The Impact of Monetary Incentives on Delay Discounting Within a Year-Long Physical Activity Intervention.

BACKGROUND: Delay discounting is the depreciation in a reward's perceived value as a function of the time until receipt. Monetary incentive programs that provide rewards contingent on meeting daily physical activity (PA) goals may change participants' delay discounting preferences. PURPOSE: Determine if monetary incentives provided in close temporal proximity to meeting PA goals changed delay discounting, and if such changes mediated intervention effects. METHODS: Inactive adults (n = 512) wore accelerometers during a 12-month intervention where they received proximal monetary incentives for meeting daily moderate-to-vigorous PA (MVPA) goals or delayed incentives for study participation. Delay discount rate and average MVPA were assessed at baseline, end of intervention, and a 24-month follow-up. Using structural equation modeling, we tested effects of proximal versus delayed rewards on delay discounting and whether any changes mediated intervention effects on MVPA. PA self-efficacy was also evaluated as a potential mediator, and both self-efficacy and delay discounting were assessed as potential moderators of intervention effects. RESULTS: Proximal rewards significantly increased participants' delay discounting (ß = 0.238, confidence interval [CI]: -0.078, 0.380), indicating greater sensitivity to reinforcement timing. This change did not mediate incentive-associated increases in MVPA at the end of the 12-month intervention (ß = -0.016, CI: -0.053, 0.019) or at a 24-month follow-up (ß = -0.020, CI: -0.059, 0.018). Moderation effects were not found. CONCLUSIONS: Incentive-induced increases in delay discounting did not deleteriously impact MVPA. This finding may help assuage concerns about using monetary incentives for PA promotion, but further research regarding the consequences of changes in delay discounting is warranted.

This study examined the effects of providing proximal monetary incentives for meeting daily exercise goals on people's tendency to value immediate versus delayed rewards. Inactive adults (n = 512) participated in a year-long program where they wore an accelerometer each day and received either (i) small monetary rewards in close temporal proximity to instances of meeting daily exercise goals or (ii) larger rewards at 2-month intervals for ongoing participation. Those receiving proximal incentives showed an increased preference for immediate rewards, yet this reported change did not compromise long-term physical activity gains. In a comparison analysis, we found that proximal monetary incentives were not associated with changes in exercise self-efficacy. Overall, the findings suggest that monetary incentives for exercise do not negatively impact people's activity levels, though more research is needed to fully understand the implications of changes in reward timing preferences.

Time of Day Preferences and Daily Temporal Consistency for Predicting the Sustained Use of a Commercial Meditation App: Longitudinal Observational Study.

BACKGROUND: The intensive data typically collected by mobile health (mHealth) apps allows factors associated with persistent use to be investigated, which is an important objective given users' well-known struggles with sustaining healthy behavior. OBJECTIVE: Data from a commercial meditation app (n=14,879; 899,071 total app uses) were analyzed to assess the validity of commonly given habit formation advice to meditate at the same time every day, preferably in the morning. METHODS: First, the change in probability of meditating in 4 nonoverlapping time windows (morning, midday, evening, and late night) on a given day over the first 180 days after creating a meditation app account was calculated via generalized additive mixed models. Second, users' time of day preferences were calculated as the percentage of all meditation sessions that occurred within each of the 4 time windows. Additionally, the temporal consistency of daily meditation behavior was calculated as the entropy of the timing of app usage sessions. Linear regression was used to examine the effect of time of day preference and temporal consistency on two outcomes: (1) short-term engagement, defined as the number of meditation sessions completed within the sixth and seventh month of a user's account, and (2) long-term use, defined as the days until a user's last observed meditation session. RESULTS: Large reductions in the probability of meditation at any time of day were seen over the first 180 days after creating an account, but this effect was smallest for morning meditation sessions (63.4% reduction vs reductions ranging from 67.8% to 74.5% for other times). A greater proportion of meditation in the morning was also significantly associated with better short-term engagement (regression coefficient B=2.76, P<.001) and long-term use (B=50.6, P<.001). The opposite was true for late-night meditation sessions (short-term: B=-2.06, P<.001; long-term: B=-51.7, P=.001). Significant relationships were not found for midday sessions (any outcome) or for evening sessions when examining long-term use. Additionally, temporal consistency in the performance of morning meditation sessions was associated with better short-term engagement (B=-1.64, P<.001) but worse long-term use (B=55.8, P<.001). Similar-sized temporal consistency effects were found for all other time windows. CONCLUSIONS: Meditating in the morning was associated with higher rates of maintaining a meditation practice with the app. This is consistent with findings from other studies that have hypothesized that the strength of existing morning routines and circadian rhythms may make the morning an ideal time to build new habits. In the long term, less temporal consistency in meditation sessions was associated with more persistent app use, suggesting there are benefits from maintaining flexibility in behavior performance. These findings improve our understanding of how to promote enduring healthy lifestyles and can inform the design of mHealth strategies for maintaining behavior changes.

