BackgroundThe use of smartphone apps to monitor and deliver health care guidance and interventions has received considerable attention recently, particularly with regard to behavioral disorders, stress relief, negative emotional state, and poor mood in general. Unfortunately, there is little research investigating the long-term and repeated effects of apps meant to impact mood and emotional state.ObjectiveWe aimed to investigate the effects of both immediate point-of-intervention and long-term use (ie, at least 10 engagements) of a guided meditation and mindfulness smartphone app on users’ emotional states. Data were collected from users of a mobile phone app developed by the company Stop, Breathe & Think (SBT) for achieving emotional wellness. To explore the long-term effects, we assessed changes in the users’ basal emotional state before they completed an activity (eg, a guided meditation). We also assessed the immediate effects of the app on users’ emotional states from preactivity to postactivity.MethodsThe SBT app collects information on the emotional state of the user before and after engagement in one or several mediation and mindfulness activities. These activities are recommended and provided by the app based on user input. We considered data on over 120,000 users of the app who collectively engaged in over 5.5 million sessions with the app during an approximate 2-year period. We focused our analysis on users who had at least 10 engagements with the app over an average of 6 months. We explored the changes in the emotional well-being of individuals with different emotional states at the time of their initial engagement with the app using mixed-effects models. In the process, we compared 2 different methods of classifying emotional states: (1) an expert-defined a priori mood classification and (2) an empirically driven cluster-based classification.ResultsWe found that among long-term users of the app, there was an association between the length of use and a positive change in basal emotional state (4% positive mood increase on a 2-point scale every 10 sessions). We also found that individuals who were anxious or depressed tended to have a favorable long-term emotional transition (eg, from a sad emotional state to a happier emotional state) after using the app for an extended period (the odds ratio for achieving a positive emotional state was 3.2 and 6.2 for anxious and depressed individuals, respectively, compared with users with fewer sessions).ConclusionsOur analyses provide evidence for an association between both immediate and long-term use of an app providing guided meditations and improvements in the emotional state.

Glaucoma disproportionately affects individuals of African descent. Prior studies of the PIEZO1 mechanoreceptor have suggested a possible role in glaucoma pathophysiology. Here, we investigated associations between a Piezo1 gain-of-function variant common in individuals of African descent with glaucoma-related phenotypes. We analyzed whole genome sequences to identify Piezo1 variants and their frequencies among 1565 human participants. For the most common variant (e756del), we compared phenotypes between heterozygotes, homozygotes, and wildtypes. Longitudinal mixed effects models of visual field mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were used to evaluate progression. Based on trends in the models, further investigation was conducted using Piezo1 gain-of-function mice. About 30% of African descent individuals had at least one e756del allele. There were trends suggesting e756del was associated with higher IOPs, thinner RNFLs, lower optic nerve head capillary densities, and greater decreases in MD and RNFL thickness over time, but these did not reach statistical significance. Among mice, increased Piezo1 activity was not significantly associated with IOP or retinal ganglion cell density. Our study confirms that the Piezo1 e756del gain-of-function variant is a frequent polymorphism present in African descent individuals but is unrelated to examined differences in glaucoma phenotypes. Ongoing work is needed to elucidate the role of Piezo1-mediated mechanotransduction in glaucoma.

Background The increasing demand for mental health care, a lack of mental health care providers, and unequal access to mental health care services have created a need for innovative approaches to mental health care. Digital device apps, including digital therapeutics, that provide recommendations and feedback for dealing with stress, depression, and other mental health issues can be used to adjust mood and ultimately show promise to help meet this demand. In addition, the recommendations delivered through such apps can also be tailored to an individual’s needs (ie, personalized) and thereby potentially provide greater benefits than traditional “one-size-fits-all” recommendations. Objective This study aims to characterize individual transitions from one emotional state to another during the prolonged use of a digital app designed to provide a user with guided meditations based on their initial, potentially negative, emotional state. Understanding the factors that mediate such transitions can lead to improved recommendations for specific mindfulness and meditation interventions or activities (MMAs) provided in mental health apps. Methods We analyzed data collected during the use of the Stop, Breathe & Think (SBT) mindfulness app. The SBT app prompts users to input their emotional state before and immediately after engaging with MMAs recommended by the app. Data were collected from more than 650,000 SBT users engaging in nearly 5 million MMAs. We limited the scope of our analysis to users with 10 or more MMA sessions that included at least 6 basal emotional state evaluations. Using clustering techniques, we grouped emotions recorded by individual users and then applied longitudinal mixed effect models to assess the associations between individual recommended MMAs and transitions from one group of emotions to another. Results We found that basal emotional states have a strong influence on transitions from one emotional state to another after MMA engagement. We also found that different MMAs impact these transitions, and many were effective in eliciting a healthy transition but only under certain conditions. In addition, we observed gender and age effects on these transitions. Conclusions We found that the initial emotional state of an SBT app user determines the type of SBT MMAs that will have a favorable effect on their transition from one emotional state to another. Our results have implications for the design and use of guided mental health recommendations for digital device apps.

<p>Table S2</p>

<p>Table S2</p>