Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis

Background

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor.

Objective

This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status.

Methods

Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs).

Results

Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79).

Conclusions

Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Exploring person-specific associations of situational motivation and readiness with leisure-time physical activity effort and experience

Identifying determinants of leisure-time physical activity (LTPA) often relies on population-level (nomothetic) averages, potentially overlooking person-specific (idiographic) associations. This study uses an idiographic perspective to explore how subjective readiness and motives for LTPA relate to volitional effort (duration, intensity) and affective experience (pleasure, displeasure). We also highlight the potential for different interpretations when data are averaged within individuals and assessed using a variable-centered approach. Participants (N = 22, 25±8 years old, 54.5% women) were asked to continue their regular PA patterns for 10 weeks. Ecological momentary assessment procedures allowed participants to provide pre-activity reports (physical, cognitive, emotional readiness and situational motive for activity) and post-activity reports (activity type, duration, perceived exertion, ratings of affective valence). Spearman rank correlation was implemented to interpret within- and between-person associations. Data visualization approaches were used to showcase person-specific differences in associations. Participants provided 519 reports of LTPA (24±11 events/person), which displayed between- and within-person variety in type, duration, intensity, and affective experience. Exemplar cases highlight discrepancies in interpretation based on level of analysis, such that the nomothetic association (rho = .42, p = .05; 95% CI -.02, .72) between motive to replenish energy and LTPA duration was observed in only one within-person analysis (41% were weak-to-large inverse effects). Alternatively, the negligible nomothetic association (rho = .02, p = .93; 95% CI -.41, .44) between physical readiness and LTPA-related affect did not reflect the 59% of within-person analyses showing moderate-to-large, positive effects. Future research aiming to identify determinants of LTPA effort and experience should integrate contemporary, idiographic analyses in early-stage research for developing person-specific strategies for LTPA promotion.

Precision public health in the era of genomics and big data

Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level-thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for 'precision' to enhance traditional public health approaches.

Precision prevention in worksite health-A scoping review on research trends and gaps

OBJECTIVES

To map the current state of precision prevention research in the workplace setting, specifically to study contexts and characteristics, and to analyze the precision prevention approach in the stages of risk assessment/data monitoring, data analytics, and the health promotion interventions implemented.

METHODS

Six international databases were searched for studies published between January 2010 and May 2023, using the term "precision prevention" or its synonyms in the context of worksite health promotion.

RESULTS

After screening 3,249 articles, 129 studies were reviewed. Around three-quarters of the studies addressed an intervention (95/129, 74%). Only 14% (18/129) of the articles primarily focused on risk assessment and data monitoring, and 12% of the articles (16/129) mainly included data analytics studies. Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. The main data analyses of all studies were regression analyses (44% with analyses of variance or linear mixed models), whereas machine learning methods (e.g., Algorithms, Markov Models) were conducted only in 8% of the articles.

DISCUSSION

Although there is a growing number of precision prevention studies in the workplace, there are still research gaps in applying new data analysis methods (e.g., machine learning) and implementing innovative study designs. In the future, it is desirable to take a holistic approach to precision prevention in the workplace that encompasses all the stages of precision prevention (risk assessment/data monitoring, data analytics and interventions) and links them together as a cycle.

Seeking an Integrated Approach to Trauma and Problematic Sexual Behaviors in Adolescents: Learning from Practitioners

More than a third of all sexual crimes are committed by adolescents. In addition, many adolescents struggle with problematic sexual behaviors (PSB) that may not rise to a criminal offense, but cause harm to themselves and those around them. A significant number of these adolescents also have histories of their own trauma; yet there are no treatment models that integrate both PSB and trauma into one comprehensive treatment for this population. This qualitative study's aim was to use the expertise of clinicians who work with adolescents with PSB as well as clients and their caregivers who had completed PSB treatment to understand what they believe are the necessary components and elements needed for an integrated model that seeks to simultaneously address both PSB and the consequences of traumatic experiences. Twenty-six mental health professionals and one former client and his caregiver participated in focus groups (three with clinicians and one with the caregiver and former client) to share their experiences and expertise regarding an integrated model. The thematic analysis procedure yielded six major themes: family, external systems, treatment structure, therapeutic themes, specialized PSB targets, and trauma and PSB integration. Each of these major themes was comprised of subthemes that are presented as well. Implications for practice and future research are discussed, including that clinicians could benefit from guidance and structure to assist them in structuring their interventions to address the multiple needs of their clients that seek to reduce their risk of reoffending while simultaneously enhancing the quality of their lives.

Assessing organizational readiness for the Clean Cuts and Sharp Minds Collective: a barbershop health promotion network

BACKGROUND

Black men have among the lowest life expectancy in the United States. Alarmingly, these men are underrepresented in health promotion efforts. There are well-documented barriers to recruiting and retaining Black men in health promotion efforts, such as exclusionary research practices - many researchers may be hesitant to reach Black men in culturally unique spaces, such as barbershops. Despite these practices, qualitative research among Black men unanimously find that Black men are interested in health promotion efforts. The Clean Cuts and Sharp Minds Collective (CCSMC) was designed to bridge this gap. The objectives of the CCSMC are to train barbers to be lay advocates for their clients, train barbers to be research partners, and serve as a nexus between barbers interested in health promotion at their shops and researchers interested in implementing such efforts. The present study sought to assess the organizational readiness of barbershops in South Carolina (SC) to participate in the CCSMC.

