OBJECTIVES: This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction types: Human-Object Activities (HOA), such as blood pressure measurement, and Human-Human Activities (HHA), such as gland palpation. MATERIAL AND METHOD: A multi-component ensemble transfer learning framework was developed that integrates spatial and temporal feature analysis. The model comprises: (1) a CNN-LSTM module for spatial feature extraction and sequential modelling, (2) an ensemble of EfficientNet-B7, DenseNet-121, and Inception-v3 to capture diverse spatial representations, and (3) a fusion module that concatenates outputs from both streams, refined by an attention mechanism to prioritise salient features. Transfer learning was applied to fine-tune pre-trained networks on GP consultation video data. Model performance was evaluated using five-fold stratified video-level cross-validation, reporting mean ± SD for precision, recall, F1-score, specificity, Cohen's κ, and PR-AUC. RESULTS: The fusion model achieved robust overall performance, with a precision of 92.1 ± 1.4%, recall of 89.9 ± 1.8%, F1-score of 90.9 ± 1.5%, specificity of 93.1 ± 1.3%, Cohen's κ of 0.90 ± 0.02, and PR-AUC of 0.935 ± 0.02. It consistently outperformed ten state-of-the-art baselines, while ablation analysis showed F1-score improvements of 17% over CNN-LSTM and 16% over the ensemble model, confirming the benefit of combining spatial and temporal analysis. CONCLUSION: The proposed fusion framework accurately recognises physical examinations in GP consultations and supports future telehealth and diagnostic research.

While families have a central role in shaping individual choices and behaviors, healthcare largely focuses on treating individuals or supporting self-care. However, a family is also a health unit. We argue that family informatics is a necessary evolution in scope of health informatics. To deal with the needs of individuals, we must ensure technologies account for the role of their families and may require new classes of digital service. Social networks can help conceptualize the structure, composition, and behavior of families. A family network can be seen as a multiagent system with distributed cognition. Digital tools can address family needs in (1) sensing and monitoring; (2) communicating and sharing; (3) deciding and acting; and (4) treating and preventing illness. Family informatics is inherently multidisciplinary and has the potential to address unresolved chronic health challenges such as obesity, mental health, and substance abuse, support acute health challenges, and to improve the capacity of individuals to manage their own health needs.

OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically. MATERIALS AND METHODS: We propose a deep learning approach to classify activities of patient work collected from wearable cameras, thereby studying self-management routines more effectively. Twenty-six people with type 2 diabetes and comorbidities wore a wearable camera for a day, generating more than 400 h of video across 12 daily activities. To classify these video images, a weighted ensemble network that combines Linear Discriminant Analysis, Deep Convolutional Neural Networks, and Object Detection algorithms is developed. Performance of our model is assessed using Top-1 and Top-5 metrics, compared against manual classification conducted by 2 independent researchers. RESULTS: Across 12 daily activities, our model achieved on average the best Top-1 and Top-5 scores of 81.9 and 86.8, respectively. Our model also outperformed other non-ensemble techniques in terms of Top-1 and Top-5 scores for most activity classes, demonstrating the superiority of leveraging weighted ensemble techniques. CONCLUSIONS: Deep learning can be used to automatically classify daily activities of patient work collected from wearable cameras with high levels of accuracy. Using wearable cameras and a deep learning approach can offer an alternative approach to investigate patient work, one not subjected to biases commonly associated with self-report methods.

OBJECTIVE: To understand the nature of health consumer self-management workarounds during the COVID-19 pandemic; to classify these workarounds using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework; and to see how digital tools had assisted these workarounds. MATERIALS AND METHODS: We assessed 15 self-managing elderly patients with Type 2 diabetes, multiple chronic comorbidities, and low digital literacy. Interviews were conducted during COVID-19 lockdowns in May-June 2020 and participants were asked about how their self-management had differed from before. Each instance of change in self-management were identified as consumer workarounds and were classified using the SAMR framework to assess the extent of change. We also identified instances where digital technology assisted with workarounds. RESULTS: Consumer workarounds in all SAMR levels were observed. Substitution, describing change in work quality or how basic information was communicated, was easy to make and involved digital tools that replaced face-to-face communications, such as the telephone. Augmentation, describing changes in task mechanisms that enhanced functional value, did not include any digital tools. Modification, which significantly altered task content and context, involved more complicated changes such as making video calls. Redefinition workarounds created tasks not previously required, such as using Google Home to remotely babysit grandchildren, had transformed daily routines. DISCUSSION AND CONCLUSION: Health consumer workarounds need further investigation as health consumers also use workarounds to bypass barriers during self-management. The SAMR framework had classified the health consumer workarounds during COVID, but the framework needs further refinement to include more aspects of workarounds.

