Effects of goal-setting and of goal levels on weight loss induced by self-monitoring of caloric intake.

1981 ◽  
Vol 13 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Pierre Baron ◽  
Robert G. Watters
Keyword(s):  
Weight Loss ◽  
Goal Setting ◽  
Caloric Intake ◽  
Self Monitoring

Behavior Modification In The Treatment Of Obesity

2018 ◽  
Author(s):  
Paul Davidson
Keyword(s):  
Weight Loss ◽  
Stages Of Change ◽  
Sense Of Self ◽  
Relevant Literature ◽  
Caloric Intake ◽  
Control Treatment ◽  
Patient Specific ◽  
Self Monitoring ◽  
Treatment Of Obesity ◽  
Change Stimulus

This review addresses the three most common components used in helping individuals deal with weight loss from a behavioral perspective. Relevant literature and recent findings are reviewed and summarized, showing that programs containing behavioral techniques, along with an emphasis on diet and exercise, tend to lead to improved results. Factors related to better weight loss outcomes include assessing empathically, setting reasonable goals, enhancing a sense of self-determination, seeing a patient more frequently, focusing on decreasing caloric intake, and encouraging regular physical activity. Newer technologies, such as Internet- and smartphone-based interventions, seem promising but lack sufficient research evidence at this point. It is also clear that just as reasons for weight gain are patient specific, treatments likely do best when they are more highly individualized. This review contains 3 figures, 2 tables and 33 references Key words: behavior, cognitive restructuring, decision tree, 5As, intervention, mindfulness, modification, motivation, obesity, relapse prevention, self-monitoring, stages of change, stimulus control, treatment, weight loss

Session 3: Goal Setting; Weighing and Measuring

2016 ◽  
pp. 27-34
Author(s):  
Evan M. Forman ◽  
Meghan L. Butryn
Keyword(s):  
Weight Loss ◽  
Goal Setting ◽  
Caloric Intake ◽  
Short Term ◽  
Total Caloric Intake ◽  
Food And Beverages ◽  
Over Time

This chapter (Session 3) focuses on teaching clients how to set effective weight loss goals by choosing goals that are reasonable, active, short term, and time limited. Methods of evaluating goals are presented, such as tracking progress over time and sharing goals with others. This chapter also discusses the importance of weighing and measuring food and beverages by using measuring utensils or prepackaged meal options to promote accurate recording of total caloric intake.

scholarly journals Delivering a Group-Mediated Weight Loss and Activity Program to Older Adults With Chronic Pain in the MORPH Study

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 281-282
Author(s):  
Amber Brooks ◽  
Barbara Nicklas ◽  
W Jack Rejeski ◽  
Jason Fanning
Keyword(s):  
Older Adults ◽  
Chronic Pain ◽  
Weight Loss ◽  
Sedentary Behavior ◽  
Interpersonal Communication ◽  
Group Intervention ◽  
Caloric Intake ◽  
Self Monitoring ◽  
Intervention Delivery ◽  
The Impact

Abstract Chronic pain in aging is a potent cause and consequence of obesity, inactivity, and prolonged sedentary behavior, making these especially important targets for behavioral intervention. This study aimed to refine a theory-based group-mediated diet and sedentary behavior intervention for older adults with chronic pain. Participants (N=28) attended 12 weekly group meetings generally in home via WebEx and used an mHealth self-monitoring app as they attempted to move more often and reduce caloric intake. Relative to a control condition, the program produced improvements in physical function (η^2=.08), pain intensity (η^2=.12), sedentary time (η^2=.07), and weight loss (η^2=.21). Key findings related to effective remote group intervention delivery included: (1) the importance of a self-efficacy-enhancing technology orientation; (2) the value of small group bonding activities to seed communication; and (3) the impact of software choice on interpersonal communication. We will discuss the value of these findings for future remote intervention design.

scholarly journals Persuasive System Design Principles and Behavior Change Techniques to Stimulate Motivation and Adherence in Electronic Health Interventions to Support Weight Loss Maintenance: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Rikke Aune Asbjørnsen ◽  
Mirjam Lien Smedsrød ◽  
Lise Solberg Nes ◽  
Jobke Wentzel ◽  
Cecilie Varsi ◽  
...  
Keyword(s):  
Weight Loss ◽  
Behavior Change ◽  
Goal Setting ◽  
Weight Loss Maintenance ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Support Weight ◽  
And Behavior ◽  
Weight Loss Interventions

