Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention
Abstract Background Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact. Methods This study examined self-monitoring data from 61 Chinese adults who participated in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-024-17848-9 |
| _version_ | 1797272869520015360 |
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| author | Hai-Bo Tang Nurul Iman Binti Abdul Jalil Chee-Seng Tan Ling He Shu-Juan Zhang |
| author_facet | Hai-Bo Tang Nurul Iman Binti Abdul Jalil Chee-Seng Tan Ling He Shu-Juan Zhang |
| author_sort | Hai-Bo Tang |
| collection | DOAJ |
| description | Abstract Background Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact. Methods This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants’ daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants’ qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups. Results After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories. Conclusion The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters’ weight loss motivation and promote adherence to self-monitoring practices. |
| first_indexed | 2024-03-07T14:36:28Z |
| format | Article |
| id | doaj.art-037b9450d68a49c1bdf5434e16ca0c28 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-07T14:36:28Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-037b9450d68a49c1bdf5434e16ca0c282024-03-05T20:37:23ZengBMCBMC Public Health1471-24582024-01-0124111310.1186/s12889-024-17848-9Why more successful? An analysis of participants’ self-monitoring data in an online weight loss interventionHai-Bo Tang0Nurul Iman Binti Abdul Jalil1Chee-Seng Tan2Ling He3Shu-Juan ZhangFaculty of Education, Yibin UniversityDepartment of Psychology and Counselling, Universiti Tunku Abdul RahmanSchool of Psychology, College of Liberal Arts Wenzhou-Kean UniversityFaculty of Education, Yibin UniversityAbstract Background Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact. Methods This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants’ daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants’ qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups. Results After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories. Conclusion The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters’ weight loss motivation and promote adherence to self-monitoring practices.https://doi.org/10.1186/s12889-024-17848-9Online interventionContent analysisSelf-monitoringWeight lossGroup counseling |
| spellingShingle | Hai-Bo Tang Nurul Iman Binti Abdul Jalil Chee-Seng Tan Ling He Shu-Juan Zhang Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention BMC Public Health Online intervention Content analysis Self-monitoring Weight loss Group counseling |
| title | Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention |
| title_full | Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention |
| title_fullStr | Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention |
| title_full_unstemmed | Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention |
| title_short | Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention |
| title_sort | why more successful an analysis of participants self monitoring data in an online weight loss intervention |
| topic | Online intervention Content analysis Self-monitoring Weight loss Group counseling |
| url | https://doi.org/10.1186/s12889-024-17848-9 |
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