Dataset Information

Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating.

ABSTRACT:

Background

Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting).

Objective

The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions.

Methods

This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented.

Results

The findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet.

Conclusions

This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.

SUBMITTER: Vega J

PROVIDER: S-EPMC9086876 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone-based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence-related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups.ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy.MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm.ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001).ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future.

Project description:BackgroundDespite the worldwide growth in mobile health (mHealth) tools and the possible benefits for both patients and health care providers, the overall adoption levels of mHealth tools by health professionals remain relatively low.ObjectiveThis study aimed (1) to investigate attitudes of health care providers and mHealth experts toward mHealth tools in the health context in general, and this study aimed (2) to test the acceptability and feasibility of a specific mHealth tool for patients with an eating disorder (ED), called TCApp, among patients and ED specialists.MethodsTo this purpose, we conducted an explorative qualitative study with 4 in-depth group discussions with several groups of stakeholders: our first focus group was conducted with 11 experts on mHealth from the Catalan Association of Health Entities; the second focus group included 10 health care professionals from the Spanish College of Doctors of Barcelona; the third focus group involved 9 patients with an ED who had used the TCApp over a 12-week period, and the fourth and last focus group involved 8 ED specialists who had monitored such ED patients on the Web.ResultsThe focus groups showed that health care providers and mHealth experts reported barriers for mHealth adoption more often than facilitators, indicating that mHealth techniques are difficult to obtain and use. Most barriers were attributed to external factors relating to the human or organizational environment (ie, lack of time because of workload, lack of direct interest on a legislative or political level) rather than being attributed to internal factors relating to individual obstacles. The results of the mHealth intervention study indicate that the TCApp was considered as easy to use and useful, although patients and the ED specialists monitoring them on the Web reported different adoption problems, such as the inability to personalize the app, a lack of motivational and interactive components, or difficulties in adhering to the study protocol.ConclusionsIn general, this paper indicates that both health professionals and patients foresee difficulties that need to be addressed before comprehensive adoption and usage of mHealth techniques can be effectively implemented. Such findings are in line with previous studies, suggesting that although they acknowledge their possible benefits and cost-effectiveness, health care providers are quite resistant and conservative about integrating mHealth technologies in their daily practice.

Project description:ObjectivesElevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability.MethodsParticipants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability.ResultsIndividuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2 = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not.DiscussionGlucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study.Public significanceThis study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

Project description:BackgroundUnderstanding the characteristics of commenters on mental health-related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics.ObjectiveUsing eating disorder-related (EDR) forums as an example, this study detailed a methodology that aimed to determine subtypes of mental health-related forums and profile their commenters based on the other forums to which they contributed.MethodsThe researchers identified all public EDR forums (with ≥500 contributing commenters between March 2017 and February 2018) on a large Web-based discussion platform (Reddit). A mixed-methods approach comprising network analysis with community detection, text mining, and manual review identified subtypes of EDR forums. For each subtype, another network analysis with community detection was conducted using the EDR forum commenter overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR forum commenters.ResultsSix subtypes of EDR forums were identified, to which 14,024 commenters had contributed. The results focus on 2 subtypes-proeating disorder and thinspiration-and communities of commenters within both subtypes. Within the proeating disorder subtype, 3 communities of commenters were detected that related to the body and eating, mental health, and women, appearance, and mixed topics. With regard to the thinspiration group, 78.17% (849/1086) of commenters had also commented on pornographic forums and 16.66% (181/1086) had contributed to proeating disorder forums.ConclusionsThe article exemplifies a methodology that provides insight into subtypes of mental health-related forums and the characteristics of their commenters. The findings have implications for future research and Web-based psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses or utilize the methodology to investigate other mental health-related forums.