Momentary changes in heart rate variability can detect risk for emotional eating episodes

Juarascio, Adrienne S.; Crochiere, Rebecca J; Tapera, Tinashe M.; Palermo, Madeline; Zhang, Fengqing

doi:10.1016/j.appet.2020.104698

Cited by 21 publications

(17 citation statements)

References 69 publications

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“…Of particular relevance to iCAT+, wearables that can detect physiological signals of rising negative affect (e.g., heart rate variability and skin conductance) may be particularly promising. Passive sensing technologies have been employed in studies that predict other risk behaviours (e.g., alcohol use, smoking; Gustafson et al, 2014; Naughton et al, 2016) and recent results from our team indicate that wearable sensors that can detect heart rate variability can prospectively predict emotional eating episodes (Godfrey et al, 2019; Juarascio, Crochiere, Tapera, Palermo, & Zhang, 2020).…”

Section: Discussionmentioning

confidence: 99%

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

Juarascio

Hunt

Lesser

et al. 2020

Euro Eating Disorders Rev

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Objective Although current treatments are effective for some patients with eating disorders, a large number of patients remain partially or fully symptomatic post‐treatment. This may be related to poor utilization of treatment skills outside of the therapy office. Smartphone applications that can detect and intervene during moments of need could facilitate such skill use between sessions. Method Individuals (N = 16) participated in a small pilot open trial where they received 21 sessions of in‐person Integrative Cognitive‐Affective Therapy (ICAT) therapy an app (iCAT+) that delivers ecological momentary interventions (EMI) in response to user‐entered data. Data were collected on the feasibility and acceptability of this treatment approach and on preliminary indicators of treatment outcomes. Results Participants found iCAT+ as a treatment augmentation acceptable and indicated it had clinical utility as an adjunct to in‐person therapy, although analyses indicated poor compliance with data entry needed to trigger EMI delivery. This suggests that long‐term use of EMI requiring ongoing data entry is infeasible. Conclusions We describe lessons learned from our initial pilot trial and future directions for the development of impactful EMI systems that can be used to augment in‐person therapies.

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Section: Discussionmentioning

confidence: 99%

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

Juarascio

Hunt

Lesser

et al. 2020

Euro Eating Disorders Rev

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“…Of the studies on optimising goal setting, five were on optimising eating behaviour goals (e.g., eating at a certain time of the day and energy intake) (48,49,53) and six were on optimising physical activity goals (e.g., type of physical activity and energy expenditure) (26,(51)(52)(53)(54)(55) . Of the ten studies on self-control, five were on controlling eating behaviours (48,49,107) , three were on controlling physical activity performance (26,52,54) and two were on both (51,53) . Only six of these studies reported weight loss of which two were significant (26,107) .…”

Section: Self-regulation Of Weight Loss-related Behavioursmentioning

confidence: 99%

“…Of the ten studies on self-control, five were on controlling eating behaviours (48,49,107) , three were on controlling physical activity performance (26,52,54) and two were on both (51,53) . Only six of these studies reported weight loss of which two were significant (26,107) . With only 15•2 % of the included studies examining strategies to exert self-control over weight-related behaviours, more research is needed to explore the potential of AI on improving weight-related behavioural changes for weight loss.…”

Section: Self-regulation Of Weight Loss-related Behavioursmentioning

confidence: 99%

“…We categorised the included articles into three AI applications, namely machine perception (n 50), predictive analytics only (n 6) (47,55,82,105,107,111) and real-time analytics with personalised micro-interventions (n 10) (26,(49)(50)(51)(52)(53)(54)107) (Fig. 3).…”

Section: Functions Of Artificial Intelligence In Self-regulation Of Weight Loss-related Behavioursmentioning

confidence: 99%

“…Predictive analytics: goal setting and action planning optimisation Six studies showed the use of AI to predict weight loss (n 1) (82) , adherence to personalised physical activity goals (n 2) (55,105) , dietary lapses (n 2) (47,107) and episodes of emotional eating (n 1) (Table 2). Only one study collected primary data using the ecological momentary assessment (EMA), which was also the only one that reported a mean dietary lapse frequency of 3•5 per week.…”

Section: Machine Perception: Self-monitoringmentioning

confidence: 99%

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The potential of artificial intelligence in enhancing adult weight loss: a scoping review

Chew

Ang

Lau

2021

Public Health Nutr.

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Objective: To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss. Design: A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O’Malley’s five-step framework. Eight databases (CINAHL, Cochrane–Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k= 0.96). Results: 66 out of 5573 potential studies were included, representing more than 2,031 participants. Three tenets of self-regulation were identified - self-monitoring (n=66, 100%), optimization of goal-setting (n=10, 15.2%) and self-control (n= 10, 15.2%). Articles were also categorised into three AI applications namely machine perception (n=50), predictive analytics only (n=6), and real-time analytics with personalised micro-interventions (n=10). Machine perception focused on recognizing food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalized nudges/prompts. Only six studies reported average weight losses (2.4% to 4.7%) of which two were statistically significant. Conclusion: The use of AI for weight loss is still undeveloped. Based on this study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.

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Measures of nutrition status and health for weight‐inclusive patient care: A narrative review

Mauldin,

Pignotti,

Gieng

2024

Nut in Clin Prac

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In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non–weight‐related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

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Momentary changes in heart rate variability can detect risk for emotional eating episodes

Cited by 21 publications

References 69 publications

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

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

The potential of artificial intelligence in enhancing adult weight loss: a scoping review

Measures of nutrition status and health for weight‐inclusive patient care: A narrative review

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