When a behavior is monitored, it is likely to change, even if no change may be intended. This phenomenon is known as measurement reactivity. We investigated systematic changes in accelerometer-based measures over the days of monitoring as an indicator of measurement reactivity in an adult population. One hundred seventy-one participants from the general population (65% women; mean age = 55 years, range: 42-65 years) wore accelerometers for 7 consecutive days to measure sedentary behavior and physical activity (PA). Latent growth models were used (a) to investigate changes in accelerometer wear time over the measurement days and (b) to identify measurement reactivity indicated by systematic changes in sedentary time (ST), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Over the measurement days, participants reduced accelerometer wear time by trend (rate of change [b] = -4.7 min/d, P = .051, Cohen's d = .38), increased ST (b = 2.4 min/d, P = .018, d = .39), and reduced LPA (b = -2.4 min/d, P = .015, d = .38). Participants did not significantly reduce MVPA (P = .537). Our data indicated that accelerometry might generate reactivity. Small effects on ST and LPA were found. Thus, the validity of accelerometer-based data on ST and LPA may be compromised. Systematic changes observed in accelerometer wear time may further bias accelerometer-based measures. MVPA seems to be less altered due to the presence of an accelerometer.

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Model-based predictions of the impact of land management practices on nutrient loading require measured nutrient flux data for model calibration and evaluation. Consequently, uncertainties in the monitoring data resulting from sample collection and load estimation methods influence the calibration, and thus, the parameter settings that affect the modeling results. To investigate this influence, we compared three different time-based sampling strategies and four different load estimation methods for model calibration and compared the results. For our study, we used the river basin model Soil and Water Assessment Tool on the intensively managed loess-dominated Parthe watershed (315 km(2)) in Central Germany. The results show that nitrate-N load estimations differ considerably depending on sampling strategy, load estimation method, and period of interest. Within our study period, the annual nitrate-N load estimation values for the daily composite data set have the lowest ranges (between 9.8% and 15.7% maximum deviations related to the mean value of all applied methods). By contrast, annual estimation results for the submonthly and the monthly data set vary in greater ranges (between 24.9% and 67.7%). To show differences between the sampling strategies, we calculated the percentage deviation of mean load estimations of submonthly and monthly data sets as related to the mean estimation value of the composite data set. For nitrate-N, the maximum deviation is 64.5% for the submonthly data set in the year 2000. We used average monthly nitrate-N loads of the daily composite data set to calibrate the model to achieve satisfactory simulation results [Nash-Sutcliffe efficiency (NSE) 0.52]. Using the same parameter settings with submonthly and monthly data set, the NSE dropped to 0.42 and 0.31, respectively. Considering the different results from the monitoring strategy and the load estimation method, we recommend both the implementation of optimized monitoring programs and the use of multiple load estimation methods to improve water quality characterization and provide appropriate model calibration and evaluation data.

It was our aim to develop a questionnaire for patients with chronic musculoskeletal diseases to self-report their health education literacy, to analyse the psychometric properties of the instrument and to test hypotheses concerning sociodemographic predictors of health education literacy. A total of 577 patients with chronic back pain or osteoarthritis who underwent inpatient rehabilitation were surveyed. The resulting 'HELP questionnaire' (health education literacy of patients with chronic musculoskeletal diseases) consists of 18 items and three scales (comprehension of medical information, applying medical information, communicative competence in provider interactions). The instrument's psychometric properties are good (Cronbach's alpha between 0.88 and 0.95, unidimensionality and Rasch model fit established). Our sample's average level of self-reported health education literacy is quite high. However, 20-30% of the patients admitted to having difficulty understanding important aspects of health education programmes (i.e. comprehending what medical information means in relation to their disease). The variance explained by sociodemographic and basic medical variables is small (4-8%). Greater effort is required to make health education programmes easier to understand. There is a need for more research on interindividual variability of complex aspects of health literacy.

BackgroundMeasuring physical activity (PA) and sedentary time (ST) by self-report or device as well as assessing related health factors may alter those behaviors. Thus, in intervention trials assessments may bias intervention effects. The aim of our study was to examine whether leisure-time PA, transport-related PA, and overall ST measured via self-report vary after assessments and whether a brief tailored letter intervention has an additional effect.MethodsAmong a sample of subjects with no history of myocardial infarction, stroke, or vascular intervention, a number of 175 individuals participated in a study comprising multiple repeated assessments. Of those, 153 were analyzed (mean age 54.5 years, standard deviation = 6.2; 64% women). At baseline, participants attended a cardiovascular examination (standardized measurement of blood pressure and waist circumference, blood sample taking) and wore an accelerometer for seven days. At baseline and after 1, 6, and 12 months, participants completed the International Physical Activity Questionnaire. A random subsample received a tailored counseling letter intervention at month 1, 3, and 4. Changes in PA and ST from baseline to 12-month follow-up were analyzed using random-effects modelling.ResultsFrom baseline to 1-month assessment, leisure-time PA did not change (Incidence rate ratio = 1.13, p = .432), transport-related PA increased (Incidence rate ratio = 1.45, p = .023), and overall ST tended to decrease (b = − 1.96, p = .060). Further, overall ST decreased from month 6 to month 12 (b = − 0.52, p = .037). Time trends of the intervention group did not differ significantly from those of the assessment-only group.ConclusionsResults suggest an effect of measurements on PA and ST. Data of random-effects modelling results revealed an increase of transport-related PA after baseline to 1-month assessment. Decreases in overall ST may result from repeated assessments. A brief tailored letter intervention seemed to have no additional effect. Thus, measurement effects should be considered when planning intervention studies and interpreting intervention effects.Trial registrationClinicalTrials.gov NCT02990039. Registered 7 December 2016. Retrospectively registered.