Social connections have a significant impact on health across age groups, including older adults. Loneliness and social isolation are known risk factors for Alzheimer’s disease and related dementias (ADRD). Yet, we did not find a review focused on meta-analyses and systematic reviews of studies that had examined associations of social connections with cognitive decline and trials of technology-based and other social interventions to enhance social connections in people with ADRD.

We conducted a scoping review of 11 meta-analyses and systematic reviews of social connections as possible determinants of cognitive decline in older adults with or at risk of developing ADRD. We also examined eight systematic reviews of technology-based and other social interventions in persons with ADRD.

The strongest evidence for an association of social connections with lower risk of cognitive decline was related to social engagement and social activities. There was also evidence linking social network size to cognitive function or cognitive decline, but it was not consistently significant. A number of, though not all, studies reported a significant association of marital status with risk of ADRD. Surprisingly, evidence showing that social support reduces the risk of ADRD was weak. To varying degrees, technology-based and other social interventions designed to reduce loneliness in people with ADRD improved social connections and activities as well as quality of life but had no significant impact on cognition. We discuss strengths and limitations of the studies included.

Social engagement and social activities seem to be the most consistent components of social connections for improving cognitive health among individuals with or at risk for ADRD. Socially focused technology-based and other social interventions aid in improving social activities and connections and deserve more research.

Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.

To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.

Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.

Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.

Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.

Electronic health records (EHRs) are a significant contributor to physicians’ low satisfaction, reduced engagement and increased burnout. Yet the majority of evidence around EHR and physician harms is based on self-reported screen time, which may both over- and underreport actual exposure.

The purpose of this study was to examine how objective EHR use correlates with physician well-being and to develop preliminary recommendations for well-being-based EHR interventions.

Prior to the onset of COVID-19, psychiatry residents and attending physicians working in an out-patient clinic at an academic medical centre provided consent for access to EHR-usage logs and completed a well-being assessment made up of three scales: the Maslach Burnout Inventory, the Urecht Work Engagement Scale and the Professional Quality of Life Measure. Survey responses and objective EHR data were analysed with descriptive statistics.

Responses were obtained from 20 psychiatry residents (total eligible residents n = 27; 74% participation) and 16 clinical faculty members (total eligible faculty n = 24; 67% participation) with an overall response rate of 71% (total eligible residents and faculty n = 51 and total residents and faculty who completed survey n = 36). Moderate correlations for multiple well-being domains emerged in analysis for all participants, especially around the time spent per note and patient visits closed the same day.

EHR-usage logs represent an objective tool in the evaluation and enhancement of physician well-being. Results from our pilot study suggest that metrics for note writing efficiency and closing patient visits the same day are associated with physician well-being. These metrics will be important to study in ongoing efforts involving well-being-based EHR interventions.

Despite significant advancements in healthcare technology, digital health solutions – especially those for serious mental illnesses – continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India.

Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality.

Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement.

People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary – not substitutive – of therapeutic care from a clinician.

The prevalence of neurodegenerative disorders demands methods of accessible assessment that reliably captures cognition in daily life contexts. We investigated the feasibility of smartphone cognitive assessment in people with Parkinson’s disease (PD), who may have cognitive impairment in addition to motor-related problems that limit attending in-person clinics. We examined how daily-life factors predicted smartphone cognitive performance and examined the convergent validity of smartphone assessment with traditional neuropsychological tests.

Twenty-seven nondemented individuals with mild–moderate PD attended one in-lab session and responded to smartphone notifications over 10 days. The smartphone app queried participants 5x/day about their location, mood, alertness, exercise, and medication state and administered mobile games of working memory and executive function.

Response rate to prompts was high, demonstrating feasibility of the approach. Between-subject reliability was high on both cognitive games. Within-subject variability was higher for working memory than executive function. Strong convergent validity was seen between traditional tests and smartphone working memory but not executive function, reflecting the latter’s ceiling effects. Participants performed better on mobile working memory tasks when at home and after recent exercise. Less self-reported daytime sleepiness and lower PD symptom burden predicted a stronger association between later time of day and higher smartphone test performance.

These findings support feasibility and validity of repeat smartphone assessments of cognition and provide preliminary evidence of the effects of context on cognitive variability in PD. Further development of this accessible assessment method could increase sensitivity and specificity regarding daily cognitive dysfunction for PD and other clinical populations.

Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients’ symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals.

Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness.

SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2.

The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health.

The digital tools developed as a result of this study, and participants’ experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.

