A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot

BACKGROUND

One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.

METHODS

Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.

RESULTS

The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.

CONCLUSIONS

It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

Associations of smartphone usage patterns with sleep and mental health symptoms in a clinical cohort receiving virtual behavioral medicine care: a retrospective study

Study Objectives

We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care.

Methods

Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2300 patients receiving standard-of-care virtual mental healthcare across more than 111 000 days were retrospectively analyzed.

Results

Mean ESP duration was 8.4 h (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001).

Conclusions

Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e. greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression.

Labelling by axonal transport of myelin-associated proteins in the rabbit visual pathway

After intraocular injections of [3H]leucine, six regions of the visual pathway of adult rabbit were used to study the spatio-temporal pattern of the slow anterograde axonal transport of radioactive proteins associated with the particulate fraction, the water-soluble fraction and the myelin fraction. Unlike other fractions, myelin-associated labelled proteins represented a time-constant (for a given region) percentage of total tissue radioactivity. This percentage increased from the first half to the second half of the optic nerve and remained high in the chiasma and tract. The peak specific radioactivity of myelin decreased in the same direction. Myelin proteins were separated by sodium dodecyl sulphate/polyacrylamide-gel electrophoresis and the labelling patterns obtained in different regions and at different survival times were compared. At the peak of myelin radioactivity of a given region the label was typically associated with four protein bands, L1, L2, L3 and L4, of 40000, 44000, 62000, and 68000 mol.wts. respectively. The basic protein, the proteolipid protein and the W1 component (mol.wt. 51000-53000) of the Wolfgram proteins were not significantly labelled. The radioactivity associated with the W2 component (mol.wt 60000) of the Wolfgram proteins could be derived from the closely migrating L3 component. At shorter survival times no clear labelling pattern could be detected. At longer survival times radioactivity was almost totally localized around band L3. The results presented underline the importance of choosing appropriate experimental conditions to obtain a consistent labelling pattern of myelin-associated proteins and to investigate the possible mechanism responsible for this phenomenon.