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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.

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 R

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.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.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.Principles of reinforcement and behavioral economics directed intervention design.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.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; p0.001) 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; p0.01). 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 (p0.001). 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.This study offers automated and scalable-behavior change strategies for increasing walking among adults most at-risk for chronic diseases attributed to sedentary lifestyles.This study is registered at www.clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT02717663).

BACKGROUND: 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. METHODS: 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. RESULTS: Among smokers reporting < a full ban, U.S.-born and foreign-born Latinos had fewer in-home smoking events than US-born Whites (p

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.

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.

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, PB=50.6, PB=–2.06, PB=–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, PB=55.8, P 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.

Background: Adaptive behavioral interventions that automatically adjust in real-time to participants’ changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. Purpose: JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. Methods: A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Results: Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared to a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. Conclusions: The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

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.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.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.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.

Background: 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.Methods: 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.Results: Among smokers reporting < a full ban, U.S.-born and foreign-born Latinos had fewer in-home smoking events than US-born Whites (p

Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

The original article has been corrected to update figures 1, 4, and 5 captions and the corrected captions are listed below

Little change over the decades has been seen in adults meeting moderate-to-vigorous physical activity (MVPA) guidelines. Numerous individual-level interventions to increase MVPA have been designed, mostly static interventions without consideration for neighborhood context. Recent technologies make adaptive interventions for MVPA feasible. Unlike static interventions, adaptive intervention components (e.g., goal setting) adjust frequently to an individual's performance. Such technologies also allow for more precise delivery of “smaller, sooner incentives” that may result in greater MVPA than “larger, later incentives”. Combined, these factors could enhance MVPA adoption. Additionally, a central tenet of ecological models is that MVPA is sensitive to neighborhood environment design; lower-walkable neighborhoods constrain MVPA adoption and maintenance, limiting the effects of individual-level interventions. Higher-walkable neighborhoods are hypothesized to enhance MVPA interventions. Few prospective studies have addressed this premise. This report describes the rationale, design, intervention components, and baseline sample of a study testing individual-level adaptive goal-setting and incentive interventions for MVPA adoption and maintenance over 2 years among adults from neighborhoods known to vary in neighborhood walkability. We scaled these evidenced-based interventions and tested them against static-goal-setting and delayed-incentive comparisons in a 2 × 2 factorial randomized trial to increase MVPA among 512 healthy insufficiently-active adults. Participants (64.3% female, M age = 45.5 ± 9.1 years, M BMI = 33.9 ± 7.3 kg/m2, 18.8% Hispanic, 84.0% White) were recruited from May 2016 to May 2018 from block groups ranked on GIS-measured neighborhood walkability and socioeconomic status (SES) and classified into four neighborhood types: “high walkable/high SES,” “high walkable/low SES,” “low walkable/high SES,” and “low walkable/low SES.” Results from this ongoing study will provide evidence for some of the central research questions of ecological models.

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.

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.

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.

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; PP 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. CLINICALTRIAL

Gay men are underrepresented in research on sexual satisfaction. We examined sexual satisfaction and over 50 sexual practices in an online U.S. national survey of men in relationships. Coarsened exact matching created comparable samples of heterosexual (

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.

Background WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects. Objective The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention. Methods A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation. Results All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation. Conclusions This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement. Trial Registration Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663 International Registered Report Identifier (IRRID) RR2-10.1016/j.cct.2019.05.001

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. 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 a current cannabis user if they reported any use over the previous 30 days. Multivariable linear regression estimated minutes engaged in sedentary behavior/physical activity for current versus non-current cannabis users. 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’ objectively-measured sedentary behavior/physical activity did not significantly differ from non-current users. There were no significant associations between self-reported moderate-to-vigorous physical activity and cannabis use. 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 finding that current cannabis use is not significantly associated with sedentary behavior/physical activity in order to fully assess the potential public health impact of recent increases in cannabis use.

BACKGROUND WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects. OBJECTIVE The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention. METHODS A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation. RESULTS All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation. CONCLUSIONS This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement. CLINICALTRIAL Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663 INTERNATIONAL REGISTERED REPORT RR2-10.1016/j.cct.2019.05.001

Investigating drivers’ injury level and detecting contributing factors that aggravate the damage level imposed on drivers and vehicles is a critical subject in the field of crash analysis. In this ...

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.

The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data set, this paper modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of random forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers’ instantaneous decisions and identify their aggressive driving behavior. The results of this paper inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles and vehicles-to-infrastructure applications.

INTRODUCTION: Exposure to fine particulate matter in the home from sources such as smoking, cooking, and cleaning may put residents, especially children, at risk for detrimental health effects. A randomized clinical trial was conducted from 2011 to 2016 to determine whether real-time feedback in the home plus brief coaching of parents or guardians could reduce fine particle levels in homes with smokers and children. DESIGN: A randomized trial with two groups—intervention and control. SETTING/PARTICIPANTS: A total of 298 participants from predominantly low-income households with an adult smoker and a child aged 15,000 particles/0.01 ft(3)). Linear mixed models were used to analyze the differential change in the outcomes over time by group, during 2016–2017. RESULTS: Intervention homes had significantly larger reductions than controls in daily geometric mean particle concentrations (18.8% reduction vs 6.5% reduction, p

Adaptive behavioral interventions that automatically adjust in real-time to participants’ changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

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 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.

