Drivers of weight loss in a CDC-recognized digital diabetes prevention program

Telehealth Technologies in Diabetes Self-management and Education

BACKGROUND

The coronavirus pandemic of 2019 (COVID-19) forced worldwide recognition and implementation of telehealth as a means of providing continuity of care by varied health care institutions. Diabetes is a global health threat with rates that continue to accelerate, thereby causing an increased need for clinicians to provide diabetes care and education to keep up with demand. Utilizing technology to provide education via phone/smartphone, video/audio, web, text message, mobile apps, or a combination of these methods can help improve patient access and clinical outcomes, cut costs, and close gaps in care.

METHODS

While numerous publications have summarized the various tools and technologies available for capturing remote clinical data and their relevance to diabetes care and self-management, this review focuses on self-educational telehealth tools available for diabetes self-management, their advantages and disadvantages, and factors that need to be considered prior to implementation. Recent relevant studies indexed by PubMed were included.

RESULTS

The widespread use and popularity of phones/smartphones, tablets, computers, and the Internet by patients of all age groups, cultures, socioeconomic and geographic areas allow for increased outreach, flexibility, and engagement with diabetes education, either in combination or as an adjunct to traditional in-person visits. Demonstrated benefits of using health technologies for diabetes self-management education include improved lifestyle habits, reduced hemoglobin A1C levels, decreased health care costs, and better medication adherence. Potential drawbacks include lack of regulation, need for staff training on methodologies used, the requirement for patients to be tech savvy, privacy concerns, lag time with technology updates/glitches, and the need for more long-term research data on efficacy.

CONCLUSIONS

Telehealth technologies for diabetes self-education improve overall clinical outcomes and have come a long way. With increasing numbers of patients with diabetes, it is expected that more optimal and user-friendly methodologies will be developed to fully engage and help patients communicate with their physicians.

Weight loss and lifestyle change among high-risk individuals enrolled in a digital diabetes prevention program: A longitudinal study of private and public health insurance members in Western New York

Weight loss is critical to reduce diabetes risk. Diabetes Prevention Programs (DPPs) are effective for weight loss, although less is known about digital DPPs. This study explores the association between Brook+, a 52-week digital DPP, and weight loss. This longitudinal observational study uses a sample of 5,516 private, Medicare, and Medicaid health insurance members from Western New York enrolled into Brook+ between December 2020 and December 2022. We used multivariable generalized linear regression models to estimate the association between completion of the Brook+ program, overall and stratified by health insurance type, and 5% weight loss using odds ratios (OR) and 95% confidence intervals (CI), adjusted for age and gender. We also estimated average weight loss from baseline associated with high engagement with the program using adjusted linear regression models. In the pooled sample, those who completed the Brook + program had 21% increased odds of 5% weight loss (OR = 1.21 95% CI: (1.02 - 1.44)). Among users enrolled in private, Medicare, and Medicaid health insurance, program completion was associated with 21%, 33%, and 13% increased odds of 5% weight loss, compared to those who did not complete Brook+. Interacting with health coaches, increased physical activity, and meal logging were all significant predictors of weight loss. Our results suggest that digital DPPs are promising for large-scale diabetes prevention via weight loss and lifestyle change.

Engagement with a nationally-implemented digital behaviour change intervention: Usage patterns over the 9-month duration of the National Health Service Digital Diabetes Prevention Programme

Background

Digital behaviour change interventions may offer a scalable way to promote weight loss by increasing physical activity and improving diet. However, user engagement is necessary for such benefits to be achieved. There is a dearth of research that assesses engagement with nationally implemented digital programmes offered in routine practice. The National Health Service Digital Diabetes Prevention Programme (NHS-DDPP) is a nine-month digital behaviour change intervention delivered by independent providers for adults in England who are at high risk of developing type 2 diabetes. This study reports engagement with the NHS-DDPP for users enrolled onto the programme over the nine-month duration.

