Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial (Preprint)

BACKGROUND Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie., minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness. OBJECTIVE This study examined weekly patterns of adherence to steps-based and minutes-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention. METHODS Participants were prescribed weekly PA goals for steps (7,000 to 10,000 steps/day) and minutes of MVPA (50 to 200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as “adherent” if they self-monitored their PA and met the program PA goal, “suboptimally adherent” if they self-monitored but did not meet the program goal, or “nonadherent” if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression. RESULTS Individuals (N=212) were predominantly middle-aged females with obesity, and 31.6% self-identified as a racial/ethnic minority. Initially, 34.4% were categorized as “adherent” to steps-based goals (51.9% “suboptimally adherent” and 13.7% “nonadherent”), and there was a high probability of either remaining “suboptimally adherent” from week-to-week or transitioning to a “nonadherent” status. On the other hand, 70.3% of individuals started out “adherent” to minutes-based goals (16.0% “suboptimally adherent” and 13.7% “nonadherent”), with “suboptimally adherent” seen as the most variable status. During the graded goal phase, individuals were more likely to transition to a less adherent status for minutes-based goals (OR 1.39, 95% CI 1.31-1.48) compared to steps-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minutes-based goals: OR 1.06, 95% CI 1.05, 1.08 versus steps-based goals: OR 1.07, 95% CI 1.05, 1.08). CONCLUSIONS States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial two months to bring more people towards “adherent” behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. While this study describes the probability of adhering to steps-based and minutes-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions. CLINICALTRIAL This study was a secondary analysis of a pre-registered randomized trial (Trial Registration: ClinicalTrials.gov NCT02688621).

“Perfect Leader, Perfect Leadership?” Linking Leaders’ Perfectionism to Monitoring, Transformational, and Servant Leadership Behavior

Despite the growing interest in perfectionism and its many facets, there is a lack of research on this phenomenon in the context of leadership. Attending to this deficit, the present study is the first to investigate the relationship between the three facets of perfectionism (self-oriented, socially prescribed, and other-oriented perfectionism) and three types of self-rated leadership behavior. In Study 1 (N = 182), leaders’ perfectionism and its association to their organizational, goal-oriented leadership behavior—self-rated as transactional (management by exception) and transformational leadership—is explored. In Study 2 (N = 185), the relationship of leaders’ perfectionism to their servant leadership as a people-centered leadership behavior is investigated. In line with the perfectionism social disconnection model (PSDM), we assume other-oriented and socially prescribed perfectionism to be positively related to management by exception (i.e., monitoring behavior) and negatively related to transformational and servant leadership, whereas the opposite pattern is primarily predicted for self-oriented perfectionism. Our findings in Study 1 reveal a negative relationship between leaders’ self-oriented perfectionism as well as positive relationships to their other-oriented and socially prescribed perfectionism in management by exception, while no substantial correlations with transformational leadership have emerged. In Study 2, a negative association between other-oriented perfectionism and the forgiveness dimension of servant leadership is revealed, indicating a possible barrier to building interpersonal relationships of acceptance and trust. Additionally, self-oriented perfectionism has been proven to be a rather favorable trait in servant leadership.

Onset of smoking amongst middle school students in Switzerland

Smoking is a major concern amongst youth in Switzerland. This study aims to understand the longitudinal drivers of smoking initiation among middle school students in Switzerland. Data was collected as part of an ongoing longitudinal survey study. Participants were students from middle schools in Switzerland. 1076 adolescents were surveyed in four waves from 2015 to 2019. All participants were non-smokers at the start of the study, when they were aged 10 respectively 11. Furthermore, data from adolescents' parents were collected, including their smoking behavior, perceived quality of relation with their child, as well as parental monitoring behavior. A survival analysis was carried out, describing if and when the event of smoking initiation occurs among adolescents. Our life table showed that 31% (n = 330) of participants started smoking between the first and fourth years of their schooling. The proportion of non-smoking participants decreased yearly. The proportions of non-smokers were 0.98, 0.93, 0.86, and 0.83 in 2016, 2017, 2018 and 2019 respectively. A life table was used to describe and summarize the sample distribution of smoking initiation and the percentage of risk. Second, a Discrete-Time Hazard Model was tested with parental smoking behavior, perceived quality of relation with their child and parental monitoring behavior as drivers to predict adolescents' smoking initiation. The findings show that the number of smokers and new smokers increases over time among adolescents. Parental smoking behavior and the perceived quality of relationship with their offspring are factors that influence an adolescent's smoking initiation. This suggests that there may be sharing and normative influences amongst the cohorts as students move up the school grades. Future studies should investigate personal and environmental factors that contribute to smoking initiation amongst adolescents. Key messages Findings show that the number of smokers and new smokers increases over time among adolescents. Sharing and normative influences among adolescents as they move up the school grades may influence smoking initiation.

Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

Background Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors. Objective The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics. Methods This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test. Results The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A1c level were more likely to be in the low and waning engagement group. Conclusions We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition. International Registered Report Identifier (IRRID) RR2-10.2196/13517