Severe processing capacity limits for sub-lexical features of letter strings.

When reading, the visual system is confronted with many words simultaneously. How much of that information can a reader process at once? Previous studies demonstrated that low-level visual features of multiple words are processed in parallel, but lexical attributes are processed serially, for one word at a time. This implies that an internal bottleneck lies somewhere between early visual and lexical analysis. We used a dual-task behavioral paradigm to investigate whether this bottleneck lies at the stage of letter recognition or phonological decoding. On each trial, two letter strings were flashed briefly, one above and one below fixation, and then masked. In the letter identification experiment, participants indicated whether a vowel was present in a particular letter string. In the phonological decoding experiment, participants indicated whether the letter string was pronounceable. We compared accuracy in a focused attention condition, in which participants judged only one of the two strings, with accuracy in a divided attention condition, in which participants judged both strings independently. In both experiments, the cost of dividing attention was so large that it supported a serial model: participants were able to process only one letter string per trial. Furthermore, we found a stimulus processing trade-off that is characteristic of serial processing: When participants judged one string correctly, they were less likely to judge the other string correctly. Therefore, the bottleneck that constrains word recognition under these conditions arises at a sub-lexical level, perhaps due to a limit on the efficiency of letter recognition.

The use of virtual complementary and integrative therapies by neurology outpatients: An exploratory analysis of two cross-sectional studies assessing the use of technology as treatment in an academic neurology department in New York City.

Background: Prior to the COVID-19 pandemic, about half of patients from populations that sought care in neurology tried complementary and integrative therapies (CITs). With the increased utilization of telehealth services, we sought to determine whether patients also increased their use of virtual CITs. Methods: We examined datasets from two separate cross-sectional surveys that included cohorts of patients with neurological disorders. One was a dataset from a study that examined patient and provider experiences with teleneurology visits; the other was a study that assessed patients with a history of COVID-19 infection who presented for neurologic evaluation. We assessed and reported the use of virtual (and non-virtual) CITs using descriptive statistics, and determined whether there were clinical characteristics that predicted the use of CITs using logistic regression analyses. Findings: Patients who postponed medical treatment for non-COVID-19-related problems during the pandemic were more likely to seek CITs. Virtual exercise, virtual psychotherapy, and relaxation/meditation smartphone applications were the most frequent types of virtual CITs chosen by patients. In both studies, age was a key demographic factor associated with mobile/virtual CIT usage. Interpretations: Our investigation demonstrates that virtual CIT-related technologies were utilized in the treatment of neurologic conditions during the pandemic, particularly by those patients who deferred non-COVID-related care.

Assessment of Smartphone Apps for Common Neurologic Conditions (Headache, Insomnia, and Pain): Cross-sectional Study.

BACKGROUND: There are thousands of apps for individuals struggling with headache, insomnia, and pain, but it is difficult to establish which of these apps are best suited for patients' specific needs. If clinicians were to have access to a platform that would allow them to make an informed decision on the efficacy and feasibility of smartphone apps for patient care, they would feel confident in prescribing specific apps. OBJECTIVE: We sought to evaluate the quality of apps for some of the top common, disabling neurologic conditions (headache, insomnia, and pain) based on principles derived from the American Psychiatric Association's (APA) app evaluation model. METHODS: We used the Mobile Health Index and Navigation database and expanded upon the database's current supported conditions by adding 177 new app entries. Each app was rated for consistency with the APA's app evaluation model, which includes 105 objective questions based on the following 5 major classes of consideration: (1) accessibility, (2) privacy and security, (3) clinical foundation, (4) engagement style, and (5) interoperability. These characteristics were evaluated to gain a broader understanding of the significant features of each app category in comparison against a control group. RESULTS: Approximately 90% (187/201) of all apps evaluated were free to download, but only 50% (63/201) of headache- and pain-related apps were truly free. Most (87/106, 81%) sleep apps were not truly free to use. The apps had similar limitations with limited privacy, accessibility, and crisis management resources. For example, only 17% (35/201) of the apps were available in Spanish. The apps offered mostly self-help tools with little tailoring; symptom tracking was the most common feature in headache- (32/48, 67%) and pain-related apps (21/47, 45%), whereas mindfulness was the most common feature in sleep-related apps (73/106, 69%). CONCLUSIONS: Although there are many apps for headache, pain, and insomnia, all 3 types of apps have room for improvement around accessibility and privacy. Pain and headache apps share many common features, whereas insomnia apps offer mostly mindfulness-based resources. Given the many available apps to pick from, clinicians and patients should seek apps that offer the highest-quality features, such as complete privacy, remedial features, and the ability to download the app at no cost. These results suggest that there are many opportunities for the improvement of apps centered on headache, insomnia, and pain.

Grit predicts less severe hoarding symptoms among patients seeking treatment at an anxiety disorders clinic.

Grit is associated with positive outcomes in nonclinical samples. However, no studies have examined grit in relation to psychopathology in patients with clinical mood or anxiety disorders. Research and clinical experience suggest that individuals who hoard struggle with characteristics associated with grit, such as task persistence, impulsivity, and self-control. The authors tested the hypothesis that hoarding symptoms are associated with less grit in a sample of individuals (N = 72) presenting for treatment to an anxiety disorders clinic. After covarying symptoms of the four mood and anxiety disorders most commonly comorbid with hoarding disorder (viz. depression, generalized anxiety, social anxiety, and obsessive-compulsive disorder), the authors found that hoarding symptoms were associated with less grit, and the effects were medium-to-large. These results indicate that grit is worthy of investigation in individuals with hoarding disorder using methodologies that permit inferences about causality, and with attention to clinical implications for prevention or treatment.

Can a rapid measure of self-exposure to drugs of abuse provide dimensional information on depression comorbidity?

BACKGROUND AND OBJECTIVES: Addictions to heroin or to cocaine are associated with substantial psychiatric comorbidity, including depression. Poly-drug self-exposure (eg, to heroin, cocaine, cannabis, or alcohol) is also common, and may further affect depression comorbidity. METHODS: This case-control study examined the relationship of exposure to the above drugs and depression comorbidity. Participants were recruited from methadone maintenance clinics, and from the community. Adult male and female participants (n = 1,201) were ascertained consecutively by experienced licensed clinicians. The instruments used were the SCID-I, and Kreek-McHugh-Schluger-Kellogg (KMSK) scales, which provide a rapid dimensional measure of maximal lifetime self-exposure to each of the above drugs. This measure ranges from no exposure to high unit dose, high frequency, and long duration of exposure. RESULTS: A multiple logistic regression with stepwise variable selection revealed that increasing exposure to heroin or to cocaine was associated greater odds of depression, with all cases and controls combined. In cases with an opioid dependence diagnosis, increasing cocaine exposure was associated with a further increase in odds of depression. However, in cases with a cocaine dependence diagnosis, increasing exposure to either cannabis or alcohol, as well as heroin, was associated with a further increase in odds of depression. DISCUSSION AND CONCLUSIONS: This dimensional analysis of exposure to specific drugs provides insights on depression comorbidity with addictive diseases, and the impact of poly-drug exposure. SCIENTIFIC SIGNIFICANCE: A rapid analysis of exposure to drugs of abuse reveals how specific patterns of drug and poly-drug exposure are associated with increasing odds of depression. This approach detected quantitatively how different patterns of poly-drug exposure can result in increased odds of depression comorbidity, in cases diagnosed with opioid versus cocaine dependence. (Am J Addict 2017;26:632-639).