Obesogenic environments and cardiovascular disease: a path analysis using US nationally representative data.

INTRODUCTION: People living in obesogenic environments, with limited access to healthful food outlets and exercise facilities, generally have poor health. Previous research suggests that behavioral risk factors and indicators of physiological functioning may mediate this link; however, no studies to date have had the requisite data to investigate multi-level behavioral and physiological risk factors simultaneously. The present study conducted serial and parallel mediation analyses to examine behavioral and physiological pathways explaining the association between environmental obesogenicity and cardiovascular disease (CVD). METHODS: This cross-sectional observational study used data from the 2012-2016 Health and Retirement Study, a representative survey of US older adults (n = 12,482, mean age 65.9). Environmental obesogenicity was operationalized as a combined score consisting of nine environmental measures of food and physical activity. CVD and health-compromising behaviors (diet, alcohol consumption, smoking, and exercise) were self-reported. Physiological dysregulation was assessed with measured blood pressure, heart rate, HbA1c, cholesterol levels, BMI, and C-reactive protein. The Hayes Process Macro was used to examine serial and parallel paths through health-compromising behaviors and physiological dysregulation in the environmental obesogenicity-CVD link. RESULTS: People living in more obesogenic environments had greater odds of self-reported CVD (odds ratio = 1.074, 95% confidence interval (CI): 1.028, 1.122), engaged in more health-compromising behaviors (ß = 0.026, 95% CI: 0.008, 0.044), and had greater physiological dysregulation (ß = 0.035, 95% CI: 0.017, 0.054). Combined, health-compromising behaviors and physiological dysregulation accounted for 7% of the total effects of environmental obesogenicity on CVD. CONCLUSION: Behavioral and physiological pathways partially explain the environmental obesogenicity-CVD association. Obesogenic environments may stymie the success of cardiovascular health-promotion programs by reducing access to resources supporting healthy lifestyles.

Debunking Lesbian Bed Death: Using Coarsened Exact Matching to Compare Sexual Practices and Satisfaction of Lesbian and Heterosexual Women.

The current study examined the prevalence and correlates of over 50 sexual practices in a national survey of heterosexual and lesbian women in relationships. Coarsened exact matching was used to create comparable samples of heterosexual (n = 2510) and lesbian (n = 283) women on six demographic factors, including relationship length. Heterosexual and lesbian women were equally likely to be sexually satisfied (66% heterosexual women vs. 68% lesbian women). Compared to heterosexuals, lesbians were more likely to report having sex 0-1 times per month (11% vs. 23%) and were less likely to report having sex greater than once per month (89% vs. 77%). Among women who had been in relationships for longer than 5 years, heterosexual women were less likely than lesbian women to report having sex 0-1 times per month (15%; 42%). This steeper drop in sexual frequency among lesbian women than heterosexual women has pejoratively been labeled lesbian bed death. Rather than accept the label "lesbian bed death" as characterizing these sexual relationships, we turn our attention to what we call lesbian bed intimacies: the myriad ways that lesbian women incorporate behaviors promoting emotional connection, romance, and mood setting, as well as relying on a wide variety of specific sexual acts (e.g., use of sex toys) and sexual communication. Compared to heterosexual women, lesbian women were more likely to usually to always receive oral sex during sex in the past month (28%; 47%) and to use sex toys in the past year (40%; 62%). In their last sexual encounter, lesbian women were more likely to say "I love you" (67%; 80%), have sex longer than 30 min (48%; 72%), and engage in gentle kissing (80%; 92%). These intimacies likely help explain why sexual satisfaction was similar in these groups despite notable differences in sexual frequency.

Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study.

BACKGROUND: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits. OBJECTIVE: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits based on temporal similarity and estimate the association between temporal similarity and meditation app users' perceived health benefits. METHODS: App-use data from June 2019 to June 2020 were analyzed for 2771 paying subscribers of a meditation mobile phone app, of whom 86.06% (2359/2771) were female, 72.61% (2012/2771) were college educated, 86.29% (2391/2771) were White, and 60.71% (1664/2771) were employed full-time. Participants volunteered to complete a survey assessing their perceived changes in physical and mental health from using the app. Receiver operating characteristic curve analysis was used to evaluate the ability of the temporal similarity measure to predict future behavior, and variable importance statistics from random forest models were used to corroborate these findings. Logistic regression was used to estimate the association between temporal similarity and self-reported physical and mental health benefits. RESULTS: The temporal similarity of users' daily app use before completing the survey, as measured by the dynamic time warping (DTW) distance between app use on consecutive days, significantly predicted app use at 28 days and at 6 months after the survey, even after controlling for users' demographic and socioeconomic characteristics, total app sessions, duration of app use, and number of days with any app use. In addition, the temporal similarity measure significantly increased in the area under the receiver operating characteristic curve (AUC) for models predicting any future app use in 28 days (AUC=0.868 with DTW and 0.850 without DTW; P<.001) and for models predicting any app use in 6 months (AUC=0.821 with DTW and 0.802 without DTW; P<.001). Finally, a 1% increase in the temporal similarity of users' daily meditation practice with the app over 6 weeks before the survey was associated with increased odds of reporting mental health improvements, with an odds ratio of 2.94 (95% CI 1.832-6.369). CONCLUSIONS: The temporal similarity of the meditation app use was a significant predictor of future behavior, which suggests that this measure can identify reflexive meditation habits. In addition, temporal similarity was associated with greater perceived mental health benefits, which demonstrates that additional mental health benefits may be derived from forming reflexive meditation habits.

Cannabis use, sedentary behavior, and physical activity in a nationally representative sample of US adults.

BACKGROUND: Prior research examining the relationship between cannabis use, sedentary behavior, and physical activity has generated conflicting findings, potentially due to biases in the self-reported measures used to assess physical activity. This study aimed to more precisely explore the relationship between cannabis use and sedentary behavior/physical activity using objective measures. METHODS: Data were obtained from the 2005-2006 National Health and Nutrition Examination Survey. A total of 2,092 participants (ages 20-59; 48.8% female) had accelerometer-measured sedentary behavior, light physical activity, and moderate-to-vigorous physical activity. Participants were classified as light, moderate, frequent, or non-current cannabis users depending on how often they used cannabis in the previous 30 days. Multivariable linear regression estimated minutes in sedentary behavior/physical activity by cannabis use status. Logistic regression modeled self-reported moderate-to-vigorous physical activity in relation to current cannabis use. RESULTS: Fully adjusted regression models indicated that current cannabis users' accelerometer-measured sedentary behavior did not significantly differ from non-current users. Frequent cannabis users engaged in more physical activity than non-current users. Light cannabis users had greater odds of self-reporting physical activity compared to non-current users. CONCLUSIONS: This study is the first to evaluate the relationship between cannabis use and accelerometer-measured sedentary behavior and physical activity. Such objective measures should be used in other cohorts to replicate our findings that cannabis use is associated with greater physical activity and not associated with sedentary behavior in order to fully assess the potential public health impact of increases in cannabis use.

Burstiness and Stochasticity in the Malleability of Physical Activity.

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability.

Randomised controlled trial of real-time feedback and brief coaching to reduce indoor smoking.