METHODS

Barbers in SC were invited to complete a modified version of the Readiness Thinking Tool to assess organizational readiness to participate in the CCSMC.

RESULTS

Thirty-six (36; mean age = 41.12; 94.4% identified as Black; 91.7% identified as male) barbers completed the organizational readiness assessment. Results indicated that there was a high level of motivation, innovation-specific capacity, and general capacity within barbershops to participate in the CCSMC. Additionally, many barbers indicated that there would be widespread support to join the CCSMC.

CONCLUSIONS

The results from the present study highlight exciting opportunities and future directions for barbershop-academic partnerships. Such partnerships have the potential to promote health equity among, and in partnership with, Black men.

Research Priorities in Psychiatric Mental Health Nursing: Funding Availability, Recently Published Work, and Future Directions for Advancing Our Science

BACKGROUND

Identifying and sharing psychiatric mental health (PMH) research priorities can help identify gaps in the literature, determine training needs for nurses, and assist investigators in locating funding.

AIMS

The aims for this project included: (1) identify PMH nursing specialty research priorities from large, public funding entities; (2) compare the identified research priorities and scholarly articles published in 2020; and (3) outline future directions for advancing the science of PMH nursing.

METHODS

Strategic plans of funding entities for PMH research were reviewed for research priorities. Summarized priorities were then compared with publications from peer-reviewed PMH nursing journals. Work conducted in the United States was extracted and categorized as original research, evidence-based practice (EBP), or quality improvement (QI), then design and population.

RESULTS

Priorities from 13 funding entities comprised three domains (education, research, and practice), with nine areas, 30 topics, and subtopics. Eight journals produced 432 articles with 99 (23%) conducted in the United States. Among these journals, publications fitting research priorities ranged from 1 to 33 articles. Of the 99 articles, 85% were original research and 15% were either EBP or QI. Descriptive articles comprised 64% and 23% tested interventions. Populations studied included workforce; adults; infants, children, and adolescents; and parents.

CONCLUSIONS

All published articles by the U.S. PMH nurses matched the funding priorities, but not all funding priorities were addressed by 2020 publications. The majority of work was conducted outside of the United States and most published from the United States was descriptive, contrary to many United States funders' priorities for interventional research.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Trajectories of opioid consumption as predictors of patient-reported outcomes among individuals attending multidisciplinary pain treatment clinics

PURPOSE

This study aimed to identify opioid consumption trajectories among persons living with chronic pain (CP) and put them in relation to patient-reported outcomes 6 months after initiating multidisciplinary pain treatment.

METHODS

This study used data from the Quebec Pain Registry (2008-2014) linked to longitudinal Quebec health insurance databases. We included adults diagnosed with CP and covered by the Quebec public prescription drug insurance plan. The daily cumulative opioid doses in the first 6 months after initiating multidisciplinary pain treatment were transformed into morphine milligram equivalents. An individual-centered approach involving principal factor and cluster analyses applied to longitudinal statistical indicators of opioid use was conducted to classify trajectories. Multivariate regression models were applied to evaluate the associations between trajectory group membership and outcomes at 6-month follow-up (pain intensity, pain interference, depression, and physical and mental health-related quality of life).

RESULTS

We identified three trajectories of opioid consumption: "no or very low and stable" opioid consumption (n = 2067, 96.3%), "increasing" opioid consumption (n = 40, 1.9%), and "decreasing" opioid consumption (n = 39, 1.8%). Patients in the "no or very low and stable" trajectory were less likely to be current smokers, experience polypharmacy, use opioids or benzodiazepine preceding their first visit, or experience pain interference at treatment initiation. Patients in the "increasing" opioid consumption group had significantly greater depression scores at 6-month compared to patients in the "no or very low and stable" trajectory group.

CONCLUSION

Opioid consumption trajectories do not seem to be important determinants of most PROs 6 months after initiating multidisciplinary pain treatment.

Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation

Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.

Data Quality in Health Research: Integrative Literature Review

BACKGROUND

Decision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance.

OBJECTIVE

Through an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality.

METHODS

A search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases.

RESULTS

After analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches.

CONCLUSIONS

The main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1101/2022.05.31.22275804.

Moderators of a Diet and Physical Activity Intervention: who Responds Best to Sequential vs. Simultaneous Approaches

BACKGROUND

Given that low physical activity levels and poor dietary intake are co-occurring risk factors for chronic disease, there is a need for interventions that target both health behaviors, either sequentially or simultaneously. Little is known about participant characteristics that are associated with better or worse response to sequential and simultaneous interventions.

METHOD

The 12-month Counseling Advice for Lifestyle Management (CALM) randomized trial (N = 150; Mage = 55.3 years) targeted these two behaviors either via a sequential approach - dietary advice first then exercise advice added ("Diet-First") or exercise advice first then dietary advice added ("Exercise-First") - or via a simultaneous approach. The objective was to examine demographic, clinical, and psychosocial moderators of intervention effects on 12-month change in (1) moderate-to-vigorous physical activity (MVPA), (2) fruit/vegetable intake, (3) caloric intake from saturated fat, and (4) weight. Hierarchical regressions first compared Diet-First to Exercise-First, followed by comparisons of these arms combined ("sequential") to the simultaneous arm.

