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BACKGROUND: The rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need. OBJECTIVE: This study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk. METHOD: Sixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC). RESULTS: For both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51-0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43-0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52-0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65-0.87). Participants reported the experience comfortable and shared their feelings. CONCLUSION: MDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks.

A majority of the 34 million people who currently smoke cigarettes report wanting to quit smoking yet most attempts to quit end in relapse. A mindfulness based intervention (MBI) is an intervention package used to reinforce an individual's practice of mindfulness meditation in daily life. MBIs delivered by phone app offer daily prompts to guide bouts of mindfulness meditation, that is, sustained attention to moment-by-moment experience without behavior reactivity. Daily bouts of mindfulness meditation offer individuals a replacement behavior for smoking during a quit attempt, and MBI app instruction aims to increase an individual's skill in non-reactivity when they experience cravings. Our study objective is to test the effect of an app-based MBI on abstinence during a scheduled quit attempt among people who currently smoke and who are willing to make a voluntary quit attempt on a selected near-term date. Our delivery of a smartphone app-based MBI occurs daily for the two weeks preceding a planned quit date. Study participants are randomized to an app-based MBI or psychoeducation control group and asked to self-administer their intervention two times a day in 10-min segments for 14 sequential days for a total of 280 min. This preparatory period capitalizes on time to help participants orient toward a near-term goal to quit smoking, practice mindfulness meditation, and increase the skill of non-reactivity during states of craving to smoke. We recruit people who smoke cigarettes from all 58 counties in California. We test the hypothesis that an app-based MBI will outperform a time-matched psychoeducation control on increasing smoking abstinence. Findings contribute knowledge to the addictions treatment field about whether MBIs delivered by app can increase smoking cessation in a geographically-diverse sample.

Mindfulness-based interventions (MBIs) may offer a means for Latinx families to ameliorate stress, enhance emotion regulation, and foster social support. We assessed pilot data from Latinx parents in Eastside Los Angeles (n = 27) matched with their children aged 10-16 (n = 32) to determine whether participation in a community-derived MBI was associated with greater improvements in dispositional mindfulness, perceived stress, emotion regulation, and family social support compared to a control condition. Compared to the control group, parents in the MBI group showed greater reductions in perceived stress scale (PSS) scores (B =  - 2.94, 95% CI [- 5.58, - 0.39], p = 0.029), while their children reported greater increases in perceived social support from family (B = 2.32, 95% CI [0.26, 4.38], p = 0.027). Findings show a community-derived MBI may improve stress in Latinx parents and social support for their children.

Interaction between public and private sector real wages in the U.S. are examined over the periods 1952–2019, 1952–1972, and 1973–2019. Industries considered include all private industries and five broad industry subcategories. Measures of U.S. state & local governments and federal government are considered, and their changing contributions to total employment across periods provides context for wage leadership. Private sector wages caused federal wages as federal employment's share of total employment decreased throughout the periods. In contrast, state & local government's share of total employment grew substantially in the early period, with their wages causing private sector wages. In the later period, state & local government's share of total employment stabilized and their wages cause those in half of the private sectors to be considered, with feedback in the remaining industries. [ABSTRACT FROM AUTHOR]

The aim of this analysis was to test if changes in insomnia symptoms and global sleep quality are associated with coinciding changes in depressed mood among older adults. We report on results yielded from secondary analysis of longitudinal data from a clinical trial of older adults (N = 49) aged 55 to 80 years who reported at least moderate levels of sleep problems. All measures were collected at baseline and after the trial ten weeks later. We computed change scores for two separate measures of disturbed sleep, the Athens Insomnia Scale (AIS) and the Pittsburgh Sleep Quality Index (PSQI), and tested their association with change in depressed mood (Beck Depression Inventory-II; BDI-II) in two separate linear regression models adjusted for biological covariates related to sleep (sex, age, body mass index, and NF-κB as a biological marker previously correlated with insomnia and depression). Change in AIS scores was associated with change in BDI-II scores (β = 0.38, p < 0.01). Change in PSQI scores was not significantly associated with change in BDI-II scores (β = 0.17, p = 0.26). Our findings suggest that improvements over ten weeks in insomnia symptoms rather than global sleep quality coincide with improvement in depressed mood among older adults.