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This study examined the between-person associations of seven health behaviors in adults with obesity participating in a weight loss intervention, as well as the covariations between these behaviors within-individuals across the intervention. The present study included data from a 12-month weight loss trial (N = 278). Seven health behaviors (physical activity, sedentary behavior, sleep duration, and consumption of fruits, vegetables, total fat and added sugar) were measured at baseline, 6- and 12-months. Between- and within-participants network analyses were conducted to examine how these behaviors were associated through the 12-month intervention and covaried across months. At the between-participants level, associations were found within the different diet behaviors and between total fat and sedentary behaviors. At the within-participants level, covariations were found between sedentary and diet behaviors, and within diet behaviors. Findings suggest that successful multiple health behaviors change interventions among adults with obesity will need to (1) simultaneously target sedentary and diet behaviors; and (2) prevent potentialcompensatory behaviors in the diet domain.

Summary: Background: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study. Methods: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p

Contains fulltext : 234332.pdf (Publisher’s version ) (Open Access) Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant’s individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses. 17 p.

Objective Despite evidence that goal setting is valuable for physical activity promotion, recent studies highlighted a potential oversimplification in the application of this behavior change technique. While more difficult performance goals might trigger higher physical activity levels, higher performance goals might concurrently be more difficult to achieve, which could reduce long-term motivation. This study examined (a) the association between performance goal difficulty and physical activity and (b) the association between performance goal difficulty and goal achievement. Method This study used data from an e-Health intervention among inactive overweight adults (n = 20). The study duration included a 2-week baseline period and an intervention phase of 80 days. During the intervention, participants received a daily step goal experimentally manipulated by taking participants' baseline physical activity median (i.e., number of steps) multiplied by a pseudorandom factor ranging from 1 to 2.6. A continuous measure of goal achievement was inferred for each day by dividing the daily number of steps by the goal prescribed that day. Linear and generalized additive models were fit for each participant. Results The results confirm that, for a majority of the participants involved in the study, performance goal difficulty was positively and significantly associated with physical activity (n = 14), but, concurrently, negatively and significantly associated with goal achievement (n = 19). These associations were mainly linear. Conclusion At the daily level, setting a higher physical activity goal leads to engaging in higher physical activity levels, but concurrently lower goal achievement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

BACKGROUND: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals’ current context. PURPOSE: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps’ activity suggestions to optimize the intervention. METHODS: We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS: Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION: Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION: This study was registered on ClinicalTrials.gov number NCT03225521.

Background Consumption of fruits and vegetables (F/V) among elementary school-aged children remains inadequate, especially among low-income children. The US Department of Agriculture's Fresh Fruit and Vegetable Program (FFVP) provides F/V as snacks to children during the school day, outside of school meals. School-based initiatives are successful in changing behaviors in school settings; however, their influence on behaviors outside of schools needs investigation. Objective To examine whether FFVP participation is associated with F/V requests at stores, self-efficacy to ask for and choose F/V at home, and F/V consumption. Design Cross-sectional study. Participant/setting Fourth graders in six classrooms (n=296) from three urban, low-income school districts in Phoenix, AZ, were surveyed during 2015; one FFVP and one non-FFVP school from each district that were similar in school size, percent free/reduced-price meal eligibility, and race/ethnicity of enrolled students were selected. Main outcome measures Children's self-reported F/V requests during shopping, their self-efficacy to ask for and choose F/V at home, and F/V consumption on the previous day (non-FFVP school day) were measured using questions adapted from validated surveys. Statistical analysis Multivariable mixed-effect regression models, adjusting for clustering of students within classes and classes within schools were explored. Results In models adjusting for individual-level factors (ie, age and sex) only, several significant positive associations were observed between school FFVP participation and healthier F/V outcomes. After additionally adjusting for school-level factors (ie, total enrollment and % Hispanic/Latino students) significant associations were observed between school FFVP participation and more requests for vegetables during shopping ( P P =0.004), stronger preferences for vegetables ( P P =0.006). Conclusions School FFVP participation was associated with more requests for vegetables during shopping and higher self-efficacy to make healthy choices at home, suggesting the influence of the FFVP may extend beyond the school day.

