Alzheimer's Disease Neuroimaging Initiative (mean age=75 years). We also tested four additional epigenetic measures of aging: the Horvath clock, the Hannum clock, PhenoAge, and GrimAge. Across all datasets (total N observations=3,380; total N individuals=2,322), faster DunedinPACE was associated with lower total brain volume, lower hippocampal volume, and thinner cortex. In two datasets, faster DunedinPACE was associated with greater burden of white matter hyperintensities. Across all measures, DunedinPACE and GrimAge had the strongest and most consistent associations with brain phenotypes. Our findings suggest that single timepoint measures of multi-organ decline such as DunedinPACE could be useful for gauging nervous system health.

Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities.

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

Associations Between Thinner Retinal Neuronal Layers and Suboptimal Brain Structural Integrity in a Middle-Aged Cohort.

Purpose: The retina has potential as a biomarker of brain health and Alzheimer's disease (AD) because it is the only part of the central nervous system which can be easily imaged and has advantages over brain imaging technologies. Few studies have compared retinal and brain measurements in a middle-aged sample. The objective of our study was to investigate whether retinal neuronal measurements were associated with structural brain measurements in a middle-aged population-based cohort. Participants and Methods: Participants were members of the Dunedin Multidisciplinary Health and Development Study (n=1037; a longitudinal cohort followed from birth and at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and most recently at age 45, when 94% of the living Study members participated). Retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness were measured by optical coherence tomography (OCT). Brain age gap estimate (brainAGE), cortical surface area, cortical thickness, subcortical grey matter volumes, white matter hyperintensities, were measured by magnetic resonance imaging (MRI). Results: Participants with both MRI and OCT data were included in the analysis (RNFL n=828, female n=413 [49.9%], male n=415 [50.1%]; GC-IPL n=825, female n=413 [50.1%], male n=412 [49.9%]). Thinner retinal neuronal layers were associated with older brain age, smaller cortical surface area, thinner average cortex, smaller subcortical grey matter volumes, and increased volume of white matter hyperintensities. Conclusion: These findings provide evidence that the retinal neuronal layers reflect differences in midlife structural brain integrity consistent with increased risk for later AD, supporting the proposition that the retina may be an early biomarker of brain health.

Clinical utility of a mHealth assisted intervention for activity modulation in chronic pain: The pilot implementation of pain ROADMAP.

BACKGROUND: Being able to successfully self-regulate one's activity levels is a key adaptation strategy for many people with chronic pain. This study aimed to explore the clinical utility of a mobile health platform (Pain ROADMAP) for assisting with the delivery of a tailored activity modulation intervention for people with persistent pain. METHODS: Twenty adults with chronic pain undertook 1-week monitoring intervals which involved wearing an Actigraph activity monitor and entering pain, opioid use, and activity participation data into a custom-made phone app. The Pain ROADMAP online portal integrated and analysed the data to detect activities that caused a severe pain exacerbation and provided summary statistics pertaining the data collected. As part of a 15-week treatment protocol, participants received feedback from three dispersed Pain ROADMAP monitoring periods. Treatment focused on adapting pain-provoking activities, gradually increase goal-related activity and optimizing routine. RESULTS: Results revealed good participant acceptability of monitoring procedures and reasonable adherence to both monitoring procedures and clinical follow-up appointments. Preliminary efficacy was established through clinically meaningful decreases in overactivity behaviour, pain variation, opioid use, depression, activity avoidance, and increases in productivity. No adverse outcomes were observed. CONCLUSION: The results of this study provide initial support for the clinical utility of mHealth assisted activity modulation interventions that involve remote monitoring. SIGNIFICANCE: This is the first study to demonstrate how mHealth innovations that utilise ecological momentary assessment can be successfully integrated with wearable technologies to provide a tailored activity modulation intervention that is both highly valued by people with chronic pain and assists individuals to make constructive behavioural changes. Adaptions such as low costs sensors, increased customisability and gamification may be important for enhanced uptake, adherence and scalability.

Functional Topography of the Neocortex Predicts Covariation in Complex Cognitive and Basic Motor Abilities.

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g., hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

Improving risk indexes for Alzheimer's disease and related dementias for use in midlife.