Objective: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement. Results: The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions: The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration: The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

OBJECTIVES: We conducted a meta-review to determine the reporting quality of user-centered digital interventions for the prevention and management of cardiometabolic conditions. MATERIALS AND METHODS: Using predetermined inclusion criteria, systematic reviews published between 2010 and 2015 were identified from 3 databases. To assess whether current evidence is sufficient to inform wider uptake and implementation of digital health programs, we assessed the quality of reporting of research findings using (1) endorsement of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, (2) a quality assessment framework (eg, Cochrane risk of bias assessment tool), and (3) 8 parameters of the Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth (CONSORT-eHEALTH) guidelines (developed in 2010). RESULTS: Of the 33 systematic reviews covering social media, Web-based programs, mobile health programs, and composite modalities, 6 reported using the recommended PRISMA guidelines. Seven did not report using a quality assessment framework. Applying the CONSORT-EHEALTH guidelines, reporting was of mild to moderate strength. DISCUSSION: To our knowledge, this is the first meta-review to provide a comprehensive analysis of the quality of reporting of research findings for a range of digital health interventions. Our findings suggest that the evidence base and quality of reporting in this rapidly developing field needs significant improvement in order to inform wider implementation and uptake. CONCLUSION: The inconsistent quality of reporting of digital health interventions for cardiometabolic outcomes may be a critical impediment to real-world implementation.

OBJECTIVE: Our aim was to evaluate the use and effectiveness of interventions using social networking sites (SNSs) to change health behaviors. MATERIALS AND METHODS: Five databases were scanned using a predefined search strategy. Studies were included if they focused on patients/consumers, involved an SNS intervention, had an outcome related to health behavior change, and were prospective. Studies were screened by independent investigators, and assessed using Cochrane's 'risk of bias' tool. Randomized controlled trials were pooled in a meta-analysis. RESULTS: The database search retrieved 4656 citations; 12 studies (7411 participants) met the inclusion criteria. Facebook was the most utilized SNS, followed by health-specific SNSs, and Twitter. Eight randomized controlled trials were combined in a meta-analysis. A positive effect of SNS interventions on health behavior outcomes was found (Hedges' g 0.24; 95% CI 0.04 to 0.43). There was considerable heterogeneity (I(2) = 84.0%; T(2) = 0.058) and no evidence of publication bias. DISCUSSION: To the best of our knowledge, this is the first meta-analysis evaluating the effectiveness of SNS interventions in changing health-related behaviors. Most studies evaluated multi-component interventions, posing problems in isolating the specific effect of the SNS. Health behavior change theories were seldom mentioned in the included articles, but two particularly innovative studies used 'network alteration', showing a positive effect. Overall, SNS interventions appeared to be effective in promoting changes in health-related behaviors, and further research regarding the application of these promising tools is warranted. CONCLUSIONS: Our study showed a positive effect of SNS interventions on health behavior-related outcomes, but there was considerable heterogeneity. Protocol registration The protocol for this systematic review is registered at http://www.crd.york.ac.uk/PROSPERO with the number CRD42013004140.

