Predictors of relapse among smokers: Transtheoretical effort variables, demographics, and smoking severity

The present longitudinal study investigates baseline assessments of static and dynamic variables, including demographic characteristics, smoking severity, and Transtheoretical Model of Behavior Change (TTM) effort variables (Decisional Balance (i.e. Pros and Cons), Situational Temptations, and Processes of Change) of relapse among individuals who were abstinent at 12 months. The study sample (N = 521) was derived from an integrated dataset of four population-based smoking cessation interventions. Several key findings included: Participants who were aged 25–44 and 45–64 (OR = .43, p = .01 and OR = .40, p = .01, respectively) compared to being aged 18–24 were less likely to relapse at follow-up. Participants in the control group were more than twice as likely to relapse (OR = 2.17, p = .00) at follow-up compared to participants in the treatment group. Participants who reported higher Habit Strength scores were more likely to relapse (OR = 1.05, p = .02). Participants who had higher scores of Reinforcement Management (OR = 1.05, p = .04) and Self-Reevaluation (OR = 1.08, p = .01) were more likely to relapse. Findings add to one assumption that relapsers tend to relapse not solely due to smoking addiction severity, but due to immediate precursor factors such as emotional distress. One approach would be to provide additional expert guidance on how smokers can manage stress effectively when they enroll in treatment at any stage of change

Development and Validation of the Cognitive Behavioral Physical Activity Questionnaire

Purpose: Develop and demonstrate preliminary validation of a brief questionnaire aimed at assessing social cognitive determinants of physical activity (PA) in a college population. Design: Quantitative and observational. Setting: A midsized northeastern university. Subjects: Convenience sample of 827 male and female college students age 18 to 24 years. Measures: International Physical Activity Questionnaire and a PA stage-of-change algorithm. Analysis: A sequential process of survey development, including item generation and data reduction analyses by factor analysis, was followed with the goal of creating a parsimonious questionnaire. Structural equation modeling was used for confirmatory factor analysis and construct validation was confirmed against self-reported PA and stage of change. Validation analyses were replicated in a second, independent sample of 1032 college students. Results: Fifteen items reflecting PA self-regulation, outcome expectations, and personal barriers explained 65% of the questionnaire data and explained 28.6% and 39.5% of the variance in total PA and moderate-to-vigorous–intensity PA, respectively. Scale scores were distinguishable across the stages of change. Findings were similar when the Cognitive Behavioral Physical Activity Questionnaire (CBPAQ) was tested in a similar and independent sample of college students (40%; R2 moderate-to-vigorous–intensity PA = .40; p \u3c .001). Conclusion: The CBPAQ successfully explains and predicts PA behavior in a college population, warranting its incorporation into future studies aiming at understanding and improving on PA behavior in college students

Age-specific trends in health-related quality of life among US adults: Findings from National Health and Nutrition Examination Survey, 2001-2016

Purpose Health-related quality of life (HRQoL) is an important indicator of population health, yet no age-specific trend analyses in HRQoL have been conducted with a nationally representative sample since 2004. Therefore, to address this gap, an age-specific trend analysis of HRQoL was conducted using National Health and Nutrition Examination Surveys (NHANES) data. Methods NHANES 2001–2016 data (8 cycles) were examined to evaluate trends in HRQoL by age group (young adults: 21–39, middle-aged: 40–64, older adults: 65+). HRQoL was assessed by self-reported health (SRH) and number of physically unhealthy, mentally unhealthy, and inactive days to due to physical or mental health in the past 30 days. Multiple linear or logistic regression analyses explored trends in HRQoL by age group, adjusting for demographics over time. Results Analysis revealed increasing fair/poor SRH over time for the entire sample (β = 0.34, 95% CI 0.08, 0.60, p = 0.011). However, age-specific analysis identified a bi-annual increase in fair/poor SRH only among young adults (β = 0.49, 95% CI 0.22, 0.76, p \u3c 0.001) and a decrease among older adults (β = − 0.60, 95% CI − 1.14, − 0.06, p = 0.03). Closer inspection revealed increasing fair/poor SRH increased among young women (β = 0.52, 95% CI 0.11, 0.93, p = 0.013) and young men (β = 0.46, 95% CI 0.04, 0.88, p = 0.03) but decreased among older women (β = − 0.81, 95% CI − 1.59, − 0.03, p = 0.042) over time. Analyses also determined that there was a trend for a decreasing number of physically unhealthy days among young adults (p \u3c 0.001), although no trends were observed for the other HRQoL items. Conclusions Although there was a significant trend over time for increasing fair/poor SRH when considering the entire sample, this trend was not consistent between age groups or sexes. Given increasing fair/poor SRH among young adults, there is a need to understand and address factors relating to HRQoL among this age group