Methods

Anonymous usage data was obtained for a cohort of service users (n = 1826) enrolled on the NHS-DDPP with three independent providers, between December 2020 and June 2021. Usage data were obtained for time spent in app, and frequency of use of NHS-DDPP intervention features in the apps including self-monitoring, goal setting, receiving educational content (via articles) and social support (via health coaches and group forums), to allow patterns of usage of these key features to be quantified across the nine-month intervention. Median usage was calculated within nine 30-day engagement periods to allow a longitudinal analysis of the dose of usage for each feature.

Results

App usage declined from a median of 32 min (IQR 191) in month one to 0 min (IQR 14) in month nine. Users self-monitored their behaviours (e.g., physical activity and diet) a median of 117 times (IQR 451) in the apps over the nine-month programme. The open group discussion forums were utilised less regularly (accessed a median of 0 times at all time-points). There was higher engagement with some intervention features (e.g., goal setting) when support from a health coach was linked to those features.

Conclusions

App usage decreased over the nine-month programme, although the rate at which the decrease occurred varied substantially between individuals and providers. Health coach support may promote engagement with specific intervention features. Future research should assess whether engagement with particular features of digital diabetes prevention programmes is associated with outcomes such as reduced bodyweight and HbA1c levels.

Health Coaching for Weight Loss Among Overweight and Obese Individuals in Saudi Arabia: A Retrospective Analysis

Background Health coaching is an increasingly used strategy to help in adopting lifestyle changes for weight loss. While Saudi Arabia has one of the highest obesity prevalences worldwide, research on lifestyle interventions for weight loss is limited. Aim We aimed to investigate the effectiveness of health coaching for weight loss among the Saudi population in real-world primary healthcare settings. Methods This is a retrospective observational study. Secondary data from the health coach national program in the Eastern Health Cluster were retrieved. Obese and overweight individuals aged 15 years or older with weight-related goals who completed at least 12 weeks of coaching were included in the analysis. The primary outcomes are weight change (kg) and weight change percent (%) of the initial weight. We further compared the weight change% between different follow-up methods (i.e., physical, virtual, and hybrid) and studied the factors associated with -5% weight loss. Results In total, 465 participants were included in the analysis, with a female predominance (66.2%) and a median initial weight of 90 kg (interquartile range (IQR): 77, 101). The median follow-up duration was 127 days (IQR: 101, 157), and the median total number of coaching sessions was three (IQR: 2, 5). The mean weight change was -2.68 kg (95% confidence interval (CI): -3.12, -2.24), p<0.001. Comparing each follow-up group, no statistically significant difference was found when controlling for number of visits (p=0.059). The adjusted means for weight change% were -3.77%, -2.59%, and -2.54% for hybrid, physical, and virtual visits, respectively. Factors that were associated with achieving at least -5% weight loss were male sex (adjusted odds ratio (aOR)=1.87, 95% CI: 1.16, 3.02), five or more total coaching visits (aOR=5.23, 95% CI: 2.88, 9.50), longer follow-up duration (aOR=1.09, 95% CI: 1.03, 1.15), and having a weight management goal (aOR=4.5, 95% CI: 1.63, 12.45) as the reason for initial coaching visit. Conclusion We found statistically significant weight change among clients who completed 12 weeks of coaching in primary care settings. The findings in this paper contribute to the importance of lifestyle interventions for weight loss among the Saudi population. However, stronger controlled studies are needed to confirm this finding.

Atopic Dermatitis and Water: Is There an Optimum Water Intake Level for Improving Atopic Skin?

