Does Brief Telephone Support Improve Engagement With a Web-Based Weight Management Intervention? Randomized Controlled Trial

Dennison, Laura; Morrison, Leanne; Lloyd, Scott; Phillips, Dawn; Stuart, Beth; Williams, Sarah; Bradbury, Katherine; Roderick, Paul; Murray, Elizabeth; Michie, Susan; Little, Paul; Yardley, Lucy

doi:10.2196/jmir.3199

Cited by 90 publications

(97 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We found that overall engagement with the intervention was low, but at a similar level (50%) found in a systematic review of use of web-based behaviour change interventions [44]. Other studies have found that engagement with digital interventions can be boosted by telephone-based coaching [45] or professional support [46], however this would affect the potential reach of such an intervention delivered at scale. Provision of frequent updates of the intervention, which was not possible in our study due to the data sharing relationship with the participating supermarket, has also been shown to increase engagement with digital behaviour change interventions [46,47].…”

Section: Discussionsupporting

confidence: 54%

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study (Preprint)

Harrington¹,

Scarborough²,

Hodgkins³

et al. 2018

Preprint

View full text Add to dashboard Cite

BACKGROUND Most food in the United Kingdom is purchased in supermarkets, and many of these purchases are routinely tracked through supermarket loyalty card data. Using such data may be an effective way to develop remote public health interventions and to measure objectively their effectiveness at changing food purchasing behavior. OBJECTIVE The Front-of-pack food Labels: Impact on Consumer Choice (FLICC) study is a pilot randomized controlled trial of a digital behavior change intervention. This pilot trial aimed to collect data on recruitment and retention rates and to provide estimates of effect sizes for the primary outcome (healthiness of ready meals and pizzas purchased) to inform a larger trial. METHODS The intervention consisted of a website where participants could access tailored feedback on previous purchases of ready meals and pizzas, set goals for behavior change, and model and practice the recommended healthy shopping behavior using traffic light labels. The control consisted of Web-based information on traffic light labeling. Participants were recruited via email from a list of loyalty card holders held by the participating supermarket. All food and drink purchases for the participants for the 6 months before recruitment, during the 6-week intervention period, and during a 12-week washout period were transferred to the research team by the participating supermarket. Healthiness of ready meals and pizzas was measured using a predeveloped scale based solely on the traffic light colors on the foods. Questionnaires were completed at recruitment, end of the intervention, and end of washout to estimate the effect of the intervention on variables that mediate behavior change (eg, belief and intention formation). RESULTS We recruited 496 participants from an initial email to 50,000 people. Only 3 people withdrew from the study, and purchase data were received for all other participants. A total of 208 participants completed all 3 questionnaires. There was no difference in the healthiness of purchased ready meals and pizzas between the intervention and control arms either during the intervention period (P=.32) or at washout (P=.59). CONCLUSIONS Although the FLICC study did not find evidence of an impact of the intervention on food purchasing behavior, the unique methods used in this pilot trial are informative for future studies that plan to use supermarket loyalty card data in collaboration with supermarket partners. The experience of the trial showcases the possibilities and challenges associated with the use of loyalty card data in public health research. CLINICALTRIAL ISRCTN Registry ISRCTN19316955; http://www.isrctn.com/ISRCTN19316955 (Archived by WebCite at http://www.webcitation.org/76IVZ9WjK)

show abstract

Section: Discussionsupporting

confidence: 54%

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study (Preprint)

Harrington¹,

Scarborough²,

Hodgkins³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Example components of delivery packages could include system redesign where use of DHI becomes standard clinical practice, 17 ad hoc referral from a clinician, 18 supported access (eg. face-to-face, 19 by telephone, 20 or email 21 ), hosting on a trusted portal (e.g. NHS Choices), marketing via public health campaigns, or embedding in a social network.…”

Section: Paper Structurementioning

confidence: 99%

Evaluating Digital Health Interventions

Murray

Hekler

Andersson

et al. 2016

American Journal of Preventive Medicine

Self Cite

602

467

View full text Add to dashboard Cite

Digital health interventions (DHI) have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety and personalisation. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of DHI. However, evaluations of DHI present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the Research Questions (RQs) needed to appraise DHIs. As DHI are at the intersection of biomedical, behavioural, computing and engineering research, methods drawn from all these disciplines are required. Relevant RQs include defining the problem and the likely benefit of the DHI, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost-effectiveness and harms. While Randomised Controlled Trials (RCTs) are important for evaluation of effectiveness and cost-effectiveness, they are best undertaken only when: a) the intervention and its delivery package are stable; b) these can be implemented with high fidelity and c) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at less cost). Broadening the portfolio of RQs and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice and research.

show abstract

“…Many articles have been published showing the utility of mHealth devices in promoting healthy habits, weight loss reduction, and functional management of comorbidities 16,97,103–119. One interesting telephone-based CBT intervention, planned to improve eating and psychosocial functioning, has been tested in a preliminary study by Sockalingam et al120 Another interesting project was the positive online weight reduction, a web-based weight management intervention focused on supplementing a telephone-based support in web-based obesity treatment protocols 121. Many of these interventions include cognitive, behavioral, or cognitive behavioral techniques compatible with a CBT approach.…”

Section: Cbt and Mhealth: The Integration Of Internet-based Technologmentioning

confidence: 99%

Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives

Castelnuovo¹,

Pietrabissa²,

Manzoni³

et al. 2017

PRBM

125

View full text Add to dashboard Cite

Obesity is a chronic condition associated with risk factors for many medical complications and comorbidities such as cardiovascular diseases, some types of cancer, osteoarthritis, hypertension, dyslipidemia, hypercholesterolemia, type-2 diabetes, obstructive sleep apnea syndrome, and different psychosocial issues and psychopathological disorders. Obesity is a highly complex, multifactorial disease: genetic, biological, psychological, behavioral, familial, social, cultural, and environmental factors can influence in different ways. Evidence-based strategies to improve weight loss, maintain a healthy weight, and reduce related comorbidities typically integrate different interventions: dietetic, nutritional, physical, behavioral, psychological, and if necessary, pharmacological and surgical ones. Such treatments are implemented in a multidisciplinary context with a clinical team composed of endocrinologists, nutritionists, dietitians, physiotherapists, psychiatrists, psychologists, and sometimes surgeons. Cognitive behavioral therapy (CBT) is traditionally recognized as the best established treatment for binge eating disorder and the most preferred intervention for obesity, and could be considered as the first-line treatment among psychological approaches, especially in a long-term perspective; however, it does not necessarily produce a successful weight loss. Traditional CBT for weight loss and other protocols, such as enhanced CBT, enhanced focused CBT, behavioral weight loss treatment, therapeutic education, acceptance and commitment therapy, and sequential binge, are discussed in this review. The issue of long-term weight management of obesity, the real challenge in outpatient settings and in lifestyle modification, is discussed taking into account the possible contribution of mHealth and the stepped-care approach in health care.

show abstract

Does Brief Telephone Support Improve Engagement With a Web-Based Weight Management Intervention? Randomized Controlled Trial

Cited by 90 publications

References 33 publications

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study (Preprint)

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study (Preprint)

Evaluating Digital Health Interventions

Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives

Contact Info

Product

Resources

About