Adding evidence-based behavioral weight loss strategies to a statewide wellness campaign: a randomized clinical trial

An Adaptive Telephone Coaching Intervention for Patients in an Online Weight Loss Program: A Randomized Clinical Trial

Importance

Weight loss (WL) during the first month of a behavioral program is associated with longer-term WL. Testing of translatable and adaptive obesity programs is needed.

Objective

To compare brief, extended, and no telephone coaching for individuals with suboptimal response (ie, 1-month WL <4%) within an online WL program.

Design, Setting, and Participants

This randomized clinical trial with enrollment between March 2019 and April 2022 (data collection completed May 2023) was conducted at an academic research center in the US. Eligible participants included adults aged 18 to 70 years with daily access to internet and a body mass index between 25 and 45.

Interventions

All participants received an automated online WL program (4 months) and WL maintenance program (8 months), consisting of video lessons, self-monitoring, and personalized feedback. Participants were randomized, such that individuals with suboptimal response received either brief telephone coaching (3 calls during weeks 5-8), extended telephone coaching (12 calls during weeks 5-16), or no coaching (control). Coaching included education, problem solving, and goal setting, and promoted engagement with the online program.

Main Outcomes and Measures

The primary outcomes were percent weight change and proportion of participants achieving 5% or greater WL at 4 and 12 months. A priori hypotheses for WL were that WL for extended coaching would be greater than for brief coaching, and both extended and brief coaching would be greater than no coaching (control). A longitudinal mixed-effects model with participant-specific intercept was used to examine intervention effects on percent WL at 4 and 12 months. Secondary analyses focused on program engagement and cost/kilogram of WL.

Results

The study included a total of 437 participants who reported WL at 1 month (mean [SD] age, 50.8 [11.4] years; mean [SD] BMI, 34.6 [5.0]; 305 female [69.8%] and 132 male [30.2%]) with 148 randomized to extended coaching, 143 assigned to brief coaching, and 146 assigned to the control group. Of all participants, 346 (79.2%) were considered to have a suboptimal response. WL at 4 months was significantly greater in the extended coaching group (mean [SD] WL, -7.0% [5.1%]) and brief coaching group (mean [SD] WL, -6.2% [4.7%]) vs the control group (mean [SD] WL, -4.5% [4.7%]) (P < .001). Similarly, the proportion of participants achieving 5% or greater WL at 4 months was greater in the extended coaching group (89 participants [65.9%]) and brief coaching group (77 participants [58.5%]) vs control group (46 participants [36.5%]) (P < .001). At 12 months, a similar pattern was observed for achievement of 5% WL or greater (extended coaching, 63 participants [48.1%]; brief coaching, 58 participants [45.9%]; control, 38 participants [32.8%]; P = .03). Percent WL at 12 months was significantly higher in extended coaching vs control (mean [SD] WL for extended coaching, -5.5% [6.7%]; mean [SD] WL for control, -3.9% [7.4%]; P = .03) but not for brief coaching (mean [SD] WL, -4.9% [6.1%]).Both the brief and extended coaching groups watched more lessons and self-monitored on more days compared with the control group. The cost per additional kilogram of WL, beyond that of the control group, was $50.09 for brief coaching and $92.65 for extended coaching.

Conclusions and Relevance

In this randomized clinical trial testing an adaptive intervention, the provision of coaching for individuals with suboptimal response improved WL and was cost-effective; further testing in clinical settings (eg, health care systems) is warranted.

Trial Registration

ClinicalTrials.gov Identifier: NCT03867981.

Pragmatic Implementation of Online Obesity Treatment and Maintenance Interventions in Primary Care: A Randomized Clinical Trial

Importance

Behavioral weight loss interventions have achieved success in primary care; however, to our knowledge, pragmatic implementation of a fully automated treatment that requires little researcher oversight has not been tested. Moreover, weight loss maintenance remains a challenge.

Objective

To evaluate the long-term effectiveness of an automated, online, behavioral obesity treatment program (Rx Weight Loss [RxWL]) at 12 months (primary end point) and 24 months when delivered pragmatically in primary care and to compare the effectiveness of 3 weight loss maintenance approaches.

Design, Setting, and Participants

This randomized clinical trial of RxWL, an online weight loss program, recruited patients from a Rhode Island primary care network with approximately 60 practices and 100 physicians. Eligible participants were primary care patients aged 18 to 75 years with overweight or obesity who were referred by their nurse care manager and enrolled between 2018 and 2020. All participants were included in the intention-to-treat analysis, whereas only those who engaged with maintenance intervention were included in the per-protocol analysis. Data were analyzed from August 2022 to September 2023.

Interventions

All participants were offered the same 3-month weight loss program, with randomization to one of three 9-month maintenance programs: control intervention (monthly online newsletters), monthly intervention (9 monthly video lessons and 1 week of self-monitoring per month), or refresher intervention (an introductory session and two 4-week periods of lessons and self-monitoring at 7 and 10 months).

Main Outcomes and Measures

The primary outcome was weight change at 12 months using height and weight data collected from electronic medical records through 24 months.

Results

Among the 540 participants (mean [SD] age, 52.8 [13.4] years; 384 females [71.1%]) in the intention-to-treat analysis, mean estimated 3-month weight loss was 3.60 (95% CI, -4.32 to -2.88) kg. At the 12-month primary end point, the amount of weight regained in the monthly (0.37 [95% CI, -0.06 to 0.81] kg) and refresher (0.45 [95% CI, 0.27 to 0.87] kg) maintenance groups was significantly less than that in the newsletter control maintenance group (1.28 [95% CI, 0.85 to 1.71] kg; P = .004). The difference in weight regain between the monthly and refresher maintenance groups was not statistically significant. This pattern persisted at 24 months. In the per-protocol analysis of 253 participants, mean weight loss at the end of the initial 3-month intervention was 6.19 (95% CI, -7.25 to -5.13) kg. Similarly, at 12 months there was less weight regain in the monthly (0.61 kg) and refresher (0.96 kg) maintenance groups than in the newsletter control maintenance group (1.86 kg).

Conclusions and Relevance

Results of this randomized clinical trial indicate that pragmatic implementation of a 12-month automated, online, behavioral obesity treatment that includes 9 months of active maintenance produces clinically significant weight loss over 2 years in primary care patients with overweight or obesity. These findings underscore the importance of providing ongoing maintenance intervention to prevent weight regain.

Trial Registration

ClinicalTrials.gov Identifier: NCT03488212.

Impact of social connectedness on weight loss outcomes in an online program

Obesity is linked to many negative health consequences. While online behavioral weight loss programs (BWL) are an effective treatment for obesity, weight losses are modest. Social connectedness has been found to improve weight loss outcomes and previous findings suggests that it may be especially important for people of color. The present study investigated the impact of social connectedness (structural connectedness, or network size; relationship quality, and functional connectedness, or social support) on weight loss outcomes in an online BWL program and whether Black race or Hispanic ethnicity moderates the relationship between social connectedness and weight loss. Participants (N = 387) enrolled in a 16-week online BWL program and completed measures of social connectedness before treatment and had their weight measured. Individuals with less structural connectedness (smaller social networks) had greater weight losses. Further, higher levels of functional connectedness (affectionate support, positive support, and relationship quality) mediated the relationship between smaller network size and better weight loss outcomes. Black race / Hispanic ethnicity did not moderate the relationship between social connectedness and weight loss. These findings suggest that the quality of one's relationships, not the size of one's social network, is important for weight loss. Future studies may examine whether online BWL programs that build relationship quality and affectionate and positive support in participants' existing social networks improve overall weight loss outcomes.

