ObjectivesPedometers are an effective self-monitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged ...(eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers.Design and participantsA cross-sectional national telephone survey was conducted in Australia with 1349 respondents.Outcome measuresRegression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented.ResultsOver one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, ‘I don't think it would help me’ (39%), and ‘I don't want to increase my activity’ (47%).ConclusionsActivity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate.
Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on ...associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.
In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m
) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.
Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m
(SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; p = 0.047) and the daily number of >30-minute bouts was positively associated with WC (b = 0.330; p = 0.001). All other associations were not statistically significant.
The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.
Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
OBJECTIVE:This study aims to examine the relationship of lifestyle behaviors (physical activity, work and non-work sitting time, sleep quality, and sleep duration) with presenteeism while controlling ...for sociodemographics, work- and health-related variables.
METHODS:Data were collected from 710 workers (aged 20 to 76 years; 47.9% women) from randomly selected Australian adults who completed an online survey. Linear regression was used to examine the relationship between lifestyle behaviors and presenteeism.
RESULTS:Poorer sleep quality (standardized regression coefficients B = 0.112; P < 0.05), suboptimal duration (B = 0.081; P < 0.05), and lower work sitting time (B = −0.086; P < 0.05) were significantly associated with higher presenteeism when controlling for all lifestyle behaviors. Engaging in three risky lifestyle behaviors was associated with higher presenteeism (B = 0.150; P < 0.01) compared with engaging in none or one.
CONCLUSIONS:The results of this study highlight the importance of sleep behaviors for presenteeism and call for behavioral interventions that simultaneously address sleep in conjunction with other activity-related behaviors.
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BFBNIB, NMLJ, NUK, PNG, UL, UM, UPUK
This study aims to analyze psychometric properties and validity of the Compulsive Internet Use Scale (CIUS) and the Internet Addiction Test (IAT) and, second, to determine a threshold for the CIUS ...which matches the IAT cut-off for detecting problematic Internet use. A total of 292 subjects with problematic or pathological gambling (237 men, 55 women) aged 14–63 years and with private Internet use for at least 1 h per working or weekend day were recruited via different recruitment channels. Results include that both scales were internally consistent (Cronbach’s α = 0.9) and had satisfactory convergent validity (r = 0.75; 95% CI 0.70–0.80). The correlation with duration of private Internet use per week was significantly higher for the CIUS (r = 0.54) compared to the IAT (r = 0.40). Among all participants, 25.3% were classified as problematic Internet users based on the IAT with a cut-off ≥40. The highest proportion of congruent classified cases results from a CIUS cut-off ≥18 (sensitivity 79.7%, specificity 79.4%). However, a higher cut-off (≥21) seems to be more appropriate for prevalence estimation of problematic Internet use.
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Abstract
Background
The Patient Health Questionnaire-8 (PHQ-8) is a screening questionnaire of depressive symptoms. However, it is unknown whether it is equivalent across time and between groups of ...individuals. The aim of our paper was to test whether the PHQ-8 has the same meaning in two groups of individuals over time.
Methods
Primary care patients were proactively recruited from three German cities. PHQ-8 data from a baseline assessment (
n
= 588), two assessments during the intervention (
n
= 246/225), and a six (
n
= 437) and 12 months (
n
= 447) follow-up assessment were first used to examine the factor structure of the PHQ-8 by confirmatory factor analysis (CFA). The best fitting factor solution was then used to test longitudinal invariance across time and between intervention and control group by Multiple Group CFA.
Results
A two-factor structure consistently showed the best model fit. Only configural longitudinal invariance was evidenced when the baseline assessment was included in the analysis. Without the baseline assessment, strict longitudinal invariance was shown across the intervention and the follow-up assessments. Scalar invariance was established between the intervention and control group for the baseline assessment and strict invariance between groups and across the 6- and 12-month follow-up assessments.
Conclusions
The lack of longitudinal invariance might be attributed to various differences between the baseline assessments and all following assessments, e.g., assessment mode (iPad vs telephone), potential changes in symptom perception, and setting.
Trial registration
DRKS0001163
5, date of trial registration: 20.01.2017;
DRKS00011637
, date of trial registration: 25.01.2017.
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Highlights • We examine the validity of a pouch-worn activPAL3c accelerometer. • Data from pouch-mounted and skin-mounted devices are not significantly different. • An elasticised pouch may offer a ...useful alternative to skin mounting.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
An important step in Internet addiction research is to develop standardized instruments for assessing Internet addiction-related symptoms. The Compulsive Internet Use Scale (CIUS) is a promising ...brief questionnaire. The aim of this study was to examine the factor structure of a German version of the CIUS with confirmatory factor analysis in a general population sample. In addition, the best fitting structure was tested for factorial invariance across sex, age, education level, and weekly Internet use. We used a weighted general population sample (N=8,132) of 14-64 years olds spending at least 1 hour online for private purposes per typical working or weekend day. Findings include that a one-factor model was found to fit well. It was invariant across sex, age, education level, and weekly Internet use. The findings support the validity of the CIUS as a short screening instrument.