BACKGROUND Maintaining weight after weight loss is a major health challenge, and eHealth (electronic health) solutions may be a way to meet this challenge. Application of behavior change techniques (BCTs) and persuasive system design (PSD) principles in eHealth development may contribute to the design of technologies that positively influence behavior and motivation to support the sustainable health behavior change needed. OBJECTIVE This review aimed to identify BCTs and PSD principles applied in eHealth interventions to support weight loss and weight loss maintenance, as well as techniques and principles applied to stimulate motivation and adherence for long-term weight loss maintenance. METHODS A systematic literature search was conducted in PsycINFO, Ovid MEDLINE (including PubMed), EMBASE, Scopus, Web of Science, and AMED, from January 1, 2007 to June 30, 2018. Arksey and O’Malley’s scoping review methodology was applied. Publications on eHealth interventions were included if focusing on weight loss or weight loss maintenance, in combination with motivation or adherence and behavior change. RESULTS The search identified 317 publications, of which 45 met the inclusion criteria. Of the 45 publications, 11 (24%) focused on weight loss maintenance, and 34 (76%) focused on weight loss. Mobile phones were the most frequently used technology (28/45, 62%). Frequently used wearables were activity trackers (14/45, 31%), as well as other monitoring technologies such as wireless or digital scales (8/45, 18%). All included publications were anchored in behavior change theories. Feedback and monitoring and goals and planning were core behavior change technique clusters applied in the majority of included publications. Social support and associations through prompts and cues to support and maintain new habits were more frequently used in weight loss maintenance than weight loss interventions. In both types of interventions, frequently applied persuasive principles were self-monitoring, goal setting, and feedback. Tailoring, reminders, personalization, and rewards were additional principles frequently applied in weight loss maintenance interventions. Results did not reveal an ideal combination of techniques or principles to stimulate motivation, adherence, and weight loss maintenance. However, the most frequently mentioned individual techniques and principles applied to stimulate motivation were, personalization, simulation, praise, and feedback, whereas associations were frequently mentioned to stimulate adherence. eHealth interventions that found significant effects for weight loss maintenance all applied self-monitoring, feedback, goal setting, and shaping knowledge, combined with a human social support component to support healthy behaviors. CONCLUSIONS To our knowledge, this is the first review examining key BCTs and PSD principles applied in weight loss maintenance interventions compared with those of weight loss interventions. This review identified several techniques and principles applied to stimulate motivation and adherence. Future research should aim to examine which eHealth design combinations can be the most effective in support of long-term behavior change and weight loss maintenance.

Session 3: Goal Setting; Weighing and Measuring

2016 ◽  
pp. 25-30
Author(s):  
Evan M. Forman ◽  
Meghan L. Butryn
Keyword(s):  
Weight Loss ◽  
Goal Setting ◽  
Caloric Intake ◽  
Short Term ◽  
Total Caloric Intake ◽  
Food And Beverages ◽  
Over Time

This chapter (Session 3) focuses on teaching clients how to set effective weight loss goals by choosing goals that are reasonable, active, short term, and time limited. Methods of evaluating goals are presented, such as tracking progress over time and sharing goals with others. This chapter also discusses the importance of weighing and measuring food and beverages by using measuring utensils or prepackaged meal options to promote accurate recording of total caloric intake.

scholarly journals Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study

10.2196/18741 ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. e18741
Author(s):  
Gregory Farage ◽  
Courtney Simmons ◽  
Mehmet Kocak ◽  
Robert C Klesges ◽  
G Wayne Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Linear Regression ◽  
Controlled Trial ◽  
Caloric Intake ◽  
Secondary Analysis ◽  
Principal Component ◽  
Exercise Frequency ◽  
Self Monitoring ◽  
Substantial Impact ◽  
Predict Weight Loss

Background Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. Objective The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. Methods In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. Results On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). Conclusions We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. Trial Registration ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

scholarly journals Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study (Preprint)

2020 ◽  
Author(s):  
Gregory Farage ◽  
Courtney Simmons ◽  
Mehmet Kocak ◽  
Robert C Klesges ◽  
G Wayne Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Linear Regression ◽  
Controlled Trial ◽  
Caloric Intake ◽  
Secondary Analysis ◽  
Principal Component ◽  
Exercise Frequency ◽  
Self Monitoring ◽  
Substantial Impact ◽  
Predict Weight Loss

BACKGROUND Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. OBJECTIVE The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial <i>Lose It!</i> app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. METHODS In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. RESULTS On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; <i>P</i>=.001), self-monitored caloric intake (second PC; <i>P</i>=.001), and the frequency of self-weighing at 8 weeks (<i>P</i>=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an <i>R</i><sup>2</sup> value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The <i>R</i><sup>2</sup> value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: <i>P</i>=.77; 12 months: <i>P</i>=.15). CONCLUSIONS We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. CLINICALTRIAL ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

scholarly journals Examination of Nutrition Literacy and Quality of Self-monitoring in Behavioral Weight Loss