Characterization of early non-responders within behavioral weight loss treatment

Objective

Given that low early (4 weeks) weight loss (WL) predicts longer-term WL, the purpose of this study was to identify factors associated with poor early WL.

Methods

438 adults with overweight/obesity participating in an Internet-delivered behavioral WL program provided weights at baseline and 4 weeks. Participants were stratified by percent WL at 4 weeks: LOW: <2% WL, MEDIUM: 2 to <4% WL, HIGH: ≥4% WL and groups were compared on baseline variables (demographics, physical activity, and psychosocial measures) and 4-week intervention adherence.

Results

37.4%, 40.9%, and 21.7% of participants had LOW, MEDIUM, and HIGH early WL respectively. LOW was more likely to be female compared to HIGH and less likely to be non-Hispanic White compared to MEDIUM and HIGH (p's<0.05). After controlling for demographic differences, LOW had lower baseline physical activity compared to HIGH and watched fewer video lessons, self-monitored calorie intake and weight on fewer days, and were less likely to achieve the exercise goal compared to MEDIUM and HIGH (p's<0.05).

Conclusion

Findings can inform future adaptive interventions which tailor treatment based upon early WL to improve WL outcomes for more individuals.

Physical activity changes during an automated online weight loss program

Evidence-based online behavioral weight loss (BWL) treatment targets a combination of diet, physical activity, and behavioral skills training. While weight loss outcomes are well documented, little is known about changes in physical activity. This study examined changes in objectively measured physical activity across the energy expenditure spectrum during a fully automated, online BWL program. Adults with overweight or obesity (n = 63) completed a 12-week, online BWL program. Participants wore an accelerometer for 7 days and body weight was measured in-clinic at pre- and post-treatment. At post-treatment, mean daily moderate-to-vigorous physical activity increased by about 4 min (SE = 1.59, p = 0.01). There were no statistically significant changes in light physical activity or time spent sedentary (p's > 0.05). Despite only minimal changes in moderate-to-vigorous physical activity overall, larger increases correlated with greater weight loss (r = - 0.28, p = 0.02), which averaged 6.1% of baseline weight at post-treatment. Though achieving important weight loss outcomes, online programs may fail to produce clinically relevant improvements in physical activity, which can put weight loss maintenance at risk.

Pragmatic implementation of a fully automated online obesity treatment in primary care

OBJECTIVE

Behavioral obesity treatment implemented in primary care is efficacious but typically involves face-to-face or phone contact. This study evaluated enrollment, engagement, and 12-week weight loss in a fully automated online behavioral weight-loss intervention implemented pragmatically in a primary care network.

METHODS

As part of routine primary care, providers and nurse care managers offered a no-cost online obesity treatment program to 1,721 patients. Of these, 721 consented and were eligible (aged 18-75 years with BMI ≥ 25 kg/m² and internet access), and 464 started the program. The program included 12 weekly online lessons, a self-monitoring platform, and automated feedback.

RESULTS

More than one-quarter of patients who were offered the program (26%) initiated treatment. In intent-to-treat analyses using all data available, mean 12-week weight change was -5.10% (SE = 0.21). Patients who submitted their weights on all 12 weeks (37% of 464) lost an estimated 7.2% body weight versus 3.4% in those submitting less frequently.

CONCLUSIONS

This fully automated online program, implemented into the routine workflow of a primary care setting without any human counseling or researcher involvement, produced clinically meaningful short-term weight loss. Greater program engagement was associated with greater weight loss; efforts are needed to understand barriers to engagement.

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

BACKGROUND

Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT.

OBJECTIVE

The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions.

METHODS

Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment.

RESULTS

The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing.

CONCLUSIONS

This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss).

TRIAL REGISTRATION

ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/33568.

Scaling a Community-Wide Campaign Intervention to Manage Hypertension and Weight Loss

Public health impacts can be achieved when evidence-based interventions are implemented to those most in need. Too often implementation never or slowly occurs. The community-wide campaign intervention Tu Salud ¡Si Cuenta! has evidence of improving health outcomes related to chronic disease among low-income, Latinos. Using the RE-AIM Framework, this study examined if the scaled-up version of the intervention is associated with improvements in hypertension and obesity in 12 locations. Each element of the RE-AIM framework was examined. For "Effectiveness," we examined outcomes overall and by implementing location. We used linear and logistic regression to assess if exposure in the intervention was associated with improvement in hypertension and weight loss. Participants were stratified into "low exposure" (2-3 outreach visits) vs. "high exposure" (4-5 outreach visits). Based on the RE-AIM Framework, the intervention "reached" its intended population of low-income Latinos, demonstrated "effectiveness" in improving hypertension and obesity, was "adopted" at a high level in all but one site, was "implemented" with fidelity to the intervention model with moderate success across locations, and showed high "maintenance" over time. For effectiveness specifically, we found that out of 5,019 participants, 2,508 (50%) had a baseline hypertensive blood pressure (BP) reading. Of the 2,508, 1,245 (49.9%) recovered to normal blood pressure or pre-hypertension stage by last follow-up. After adjusting for baseline BP and potential confounders in multivariable linear regression models, the high exposure group had significantly more reduction in systolic BP (adjusted mean difference in % change = -0.96; p = 0.002) and diastolic BP (adjusted mean difference in % change = -1.61; p < 0.0001) compared to the low exposure group. After controlling for baseline weight and other confounders, the high exposure group had significantly greater decrease in weight compared to the low exposure group (adjusted mean difference in % change = -1.28; p < 0.0001). Results from the multivariable logistic regression models indicated that compared to the low exposure group the high exposure group was more likely to achieve a clinically significant minimum 5% weight loss [adjusted odds ratio (OR) = 2.97; p < 0.0001). This study contributes evidence that a Community-Wide Campaign model holds promise for addressing hypertension and obesity among low-income Latinos.

Wellness Coaching: An Intervention to Increase Healthy Behavior in Breast Cancer Survivors

BACKGROUND

Regular physical activity after breast cancer diagnosis improves survival rates and quality of life (QOL). However, breast cancer survivors rarely meet guidelines for recommended levels of physical activity. Wellness coaching interventions (WCIs) have improved exercise and health behaviors in other patient populations.

OBJECTIVES

This study assessed the feasibility and effectiveness of WCIs for increasing physical activity in breast cancer survivors; secondary measures included changes in dietary habits, weight, and QOL.

METHODS

20 obese or overweight breast cancer survivors who recently completed active breast cancer treatment were recruited into a single-arm, 12-week WCI pilot feasibility study. The intervention was comprised of one in-person wellness coaching visit followed by four telephone calls over 12 weeks and 12 weekly emails containing wellness recommendations.

FINDINGS

Significant improvements from pre- to postintervention were seen in physical activity level, dietary habits, and in some aspects of QOL. Forty percent of participants achieved the 3% postintervention weight-loss goal, but this was not sustained at 30 weeks.