Knowledge of a person's risk for Alzheimer's disease and related dementias (ADRDs) is required to triage candidates for preventive interventions, surveillance, and treatment trials. ADRD risk indexes exist for this purpose, but each includes only a subset of known risk factors. Information missing from published indexes could improve risk prediction. In the Dunedin Study of a population-representative New Zealand-based birth cohort followed to midlife (N = 938, 49.5% female), we compared associations of four leading risk indexes with midlife antecedents of ADRD against a novel benchmark index comprised of nearly all known ADRD risk factors, the Dunedin ADRD Risk Benchmark (DunedinARB). Existing indexes included the Cardiovascular Risk Factors, Aging, and Dementia index (CAIDE), LIfestyle for BRAin health index (LIBRA), Australian National University Alzheimer's Disease Risk Index (ANU-ADRI), and risks selected by the Lancet Commission on Dementia. The Dunedin benchmark was comprised of 48 separate indicators of risk organized into 10 conceptually distinct risk domains. Midlife antecedents of ADRD treated as outcome measures included age-45 measures of brain structural integrity [magnetic resonance imaging-assessed: (i) machine-learning-algorithm-estimated brain age, (ii) log-transformed volume of white matter hyperintensities, and (iii) mean grey matter volume of the hippocampus] and measures of brain functional integrity [(i) objective cognitive function assessed via the Wechsler Adult Intelligence Scale-IV, (ii) subjective problems in everyday cognitive function, and (iii) objective cognitive decline measured as residualized change in cognitive scores from childhood to midlife on matched Weschler Intelligence scales]. All indexes were quantitatively distributed and proved informative about midlife antecedents of ADRD, including algorithm-estimated brain age (ß's from 0.16 to 0.22), white matter hyperintensities volume (ß's from 0.16 to 0.19), hippocampal volume (ß's from -0.08 to -0.11), tested cognitive deficits (ß's from -0.36 to -0.49), everyday cognitive problems (ß's from 0.14 to 0.38), and longitudinal cognitive decline (ß's from -0.18 to -0.26). Existing indexes compared favourably to the comprehensive benchmark in their association with the brain structural integrity measures but were outperformed in their association with the functional integrity measures, particularly subjective cognitive problems and tested cognitive decline. Results indicated that existing indexes could be improved with targeted additions, particularly of measures assessing socioeconomic status, physical and sensory function, epigenetic aging, and subjective overall health. Existing premorbid ADRD risk indexes perform well in identifying linear gradients of risk among members of the general population at midlife, even when they include only a small subset of potential risk factors. They could be improved, however, with targeted additions to more holistically capture the different facets of risk for this multiply determined, age-related disease.

Diminished Structural Brain Integrity in Long-term Cannabis Users Reflects a History of Polysubstance Use.

BACKGROUND: Cannabis legalization and use are outpacing our understanding of its long-term effects on brain and behavior, which is fundamental for effective policy and health practices. Existing studies are limited by small samples, cross-sectional measures, failure to separate long-term from recreational use, and inadequate control for other substance use. Here, we address these limitations by determining the structural brain integrity of long-term cannabis users in the Dunedin Study, a longitudinal investigation of a population-representative birth cohort followed to midlife. METHODS: We leveraged prospective measures of cannabis, alcohol, tobacco, and other illicit drug use in addition to structural neuroimaging in 875 study members at age 45 to test for differences in both global and regional gray and white matter integrity between long-term cannabis users and lifelong nonusers. We additionally tested for dose-response associations between continuous measures of cannabis use and brain structure, including careful adjustments for use of other substances. RESULTS: Long-term cannabis users had a thinner cortex, smaller subcortical gray matter volumes, and higher machine learning-predicted brain age than nonusers. However, these differences in structural brain integrity were explained by the propensity of long-term cannabis users to engage in polysubstance use, especially with alcohol and tobacco. CONCLUSIONS: These findings suggest that diminished midlife structural brain integrity in long-term cannabis users reflects a broader pattern of polysubstance use, underlining the importance of understanding comorbid substance use in efforts to curb the negative effects of cannabis on brain and behavior as well as establish more effective policy and health practices.

Association of Treatable Health Conditions During Adolescence With Accelerated Aging at Midlife.

