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BackgroundDepression is one of the most common mental health disorders and severely impacts one’s physical, psychological, and social functioning. To address access barriers to care, we developed Ascend—a smartphone-delivered, therapist-supported, 8-week intervention based on several evidence-based psychological treatments for depression and anxiety. A previous feasibility study with 102 adults with elevated depression reported that Ascend is associated with a postintervention reduction in depression symptoms. ObjectiveWe aimed to examine whether Ascend is associated with a reduction in symptoms of anxiety, and importantly, whether reductions in symptoms of depression and anxiety are maintained up to 12-months postintervention. MethodsWe assessed whether the previously reported, end-of-treatment improvements seen in the 102 adults with elevated symptoms of depression extended up to 12 months posttreatment for depression symptoms (measured by the Patient Health Questionnaire-9 [PHQ-9]) and up to 6 months posttreatment for anxiety symptoms (added to the intervention later and measured using the Generalized Anxiety Disorder-7 [GAD-7] scale). We used linear mixed effects models with Tukey contrasts to compare time points and reported intention-to-treat statistics with a sensitivity analysis. ResultsThe intervention was associated with reductions in symptoms of depression that were maintained 12 months after the program (6.67-point reduction in PHQ-9 score, 95% CI 5.59-7.75; P

Background: There is growing interest in digital platforms as a means of implementing scalable, accessible, and cost-effective mental health interventions in the workplace. However, little is known about the efficacy of such interventions when delivered to employee groups. Objective: This study aims to evaluate the feasibility and preliminary efficacy of a digital mental health platform for the workplace, which incorporates evidence-based practices such as cognitive behavioral therapy and acceptance and commitment therapy. A total of 3 brief, unguided interventions designed to address stress, anxiety, and resilience, respectively, are evaluated. The primary aim is to determine the feasibility of the study methods and interventions in preparation for a definitive randomized controlled trial. Methods: The study used a fully remote, parallel, multi-arm, external pilot randomized controlled trial, with 3 intervention arms and a no-intervention control group. Participants were working adults representative of the general UK population with respect to age, sex, and ethnicity who were recruited from a web-based participant platform. Primary outcomes included objective and self-report measures of feasibility, acceptability, engagement, transferability, relevance, and negative effects. Secondary outcomes included 4 self-report measures of mental health and well-being, completed at baseline (time point 0 [t0]), postintervention (time point 1 [t1]), and the 1-month follow-up (time point 2 [t2]). Secondary outcomes were analyzed via linear mixed-effects models using intention-to-treat principles. Preregistered criteria for progression to a definitive trial were evaluated. Results: Data were collected between January and March of 2021. A total of 383 working adult participants meeting trial eligibility were randomized, of whom 356 (93%) were retained at t2. Objective engagement data showed that 67.8% (196/289) of participants randomized to an intervention arm completed their intervention. Overall, 87.1% (203/233) of participants reported being satisfied or very satisfied with their intervention and rated the quality of their intervention as good or excellent. All intervention groups reported significantly greater improvements than the control group on at least one secondary outcome at t1, with between-group Hedges g effect sizes for the pooled interventions ranging from 0.25 (95% CI 0.05-0.46) to 0.43 (95% CI 0.23-0.64). All the improvements were maintained at t2. Conclusions: The study methods were feasible, and all preregistered criteria for progression to a definitive trial were met. Several minor protocol amendments were noted. Preliminary efficacy findings suggest that the study interventions may result in improved mental health outcomes when offered to working adults. [ABSTRACT FROM AUTHOR]

Background: Previous evidence suggests that mindfulness training may improve aspects of psychosocial well-being. Whilst mindfulness is traditionally taught in person, consumers are increasingly turning to mindfulness-based smartphone apps as an alternative delivery medium for training. Despite this growing trend, few studies have explored whether mindfulness delivered via a smartphone app can enhance psychosocial well-being within the general public. Methods: The present pilot randomised controlled trial compared the impact of engaging with the self-guided mindfulness meditation (MM) app ‘Headspace’ (n = 38) for a period of 10 or 30 days, to a wait-list (WL) control (n = 36), using a cohort of adults from the general population. The Satisfaction with Life Scale, Perceived Stress Scale, and Wagnild Resilience Scale were administered online at baseline and after 10 and 30 days of the intervention. Results: Twelve participants (MM n = 9, WL n = 3) were lost to follow-up for unknown reasons. Relative to the WL control, the MM app positively impacted self-reported satisfaction with life, stress, and resilience at day 10, with further improvements emerging at day 30 (Cohen’s d = 0.57, 1.42, 0.63 respectively). The rate of improvement was largest at the 10-day assessment point, dropping moderately by day 30. Participants that rated the MM app as easy to engage with experienced the largest self-reported benefits. Moreover, the MM app was able to protect against an unexpected increase in perceived stress that emerged in the control group. Conclusions: This pilot randomised controlled trial shows that self-reported improvements in psychosocial outcomes can be achieved at low cost through short-term engagement with a mindfulness-based smartphone app, and should be followed up with more substantive studies. Trial registration: ISRCTN . [ABSTRACT FROM AUTHOR]

Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load—a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders. [ABSTRACT FROM AUTHOR]