Impact of income and perceived stress on engagement and weight loss outcomes in an online behavioral weight loss program

Lower income is associated with greater stress, and stress has been shown to undermine treatment engagement and weight loss outcomes in face-to-face interventions. The present study examined whether lower income predicts treatment engagement and weight loss outcomes during an online behavioral weight loss (BWL) intervention. A total of N = 260 participants (79.2% female; 50.7 ± 11.9 years of age; 60.3% non-Hispanic White) were enrolled into an online BWL program and had their income, stress, program engagement, and weight measured. Results showed that stress fully mediated the effect of income on engagement in the online program. Further, lower income predicted poorer weight loss outcomes, and this effect was partially mediated by perceived stress. This is the first study to demonstrate that lower income is associated with poorer engagement and weight loss outcomes in an online weight loss program, underscoring the need for systems-level stress reduction programs and individual level stress management tools, particularly for individuals with lower income.

Novel behavioral interventions to improve long-term weight loss: A randomized trial of acceptance and commitment therapy or self-regulation for weight loss maintenance

Maintenance of weight loss is hard to achieve, and novel interventions are needed to improve long-term outcomes. In this pilot randomized controlled trial, N = 188 participants received an online, 12-week weight loss intervention and N = 102 who lost ≥ 5% were then randomly assigned to a 1-day, 5-h workshop based on Acceptance and Commitment Therapy (ACT), Self-Regulation (SR), or no workshop (Control) with 3 months of limited email follow-up. Assessments were conducted at baseline, 3, 6, 12, 18, and 24 months. The primary outcome was percent weight change; secondary outcomes were weight-related experiential avoidance and health values-consistent behavior. ACT had greater overall weight loss (-7.18%, SE = 1.33) when compared to Control (-1.15%, SE = 1.50; p = .03). Post hoc analyses showed that ACT had significantly greater weight losses than Control (6.11%, β = -2.03, p = .048) among those with lower initial weight loss (5-7%), and significantly greater weight loss than SR (6.19%, β = -1.77, p = .05) among those with the highest initial weight losses (10% +). There is potential for continuing to develop ACT in a limited interventionist-contact format with modifications. This pilot study represents an innovative model for behavioral weight loss by reversing the typical treatment intensity model with the aim of providing interventionist support during a critical period after initial weight loss. REGISTRATION: Clinicaltrials.org #NCT02156752 https://www.clinicaltrials.gov/ct2/show/NCT02156752 .

Self-Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity

OBJECTIVE

Self-monitoring is a core component of behavioral obesity treatment, but it is unknown how digital health has been used for self-monitoring, what engagement rates are achieved in these interventions, and how self-monitoring and weight loss are related.

METHODS

This systematic review examined digital self-monitoring in behavioral weight loss interventions among adults with overweight or obesity. Six databases (PubMed, Embase, Scopus, PsycInfo, CINAHL, and ProQuest Dissertations & Theses) were searched for randomized controlled trials with interventions ≥ 12 weeks, weight outcomes ≥ 6 months, and outcomes on self-monitoring engagement and their relationship to weight loss.

RESULTS

Thirty-nine studies from 2009 to 2019 met inclusion criteria. Among the 67 interventions with digital self-monitoring, weight was tracked in 72% of them, diet in 81%, and physical activity in 82%. Websites were the most common self-monitoring modality, followed by mobile applications, wearables, electronic scales, and, finally, text messaging. Few interventions had digital self-monitoring engagement rates ≥ 75% of days. Rates were higher in digital- than in paper-based arms in 21 out of 34 comparisons and lower in just 2. Interventions with counseling had similar rates to standalone interventions. Greater digital self-monitoring was linked to weight loss in 74% of occurrences.

CONCLUSIONS

Self-monitoring via digital health is consistently associated with weight loss in behavioral obesity treatment.

Study protocol for a randomized controlled trial comparing two low-intensity weight loss maintenance interventions based on acceptance and commitment therapy or self-regulation

BACKGROUND

Weight regain is common following behavioral obesity treatment and attenuates many of the benefits of initial weight loss. This paper describes a randomized controlled trial that will evaluate the efficacy of two low-contact weight loss maintenance interventions based on Acceptance and Commitment Therapy (ACT) and self-regulation (SR). Potential mechanisms of action and moderators of treatment effects will also be evaluated.

METHODS

Adults (anticipated N = 480) with overweight or obesity will complete an initial 3-month online weight loss program (Phase 1). Participants who achieve ≥4 kg weight loss (anticipated N = 288) will then be randomized to an ACT or SR weight loss maintenance intervention. Both interventions will entail four 2.5 h, face-to-face, group-based workshop sessions and 6 months of email contact. Assessments will be conducted at phase 1 baseline, phase 1 completion/pre-randomization, and 6, 12, 18, 24, and 30 months post-randomization. The primary outcome will be weight change for the period from randomization to 30 months. Potential process measures including ACT-based constructs (e.g., psychological acceptance, values-consistent behavior), self-weighing frequency, and motivation will be also be assessed, as will potential moderators (e.g., initial weight loss).

CONCLUSIONS

This study will compare the efficacy of two intervention approaches (ACT and SR) delivered in a scalable workshop format for long-term weight loss maintenance. Future research could examine efficacy and cost-effectiveness of these approaches in real world settings.

Food reward sensitivity, impulsivity, and weight change during and after a 3-month weight loss program

Background

Greater sensitivity to food rewards and higher levels of impulsivity (and an interaction between these variables, termed “reinforcement pathology”) have been associated with obesity in cross-sectional studies. Less is known regarding how these constructs may impact attempts at weight loss or longer-term weight loss maintenance.

Methods

We provided 75 adults (69%Female, 84%White, age = 50.8y, BMI = 31.2kg/m²) with a 3-month Internet-based weight loss program and assessed weight, food reward sensitivity (via the Power of Food Scale [PFS]), and impulsivity (via Go No-Go [GNG] and Delay Discounting [DD] computer tasks) at baseline and at Months 3, 6, 9, and 12. No additional intervention was provided Months 3–12. Multi-level mixed-effect models were used to examine changes in PFS, GNG, and DD over time and associations between these measures and weight loss/regain.

Results

Participants lost 6.0±1.1kg Months 0–3 and regained 2.4±1.1kg Months 3–12. Across time points, higher PFS scores were associated with higher weight, p = .007; however, there were no significant associations between GNG or DD and weight nor between the interactions of PFS and GNG or DD and weight, ps>.05. There were significant decreases from Months 0–3 in PFS, GNG, and DD, ps < .05; however, neither baseline values nor changes were significantly associated with weight change and there were no significant associations between the interactions of PFS and GNG or DD and weight change, ps>.05.

Conclusion

Results demonstrated an association between food reward sensitivity and weight. Further, decreases in both food reward sensitivity and impulsivity were observed during an initial weight loss program, but neither baseline levels nor improvements were associated with weight change. Taken together, results suggest that the constructs of food reward sensitivity, impulsivity, and reinforcement pathology may have limited clinical utility within behavioral weight management interventions. Future intervention studies should examine whether food-related impulsivity tasks lead to a similar pattern of results.

Evaluation of intervention components to maximize outcomes of behavioral obesity treatment delivered online: A factorial experiment following the multiphase optimization strategy framework

BACKGROUND

Behavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss.