Background Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. Method A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1–2 was used to inform personalized daily step goals delivered in weeks 3–14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20–$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. Results Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. Conclusion The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p

Although group-level evidence supports the use of behavioral interventions to enhance cognitive and emotional well-being, different interventions may be more acceptable or effective for different people. N-of-1 trials are single-patient crossover trials designed to estimate treatment effectiveness in a single patient. We designed a mobile health (mHealth) supported N-of-1 trial platform permitting US adult volunteers to conduct their own 30-day self-experiments testing a behavioral intervention of their choice (deep breathing/meditation, gratitude journaling, physical activity, or helpful acts) on daily measurements of stress, focus, and happiness. We assessed uptake of the study, perceived usability of the N-of-1 trial system, and influence of results (both reported and perceived) on enthusiasm for the chosen intervention (defined as perceived helpfulness of the chosen intervention and intent to continue performing the intervention in the future). Following a social media and public radio campaign, 447 adults enrolled in the study and 259 completed the post-study survey. Most were highly educated. Perceived system usability was high (mean scale score 4.35/5.0, SD 0.57). Enthusiasm for the chosen intervention was greater among those with higher pre-study expectations that the activity would be beneficial for them (p < 0.001), those who obtained more positive N-of-1 results (as directly reported to participants) (p < 0.001), and those who interpreted their N-of-1 study results more positively (p < 0.001). However, reported results did not significantly influence enthusiasm after controlling for participants' interpretations. The interaction between pre-study expectation of benefit and N-of-1 results interpretation was significant (p < 0.001), such that those with the lowest starting pre-study expectations reported greater intervention enthusiasm when provided with results they interpreted as positive. We conclude that N-of-1 behavioral trials can be appealing to a broad albeit highly educated and mostly female audience, that usability was acceptable, and that N-of-1 behavioral trials may have the greatest utility among those most skeptical of the intervention to begin with.

Text4baby (T4b), a free nation-wide mobile health information service, delivers health-related text messages (SMS) to pregnant women. The objective of this study was to determine the effectiveness of physical activity (PA) specific SMS to improve PA in pregnant women (vs standard T4b) and the most effective dose/timing of PA-specific SMS to improve PA. Pregnant women (N = 80) were randomized to one of four groups that differed in frequency and time of SMS. The Fitbit™ Flex measured PA. Data were analyzed using mixed model analyses. There were no increases in PA regardless of frequency or time. Those that received six PA SMS/week had greater decreases in activity and greater increases in sedentary time. SMS may not be a “potent” enough strategy to improve PA. Future studies should explore a modified focus on behavior change (e.g., decrease sedentary activity, increase light activity) and incorporate SMS as part of a multi-level approach with other evidence-based strategies.

Significant gaps exist in the published literature regarding the treatment of overweight/obesity in preschool-aged children, especially in primary care settings. Parental influence plays an important factor in the development of healthy behaviors in children, yet there is no consensus about why some behavior change intervention strategies for parents of young children are more influential and effective than others.The purpose of this secondary data analysis was to assess correlations among the study variables (healthy lifestyle beliefs, perceived difficulty, and healthy lifestyle behaviors) in parents of overweight/obese preschool children. A second aim explored if the parent's level of cognitive beliefs and perceived difficulty of engaging in healthy lifestyle behaviors correlated with text messaging cognitive behavioral support.Fifteen preschool-parent dyads from primary care clinics completed a 7-week cognitive behavioral skills building intervention. Beck's Cognitive Theory guided the intervention content, and Fogg's Behavior Model guided the implementation. The intervention was delivered using a combination of face-to-face clinic visits and ecological momentary interventions using text messaging.Supported are the interconnected relationships among the study variables, that is, parental healthy lifestyle beliefs, thoughts, and behaviors. At baseline, parental healthy lifestyle belief scores significantly correlated with perceived difficulty (rs = 0.598, p.05) and healthy lifestyle behaviors (rs = 0.545, p.05). These associations strengthened after the intervention. Furthermore, as parental healthy lifestyle beliefs increased and perceived difficulty lessened, their response rate and subsequent feedback lessened to the static text messaging support.Findings from this study support the interconnections between parents' thoughts, feelings, and actions toward healthy lifestyles. As parental beliefs became stronger through cognitive behavioral skills building and tailored text messaging, the need for general support via text messaging lessened, warranting additional research.