Water is a vital nutrient with innumerable functions for every living cell. The functions of human skin include protection against dehydration of the body. Atopic dermatitis (AD) is a chronic pruritic inflammatory skin disease that presents with dry skin, erythematous and eczematous lesions, and lichenification. This paper discusses the question of whether extra water intake in children with AD affects skin hydration and the skin barrier function. Among the methods used to treat dry skin, topical leave-on products are the first-line treatment, intended to improve hydration and the skin barrier function. The effectiveness of adequate water intake as a measure to treat dry skin is still under debate. Normal skin hydration increases with dietary water intake, particularly in those with prior lower water consumption. Skin dryness in AD is instrumental to the itch and inflammation cycle, contributing to barrier impairment and aggravating disease severity and flares. Certain emollients provide significant hydration to AD skin, with relief of dryness and reduction in barrier impairment, disease severity, and flares. Further investigations are needed to evaluate the optimum water intake levels in children with AD, as important questions remain unanswered, namely, does oral hydration provide relief of skin dryness and reduce barrier impairment, disease severity, and flares; is there any additional benefit from using mineral or thermal spring water; or is there a need to specifically study the fluid/water intake in children with AD and food allergy (FA) restrictions?

Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence

Objective

The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC).

Methods

We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n  =  191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n  =  223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members.

Results

CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M  =  5.3%, SE  =  .8 vs. M  =  3.3%, SE  =  .8; p  =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex (β = .11; P  =  .009), weeks with ≥2 weigh-ins (β = .68; P < .0001), and days with an AI-powered coaching exchange (β = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss.

Conclusions

An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

Innovations in Cardio-oncology Resulting from the COVID-19 Pandemic

The COVID pandemic has transformed our approach to patient care, research, and training in cardio-oncology. While the early phases of the COVID pandemic were exceptionally frightening, we now can reflect on the innovative changes that brought more effective and patient-centered care to our doorsteps: expansion of telemedicine, integration of digital health, wider adoption of cardiac biomarkers, consolidation, and coordination of cardio-oncology testing. Normally, it takes years for health care systems to adopt new technology or modify patient care pathways; however, COVID pushed healthcare providers and the health systems to change at warp speed. All of these innovations have improved our efficacy and provided a more “patient-centered” approach for our cardio-oncology patients. The changes we have made in cardio-oncology will likely remain well beyond the pandemic and continue to grow improving the cardiovascular care of oncology patients.

What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients

Background: Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss. Objectives: This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI. Methods: Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months. Results: Overall relative weight loss was −1.63 kg at 1 month, −3.61 kg at 3 months, −5.28 kg at 6 months, and −6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss. Conclusions: The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.

A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention

OBJECTIVE

This study tested the efficacy of a lower-burden, simplified dietary self-monitoring approach compared with a standard calorie monitoring approach for self-monitoring adherence and weight loss in a mobile-delivered behavioral weight loss intervention.

METHODS

Participants (n = 72) with overweight or obesity who had children 2 to 12 years of age living in the home were randomly assigned to a group that used simplified dietary self-monitoring of high-calorie foods (Simplified) or a group that tracked calories (Standard). Both groups received a wireless scale, Fitbit trackers, and a 6-month intervention delivered via a smartphone application with lessons, text messages, and weekly personalized feedback messages.

RESULTS

Percentage weight loss at 6 months was 5.7% (95% CI: -8.3% to -3.2%) in the Standard group and 4.0% (95% CI: -5.7% to -2.3%) in the Simplified group, which was not significantly different. Similar proportions reached 5% weight loss at 6 months (43.2% in Standard and 42.9% in Simplified). There were no differences in number of dietary tracking days or change in average daily caloric intake between groups.

CONCLUSIONS

Two mobile-delivered weight loss interventions produced clinically meaningful levels of weight loss at 6 months, with no differences in dietary tracking adherence or dietary intake. The results suggest that simplified monitoring of high-calorie foods could be a promising alternative to calorie monitoring.

Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial

BACKGROUND

Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness.

OBJECTIVE

We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors.

METHODS

This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors.

RESULTS

More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months.

CONCLUSIONS

Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03156478; https://clinicaltrials.gov/ct2/show/NCT03156478.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12889-019-6574-y.

Duration of Treatment in a Weight Loss Program Using a Mobile App is Associated with Successful Weight Loss During the COVID-19 Pandemic

PURPOSE

We aimed to explore the independent factors associated with successful weight loss using a mobile app during the COVID-19 pandemic.