RESULTS

Older age, higher baseline BMI, and lower social support were associated with higher MVPA in Exercise-First vs. Diet-First, while lower tangible support was associated with higher fruit/vegetable intake in Exercise-First but not in Diet-First. Poor sleep was associated with higher levels of MVPA in the sequential arm than in the simultaneous arm. Lower vitality was associated with greater weight loss in the sequential arm than in the simultaneous arm, while the opposite was true for those who were not married.

CONCLUSION

Identifying moderators of treatment response can allow the behavioral medicine field to enhance intervention efficacy by matching participant subgroups to their best-fitting interventions.

TRIAL REGISTRATION

NCT00131105.

Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care

Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions

BACKGROUND

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept.

OBJECTIVE

The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d).

METHODS

We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI.

RESULTS

As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023.

CONCLUSIONS

This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52161.

In Which Situations Do We Eat? A Diary Study on Eating Situations and Situational Stability

Eating situations are crucial for understanding and changing eating behavior. While research on individual situational dimensions exists, little is known about eating situations as a whole. This study aimed to fill this gap by identifying eating situations as combinations of multiple situational dimensions and describing how stable individuals eat in those situations. In a five-day online diary study, 230 participants reported a total of 2461 meals and described the corresponding eating situation using predefined situational dimensions. Divisive hierarchical cluster analyses were conducted separately for breakfast, lunch, and dinner, resulting in unique cluster solutions that characterized the most common eating situations. The most common breakfast situations were characterized by a combination of the dimensions social, affect, and hunger. The most common lunch and dinner situations were characterized by varying combinations of the dimensions social, affect, and activity. Based on the identified situations, a situational stability index was developed to describe how stable individuals eat in the same situations. The findings suggest high interindividual differences in situational stability, which were associated with socio-demographic characteristics like age or employment. This study enhances our understanding of the situational aspects of eating behavior while offering tools to describe eating situations and situational stability.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

A remotely delivered, peer-led intervention to improve physical activity and quality of life in younger breast cancer survivors

Younger breast cancer survivors (YBCS) consistently report poorer quality of life (QOL) than older survivors. Increasing physical activity (PA) may improve QOL, but this has been understudied in YBCS. This single arm pilot study evaluated the feasibility and acceptability of a 3-month, peer-delivered, remote intervention to increase PA and improve QOL in YBCS. Data were collected from October 2019 - July 2020. Participants (n = 34, 43.1 ± 5.5 years old, 46 ± 34.4 months post-diagnosis, BMI = 30.2 ± 7.4 kg/m2) completed six video sessions with a trained peer mentor; self-monitored PA with a Fitbit activity tracker; and interacted with a private Fitbit Community for social support. At baseline, 3-and 6-months, participants completed QOL questionnaires and PA was measured through accelerometer (moderate-to-vigorous PA [MVPA]) and self-report (strength and flexibility). A parallel mixed-methods approach (qualitative interviews and quantitative satisfaction survey at 3-months) explored intervention feasibility and acceptability. One-way repeated-measures ANOVAs examined impacts on PA and QOL at 3-and 6-months. The intervention was feasible as evidenced by efficient recruitment, high retention, and adherence to intervention components. Remote delivery, working with a peer mentor, and using Fitbit tools were highly acceptable. From baseline to 3-months, participants increased time spent in objectively measured MVPA, strength, and flexibility exercises, and reported meaningful improvements to body image, fatigue, anxiety, and emotional support. A fully remote, peer-to-peer intervention is an acceptable and promising strategy to increase PA and improve QOL in YBCS. Refinements to the intervention and its delivery should be further assessed in future studies, toward the goal of disseminating an evidence-based, scalable intervention to the growing number of YBCS.Trial registration Prospectively registered as NCT04064892.

Establishing the Feasibility and Acceptability of a Caregiver Targeted Intervention to Improve Pain Assessment Among Persons With Dementia

Background and Objectives

Despite its prevalence and impact, pain is underdetected and undermanaged in persons with dementia. Family caregivers are well positioned to detect pain and facilitate its management in their care recipients, but they lack training in symptom recognition and communication. This study reports findings from a pilot trial evaluating the Pain Identification and Communication Toolkit (PICT), a multicomponent intervention that provides training in observational pain assessment and coaching in pain communication techniques.

Research Design and Methods

Family caregivers of persons with comorbid pain and moderate-to-advanced dementia were randomly assigned to PICT (n = 19) or a control condition (n = 15). Caregivers in the PICT group participated in four weekly sessions delivered by telephone with a trained interventionist; caregivers in the control group received an information pamphlet about pain and dementia. All participants completed surveys at baseline and 12 weeks. Caregivers in the intervention group also completed semistructured interviews at 12 weeks. Quantitative data were analyzed using descriptive statistics and t tests; qualitative data were analyzed using content analysis.

Results

All participants (100%) in the PICT group completed the intervention and most completed the 12-week assessment (94%). PICT randomized caregivers reported that the intervention helped them to feel more confident in their ability to recognize (67%) and communicate about pain symptoms (83%). At 12 weeks, caregivers in the PICT group showed a statistically significant improvement in self-efficacy in pain-related communication. In qualitative interviews, caregivers emphasized the utility of PICT's components, including pain assessment tools, and offered considerations for future enhancements, such as technology-based adaptations and integration within care delivery systems.

