Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Introduction

Major depressive disorder is a leading cause of disability worldwide, impacting the lives of more than 264 million people globally, according to the World Health Organization [1]. The COVID-19 pandemic has further increased the number of people experiencing depressive symptoms [2]. Despite its prevalence, depression often remains undiagnosed and untreated. In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment [3].

Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost [4]. Undiagnosed and untreated depression has significant economic consequences, adding an economic burden of over US $200 billion annually in the United States alone [5]. Thus, it is essential to make the detection and monitoring of depression symptoms easier and more affordable.

An increasingly explored and promising way to accomplish this is through person-generated health data (PGHD) in the form of self-reports and data from consumer-grade wearable devices [6]. Multiple studies have shown that early indicators of changes in depression status can be detected from PGHD in the form of social media use [7] or physical activity patterns [8]. For example, a recent study, using consumer wearable devices to track the sleep of 368 participants, found several strong associations (Z-scores up to 6.19) between sleep features and self-reported depression [9]. Another study showed that activity features collected for 23 participants could accurately (κ=0.773) classify individuals with depression from controls, and predict changes in depression status over a 2-week period [10]. Although these studies are limited in sample size and time duration to generalize across larger populations, they demonstrate the potential of this approach versus more burdensome active assessments [11].

In this work, we present PSYCHE-D (Prediction of Severity Change-Depression), a 2-phase prediction model that uses PGHD to predict longitudinal changes in an individual’s depression severity level (Figure 1). Input data include self-reported sociodemographic data and medical history, and objective behavioral data derived from consumer-grade wearables. The presented model has been developed using the largest longitudinal cohort study ever considered for depression at the time of publication [12], collecting PGHD over a 1-year period from more than 10,000 participants.

In previous work, we presented initial results [13] for the first phase of the model, and exploratory results for the second phase are also available [14]. These initial results demonstrate the feasibility of the PSYCHE-D approach, yet the stepwise development approach creates the possibility of data leakage between the phases and therefore misleading results. This work presents results from an improved pipeline that eliminates the leakage, thus ensuring generalizable results and laying the foundation for a very low–burden, consumer-facing, personalized system that could improve engagement and outcomes in people with depression.

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Methods

Data Collection

The data used in this work are part of the DiSCover (Digital Signals in Chronic Pain) Project (ClinicalTrials.gov identifier: NCT03421223). The DiSCover Project is a 1-year longitudinal study consisting of 10,036 individuals in the United States, who, between January 2018 and January 2020, provided data from consumer-grade wearable devices and completed surveys about their mental health and lifestyle changes quarterly and monthly, respectively. Detailed design and baseline participant characteristics are described in the report by Lee et al [12].

The data subset used in this work comprises the following:

1. Wearable PGHD: Step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study were collected.
2. Screener survey: Prior to the study, participant self-reported sociodemographic information, as well as comorbidities were collected.
3. Lifestyle and medication changes (LMC) survey: Every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month.
4. Patient Health Questionnaire-9 (PHQ-9) score: Every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be a reliable and valid measure of depression severity [15].

From these input sources, we defined a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, eg, demographic features) and dynamic (varying with time for a given participant, eg, behavioral features derived from consumer-grade wearables). Feature extraction and engineering are described in Multimedia Appendix 1.

Data Processing

Data Filtering Process

Figure 2 outlines the processing of the initial data set into the samples used for developing the phase 1c model. Of the 10,036 enrolled participants, 9961 passed the survey response quality control, defined as completion of the PHQ-9 for at least two contiguous quarters, as well as the LMC survey for the same month as the second PHQ-9. Additional filtering, based on the density of available activity data in the 2 weeks matching the PHQ-9 recall period, was performed according to standards proposed in the literature [16,17]. We ultimately obtained a total of 10,866 samples from 4036 unique participants.

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Data Set Construction

Initial data exploration showed that the evolution of PHQ-9 scores over 3-month intervals was constant throughout the study period, when grouping by demographic variables, such as sex, age, race, and geographic location. Based on this observation, we divided the data into 3-month long, nonoverlapping, independent samples. We used the notations “SM0” (sample month 0), “SM1,” “SM2,” and “SM3” to refer to relative time points within each sample. Each 3-month sample consisted of 1 set of screener survey responses, PHQ-9 survey responses at SM0 and SM3, LMC survey responses at SM3 (as well as SM1 and SM2, if available), and wearable PGHD for SM3 (as well as SM1 and SM2, if available). The wearable PGHD included data collected from 8 to 14 days prior to the PHQ-9 label generation date (SM1 and SM2 in phase 1, SM3 in phase 2).