During the COVID-19 pandemic, the use of telemedicine as a way to reduce COVID-19 infections was noted and consequently deregulated. However, the degree of telemedicine regulation varies from country to country, which may alter the widespread use of telemedicine. This study aimed to clarify the telepsychiatry regulations for each collaborating country/region before and during the COVID-19 pandemic.

We used snowball sampling within a global network of international telepsychiatry experts. Thirty collaborators from 17 different countries/regions responded to a questionnaire on barriers to the use and implementation of telepsychiatric care, including policy factors such as regulations and reimbursement at the end of 2019 and as of May 2020.

Thirteen of 17 regions reported a relaxation of regulations due to the pandemic; consequently, all regions surveyed stated that telepsychiatry was now possible within their public healthcare systems. In some regions, restrictions on prescription medications allowed via telepsychiatry were eased, but in 11 of the 17 regions, there were still restrictions on prescribing medications via telepsychiatry. Lower insurance reimbursement amounts for telepsychiatry consultations v. in-person consultations were reevaluated in four regions, and consequently, in 15 regions telepsychiatry services were reimbursed at the same rate (or higher) than in-person consultations during the COVID-19 pandemic.

Our results confirm that, due to COVID-19, the majority of countries surveyed are altering telemedicine regulations that had previously restricted the spread of telemedicine. These findings provide information that could guide future policy and regulatory decisions, which facilitate greater scale and spread of telepsychiatry globally.

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Although apps are increasingly being used to support the diagnosis, treatment and management of mental illness, there is no single means through which costs associated with mental apps are being reimbursed. Furthermore, different apps are amenable to different means of reimbursement as not all apps generate value in the same way.

To provide insights into how apps are currently generating value and being reimbursed across the world, with a particular focus on the situation in the USA.

An international team performed secondary research on how apps are being used and on common pathways to remuneration.

The uses of apps today and in the future are reviewed, the nature of the value delivered by apps is summarised and an overview of app reimbursement in the USA and other countries is provided. Recommendations regarding how payments might be made for apps in the future are discussed.

Currently, apps are being reimbursed through channels with other original purposes. There may be a need to develop an app-specific channel for reimbursement which is analogous to the channels used for devices, drugs and laboratory tests.

User experience (UX) plays a key role in uptake and usage of mental health smartphone interventions, yet remains underinvestigated. This review aimed to characterize and compare UX evaluation approaches that have been applied in this specific context, and to identify implications for research and practice.

A narrative review was conducted of UX-themed studies published in PubMed, PsycINFO, and Scopus up to February 2019. Eligible studies reported on data reflecting users' interactions with a smartphone intervention for any mental health condition. Studies were categorized into “situated” versus “construct-based” methods according to whether or not an established UX construct was used to acquire and analyze data.

Situated approaches used bespoke UX metrics, including quantitative measures of usage and performance, as well as grounded interview data. Construct-based approaches such as assessments of usability and acceptability were based on conceptual frameworks, with methodologically stronger versions featuring construct definitions, validated measurement tools, and an ability to compare data across studies. Constructs and measures were sometimes combined to form bespoke construct-based approaches.

Both situated and construct-based UX data may provide benefits during design and implementation of a mental health smartphone intervention by helping to clarify the needs of users and the impact of new features. Notable however was the omission of UX methods, such as split testing. Future research should consider these unaddressed methods, aim to improve the rigor of UX assessment, integrate their use alongside clinical outcomes, and explore UX assessment of more complex, adaptive interventions.

L.M.T. owns shares in Safari Health Inc – a digital health technology company.

As mental healthcare expands to smartphone apps and other technologies that may offer therapeutic interventions without a therapist involved, it is important to assess the impact of non-traditional therapeutic relationships.

To determine if there were any meaningful data regarding the digital therapeutic alliance in smartphone interventions for serious mental illnesses.

A literature search was conducted in four databases (PubMed, PsycINFO, Embase and Web of Science).

There were five studies that discuss the therapeutic alliance when a mobile application intervention is involved in therapy. However, in none of the studies was the digital therapeutic alliance the primary outcome. The studies looked at different mental health conditions, had different duration of technology use and used different methods for assessing the therapeutic alliance.

Assessing and optimising the digital therapeutic alliance holds the potential to make tools such as smartphone apps more effective and improve adherence to their use. However, the heterogeneous nature of the five studies we identified make it challenging to draw conclusions at this time. A measure is required to evaluate the digital therapeutic alliance.

H.H. is an employee of Verily Life Sciences and owns equity in this company. The views expressed here are those of the authors and are not official views of Verily Life Sciences.