Goal setting and achievement for walking: A series of N-of-1 digital interventions

Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.

Parent, child, and family outcomes following Acceptance And Commitment Therapy for parents of autistic children: A randomized controlled trial

LAY ABSTRACT

Parents of autistic children commonly experience difficulties with their own mental health. This study looked at the effects of a brief group-based Acceptance and Commitment Therapy program, developed for parents of autistic children, youth, and adults. ACT focuses on increasing psychological flexibility, which is the ability to be mindful and accepting of difficult thoughts and experiences, shown to be important for mental wellness. Participants included 54 parents of autistic people, ages 3-34. Parents were randomly divided into two groups: a Treatment group that received the intervention right away, and a Waitlist group that completed the program after the Treatment group completed the trial. All parents filled out questionnaires right before the program began, and at 3, 7, and 17 weeks after randomization. Compared to the group that was waiting to participate in the program, parents in the Treatment group reported greater improvements in depression and family distress, and these improvements were still present 4 months later. Parents in the Treatment group also reported short-term improvements in their positive feelings and personal goals, compared to those waiting. Results showed that ACT may help improve some aspects of mental health for parents of autistic children, but further research is recommended.

Predictive factors of students' physical activity in different learning interest phases: A cluster analysis of physical education

INTRODUCTION

This study investigates the relationships among students' perception of the sports environment, expectancy value, achievement goals, physical activity, sports knowledge and skills in physical education (PE) in different learning interest phases.

METHOD

This cross-sectional study used a sample of 4367 Chinese primary and secondary school students enrolled in PE classes, Mage  = 12.1, SD = 2.9, 48.9% males. Validated questionnaires assessed their physical activity, expectancy value, achievement goals, and sports knowledge and skills. Cluster analysis examined four student profiles in relation to their learning interest in PE. For each profile identified, correlation and multiple regression analyses examined the relationships among students' expectancy value, achievement goals, sports knowledge and skills, and physical activity.

RESULTS

Four different student interest profiles were identified, revealing a continuum from the "triggered situational interest" (TSI) to the "well-developed individual interest" (WII) phases. The results confirmed the key role played by students' perception of the family sports environment and sports knowledge and skills in their PE interest in all four phases, while sex, perception of the school and social sports environment, achievement goals and expectancy value had different predictive effects on students' physical activity in different interest phases.

CONCLUSIONS

The findings from this study provide a new lens by which to divide the phases of learning interest in PE. Students' characteristics differed among the four different learning interest phases, and the impacts on physical activity varied. Future research is needed to consider the interest phase of adolescents to make more targeted guidance measures when improving physical activities.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

BACKGROUND

The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking.

METHODS

We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months.

DISCUSSION

The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05351359, 28/04/2022).

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.

Digital intervention promoting physical activity among obese people (DIPPAO) randomised controlled trial: study protocol

INTRODUCTION

Physical inactivity and excessive sedentary behaviours are major preventable causes in both the development and the treatment of obesity and type 2 diabetes mellitus (T2DM). Nevertheless, current programmes struggle to engage and sustain physical activity (PA) of patients over long periods of time. To overcome these limitations, the Digital Intervention Promoting Physical Activity among Obese people randomised controlled trial (RCT) aims to evaluate the effectiveness of a group-based digital intervention grounded on gamification strategies, enhanced by social features and informed by the tenets of the self-determination theory and the social identity approach.

METHODS AND ANALYSIS

This trial is a two-arm parallel RCT testing the effectiveness of the Kiplin digital intervention on obese and patients with T2DM in comparison to the usual supervised PA programme of the University Hospital of Clermont-Ferrand, France. A total of 50 patients will be randomised to one of the two interventions and will follow a 3-month programme with a 6-month follow-up postintervention. The primary outcome of the study is the daily step count change between the baseline assessment and the end of the intervention. Accelerometer data, self-reported PA, body composition and physical capacities will also be evaluated. To advance our understanding of complex interventions like gamified and group-based ones, we will explore several psychological mediators relative to motivation, enjoyment, in-group identification or perceived weight stigma. Finally, to assess a potential superior economic efficiency compared with the current treatment, we will conduct a cost-utility analysis between the two conditions. A mixed-model approach will be used to analyse the change in outcomes over time.

ETHICS AND DISSEMINATION

The research protocol has been reviewed and approved by the Local Human Protection Committee (CPP Ile de France XI, No 21 004-65219). Results will inform the Kiplin app development, be published in scientific journals and disseminated in international conferences.

TRIAL REGISTRATION NUMBER

NCT04887077.

The Niche of n-of-1 Trials in Precision Medicine for Weight Loss and Obesity Treatment: Back to the Future

PURPOSE OF REVIEW

The n-of-1 clinical trials are considered the epitome of individualized health care. They are employed to address differences in treatment response and adverse events between patients, in a comparative effectiveness manner, extending beyond the delivery of horizontal recommendations for all.

RECENT FINDINGS

The n-of-1 design has been applied to deliver precision exercise interventions, through eHealth and mHealth technologies. Regarding personalized and precision medical nutrition therapy, few trials have implemented dietary manipulations and one series of n-of-1 trials has applied comprehensive genetic data to improve body weight. With regard to anti-obesity medication, pharmacogenetic data could be applied using the n-of-1 trial design, although none have been implemented yet. The n-of-1 clinical trials consist of the only tool for the delivery of evidence-based, personalized obesity treatment (lifestyle and pharmacotherapy), reducing non-responders, while tailoring the best intervention to each patient, through "trial and error". Their application is expected to improve obesity treatment and mitigate the epidemic.

Fitness center use and subsequent achievement of exercise goals. A prospective study on long-term fitness center members

Background

Knowledge on the relationship between fitness center use and long-term members’ subsequent goal achievement is limited. Therefore, the aim was to investigate the prospective association between the use of fitness centers during 18 months and subsequent self-reported goal achievement among long-term members.

Methods

This was a registry- and survey-based longitudinal study of 2851 people who had been members at a Norwegian fitness center chain for more than two years. Fitness center use from December 2016 to June 2018 was obtained from registry data. Subsequent goal achievement was measured in a survey in June 2018, assessed by a 1–100 visual analogue scale, and a score between 0 and 50 was defined as low goal achievement.

Results

Visiting the fitness center frequently and regularly, and having frequent group activity bookings were associated with higher subsequent self-reported goal achievement. Participants with fewest visits (1–57 days) during 18 months were more likely to report low goal achievement than participants with most visits (118–543 days) (OR = 8.5; 95% CI 6.3–11.4). Fitness trainer bookings was not clearly associated with subsequent goal achievement.

Conclusions

Frequent and regular long-term fitness center use were associated with higher subsequent self-reported goal achievement.

Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior

Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.