IMPORTANCE: Biological aging is a distinct construct from health; however, people who age quickly are more likely to experience poor health. Identifying pediatric health conditions associated with accelerated aging could help develop treatment approaches to slow midlife aging and prevent poor health in later life. OBJECTIVE: To examine the association between 4 treatable health conditions in adolescence and accelerated aging at midlife. DESIGN, SETTING, AND PARTICIPANTS: This cohort study analyzed data from participants in the Dunedin Study, a longitudinal investigation of health and behavior among a birth cohort born between April 1, 1972, and March 31, 1973, in Dunedin, New Zealand, and followed up until age 45 years. Participants underwent an assessment at age 45 years and had data for at least 1 adolescent health condition (asthma, smoking, obesity, and psychological disorders) and outcome measure (pace of aging, gait speed, brain age, and facial age). Data analysis was performed from February 11 to September 27, 2021. EXPOSURES: Asthma, cigarette smoking, obesity, and psychological disorders were assessed at age 11, 13, and 15 years. MAIN OUTCOMES AND MEASURES: The outcome was a midlife aging factor composite score comprising 4 measures of biological aging: pace of aging, gait speed, brain age (specifically, BrainAGE score), and facial age. RESULTS: A total of 910 participants (459 men [50.4%]) met the inclusion criteria, including an assessment at age 45 years. Participants who had smoked daily (0.61 [95% CI, 0.43-0.79] SD units), had obesity (0.82 [95% CI, 0.59-1.06] SD units), or had a psychological disorder diagnosis (0.43 [95% CI, 0.29-0.56] SD units) during adolescence were biologically older at midlife compared with participants without these conditions. Participants with asthma were not biologically older at midlife (0.02 [95% CI, -0.14 to 0.19] SD units) compared with those without asthma. These results remained unchanged after adjusting for childhood risk factors such as poor health, socioeconomic disadvantage, and adverse experiences. CONCLUSIONS AND RELEVANCE: This study found that adolescent smoking, obesity, and psychological disorder diagnoses were associated with older biological age at midlife. These health conditions could be treated during adolescence to reduce the risk of accelerated biological aging later in life.

What Do People Want in a Smoking Cessation App? An Analysis of User Reviews and App Quality.

INTRODUCTION: Mobile smoking cessation (mCessation) apps have the potential to complement and enhance existing interventions, but many are of low quality. Exploring app reviews can provide a broader understanding of user experiences and engagement, to enhance the quality, acceptability, and effectiveness of future developments. METHODS: Publicly available user reviews and ratings of smoking cessation apps were mined from Google Play and the App Store via a targeted two-stage search strategy. English language smoking cessation apps with at least 20 consumer reviews between 2011 and 2020 were included. User reviews were thematically analyzed using Braun and Clarke's framework. Apps were independently scored using the Mobile Apps Rating Scale (MARS) and compared to average user star ratings. RESULTS: Forty-eight versions of 42 apps, encompassing 1414 associated reviews, met eligibility criteria. Inductive coding of reviews produced 1084 coding references including reviews coded across multiple nodes. Themes generated included: (1) supportive characteristics/tools; (2) useability; (3) influence on smoking behavior; (4) benefits of quitting; and (5) role as a supplementary tool for quitting. The mean MARS score of 36 free and accessible apps was 3.10 (SD 0.71) with mean scores ranging from 2.00 to 4.47. An inverse relationship between MARS scores and average user star ratings was observed. CONCLUSIONS: App personalization, relationality, functionality, and credibility were important to users, and should be considered as key design components for future apps. Differences between user star ratings and MARS scores may illustrate competing priorities of consumers and researchers, and the importance of a codesign development method. IMPLICATIONS: This is the first study to use unsolicited user reviews from a large population to understand the general mCessation user experience in relation to making a quit attempt. Our findings highlight specific features favored and disliked by users, including their influence on engagement, and supports previous findings that mCessation applications need to be highly tailorable, functional, credible, and supportive. We recommend a consumer-driven, co-design approach for future mCessation app developments to optimize user acceptability and engagement.

Association of subcortical gray-matter volumes with life-course-persistent antisocial behavior in a population-representative longitudinal birth cohort.

Neuropsychological evidence supports the developmental taxonomy theory of antisocial behavior, suggesting that abnormal brain development distinguishes life-course-persistent from adolescence-limited antisocial behavior. Recent neuroimaging work confirmed that prospectively-measured life-course-persistent antisocial behavior is associated with differences in cortical brain structure. Whether this extends to subcortical brain structures remains uninvestigated. This study compared subcortical gray-matter volumes between 672 members of the Dunedin Study previously defined as exhibiting life-course-persistent, adolescence-limited or low-level antisocial behavior based on repeated assessments at ages 7-26 years. Gray-matter volumes of 10 subcortical structures were compared across groups. The life-course-persistent group had lower volumes of amygdala, brain stem, cerebellum, hippocampus, pallidum, thalamus, and ventral diencephalon compared to the low-antisocial group. Differences between life-course-persistent and adolescence-limited individuals were comparable in effect size to differences between life-course-persistent and low-antisocial individuals, but were not statistically significant due to less statistical power. Gray-matter volumes in adolescence-limited individuals were near the norm in this population-representative cohort and similar to volumes in low-antisocial individuals. Although this study could not establish causal links between brain volume and antisocial behavior, it constitutes new biological evidence that all people with antisocial behavior are not the same, supporting a need for greater developmental and diagnostic precision in clinical, forensic, and policy-based interventions.