Maintaining exercise and healthful eating in older adults: The SENIOR project II: Study design and methodology

The Study of Exercise and Nutrition in Older Rhode Islanders (SENIOR) Project II is an intervention study to promote the maintenance of both exercise and healthful eating in older adults. It is the second phase of an earlier study, SENIOR Project I, that originally recruited 1277 community-dwelling older adults to participate in behavior-specific interventions designed to increase exercise and/or fruit and vegetable consumption. The general theoretical framework for this research is the Transtheoretical Model (TTM) of Health Behavior Change. The current intervention occurs over a 48-month period, using a manual, newsletters, and phone coaching calls. Annual assessments collect standardized data on behavioral outcomes (exercise and diet), TTM variables (stage of change and self-efficacy), psychosocial variables (social support, depression, resilience, and life satisfaction), physical activity and functioning (SF-36, Up and Go, Senior Fitness Test, and disability assessment), cognitive functioning (Trail Making Test and Forward and Backward Digit Span), physical measures (height, weight, and waist circumference), and demographics. The SENIOR Project II is designed to answer the following question as its primary objective: (1) Does an individualized active-maintenance intervention with older adults maintain greater levels of healthful exercise and dietary behaviors for 4 years, compared to a control condition? In addition, there are two secondary objectives: (2) What are the psychosocial factors associated with the maintenance of health-promoting behaviors in the very old? (3) What are the effects of the maintenance of health-promoting behaviors on reported health outcomes, psychosocial measures, anthropometrics, and cognitive status

Common Factors Predicting Long-term Changes in Multiple Health Behaviors

This study was designed to assess if there are consistent treatment, stage, severity, effort and demographic effects which predict long-term changes across the multiple behaviors of smoking, diet and sun exposure. A secondary data analysis integrated data from four studies on smoking cessation (N = 3927), three studies on diet (N = 4824) and four studies on sun exposure (N = 6465). Across all three behaviors, behavior change at 24 months was related to treatment, stage of change, problem severity and effort effects measured at baseline. There were no consistent demographic effects. Across multiple behaviors, long-term behavior changes are consistently related to four effects that are dynamic and open to change. Behavior changes were not consistently related to static demographic variables. Future intervention research can target the four effects to determine if breakthroughs can be produced in changing single and multiple behaviors

Baseline transtheoretical and dietary behavioral predictors of dietary fat moderation over 12 and 24 months

Longitudinal predictors of dietary behavior change are important and in need of study. This secondary data analysis combined primary data across three randomized trials to examine transtheoretical model (TTM) and specific dietary predictors of successful dietary change at 12 and 24 months separately in treatment and control groups (N = 4178). The treatment group received three TTM-tailored print interventions over 12 months between 1995 and 2000. Chi-square and MANOVA analyses were used to examine baseline predictors of dietary outcome at 12 and 24 months. Last, a multivariable logistic regression was conducted with all baseline variables included. Across all analyses in both treatment and control groups, the most robust predictors of successful change were for TTM-tailored treatment group, preparation stage of change, and increased use of dietary behavior variables such as moderating fat intake, substitution of lower fat foods, and increasing intake of healthful foods. These results provide strong evidence for treatment, stage and behavioral dietary severity effects predicting dietary behavior change over time, and for targeting these variables with the strongest relationships to outcome in interventions, such as TTM-tailored dietary interventions

Baseline Stage, Severity, and Effort Effects Differentiate Stable Smokers from Maintainers and Relapsers

This cross-sectional study (N = 4,144) compared three longitudinal dynatypes (Maintainers, Relapsers, and Stable Smokers) of smokers on baseline demographics, stage, addiction severity, and transtheoretical model effort effect variables. There were significant small-to-medium-sized differences between the Stable Smokers and the other two groups on stage, severity, and effort effect variables in both treatment and control groups. There were few significant, very small differences on baseline effort variables between Maintainers and Relapsers in the control, but not the treatment group. The ability to identify Stable Smokers at baseline could permit enhanced tailored treatments that could improve population cessation rates

Treated individuals who progress to action or maintenance for one behavior are more likely to make similar progress on another behavior: Coaction results of a pooled data analysis of three trials

