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AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

 

Individuals such as medical interns who work in high-stress environments often face mental health challenges including depression and anxiety. These challenges are exacerbated by the limited access to traditional mental health services due to demanding work schedules. In this context, mobile health interventions such as push notifications targeting behavioral modification to improve mental health outcomes could deliver much needed support. In this work, we study the effectiveness of these interventions on subgroups, by studying the conditional average causal effect of these interventions. We design a two step approach for estimating the conditional average causal effect of interventions and identifying specific subgroups of the population who respond positively or negatively to the interventions. The first step of our approach follows existing causal effect estimation approaches, while the second step involves a novel tree-based approach to identify subgroups who respond to the treatment. The novelty in the second step stems from a pruning approach that deploys hypothesis testing to identify subgroups experiencing a statistically significant positive or negative causal effect. Using a semi-simulated dataset, we show that our approach retrieves affected subpopulations with a higher precision than alternatives while maintaining the same recall and accuracy. Using a real dataset with randomized push interventions among the medical intern population at a large hospital, we show how our approach can be used to identify subgroups who might benefit the most from interventions. 

Individuals such as medical interns who work in high-stress environments often face mental health challenges including depression and anxiety. These challenges are exacerbated by the limited access to traditional mental health services due to demanding work schedules. In this context, mobile health interventions such as push notifications targeting behavioral modification to improve mental health outcomes could deliver much needed support. In this work, we study the effectiveness of these interventions on subgroups, by studying the conditional average causal effect of these interventions. We design a two step approach for estimating the conditional average causal effect of interventions and identifying specific subgroups of the population who respond positively or negatively to the interventions. The first step of our approach follows existing causal effect estimation approaches, while the second step involves a novel tree-based approach to identify subgroups who respond to the treatment. The novelty in the second step stems from a pruning approach that deploys hypothesis testing to identify subgroups experiencing a statistically significant positive or negative causal effect. Using a semi-simulated dataset, we show that our approach retrieves affected subpopulations with a higher precision than alternatives while maintaining the same recall and accuracy. Using a real dataset with randomized push interventions among the medical intern population at a large hospital, we show how our approach can be used to identify subgroups who might benefit the most from interventions. 

Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.