Modeling

Figure 1 illustrates the overall approach, and the inputs and outputs for phase 1c and phase 2c. Figure 3 illustrates the modeling approach, which is explained in more detail below. The key design feature is that the models are combined into a single combined pipeline, and participant-based train-test partitioning is performed once at the start, in order to eliminate the possibility of data leakage. The combined pipeline is thus fitted on 1 set of participants and tested on another set of previously unseen participants.

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Phase 1c: Categorization of the Intermediate PHQ-9

The goal of the phase 1c model was to predict participants’ PHQ-9 score categories from sociodemographic, medical, and wearable PGHD. The initial version of phase 1c is described in the report by Makhmutova et al [13]. Here, we describe an improved variant that has been adapted to reduce overfitting. The Light Gradient Boosting Machine (LightGBM) algorithm with Dropouts Meet Multiple Additive Regression Trees (DART) boosting [18], an ensemble model of boosted regression trees with dropout, was chosen due to its ability to handle sparse data and the ability to tune an additional dropout parameter to reduce overfitting. Feature selection removed highly correlated features, and used recursive feature elimination [19] in order to eliminate features that had lower contributions to model performance. Model performance was primarily measured using quadratic weighted Cohen κ [20], with adjacent accuracy (ie, fraction of samples predicted at most one off from the target value), balanced accuracy, and weighted F1-scores as secondary performance metrics. We performed randomized search 5-fold cross-validation to tune the hyperparameters of our LightGBM model. We chose to perform a 5-fold cross-validation to reduce the impact of overfitting. We reported the performance metrics of the best tuned models with 95% CIs across 5 training runs (5 outer shuffle splits). Further details on hyperparameters are reported in Multimedia Appendix 1 and elsewhere [14]. Due to the very large feature space that covers a range of static and dynamic input features, we constructed the model in 3 steps. We first performed an extensive exploration accessing the best feature subsets of each type of input. We then carried out an initial optimization on input sets, which combined different types of input and considered an initial estimation of model error. Subsequently, we conducted a final tuning to obtain the best performing model. The output of phase 1c generated intermediate monthly PHQ-9 score categories for SM1, denoting sample month 1, and SM2.

Phase 2c: Prediction of Longitudinal PHQ-9 Change

In phase 2c, we predicted an increase in the PHQ-9 category using the participants’ PHQ-9 scores from SM0, intermediate generated PHQ-9 categories at SM1 and SM2 as well as the generated probabilities of each PHQ-9 category for SM1 and SM2, and LMC survey responses and wearable PGHD collected over the 2 weeks prior to final PHQ-9 completion at SM3. We also used the screener survey responses as input features to control for sociodemographic factors. To compute the target variable in each sample in the phase 2c model, we observed whether there was an increase in the PHQ-9 category between SM0 and SM3.

A similar model construction procedure was used for phase 2c as for phase 1c. The feature selection procedure consisted of reducing the initial number of input features through the removal of highly correlated features and selecting the most important features using recursive feature elimination with cross-validation for the largest sets of input features, grouped by source. Then, we performed forward sequential feature selection [21], a greedy method that has been successfully used to develop digital measures in mental health studies [22], to identify the optimal features. We then again used LightGBM DART, as it has been shown to deliver high accuracy in comparable classification tasks [18], is able to handle sparse data, and generates interpretable models.

Specificity and area under the precision-recall curve (AUPRC) [23] were prioritized as performance metrics. Feature importance was assessed using a combination of the following 2 key metrics: “gain” importance and “split” importance [24]. Gain importance measures the improvement in accuracy that a feature provides, while split importance considers the number of times the feature is used in a model. Taken together, these metrics help us understand which features contribute the most to the “decisions” that the model makes.

The construction of the PSYCHE-D combined pipeline consisting of phase 1c, followed by phase 2c, is summarized in Figure 3. The diagram also illustrates the participant-based splitting approach used to ensure that we generate predictions on previously unseen participants, to evaluate the approach’s generalization capabilities. Further details are presented in Multimedia Appendix 1.

Code Availability

The codes of the models in this study, along with their trained weights, are available on GitHub [25].

Data Availability

Data are made available to academic researchers on Zenodo [26].

Ethics Approval

This study received expedited review and Institutional Review Board (IRB) approval from the Western Institutional Review Board-Copernicus Group (IRB study number: 1181760; protocol number: 20172916; initial approval date: December 21, 2017).

Results

Overview

In the following section, we present the performance and informative features for the combined pipeline. Importantly, we wanted to build the model in a manner representative of how such a model might be deployed “in the real world.” In such a situation, a trained model (eg, as part of an app) would need to make predictions for participants that the model is naive to, that is, people who have just downloaded the app and perhaps only filled out the baseline assessments, and did not contribute data used in the model construction. This pipeline is therefore designed to test the generalizability of the models by eliminating any data leakage, and using a participant-based validation strategy, that is, the model is tested on participants that it is completely naive to. Results for the 2 phases are presented separately.

