Momentary Self-regulation: Scale Development and Preliminary Validation.

BACKGROUND: Self-regulation refers to a person's ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual level; however, individual-level assessments do not allow the examination of dynamic patterns of intraindividual variability in self-regulation and thus cannot aid in understanding potential malleable processes of self-regulation that may occur in response to the daily environment. OBJECTIVE: This study aims to develop a brief, psychometrically sound momentary self-regulation scale that can be practically administered through participants' mobile devices at a momentary level. METHODS: This study was conducted in 2 phases. In the first phase, in a sample of 522 adults collected as part of a larger self-regulation project, we examined 23 previously validated assessments of self-regulation containing 594 items in total to evaluate the underlying structure of self-regulation via exploratory and confirmatory factor analyses. We then selected 20 trait-level items to be carried forward to the second phase. In the second phase, we converted each item into a momentary question and piloted the momentary items in a sample of 53 adults over 14 days. Using the results from the momentary pilot study, we explored the psychometric properties of the items and assessed their underlying structure. We then proposed a set of subscale and total score calculations. RESULTS: In the first phase, the selected individual-level items appeared to measure 4 factors of self-regulation. The factors identified were perseverance, sensation seeking, emotion regulation, and mindfulness. In the second phase of the ecological momentary assessment pilot, the selected items demonstrated strong construct validity as well as predictive validity for health risk behaviors. CONCLUSIONS: Our findings provide preliminary evidence for a 12-item momentary self-regulation scale comprising 4 subscales designed to capture self-regulatory dynamics at the momentary level.

Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder.

Introduction: Self-regulation has been implicated in health risk behaviors and is a target of many health behavior interventions. Despite most prior research focusing on self-regulation as an individual-level trait, we hypothesize that self-regulation is a time-varying mechanism of health and risk behavior that may be influenced by momentary contexts to a substantial degree. Because most health behaviors (e.g., eating, drinking, smoking) occur in the context of everyday activities, digital technologies may help us better understand and influence these behaviors in real time. Using a momentary self-regulation measure, the current study (which was part of a larger multi-year research project on the science of behavior change) used ecological momentary assessment (EMA) to assess if self-regulation can be engaged and manipulated on a momentary basis in naturalistic, non-laboratory settings. Methods: This one-arm, open-label exploratory study prospectively collected momentary data for 14 days from 104 participants who smoked regularly and 81 participants who were overweight and had binge-eating disorder. Four times per day, participants were queried about momentary self-regulation, emotional state, and social and environmental context; recent smoking and exposure to smoking cues (smoking sample only); and recent eating, binge eating, and exposure to binge-eating cues (binge-eating sample only). This study used a novel, momentary self-regulation measure comprised of four subscales: momentary perseverance, momentary sensation seeking, momentary self-judgment, and momentary mindfulness. Participants were also instructed to engage with Laddr, a mobile application that provides evidence-based health behavior change tools via an integrated platform. The association between momentary context and momentary self-regulation was explored via mixed-effects models. Exploratory assessments of whether recent Laddr use (defined as use within 12 h of momentary responses) modified the association between momentary context and momentary self-regulation were performed via mixed-effects models. Results: Participants (mean age 35.2; 78% female) in the smoking and binge-eating samples contributed a total of 3,233 and 3,481 momentary questionnaires, respectively. Momentary self-regulation subscales were associated with several momentary contexts, in the combined as well as smoking and binge-eating samples. For example, in the combined sample momentary perseverance was associated with location, positively associated with positive affect, and negatively associated with negative affect, stress, and tiredness. In the smoking sample, momentary perseverance was positively associated with momentary difficulty in accessing cigarettes, caffeine intake, and momentary restraint in smoking, and negatively associated with temptation and urge to smoke. In the binge-eating sample, momentary perseverance was positively associated with difficulty in accessing food and restraint in eating, and negatively associated with urge to binge eat. While recent Laddr use was not associated directly with momentary self-regulation subscales, it did modify several of the contextual associations, including challenging contexts. Conclusions: Overall, this study provides preliminary evidence that momentary self-regulation may vary in response to differing momentary contexts in samples from two exemplar populations with risk behaviors. In addition, the Laddr application may modify some of these relationships. These findings demonstrate the possibility of measuring momentary self-regulation in a trans-diagnostic way and assessing the effects of momentary, mobile interventions in context. Health behavior change interventions may consider measuring and targeting momentary self-regulation in addition to trait-level self-regulation to better understand and improve health risk behaviors. This work will be used to inform a later stage of research focused on assessing the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and health outcomes. Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT03352713.

Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation.

Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation and the measures used to assess these constructs. To facilitate the integration of cross-disciplinary measures of self-regulation, we estimated product-moment and distance correlations among 60 cross-disciplinary measures of self-regulation (23 self-report surveys, 37 cognitive tasks) and measures of health and substance use based on 522 participants. The correlations showed substantial variability, though the surveys demonstrated greater convergent validity than did the cognitive tasks. Variables derived from the surveys only weakly correlated with variables derived from the cognitive tasks (M = .049, range = .000 to .271 for the absolute value of the product-moment correlation; M = .085, range = .028 to .241 for the distance correlation), thus challenging the notion that these surveys and cognitive tasks measure the same construct. We conclude by outlining several potential uses for this publicly available database of correlations.

Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia.

Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients' individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models' prediction accuracy but also provided better interpretability for how patients' behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient's condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient's condition starts deteriorating without requiring extra effort from patients and clinicians.

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient's condition worsens. OBJECTIVE: In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse. METHODS: Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied. RESULTS: A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events. CONCLUSIONS: Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.

Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects.

The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.

Working memory training and high magnitude incentives for youth cannabis use: A SMART pilot trial.

The purpose of this sequential multiple-assignment randomization treatment pilot study was to examine if (a) adding working memory training to contingency management (CM) for youth with cannabis use disorder (CUD) and (b) switching nonresponding youth to higher magnitude CM incentives boosts outcomes. In Phase 1, youth with CUD (n = 59, M age = 16, male = 71%) attending an intensive outpatient program were randomly assigned to 14 weeks of CM only or CM plus working memory training (WMT). In Week 4, a Phase 2 treatment was assigned. Those with negative urine drug tests (responders) continued in their Phase 1 treatment. Those who were drug positive (nonresponders) were randomly assigned to remain in their Phase 1 treatment or to higher magnitude CM. Zero-inflated negative binomial models comparing those assigned to CM versus CM + WMT indicated no differences in the likelihood of having ≥ 1 week of continuous abstinence or longer abstinence duration. Those assigned to WMT showed greater but nonsignificant improvements in working memory (n = 35; ß = .69, p = .06). Working memory improvements were associated with achieving any abstinence (odds ratio = 3.50, 95% CI [1.01, 12.10], p = .05). Phase 2 randomization to higher magnitude CM did not boost outcomes. Overall results suggest that WMT appears promising, but the sample size was small, attrition was high, and replication is important. Alternative strategies should continue to be explored to improve outcomes for adolescent substance use disorders, such as different approaches for nonresponders, tailoring to other baseline or response characteristics, or more robust first-line interventions. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Perceived safety of cannabis intoxication predicts frequency of driving while intoxicated.

Driving under the influence of cannabis (DUIC) is a public health concern, and data are needed to develop screening and prevention tools. Measuring the level of intoxication that cannabis users perceive as safe for driving could help stratify DUIC risk. This study tested whether intoxication levels perceived as safe for driving predicted past-month DUIC frequency. Online survey data were collected in 2017 from a national sample of n = 3010 past-month cannabis users with lifetime DUIC (age 18+). Respondents indicated past-month DUIC frequency, typical cannabis intoxication level (1-10 scale), and cannabis intoxication level perceived as safe for driving (0-10 scale). Approximately 24%, 38%, 13%, and 24% of respondents engaged in DUIC on 0, 1-9, 10-19, and 20-30 days respectively in the past month. Among these four DUIC frequency groups, median typical intoxication varied little (5-6), but median intoxication perceived as safe for driving varied widely (3-8). Higher intoxication levels perceived as safe for driving corresponded to frequent DUIC (Spearman's rho: 0.46). For each unit increase in intoxication level perceived as safe for driving, the odds of past-month DUIC increased 18% to 68% (multinomial logistic regression odds ratio - MOR1-9 days: 1.18, 95% CI: 1.13-1.23; MOR10-19 days: 1.40, 95% CI: 1.30-1.50; MOR20-30 days: 1.68, 95% CI: 1.57-1.80). In this targeted sample of past-month cannabis users, DUIC frequency varied widely, but daily/near-daily DUIC was common (24%). Measuring intoxication levels perceived as safe for driving permits delineation of past-month DUIC frequency. This metric has potential as a component of public health prevention tools.

Item Response Theory Analysis of the Five Facet Mindfulness Questionnaire and its Short Forms.