BACKGROUND: Previous secondhand smoke (SHS) reduction interventions have provided only delayed feedback on reported smoking behaviour, such as coaching, or presenting results from child cotinine assays or air particle counters. DESIGN: This SHS reduction trial assigned families at random to brief coaching and continuous real-time feedback (intervention) or measurement-only (control) groups. PARTICIPANTS: We enrolled 298 families with a resident tobacco smoker and a child under age 14. INTERVENTION: We installed air particle monitors in all homes. For the intervention homes, immediate light and sound feedback was contingent on elevated indoor particle levels, and up to four coaching sessions used prompts and praise contingent on smoking outdoors. Mean intervention duration was 64 days. MEASURES: The primary outcome was 'particle events' (PEs) which were patterns of air particle concentrations indicative of the occurrence of particle-generating behaviours such as smoking cigarettes or burning candles. Other measures included indoor air nicotine concentrations and participant reports of particle-generating behaviour. RESULTS: PEs were significantly correlated with air nicotine levels (r=0.60) and reported indoor cigarette smoking (r=0.51). Interrupted time-series analyses showed an immediate intervention effect, with reduced PEs the day following intervention initiation. The trajectory of daily PEs over the intervention period declined significantly faster in intervention homes than in control homes. Pretest to post-test, air nicotine levels, cigarette smoking and e-cigarette use decreased more in intervention homes than in control homes. CONCLUSIONS: Results suggest that real-time particle feedback and coaching contingencies reduced PEs generated by cigarette smoking and other sources. TRIAL REGISTRATION NUMBER: NCT01634334; Post-results.

Delay Discount Rate Moderates a Physical Activity Intervention Testing Immediate Rewards.

Financial incentives can increase physical activity (PA), but differences in the immediacy of reward delivery and individual differences in delay discount rates (i.e., higher discount values associated with less tolerance for delayed rewards) may explain differential responding. The current study tested whether delay discount rate moderated the relative effectiveness of immediate financial rewards on increasing daily PA. Inactive, overweight adults (ages 18-60, N = 96) were randomized to receive either smaller, immediate goal-contingent rewards or larger, delayed rewards for participation. Delay discount rates were derived for those who completed the Monetary Choice Questionnaire (N = 85). Linear mixed models tested interactions between discount rate and intervention arm on changes in mean daily Fitbit-measured steps from baseline to intervention phases, and rates of change during the intervention phase. Across all groups, participants increased by 2258 steps/day on average from baseline to intervention and declined by 9 steps/day across the 4-month intervention phase. The mean increase in daily steps was greater for immediate reward-arm participants across all discount rates. Descriptive exploration of reward effects by delay discount rate suggested that the magnitude of reward effects decreased at higher discount rates. During the 4-month intervention phase, rates of decline in daily steps were similar in both reward arms, but declines became more pronounced at higher discount rates. Overall, intervention efficacy decreased with less tolerance for delays. The importance of financial reward immediacy for increasing PA appears to increase with greater delay discount rates.

Fractal Self-Structure and Psychological Resilience.

Since the mid 1980's, mainstream social psychology investigations of self-complexity and psychopathology have produced contradictory results. These results are likely the result of a lack of theoretical and methodological grounding in complexity theory. The current study proposes that the self has an interconnected fractal structure, and that this structure may be reflected within inverse-power law (IPL) distributions of response times to self-related questions. MMPI-2 item response sets (N = 300) were selected from a larger pool of 1,881 forensic administrations. Self-complexity was operationalized as the inverse of the shape parameter (?) of the frequency distribution of reaction times to MMPI-2 items (n = 567) for each participant. It was predicted that: (a) these distributions would generally have strong fits with IPL distributions; and (b) that ? would tend to be correlated with pathology among the MMPI-2 scale scores. The results confirmed that the response-time distributions tended to fit IPLs (mean R2 = .94; range: .64 to .99). Furthermore, 18 of 45 correlations between ? and MMPI-2 scale scores associated with pathology were statistically significant, suggesting that rigidity in fractal self-structure is associated with broadband psychopathology. A follow up principal components analysis of the 45 individual scale scores across the participants confirmed this conclusion, producing three latent components, each of which was significantly correlated with ?, and each of which had a broad variety of scales with factor loadings > |.5|. These results may provide a first step toward a practical complexity-science approach to measuring the structural resilience of the self, and viewing the self as a complex self-organizing system.

A Markov approach for increasing precision in the assessment of data-intensive behavioral interventions.

