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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. 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. 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. 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. ISRCTN ISRCTN34618894.

Mindfulness training, which involves observing thoughts and feelings without judgment or reaction, has been shown to improve aspects of psychosocial well-being when delivered via in-person training programs such as mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT). Less is known about the efficacy of digital training mediums, such as smartphone apps, which are rapidly rising in popularity. In this study, novice meditators were randomly allocated to an introductory mindfulness meditation program or to a psychoeducational audiobook control featuring an introduction to the concepts of mindfulness and meditation. The interventions were delivered via the same mindfulness app, were matched across a range of criteria, and were presented to participants as well-being programs. Affect, irritability, and two distinct components of stress were measured immediately before and after each intervention in a cohort of healthy adults. While both interventions were effective at reducing stress associated with personal vulnerability, only the mindfulness intervention had a significant positive impact on irritability, affect, and stress resulting from external pressure (between group Cohen’s d  = 0.44, 0.47, 0.45, respectively). These results suggest that brief mindfulness training has a beneficial impact on several aspects of psychosocial well-being, and that smartphone apps are an effective delivery medium for mindfulness training.

A rise in the prevalence of depression underscores the need for accessible and effective interventions. The objectives of this study were to determine if the addition of a treatment component showing promise in treating depression, heart rate variability-biofeedback (HRV-B), to our original smartphone-based, 8-week digital intervention was feasible and whether patients in the HRV-B (“enhanced”) intervention were more likely to experience clinically significant improvements in depressive symptoms than patients in our original (“standard”) intervention. We used a quasi-experimental, non-equivalent (matched) groups design to compare changes in symptoms of depression in the enhanced group (n = 48) to historical outcome data from the standard group (n = 48). Patients in the enhanced group completed a total average of 3.86 h of HRV-B practice across 25.8 sessions, and were more likely to report a clinically significant improvement in depressive symptom score post-intervention than participants in the standard group, even after adjusting for differences in demographics and engagement between groups (adjusted OR 3.44, 95% CI [1.28–9.26], P = .015). Our findings suggest that adding HRV-B to an app-based, smartphone-delivered, remote intervention for depression is feasible and may enhance treatment outcomes.

The proliferation of artificial intelligence (AI)–based mental health chatbots, such as those on platforms like OpenAI’s GPT Store and Character. AI, raises issues of safety, effectiveness, and ethical use; they also raise an opportunity for patients and consumers to ensure AI tools clearly communicate how they meet their needs. While many of these tools claim to offer therapeutic advice, their unregulated status and lack of systematic evaluation create risks for users, particularly vulnerable individuals. This viewpoint article highlights the urgent need for a standardized framework to assess and demonstrate the safety, ethics, and evidence basis of AI chatbots used in mental health contexts. Drawing on clinical expertise, research, co-design experience, and the World Health Organization’s guidance, the authors propose key evaluation criteria: adherence to ethical principles, evidence-based responses, conversational skills, safety protocols, and accessibility. Implementation challenges, including setting output criteria without one “right answer,” evaluating multiturn conversations, and involving experts for oversight at scale, are explored. The authors advocate for greater consumer engagement in chatbot evaluation to ensure that these tools address users’ needs effectively and responsibly, emphasizing the ethical obligation of developers to prioritize safety and a strong base in empirical evidence.

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.

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. 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. 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. 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). 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. International Standard Randomised Controlled Trial Number (ISRCTN) ISRCTN13067492; https://www.isrctn.com/ISRCTN13067492.

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. 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. 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. 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. 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. ISRCTN Registry 80309011; http://www.isrctn.com/ISRCTN80309011.

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. 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. 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. 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. 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.

Human decisions can be habitual or goal-directed, also known as model-free (MF) or model-based (MB) control. Previous work suggests that the balance between the two decision systems is impaired in psychiatric disorders such as compulsion and addiction, via overreliance on MF control. However, little is known whether the balance can be altered through task training. Here, 20 healthy participants performed a well-established two-step task that differentiates MB from MF control, across five training sessions. We used computational modelling and functional near-infrared spectroscopy to assess changes in decision-making and brain hemodynamic over time. Mixed-effects modelling revealed overall no substantial changes in MF and MB behavior across training. Although our behavioral and brain findings show task-induced changes in learning rates, these parameters have no direct relation to either MF or MB control or the balance between the two systems, and thus do not support the assumption of training effects on MF or MB strategies. Our findings indicate that training on the two-step paradigm in its current form does not support a shift in the balance between MF and MB control. We discuss these results with respect to implications for restoring the balance between MF and MB control in psychiatric conditions.

Abstract Study Objectives Many adults without a diagnosed sleep disorder report poor sleep. Recently, there has been a dramatic increase in the use of app-based audio tools to aid sleep by the general public, yet there is a paucity of evidence on whether such tools are efficacious. This study evaluated the feasibility and preliminary efficacy of two categories of audio tools, comprising music and narrated stories, featured on the Unmind app. Methods We conducted an online, parallel, multi-arm, external pilot randomized controlled trial, with two intervention arms and a waitlist (WL) control group. Participants were working adults who were screened for poor sleep. Feasibility was assessed via objective and self-report measures. Preliminary efficacy was evaluated via self-report measures of sleep disturbance, work productivity, and other mental health outcomes, captured at baseline (t0) and following a 4-week intervention period (t1), and analyzed using mixed effects models with intention-to-treat principles. Results Three hundred participants were randomized, and 92% were retained at t1. 90.5% of participants completed at least one intervention session. 82.1% reported being “satisfied” or “very satisfied” with their intervention, and 84.3% rated their intervention as “good” or “excellent.” The between-group Hedges’ g effect size for sleep disturbance was 0.92 [0.63–1.22] and 1.09 [0.80–1.39] for the two interventions compared to the WL group. Conclusions Both interventions are feasible and acceptable. Preliminary efficacy findings suggest that audio tools designed to aid sleep could have widespread financial and public health implications, and should be investigated in a definitive trial. Clinical Trial International Standard Randomized Controlled Trial Number (ISRCTN), 12614821, http://www.isrctn.com/ISRCTN12614821. Graphical abstract Graphical Abstract