2018 ◽  
Vol 52 (9) ◽  
pp. 809-816 ◽  
Author(s):  
Diane L Rosenbaum ◽  
Margaret H Clark ◽  
Alexandra D Convertino ◽  
Christine C Call ◽  
Evan M Forman ◽  
...  
Keyword(s):  
Weight Loss ◽  
Caloric Intake ◽  
Secondary Analysis ◽  
Behavioral Weight Loss ◽  
Food Record ◽  
Obese Adults ◽  
Baseline Measure ◽  
Self Monitoring ◽  
Session Attendance

Abstract Background Few have examined nutrition literacy (i.e., capacity to process and make informed nutritional decisions) in behavioral weight loss. Nutrition literacy (NL) may impact necessary skills for weight loss, contributing to outcome disparities. Purpose The study sets out to identify correlates of NL; evaluate whether NL predicted weight loss, food record completion and quality, and session attendance; and investigate whether the relations of race and education to weight loss were mediated by NL and self-monitoring. Methods This is a secondary analysis of 6-month behavioral weight loss program in which overweight/obese adults (N = 320) completed a baseline measure of NL (i.e., Newest Vital Sign). Participants self-monitored caloric intake via food records. Results NL was lower for black participants (p < .001) and participants with less education (p = .002). Better NL predicted better 6-month weight loss (b = −.63, p = .04) and food record quality (r = .37, p < .001), but not food record completion or attendance (ps > 0.05). Black participants had lower NL, which was associated with poorer food record quality, which adversely affected weight loss. There was no indirect effect of education on weight loss through NL and food record quality. Conclusions Overall, results suggest that lower NL is problematic for weight loss. For black participants, NL may indirectly impact weight loss through quality of self-monitoring. This might be one explanation for poorer behavioral weight loss outcomes among black participants. Additional research should investigate whether addressing these skills through enhanced treatment improves outcomes. Clinical trial information NCT02363010.

Mobile Applications for Health Management of Older Adults: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Nurul Asilah Ahmad ◽  
Shahrul Azman Mohd Noah ◽  
Arimi Fitri Mat Ludin ◽  
Suzana Shahar ◽  
Noorlaili Mohd Tohit
Keyword(s):  
Older Adults ◽  
Goal Setting ◽  
Mobile Applications ◽  
Health Management ◽  
Mobile Apps ◽  
Personalized Feedback ◽  
Self Monitoring ◽  
App Store ◽  
Evidenced Based ◽  
Google Play

BACKGROUND Currently, the use of smartphones to deliver health-related content has experienced a rapid growth, with more than 165,000 mobile health (mHealth) applications currently available in the digital marketplace such as iOS store and Google Play. Among these, there are several mobile applications (mobile apps) that offer tools for disease prevention and management among older generations. These mobile apps could potentially promote health behaviors which will reduce or delay the onset of disease. However, no review to date that has focused on the app marketplace specific for older adults and little is known regarding its evidence-based quality towards the health of older adults. OBJECTIVE The aim of this review was to characterize and critically appraise the content and functionality of mobile apps that focuses on health management and/or healthy lifestyle among older adults. METHODS An electronic search was conducted between May 2019 to December 2019 of the official app store for two major smartphone operating systems: iPhone operating system (iTunes App Store) and Android (Google Play Store). Stores were searched separately using predetermined search terms. Two authors screened apps based on information provided in the app description. Metadata from all included apps were abstracted into a standard assessment criteria form. Evidenced based strategies and health care expert involvement of included apps was assessed. Evidenced based strategies included: self-monitoring, goal setting, physical activity support, healthy eating support, weight and/or health assessment, personalized feedback, motivational strategies, cognitive training and social support. Two authors verified the data with reference to the apps and downloaded app themselves. RESULTS A total of 16 apps met the inclusion criteria. Six out of 16 (37.5%) apps were designed exclusively for the iOS platform while ten out of 16 (62.5%) were designed for Android platform exclusively. Physical activity component was the most common feature offered in all the apps (9/16, 56.3%) and followed by cognitive training (8/16, 50.0%). Diet/nutrition (0/16, 0%) feature, however, was not offered on all reviewed mobile apps. Of reviewed apps, 56.3% (9/16) provide education, 37.5% (6/16) provide self-monitoring features, 18.8% (3/16) provide goal setting features, 18.5% (3/16) provide personalized feedback, 6.3% (1/16) provide social support and none of the reviewed apps offers heart rate monitoring and reminder features to the users. CONCLUSIONS All reviewed mobile apps for older adults in managing health did not focused on diet/nutrition component, lack of functional components and lack of health care professional involvement in their development process. There is also a need to carry out scientific testing prior to the development of the app to ensure cost effective and its health benefits to older adults. Collaborative efforts between developers, researchers, health professionals and patients are needed in developing evidence-based, high quality mobile apps in managing health prior they are made available in the app store.

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