Discussion and Implications

This pilot trial demonstrates that PICT is feasible to implement, acceptable to caregivers, and has the potential to improve confidence in recognizing and communicating about pain. Results support conducting a fully powered efficacy trial, an important step toward future integration into real-world care delivery.

Clinical Trial Registration Number

NCT03853291.

Recommendations for post-implementation adaptations to optimize family navigation in pediatric primary care: a qualitative study with parents and navigators

BACKGROUND

Family Navigation (FN) is an evidence-based care management intervention designed to reduce disparities in access to care by providing families with individually tailored support and care coordination. Early data suggest FN is effective, but effectiveness is significantly influenced by both contextual (e.g. setting) and individual (e.g., ethnicity) variables. To better understand how FN could be tailored to address this variability in effectiveness, we set forth to explore proposed adaptations to FN by both navigators and families who received FN.

METHODS

This study was a nested qualitative study set within a larger randomized clinical trial of FN to improve access to autism diagnostic services in urban pediatric primary care practices in Massachusetts, Pennsylvania, and Connecticut serving low-income, racial and ethnic minority families. Following FN implementation, key informant interviews were conducted based on the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) with a purposeful sample of parents of children who received FN (n = 21) and navigators (n = 7). Interviews were transcribed verbatim and were coded using framework-guided rapid analysis to categorize proposed adaptations to FN.

RESULTS

Parents and navigators proposed 38 adaptations in four domains: 1) content of the intervention (n = 18), 2) context of the intervention (n = 10), 3) training and evaluation (n = 6), and 4) implementation and scale-up (n = 4). The most frequently endorsed adaptation recommendations focused on content (e.g., lengthening FN, providing parents with additional education on autism and parenting children with autism) and implementation (e.g., increasing access to navigation). Although probes targeted critical feedback, parents and navigators were overwhelmingly positive about FN.

CONCLUSIONS

This study builds upon prior FN effectiveness and implementation research by providing concrete areas for adaptation and refinement of the intervention. Recommendations by parents and navigators have the potential to inform improvement of existing navigation programs and development of new programs in similarly underserved populations. These findings are critical as adaptation (cultural and otherwise) is an important principle in the field of health equity. Ultimately, adaptations will need to be tested to determine clinical and implementation effectiveness.

TRIAL REGISTRATION

ClinicalTrials.gov, registration number NCT02359084, February 9, 2015.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App

INTRODUCTION

Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports.

AIMS AND METHODS

We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested.

RESULTS

Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000).

CONCLUSIONS

Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants.

IMPLICATIONS

This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

N-of-1 trials provide a higher level of evidence than randomized controlled trials for determining which treatment works best for an individual, and the design readily accommodates testing of personalized nutrition. The aim of this systematic review was to synthesize nutrition-related studies using an N-of-1 design. The inclusion criterion was adult participants; the intervention/exposure was any nutrient, food, beverage, or dietary pattern; the comparators were baseline values, a control condition untreated or placebo, or an alternate treatment, alongside any outcomes such as changes in diet, body weight, biochemical outcomes, symptoms, quality of life, or a disease outcome resulting from differences in nutritional conditions. The information sources used were Medline, Embase, Scopus, Cochrane Central, and PsychInfo. The quality of study reporting was assessed using the Consort Extension for N-of-1 trials (CENT) statement or the STrengthening Reporting of OBservational Studies in Epidemiology (STROBE) guidelines, as appropriate. From 211 articles screened, a total of 7 studies were included and were conducted in 5 countries with a total of 83 participants. The conditions studied included prediabetes, diabetes, irritable bowel syndrome, weight management, and investigation of the effect of diet in healthy people. The quality of reporting was mostly adequate, and dietary assessment quality varied from poor to good. The evidence base is small, but served to illustrate the main characteristics of N-of-1 study designs and considerations for moving research forward in the era of personalized medical nutrition therapy.

Symptoms and Comorbidities Differ Based on Race and Weight Status in Persons with HIV in the Northern United States: a Cross-Sectional Study

BACKGROUND

Persons with HIV (PWHIV) on highly active antiretroviral treatments (HAART) may require specialized care based on health and demographic indicators. This study investigated the association of comorbidities, race, weight status, and gastrointestinal (GI) and cardiovascular (CV) symptoms among PWHIV.

METHODS

The Symptom Checklist, Co-Morbidity Questionnaire, and Sociodemographic Questionnaire were used to assess weight status and GI and CV symptoms among 283 PWHIV. Data were analyzed using latent class analysis on John's Macintosh Project 13 Platform.

RESULTS

Participants were majority Black (50%), 69% male, and 35% AIDS diagnosed. Ages were 25 to 66. Clusters included least symptomatic status, weight gain, and weight loss by Black and non-Black participants. The non-Black weight gain cluster reported a higher incidence of AIDS (70.6% vs 38.2%), nausea (70.6% vs 17.6%), diarrhea (70.6% vs 26.5%), and shortness of breath (58.8% vs 20.6%) compared to the Black weight gain cluster. The Black weight loss cluster reported a higher incidence of CV symptoms such as chest palpitations (42.2% vs 2.7%), chest pain (44.4% vs 8.1%), and shortness of breath (73.3% vs 35.1%). Moreover, the Black weight loss cluster reported a higher incidence of all GI symptoms with the most prominent being diarrhea (71.1% vs 48.6%) compared to the non-Black weight loss cluster.

