How self-monitoring and motivation fuel online weight loss success

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recent BMC Public Health The study analyzed self-monitoring data from Chinese adults who participated in a group weight loss intervention using a mixed-methods approach.

Study: Why more successful?  Analysis of self-monitoring data from participants in an online weight loss intervention.  Image credit: Ground Pictures / Shutterstock.com Study: Why more successful? Analysis of self-monitoring data from participants in an online weight loss intervention. Image credit: Ground Pictures / Shutterstock.com

Background

According to the World Health Organization (WHO), 1.9 billion adults were overweight in 2016. This global public health problem has reached alarming proportions in China, significantly increasing the risk of several diseases, including cancer, diabetes and cardiovascular disease.

Self-monitoring, which increases self-awareness, encourages desired behavior, and reduces maladaptive behavior, can be achieved through setting specific goals and logging progress. Changes in body weight, exercise and diet are routinely monitored by participants in weight loss interventions. Indeed, obese individuals who periodically monitored their diet and body weight experienced a more beneficial response to the intervention.

Researchers analyzed dieters’ self-monitoring behaviors both quantitatively and qualitatively; However, few have used a mixed-methods approach for this purpose. Significant advantages of mixed-methods approaches include the ability to elucidate relationships between weight loss and various self-monitoring indicators and ultimately reduce bias to develop reliable insights into self-monitoring.

About the study

The current study analyzed self-monitoring data from 61 Chinese adults who participated in a five-week online weight loss intervention group. In addition to providing information about their weight loss motivation and body mass index (BMI) values, study participants engaged in daily quantitative monitoring, which included parameters such as caloric intake and sedentary behavior, as well as qualitative self-monitoring, which involved Daily log of weight loss progress.

A scoring rule evaluates the timeliness of the data. A one-way repeated measures ANOVA was used to analyze the dynamics of self-monitoring indices.

Regression and correlation analyzes were performed to explore the relationship between weight change, self-monitoring indices and baseline data. Participants were divided into three categories based on their weight loss results, and their qualitative data were assessed using content analysis.

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Some fluctuations in self-monitoring data were observed across interventions. Furthermore, some of the participants’ baseline characteristics and self-monitoring behaviors were positively associated with their final weight loss outcomes. Across weight loss categories, heterogeneity was observed in qualitative self-monitoring data.

During the weight loss process, a gradual decrease in calorie intake was observed, thus suggesting a learning behavior among the participants. Over the past week, participants have shown some variation in commitment levels, leading to concerns about a rebound in calorie intake.

Satisfaction with weight loss was highest during the first week and gradually decreased. This decline in satisfaction was consistent with their weight loss, highlighting the link between effort and outcome.

Motivation to lose weight, baseline BMI, and timeliness of completion of daily self-monitoring data predicted final weight loss. The relationship between weight loss, daily physical activity expenditure, and daily caloric intake was insignificant. Additionally, no significant relationship was observed between weight loss and daily mood.

Qualitative analysis of participants’ daily logs revealed four categories: eating behavior, weight loss awareness, physical activity, and perception of change, with the latter being the most frequently mentioned. This was followed by weight loss awareness, eating behavior and physical activity.

Asymmetry was observed in the probability distribution of participants’ daily log frequencies. The poor and moderate weight loss groups reported lower observation frequencies in all four categories than the excellent group. The excellent group reported higher frequencies of adjustment in eating habits, self-awareness, difficulties, and greater endurance.

Conclusion

An inconsistent pattern of self-monitoring behavior was observed among individuals undergoing a group weight loss intervention. More specifically, a high level of self-monitoring was identified during the initial weeks of weight loss, followed by a gradual decline.

Higher levels of motivation, higher baseline BMI, and more significant weight loss were reported by those who self-monitored regularly. Furthermore, more detailed and frequent content was reported in texts submitted by successful participants.

These findings suggest that weight loss motivation and self-monitoring should be emphasized. The use of digital technologies can be beneficial, as they can promote greater weight loss awareness and healthy eating habits.

In the future, more studies with larger sample sizes and precise measurement tools are needed to assess daily caloric expenditure and intake.



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