OBJECTIVES: The Five Facet Mindfulness Questionnaire (FFMQ) is a self-report measure of mindfulness with forms of several different lengths, including the FFMQ-39, FFMQ-24, and FFMQ-15. We use item response theory analysis to directly compare the functioning of these three forms. METHODS: Data were drawn from a non-clinical Amazon Mechanical Turk study (N = 522) and studies of aftercare treatment of individuals with substance use disorders (combined N = 454). The item and test functioning of the three FFMQ forms were studied and compared. RESULTS: All 39 items were strongly related to the facet latent variables, and the items discriminated over a similar range of the latent mindfulness constructs. Items provided more information in the low to medium range of latent mindfulness than in the high range. Scores in three of the five FFMQ-39 facets were unreliable when measuring individuals in the high range of latent mindfulness, resulting from ceiling effects in item responses. Reliability in the high range of mindfulness was further reduced in the FFMQ-24 and FFMQ-15, such that short forms may be ill-suited for applications that require reliable measurement in the high range. CONCLUSIONS: Results suggest the existing FFMQ item pool cannot be reduced without negatively affecting either overall reliability or the span of mindfulness over which reliability is assessed. Conditional test reliability curves and item functioning parameters can aid investigators in tailoring their choice of FFMQ form to the reliability they hope to achieve and to the range of latent mindfulness over which they must reliably measure.

Incentivizing healthy lifestyle behaviors to reduce cardiovascular risk in people with serious mental illness: An equipoise randomized controlled trial of the wellness incentives program.

BACKGROUND: Medicaid recipients with serious mental illness die 25-30 years earlier than people in the general population due to health conditions that are modifiable through lifestyle changes. Cardiovascular diseases from excess weight, smoking, and sedentary lifestyle contribute substantially to this life expectancy disparity. The current study evaluated the impact of incentives on participation in weight management programming (for overweight and obese adults) and smoking cessation treatment (for regular smokers). METHODS: Participants were Medicaid recipients with disabling mental illness receiving services at any one of 10 community mental health centers across New Hampshire. Using an equipoise stratified randomized design, n = 1348 were enrolled and assigned to one of four weight management programs (Healthy Choices Healthy Changes: HCHC) and n = 661 were enrolled and assigned to one of three smoking cessation interventions (Breathe Well Live Well: BWLW). Following assignment to an intervention, participants were randomized to receive financial incentives (to attend weight management programs, or to achieve abstinence from smoking) or not. Data were collected at baseline and every 3 months for 12 months. DISCUSSION: New Hampshire's HCHC and BWLW programs were designed to address serious and preventable health disparities by providing incentivized health promotion programs to overweight/obese and/or tobacco-smoking Medicaid beneficiaries with mental illness. This study was an unprecedented opportunity to evaluate an innovative statewide implementation of incentivized health promotion targeting the most at-risk and costly beneficiaries. If proven effective, this program has the potential to serve as a national model for widespread implementation.

Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: Protocol for a randomized controlled trial.

BACKGROUND: Individuals with serious mental illness (SMI) such as schizophrenia and bipolar disorder face a higher risk of early death due to cardiovascular disease and other preventable chronic illnesses. Young adulthood is a critical window of development for lifestyle interventions to improve the long-term health and quality of life in this population. Fit Forward is an NIH-funded randomized clinical trial examining the effectiveness of a group lifestyle intervention (PeerFIT) enhanced with mobile health technology compared to one-on-one mobile lifestyle coaching with Basic Education in fitness and nutrition supported by a wearable Activity Tracking device (BEAT) in achieving clinically significant weight loss and improved cardiorespiratory fitness in young adults with SMI. METHODS: Fit Forward targets 144 young adults (18 to 35 years) with SMI and a body mass index (BMI) of ≥25 receiving public mental health services. In a two-arm randomized clinical trial, participants will be randomly assigned with equal probability to PeerFIT or BEAT, stratified by birth sex and psychiatric diagnosis. Participants will be assessed at baseline, 6, and 12 months. The primary outcome is cardiovascular risk reduction indicated by either clinically significant weight loss (5% or greater) or increased fitness (>50 m on the 6-Minute Walk Test). Secondary outcomes include change in BMI, lipids, and hemoglobin A1c. Perceived self-efficacy for exercise and peer support will be evaluated as mechanisms underlying intervention effects. CONCLUSION: If effective, PeerFIT will provide a potentially scalable approach to addressing health risks among young adults with SMI in mental health settings. TRIALS REGISTRATION: ClinicalTrials.gov, NCT02815813.