STUDY DESIGN

Five experimental intervention components will be tested as adjuncts to an established 12-month online BLI: virtual reality for BLI skills training, interactive video feedback, tailored intervention to promote physical activity, skills for dysregulated eating, and social support combined with friendly competition. Following the Multiphase Optimization Strategy (MOST) framework, the components will first be refined and finalized during Preparation Phase pilot testing and then evaluated in a factorial experiment with 384 adults with overweight or obesity. A priori optimization criteria that balance efficacy and efficiency will be used to create a finalized treatment package that produces the best weight loss outcomes with the fewest intervention components. Mediation analysis will be conducted to test hypothesized mechanisms of action and a moderator analysis will be conducted to understand for whom and under what circumstances the interventions are effective.

CONCLUSION

This study will provide important information about intervention strategies that are useful for improving outcomes of online BLI. The finalized treatment package will be suitable for testing in a future randomized trial in the MOST Evaluation Phase.

Episodic future thinking, delay discounting, and exercise during weight loss maintenance: The PACE trial

OBJECTIVES

Weight loss maintenance (WLM) is the next major challenge in obesity treatment. While most individuals who lose weight intend to keep their weight off, weight regain is common. Temporal Self-Regulation Theory posits that whether intentions lead to behavior depends on self-regulatory capacity, including delay discounting (DD; the tendency to discount a larger future reward in favor of a smaller immediate reward). Episodic Future Thinking (EFT; mental imagery of a future event for which a health goal is important) may improve DD and promote behavior change. Described herein is a trial protocol designed to examine whether EFT improves DD within the context of weight loss maintenance.

METHOD

Participants who lose ≥5% of initial body weight in an online behavioral weight loss intervention will be randomly assigned to a standard weight loss maintenance program (WLM-STD) or a weight loss maintenance program plus EFT (WLM + EFT). Both interventions involve periodic phone and in-person treatment sessions. Participants in WLM + EFT will engage in daily EFT training via smartphone. To control for contact, participants in WLM-STD will engage in daily Healthy Thinking (reviewing strategies for weight management) on their smartphone. Our primary hypothesis is that WLM + EFT will yield better improvements in DD compared to WLM-STD. We will also explore whether DD mediates the relationship between intervention allocation and physical activity (secondary outcome). Weight and contextual variables will be explored.

CONCLUSIONS

This study is the first to test whether EFT improves DD within the context of weight loss maintenance; results from this experimental medicine approach could have important implications for understanding the impact of both EFT and DD on sustained behavior change. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Understanding Self-Guided Web-Based Educational Interventions for Patients With Chronic Health Conditions: Systematic Review of Intervention Features and Adherence

Background

Chronic diseases contribute to 71% of deaths worldwide every year, and an estimated 15 million people between the ages of 30 and 69 years die mainly because of cardiovascular disease, cancer, chronic respiratory diseases, or diabetes. Web-based educational interventions may facilitate disease management. These are also considered to be a flexible and low-cost method to deliver tailored information to patients. Previous studies concluded that the implementation of different features and the degree of adherence to the intervention are key factors in determining the success of the intervention. However, limited research has been conducted to understand the acceptability of specific features and user adherence to self-guided web interventions.

Objective

This systematic review aims to understand how web-based intervention features are evaluated, to investigate their acceptability, and to describe how adherence to web-based self-guided interventions is defined and measured.

Methods

Studies published on self-guided web-based educational interventions for people (≥14 years old) with chronic health conditions published between January 2005 and June 2020 were reviewed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement protocol. The search was performed using the PubMed, Cochrane Library, and EMBASE (Excerpta Medica dataBASE) databases; the reference lists of the selected articles were also reviewed. The comparison of the interventions and analysis of the features were based on the published content from the selected articles.

Results

A total of 20 studies were included. Seven principal features were identified, with goal setting, self-monitoring, and feedback being the most frequently used. The acceptability of the features was measured based on the comments collected from users, their association with clinical outcomes, or device adherence. The use of quizzes was positively reported by participants. Self-monitoring, goal setting, feedback, and discussion forums yielded mixed results. The negative acceptability was related to the choice of the discussion topic, lack of face-to-face contact, and technical issues. This review shows that the evaluation of adherence to educational interventions was inconsistent among the studies, limiting comparisons. A clear definition of adherence to an intervention is lacking.

Conclusions

Although limited information was available, it appears that features related to interaction and personalization are important for improving clinical outcomes and users’ experience. When designing web-based interventions, the selection of features should be based on the targeted population’s needs, the balance between positive and negative impacts of having human involvement in the intervention, and the reduction of technical barriers. There is a lack of consensus on the method of evaluating adherence to an intervention. Both investigations of the acceptability features and adherence should be considered when designing and evaluating web-based interventions. A proof-of-concept or pilot study would be useful for establishing the required level of engagement needed to define adherence.

The Assessment of Supportive Accountability in Adults Seeking Obesity Treatment: Psychometric Validation Study

BACKGROUND

Technology-mediated obesity treatments are commonly affected by poor long-term adherence. Supportive Accountability Theory suggests that the provision of social support and oversight toward goals may help to maintain adherence in technology-mediated treatments. However, no tool exists to measure the construct of supportive accountability.

OBJECTIVE

This study aimed to develop and psychometrically validate a supportive accountability measure (SAM) by examining its performance in technology-mediated obesity treatment.

METHODS

Secondary data analyses were conducted in 2 obesity treatment studies to validate the SAM (20 items). Study 1 examined reliability, criterion validity, and construct validity using an exploratory factor analysis in individuals seeking obesity treatment. Study 2 examined the construct validity of SAM in technology-mediated interventions involving different self-monitoring tools and varying amounts of phone-based interventionist support. Participants received traditional self-monitoring tools (standard, in-home self-monitoring scale [SC group]), newer, technology-based self-monitoring tools (TECH group), or these newer technology tools plus additional phone-based support (TECH+PHONE group). Given that the TECH+PHONE group involves more interventionist support, we hypothesized that this group would have greater supportive accountability than the other 2 arms.

RESULTS

In Study 1 (n=353), the SAM showed strong reliability (Cronbach α=.92). A factor analysis revealed a 3-factor solution (representing Support for Healthy Eating Habits, Support for Exercise Habits, and Perceptions of Accountability) that explained 69% of the variance. Convergent validity was established using items from the motivation for weight loss scale, specifically the social regulation subscale (r=0.33; P<.001) and social pressure for weight loss subscale (r=0.23; P<.001). In Study 2 (n=80), the TECH+PHONE group reported significantly higher SAM scores at 6 months compared with the SC and TECH groups (r2=0.45; P<.001). Higher SAM scores were associated with higher adherence to weight management behaviors, including higher scores on subscales representing healthy dietary choices, the use of self-monitoring strategies, and positive psychological coping with weight management challenges. The association between total SAM scores and percent weight change was in the expected direction but not statistically significant (r=-0.26; P=.06).

CONCLUSIONS

The SAM has strong reliability and validity across the 2 studies. Future studies may consider using the SAM in technology-mediated weight loss treatment to better understand whether support and accountability are adequately represented and how supportive accountability impacts treatment adherence and outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01999244; https://clinicaltrials.gov/ct2/show/NCT01999244.

Effectiveness of a self-regulation intervention for weight loss: A randomized controlled trial

OBJECTIVES

To evaluate effectiveness and acceptability of a novel intervention, based on self-regulation theory, for weight loss.

