Evidence map›Paper›PMID 38287333›Full record

ArticleBMC public health2024

Why more successful? An analysis of participants' self-monitoring data in an online weight loss intervention.

Hai-Bo Tang, Nurul Iman Binti Abdul Jalil, Chee-Seng Tan, Ling He, Shu-Juan Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.5field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Hai-Bo TangFaculty of Education, Yibin University, Yibin, 644000, China. tanghaibo555@163.com.
Nurul Iman Binti Abdul JalilDepartment of Psychology and Counselling, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia.
Chee-Seng TanSchool of Psychology, College of Liberal Arts Wenzhou-Kean University, Wenzhou, Zhejiang province, 325060, China.
Ling HeFaculty of Education, Yibin University, Yibin, 644000, China.
Shu-Juan Zhang, Sichuan Tianfu New District No. 3 Middle School, Chengdu, 610213, China.
Yibin University · CNUniversiti Tunku Abdul Rahman · MYWenzhou-Kean University · CN

Funding

Sichuan Mental Health Education Research Center No. XLJKJY202129BThe special research grant for pedagogy of Yibin University No. 0216014102
6 · The paper itself

Abstract

backgroundSelf-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.

methodsThis 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.

resultsAfter 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.

conclusionThe 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.

Indexed as

Health BehaviorWeight LossAdultBehavior TherapyBody Mass IndexEnergy IntakeHumansContent analysisGroup counselingOnline interventionSelf-monitoringWeight loss

Identifiers

PMID38287333
PMCPMC10826064
OpenAlexW4391324559

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.