CONCLUSIONS

The existing racial disparities in health-related quality of life for PWHIV may be improved through precision health and nutrition modifications. Continued research is needed investigating differential health outcomes among PWHIV on HAART.

CLINICAL TRIAL REGISTRATION NUMBER

NCT00222716. Registered 22 September 2005. Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT00222716?term=NCT00222716&draw=2&rank=1.

Factors that influence health literacy in patients with coronary artery disease

OBJECTIVE

to investigate the factors that exert an influence on health literacy in patients with coronary artery disease.

METHODS

a crosssectional study, including 122 patients with coronary diseases (60.7% male; 62.07 ± 8.8 years old). Health literacy and specific knowledge about the disease were evaluated through interviews with the participants by means of the Short Test of Functional Health Literacy in Adults and the Short version of the coronary artery disease education questionnaire. The data were described by means of central tendency measures and frequencies. The factors that exert an influence on health literacy were determined by means of a linear regression model. The significance level adopted was 5%. The study was approved by the Research Ethics Committee.

RESULTS

age and arterial hypertension presented an inverse and significant relationship with health literacy. On the other hand, higher schooling levels and having a job were associated with better scores in the health literacy instrument. Specific knowledge about the disease did not exert any influence on health literacy. The variables included in the regression model explained 55.3% of inadequate literacy.

CONCLUSION

this study, knowledge about the disease exerts no influence on health literacy: however, the professionals should consider the sociodemographic and clinical factors to plan the interventions.

The Psychology of Athletic Endeavor

A considerable amount of human behavior occurs within the context of sports. In recent years there have been notable advances in psychological science research applied to understanding athletic endeavor. This work has utilized a number of novel theoretical, methodological, and data analytic approaches. We review the current evidence related to developmental considerations, intrapersonal athlete factors, group processes, and the role of the coach in explaining how athletes function within the sport domain. This body of work sheds light on the diverse ways in which psychological processes contribute to athletic strivings. It also has the potential to spark interest in domains of psychology concerned with achievement as well as to encourage cross-domain fertilization of ideas.

Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity

BACKGROUND

Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase.

METHODS

To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi²-tests.

RESULTS

First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X² = 16.92, P < .001; X² = 20.34, P < .001; X² = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans.

CONCLUSIONS

The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level.

TRIAL REGISTRATION

The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

Real-world data for precision public health of noncommunicable diseases: a scoping review

BACKGROUND

Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation.

RESULTS

Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear.

CONCLUSIONS

Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.

An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice

Innovative approaches are needed to make health communication research and practice more timely, responsive, and effective in a rapidly changing information ecosystem. In this paper we provide an overview of strategies that can enhance the delivery and effectiveness of health communication campaigns and interventions, as well as research approaches that can generate useful data and insights for decisionmakers and campaign designers, thereby reducing the research-to-practice gap. The discussion focuses on the following approaches: digital segmentation and microtargeting, social media influencer campaigns, recommender systems, adaptive interventions, A/B testing, efficient message testing protocols, rapid cycle iterative message testing, megastudies, and agent-based modeling. For each method highlighted, we also outline important practical and ethical considerations for utilizing the approach in the context of health communication research and practice, including issues related to transparency, privacy, equity, and potential for harm.

Advancing digital health Equity: Directions for behavioral and social science research

The field of digital health is evolving rapidly and encompasses a wide range of complex and changing technologies used to support individual and population health. The COVID-19 pandemic has augmented digital health expansion and significantly changed how digital health technologies are used. To ensure that these technologies do not create or exacerbate existing health disparities, a multi-pronged and comprehensive research approach is needed. In this commentary, we outline five recommendations for behavioral and social science researchers that are critical to promoting digital health equity. These recommendations include: (i) centering equity in research teams and theoretical approaches, (ii) focusing on issues of digital health literacy and engagement, (iii) using methods that elevate perspectives and needs of underserved populations, (iv) ensuring ethical approaches for collecting and using digital health data, and (v) developing strategies for integrating digital health tools within and across systems and settings. Taken together, these recommendations can help advance the science of digital health equity and justice.

Inclusion of Social and Structural Determinants of Health to Advance Understanding of their Influence on the Biology of Chronic Disease

Social Determinants of Health (SDOH) consider social, political, and economic factors that contribute to health disparities in patients and populations. The most common health-related SDOH exposures are food and housing insecurity, financial instability, transportation needs, low levels of education, and psychosocial stress. These domains describe risks that can impact health outcomes more than health care. Epidemiologic and translational research demonstrates that SDOH factors represent exposures that predict harm and impact the health of individuals. International and national guidelines urge health professionals to address SDOH in clinical practice and public health. The further implementation of these recommendations into basic and translational research, however, is lagging. Herein, we consider a precision health framework to describe how SDOH contributes to the exposome and exacerbates physiologic pathways that lead to chronic disease. SDOH factors are associated with various forms of stressors that impact physiological processes through epigenetic, inflammatory, and redox regulation. Many SDOH exposures may add to or potentiate the pathologic effects of additional environmental exposures. This overview aims to inform basic life science and translational researchers about SDOH exposures that can confound associations between classic biomedical determinants of disease and health outcomes. To advance the study of toxicology through either qualitative or quantitative assessment of exposures to chemical and biological substances, a more complete environmental evaluation should include SDOH exposures. We discuss common approaches to measure SDOH factors at individual and population levels and review the associations between SDOH risk factors and physiologic mechanisms that influence chronic disease. We provide clinical and policy-based motivation to encourage researchers to consider the impact of SDOH exposures on study results and data interpretation. With valid measures of SDOH factors incorporated into study design and analyses, future toxicological research may contribute to an evidence base that can better inform prevention and treatment options, to improve equitable clinical care and population health. © 2022 Wiley Periodicals LLC.