Pilot randomized trial of MOMENT, a motivational counseling-plus-ecological momentary intervention to reduce marijuana use in youth.

BACKGROUND: Ecological momentary interventions (EMIs) influence behavior in real time, in real life. We evaluated trial feasibility and preliminary efficacy of MOMENT, a counseling-plus-EMI to reduce frequent marijuana use in youth in primary care. METHODS: Primary care patients age 15-24 years using marijuana at least 3 times/week were randomized to MOMENT [motivational enhancement therapy (MET)/smartphone-based momentary assessment/responsive motivational messaging] vs. No-messages (MET/momentary assessment) vs. MET-only. In MOMENT, two MET sessions were followed by 2 weeks of momentary assessment of marijuana use and factors related to use, with motivational messaging displayed after report of marijuana triggers, desire, use, and effort to avoid use. We evaluated study feasibility (recruitment, retention, and response rates; feedback survey responses) and explored intervention effects on marijuana desire and use at three months with linear mixed effects modeling. RESULTS: Seventy youth [mean (M) =20.7 years, 60% female] were assigned to MOMENT (n=27), No-messages (n=15; assignment suspended to enrich other arms), or MET-only (n=28). Most attrition occurred during baseline, before MET. Of those completing MET session 1, 82% completed their assigned treatment and 79% provided 3-month data. Participants highly rated acceptability; comments reflected changing motivation and behavior. Across arms, participants reported significantly lower marijuana use, desire, and problems at follow-up vs. baseline. Momentary marijuana desire declined more in MOMENT vs. MET-only. Marijuana use following a targeted context or behavior was less likely in MOMENT and No-messages, vs. MET-only. CONCLUSIONS: The MOMENT intervention is feasible to deliver, acceptable, and potentially efficacious in reducing marijuana desire and use among adolescent and young adults in primary care. A larger randomized trial to evaluate efficacy is warranted.

Sexual orientation and salivary alpha-amylase diurnal rhythms in a cohort of U.S. young adults.

Sexual minorities in the United States are at elevated risk of prejudice, discrimination, and violence victimization due to stigma associated with their sexual orientation. These stressors may contribute to physiological stress responses and changes in the regulation of the sympathetic nervous system (SNS). To date, no studies have examined the associations among minority sexual orientation, recent stressful events, and diurnal salivary alpha-amylase (sAA) patterns. The present study included 1663 young adults ages 18-32 years (31% men, 69% women) from the Growing Up Today Study, a prospective cohort of U.S. youth. Participants provided five saliva samples over the course of one day to estimate diurnal sAA patterns. Sexual orientation groups included completely heterosexual with no same-sex partners (CH; referent), mostly heterosexual/completely heterosexual with same-sex partners, and gay/lesbian/bisexual (LB or GB). Sex-stratified multilevel models were fit to evaluate the association of sexual orientation with diurnal patterns of log sAA. The association of recent stressful events was also evaluated. Among women, sexual minorities scored significantly higher than CH on perceived stress and number of stressful events in the past month (p < 0.05). Among men, sexual minorities scored higher than CH on perceived stress but not recent stressful events. In multivariable models, recent stressful events were not associated with sAA patterns, but significant sexual orientation group differences in sAA diurnal rhythm were observed among women though not among men. Compared to CH women, LB showed a blunted awakening response and elevated sAA levels across the day, both indicators consistent with SNS dysregulation. Findings suggest dysregulation of stress physiology in LB women, but not other sexual minority women or men, relative to same-sex heterosexuals. Observed dysregulation may relate to exposure among LB women to chronic stressors associated with sexual orientation stigma, although these relations and differences by sex warrant further study.

Mobile Health (mHealth) Versus Clinic-Based Group Intervention for People With Serious Mental Illness: A Randomized Controlled Trial.