Actions can lead to an immediate reward or punishment and a complex set of delayed outcomes. Adaptive choice necessitates the brain track and integrate both of these potential consequences. Here, we designed a sequential task whereby the decision to exploit or forego an available offer was contingent on comparing immediate value and a state-dependent future cost of expending a limited resource. Crucially, the dynamics of the task demanded frequent switches in policy based on an online computation of changing delayed consequences. We found that human subjects choose on the basis of a near-optimal integration of immediate reward and delayed consequences, with the latter computed in a prefrontal network. Within this network, anterior cingulate cortex (ACC) was dynamically coupled to ventromedial prefrontal cortex (vmPFC) when adaptive switches in choice were required. Our results suggest a choice architecture whereby interactions between ACC and vmPFC underpin an integration of immediate and delayed components of value to support flexible policy switching that accommodates the potential delayed consequences of an action. [ABSTRACT FROM AUTHOR]

Subjects routinely control the vigor with which they emit motoric responses. However, the bulk of formal treatments of decision-making ignores this dimension of choice. A recent theoretical study suggested that action vigor should be influenced by experienced average reward rate and that this rate is encoded by tonic dopamine in the brain. We previously examined how average reward rate modulates vigor as exemplified by response times and found a measure of agreement with the first suggestion. In the current study, we examined the second suggestion, namely the potential influence of dopamine signaling on vigor. Ninety healthy subjects participated in a double-blind experiment in which they received one of the following: placebo, L-DOPA (which increases dopamine levels in the brain), or citalopram (which has a selective, if complex, effect on serotonin levels). Subjects performed multiple trials of a rewarded odd-ball discrimination task in which we varied the potential reward over time in order to exercise the putative link between vigor and average reward rate. Replicating our previous findings, we found that a significant fraction of the variance in subjects' responses could be explained by our experimentally manipulated changes in average reward rate. Crucially, this relationship was significantly stronger under L-Dopa than under Placebo, suggesting that the impact of average reward levels on action vigor is indeed subject to a dopaminergic influence. [ABSTRACT FROM AUTHOR]

Background: Depressive symptoms are highly prevalent and have broad-ranging negative implications. Digital interventions are increasingly available in the workplace context, but supporting evidence is limited., Objective: This study aimed to evaluate the feasibility, acceptability, and preliminary efficacy of 3 digital interventions for depressive symptoms in a sample of UK-based working adults experiencing mild to moderate symptoms., Methods: This was a parallel, multiarm, pilot randomized controlled trial. Participants were allocated to 1 of 3 digital interventions or a waitlist control group and had 3 weeks to complete 6 to 8 short self-guided sessions. The 3 interventions are available on the Unmind mental health app for working adults and draw on behavioral activation, cognitive behavioral therapy, and acceptance and commitment therapy. Web-based assessments were conducted at baseline, postintervention (week 3), and at 1-month follow-up (week 7). Participants were recruited via Prolific, a web-based recruitment platform, and the study was conducted entirely on the web. Feasibility and acceptability were assessed using objective engagement data and self-reported feedback. Efficacy outcomes were assessed using validated self-report measures of mental health and functioning and linear mixed models with intention-to-treat principles., Results: In total, 2003 individuals were screened for participation, of which 20.22% (405/2003) were randomized. A total of 92% (373/405) of the participants were retained in the study, 97.4% (295/303) initiated their allocated intervention, and 66.3% (201/303) completed all sessions. Moreover, 80.6% (229/284) of the participants rated the quality of their allocated intervention as excellent or good, and 79.6% (226/284) of the participants were satisfied or very satisfied with their intervention. All active groups showed improvements in well-being, functioning, and depressive and anxiety symptoms compared with the control group, which were maintained at 4 weeks. Hedges g effect sizes for depressive symptoms ranged from -0.53 (95% CI -0.25 to -0.81) to -0.74 (95% CI -0.45 to -1.03)., Conclusions: All interventions were feasible and acceptable, and the preliminary efficacy findings indicated that their use may improve depressive symptoms, well-being, and functioning. The predefined criteria for a definitive trial were met., Trial Registration: International Standard Randomised Controlled Trial Number (ISRCTN) ISRCTN13067492; https://www.isrctn.com/ISRCTN13067492., (©Rachael Wallis Taylor, Rhian Male, Marcos Economides, Heather Bolton, Kate Cavanagh. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.06.2023.)

Background: Unmind is a workplace, digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalized recommendations, while providing employers with aggregate information about the MHWB of their workforce., Objective: Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel well-being index for digital use, to capture symptoms of common mental health problems and key aspects of positive well-being., Methods: In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (n=1104) of UK adults, and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (n=976), along with a battery of existing measures, with 238 participants retaking the Umind Index after 1 week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations., Results: Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures and generally diverge from personality measures. Reliability was good or excellent across all subscales., Conclusions: The Unmind Index is a robust measure of MHWB that can help to identify target areas for intervention in nonclinical users of a mental health app. We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs., (©Anika Sierk, Eoin Travers, Marcos Economides, Bao Sheng Loe, Luning Sun, Heather Bolton. Originally published in JMIR Mental Health (https://mental.jmir.org), 17.01.2022.)