Importance of early weight loss and other predictors of lower weight loss in a commercial program: A secondary data analysis
Objective There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer‐term weight loss. This secondary analysis of data from a commercial program there...
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| Published in | Obesity science & practice Vol. 10; no. 1; pp. e724 - n/a |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
John Wiley & Sons, Inc
01.02.2024
John Wiley and Sons Inc Wiley |
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| Online Access | Get full text |
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| Abstract | Objective
There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer‐term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit.
Methods
Client charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss.
Results
The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold.
Conclusions
Lower 4‐week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals.
This analysis adds to the growing evidence suggesting that weight loss during the early weeks of an intervention may be predictive of longer‐term success. To our knowledge, this analysis is also the first to evaluate the sensitivity and specificity of various weight loss thresholds within the context of a commercial weight loss program. An additional unique aspect of this study, not typically evaluable in clinical trials that have a defined study length or within research trials which incentivize participants for study completion, is the evaluation of the relationship between early weight loss and time spent in the program. |
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| AbstractList | This analysis adds to the growing evidence suggesting that weight loss during the early weeks of an intervention may be predictive of longer‐term success. To our knowledge, this analysis is also the first to evaluate the sensitivity and specificity of various weight loss thresholds within the context of a commercial weight loss program. An additional unique aspect of this study, not typically evaluable in clinical trials that have a defined study length or within research trials which incentivize participants for study completion, is the evaluation of the relationship between early weight loss and time spent in the program. Objective There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer‐term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit. Methods Client charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss. Results The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold. Conclusions Lower 4‐week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals. This analysis adds to the growing evidence suggesting that weight loss during the early weeks of an intervention may be predictive of longer‐term success. To our knowledge, this analysis is also the first to evaluate the sensitivity and specificity of various weight loss thresholds within the context of a commercial weight loss program. An additional unique aspect of this study, not typically evaluable in clinical trials that have a defined study length or within research trials which incentivize participants for study completion, is the evaluation of the relationship between early weight loss and time spent in the program. ObjectiveThere is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer-term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit.MethodsClient charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss.ResultsThe odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold.ConclusionsLower 4-week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals. There is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer-term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit. Client charts of adults with overweight or obesity ( = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss. The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, < 0.01) compared with those who did not meet the 2% threshold. Lower 4-week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals. Abstract Objective There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer‐term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit. Methods Client charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss. Results The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold. Conclusions Lower 4‐week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals. There is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer-term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit.ObjectiveThere is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer-term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit.Client charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss.MethodsClient charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss.The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold.ResultsThe odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold.Lower 4-week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals.ConclusionsLower 4-week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals. |
| Author | Bell, Marjorie Coleman, Christopher D. Kiel, Jessica R. Unick, Jessica L. Jonnalagadda, Satya S. Maki, Kevin C. Guarneiri, Liana L. Wilcox, Meredith L. |
| AuthorAffiliation | 3 Indiana University Department of Applied Health Science School of Public Health‐Bloomington Bloomington Indiana USA 4 The Miriam Hospital's Weight Control and Diabetes Research Center Warren Alpert Medical School at Brown University Providence Rhode Island USA 1 Department of Scientific and Clinical Affairs Medifast, Inc. Baltimore Maryland USA 2 Midwest Biomedical Research Addison Illinois USA |
| AuthorAffiliation_xml | – name: 3 Indiana University Department of Applied Health Science School of Public Health‐Bloomington Bloomington Indiana USA – name: 4 The Miriam Hospital's Weight Control and Diabetes Research Center Warren Alpert Medical School at Brown University Providence Rhode Island USA – name: 1 Department of Scientific and Clinical Affairs Medifast, Inc. Baltimore Maryland USA – name: 2 Midwest Biomedical Research Addison Illinois USA |
| Author_xml | – sequence: 1 givenname: Christopher D. orcidid: 0000-0003-0580-0122 surname: Coleman fullname: Coleman, Christopher D. email: christopher.coleman@medifastinc.com organization: Medifast, Inc – sequence: 2 givenname: Jessica R. orcidid: 0000-0001-9808-0405 surname: Kiel fullname: Kiel, Jessica R. organization: Medifast, Inc – sequence: 3 givenname: Liana L. surname: Guarneiri fullname: Guarneiri, Liana L. organization: Midwest Biomedical Research – sequence: 4 givenname: Marjorie surname: Bell fullname: Bell, Marjorie organization: Midwest Biomedical Research – sequence: 5 givenname: Meredith L. surname: Wilcox fullname: Wilcox, Meredith L. organization: Midwest Biomedical Research – sequence: 6 givenname: Kevin C. surname: Maki fullname: Maki, Kevin C. organization: School of Public Health‐Bloomington – sequence: 7 givenname: Jessica L. orcidid: 0000-0002-6807-9515 surname: Unick fullname: Unick, Jessica L. organization: Warren Alpert Medical School at Brown University – sequence: 8 givenname: Satya S. surname: Jonnalagadda fullname: Jonnalagadda, Satya S. organization: Medifast, Inc |
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| Copyright | 2023 Jason Pharmaceuticals, Inc and The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd. 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | Objective
There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the... There is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early... This analysis adds to the growing evidence suggesting that weight loss during the early weeks of an intervention may be predictive of longer‐term success. To... ObjectiveThere is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the... Abstract Objective There is substantial inter‐individual variability in response to weight loss interventions and emerging evidence suggests that weight loss... |
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| SubjectTerms | Body fat Intervention meal replacements non‐responder Original Overweight Variance analysis Weight Weight control weight loss |
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| Title | Importance of early weight loss and other predictors of lower weight loss in a commercial program: A secondary data analysis |
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