OBJECTIVE: mHealth approaches that use mobile phones to deliver interventions can help improve access to care for people with serious mental illness. The goal was to evaluate how mHealth performs against more traditional treatment. METHODS: A three-month randomized controlled trial was conducted of a smartphone-delivered intervention (FOCUS) versus a clinic-based group intervention (Wellness Recovery Action Plan [WRAP]). Participants were 163 clients, mostly from racial minority groups and with long-term, serious mental illness (schizophrenia or schizoaffective disorder, 49%; bipolar disorder, 28%; and major depressive disorder, 23%). Outcomes were engagement throughout the intervention; satisfaction posttreatment (three months); and improvement in clinical symptoms, recovery, and quality of life (assessed at baseline, posttreatment, and six months). RESULTS: Participants assigned to FOCUS were more likely than those assigned to WRAP to commence treatment (90% versus 58%) and remain fully engaged in eight weeks of care (56% versus 40%). Satisfaction ratings were comparably high for both interventions. Participants in both groups improved significantly and did not differ in clinical outcomes, including general psychopathology and depression. Significant improvements in recovery were seen for the WRAP group posttreatment, and significant improvements in recovery and quality of life were seen for the FOCUS group at six months. CONCLUSIONS: Both interventions produced significant gains among clients with serious and persistent mental illnesses who were mostly from racial minority groups. The mHealth intervention showed superior patient engagement and produced patient satisfaction and clinical and recovery outcomes that were comparable to those from a widely used clinic-based group intervention for illness management.

Implementation of a Lifestyle Intervention for People With Serious Mental Illness in State-Funded Mental Health Centers.

OBJECTIVE: The purpose of this study was to evaluate health outcomes of a state-supported implementation in community mental health settings of an evidence-based lifestyle intervention for overweight and obese adults with serious mental illness. METHODS: Weight and fitness outcomes were evaluated for 122 overweight or obese adults with serious mental illness in four community mental health centers (CMHCs) that were participating in a phased statewide implementation of the In SHAPE lifestyle intervention. Six- and 12-month outcomes were compared between two CMHCs that implemented In SHAPE in the first 12 months and two CMHCs with similar characteristics that implemented In SHAPE in a subsequent phase in the statewide implementation 12 months later. RESULTS: Participants in the two In SHAPE sites (N=63 participants) lost significantly more weight (p=.003) and showed greater improvement in fitness (p=.011) compared with participants at the two usual care control sites (N=59 participants). At six months, nearly half (49%) of In SHAPE participants and at 12 months more than half (60%) of In SHAPE participants showed clinically significant cardiovascular risk reduction defined as ≥5% weight loss or improved fitness (>50 m [164 feet] increase on the six-minute walk test). The difference between the In SHAPE and control groups was not statistically significant. CONCLUSIONS: This natural experiment demonstrated promising public health benefits of a practical implementation of health promotion programming for overweight and obese adults with serious mental illness and offers a potential model for reducing risk of early mortality among individuals served by state-funded mental health centers nationwide.

Body Mass Index and Rural Status on Self-Reported Health in Older Adults: 2004-2013 Medicare Expenditure Panel Survey.

PURPOSE: To ascertain whether rural status impacts self-reported health and whether the effect of rural status on self-reported health differs by obesity status. METHODS: We identified 22,307 subjects aged ≥60 from the Medical Expenditure Panel Survey 2004-2013. Body mass index (BMI) was categorized as underweight, normal, overweight, or obese. Physical and mental component scores of the Short Form-12 assessed self-reported health status. Rural/urban status was defined using metropolitan statistical area. Weighted regression models ascertained the relative contribution of predictors (including rural and BMI) on each subscale. FINDINGS: Mean age was 70.7 years. Rural settings had higher proportions classified as obese (30.7 vs 27.6%; P < .001), and rural residents had lower physical health status (41.7 ± 0.3) than urban (43.4 ± 0.1; P < .001). Obese or underweight persons had lower physical health status (39.5 ± 0.20 and 37.0 ± 0.82, respectively) than normal (44.7 ± 0.18) or overweight (44.6 ± 0.16) persons (P < .001). BMI category stratification was associated with differences in physical health between rural/urban by BMI. Individuals classified as underweight or obese had lower physical health compared to normal, while the differences were less pronounced for mental health. No differences in mental health existed between rural/urban status. A BMI * rural interaction was significant for physical but not mental health. CONCLUSIONS: Rural residents report lower self-reported physical health status compared to urban residents, particularly older adults who are obese or underweight. No interaction was observed between BMI and rural status.

Factors Associated With Accelerated Hospitalization and Re-hospitalization Among Medicare Home Health Patients.