Precision health in behaviour change interventions: A scoping review

Precision health seeks to optimise behavioural interventions by delivering personalised support to those in need, when and where they need it. Conceptualised a decade ago, progress toward this vision of personally relevant and effective population-wide interventions continues to evolve. This scoping review aimed to map the state of precision health behaviour change intervention research. This review included studies from a broader precision health review. Six databases were searched for studies published between January 2010 and June 2020, using the terms 'precision health' or its synonyms, and including an intervention targeting modifiable health behaviour(s) that was evaluated experimentally. Thirty-one studies were included, 12 being RCTs (39%), and 17 with weak study design (55%). Most interventions targeted physical activity (27/31, 87%) and/or diet (24/31, 77%), with 74% (23/31) targeting two to four health behaviours. Interventions were personalised via human interaction in 55% (17/31) and digitally in 35% (11/31). Data used for personalising interventions was largely self-reported, by survey or diary (14/31, 45%), or digitally (14/31, 45%). Data was mostly behavioural or lifestyle (20/31, 65%), and physiologic, biochemical or clinical (15/31, 48%), with no studies utilising genetic/genomic data. This review demonstrated that precision health behaviour change interventions remain dependent on human-led, low-tech personalisation, and have not fully considered the interaction between behaviour and the social and environmental contexts of individuals. Further research is needed to understand the relationship between personalisation and intervention effectiveness, working toward the development of sophisticated and scalable behaviour change interventions that have tangible public health impact.

The Family of Single-Case Experimental Designs

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case-which can be an individual, clinic, or community-ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design-including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs-are also discussed.

Demand Coupling Drives Neurodegeneration: A Model of Age-Related Cognitive Decline and Dementia

The societal burden of Alzheimer's Disease (AD) and other major forms of dementia continues to grow, and multiple pharmacological agents directed towards modifying the pathological "hallmarks" of AD have yielded disappointing results. Though efforts continue towards broadening and deepening our knowledge and understanding of the mechanistic and neuropathological underpinnings of AD, our previous failures motivate a re-examination of how we conceptualize AD pathology and progression. In addition to not yielding effective treatments, the phenotypically heterogeneous biological processes that have been the primary area of focus to date have not been adequately shown to be necessary or sufficient to explain the risk and progression of AD. On the other hand, a growing body of evidence indicates that lifestyle and environment represent the ultimate level of causation for AD and age-related cognitive decline. Specifically, the decline in cognitive demands over the lifespan plays a central role in driving the structural and functional deteriorations of the brain. In the absence of adequate cognitive stimulus, physiological demand-function coupling leads to downregulation of growth, repair, and homeostatic processes, resulting in deteriorating brain tissue health, function, and capacity. In this setting, the heterogeneity of associated neuropathological tissue hallmarks then occurs as a consequence of an individual's genetic and environmental background and are best considered downstream markers of the disease process rather than specific targets for direct intervention. In this manuscript we outline the evidence for a demand-driven model of age-related cognitive decline and dementia and why it mandates a holistic approach to dementia treatment and prevention that incorporates the primary upstream role of cognitive demand.

College as a Developmental Context for Emerging Adulthood in Autism: A Systematic Review of What We Know and Where We Go from Here

Autistic individuals often struggle to successfully navigate emerging adulthood (EA). College is an increasingly common context in which individuals learn and hone the necessary skills for adulthood. The goal of this paper is to systematically review and assess the existing research on college as a context of EA development in autistic individuals, particularly in terms of understanding whether and how this context might be critically different for those who are typically developing or developing with other disabilities. Our findings indicate that ASD college students report feeling prepared academically, but exhibit weaknesses in daily living and social skills. Interventions largely focus on social skills, and rarely evaluate outcomes relevant to college success or longer-term emerging adulthood independence. We conclude with hypotheses and recommendations for future work that are essential for understanding and supporting ASD students as they navigate potentially unique challenges in college and their transition to independence during EA.

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning

BACKGROUND

The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors.

PURPOSE

To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics.

METHODS

In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2).

RESULTS

We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design.

CONCLUSIONS

By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1—digital public health workflows; Horizon 2—population health data and analytics; Horizon 3—precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

Impact of BRCA Status on Reproductive Decision-Making and Self-Concept: A Mixed-Methods Study Informing the Development of Tailored Interventions

This mixed-methods study sought to deepen our understanding of self-concept and experiences in balancing cancer risk/reproductive decisions after learning of BRCA+ status. First, a quantitative survey of BRCA+ women (n = 505) examined the childbearing status, risk-reducing surgery, and self-concept. At the time of testing, 307/505 (60.8%) women were of reproductive age (<40 years-old), 340/505 (67.3%) had children, and 317/505 (62.8%) had undergone risk-reducing surgery. A younger age at the time of the testing was significantly associated with the decision to have children after learning BRCA+ status or undergoing risk-reducing surgery (p < 0.001). Compared to older women, BRCA+ women of reproductive age, exhibited a more negative self-concept with significantly higher vulnerability ratings (p < 0.01). Women with a cancer diagnosis exhibited a more negative mastery ratings and worse vulnerability ratings (p < 0.01) than women without a cancer history. Compared to childless counterparts, significantly higher vulnerability ratings were observed among BRCA+ women who had children before learning their BRCA status and/or undergoing risk-reducing surgery (p < 0.001). Subsequently, a subset of women (n = 40) provided in-depth interviews to explore their experiences in decision-making. The interviews provided insights into the effects of BRCA status on decisions regarding relationships, childbearing, cancer risk management, and communicating BRCA risk to children. Integrating quantitative and qualitative findings identifies targets for tailored interventions to enhance precision health for BRCA+ women of reproductive age.