Lifestyle behaviors across the 24-h spectrum (i.e., sleep, sedentary, and active behaviors) drive metabolic risk. We describe the development and process evaluation of BeWell24, a multicomponent smartphone application (or “app”) that targets behavior change in these interdependent behaviors. A community-embedded iterative design framework was used to develop the app. An 8-week multiphase optimization strategy design study was used to test the initial efficacy of the sleep, sedentary, and exercise components of the app. Process evaluation outcomes included objectively measured app usage statistics (e.g., minutes of usage, self-monitoring patterns), user experience interviews, and satisfaction ratings. Participants (N = 26) logged approximately 60 % of their sleep, sedentary, and exercise behaviors, which took 3–4 min/day to complete. Usage of the sleep and sedentary components peaked at week 2 and remained high throughout the intervention. Exercise component use was low. User experiences were mixed, and overall satisfaction was modest.

Consumers are increasingly seeking out alternative sources of unprocessed foods grown and harvested in close proximity to where they live. Subsequently, the demand for locally grown agricultural goods is rising. Farmers’ markets (both seasonal and year-round), in particular, have seen a resurgence. In the early 1990s, for example, less than 2000 markets existed in the USA. By 2012, 7864 farmers’ markets had been established(1). As the farmers’ market trend continues to grow, consensus has developed around the use of farmers’ markets as a strategy to improve access to healthy, whole foods; combat food insecurity in urban areas; and potentially improve healthy eating behaviours(2,3). Federal health-related reports and published guidance, such as the 2010 Dietary Guidelines for Americans(4), include language identifying farmers’ markets as an important strategy for healthful nutrition across communities, including low-income, food-insecure populations. The US Dietary Guidelines, for example, include a chapter focused on facilitating healthy choices for Americans. Two of the three principles to which the Guidelines adhere include: (i) ensuring that all Americans have equal access to nutritious foods; and (ii) facilitating individual behaviour change through environmental strategies(4). The Guidelines note that communities can successfully implement these principles by improving access to healthy food outlets such as farmers’ markets(4–7). Despite the growing number of farmers’ markets and associated sales, recent US Department of Agriculture data suggest that only 0·15 % of money spent on food for home consumption is spent at farmers’ markets(8). Similarly, Supplemental Nutrition Assistance Program (SNAP) benefits are underutilized at such venues, with only 0·016 % of all benefits occurring at farmers’ markets(8). The reasons for underutilization are not fully known. However, because markets can be introduced directly within low-income areas and their potential to increase healthy food access and improve healthy food choices, researchers have recently called for the development of more local foods programmes like farmers’ markets in low-income communities and elsewhere(3,9,10). However, little is known about what within-market factors might enhance or detract from shoppers’ experiences. The purpose of the present study was to understand, using innovative technology that captures real-time perceptions, factors that enhanced or detracted from shoppers’ experiences in an urban farmers’ market in a large metropolitan city in the USA.

Sleep and sedentary and active behaviors are linked to cardiovascular disease risk biomarkers, and across a 24-hour day, increasing time in 1 behavior requires decreasing time in another. We explored associations of reallocating time to sleep, sedentary behavior, or active behaviors with biomarkers. Data (n = 2,185 full sample; n = 923 fasting subanalyses) from the cross-sectional 2005-2006 US National Health and Nutrition Examination Survey were analyzed. The amounts of time spent in sedentary behavior, light-intensity activity, and moderate-to-vigorous physical activity (MVPA) were derived from ActiGraph accelerometry (ActiGraph LLC, Pensacola, Florida), and respondents reported their sleep duration. Isotemporal substitution modeling indicated that, independent of potential confounders and time spent in other activities, beneficial associations (P < 0.05) with cardiovascular disease risk biomarkers were associated with the reallocation of 30 minutes/day of sedentary time with equal time of either sleep (2.2% lower insulin and 2.0% lower homeostasis model assessment of β-cell function), light-intensity activity (1.9% lower triglycerides, 2.4% lower insulin, and 2.2% lower homeostasis model assessment of β-cell function), or MVPA (2.4% smaller waist circumference, 4.4% higher high-density lipoprotein cholesterol, 8.5% lower triglycerides, 1.7% lower glucose, 10.7% lower insulin, and 9.7% higher homeostasis model assessment of insulin sensitivity. These findings provide evidence that MVPA may be the most potent health-enhancing, time-dependent behavior, with additional benefit conferred from light-intensity activities and sleep duration when reallocated from sedentary time.

Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Time-intensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N = 34; 88% women, 64.1 ± 8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; n = 10) or did not (n = 24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and nonmaintainers. We found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, nonmaintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, percentage of outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.

BACKGROUND: The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. METHODS: We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. RESULTS: Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). CONCLUSIONS: StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users.

The physical environment is thought to influence walking; however, daily variations in perceived environment have received little attention. The current study sought to examine if key within-person factors (i.e., implementation intentions, social support, affect and self-efficacy) would be associated with walking and if perceived access to supportive environments (e.g., access to nice walking paths) and perceived environmental barriers (e.g., bad weather and safety issues) were uniquely associated with walking after controlling for other constructs. Participants (N = 14, 50.0% men, 78.6% White, M age = 59.4±6.4) were in the intervention arm of an 8-week controlled trial promoting walking via personal digital assistants. Participants completed electronic surveys twice a day (total entries = 804) in which they reported brisk walking levels and psychosocial and environmental factors. Multilevel modelling was used to examine within-person variations in constructs as determinants of walking. Results suggested that daily variations in implementation intentions, social support and positive affect were positively associated with walking. Further, perceived access to supportive environments, though not perceived environmental barriers, was positively associated with walking after controlling for other constructs (p < 0.05). Future research should explore intervention components that target context-specific information about perceived access to supportive environments as part of a broader perspective on intervention development.

Regular exercise can improve sleep quality, but for whom and by what means this occurs remain unclear. We examined moderators and mediators of objective sleep improvements in a 12-month randomized controlled trial among initially underactive midlife and older adults reporting mild/moderate sleep complaints. Participants (N=66, 67% women, 55–79 years) were randomized to moderate-intensity exercise or health education control. Putative moderators were gender, age, and baseline physical function, self-reported global sleep quality, and physical activity levels. Putative mediators were changes in BMI, depressive symptoms, and physical function at 6 months. Objective sleep outcomes measured by in-home PSG were percent time in Stage 1 sleep, percent time in Stage 2 sleep, and number of awakenings during the first third of sleep at 12 months. Baseline physical function and sleep quality moderated changes in Stage 1 sleep; individuals with higher initial physical function (p=0.01) and poorer sleep quality (p=0.03) had greater improvements. Baseline physical activity level moderated changes in Stage 2 sleep (p=0.04) and number of awakenings (p=0.01); more sedentary individuals had greater improvements. Decreased depressive symptoms (CI:−1.57 to −0.02) mediated change in Stage 1 sleep. Decreased depressive symptoms (CI: −0.75 to −0.01), decreased BMI (CI:−1.08 to −0.06), and increased physical function (CI:0.01 to 0.72) mediated change in number of awakenings. In conclusion, initially less active individuals with higher initial physical function and poorer sleep quality improved the most. Affective, functional, and metabolic mediators specific to different parameters of sleep architecture were suggested. Collectively, the results indicate strategies to more efficiently treat poor sleep through exercise in older adults.

BACKGROUND: Health education programs for promoting a healthful diet have shown limited success in clinical trials. PURPOSE: This paper aims to examine whether an innovative educational course focused on societal-level issues related to food and food production (Food and Society) would promote healthful eating among college students. METHODS: A quasi-experimental non-RCT was conducted to compare changes in eating behaviors among students taking the Food and Society course (n = 28) versus students taking health-related human biology courses about obesity, health psychology, and community health assessment (n = 72). All participants were undergraduates. A Food Frequency Questionnaire was administered at the beginning and end of the four courses taught from January through March 2009. Students in the Food and Society course read selected portions of popular books and essays (e.g., Michael Pollan’s Omnivore’s Dilemma) and watched documentaries (e.g., Aaron Woolf’s King Corn) highlighting environmental, ethical, social justice, cultural, political, and agricultural issues related to food and food production, and discussed these major themes during class sessions. In addition, students were required to (1) write an Op-Ed article and (2) create a brief YouTube video focused on themes discussed in the course. RESULTS: The students who took the Food and Society course reported signifıcantly improving their healthful eating (F[2, 97] = 5.72, p = 0.02), with greatest improvements in increased vegetable (F[2, 97] = 10.96, p = 0.001) and decreased high-fat dairy (F[2, 97] = 5.39, p = 0.02) intakes relative to the comparison group. CONCLUSIONS: The results suggest that it may be possible to change dietary behaviors in college students by focusing on social, ethical, cultural, and environmental issues related to food and food production.

