Enhancing Integrative Cognitive-Affective Therapy with ecological momentary interventions: A pilot trial

Which dimensions of emotion dysregulation predict the onset and persistence of eating disorder behaviours? A prospective study

BACKGROUND

Theoretical models highlight the importance of emotion dysregulation as a key risk and maintaining factor for eating disorders. However, most studies testing these theories are cross-sectional. It remains unclear which dimensions of emotion dysregulation account for the onset and persistence of eating disorder behaviours over time.

METHODS

To address these gaps, data were analyzed from 1321 adult women who completed study measures at baseline and eight-month follow-up. The dimensions of emotion dysregulation assessed were five subscales from the abbreviated 16-item Difficulties in Emotion Regulation Scale. Outcomes included the onset (versus asymptomatic) and persistence (versus remission) of binge eating and compensatory behaviours.

RESULTS

Univariate logistic regressions showed that, among initially asymptomatic women, higher baseline levels of each emotion dysregulation dimension (except the "goals" subscale) predicted the onset of binge eating and compensatory behaviours at follow-up. Each dimension also predicted the persistence of compensatory behaviours at follow-up among women endorsing these behaviours at baseline, while the "impulse", "strategies", and "non-acceptance" dimensions predicted the persistence of binge eating. In multivariate analyses, only the "strategies" dimension predicted the onset and persistence of binge eating, while the "non-acceptance" dimension predicted the onset and persistence of compensatory behaviours.

LIMITATIONS

Only a limited number of emotion dysregulation dimensions were tested.

CONCLUSION

Findings demonstrate the importance of emotion dysregulation dimensions in accounting for the onset and maintenance of eating disorder behaviours. The delivery of specific intervention strategies designed to address emotion dysregulation may depend on the risk and symptom profile of an individual.

Ecological momentary interventions for mental health: A scoping review

BACKGROUND

The development of mobile computing technology has enabled the delivery of psychological interventions while people go about their everyday lives. The original visions of the potential of these "ecological momentary interventions" were presented over a decade ago, and the widespread adoption of smartphones in the intervening years has led to a variety of research studies exploring the feasibility of these aspirations. However, there is a dearth of research describing the different dimensions, characteristics, and features of these interventions, as constructed.

OBJECTIVE

To provide an overview of the definitions given for "ecological momentary interventions" in the treatment of common mental health disorders, and describe the set of technological and interaction possibilities which have been used in the design of these interventions.

METHODS

A systematic search identified relevant literature published between 2009 and 2020 in the PubMed, PsycInfo, and ACM Guide to the Computing Literature databases. Following screening, data were extracted from eligible articles using a standardized extraction worksheet. Selected articles were then thematically categorized.

RESULTS

The search identified 583 articles of which 64 met the inclusion criteria. The interventions target a range of mental health problems, with diverse aims, intervention designs and evaluation approaches. The studies employed a variety of features for intervention delivery, but recent research is overwhelmingly comprised of studies based on smartphone apps (30 of 42 papers that described an intervention). Twenty two studies employed sensors for the collection of data in order to provide just-in-time support or predict psychological states.

CONCLUSIONS

With the shift towards smartphone apps, the vision for EMIs has begun to be realised. Recent years have seen increased exploration of the use of sensors and machine learning, but the role of humans in the delivery of EMI is also varied. The variety of capabilities exhibited by EMIs motivates development of a more precise vocabulary for capturing both automatic and human tailoring of these interventions.