Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

Abstract

Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA) but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place.In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE)). We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425 days of EMA data) who were diagnosed with a major depressive disorder according to DSM-IV.Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11). The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant.Short term mood prediction remains a difficult task, but from this research we can conclude that sophisticated machine learning algorithms/setups can result in accurate performance. For future work, we want to use more data from the mobile phone to improve predictive performance of short term mood.

Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data

“…Our findings complement a body of published work by other investigators who have used machine learning with GPS-based, EMA-based, or sensor-based inputs to predict drug use, 23 smoking, 24,25 exercising, 26 diet-related behaviors, [27][28][29][30] and mood changes, [31][32][33][34][35] on time scales ranging from hours to days. A closely related body of work used similar inputs for automated detection of current (not future) cigarette cravings, 36 food cravings, 37 stress, [38][39][40][41] drinking, 42 manic episodes, 43,44 and mood.…”

Affect Recall Bias: Being Resilient by Distorting Reality

“…Rather than representing processing errors, these positive biases have been shown to be protective factors for mental health (i.e., useful resources to maintain and promote well-being and happiness). Consistent with this perspective, a growing body of studies has focused on the importance of a future-oriented disposition (Colombo et al 2020a, b) and, more specifically, the repercussions that future perception has on mental health (Mikus et al 2017;Weinstein 1980). Accordingly, a new construct called "openness to the future" has been proposed, which refers to the "positive expectations about what life may bring, a sense of competence and ability to cope with events, the anticipation, planning and perseverance to reach an outcome even in the face of adversity, and the acceptance of what cannot be resolved or predicted".…”

Biased Affective Forecasting: A Potential Mechanism That Enhances Resilience and Well-Being

“…These cognitive biases are likely to increase the perception of owning successful copying skills (Brown, 1993), which in turn enhances motivation and enthusiasm while carrying out actions (Taylor and Gollwitzer, 1995). Similarly, a positive futureoriented disposition and openness to the future (i.e., having positive expectations and a general disposition of acceptance toward the future) have been shown to be protective factors for mental health and to be positively associated with well-being (Weinstein, 1980;Mikus et al, 2017;Botella et al, 2018).…”