Refine
Document Type
- Article (3)
- Doctoral Thesis (2)
Language
- English (5) (remove)
Has Fulltext
- yes (5)
Is part of the Bibliography
- no (5)
Keywords
- prevention (5) (remove)
Institute
Publisher
- MDPI (2)
- Frontiers Media S.A. (1)
Background
Few studies have assessed trajectories of alcohol use in the general population, and even fewer studies have assessed the impact of brief intervention on the trajectories. Especially for low-risk drinkers, it is unclear what trajectories occur, whether they benefit from intervention, and if so, when and how long. The aims were first, to identify alcohol use trajectories among at-risk and among low-risk drinkers, second, to explore potential effects of brief alcohol intervention and, third, to identify predictors of trajectories.
Methods
Adults aged 18-64 years were screened for alcohol use at a municipal registration office. Those with alcohol use in the past 12 months (N = 1646; participation rate: 67%) were randomized to assessment plus computer-generated individualized feedback letters or assessment only. Outcome was drinks/week assessed at months 3, 6, 12, and 36. Alcohol risk group (at-risk/low-risk) was determined using the Alcohol Use Disorders Identification Test–Consumption. Latent class growth models were estimated to identify alcohol use trajectories among each alcohol risk group. Sex, age, school education, employment status, self-reported health, and smoking status were tested as predictors.
Results
For at-risk drinkers, a light-stable class (46%), a medium-stable class (46%), and a high-decreasing class (8%) emerged. The light-stable class tended to benefit from intervention after 3 years (Incidence Rate Ratio, IRR=1.96; 95% Confidence Interval, CI: 1.14–3.37). Male sex, higher age, more years of school, and current smoking decreased the probability of belonging to the light-stable class (p-values<0.05). For low-risk drinkers, a very light-slightly increasing class (72%) and a light-increasing class (28%) emerged. The very light-slightly increasing class tended to benefit from intervention after 6 months (IRR=1.60; 95% CI: 1.12–2.28). Male sex and more years of school increased the probability of belonging to the light-increasing class (p-value < 0.05).
Conclusion
Most at-risk drinkers did not change, whereas the majority of low-risk drinkers increased alcohol use. There may be effects of alcohol feedback, with greater long-term benefits among persons with low drinking amounts. Our findings may help to identify refinements in the development of individualized interventions to reduce alcohol use.
Little is known about the (co-)occurrence of smoking, alcohol at-risk drinking, physical inactivity and overweight, and the motivation to change these behavioral health risk factors (HRFs) in older general hospital patients with cardiovascular disease. Between October and December 2016, all consecutively admitted patients aged 50 to 79 years were proactively recruited on 3 cardiology wards and asked to participate in a survey on HRFs and behavior change motivation. Of the eligible patients, 80.4% participated in the survey (n = 328). The mean age was 66.5 years (standard deviation 9.0), and 65.5% were male. At least 1 HRF was present in 91.8% (n = 280), at least 2 HRFs in 54.4% (n = 166), and 3 or 4 HRFs in 12.1% (n = 37) of participants. The proportion of older adults who contemplated or were changing or planning to change their behavior to meet health behavior recommendations ranged between 66.0% (smoking) and 93.2% (alcohol consumption). The results indicate a notable co-occurrence of behavioral HRFs in older patients with cardiovascular disease. The majority of older adults were at least considering changing the respective behavior. To prevent and treat diseases efficiently, hospitalization may be a suitable moment for systematic multiple HRF screening and intervention.
Background: Sedentary behavior (SB) is a modifiable behavior with increasing prevalence worldwide. There is emerging evidence that time spend in SB and the manner in which SB is accumulated over time is associated with cardiovascular and cardiometabolic health. The requirement for SB data to be accurately measured is minimization, or at least accurate quantification of human-related sources of measurement errors such as accelerometer measurement reactivity (AMR). The present thesis was to examine SB and their associations with cardiovascular and cardiometabolic health, and to focus on challenges related to the assessment of SB. The first aim of the thesis was to identify patterns of SB describing how individuals accumulate their time spend in SB day-by-day over one week, and to examine how these patterns are associated with cardiorespiratory fitness as a marker for cardiovascular health (paper 1). The second aim of the thesis was to examine the multiple types of SB, and how this is associated with a clustered cardiometabolic risk score (CMRS; paper 2). The third aim of the thesis was to examine AMR and the reproducibility in SB and physical activity (PA) in two measurement periods, and to quantify AMR as a confounder for the estimation of the reproducibility of SB and PA data (paper 3).
