Use of passively collected actigraphy data to detect individual depressive symptoms in a... 2024 Price et al

Full title: Use of passively collected actigraphy data to detect individual depressive symptoms in a clinical subpopulation and a general population.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data.

The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data.

Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58–0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56–0.60) in the general population (n = 8,378).

Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD.

Impact Statement
The coupling of deep learning methods with passive monitoring of an individual’s naturalistic movement provides a unique opportunity to detect depressive symptoms without the necessity for frequent clinical visits or self-report measures. The present work builds upon previous efforts to evaluate which depressive symptoms are best captured by passively collected physical activity data, and how this differs between individuals in the general population and individuals who meet criteria for depression.

Our findings provide insight into which individual depressive symptoms may be best detected by passively collected physical activity data, providing important assessment and treatment implications for depression.

Paywall, https://psycnet.apa.org/doiLanding?doi=10.1037/abn0000933

 

Aside from being a terrible idea with no possible good outcome, studies like this just shows how medicine is as totally clueless as ever about depression and has not actually made any progress in understanding what it is, let alone how to differentiate it from anything else. In fact the complete turnaround in acceptance, going completely overboard with labeling it everywhere instead of denying that it's a real thing, is probably one of the single worst mistakes the profession has ever done. They're still decades away from being able to do this safely.

It would actually be more productive to fund efforts to build a machine that could erase everything ever said or written about similar health problems, including from people's memories, to perform a complete reset from scratch than continuing on with stuff like this. Not that it's possible to build such a machine, but neither is achieving anything with the complete mess we have right now.