Scale-up of Digital Innovations in Health Care: Expert Commentary on Enablers and Barriers

Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.

Adverse childhood experiences in children and adolescents with sickle cell disease: A retrospective cohort study

BACKGROUND

Adverse childhood experiences (ACEs) are linked to poor health outcomes; however, the relationship between ACEs and health outcomes among children and adolescents with sickle cell disease (SCD) has limited documentation in the published literature.

PROCEDURE

This retrospective cohort study involved 45 children and 30 adolescents. Participants were screened using the Center for Youth Wellness ACE Questionnaire. Parents completed the questionnaire for children. Adolescents provided self-report. ACEs were treated as continuous and categorical scales: 0-1 verus ≥2 original ACEs (individual and/or familial level); 0-1 versus ≥2 additional ACEs (community level); and 0-3 versus ≥4 expanded ACEs (original + additional). Pain and acute chest syndrome events were compared using Wilcoxon rank-sum tests, and correlated with cumulative ACE scores using Spearman's correlation. Multivariable models were fitted to examine the association between ACEs and pain/acute chest syndrome.

RESULTS

The cumulative number of original ACEs positively correlated with acute chest syndrome events (rho = .53, p = .003) and pain (rho = .40, p = .028) among adolescents. Adolescents with ≥2 versus 0-1 original ACEs had a higher number of acute chest syndrome events (4.9 ± 2.6 vs. 1.6 ± 2.2, p = .002); however, this association was confounded by asthma. Acute chest syndrome events and hospitalizations for pain did not differ among child ACE groups. Emergency department (ED) pain visits were higher among children with ≥4 versus 0-3 expanded ACEs (1.6 ± 2.8 vs. 3.3 ± 3.2, p = .042), even after controlling for SCD genotype, asthma, disease-modifying treatment, and follow-up years (p = .027).

CONCLUSION

ACEs are linked to increased morbidity among children and adolescents with SCD. Prospective studies are needed to further understand this relationship and test ACE-protective remedies.

Precision Health in Taiwan: A Data-Driven Diagnostic Platform for the Future of Disease Prevention

“Precision medicine” has revolutionized how we respond to diseases by using an individual’s genomic data and lifestyle and environment-related information to create an effective personalized treatment. However, issues surrounding regulations, medical insurance payments and the use of patients’ medical data, have delayed the development of precision medicine and made it difficult to achieve “true” personalization. We therefore recommend that precision medicine be transformed into precision health: a novel and generalized platform of tools and methods that could prevent, manage, and treat disease at a population level. “Precision health,” one of six core strategic industries highlighted in Taiwan’s vision for 2030, uses various physiological data, genomic data, and external factors, to develop unique “preventative” solutions or therapeutic strategies. For Taiwan to implement precision health, it has to address three challenges: (1) the high-cost issue of precision health; (2) the harmonization issues surrounding integration and transmission of specimen and data; (3) the legal issue of combining information and communications technology (ICT) with Artificial Intelligence (AI) for medical use. In this paper, we propose an innovative framework with six recommendations for facilitating the development of precision health in Taiwan, including a novel model of precise telemedicine with AI-aided technology. We then describe how these tools can be proactively applied in early response to the COVID-19 crisis. We believe that precision health represents an important shift to more proactive and preventive healthcare that enables people to lead healthier lives.

Parents' Experience With a Mobile Health Intervention to Influence Human Papillomavirus Vaccination Decision Making: Mixed Methods Study

BACKGROUND

Human papillomavirus (HPV)-attributed cancers are preventable, yet HPV vaccination rates severely lag behind other adolescent vaccinations. HPVcancerFree (HPVCF) is a mobile health (mHealth) intervention developed to influence parental HPV vaccination decision making by raising awareness of HPV, reducing HPV vaccination barriers, and enabling HPV vaccination scheduling and reminders through a smartphone app. Evaluating the user experience of mHealth interventions is a vital component in assessing their quality and success but tends to be underreported in mHealth intervention evaluation.

OBJECTIVE

We aimed to evaluate the user experience of HPVCF, an HPV cancer prevention app designed for a pediatric clinic network, using mixed methods data collected from log files, survey measures, and qualitative feedback.

METHODS

Study data were evaluated from parents in a large US pediatric clinic network using HPVCF in the treatment study condition of a group randomized controlled trial. Log data captured HPVCF retention and use. Postintervention rating scales and items assessed HPVCF utility, usefulness, understandability, appeal, credibility, and perceived impact. Overall quality was evaluated using the user version of the Mobile Application Rating Scale (uMars). Open-ended responses assessed parent recommendations for HPVCF enhancement.