Objective: This study compared, in treatment and control groups, the phenomena of coaction, which is the probability that taking effective action on one behavior is related to taking effective action on a second behavior. Methods: Pooled data from three randomized trials of Transtheoretical Model (TTM) tailored interventions (n = 9461), completed in the U.S. in 1999, were analyzed to assess coaction in three behavior pairs (diet and sun protection, diet and smoking, and sun protection and smoking). Odds ratios (ORs) compared the likelihood of taking action on a second behavior compared to taking action on only one behavior. Results: Across behavior pairs, at 12 and 24 months, the ORs for the treatment group were greater on an absolute basis than for the control group, with two being significant. The combined ORs at 12 and 24 months, respectively, were 1.63 and 1.85 for treatment and 1.20 and 1.10 for control. Conclusions: The results of this study with addictive, energy balance and appearance-related behaviors were consistent with results found in three studies applying TTM tailoring to energy balance behaviors. Across studies, there was more coaction within the treatment group. Future research should identify predictors of coaction in more multiple behavior change interventions

Health-related quality of life following a clinical weight loss intervention among overweight and obese adults: intervention and 24 month follow-up effects

BACKGROUND: Despite a growing literature on the efficacy of behavioral weight loss interventions, we still know relatively little about the long terms effects they have on HRQL. Therefore, we conducted a study to investigate the immediate post-intervention (6 months) and long-term (12 and 24 months) effects of clinically based weight management programs on HRQL. METHODS: We conducted a randomized clinical trial in which all participants completed a 6 month clinical weight loss program and were randomized into two 6-month extended care groups. Participants then returned at 12 and 24 months for follow-up assessments. A total of 144 individuals (78% women, M age = 50.2 (9.2) yrs, M BMI = 32.5 (3.8) kg/m(2)) completed the 6 month intervention and 104 returned at 24 months. Primary outcomes of weight and HRQL using the SF-36 were analyzed using multivariate repeated measures analyses. RESULTS: There was complete data on 91 participants through the 24 months of the study. At baseline the participants scored lower than U.S. age-specific population norms for bodily pain, vitality, and mental health. At the completion of the 6 month clinical intervention there were increases in the physical and mental composite measures as well as physical functioning, general health, vitality, and mental health subscales of the SF-36. Despite some weight regain, the improvements in the mental composite scale as well as the physical functioning, vitality, and mental health subscales were maintained at 24 months. There were no significant main effects or interactions by extended care treatment group or weight loss group (whether or not they maintained 5% loss at 24 months). CONCLUSION: A clinical weight management program focused on behavior change was successful in improving several factors of HRQL at the completion of the program and many of those improvements were maintained at 24 months. Maintaining a significant weight loss (> 5%) was not necessary to have and maintain improvements in HRQL

National Scouting Combine Scores as Performance Predictors in the National Football League

Vincent, LM, Blissmer, BJ, and Hatfield, DL. National Scouting Combine scores as performance predictors in the National Football League. J Strength Cond Res 33(1): 104-111, 2019 - The National Football League (NFL) hosts an annual scouting combine to evaluate the approximately 300 elite college football players who are most likely to be selected in the upcoming NFL draft. Given the public interest, player obligations, coaching staff commitments, and business aspects of the combine, several questions have arose in recent years concerning the applicability of combine scores to eventual draft NFL performance. The primary purpose of this study is to investigate the relationship between specified National Scouting Combine (NSC) scores and measures of performance by player position. A secondary aim was to determine whether correlated variables could predict player performance at the quarterback (QB), running back (RB), wide receiver (WR), defensive end (DE), defensive tackle (DT), and linebacker (LB) positions. Subjects in this study were combine participants between the years 2005-2010 who subsequently played in the NFL. The positional groups investigated were QBs (N = 44), RBs (N = 82), WRs (N = 116), LBs (N = 139), DEs (N = 59), and DTs (N = 72). Combine raw scores for 40-yd dash time, countermovement vertical jump (CMVJ) height, standing long jump (SLJ) distance, and pro-agility time were recorded. Measures of horizontal and vertical power were calculated for the 40-yd dash and CMVJ. Combine scores and on-field positional statistics for the first 4 years for QBs and 3 years of all other players\u27 careers were analyzed to investigate relationships. Significant correlations were shown between at least one combine measure and on-field success at every position. Hierarchal regression showed combine measures could predict between 4% and 62% of the variance for individual on-field variables. Quarterback rushing yards were significantly correlated with 40T, CMVJ, vertical jump power (VJP), vertical jump relative power (VJRP), and horizontal power (HP), and those factors accounted for 62.2% of the total variance. Horizontal power and VJP were predictive of QB rushing attempts (r2 = 0.370). At RB, 40T and SLJ combined were predictive of total rushing yards (r2 = 0.200), rushing attempts (r2 = 0.195), and yards per game (r2 = 0.197). Power variables were predictive of total tackles for DEs\u27 40HP (r2 = 0.096) and VJP (r2 = 0.018), accounting for a total of 21% of the variance. The current study suggests that combine tests are modest predictors of future performance. Should the NFL change the current NSC testing battery, the addition of horizontal and vertical power measurements, as well as position-specific skill tests are recommended