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals' responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one state to another. State transition probabilities are summarized separately in phase-specific transition matrices. Comparing transition matrices illuminates intricate, quantifiable differences in behavior between intervention phases. Statistical significance is estimated via bootstrapping techniques. This paper introduces the methodology via three case studies from a secondhand smoke reduction trial utilizing real-time air particle sensors. Analysis enabled the identification of complex phenomena such as avoidance and escape behavior in response to punitive contingencies for tobacco use. Additionally, the largest changes in behavior dynamics were associated with the introduction of behavioral feedback. The Markov approach's ability to elucidate subtle behavioral details has not typically been feasible with standard methodologies, mainly due to historical limitations associated with infrequent repeated measures. These results suggest that the evaluation of intervention effects in data-intensive single-case designs can be enhanced, providing rich information that can ultimately be used to develop interventions uniquely tailored to specific individuals.

Fatalism, Diabetes Management Outcomes, and the Role of Religiosity.

This study aimed to determine whether fatalistic beliefs were associated with elevated levels of glycated hemoglobin (HbA1c) and to establish the role of religiosity in this relationship. A cross-sectional survey was conducted on a sample of 183 Jewish adults with diabetes visiting a large medical center in northern Israel. Self-administered questionnaires assessed level of religiosity, fatalistic beliefs, diabetes management behaviors, and demographic/personal characteristics; laboratory tests were used to measure HbA1c. Multivariate regression indicated that fatalism was significantly associated with HbA1c (ß = 0.51, p = 0.01). The association was no longer statistically significant after including self-reported religiosity in the model (ß = 0.31, p = 0.13). This phenomenon is likely due to a confounding relationship between the religious/spiritual coping component of the fatalism index and self-reported religiosity (r = 0.69). The results indicate that addressing fatalistic attitudes may be a viable strategy for improving diabetes management, but call for a better understanding of the interplay between religiosity and fatalism in this context.

Stair versus elevator use in a university residence hall setting.

OBJECTIVE: Determine the temporal and spatial characteristics of stairs versus elevator use in a university residence hall to inform future physical activity promotion efforts. PARTICIPANTS: All residents and visitors for a single, four-story residence hall dormitory building located on a college campus in Orange, CA. METHODS: Smart mat systems capable of detecting pedestrian traffic were placed in front of the stairs and elevators on each floor plus a basement. Generalized additive mixed models (GAMMs) were used to compare stair versus elevator usage at different times of the day and on different floors. RESULTS: Stair versus elevator use varied much more throughout the day on floors nearest to the ground floor, with stair use most common in the morning. Overall, the elevator was used more frequently on higher floors, with less variation throughout the day. CONCLUSION: To be most effective, future stair promotion interventions should target residents on higher floors and in the morning.

Using mobile meditation app data to predict future app engagement: an observational study.

OBJECTIVE: Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target additional behavioral support to those who are most likely to stop using the app. METHODS: Data were collected from a randomly drawn sample of 2600 new subscribers to a 1-year membership of the mobile app Calm, who started using the app between July and November of 2018. App usage data contained the duration and start time of all meditation sessions with the app over 365 days. We used these data to construct the following predictive model features: total daily sessions, total daily duration, and a measure of temporal similarity between consecutive days based on the dynamic time warping (DTW) distance measure. We then fit random forest models using increasingly longer periods of data after users subscribed to Calm to predict whether they performed any meditation sessions over 2-week intervals in the future. Model fit was assessed using the area under the receiver operator characteristic curve (AUC), and an exponential growth model was used to determine the minimal amount of data needed to reach an accurate prediction (95% of max AUC) of future engagement. RESULTS: After first subscribing to Calm, 83.1% of the sample used the Calm app on at least 1 more day. However, by day 350 after subscribing, 58.0% of users abandoned their use of the app. For the persistent users, the average number of daily sessions was 0.33 (SD = 0.02), the average daily duration of meditating was 3.93 minutes (SD = 0.25), and the average DTW distance to the previous day was 1.50 (SD = 0.17). The exponential growth models revealed that an average of 64 days of observations after subscribing to Calm are needed to reach an accurate prediction of future app engagement. DISCUSSION: Our results are consistent with existing estimates of the time required to develop a new habit. Additionally, this research demonstrates how to use app usage data to quickly and accurately predict the likelihood of users' future app abandonment. This research allows future researchers to better target just-in-time interventions towards users at risk of abandonment.

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R2) than possible with Linear Regression. We examined how well the connections between body dissatisfaction and dieting behavior could be predicted from demographic factors and measures derived from objectification theory and the tripartite-influence model. In this particular case, although Random Forest analyses sometimes provided greater predictive power than Linear Regression models, the advantages were small. More generally, however, this paper demonstrates how body image researchers might harness the power of machine learning techniques to identify previously undiscovered relations among body image variables.

