Disruptions in sleep and daily activity patterns, captured by a simple wrist-worn tracker, may signal an increased risk of relapse in people with major depressive disorder (MDD) weeks or even months before symptoms return. That is the key conclusion of new research from McMaster University which points to the growing potential of wearable technology as a tool for proactive mental health care.
Relapse remains a major challenge in depression care. Around 60% of people with MDD experience a new depressive episode within five years, even when receiving treatment. Current monitoring strategies largely depend on self-reported symptoms, which often emerge only after a relapse is already underway. The McMaster study suggests that passive data from wearables could fill this gap by identifying early biological and behavioral warning signs.
“Advances in digital technology and AI algorithms have a great potential for relapse prevention in mental health,” said Benicio Frey, professor in the Department of Psychiatry and Behavioral Neurosciences at McMaster University. “Imagine a future where a smartwatch can warn people with depression: ‘A new episode of depression is very likely coming within the next four weeks. How about seeing your health-care provider?’”
Monitoring with wearables
The study, published in JAMA Psychiatry, followed 93 adults across Canada who had previously recovered from depression. Participants wore a research-grade actigraphy device, comparable to consumer wearables such as a Fitbit or Apple Watch, for one to two years. In total, researchers analyzed more than 32,000 days of sleep and activity data, creating a detailed picture of participants’ daily rhythms over time.
The findings revealed clear patterns associated with relapse risk. Individuals with more irregular sleep schedules had nearly double the risk of experiencing a depressive relapse. One of the strongest predictors was a reduced contrast between daytime activity and nighttime rest, suggesting a breakdown in the body’s natural circadian rhythm. Increased time spent awake during the night after initially falling asleep was also linked to higher relapse risk. Notably, participants’ sleep routines often became more erratic in the weeks leading up to a relapse.
From symptoms to signals
The researchers emphasize that abnormal sleep and activity patterns have long been associated with depression relapse. What is new is the ability to detect these changes passively and continuously, without placing additional burden on patients.
“While it has long been recognized that abnormal sleep and activity patterns are associated with greater risk of depression relapse, the ability to passively detect these abnormal patterns using smart sensors opens an exciting new window of opportunity for personalizing the care of conditions that may reoccur, like depression,” the researchers note.
This approach could enable health systems to intervene earlier, targeting care to individuals at highest risk before symptoms escalate. Wearable-derived alerts might prompt timely clinical check-ins, medication adjustments, or preventive interventions, potentially reducing the severity and frequency of recurrent episodes.
Implications for digital mental health
Major depressive disorder affects millions of people worldwide and can profoundly impact how individuals feel, think, and function. Persistent symptoms such as low mood, loss of interest, guilt, and changes in appetite or sleep can significantly impair quality of life.
By shifting monitoring from episodic symptom assessment to continuous, data-driven insight, wearable technology could play a growing role in long-term depression management. While further research is needed to translate these findings into routine clinical practice, the study highlights how everyday devices may soon support more personalized, preventive mental health care, extending support well beyond the clinic.
