Motivation: key to a healthy lifestyle in people with diabetes? Current and emerging knowledge and applications.

AIM: Motivation to take up and maintain a healthy lifestyle is key to diabetes prevention and management. Motivations are driven by factors on the psychological, biological and environmental levels, which have each been studied extensively in various lines of research over the past 25 years. Here, we analyse and reflect on current and emerging knowledge on motivation in relation to lifestyle behaviours, with a focus on people with diabetes or obesity. Structured according to psychological, (neuro-)biological and broader environmental levels, we provide a scoping review of the literature and highlight frameworks used to structure motivational concepts. Results are then put in perspective of applicability in (clinical) practice. RESULTS: Over the past 25 years, research focusing on motivation has grown exponentially. Social-cognitive and self-determination theories have driven research on the key motivational concepts 'self-efficacy' and 'self-determination'. Neuro-cognitive research has provided insights in the processes that are involved across various layers of a complex cortical network of motivation, reward and cognitive control. On an environmental - more upstream - level, motivations are influenced by characteristics in the built, social, economic and policy environments at various scales, which have provided entry points for environmental approaches influencing behaviour. CONCLUSIONS: Current evidence shows that motivation is strongly related to a person's self-efficacy and capability to initiate and maintain healthy choices, and to a health climate that supports autonomous choices. Some approaches targeting motivations have been shown to be promising, but more research is warranted to sustainably reduce the burden of diabetes in individuals and populations.

Effect of a school-based intervention on physical activity and quality of life through serial mediation of social support and exercise motivation: the PESSOA program.

The aim of this study was to explore the effects of social support and behavioral regulation of exercise on physical activity (PA) and quality of life (QoL), in a Portuguese school-based intervention. We hypothesized that serial mediation effects would be present leading to greater levels of PA and QoL. The sample comprised 1042 students (549 boys), aged 10-16 years, BMI = 19.31 ± 3.51, allocated to two groups of schools: control (n = 207) and intervention (n = 835). This study will report the 24 months results of the program, which aimed to develop healthy lifestyles. Questionnaires were used to measure PA, QoL, motivation to exercise and social support. There was no direct impact of the intervention on QoL or PA. Serial mediation analyses were conducted. Social support (P < 0.019) and intrinsic motivation (P = 0.085) increased more on intervention group. Indirect effects were observed in all serial mediation models. The positive indirect effects on PA and QoL were explained by the increase on peer/parent support in serial with the increase in intrinsic motivation (P < 0.01). Parental support led to an increase on external motivation (P < 0.05), which buffered the effects of the intervention. This school-based intervention promoted the development of social support and motivational mechanisms that explained higher levels of PA and QoL.

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.

BACKGROUND: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. OBJECTIVE: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches. METHODS: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. RESULTS: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. CONCLUSIONS: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

Correction: A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

[This corrects the article DOI: 10.1371/journal.pone.0235144.].

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

BACKGROUND: Commercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions. This study aimed to evaluate methods to address missingness in data collected from commercial activity monitors. METHODS: This study utilised 1526 days of near complete data from 109 adults participating in a European weight loss maintenance study (NoHoW). We conducted simulation experiments to test a novel scaling methodology (NoHoW method) and alternative imputation strategies (overall/individual mean imputation, overall/individual multiple imputation, Kalman imputation and random forest imputation). Methods were compared for hourly, daily and 14-day physical activity estimates for steps, total daily energy expenditure (TDEE) and time in physical activity categories. In a second simulation study, individual multiple imputation, Kalman imputation and the NoHoW method were tested at different positions and quantities of missingness. Equivalence testing and root mean squared error (RMSE) were used to evaluate the ability of each of the strategies relative to the true data. RESULTS: The NoHoW method, Kalman imputation and multiple imputation methods remained statistically equivalent (p<0.05) for all physical activity metrics at the 14-day level. In the second simulation study, RMSE tended to increase with increased missingness. Multiple imputation showed the smallest RMSE for Steps and TDEE at lower levels of missingness (<19%) and the Kalman and NoHoW methods were generally superior for imputing time in physical activity categories. CONCLUSION: Individual centred imputation approaches (NoHoW method, Kalman imputation and individual Multiple imputation) offer an effective means to reduce the biases associated with missing data from activity monitors and maximise data retention.