The Role of Ethnicity and Nativity in the Correspondence between Subjective and Objective Measures of In-Home Smoking.

Studies are needed to understand the association between self-reported home smoking bans and objective measures of in-home smoking according to smokers' ethnicity/nativity. Data came from a trial that used air particle monitors to reduce children's secondhand smoke exposure in smokers' households (N = 251). Linear regressions modeled (a) full home smoking bans by ethnicity/nativity, and (b) objectively measured in-home smoking events, predicted by main and interaction effects of self-reported home smoking bans and ethnicity/nativity. Among smokers reporting < a full ban, US-born and Foreign-born Latinos had fewer in-home smoking events than US-born Whites (p < 0.001). Participants who reported a full smoking ban had a similar frequency of smoking events regardless of ethnicity/nativity. Results indicate that self-reported home smoking bans can be used as a proxy for in-home smoking. Establishing smoking bans in the households of US-born White smokers has the largest impact on potential exposure compared to other ethnicity/nativity groups.

Stability and flexibility in psychotherapy process predict outcome.

Ten good outcome and ten poor outcome psychotherapy cases were compared to investigate whether or not the temporal stability and flexibility of their process variables can predict their outcomes. Each participant was monitored daily using the Therapy Process Questionnaire (TPQ), which has 43 items and seven sub-scales, and responses over time were analyzed in terms of correlation robustness and correlation variability across the TPQ sub-scales. "Correlation robustness" and "correlation variability" are two basic characteristics of any correlation matrix: the first is calculated as the sum of the absolute values of Pearson correlation coefficients, the second as the standard deviation of Pearson correlation coefficients. The results demonstrated that the patients within the poor outcome group had lower values on both variables, suggesting lower stability and flexibility. Furthermore, a higher number of cycles of increase and decrease in correlation robustness and variability of the TPQ sub-scales was observed within good outcome psychotherapies, suggesting that, these cycles can be considered as process-markers of good-outcomes. These results provide support for the validity of these quantitative process-parameters, correlation robustness and variability, in predicting psychotherapeutic outcomes. Moreover, the results lend support to the common clinical experience of alternating periods of flexibility and integration being beneficial to good psychotherapeutic processes.

Real-time feedback of air quality in children's bedrooms reduces exposure to secondhand smoke.

INTRODUCTION: Secondhand smoke (SHS) exposure creates health risks for non-smokers and is especially detrimental to children. This study evaluated whether immediate feedback in response to poor indoor air quality in children's bedrooms can reduce the potential for SHS exposure, as measured by adherence to a World Health Organization (WHO) indoor air standard. METHODS: Homes that contained children and an adult who regularly smoked inside (n=298) had an air particle monitor installed in the child's bedroom. These devices measured the concentration of particulate matter (PM2.5) for approximately three months and, for half of the participants, immediately provided aversive feedback in response to elevated PM2.5. Hierarchical linear models were fit to the data to assess whether the intervention increased the probability that: 1) a given day was below the WHO guideline for daily exposure, and 2) a household established and maintained a smoke-free home (SFH), operationalized as achieving 30 consecutive days below the WHO guideline. The intervention's impact was calculated as group-by-time effects. RESULTS: The likelihood that a child's bedroom met the WHO indoor air quality standard on a given day increased such that the baseline versus post-baseline odds ratio (OR) of maintaining indoor PM2.5 levels below the WHO guideline was 2.38 times larger for participants who received the intervention. Similarly, the baseline versus post-baseline OR associated with achieving an SFH was 3.49 times larger for participants in the intervention group. CONCLUSIONS: The real-time intervention successfully drove clinically meaningful changes in smoking behavior that mitigated indoor PM2.5 levels in children's bedrooms and thereby reduced SHS exposure. These results demonstrate the effectiveness of targeting sensitive microenvironments by giving caregivers actionable information about children's SHS risks. Future extensions should examine additional microenvironments and focus on identifying the potential for SHS exposure before it occurs.

Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial.