Patients with Parkinson's Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication (ON). Regularized generalized linear models (RGLM), neural networks (NN), and random forest (RF) classification models were individually trained for each participant. Across all subjects, our best performing classifier on average achieved an accuracy of 92.5%. This study demonstrated that smartphone accelerometers and gyroscopes can be used to distinguish between ON versus OFF times, potentially making smartphones useful intervention tools.

Self-reported experiences of discrimination and sense of coherence (SOC) have been found to be associated with health. A face-to-face survey of Long Term Jewish Residents (LTJR), Arabs and former Soviet Union (fSU) immigrants in Israel was performed. Respondents reported their physical and mental health, self-reported experiences of discrimination, SOC and socioeconomic status. Multivariable logistic regressions and bootstrapping path analyses were performed. Discrimination was associated with health after adjusting for all other variables. SOC was also associated with health. SOC did not mediate the strong association between discrimination and health among Israeli LTJR, but was a significant mediator among Arabs and fSU immigrants. Discrimination seems to have a direct effect on health only among the majority and not among minority populations. High levels of SOC may reduce the negative effects of discrimination on health by serving as a coping resource, however only among minorities.

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.

Since 1980, many studies have evaluated whether stair-use prompts increased physical activity by quantifying changes in stair use. To more completely evaluate changes in physical activity, this study addressed the often-overlooked assessment of climbing up escalators by evaluating the degree to which stair-use sign prompts increased active ascent—defined as stair use or escalator climbing. Over 5 months, at an airport stairs/escalator point of choice, we video-recorded passersby (N = 13,544) who ascended either stairs or escalators, on 10 days with signs and 10 days without signs. Ascenders using the stairs, standing on the escalator, and climbing the escalator were compared on days with versus without signs using multivariable logistic regression. The percentage of ascenders on days with versus without signs were as follows: stair use, 6.9 versus 3.6 percent; escalator standing, 75.2 versus 76.0 percent; and escalator climbing, 18.5 versus 20.4 percent. Signs more than doubled the odds of stair use (vs. escalator use; OR = 2.25; 95% CI = 1.90–2.68; p < .001). Signs decreased the odds of escalator climbing (vs. escalator standing or stair use); OR = 0.90; 95% CI = 0.82−0.99; p = .028). Signs increased the odds of active ascent versus escalator standing by 15 percent (OR = 1.15; 95% CI = 1.05–1.25; p = .002). Although stair-use prompts increased stair use more than twofold (125%), they increased active ascent by only 15 percent, partly because escalator climbing—a behavior not targeted by the intervention—decreased. Although our results corroborated the established consensus that point-of-choice prompts increase stair use, future studies should test interventions designed to increase active ascent.

Real-time sensing and computing technologies are increasingly used in the delivery of real-time health behavior interventions. Auditory signals play a critical role in many of these interventions, impacting not only behavioral response but also treatment adherence and participant retention. Yet, few behavioral interventions that employ auditory feedback report the characteristics of sounds used and even fewer design signals specifically for their intervention. This paper describes a four-step process used in developing and selecting auditory warnings for a behavioral trial designed to reduce indoor secondhand smoke exposure. In step one, relevant information was gathered from ergonomic and behavioral science literature to assist a panel of research assistants in developing criteria for intervention-specific auditory feedback. In step two, multiple sounds were identified through internet searches and modified in accordance with the developed criteria, and two sounds were selected that best met those criteria. In step three, a survey was conducted among 64 persons from the primary sampling frame of the larger behavioral trial to compare the relative aversiveness of sounds, determine respondents' reported behavioral reactions to those signals, and assess participant's preference between sounds. In the final step, survey results were used to select the appropriate sound for auditory warnings. Ultimately, a single-tone pulse, 500 milliseconds (ms) in length that repeats every 270 ms for 3 cycles was chosen for the behavioral trial. The methods described herein represent one example of steps that can be followed to develop and select auditory feedback tailored for a given behavioral intervention.

OBJECTIVE To parameterize and validate two existing algorithms for identifying out-of-bed time using 24 h hip-worn accelerometer data from older women. APPROACH Overall, 628 women (80 ± 6 years old) wore ActiGraph GT3X+ accelerometers 24 h d-1 for up to 7 d and concurrently completed sleep-logs. Trained staff used a validated visual analysis protocol to measure in-bed periods on accelerometer tracings (criterion). The Tracy and McVeigh algorithms were adapted for optimal use in older adults. A training set of 314 women was used to choose two key thresholds by maximizing the sum of sensitivity and specificity for each algorithm and data (vertical axis, VA, and vector magnitude [VM]) combination. Data from the remaining 314 women were then used to test agreement in waking wear time (i.e. out-of-bed time while wearing the accelerometer) by computing sensitivity, specificity, and kappa comparing the algorithm output with the criterion. Waking wear time-adjusted means of sedentary time, light-intensity physical activity (light PA) and moderate-to-vigorous-intensity physical activity (MVPA) were then estimated and compared. MAIN RESULTS Waking wear time agreement with the criterion was high for Tracy_VA, Tracy_VM, McVeigh_VA, and highest for McVeigh_VM. Compared to the criterion, McVeigh_VM had mean sensitivity = 0.92, specificity = 0.87, kappa = 0.80, and overall mean difference (±SD) of -0.04 ± 2.5 h d-1. Minutes of sedentary time, light PA, and MVPA adjusted for waking wear time using the criterion measure and McVeigh_VM were not statistically different (p > 0.43|all). SIGNIFICANCE The McVeigh algorithm with optimal parameters using VM performed best compared to criterion sleep-log assisted visual analysis and is suitable for automated identification of waking wear time in older women when visual analysis is not feasible.

Cannabis use is increasing and cannabis is typically consumed by smoking. This study explored how indoor secondhand cannabis smoke (SCS) was associated with child health. As part of a larger trial, air particle monitors were placed in 298 homes of families with at least one cigarette smoker and one child under 14 years old in San Diego County, California. Assessment included past 7-day indoor cigarette and cannabis use, the youngest child's exposure to cigarette smoke, and 5 smoke-related past-year child health outcomes: emergency department use for coughing/difficulty breathing; physician diagnosis of ear infection, bronchitis/bronchiolitis, asthma, or eczema/atopic dermatitis. An ordinal measure of adverse health outcomes (0, 1, or ≥2) was regressed on reported indoor cannabis smoking—the main measure of exposure (yes/no). Of 221 parents/guardians asked about cannabis use, 192 (86.9%) provided all required data, and 29 (15.1%) reported indoor cannabis smoking; reports were supported by air particle data. Homes without indoor smoking had lower average 7-day particle concentrations (1968 particles/0.01ft3) than homes with cannabis smoking only (3131 particles/0.01ft3), cigarette smoking only (3095 particles/0.01ft3), or both cigarette and cannabis smoking (6006 particles/0.01ft3). Odds of reporting a greater number of adverse health outcomes were 1.83 (95% CI = 0.89–3.80, p = 0.10) times higher for children of families with indoor cannabis smoking vs families without cannabis smoking, after controlling for exposure to cigarette smoke and other covariates. Our results do not indicate a statistically significant association. However, the magnitude of the (non-significant) association between indoor cannabis smoking and adverse health outcomes warrants more studies., Highlights • Indoor cannabis smoking raises particle levels and risk of child particle exposure. • Smoking cannabis in-home may adversely impact child health. • This adverse impact may occur in homes with or without indoor cigarette smoking. • Health effects of secondhand cannabis smoke exposure need to be further explored.

This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in households. Proper orthogonal decomposition (POD) in conjunction with a k-means clustering algorithm assigns each data peak to one of two clusters. Aversive feedback from the monitors increased the proportion of short-duration, attenuated peaks from 38.8% to 96.6%. For each cluster, a distribution of parameters from a physics-based model of airborne particles is estimated. Peaks generated from these distributions are correctly identified by POD/clustering with >60% accuracy.

Author(s): Bellettiere, John; Carlson, Jordan A; Rosenberg, Dori; Singhania, Anant; Natarajan, Loki; Berardi, Vincent; LaCroix, Andrea Z; Sears, Dorothy D; Moran, Kevin; Crist, Katie; Kerr, Jacqueline | Abstract: BackgroundTotal sedentary time varies across population groups with important health consequences. Patterns of sedentary time accumulation may vary and have differential health risks. The purpose of this study is to describe sedentary patterns of older adults living in retirement communities and illustrate gender and age differences in those patterns.MethodsBaseline accelerometer data from 307 men and women (mean age = 84±6 years) who wore ActiGraph GT3X+ accelerometers for ≥ 4 days as part of a physical activity intervention were classified into bouts of sedentary time (l100 counts per minute). Linear mixed models were used to account for intra-person and site-level clustering. Daily and hourly summaries were examined in mutually non-exclusive bouts of sedentary time that were 1+, 5+, 10+, 20+, 30+, 40+, 50+, 60+, 90+ and 120+ minutes in duration. Variations by time of day, age and gender were explored.ResultsMen accumulated more sedentary time than women in 1+, 5+, 10+, 20+, 30+, 40+, 50+ and 60+ minute bouts; the largest gender-differences were observed in 10+ and 20+ minute bouts. Age was positively associated with sedentary time, but only in bouts of 10+, 20+, 30+, and 40+ minutes. Women had more daily 1+ minute sedentary bouts than men (71.8 vs. 65.2), indicating they break up sedentary time more often. For men and women, a greater proportion of time was spent being sedentary during later hours of the day than earlier. Gender differences in intra-day sedentary time were observed during morning hours with women accumulating less sedentary time overall and having more 1+ minute bouts.ConclusionsPatterns identified using bouts of sedentary time revealed gender and age differences in the way in which sedentary time was accumulated by older adults in retirement communities. Awareness of these patterns can help interventionists better target sedentary time and may aid in the identification of health risks associated with sedentary behavior. Future studies should investigate the impact of patterns of sedentary time on healthy aging, disease, and mortality.