What is the economic and social return on investment for telephone cancer information and support services in Australia? An evaluative social return on investment study protocol.

INTRODUCTION: Over 50% of people affected by cancer report unmet support needs. To address unmet information and psychological needs, non-government organisations such as Cancer Councils (Australia) have developed state-based telephone cancer information and support services. Due to competing demands, evidence of the value of these services is needed to ensure that future investment makes the best use of scarce resources. This research aims to determine the costs and broader economic and social value of a telephone support service, to inform future funding and service provision. METHODS AND ANALYSIS: A codesigned, evaluative social return on investment analysis (SROI) will be conducted to estimate and compare the costs and monetised benefits of Cancer Council Victoria's (CCV) telephone support line, 13 11 20, over 1-year and 3-year benefit periods. Nine studies will empirically estimate the parameters to inform the SROI and calculate the ratio (economic and social value to value invested): step 1 mapping outcomes (in-depth analysis of CCV's 13 11 20 recorded call data; focus groups and interviews); step 2 providing evidence of outcomes (comparative survey of people affected by cancer who do and do not call CCV's 13 11 20; general public survey); step 3 valuing the outcomes (financial proxies, value games); step 4 establishing the impact (Delphi); step 5 calculating the net benefit and step 6 service improvement (discrete choice experiment (DCE), 'what if' analysis). Qualitative (focus groups, interviews) and quantitative studies (natural language processing, cross-sectional studies, Delphi) and economic techniques (willingness-to-pay, financial proxies, value games, DCE) will be applied. ETHICS AND DISSEMINATION: Ethics approval for each of the studies will be sought independently as the project progresses. So far, ethics approval has been granted for the first two studies. As each study analysis is completed, results will be disseminated through presentation, conferences, publications and reports to the partner organisations.

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring.

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.

BACKGROUND: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. OBJECTIVE: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. METHODS: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. RESULTS: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. CONCLUSIONS: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.

Deep Learning to Predict Energy Expenditure and Activity Intensity in Free Living Conditions using Wrist-specific Accelerometry.

Wrist-worn accelerometers are more comfortable and yield greater compliance than hip-worn devices, making them attractive for free-living activity assessments. However, intricate wrist movements may require more complex predictive models than those applied to hip-worn devices. This study developed a novel deep learning method that predicts energy expenditure and physical activity intensity of adults using wrist-specific accelerometry. Triaxial accelerometers were worn by 119 participants on their wrist and hip for two weeks during waking hours. A deep learning model was developed from week 1 data of 60 participants and tested using week 2 data for: (i) the remaining 59 participants (Group UT), and (ii) participants used for training (Group TR). Estimates of physical activity were compared to a reference hip-specific method. Moderate-to-vigorous physical activity predicted by the wrist-model was not different to the reference method for participants in Group UT (5.9±3.1vs. 6.3±3.3 hour/week) and Group TR (6.9±3.7 vs. 7.2±4.2 hour/week). At 60-s epoch level, energy expenditure predicted by the wrist-model on Group UT was strongly correlated with the reference method (r=0.86, 95%CI: 0.84-0.87) and closely predicted activity intensity (83.7%, 95%CI: 80.9-86.5%). The deep learning method has application for wrist-worn accelerometry in free-living adults.