Patient Experiences Using a Self-Monitoring App in Eating Disorder Treatment: Qualitative Study

Lindgreen, Pil; Lomborg, Kirsten; Clausen, Loa

doi:10.2196/10253

Cited by 39 publications

(29 citation statements)

References 40 publications

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“…"Recovery Record" is perhaps one of the better-known eating disorder smartphone apps and is described in the literature by its authors 82,83 and users. 84 Another "Noom Monitor" is intended to facilitate guided self-help CBT. 85 Reviews of smartphone apps for the treatment of eating disorders are available and should be studied before proceeding with their use.…”

Section: Dasotralinementioning

confidence: 99%

Binge eating disorder revisited: what’s new, what’s different, what’s next

Citrome

2019

CNS Spectr.

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Binge eating disorder (BED) is the most common type of eating disorder. According to the most recent data available, the estimated lifetime prevalence of BED among US adults in the general population is 0.85% (men 0.42% and women 1.25%). Among psychiatric treatment populations, prevalence is several-fold higher. Although many people with BED are obese (BMI ≥ 30 kg/m2), roughly half are not. In the DSM-5, BED is defined by recurrent episodes of binge eating (eating in a discrete period of time, an amount of food larger than most people would eat in a similar amount of time under similar circumstances and a sense of lack of control over eating during the episode), occurring on average at least once a week for 3 months, and associated with marked distress. BED often goes unrecognized and thus untreated; in one study, 344 of 22,387 (1.5%) survey respondents met DSM-5 criteria for BED, but only 11 out of the 344 had ever been diagnosed with BED by a health-care provider. Psychiatric comorbidities are very common, with most adults with BED also experiencing anxiety disorders, mood disorders, impulse control disorders, or substance use disorders, suggesting that clinicians have patients in their practice with unrecognized BED. Multiple neurobiological explanations have been suggested for BED, including dysregulation in reward center and impulse control circuitry. Additionally, there is interplay between genetic influences and environmental stressors. Psychological treatments such as cognitive behavioral interventions have been recommended as first line and are supported by meta-analytic reviews; however, access to such treatments may be limited because of local availability and/or cost, and these treatments generally lead to little to no weight loss, although successfully eliminating binge eating can protect against future weight gain. Routine medication treatments for anxiety and depression do not necessarily ameliorate the symptoms of BED, but there are approved and emerging medication options, lisdexamfetamine and dasotraline, respectively, that specifically address the core drivers behind binge eating, namely obsessive thoughts and compulsive behaviors regarding food, resulting in marked decreases in binge eating behaviors as well as weight loss.

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Section: Dasotralinementioning

confidence: 99%

Binge eating disorder revisited: what’s new, what’s different, what’s next

Citrome

2019

CNS Spectr.

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“…A number of other health information technologies offer various features and solutions that overlap with our aim to use digital technologies to support timely and ongoing mental health care. For example, the Monsenso mHealth solution for mental illnesses has been designed to collect self‐report and sensor data (eg, physical activity, mobility and phone usage), trigger notifications in response to warning signs, and provide insights into the associations between behavioural patterns and mental ill health; and Recovery Record is an mHealth app for eating disorders that allows individuals to log their meals, thoughts and feelings . Logged information can then be overseen by a health professional to monitor progress and identify triggers to improve care.…”

Section: Overview Of Project Synergy: Building An Online Platform To mentioning

confidence: 99%

Project Synergy: co‐designing technology‐enabled solutions for Australian mental health services reform

Hickie

Davenport

Burns

et al. 2019

Medical Journal of Australia

109

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Summary Project Synergy aims to test the potential of new and emerging technologies to enhance the quality of mental health care provided by traditional face‐to‐face services. Specifically, it seeks to ensure that consumers get the right care, first time (delivery of effective mental health care early in the course of illness). Using co‐design with affected individuals, Project Synergy has built, implemented and evaluated an online platform to assist the assessment, feedback, management and monitoring of people with mental disorders. It also promotes the maintenance of wellbeing by collating health and social information from consumers, their supportive others and health professionals. This information is reported back openly to consumers and their service providers to promote genuine collaborative care. The online platform does not provide stand‐alone medical or health advice, risk assessment, clinical diagnosis or treatment; instead, it supports users to decide what may be suitable care options. Using an iterative cycle of research and development, the first four studies of Project Synergy (2014–2016) involved the development of different types of online prototypes for young people (i) attending university; (ii) in three disadvantaged communities in New South Wales; (iii) at risk of suicide; and (iv) attending five headspace centres. These contributed valuable information concerning the co‐design, build, user testing and evaluation of prototypes, as well as staff experiences during development and service quality improvements following implementation. Through ongoing research and development (2017–2020), these prototypes underpin one online platform that aims to support better multidimensional mental health outcomes for consumers; more efficient, effective and appropriate use of health professional knowledge and clinical skills; and quality improvements in mental health service delivery.

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“…Patients with severe EDs might be less inclined to use web-based interventions following discharge, as indicated by Jacobi et al (41). In contrast to webbased solutions, smartphone app-based interventions have the potential to increase adherence and effectiveness by providing users with reminders that may play a significant role in the buildup of desired behaviors (45), but may result in low acceptability levels, if users find them to be an obstruction to their daily routines, as indicated by Lindgreen et al (52). To be able to leverage the full potential of these interventions, future endeavors should systematically assess acceptability and focus on ways to increase adherence and acceptability rates.…”

Section: Acceptabilitymentioning

confidence: 99%

Review of Randomized Controlled Trials Using e-Health Interventions for Patients With Eating Disorders

2020

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Background: In a world of technological advancements, electronic devices and services seem to be a promising way to increase patients' engagement in treatment and to help manage their symptoms. Here, we identified and analyzed the current evidence of RCTs to evaluate the effectiveness and acceptability of e-health interventions in the eating disorder (ED) field. Methods: We screened an initial cluster of 581 papers. In the end, 12 RCTs in clinical ED cohorts were included. Results: Some studies were conceived as stand-alone interventions, while others were presented as add-ons to ED-specific treatments. Studies varied in the type of EDs under investigation and in the e-health intervention applied (with vs. without therapist support vs. blended interventions; smartphone-vs. web-based). Only four studies reported explicit acceptability measures. Out of those, two reported high acceptability, one reported low acceptability, and one reported no significant difference in acceptability between groups. Four studies reported higher effectiveness of the e-health intervention compared to the control condition, e.g., reduction in maladaptive eating behaviors. Regarding control groups, three used a wait list design and nine had another kind of intervention (e.g., faceto-face CBT, or treatment as usual) as control. Discussion: So far, the evidence for acceptability and effectiveness of e-health interventions in EDs is very limited. There is also a lack of studies in older patients, adolescents, men, sexual and ethnic minorities. Shame/stigma is discussed in the context of e-health interventions for EDs. It remains unclear how severity of EDs affects the effectiveness of e-health interventions, how patients can channel the knowledge they acquire from e-health interventions into their actual behaviors, and how such interventions can better fit the needs of the individual patient to increase acceptability and effectiveness.

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Patient Experiences Using a Self-Monitoring App in Eating Disorder Treatment: Qualitative Study

Cited by 39 publications

References 40 publications

Binge eating disorder revisited: what’s new, what’s different, what’s next

Binge eating disorder revisited: what’s new, what’s different, what’s next

Project Synergy: co‐designing technology‐enabled solutions for Australian mental health services reform

Review of Randomized Controlled Trials Using e-Health Interventions for Patients With Eating Disorders

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