DESIGN

A two-arm parallel group design was employed.

METHODS

Adult participants with a BMI ≥ 30 kg/m² and the aim to lose weight were recruited and randomized to either a control or intervention group. Both groups were asked to weigh themselves daily for eight weeks. The intervention group was encouraged to use a weight tracking app, and complete daily and weekly questionnaires to prompt action planning, reflection, and evaluation of actions. Participants chose daily actions from a menu of 53 behaviours. The primary outcome was weight change after 8 weeks, assessed using linear mixed effects models. At follow-up, 20 intervention group participants were interviewed regarding their experiences in the trial.

RESULTS

100 participants were recruited, and 98% were followed up at 8 weeks. Mean weight loss was -4.18 kg (SD = 3.84) in the intervention compared to -1.01 kg (SD = 2.67) in the control group; the adjusted difference was -3.20 kg (95% CI -4.49, -1.92). Participants rated the intervention's usefulness as 8.25 (SD = 2.04) on a scale from 1 to 10. Adherence was a significant independent predictor of weight loss success (-1.54 kg per one SD, 95% CI -2.16, -0.93), but not a mediator of the intervention effect. Participants reported that the intervention enabled them to experiment with and identify effective weight loss actions.

CONCLUSIONS

Guiding participants through the self-regulation process was feasible, acceptable to participants, and led to significantly greater short-term weight loss than unguided self-weighing.

DIAL now protocol: A randomized trial examining the provision of phone coaching to those with sub-optimal early weight loss during an Internet weight management program

BACKGROUND

While low-intensity Internet-delivered weight loss (IDWL) programs are efficacious, many patients fail to achieve clinically significant weight loss (WL). Given the positive association between 4-week and post-treatment WL, providing a more intensive intervention for those with sub-optimal 4-week WL may improve outcomes for a greater proportion of individuals. This stepped-care approach would minimize cost by reserving more aggressive treatment for those with sub-optimal early WL.

OBJECTIVE

This randomized trial examines whether the provision of brief or extended phone coaching for those with sub-optimal early WL improves 4- and 12-month WL when compared to no coaching. Secondary aims include examination of cost/kg WL and intervention engagement.

METHODS

450 individuals (age 18-70 years, BMI: 25-45 kg/m2) will be randomized to: 1) IDWL+3 weeks of coaching ('Brief'), 2) IDWL+12 weeks of coaching ('Extended'), or 3) IDWL only ('Control'). All individuals will receive a 4-month IDWL program followed by an 8-month IDWL maintenance program. At week 4, individuals will be classified as early sub-optimal responders (<4% WL) or initial responders (≥4% WL). Individuals with sub-optimal early WL randomized to 'Brief' or 'Extended' will receive 3 and 12 weeks of phone coaching respectively, starting at week 5. Those with sub-optimal early WL randomized to 'Control', and initial responders will not receive any coaching. Assessments will occur at 4 and 12 months.

DISCUSSION

Study findings can inform the development of more effective IDWL programs. This model which provides additional support to those with sub-optimal early WL can easily be translated into healthcare and community settings.

Testing the effectiveness of a weight loss intervention to enhance self-regulation in adults who are obese: protocol for a randomised controlled trial

INTRODUCTION

Previous trials finding an effect of self-monitoring on weight loss have considered the effect to be mediated by self-regulatory processes. However, a qualitative think-aloud study asking people to record thoughts and feelings during weighing showed that self-regulation occurs only rarely without further instruction. The aim of this trial is to test a novel intervention guiding people through the self-regulatory processes to see whether it facilitates weight loss.

METHODS AND ANALYSES

A parallel group, randomised controlled trial will be conducted to test the concept that a self-regulation intervention for weight loss increases weight loss compared with daily self-weighing without further support. One hundred participants with a body mass index ≥30 kg/m² will be randomised to either the control or intervention group. The control group will be asked to weigh themselves daily for 8 weeks, the intervention group will be encouraged to follow the self-regulation intervention. They will be prompted to weigh daily, track their weight using an app, plan daily actions for weight loss and reflect on their action plans on a weekly basis. This self-regulation cycle will allow them to experiment with different weight loss strategies and identify effective and sustainable actions. Primary and process outcomes will be measured at baseline and 8 weeks' follow-up. Linear regression analysis of the primary outcome, weight change, will assess the early effectiveness of the intervention. The process outcomes liking, perceived effectiveness, as well as usage and barriers with regard to the self-regulation intervention, will be assessed through qualitative analysis of follow-up interviews and quantitative analysis of adherence rates and responses to a final questionnaire.

ETHICS AND DISSEMINATION

This trial was reviewed and approved by the NHS National Research Ethics Committee and the Health Research Authority (reference number: 18/SC/0482). The findings of the trial will be published in peer reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

ISRCTN14148239, prerecruitment.

PROTOCOL VERSION

Version 1.1, 7 December 2018.

Toward a health-promoting system for cancer survivors: Patient and provider multiple behavior change

OBJECTIVE

This paper examines how and why to improve care systems for disease management and health promotion for the growing population of cancer survivors with cardiovascular multi-morbidities.

METHOD

We reviewed research characterizing cancer survivors' and their multiple providers' common sense cognitive models of survivors' main health threats, preventable causes of adverse health events, and optimal coping strategies.

RESULTS

Findings indicate that no entity in the health care system self-identifies as claiming primary responsibility to address longstanding unhealthy lifestyle behaviors that heighten survivors' susceptibility to both cancer and cardiovascular disease (CVD) and whose improvement could enhance quality of life.

CONCLUSIONS

To address this gap, we propose systems-level changes that integrate health promotion into existing survivorship services by including behavioral risk factor vital signs in the electronic medical record, with default proactive referral to a health promotionist (a paraprofessional coach adept with mobile technologies and supervised by a professional expert in health behavior change). By using the patient's digital tracking data to coach remotely and periodically report progress to providers, the health promotionist closes a gap, creating a connected care system that supports, reinforces, and maintains accountability for healthy lifestyle improvement. No comparable resource solely dedicated to treatment of chronic disease risk behaviors (smoking, obesity, physical inactivity, treatment nonadherence) exists in current models of integrated care. Integrating health promotionists into care delivery channels would remove burden from overtaxed PCPs and instantiate a comprehensive, actionable systems-level schema of health risks and coping strategies needed to have preventive impact with minimal interference to clinical work flow. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Randomized controlled trial of Web-based weight-loss intervention with human support for male workers under 40

Objectives

Human support can boost weight reduction in Internet‐based weight‐loss intervention. However, the most effective way to combine human support and the Internet for weight loss is unclear. This study aimed to examine the effects of two weight‐loss programs for male workers aged 18‐39 that combined different intensities of human support with website support compared to a delayed‐intervention group (control group; CG), in a randomized controlled trial.

Methods

Seventy‐one participants with overweight or obesity were allocated to one of three 12‐week treatment programs. The Standard Support Group (SSG) was provided support via website and two face‐to‐face group guidance sessions, at the beginning and at the end of the program along with monthly general emails throughout the program. The Enhanced Support Group (ESG) received four remote support sessions based on Supportive Accountability (SA) in addition to the SSG. The CG was provided the same program as SSG after the other two groups had completed the program. The primary outcome was body weight reduction.