Objective: Distress and low perceived social support were examined as indicators of psychosocial vulnerability in patients about to undergo heart surgery. Design: A total of 550 study patients underwent heart surgeries, including bypass grafting and valve procedures. Psychosocial interviews were conducted about five days before surgery, and biomedical data were obtained from hospital records. Main Outcome Measures: Sociodemographic, personality, religious, and biomedical factors were evaluated as predictors of psychosocial vulnerability, and all five sets of variables were evaluated as contributors to hospital length of stay (LOS). Results: Patients scoring higher on one or more indicator of presurgical psychosocial vulnerability were younger, more likely to be female, less likely to be married, less well educated, lower in dispositional optimism, higher in trait anger, and lower in religiousness. Older age, depression, low support, and low trait anger each showed an independent, prospective association with greater LOS, and several other predictors had prospective relationships with LOS that were statistically mediated by depression or perceived support. Conclusion: Evidence that multiple psychosocial factors may influence adaptation to heart surgery has implications for understanding and ameliorating presurgical distress and for improving postsurgical recovery.

Background: There is increasing interest in the use of online forums as a component of eHealth weight loss interventions. Although the research is mixed on the utility of online forums in general, results suggest that there is promise to this, particularly if the systems can be designed well to support healthful interactions that foster weight loss and continued engagement. Objective: The purpose of this study was to examine the relationship between the styles of utterances individuals make on an online weight loss forum and week-to-week fluctuations in weight. This analysis was conducted to generate hypotheses on possible strategies that could be used to improve the overall design of online support groups to facilitate more healthful interactions. Methods: A convenience sample of individuals using an online weight loss forum (N=4132) included data both on online forum use and weight check-in data. All interactions were coded utilizing the Linguistic Inquiry and Word Count (LIWC) system. Mixed model analyses were conducted to examine the relationship between these LIWC variables and weight over time. Results: Results suggested that increased use of past-tense verbs ( P =.05) and motion ( P =.02) were associated with lower weekly weights whereas increased use of conjunctions (eg, and, but, whereas; P =.001) and exclusion words (eg, but, without, exclude; P =.07) were both associated with higher weight during the weeks when these utterances were used more. Conclusions: These results provide some insights on the styles of interactions that appear to be associated with weight fluctuations. Future work should explore the stability of these findings and also explore possibilities for fostering these types of interactions more explicitly within online weight loss forums. [J Med Internet Res 2014;16(12):e254]

Introduction In 2011, San Francisco passed the first citywide ordinance to improve the nutritional standards of children’s meals sold at restaurants by preventing the giving away of free toys or other incentives with meals unless nutritional criteria were met. This study examined the impact of the Healthy Food Incentives Ordinance at ordinance-affected restaurants on restaurant response (eg, toy-distribution practices, change in children’s menus), and the energy and nutrient content of all orders and children’s-meal–only orders purchased for children aged 0 through 12 years. Methods Restaurant responses were examined from January 2010 through March 2012. Parent–caregiver/child dyads (n = 762) who were restaurant customers were surveyed at 2 points before and 1 seasonally matched point after ordinance enactment at Chain A and B restaurants (n = 30) in 2011 and 2012. Results Both restaurant chains responded to the ordinance by selling toys separately from children’s meals, but neither changed their menus to meet ordinance-specified nutrition criteria. Among children for whom children’s meals were purchased, significant decreases in kilocalories, sodium, and fat per order were likely due to changes in children’s side dishes and beverages at Chain A. Conclusion Although the changes at Chain A did not appear to be directly in response to the ordinance, the transition to a more healthful beverage and default side dish was consistent with the intent of the ordinance. Study results underscore the importance of policy wording, support the concept that more healthful defaults may be a powerful approach for improving dietary intake, and suggest that public policies may contribute to positive restaurant changes.

Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible. Objective: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention. Methods: Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data. Results: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p < .001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74). Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a “behavior change technology” that could be incorporated into mHealth technologies and scaled to reach large populations. Trial Registration: ClinicalTrials.gov NCT01793064

Purpose. To assess the feasibility of a new intervention, Ready~Steady, in terms of demand, acceptability, implementation, and limited efficacy. Design. Randomized controlled trial; repeated measures. Setting. Two rural communities in Itasca County, Minnesota. Subjects. Thirty participants were randomized to an intervention (n = 16) or attention-control (n = 14) group. Intervention. Ready~Steady combined two components: (1) motivational (motivational support, social network support, empowering education), and (2) fall-reducing physical activities (PAs; guidance to practice leg-strengthening, balance, and flexibility activities and walking). Measures. Acceptability questionnaire and Indices of Procedural Consistency (investigator developed), Community Health Activity Model Program for Seniors Questionnaire (confirmed with accelerometry), Short Physical Performance Battery, Perceived Environmental Support Scale, Social Support for Exercise Questionnaire, Goal Attainment Scale, Index of Readiness, and Index of Self-Regulation. Analysis. Descriptive statistics and a marginal approach to repeated-measures analysis of variance, using mixed-model procedures. Results. Attrition was 7% and mean attendance was 7.2 of 8 sessions, participants evaluated Ready~Steady as acceptable, and implementation fidelity was good. The intervention group improved significantly more than the attention-control group in PA behavior, F1,27 = 11.92, p = .002; fall risk (functional balance and strength), F1,27 = 14.89, p = .001; support for exercise from friends, F1,27 = 11.44, p = .002; and self-regulation, F1,26 = 38.82, p < .005. Conclusion. The Ready~Steady intervention was feasible as evidenced by low attrition and good attendance and implementation, as well as positive effects on targeted outcomes and theoretical mechanisms of change.

Few studies have evaluated how to combine dietary and physical activity (PA) interventions to enhance adherence.We tested how sequential versus simultaneous diet plus PA interventions affected behavior changes.Two hundred participants over age 44 years not meeting national PA and dietary recommendations (daily fruit and vegetable servings and percent of calories from saturated fat) were randomized to one of four 12-month telephone interventions: sequential (exercise first or diet first), simultaneous, or attention control. At 4 months, the other health behavior was added in the sequential arms.Ninety-three percent of participants were retained through 12 months. At 4 months, only exercise first improved PA, and only the simultaneous and diet-first interventions improved dietary variables. At 12 months, mean levels of all behaviors in the simultaneous arm met recommendations, though not in the exercise- and diet-first arms.We observed a possible behavioral suppression effect of early dietary intervention on PA that merits investigation.

Objective An automated telehealth counseling system, aimed at inactive midlife and older adults, was shown previously to achieve 12-month physical activity levels similar to those attained by human advisors. This investigation evaluated the sustained 18-month impacts of the automated advisor compared with human advisors. Methods Following the end of the 12-month randomized, controlled trial, participants who had been randomized to either the human advisor (n = 73) or automated advisor (n = 75) arms were followed for an additional 6 months. During that period, human or automated advisor-initiated telephone contacts ceased and participants were encouraged to initiate contact with their advisor as deemed relevant. The primary outcome was moderate-to-vigorous physical activity (MVPA), measured using the Stanford Physical Activity Recall and validated during the major trial via accelerometry. Results The two arms did not differ significantly in 18-month MVPA or the percentage of participants meeting national physical activity guidelines (ps >.50). No significant within-arm MVPA differences emerged between 12 and 18 months. Evaluation of the trajectory of physical activity change across the 18-month study period indicated that, for both arms, the greatest physical activity increases occurred during the first 6 months of intervention, followed by a relatively steady amount of physical activity across the remaining months. Conclusions The results provide evidence that an automated telehealth advice system can maintain physical activity increases at a level similar to that achieved by human advisors through 18 months. Given the accelerated use of mobile phones in developing countries, as well as industrialized nations, automated telehealth systems merit further evaluation.

Previous research from the fields of computer science and engineering highlight the importance of an iterative design process (IDP) to create more creative and effective solutions.This study describes IDP as a new method for developing health behavior interventions and evaluates the effectiveness of a dining hall-based intervention developed using IDP on college students' eating behavior and values.participants were 458 students (52.6% female, age = 19.6 ± 1.5 years [M ± SD]).The intervention was developed via an IDP parallel process. A cluster-randomized controlled study compared differences in eating behavior among students in 4 university dining halls (2 intervention, 2 control).The final intervention was a multicomponent, point-of-selection marketing campaign. Students in the intervention dining halls consumed significantly less junk food and high-fat meat and increased their perceived importance of eating a healthful diet relative to the control group.IDP may be valuable for the development of behavior change interventions.