PATIENTS AND METHODS

For this retrospective cohort study, we collected data from 45 adults in a weight loss program using a mobile app. We defined successful weight loss as a weight reduction by ≥ 5% of the baseline weight. Multivariate logistic analysis was used to assess potential factors influencing successful weight loss.

RESULTS

All subjects showed a mean 4.1 ± 4.4 kg reduction of baseline weight after using the app for a mean duration of 11 weeks (P < 0.001). Subjects in the successful weight loss group displayed a longer duration of treatment (14.6 ± 6.5 weeks vs 6.9 ± 6.0 weeks, P < 0.001), greater number of dietary records (109.2 ± 84.7 vs 54.7 ± 58.8, P = 0.002), and greater number of outpatient visits (6.1 ± 2.7 vs 3.7 ± 2.3, P < 0.001) than those in the unsuccessful weight loss group. Multivariate logistic analysis showed that duration of treatment was an independent factor associated with successful weight loss (odds ratio = 1.23, 95% confidence interval: 1.08-1.41, P = 0.003).

CONCLUSION

In a weight management program using a mobile app during the COVID-19 pandemic, the duration of treatment was found to be an independent factor of successful weight loss.

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: A Large-scale, 52-week Observational Study (Preprint)

Background

Intermittent fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile apps to help them track and adhere to their fasts and monitor changes in their weight and other biometrics.

Objective

This study aimed to quantify user retention, fasting patterns, and weight loss by users of 2 IF mobile apps. We also sought to describe and model starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change.

Methods

We assembled height, weight, fasting, and demographic data of adult users (ages 18-100 years) of the LIFE Fasting Tracker and LIFE Extend apps from 2018 to 2020. Retention for up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least 2 readings of weight and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as extended fasting hours (EFH; hours beyond 12 in a fast) averaged per day (EFH per day). Retention was modeled using a Cox proportional hazards regression. Weight loss was analyzed using linear regression.

Results

A total of 792,692 users were followed for retention based on 26 million recorded fasts. Of these, 132,775 (16.7%) users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. The survival analysis using Cox regression indicated that retention was positively associated with age and exercise and negatively associated with stress and smoking. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH per day, which displayed a positive interaction. Users with a BMI ≥40 kg/m² lost 13.9% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23 kg/m². EFH per day was an approximately linear predictor of weight loss. By week 26, users lost over 1% of their starting weight per EFH per day on average. The regression analysis using all variables was highly predictive of weight change at 26 weeks (R²=0.334) with starting BMI and EFH per day as the most significant predictors.

Conclusions

IF with LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Users who are obese lose substantial weight over time, with more weight loss in those who fast more.

Free-Living Energy Balance Behaviors Are Associated With Greater Weight Loss During a Weight Loss Program

Introduction: Free-living movement (physical activity [PA] and sedentary behavior [SB]) and eating behaviors (energy intake [EI] and food choice) affect energy balance and therefore have the potential to influence weight loss (WL). This study explored whether free-living movement and/or eating behaviors measured early (week 3) in a 14-week WL programme or their change during the intervention are associated with WL in women. Methods: In the study, 80 women (M ± SD age: 42.0 ± 12.4 years) with overweight or obesity [body mass index (BMI): 34.08 ± 3.62 kg/m²] completed a 14 week WL program focused primarily on diet (commercial or self-led). Body mass (BM) was measured at baseline, and again during week 2 and 14 along with body composition. Free-living movement (SenseWear Armband) and eating behavior (weighed food diaries) were measured for 1 week during week 3 and 12. Hierarchical multiple regression analyses examined whether early and early-late change in free-living movement and eating behavior were associated with WL. The differences in behavior between clinically significant weight losers (CWL; ≥5% WL) and non-clinically significant weight losers (NWL; ≤ 3% WL) were compared. Results: The energy density of food consumed [β = 0.45, p < 0.001] and vigorous PA [β = -0.30, p < 0.001] early in the intervention (regression model 1) and early-late change in light PA [β = -0.81 p < 0.001], moderate PA [β = -1.17 p < 0.001], vigorous PA [β = -0.49, p < 0.001], total energy expenditure (EE) [β = 1.84, p < 0.001], and energy density of food consumed [β = 0.27, p = 0.01] (regression model 2) significantly predicted percentage change in BM. Early in the intervention, CWL consumed less energy dense foods than NWL [p = 0.03]. CWL showed a small but significant increase in vigorous PA, whereas NWL showed a slight decrease in PA [p = 0.04]. Conclusion: Both early and early-late change in free-living movement and eating behaviors during a 14 week WL program are predictors of WL. These findings demonstrate that specific behaviors that contribute to greater EE (e.g., vigorous PA) and lower EI (e.g., less energy-dense foods) are related to greater WL outcomes. Interventions targeting these behaviors can be expected to increase the effectiveness of WL programs.