INTRODUCTION: Potent lifestyle interventions to increase moderate-to-vigorous physical activity are urgently needed for population-level chronic disease prevention. This trial tested the independent and joint effects of a mobile health system automating adaptive goal setting and immediate financial reinforcement for increasing daily walking among insufficiently active adults. STUDY DESIGN: Participants were randomized into a 2 (adaptive versus static goal setting) X 2 (immediate versus delayed financial incentive timing) condition factorial trial to increase walking. SETTINGS/PARTICIPANTS: Participants (N=512 adults) were recruited between 2016 and 2018 and were 64.5% female, aged 18-60 years, 18.8% Hispanic, 6.1% African American, and 83% White. INTERVENTION: Principles of reinforcement and behavioral economics directed intervention design. MAIN OUTCOME MEASURES: Participants wore accelerometers daily (133,876 day-level observations) that remotely measured moderate-to-vigorous physical activity bout minutes of ≥3 minutes/day for 1 year. Primary outcomes were between-condition differences in (1) engaging ≥1 bout of moderate-to-vigorous physical activity on each day and (2) on days with ≥1 bout, daily total moderate-to-vigorous physical activity minutes. RESULTS: Mixed-effects hurdle models tested treatment group X phase (time) interactions using an intent-to-treat approach in 2021. Engaging in any ambulatory moderate-to-vigorous physical activity was greater for Adaptive than for Static Goal groups (OR=2.34, 95% CI=2.10, 2.60 vs OR=1.66, 95% CI=1.50, 1.84; p<0.001) and for Immediate than for Static Reinforcement groups (OR=2.16 95% CI=1.94, 2.40 vs OR=1.77, 95% CI=1.59, 1.97; p<0.01). The Immediate Reinforcement group increased by 16.54 moderate-to-vigorous physical activity minutes/day, whereas the Delayed Reinforcement group increased by 9.91 minutes/day (p<0.001). The combined Adaptive Goals + Immediate Reinforcement group increased by 16.52 moderate-to-vigorous physical activity minutes/day, significantly more than that of either Delayed Reinforcement group. CONCLUSIONS: This study offers automated and scalable-behavior change strategies for increasing walking among adults most at-risk for chronic diseases attributed to sedentary lifestyles. TRIAL REGISTRATION: This study is registered at www.clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT02717663).

In-Home Cannabis Smoking Among a Cannabis-Using Convenience Sample from the Global Drug Survey: With Weighted Estimates for U.S. Respondents.

Introduction: Secondhand and thirdhand tobacco smoke exposure most often occur at home, but little is known about occurrences of in-home cannabis smoking. We ascertained in-home cannabis smoking reported by all cannabis-using (i.e., used in the last 12 months) respondents to the Global Drug Survey (GDS; international-GDS sample), and among U.S. cannabis-using respondents (US-GDS sample). Materials and Methods: We used data collected November 2019-January 2020 for the 2020 GDS, an annual anonymous, cross-sectional survey; respondents were 16+ years old, from 191 countries. We estimated any and daily in-home cannabis smoking in the last 30 days among international-GDS respondents (n=63,797), using mixed effects logistic regression. US-GDS respondents (n=6,580) were weighted to the covariate distribution of the nationally representative 2018 National Survey on Drug Use and Health cannabis-using sample, using inverse odds probability weighting, to make estimates more generalizable to the U.S. cannabis-using population. Results: For the international-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 63.9% of men, 61.9% of women, and 68.6% of nonbinary people; and by age (<25 years old=62.7%, 25-34 years old=65.0%, and 35+ years old=62.8%). Daily in-home cannabis smoking was highest among nonbinary (28.7%) and respondents 35+ years of age (28.0%). For the weighted US-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 49.8% of males and 61.2% of females; and by age (<25 years old=62.6%, 25-34 years old=41.8%, 35+ years old=57.9%). Weighted daily in-home smoking was 23.2% among males and 37.1% among females; by age (<25 years old=34.8%, 25-34 years old=27.8%, and 35+ years old=21.6%). Conclusions: There was high daily cannabis smoking in homes of international-GDS and US-GDS respondents who used cannabis in the last 12 months. In part, due to cannabis legalization, the number of users worldwide has increased over the past decade. Criminal stigma historically associated with cannabis continues to drive those users indoors. In this context, our findings support further investigation of cannabis use behavior to understand how often people are exposed to secondhand and thirdhand cannabis smoke and the consequences of that exposure.