Results

ESG participants reduced their weight significantly more than SSG and CG participants (P = 0.038, P < 0.001, respectively), and SSG participants reduced their weight significantly more than CG participants (P = 0.033).

Conclusions

The additional remote human support provided to the participants in the ESG was beneficial for weight loss in male workers. The low‐intensity program provided to the SSG was also effective. Further studies with more participants in diverse settings and with participants who are less interested in their health and weight management are needed.

A Randomized Controlled Trial of a Behavioral Weight Loss Program for Human Immunodeficiency Virus-Infected Patients

Obesity compounds the negative health effects of human immunodeficiency virus (HIV) infection. We conducted the first randomized trial of behavioral weight loss for HIV-infected patients (n = 40). Participants randomized to an Internet behavioral weight loss program had greater 12-week weight loss (mean, 4.4 ± 5.4 kg vs 1.0 ± 3.3 kg; P = .02) and improvements in quality of life than controls. Clinical Trials Registration NCT02421406.

A randomized controlled trial of a 12-week intensive lifestyle intervention program at a primary care obesity clinic for adults in western Saudi Arabia

Objectives:

To assess the ability of a 12-week primary care-based intensive lifestyle intervention (ILI), to facilitate a 5% reduction in baseline weight compared with an education-only active comparator (AC).

Methods:

A randomized clinical trial was conducted in a primary health care setting in Jeddah, Saudi Arabia between December 2014 and June 2015. Arab participants with obesity, but who were otherwise healthy (n=140), were randomized to the ILI (n=70) or AC (n=70) group. The ILI group received 8 clinical visits throughout the study. The AC group received only an initial health education session. The primary outcome was the proportion of participants who achieved clinically significant weight loss (≥5% of their baseline weight).

Results:

Participants in the ILI group were significantly more likely than those in the AC group to achieve the primary outcome (p=0.008, relative risk: 1.8 [95% confidence interval [CI]: 1.15 to 2.93). At week 12, the ILI group exhibited a mean weight decrease of 5.58 ± 5.60 kg (-5.37 ± 5.31%), significantly greater than that observed in the AC group (-2.8 ± 4.96 kg, -2.62 ± 4.34%, p=0.002), and corresponding to a weight loss advantage of 2.77 kg (95% CI: 1.01 to 4.54 kg) or 2.75% (95% CI: 1.13% to 4.37%).

Conclusion:

The 12-week primary care-based ILI program was effective in achieving a clinically meaningful weight reduction (≥5%) among Saudi and Arab patients with obesity.

Small Incentives Improve Weight Loss in Women From Disadvantaged Backgrounds

INTRODUCTION

Women from lower-income backgrounds have the highest rates of obesity. Thus, effective programs for this high-risk population are urgently needed. Evidence suggests that adding financial incentives to treatment helps to engage and promote health behavior change in lower-income populations; however, this has never been tested in women for obesity treatment. The purpose of this study was to examine whether adding small financial incentives to Internet weight loss treatment yields better weight loss outcomes in women from lower-income backgrounds compared with the same treatment without incentives. Weight losses in lower-versus higher-income women were also compared.

METHODS

Data were pooled from two randomized trials in which women (N=264) received either Internet behavioral weight loss treatment (IBWL) or IBWL plus incentives (IBWL+$). Weight was objectively assessed. Data were collected and analyzed from 2011 to 2017.

RESULTS

Women from lower-income backgrounds had significantly better weight loss outcomes in IBWL+$ compared with IBWL alone (6.4 [SD=4.9%] vs 2.6 [SD=4.6%], p=0.01). Moreover, a greater percentage achieved a ≥5% weight loss in IBWL+$ vs IBWL alone (52.6% vs 38.1%, p=0.01). Interestingly, the comparison between lower-income versus higher-income groups showed that, in IBWL alone, women with lower income achieved significantly poorer weight losses (3.4 [SD=4.2%] vs 4.9 [SD=4.0%], p=0.03). By contrast, in IBWL+$, weight loss outcomes did not differ by income status (5.0 [SD=5.6%] vs 5.3 [SD=3.8%], p=0.80), and a similar percentage of lower- versus higher-income women achieved a ≥5% weight loss (52.6% vs 53.8%, p=0.93).

CONCLUSIONS

An Internet behavioral weight loss program plus financial incentives may be an effective strategy to promote excellent weight losses in women with lower income, thereby enhancing equity in treatment outcomes in a vulnerable, high-risk population. These data also provide important evidence to support federally funded incentive initiatives for lower-income, underserved populations.

Characterizing the Pattern of Weight Loss and Regain in Adults Enrolled in a 12-Week Internet-Based Weight Management Program

OBJECTIVE

Although the trajectory of weight change during and/or after behavioral weight management interventions is believed to include a period of weight loss followed by maintenance and later regain, the sparse data produced by existing study designs (conducting assessments at 3- to 6-month intervals) have limited investigation into the precise pattern.

METHODS

Seventy-five adults were asked to self-weigh daily via "smart" scales during a 12-week, Internet-based weight loss program and for an additional 9 months with no further intervention. Longitudinal change-point mixed-effect models were used to characterize overall weight change patterns and identify when individuals moved from weight loss to maintenance/regain.

RESULTS

Analyses suggested a three-phase model. During the first phase, participants lost weight at a (mean ± SE) rate of -0.46 ± 0.04 kg/wk; after 77.66 ± 3.96 days, they transitioned to regain (0.07 ± 0.02 kg/wk). The next transition occurred at 222.55 ± 7.23 days, after which the rate of regain decreased slightly (0.06 ± 0.02 kg/wk). Exploratory analyses identified baseline/demographic factors predicting the timing of transition points and slope of weight change within phases.

CONCLUSIONS

In contrast to the hypothesized trajectory, results demonstrated that participants transitioned immediately from weight loss to regain (with no "maintenance" period) and later to a slower rate of regain. Future studies should investigate whether extended-care programs change or merely delay this pattern.

Lifestyle interventions based on the diabetes prevention program delivered via eHealth: A systematic review and meta-analysis

The objective was to describe Diabetes Prevention Program (DPP)-based lifestyle interventions delivered via electronic, mobile, and certain types of telehealth (eHealth) and estimate the magnitude of the effect on weight loss. A systematic review was conducted. PubMed and EMBASE were searched for studies published between January 2003 and February 2016 that met inclusion and exclusion criteria. An overall estimate of the effect on mean percentage weight loss across all the interventions was initially conducted. A stratified meta-analysis was also conducted to determine estimates of the effect across the interventions classified according to whether behavioral support by counselors post-baseline was not provided, provided remotely with communication technology, or face-to-face. Twenty-two studies met the inclusion/exclusion criteria, in which 26 interventions were evaluated. Samples were primarily white and college educated. Interventions included Web-based applications, mobile phone applications, text messages, DVDs, interactive voice response telephone calls, telehealth video conferencing, and video on-demand programing. Nine interventions were stand-alone, delivered post-baseline exclusively via eHealth. Seventeen interventions included additional behavioral support provided by counselors post-baseline remotely with communication technology or face-to-face. The estimated overall effect on mean percentage weight loss from baseline to up to 15months of follow-up across all the interventions was -3.98%. The subtotal estimate across the stand-alone eHealth interventions (-3.34%) was less than the estimate across interventions with behavioral support given by a counselor remotely (-4.31%), and the estimate across interventions with behavioral support given by a counselor in-person (-4.65%). There is promising evidence of the efficacy of DPP-based eHealth interventions on weight loss. Further studies are needed particularly in racially and ethnically diverse populations with limited levels of educational attainment. Future research should also focus on ways to optimize behavioral support.

Internet-delivered obesity treatment improves symptoms of and risk for depression

OBJECTIVE

In-person lifestyle interventions for obesity treatment yield significant improvements in depression. These improvements may be attributed to the excellent weight losses produced by in-person interventions. In contrast, Internet programs yield more modest weight losses, and their effect on depression is unknown. This study is the first to examine whether Internet-delivered obesity treatment impacts depressive symptoms.

METHODS

Participants (N = 136) were randomized to either a community campaign plus Internet behavioral weight loss (IBWL) or community campaign alone (Control). IBWL did not include online social support components. A measure of depressive symptoms was administered, and weight was objectively assessed.

RESULTS

Of the total sample, 24% met the clinical cutoff for elevated depression risk at baseline. IBWL participants lost more weight during treatment (P = 0.005) and experienced significantly greater improvements in depressive symptoms (P = 0.02). Among participants who met the clinical cutoff for elevated risk for depression at baseline, those assigned to IBWL had greater improvements in depressive symptoms during treatment compared to Control (P = 0.033). Consequently, at post-treatment, a smaller percentage of IBWL participants were at elevated risk for depression.

CONCLUSIONS

This study is the first to show that Internet-delivered obesity treatment improves depression risk and depressive symptoms in individuals with overweight or obesity.

Effects of breakfast eating and eating frequency on body mass index and weight loss outcomes in adults enrolled in an obesity treatment program

This study examined the effects of breakfast eating and eating frequency on objectively assessed BMI and weight loss outcomes among adults enrolled in obesity treatment. Participants completed measures of breakfast eating and eating frequency before and after treatment and had their height and weight measured. Baseline breakfast eating and eating frequency were not associated with baseline BMI (p = .34, p = .45, respectively) and did not predict weight loss during treatment (p = .36, p = .58, respectively). From pre- to post-treatment, there was no significant change in eating frequency (p = .27) and changes in eating frequency had no impact on weight loss (r = -.08, p = .23). However, increases in breakfast eating during treatment were associated with significantly better weight loss outcomes (r = .26, p < .001). Among participants who increased breakfast eating, those who had either no change or a decrease in daily eating frequency were more likely to achieve a 5% weight loss compared to those who had an increase in daily eating frequency (p = .04). These results suggest that increasing breakfast eating, while simultaneously reducing or keeping eating frequency constant, may improve outcomes in obesity treatment. Experimental studies are needed to further elucidate these effects.

Secondary data analysis from a randomized trial examining the effects of small financial incentives on intrinsic and extrinsic motivation for weight loss

Objectives

To examine whether (a) an obesity treatment involving financial incentives yields higher levels of extrinsic motivation for weight management compared to an identical intervention without incentives, (b) extrinsic motivation for weight management mediates, or accounts for, the difference in weight loss outcomes between the two interventions, and (c) there is any evidence that financial incentives and associated extrinsic motivation "crowd out" intrinsic motivation for weight control.

Methods

Participants (N=153, 80.4% Female; BMI = 33.2 ± 5.9) were randomly assigned to a 3-month Web-based behavioral weight loss program (WBWL) or the same program plus small financial incentives delivered consistent with behavioral economics and behavior change theories (WBWL+$). Weight was objectively assessed at baseline, post-treatment (month 3), and after a 9-month no-treatment follow-up phase (month 12). Intrinsic and extrinsic motivation for weight management were assessed at months 3 and 12 using a modified version of the Treatment Self-Regulation Questionnaire, with questions added to specifically target extrinsic motivation related to incentives.

Results

Compared to WBWL alone, WBWL+$ had better weight loss and higher levels of both extrinsic and intrinsic motivation for weight management (p's≤.02). Moreover, during the no-treatment follow-up phase, the trajectories of weight regain did not significantly differ between WBWL and WBWL+$ (p=.58). Extrinsic motivation was not a significant mediator of treatment outcomes.

Conclusions

Modest financial incentives delivered consistent with behavioral economics and behavior change theories do not undermine intrinsic motivation for weight management during obesity treatment; in fact, they yield higher levels of both extrinsic and intrinsic motivation. Additional research is needed to better understand the mechanisms by which incentives improve outcomes in health behavior change interventions.

What Is the Status Quo of Evidence-Based Community Health Nursing?

Background and Purpose:With an increasing emphasis on evidence-based nursing in general,evidence-based practicehas become a buzzword among community health nurses in many countries. Despite the global interests, evidence-based community health nursing is not even clearly defined in the literature and very little is known about the current status of evidence-based community health nursing. Therefore, the purpose of this article is to identify the status quo of evidence-based nursing in community health nursing through an integrative literature review.Methods:Four electronic databases were searched from the earliest year to 2016 with combinations of keywords. Twenty-six eligible articles were reviewed, and the characteristics reflecting the current status of evidence-based community health nursing were extracted.Results:Through the content analysis process, 6 characteristics were identified. First, in evidence-based community health nursing, the needs of clients, families, caregivers, and health care professionals were identified and assessed. Second, interventions were planned based on systematic reviews on various relevant sources. Third, various types of research methods were used. Fourth, available resources were assessed and used, and the findings of cost estimation, cost/effectiveness, or cost/benefit analyses were reported. Fifth, training, support, monitoring, and coordination were included as major components. Finally, the evidence used and/or found in the studies was evaluated, disseminated, and updated as the last step.Implications for Practice:Community health nurses are required to continuously integrate, implement, evaluate, disseminate, and update their evidence for future evidence-based community health nursing.

A randomized controlled trial testing an Internet delivered cost-benefit approach to weight loss maintenance

Weight loss maintenance is a significant challenge in obesity treatment. During maintenance the "costs" of adhering to weight management behaviors may outweigh the "benefits." This study examined the efficacy of a novel approach to weight loss maintenance based on modifying the cost-benefit ratio. Individuals who achieved a 5% weight loss (N=75) were randomized to one of three, 10-month maintenance interventions. All interventions were delivered primarily via the Internet. The Standard arm received traditional weight maintenance strategies. To increase benefits, or rewards, for maintenance behaviors, the two cost-benefit intervention conditions received weekly monetary rewards for self-monitoring and social reinforcement via e-coaching. To decrease behavioral costs (boredom) and increase novelty, participants in the cost-benefit conditions also monitored different evidence-based behaviors every two weeks (e.g., Weeks 1 & 2: steps; Week 3 & 4: red foods). The primary difference between the cost-benefit interventions was type of e-coach providing social reinforcement: Professional (CB Pro) or Peer (CB Peer). Study procedures took place in Providence, RI from 2013 to 2014. Retention was 99%. There were significant group differences in weight regain (p=.01). The Standard arm gained 3.5±5.7kg. In contrast, participants in CB Pro and CB Peer lost an additional 1.8±7.0kg and 0.5±6.4kg, respectively. These results suggest that an Internet delivered cost-benefit approach to weight loss maintenance may be effective for long-term weight control. In addition, using peer coaches to provide reinforcement may be a particularly economic alternative to professionals. These data are promising and provide support for a larger, longer trial.