Methods: The three papers were based on data of two different studies. For study 1, 1165 individuals aged 40 to 75 years were recruited in three different settings. Among these, 582 participated in a cardiovascular risk factor screening program including cardiopulmonary exercise testing. For the analyses of paper 1, 170 participants were eligible, agreed to wear an accelerometer, fulfilled the wearing regime, and completed the study period by wearing the accelerometer for seven consecutive days. Patterns in accelerometer data were classified based on time spent in SB per day applying growth mixture modeling. Model‐implied class‐specific peak oxygen uptake (VO2peak) means were compared using adjusted equality test of means (paper 1). The underlying study of paper 2 and 3 were based on data of a pilot study aiming to investigate the feasibility of a brief tailored letter intervention to increase PA and to reduce SB during leisure time. Among the individuals who agreed to be contacted again in study 1, a random sample of those aged between 40 and 65 years was drawn. Of those, 175 attended in a cardiovascular examination program. Assessment included giving blood sample, standardized measurement of blood pressure, waist circumference, body weight, and height at baseline, and after twelve months. Further, they agreed to complete a paper-pencil questionnaire on SB (Last 7-d Sedentary Behavior Questionnaire, SIT-Q-7d) and PA (International Physical Activity Questionnaire, IPAQ), and to receive seven-day accelerometery at baseline, and after 12 months. In addition, self-administered assessments were conducted at months one, three, four, and six after baseline. Only individuals of a random subsample (= intervention group) received up to three letters tailored to their self-reported SB and PA at months one, three, and four. For paper 2, associations between SBs and a clustered cardiometabolic risk score (CMRS) were analyzed using linear as well as quantile regression. To account for missing values at baseline, multiple imputations using chained equations were performed resulting in a total sample of 173 participants. Paper 3 comprised data of 136 individuals who participated at the baseline and twelve months assessments, and fulfilled the wearing regime. AMR was examined using latent growth modeling in each measurement period. Intraclass correlations (ICC) were calculated to examine the reproducibility of SB and PA data using two-level mixed-effects linear regression analyses.
Results: Results of paper 1 revealed four patterns of SB: 'High, stable', 'Low, increase', 'Low, decrease', and 'High, decrease'. Persons in the class 'High, stable' had significantly lower VO2peak values (M = 25.0 mL/kg/min, SD = 0.6) compared to persons in the class 'Low, increase' (M = 30.5 mL/kg/min, SD = 3.6; p = 0.001), in the class 'Low, decrease' (M = 30.1 mL/kg/min, SD = 5.0; p = 0.009), and in the class High, decrease' (M = 29.6 mL/kg/min, SD = 5.9; p = 0.032), respectively. No differences among the other classes were found. In paper 2, results revealed that the only factor positively associated with a CMRS in all regression models was watching television. Depending on the regression analysis approach used, other leisure-time SBs showed inconsistent (using a computer), or no associations (reading and socializing) with a CMRS. In paper 3, results revealed that time spent in SB increased (baseline: b = 2.3 min/d; after 12 months: b = 3.8 min/d), and time spent in light PA decreased (b = 2.0 min/day; b = 3.3 min/d). However, moderate-to-vigorous PA remained unchanged. Accelerometer wear time was reduced (b = 4.6 min/d) only at baseline. The ICC coefficients ranged from 0.42 (95% CI = 0.29 - 0.57) for accelerometer wear time to 0.70 (95% CI = 0.61 - 0.78) for moderate-to-vigorous PA. None of the regression models identified a reactivity indicator as a confounder for the reproducibility of SB and PA data.
Conclusions: The present thesis highlights SB in the field of cardiovascular and cardiometabolic research that have implications for future research. Individuals sit for different purposes and durations in multiple life domains, and the time spent in SB is accumulated in different patterns over time. Therefore, research should consider the fact that SB is embedded in an individual's daily life routine, hence might have differential effects on cardiovascular and cardiometabolic health. Further, methodological aspects have to be considered when dealing with SB. In order to detect how SB is 'independently' associated to an individual's health, an accurate measurement of SB is fundamental. Therefore, human-related sources of bias such as AMR should be taken into account when either planning studies or when interpreting data drawn from analysis of SB data.
Background: Common to most theory-based intervention approaches is the idea of supporting intentions to increase the probability of behavior change. This principle works only if (a) intentions can be explained by the hypothesized socio-cognitive constructs, and (b) people actually do what they intend to do. The overall aim of this thesis was to test these premises using two health behavior theories applied to reducing at-risk alcohol use. Method: The three papers underlying this thesis were based on data of the randomized controlled “Trial Of Proactive Alcohol interventions among job-Seekers” (TOPAS). A total of 1243 job-seekers with at-risk alcohol use were randomized to stage tailored intervention (ST), non-stage tailored intervention (NST), or control group. The ST participants (n = 426) were analyzed in paper 1. Paper 2 was based on the baseline and 3-month data provided by the NST participants (n = 433). Paper 3 was based on baseline, 3-, 6-, and 15-month data provided by the control and ST group not intending to change alcohol use (n = 629). Latent variable modeling was used to investigate the associations of social-cognitive constructs and intentional stages (paper 1), the extent to which intentions were translated into alcohol use (paper 2), and the different trajectories of alcohol use among people not intending to change as well as the ST effect on the trajectories (paper 3). Results: Persons in different intentional stages differed in the processes of change in which they engaged, in the importance placed by them on the pros and cons of alcohol use, and in the perceived ability to quit (ps < 0.01). The association between intentions and alcohol use was weak. The magnitude of this intention-behavior gap depended on the extent to which normative expectations have changed over time (p < 0.01) and was reduced when controlling for the mediating effect of temporal stability of intentions. The gap was also present among people not intending to change: Even without intervention, 35% of the persons reduced the amount of alcohol use after 15 months (p < 0.05) and 2% achieved abstinence. Persons with heavier drinking (33%) and persons with low but frequent use (30%) did not change. Persons with frequent alcohol use seem to benefit less from ST than those with occasional use, although differences were not statistically significant. Conclusions: Intentions can be quite well explained by the hypothesized socio-cognitive constructs. In a sample of persons who were, as a whole, little motivated to change, the precision of how well intentions predict subsequent alcohol use was modest though. Time and socio-contextual influences should be considered.