Background:Recent research highlights the potential value of differentiating between categories of physical activity intensities as predictors of health and well-being. This study sought to assess reliability and concurrent validity of sedentary (ie, 1 METs), low-light (ie, >1 and ≤2 METs; eg, playing cards), high-light (ie, >2 and Methods:CHAMPS data were gathered from the Seniors Neighborhood Quality of Life Study, an observational study of adults aged 65+ years conducted in 2 US regions.Results:Participants (N = 870) were 75.3 ± 6.8 years old, with 56% women and 71% white. The CHAMPS sedentary, low-light, high-light, total activity, and MVPA variables had acceptable test-retest reliability (ICCs 0.56−0.70). The CHAMPS high-light (ρ = 0.27), total activity (ρ = 0.34), and MVPA (ρ = 0.37) duration scales were moderately associated with accelerometry minutes of corresponding intensity, and the sedentary scale (ρ = 0.12) had a lower, but significant correlation. Results suggested that several CHAMPS items may be susceptible to over-reporting (eg, walking, housework).Conclusions:CHAMPS items effectively measured high-light, total activity, and MVPA in seniors, but further refinement is needed for sedentary and low-light activity.

The extent to which light-intensity physical activity contributes to health in older adults is not well known. The authors examined associations between physical activity across the intensity spectrum (sedentary to vigorous) and health and well-being variables in older adults. Two 7-day assessments of accelerometry from 2005 to 2007 were collected 6 months apart in the observational Senior Neighborhood Quality of Life Study of adults aged >65 years in Baltimore, Maryland, and Seattle, Washington. Self-reported health and psychosocial variables (e.g., lower-extremity function, body weight, rated stress) were also collected. Physical activity based on existing accelerometer thresholds for moderate/vigorous, high-light, low-light, and sedentary categories were examined as correlates of physical health and psychosocial well-being in mixed-effects regression models. Participants (N = 862) were 75.4 (standard deviation, 6.8) years of age, 56% female, 71% white, and 58% overweight/obese. After adjustment for study covariates and time spent in moderate/vigorous physical activity and sedentary behavior, low-light and high-light physical activity were positively related to physical health (all P < 0.0001) and well-being (all P < 0.001). Additionally, replacing 30 minutes/day of sedentary time with equal amounts of low-light or high-light physical activity was associated with better physical health (all P < 0.0001). Objectively measured light-intensity physical activity is associated with physical health and well-being variables in older adults.

Mobile devices are a promising channel for delivering just-in-time guidance and support for improving key daily health behaviors. Despite an explosion of mobile phone applications aimed at physical activity and other health behaviors, few have been based on theoretically derived constructs and empirical evidence. Eighty adults ages 45 years and older who were insufficiently physically active, engaged in prolonged daily sitting, and were new to smartphone technology, participated in iterative design development and feasibility testing of three daily activity smartphone applications based on motivational frames drawn from behavioral science theory and evidence. An “analytically” framed custom application focused on personalized goal setting, self-monitoring, and active problem solving around barriers to behavior change. A “socially” framed custom application focused on social comparisons, norms, and support. An “affectively” framed custom application focused on operant conditioning principles of reinforcement scheduling and emotional transference to an avatar, whose movements and behaviors reflected the physical activity and sedentary levels of the user. To explore the applications' initial efficacy in changing regular physical activity and leisure-time sitting, behavioral changes were assessed across eight weeks in 68 participants using the CHAMPS physical activity questionnaire and the Australian sedentary behavior questionnaire. User acceptability of and satisfaction with the applications was explored via a post-intervention user survey. The results indicated that the three applications were sufficiently robust to significantly improve regular moderate-to-vigorous intensity physical activity and decrease leisure-time sitting during the 8-week behavioral adoption period. Acceptability of the applications was confirmed in the post-intervention surveys for this sample of midlife and older adults new to smartphone technology. Preliminary data exploring sustained use of the applications across a longer time period yielded promising results. The results support further systematic investigation of the efficacy of the applications for changing these key health-promoting behaviors.

Background Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor. Methods Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions. Results Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p 0.12). Conclusions Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion.