Behaviour Change Communication (BCC) intervention programmes often lack documentation of successful processes. This manuscript aims to describe the development of Program Impact Pathway (PIP) using Theory of Change (ToC) approach for a mHealth BCC intervention titled ‘Mobile Solutions Aiding Knowledge for Health Improvement (M-SAKHI)’ aimed at reducing stunting in infants at 18 months of age.

The PIP was developed using ToC to design the intervention and plan its implementation. Literature review and data from previous pilots helped to identify health service gaps that needed to be addressed by the PIP of this intervention.

M-SAKHI was implemented in 244 villages under governance of forty primary health centres of Nagpur and Bhandara districts of eastern Maharashtra in central India.

The study investigators and the public health stakeholders participated in developing the PIP. M-SAKHI evaluation study recruited 2501 pregnant women who were followed up through delivery until their infants were 18 months old.

The PIP was developed, and it identified the following pathways for the final impact: (1) improving maternal and infant nutrition, (2) early recognition of maternal and infant danger signs, (3) improving access and utilisation to healthcare services, (4) improving hygiene, sanitation and immunisation practices, and (5) improving implementation and service delivery of community health workers through their training, monitoring and supervision in real time.

This paper will illustrate the significance of development of PIP for M-SAKHI. It can aid other community-based programmes to design their PIP for nutrition-based BCC interventions.

This study explores the experiences of participants receiving a mobile-based brief intervention (BI) for hazardous drinking in India, to determine characteristics that influenced engagement and examine perceived reasons for change in alcohol consumption.

Semi-structured interviews were conducted with 10 adult hazardous drinkers who received a mobile-based BI in the intervention arm of a pilot randomised control trial. Data were coded through an iterative process and analysed using thematic analysis.

Study participants reported a positive experience, with factors such as customised intervention delivery and personal motivation facilitating their engagement. Participants reported a reduction in quantity and frequency of alcohol use. This was credited to the intervention, particularly, its provision of health-related information, goal-setting content and strategies to manage drinking. Apart from alcohol reduction, participants reported improvements in diet, lifestyle, wellbeing, and familial relations.

By providing a context to explain the impact of the intervention, the learnings from this study can be used to strengthen the implementation of mobile-based interventions. This study outlines the scope for further research in digital health, such as Internet-based health interventions, and incorporating digital interventions within the ambit of existing health care programmes.

Technology-based interventions (TBIs) are a useful approach when attempting to provide therapy to more patients with psychosis.

Randomized controlled trials of outcomes of TBIs v. face-to-face interventions in psychosis were identified in a systematic search conducted in PubMed/Ovid MEDLINE. Data were extracted independently by two researchers, and standardized mean changes were pooled using a three-level model and network meta-analysis.

Fifty-eight studies were included. TBIs complementing treatment as usual (TAU) were generally superior to face-to-face interventions (g = 0.16, p ≤ 0.0001) and to specific outcomes, namely, neurocognition (g = 0.13, p ≤ 0.0001), functioning (g = 0.25, p = 0.006), and social cognition (g = 0.32, p ≤ 0.05). Based on the network meta-analysis, the effect of two TBIs differed significantly from zero; these were the TBIs cognitive training for the neurocognitive outcome [g = 0.16; 95% confidence interval (CI) 0.09–0.23] and cognitive behavioral therapy for quality of life (g = 1.27; 95% CI 0.46–2.08). The variables educational level, type of medication, frequency of the intervention, and contact during the intervention moderated the effectiveness of TBIs over face-to-face interventions in neurocognition and symptomatology.

TBIs are effective for the management of neurocognition, symptomatology, functioning, social cognition, and quality of life outcomes in patients with psychosis. The results of the network meta-analysis showed the efficacy of some TBIs for neurocognition, symptomatology, and quality of life. Therefore, TBIs should be considered a complement to TAU in patients with psychosis.

There has been a lack of health technology assessment (HTA) methods for novel digital health technologies (DHTs) such as mHealth, artificial intelligence, and robotics in Finland. The Digi-HTA method has been developed for this purpose. The aim of this study is to determine whether it would be possible to use Digi-HTA recommendations to support healthcare decision-makers. Secondly, from the perspective of companies offering different types of DHT products, this study assesses the suitability of using the Digi-HTA framework to perform HTAs for their products.

Feedback about Digi-HTA recommendations was collected from healthcare professionals. DHT companies provided input about the Digi-HTA framework. Data were collected via a web-based survey and were analyzed using qualitative methods.

Of the twenty-four healthcare professional respondents, twenty said that the Digi-HTA recommendations contained all the necessary information, and twenty-one found them useful for their work. Respondents hoped that the Digi-HTA recommendations would be better integrated into the decision-making processes and healthcare professionals would be more informed about this new HTA process. The questions of the Digi-HTA framework were applicable for different DHT products based on the responses from DHT companies (n = 8).

According to the study participants, although the Digi-HTA recommendations include clear and beneficial information, their integration into healthcare decision-making processes should be improved. Responses from DHT companies indicate that the Digi-HTA framework would be an appropriate tool for performing assessments for their products. To generalize the findings of this study, more comprehensive studies will be needed.

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.

Public health measures to curb SARS-CoV-2 transmission rates may have negative psychosocial consequences in youth. Digital interventions may help to mitigate these effects. We investigated the associations between social isolation, COVID-19-related cognitive preoccupation, worries, and anxiety, objective social risk indicators, and psychological distress, as well as use of, and attitude toward, mobile health (mHealth) interventions in youth.

Data were collected as part of the “Mental Health And Innovation During COVID-19 Survey”—a cross-sectional panel study including a representative sample of individuals aged 16–25 years (N = 666; Mage = 21.3; assessment period: May 5, 2020 to May 16, 2020).

Overall, 38% of youth met criteria for moderate or severe psychological distress. Social isolation worries and anxiety, and objective risk indicators were associated with psychological distress, with evidence of dose–response relationships for some of these associations. For instance, psychological distress was progressively more likely to occur as levels of social isolation increased (reporting “never” as reference group: “occasionally”: adjusted odds ratio [aOR] 9.1, 95% confidence interval [CI] 4.3–19.1, p < 0.001; “often”: aOR 22.2, CI 9.8–50.2, p < 0.001; “very often”: aOR 42.3, CI 14.1–126.8, p < 0.001). There was evidence that psychological distress, worries, and anxiety were associated with a positive attitude toward using mHealth interventions, whereas psychological distress, worries, and anxiety were associated with actual use.

Public health measures during pandemics may be associated with poor mental health outcomes in youth. Evidence-based digital interventions may help mitigate the negative psychosocial impact without risk of viral infection given there is an objective need and subjective demand.

Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse.

We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model.

Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1–8 and 1–12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration.

Reduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1–2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.

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.