Data from controlled trials indicate that Web-based interventions generally suffer from low engagement and high attrition. This is important because the level of exposure to intervention content is ...linked to intervention effectiveness. However, data from real-life Web-based behavior change interventions are scarce, especially when looking at physical activity promotion.
The aims of this study were to (1) examine the engagement with the freely available physical activity promotion program 10,000 Steps, (2) examine how the use of a smartphone app may be helpful in increasing engagement with the intervention and in decreasing nonusage attrition, and (3) identify sociodemographic- and engagement-related determinants of nonusage attrition.
Users (N=16,948) were grouped based on which platform (website, app) they logged their physical activity: Web only, app only, or Web and app. Groups were compared on sociodemographics and engagement parameters (duration of usage, number of individual and workplace challenges started, and number of physical activity log days) using ANOVA and chi-square tests. For a subsample of users that had been members for at least 3 months (n=11,651), Kaplan-Meier survival curves were estimated to plot attrition over the first 3 months after registration. A Cox regression model was used to determine predictors of nonusage attrition.
In the overall sample, user groups differed significantly in all sociodemographics and engagement parameters. Engagement with the program was highest for Web-and-app users. In the subsample, 50.00% (5826/11,651) of users stopped logging physical activity through the program after 30 days. Cox regression showed that user group predicted nonusage attrition: Web-and-app users (hazard ratio=0.86, 95% CI 0.81-0.93, P<.001) and app-only users (hazard ratio=0.63, 95% CI 0.58-0.68, P<.001) showed a reduced attrition risk compared to Web-only users. Further, having a higher number of individual challenges (hazard ratio=0.62, 95% CI 0.59-0.66, P<.001), workplace challenges (hazard ratio=0.94, 95% CI 0.90-0.97, P<.001), physical activity logging days (hazard ratio=0.921, 95% CI 0.919-0.922, P<.001), and steps logged per day (hazard ratio=0.99999, 95% CI 0.99998-0.99999, P<.001) were associated with reduced nonusage attrition risk as well as older age (hazard ratio=0.992, 95% CI 0.991-0.994, P<.001), being male (hazard ratio=0.85, 95% CI 0.82-0.89, P<.001), and being non-Australian (hazard ratio=0.87, 95% CI 0.82-0.91, P<.001).
Compared to other freely accessible Web-based health behavior interventions, the 10,000 Steps program showed high engagement. The use of an app alone or in addition to the website can enhance program engagement and reduce risk of attrition. Better understanding of participant reasons for reducing engagement can assist in clarifying how to best address this issue to maximize behavior change.
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Abstract Objective To assess levels of physical activity the use of objective physical activity measures like accelerometers is promising. We investigated characteristics associated with ...non-participation in accelerometry within an apparently healthy sample. Methods Among German participants of a cardiovascular examination program (CEP; 2012–2013), 470 participants aged 40–75 years were invited to wear an accelerometer for 7 days. We used multivariate logistic regression to estimate the association between non-participation and the following characteristics of participants: sex, age, education, smoking, setting of recruitment for the CEP (general medical practices, job agencies, statutory health insurance), self-reported general health, and objective health criteria such as cardiorespiratory fitness and absolute number of cardiometabolic risk factors (elevated waist circumference, blood pressure, triglycerides, blood glucose, and reduced high-density lipoprotein). Subsequently, we stratified this analysis by sex. Results Among all invited individuals, N = 235 (60.0% women) gave consent to participate in accelerometry. Women were more likely to decline participation (odds ratio, 1.7; 95% confidence interval, 1.1–2.7) compared to men. Stratified analyses revealed the absolute number of risk factors as predictor of non-participation for men (1.4; 1.01–2.0), while there was no predictor found in women. Conclusion We found a self-selection bias in participation in accelerometry. Women declined study participation more likely than men. The number of cardiometabolic risk factors decreased compliance only in men. Future studies should consider strategies to reduce this bias.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The results emphasize the validity and the psychometric quality of the AUDIT.•Strict invariance was established for the AUDIT across gender.•Strict invariance was also established across different ...assessment settings.•Confirmatory Factor Analysis shows an acceptable fit for a one-factor model.•A two-factor model is the best representation of the AUDIT’s latent construct.
The Alcohol Use Disorders Identification Test (AUDIT) is an internationally well-established screening tool for the assessment of hazardous and harmful alcohol consumption. To be valid for group comparisons, the AUDIT should measure the same latent construct with the same structure across groups. This is determined by measurement invariance. So far, measurement invariance of the AUDIT has rarely been investigated. We analyzed measurement invariance across gender and samples from different settings (i.e., inpatients from general hospital, patients from general medical practices, general population).
A sample of n = 28,345 participants from general hospitals, general medical practices and the general population was provided from six studies. First, we used Confirmatory Factor Analysis (CFA) to establish the factorial structure of the AUDIT by comparing a single-factor model to a two-factor model for each group. Next, Multiple Group CFA was used to investigate measurement invariance.
The two-factor structure was shown to be preferable for all groups. Furthermore, strict measurement invariance was established across all groups for the AUDIT.
A two-factor structure for the AUDIT is preferred. Nevertheless, the one-factor structure also showed a good fit to the data. The findings support the AUDIT as a psychometrically valid and reliable screening instrument.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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