Preference, Expected Burden, and Willingness to Use Digital and Traditional Methods to Assess Food and Alcohol Intake

We conducted an online survey to examine the preference, expected burden, and willingness of people to use four different methods of assessing food and alcohol intake such as food/drink record, 24-h recall, Remote Food Photography Method© (RFPM, via SmartIntake^® app), and a novel app (PortionSize^®) that allows the in-app portion size estimation of foods/drinks by the user. For food (N = 1959) and alcohol (N = 466) intake assessment, 67.3% and 63.3%, respectively, preferred the RFPM/SmartIntake^®, 51.9% and 53.4% preferred PortionSize^®, 48.0% and 49.3% the food records, and 32.9% and 33.9% the 24-h recalls (difference in preference across all methods was p < 0.001 for food and alcohol intake). Ratings of burden and preference of methods were virtually superimposable, and we found strong correlations between high preference and low expected burden for all methods (all ρ ≥ 0.82; all p < 0.001). Willingness (mean (SD)) to use the RFPM/SmartIntake^® (food: 6.6 (2.0); alcohol: 6.4 (2.4)) was greater than PortionSize^® (food: 6.0 (2.2); alcohol: 6.0 (2.4); all p < 0.001) and 24-h recalls (food: 6.1 (2.2); alcohol: 5.7 (2.7); p < 0.001), but not different from food records (food: 6.6 (2.0); alcohol: 6.5 (2.3); all p ≥ 0.33). Our results can be used in conjunction with existing data on the reliability and validity of these methods in order to inform the selection of methods for the assessment of food and alcohol intake.

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.

Can you see me? Participant experience of accessing a weight management programme via group videoconference to overcome barriers to engagement

Background

Engagement with conventional weight management group programmes is low.

Objective

To understand participant experience of accessing an adapted programme via videoconference.

Participants

Adults with obesity (BMI ≥ 35kg/m²), referred to an NHS Dietetics service in Wales, were offered a group videoconference weight management programme as an optional alternative to in‐person groups. Thirteen participants (mean age 48.5 ± 20.2 years, 8 female) recruited to two videoconference groups were interviewed.

Study design

A Registered Dietitian delivered a behavioural programme using Skype for Business in 10 sessions over 6 months. Participants joined the groups from any Internet‐connected device with a webcam. Participant perspectives were audiorecorded in one‐to‐one, semi‐structured interviews. Interviews were transcribed verbatim and thematically analysed using self‐determination theory as a theoretical framework.

Results

Ten themes were identified, three relating to service engagement and seven relating to behaviour change facilitation. Key themes in engagement included ‘reduced burden’, described as saving time and travel and ‘reduced threat’ as participants perceived joining a group from home as less daunting compared to attending in‐person. Despite reporting some initial technical difficulties with establishing video and audio connection, participants described beneficial peer support although not physically with other group members.

Conclusion

Accessing a group weight management programme via videoconference may be the preferred option for some participants, overcoming some of the barriers to access to standard in‐person programmes, particularly in rural areas. Participants are able to experience peer support via videoconference. During the COVID‐19 pandemic, weight management programmes could utilize videoconference groups to continue to provide support.