Researchers at Ghent University recently set out to better understand what contributes to the effectiveness of these technology-based solutions, by reviewing earlier papers that assessed their potential.
The team's review paper, published in Nature Mental Health, pinpoints types of data that are particularly helpful for detecting signs of depression, while also identifying computational models that appear to be the most effective for this specific application.
The researchers' analyses revealed that depression symptoms were typically linked with irregular sleep patterns, a reduction in movement, little physical activity and a self-reported bad mood.
In addition, models that were adjusted to consider a user's unique habits and average biological signals appeared to predict early signs of depression better than general models.
Overall, this review study confirmed the potential of data collected by portable and wearable devices for the prediction of early depressive symptoms.
In the future, it could guide the development of new mental health apps or other technological tools that detect signs of depression and share useful resources or the contacts of local mental health services with users.
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