Technology-based interventions for weight management: current randomized controlled trial evidence and future directions

Obesity is a prevalent health care issue associated with disability, premature morality, and high costs. Behavioral weight management interventions lead to clinically significant weight losses in overweight and obese individuals; however, many individuals are not able to participate in these face-to-face treatments due to limited access, cost, and/or time constraints. Technological advances such as widespread access to the Internet, increased use of smartphones, and newer behavioral self-monitoring tools have resulted in the development of a variety of eHealth weight management programs. In the present paper, a summary of the most current literature is provided along with potential solutions to methodological challenges (e.g., high attrition, minimal participant racial/ethnic diversity, heterogeneity of technology delivery modes). Dissemination and policy implications will be highlighted as future directions for the field of eHealth weight management.

Self-Expansion is Associated with Better Adherence and Obesity Treatment Outcomes in Adults

BACKGROUND

Previous studies have shown that self-expansion (e.g., increasing positive self-content via engaging in novel, rewarding activities) is associated with smoking cessation and attenuated cigarette-cue reactivity.

PURPOSE

This study examined whether self-expansion is associated with better adherence, weight loss, and physical activity (PA) outcomes within a weight loss intervention.

METHODS

Participants from Shape Up Rhode Island 2012, a Web-based community wellness initiative, took part in a randomized controlled trial that involved a 12-week behavioral weight loss intervention [1]. At baseline and post-intervention, objective weights and self-reported self-expansion and PA were obtained from 239 participants. Treatment adherence was assessed objectively.

RESULTS

Self-expansion during treatment was significantly associated with percent weight loss including clinically significant weight loss (i.e., 5 %), minutes of PA, and treatment adherence. These results held after controlling for relevant covariates.

CONCLUSIONS

This is the first study to show that self-expansion is associated with better behavioral weight loss outcomes including weight loss, adherence, and PA. These results suggest that self-expansion is a promising novel target for future research which could inform health interventions.

Social networks and social norms are associated with obesity treatment outcomes

OBJECTIVE

To examine whether overweight social network members and normative influence for obesity are associated with weight loss outcomes during obesity treatment.

METHODS

Participants (N = 214) in a behavioral weight loss trial reported (a) the weight status of various members of their social network and (b) the level of obesogenic normative influence within their social network. Weight was objectively assessed before and after treatment.

RESULTS

At baseline, participants with partners and best friends who were overweight and those with more children and relatives who were overweight had higher BMIs (P's < 0.03). However, social norms for obesity were not associated with baseline BMI. During treatment, participants lost an average of 4.4% of initial body weight, and social influence factors were adversely associated with weight loss outcomes. Having more casual friends who were overweight at baseline and being part of a social network with stronger social norms for unhealthy eating predicted poorer weight losses (P's < 0.023). Remaining social influence factors and changes in social influence were not associated with treatment outcomes.

CONCLUSIONS

Whereas weight status may "cluster" in social networks, only weight status of casual friends and normative influence for unhealthy eating were associated with obesity treatment outcomes.

Examination of whether early weight loss predicts 1-year weight loss among those enrolled in an Internet-based weight loss program

One-month weight loss (WL) predicts post-treatment WL in face-to-face interventions; however whether this holds true within Internet programs is unknown. This study examined whether 4-week WL predicts WL following a 12-week Internet program and at 6 and 12 months follow-up. 181 participants (BMI=33.4±5.5kg/m²; 83.1% female) received a 12-week behaviorally-based Internet WL program consisting of weekly video lessons. Participants were given a daily WL, calorie, and physical activity goal and asked to enter these data on the study website weekly. Personalized feedback was provided. Using 4-week WL, individuals were categorized as ‘early non-responders’ (<2.0% WL) or ‘early responders’ (≥2.0% WL). Early non-responders had significantly lower WL than early responders at 3 (-1.3±3.8% vs. -6.3±4.3%), 6 (-1.7±5.1% vs. -5.8±5.2%), and 12 months (-0.05±6.8% vs. -2.7±6.3%, p's<0.05). The odds of achieving a ≥5% WL were 8.5 (95% CI:3.3–22.1), 3.4 (95% CI:1.4-8.3), and 2.6 (95% CI:0.93-7.4) times lower in early non-responders, compared to early responders at 3, 6, and 12 months respectively. Compared to early responders, early non-responders viewed fewer video lessons and self-monitored less often across the 12-week intervention (p's<0.05). This study provides initial evidence that a 4-week WL of <2.0% places an individual at an increased risk of failing to achieve clinically significant WL following an Internet program.

eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis

A systematic review of randomized controlled trials was conducted to evaluate the effectiveness of eHealth interventions for the prevention and treatment of overweight and obesity in adults. Eight databases were searched for studies published in English from 1995 to 17 September 2014. Eighty-four studies were included, with 183 intervention arms, of which 76% (n = 139) included an eHealth component. Sixty-one studies had the primary aim of weight loss, 10 weight loss maintenance, eight weight gain prevention, and five weight loss and maintenance. eHealth interventions were predominantly delivered using the Internet, but also email, text messages, monitoring devices, mobile applications, computer programs, podcasts and personal digital assistants. Forty percent (n = 55) of interventions used more than one type of technology, and 43.2% (n = 60) were delivered solely using eHealth technologies. Meta-analyses demonstrated significantly greater weight loss (kg) in eHealth weight loss interventions compared with control (MD -2.70 [-3.33,-2.08], P < 0.001) or minimal interventions (MD -1.40 [-1.98,-0.82], P < 0.001), and in eHealth weight loss interventions with extra components or technologies (MD 1.46 [0.80, 2.13], P < 0.001) compared with standard eHealth programmes. The findings support the use of eHealth interventions as a treatment option for obesity, but there is insufficient evidence for the effectiveness of eHealth interventions for weight loss maintenance or weight gain prevention.

Benefits of adding small financial incentives or optional group meetings to a web-based statewide obesity initiative

OBJECTIVE

To examine whether adding either small, variable financial incentives or optional group sessions improves weight losses in a community-based, Internet behavioral program.

METHODS

Participants (N = 268) from Shape Up Rhode Island 2012, a 3-month Web-based community wellness initiative, were randomized to: Shape Up+Internet behavioral program (SI), Shape Up+Internet program+incentives (SII), or Shape Up+Internet program+group sessions (SIG).

RESULTS

At the end of the 3-month program, SII achieved significantly greater weight losses than SI (SII: 6.4% [5.1-7.7]; SI: 4.2% [3.0-5.6]; P = 0.03); weight losses in SIG were not significantly different from the other two conditions (SIG: 5.8% [4.5-7.1], P's ≥ 0.10). However, at the 12-month no-treatment follow-up visit, both SII and SIG had greater weight losses than SI (SII: 3.1% [1.8-4.4]; SIG: 4.5% [3.2-5.8]; SI: 1.2% [-0.1-2.6]; P's ≤ 0.05). SII was the most cost-effective approach at both 3 (SII: $34/kg; SI: $34/kg; SIG: $87/kg) and 12 months (SII: $64/kg; SI: $140/kg; SIG: $113/kg).

CONCLUSIONS

Modest financial incentives enhance weight losses during a community campaign, and both incentives and optional group meetings improved overall weight loss outcomes during the follow-up period. However, the use of the financial incentives is the most cost-effective approach.