Mixed-methods study of lifestyle wellness coaching intervention among overweight people (WellCo) (WellCo)
Main funder
Funder's project number: -
Funds granted by main funder (€)
- 120 000,00
Funding program
Project timetable
Project start date: 01/08/2018
Project end date: 31/12/2022
Summary
The purpose of this mixed-methods intervention study is to improve bodily knowledge, physical activity, body composition, weight control, and wellbeing among overweight people. The quantitative study is a quasi-experimental design with control group and data collection at baseline, post-intervention and 12-month follow-up. The qualitative study is a longitudinal design with four data collection waves and an analysis of change over time. Overweight subjects (BMI ≥ 30 kg/m2) are recruited via occupational health care in the Central Finland region and are allocated (men/women, mean age, mean BMI) to either the intervention group (n = 120) or a control group (n = 120) representing care as usual in the occupational setting.
The intervention includes both face-to-face and online coaching along with supervised and independent training. The training program contains both moderate-intensity aerobic physical activity and vigorous-intensity aerobic physical activity. Furthermore, the intervention contains a variety of methods for self-regulatory skills training; goal-setting, action planning, self-monitoring, self-reinforcement, being-present training, acceptance and imagery exercises, and bodywork exercises. The bodywork exercises focus, at first, on exploring and identifying bodily sensations and movements, and then modifying one’s own movements and physical activity based on these bodily findings.
Qualitative longitudinal datasets will be drawn from three sources; participants’ online logbooks, videotaped data of body movements, and stimulated recall interviews. The key purpose of qualitative analysis of this study is to investigate the individual narratives, critical moments and processes involved in the formulation of bodily knowledge and relationship to physical activity based on the participants’ self-reports and actual body movements and experiences. In the quantitative study, the outcomes include measures of physical activity, self-efficacy for physical activity, mental wellbeing, stress level, body composition, waist circumference (cm) and weight loss (kg). The key purpose of quantitative analysis of this study is to identify different trajectories in psychological factors, physical activity and body composition by using latent class analysis.
The intervention includes both face-to-face and online coaching along with supervised and independent training. The training program contains both moderate-intensity aerobic physical activity and vigorous-intensity aerobic physical activity. Furthermore, the intervention contains a variety of methods for self-regulatory skills training; goal-setting, action planning, self-monitoring, self-reinforcement, being-present training, acceptance and imagery exercises, and bodywork exercises. The bodywork exercises focus, at first, on exploring and identifying bodily sensations and movements, and then modifying one’s own movements and physical activity based on these bodily findings.
Qualitative longitudinal datasets will be drawn from three sources; participants’ online logbooks, videotaped data of body movements, and stimulated recall interviews. The key purpose of qualitative analysis of this study is to investigate the individual narratives, critical moments and processes involved in the formulation of bodily knowledge and relationship to physical activity based on the participants’ self-reports and actual body movements and experiences. In the quantitative study, the outcomes include measures of physical activity, self-efficacy for physical activity, mental wellbeing, stress level, body composition, waist circumference (cm) and weight loss (kg). The key purpose of quantitative analysis of this study is to identify different trajectories in psychological factors, physical activity and body composition by using latent class analysis.