Intermediate Classification of Depression Severity

Acquiring PGHD on a large scale requires a low-burden data collection approach; thus, participants were only asked to complete the PHQ-9 at sparse intervals, once every 3 months. Consequently, we were limited to a relatively small set of reference labels, with 2.07 labels on average per enrolled participant over the course of 1 year. The first phase of our approach thus generated more frequent intermediate depression severity labels, which were used in combination with self-reported reference labels to reduce the sparsity of the data set by up to 3 times.

We were able to construct a multi-class classification model that determines a participant’s depression severity for a given month, by assigning an individual to 1 of 5 ordinal PHQ-9 classes describing severity from minimal to severe [15]. The details and distributions of the observed classes are presented in Table S1 in Multimedia Appendix 1.

The best performing model, based on the LightGBM DART algorithm, after hyperparameter tuning, had a κ value of 0.476 (95% CI ±0.017) and an adjacent accuracy of 77.6%.

The performance of the model was not equal across all PHQ-9 severity categories. Comparing actual to predicted categories in a confusion matrix (Figure 4), we observed that performance was high for samples from individuals with either relatively low (minimal or mild) or high (moderately severe or severe) depression.

The model included features selected across all input sources, including demographics (gender, birth year, education, and BMI), life events and conditions at baseline (whether they had received financial assistance, experienced trauma or given birth, or were diagnosed with a range of chronic conditions), LMC (changes to medications or lifestyle), and sleep-related wearable PGHD (the number of hypersomnia nights, range of bedtime, and average ratio of the time spent asleep to the time spent in bed). A full list of the final features and their relative importance is included in Multimedia Appendix 2. This model was then used to generate intermediate PHQ-9 category labels for each individual for SM1 and SM2.

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Prediction of Longitudinal Change

The intermediate generation of depression severity labels means that each sample consisted of the PHQ-9 depression severity at SM0, the LMC surveys, wearable PGHD, and up to two generated labels that provide a weak estimate of depression severity (PHQ-9 category) at SM1 and SM2.

We posed our original aim as a binary problem as follows: can we predict increased depression severity? We defined increased depression severity as that when a participant changed the PHQ-9 category between SM0 and SM3. From our 10,866 samples, 2252 (20.7%) were thus labeled as positive cases.

The construction of the second phase model was optimized across possible input feature sets and LightGBM model hyperparameters. As summarized in Figure 3, we noted that with this approach, the optimization process also depended on the outputs generated by the first phase.

We used a range of metrics to assess performance, but prioritized sensitivity as the key metric, as our primary goal in this work was to correctly identify the highest proportion of individuals reporting increased depression severity. As the data set was highly imbalanced, with 21% of individuals in the data set reporting increased depression severity, we optimized for performance for both the majority and minority classes. We thus took into account specificity and AUPRC as secondary performance metrics, to observe the tradeoff in performance for each class.

Based on this, the best performing model selected 13 input features, with a sensitivity of 55.4% (95% CI ±0.8%), specificity of 65.3% (95% CI ±4.2%), and AUPRC of 0.31 (95% CI ±0.024). In comparison, a baseline model, which randomly assigned 20.7% positive labels, reported an AUPRC of 0.21, a sensitivity of 19.8%, and a specificity of 80.0% (averaged across 10 runs of 1000 samples).

We examined the most important features in the second phase of the combined pipeline and observed that the selected features to predict relative changes in depression were similar to the features selected to predict absolute depression in the first phase.

The most important features are presented in Figure 5, with further details in Multimedia Appendix 2. Features that were most frequently selected as strong predictors of an increase in depression severity, regardless of the cohort, were PHQ-9–related features. Specifically, the self-reported starting PHQ-9 category and the generated intermediate PHQ-9 category for SM1 were the most important features, as we can see in Figure 5. Among the static demographic and socioeconomic features, we noted that sex and having health insurance were the most important. Various self-reported LMC features were frequently selected, including medication changes (starting, stopping, and changing doses) and stress-related lifestyle changes (starting meditation and reducing stress-inducing activities), as well as reducing or stopping alcohol consumption. We observed that objective sleep features were again selected, but no specific individual wearable PGHD feature (sleep or otherwise) was sufficiently consistently selected to be included in the final model.