Background: Preventing hospitalizations and re-hospitalizations of older adults receiving Medicare home health (HH) services is a key goal for patients and care providers. This study aimed to identify factors related to greater risk of and earlier hospitalizations from HH, a key step in targeting preventive efforts. Methods: Data come from Medicare mandated start-of-care assessments from 87,780 HH patients served by 132 agencies in 32 states, collected from January 2013 to March 2015. Using parametric accelerated failure time (AFT) survival models, we evaluated the association between key patient and environmental characteristics and the hazard of and time until hospitalization and re-hospitalization. Results: In total, 15,030 hospitalizations, including 6,539 re-hospitalizations, occurred in the sample within 60 days of start of HH. Factors most strongly associated with substantially greater risk of and earlier hospitalization included male gender, history of hospitalization, polypharmacy, elevated depressive symptoms, greater functional disability, primary diagnoses of heart disease, chronic obstructive pulmonary disease, and urinary tract infection, and government-controlled agency care. In addition to these factors, black race and primary diagnosis of skin wounds were uniquely related to risk of earlier re-hospitalization. Conclusions: Results suggest that factors collected during routine HH patient assessments can provide important information to predict risk of earlier hospitalization and re-hospitalization among Medicare HH patients. Identified factors can help identify patients at greatest risk of early hospitalization and may be important targets for agencies and care providers to prevent avoidable hospitalizations.

Psychometric assessment of the marijuana adolescent problem inventory.

Cannabis is one of the most commonly used psychoactive substances among adolescents in the United States. Adolescent cannabis use has multiple consequences including academic, health, and psychiatric problems. The Marijuana Adolescent Problem Inventory (MAPI) is a 23-item scale adapted from the Rutgers Alcohol Problem Index and used in the current literature to assess cannabis use problem severity. Psychometric testing for the MAPI has yet to be reported. The current investigation assessed the psychometric characteristics of the MAPI with cannabis-using adolescents (n=727) from school and outpatient settings who enrolled in five separate randomized clinical trials focused on treatment of substance use. Findings suggested that the MAPI is internally consistent and reliable. Factor structure analyses suggested that the MAPI measures one latent construct, with no differences in factor structure between the outpatient and school settings, supporting a one-factor model. External validity of the MAPI was also demonstrated as evidenced by significant relations with concurrent diagnosis of cannabis dependence and abuse, longitudinal frequency of cannabis use, and mean times used per day. Overall, this initial test of the psychometric characteristics of the MAPI suggests that it can be considered a reliable and valid measure of problems associated with cannabis use among adolescents. Future work is now needed to replicate these findings by testing the psychometric properties of the MAPI in more diverse samples and developing a short version to be used as a brief assessment tool.

Applying novel technologies and methods to inform the ontology of self-regulation.

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

Does reduction in sciatica symptoms precede improvement in disability and physical health among those treated surgically for intervertebral disc herniation? Analysis of temporal patterns in data from the Spine Patient Outcomes Research Trial.

BACKGROUND CONTEXT: Pain, pain-related disability, and functional limitations are common consequences of intervertebral disc herniation (IDH). We hypothesized that surgical treatment reduces pain, leading to improvement in pain-related disability and, ultimately, better physical health. PURPOSE: The present study aims to evaluate pathways for improvements in quality of life during the first year after surgery for IDH by studying temporal relationships between sciatica symptoms, pain-related disability, and physical health. DESIGN: This is a secondary analysis of a randomized controlled trial using an "as treated" dataset. PATIENT SAMPLE: The sample comprised 803 patients in the Spine Patient Outcomes Research Trial. OUTCOME MEASURES: We used the Sciatica Bothersome Index, Oswestry Disability Index, and the Medical Outcomes Study Short Form 36 physical component score. METHODS: We included 803 patients in the Spine Patient Outcomes Research Trial who underwent elective decompressive surgery for IDH between 2000 and 2004. Sciatica, pain-related disability, and physical health were assessed preoperatively and at 3 and 12 months postoperatively using the Sciatica Bothersome Index, Oswestry Disability Index, and Medical Outcomes Study Short Form 36 physical component score, respectively. Temporal associations of improvement in sciatica with pain-related disability and physical health were assessed using cross-lagged path analysis. p<.05 was considered significant. No funding was received in support of the present study. The authors declare no conflicts of interest. RESULTS: Preoperatively, mean scores were 16.2±5.2 for sciatica, 54.2±20.7 for pain-related disability, and 29.8±8.4 for physical health. After adjustment for patient age and symptom duration, cross-lagged path analysis showed that sciatica reduction at 3 months was correlated with pain-related disability reduction at 3 months (ρ=.76, p<.001), and pain-related disability at 3 months was predictive of physical health at 12 months (ß=-.33, p<.001) and sciatica at 12 months (ß=.22, p<.001). CONCLUSION: Three months after surgery, patients reported reduced sciatica and pain-related disability. Improvements in pain and pain-related disability occurred within 3 months. Early reduction in pain-related disability is important because path analysis indicated that disability at 3 months was predictive of sciatica and physical health at 1 year.