RESULTS

The 98 parents were mainly female (n=94, 96%), 41 (5.67) years of age, college educated (n=55, 56%), and White and non-Hispanic (n=55, 56%) and had private health insurance for their children (n=75, 77%). Parents used HPVCF 197 times, with the average visit duration approximating 3.5 minutes. The uMARS app quality score was positively skewed (4.2/5.0). Mean ratings were highest for information (4.46 [SD 0.53]) and lowest for engagement (3.74 [SD 0.69]). In addition, of 95 parents, 45 (47%) rated HPVCF as helpful in HPV vaccination decision making and 16 (17%) attributed HPV vaccine initiation to HPVCF. Parents reported that HPVCF increased their awareness (84/95, 88%), knowledge (84/95, 88%), and HPV vaccination intentions (64/95, 67%). Most of the 98 parents rated the 4 HPVCF components as useful (72-92 [73%-94%]). Parents also agreed that HPVCF is clear (86/95, 91%), accurate (86/95, 91%), and more helpful than other HPV vaccine information they had received (89/95, 94%) and that they would recommend it to others (81/95, 85%). In addition, parents suggested ways to increase awareness and engagement with the app, along with opportunities to enhance the content and functionality.

CONCLUSIONS

HPVCF was well received by parents and performed well on indicators of quality, usefulness, utility, credibility, and perceived impact. This study contributes a multimethod and multimeasure evaluation to the growing body of literature focused on assessing the user experience of patient-focused technology-mediated applications for HPV education.

Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol

Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called “Just-in-Time Adaptive Interventions” (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.

The Role of Modifiable, Self-Empowerment-Oriented Variables to Promote Health-Related Quality of Life Among Inadequately Insured Americans

Inadequately insured Americans experience a disproportionately low health-related quality of life (HRQoL)-a multidimensional and subjective indicator of health associated with premature mortality. Although the inadequately insured are a growing and at-risk group of individuals, little research has examined factors associated with HRQoL within this population. Health Self-Empowerment Theory (HSET) and precision prevention influenced the conceptualization of this study. HSET is a health empowerment theory that recognizes the effects of certain cognitive-behavioral variables on health promotion within at-risk groups. Precision prevention advocates for individual or precise group-specific tailored and optimized health promotion approaches based on key sociodemographic variables. We investigated the impact of HSET variables on mental and physical HRQoL among 279 inadequately insured women and men who completed a questionnaire assessing HRQoL, health self-efficacy, health motivation, and active coping. Among the women in our sample, results indicate that exercise and psychological well-being self-efficacy were significantly and positively associated with mental and physical HRQoL. Among men, psychological well-being and responsible health practices self-efficacy, in addition to active coping, were significantly and positively associated with mental HRQoL. Psychological well-being self-efficacy and active coping were significantly and positively associated with physical HRQoL among men. The findings of our study suggest that HSET variables play an important role in the development of tailored HRQoL-promotion interventions for inadequately insured Americans, and that the roles of those variables may differ based on gender.

The biopsychosociotechnical model: a systems-based framework for human-centered health improvement

The biopsychosocial model is among the most influential frameworks for human-centered health improvement but has faced significant criticism- both conceptual and pragmatic. This paper extends and fundamentally re-structures the biopsychosocial model by combining it with sociotechnical systems theory. The resulting biopsychosociotechnical model addresses key critiques of the biopsychosocial model, providing a more "practical theory" for human-centered health improvement. It depicts the determinants of health as complex adaptive system of systems; includes the the artificial world (technology); and provides a roadmap for systems improvement by: differentiating between "health status" and "health and needs assessment", [promoting problem framing]; explaining health as an emergent property of the biopsychosociotechnical context [imposing a systems orientation]; focusing on "interventions" vs. "treatments" to modify the biopsychosociotechnical determinants of health, [expanding the solution space]; calling for a participatory design process [supporting systems awareness and goal-orientation]; and including intervention management to support the full lifecycle of health improvement.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Advancing understanding of dietary and movement behaviours in an Asian population through real-time monitoring: Protocol of the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA)

Background

Modifiable risk factors for non-communicable diseases, including eating an unhealthy diet and being physically inactive, are influenced by complex and dynamic interactions between people and their social and physical environment. Therefore, understanding patterns and determinants of these risk factors as they occur in real life is essential to enable the design of precision public health interventions.

Objective

This paper describes the protocol for the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). The study uses real-time data capture methods to gain a comprehensive understanding of eating and movement behaviours, including how these differ by socio-demographic characteristics and are shaped by people's interaction with their social and physical environment.

Methods

COBRA is an observational study in free-living conditions. We will recruit 1500 adults aged 21-69 years from a large prospective cohort study. Real-time data capture methods will be used for nine consecutive days: an ecological momentary assessment app with a global positioning system enabled to collect location data, accelerometers to measure movement, and wearable sensors to monitor blood glucose levels. Participants receive six EMA surveys per day between 8 a.m. and 9.30 p.m. to capture information on behavioural risk factors including eating behaviours and diet composition movement behaviours (physical activity, sedentary behaviour, sleep), and related contextual factors. The second wave of ecological momentary assessment surveys with a global positioning system enabled will be sent 6 months later. Data will be analysed using generalised linear models to examine associations between behavioural risk factors and contextual determinants.

Discussion

Findings from this study will advance our understanding of dietary and movement behaviours as they occur in real-life and inform the development of personalised interventions to prevent chronic diseases.