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Discussion

Principal Findings

PGHD represent a low burden direct connection to the patient journey, and such data have already been demonstrated to be a valuable component of models that predict health-relevant outcomes [27,28]. We present a 2-phase approach for predicting longitudinal deterioration in depression status. In phase 1c, we increased the label density by generating intermediate PHQ-9 category labels using wearable PGHD and LMC information. In the second phase, we combined self-reported and generated PHQ-9 category labels with additional recent wearable PGHD and LMC information to predict the deterioration of depression status 3 months after the initial self-report. This 2-phase approach has a very low burden and requires very little participant interaction. The information we used as input consists of simple self-reports and data from consumer-grade wearables.

Even though overall performance in phase 1c was not particularly strong (κ=0.476, 95% CI ±0.017), we were encouraged by 2 factors: the adjacent accuracy was high (77.6%), and an examination of features in the final tuned models showed good correspondence to factors known to be important risk factors for depression, for example, gender, experience of trauma, and chronic comorbidities. Large-scale studies have shown that these have an influence on depression [29]. We also observed that objective sleep features were selected. Sleep features and depressive disorders have been previously associated using low-cost wearable devices [9], PGHD [30], and smartphones [31]. Additionally, we observed that performance was not even across severity groups and was high for individuals with either relatively mild or relatively severe depression.

In phase 2c, our best performing model achieved a sensitivity of 55.4%, specificity of 65.3% (95% CI ±4.2%), and AUPRC of 0.31 (95% CI ±0.024). In comparison, simulating random assignment of 20.7% positive labels across 10 iterations of 1000 samples, we noted an AUPRC of 0.21, a sensitivity of 19.8%, and a specificity of 80.0%. This means that sensitivity nearly tripled, while specificity only slightly reduced. We prioritized sensitivity because the potential consequences of false negatives (ie, not identifying a person with deteriorating depression) is much higher than the cost of false positives (ie, incorrectly suspecting someone of deteriorating depression).

We observed that features from all input sources were selected in the best performing models, but with different relative importance. We saw that static features (ie, those defined at enrollment, which do not change afterwards) were selected, but were of relatively low importance. This included features that are known to be relevant to the risk of developing depression, including the presence of chronic comorbidities [32], ethnicity [33], financial difficulties [34], and pregnancy [35]. We also saw features derived from wearable devices, including trends in sleep onset time, percentage of sleep time spent awake, and overall number of hypersomnia days. The most important features were those generated in phase 1c, that is, the probability of an individual being in a given PHQ-9 class, summarizing features from across all input sources. The intermediate labels generated in phase 1c are inspired by the concept of “weak labeling,” which can help reduce large-scale noisy data to a signal useful for supervised learning (eg, the report by Zhan et al [36]). We noted that due to data sparsity, intermediate labeling was not always available, and thus, some samples did not have 2 intermediate PHQ-9 category labels, but sometimes had 1 or none. Nonetheless, as LightGBM was able to deal with missing values, the lack of intermediate labeling or missing PGHD values did not pose problems in the phase 2c model predictions, highlighting that the approach described in our work is indeed low-burden and robust.

From this, we were able to deduce that the average sleep onset time is a good determinant of increasing depression severity, which is consistent with previous research [9], but that variability in sleep is participant specific and not necessarily a good predictor for generalizing to other participants.

Limitations

The work presented here demonstrates the potential of a PGHD-based model for predicting long-term changes in depression status in new individuals. This initial approach nevertheless has several limitations in practice, which will be addressed in future work.

The model relies on the completion of several self-reported surveys over time. Participants were highly engaged with the year-long research study, but to lower the barrier to participation, the number of surveys could be reduced or replaced with alternative sources of data. For example, instead of LMC surveys, medication change data could be obtained through electronic health records [37] or through other consumer-grade wearables that incorporate engagement, such as the Oura ring, which allows participants to annotate days with a number of tags like medication [38].

The performance of PSYCHE-D was below our initial expectations, despite more than triple sensitivity versus a random model, and was weaker than the initial nongeneralized performance [13,14]. However, further validation and prospective data collection could seek to build off this “out of the box” performance using an active learning approach to improve individualized performance [39,40]. We also plan to perform further validation with independently generated data [41]. The study design also limits us to making predictions of depression status change over a 3-month time window. Thus, future work will focus on testing predictions beyond that time horizon.

We will also explore the application of PSYCHE to other aspects of mental health like anxiety [31], fatigue [42], and stress [22].

Outlook and Conclusion

Effective treatments for depression exist, but they must be delivered in a timely manner, as the benefits of early intervention are established for both older [43] and younger [44] patients. Moreover, the objectivity of our system provides a nonstigmatizing environment to engage people about depression [4]. We hope that this demonstration of the ability to predict long-term changes in depression using a low-burden PGHD-based approach will have great potential to deliver value to patients.