OBJECTIVE: To determine if a web-based personally controlled health management system (PCHMS) could increase the uptake of sexually transmitted infections (STI) screening among a young university population. METHODS: A non-blinded parallel-group randomized controlled trial was conducted. Participants aged 18-29 years were recruited from a university environment between April and August 2013, and randomized 1:1 to either the intervention group (immediate online PCHMS access) or control group (no PCHMS access). The study outcome was self-reported STI testing, measured by an online follow-up survey in October 2013. RESULTS: Of the 369 participants allocated to the PCHMS, 150 completed the follow-up survey, and of the 378 in the control group, 225 completed the follow-up survey. The proportion of the PCHMS group who underwent an STI test during the study period was 15.3% (23/150) compared with 7.6% (17/225) in the control group (P = .017). The difference in STI testing rates within the subgroup of sexually active participants (20.4% (23/113) of the PCHMS group compared with 9.6% (15/157) of the control group) was significantly higher (P = .027) than among non-sexually active participants. DISCUSSION: Access to the PCHMS was associated with a significant increase in participants undergoing STI testing. This is also the first study to demonstrate efficacy of a PCHMS targeting a health concern where susceptibility is generally perceived as low and the majority of infections are asymptomatic. CONCLUSION: PCHMS interventions may provide an effective means of increasing the demand for STI testing which, combined with increased opportunistic testing by clinicians, could reduce the high and sustained rates of STIs in young people.

OBJECTIVE: To assess the impact of a web-based personally controlled health management system (PCHMS) on the uptake of seasonal influenza vaccine and primary care service utilization among university students and staff. MATERIALS AND METHODS: A PCHMS called Healthy.me was developed and evaluated in a 2010 CONSORT-compliant two-group (6-month waitlist vs PCHMS) parallel randomized controlled trial (RCT) (allocation ratio 1:1). The PCHMS integrated an untethered personal health record with consumer care pathways, social forums, and messaging links with a health service provider. RESULTS: 742 university students and staff met inclusion criteria and were randomized to a 6-month waitlist (n=372) or the PCHMS (n=370). Amongst the 470 participants eligible for primary analysis, PCHMS users were 6.7% (95% CI: 1.46 to 12.30) more likely than the waitlist to receive an influenza vaccine (waitlist: 4.9% (12/246, 95% CI 2.8 to 8.3) vs PCHMS: 11.6% (26/224, 95% CI 8.0 to 16.5); χ(2)=7.1, p=0.008). PCHMS participants were also 11.6% (95% CI 3.6 to 19.5) more likely to visit the health service provider (waitlist: 17.9% (44/246, 95% CI 13.6 to 23.2) vs PCHMS: 29.5% (66/224, 95% CI: 23.9 to 35.7); χ(2)=8.8, p=0.003). A dose-response effect was detected, where greater use of the PCHMS was associated with higher rates of vaccination (p=0.001) and health service provider visits (p=0.003). DISCUSSION: PCHMS can significantly increase consumer participation in preventive health activities, such as influenza vaccination. CONCLUSIONS: Integrating a PCHMS into routine health service delivery systems appears to be an effective mechanism for enhancing consumer engagement in preventive health measures. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12610000386033. http://www.anzctr.org.au/trial_view.aspx?id=335463.

OBJECTIVE: To test whether the anchoring and order cognitive biases experienced during search by consumers using information retrieval systems can be corrected to improve the accuracy of, and confidence in, answers to health-related questions. DESIGN: A prospective study was conducted on 227 undergraduate students who used an online search engine developed by the authors to find health information and then answer six randomly assigned consumer health questions. The search engine was fitted with a baseline user interface and two modified interfaces specifically designed to debias anchoring or order effect. Each subject used all three user interfaces, answering two questions with each. MEASUREMENTS: Frequencies of correct answers pre- and post- search and confidence in answers were collected. Time taken to search and then answer a question, the number of searches conducted and the number of links accessed in a search session were also recorded. User preferences for each interface were measured. Chi-square analyses tested for the presence of biases with each user interface. The Kolmogorov-Smirnov test checked for equality of distribution of the evidence analyzed for each user interface. The test for difference between proportions and the Wilcoxon signed ranks test were used when comparing interfaces. RESULTS: Anchoring and order effects were present amongst subjects using the baseline search interface (anchoring: p < 0.001; order: p = 0.026). With use of the order debiasing interface, the initial order effect was no longer present (p = 0.34) but there was no significant improvement in decision accuracy (p = 0.23). While the anchoring effect persisted when using the anchor debiasing interface (p < 0.001), its use was associated with a 10.3% increase in subjects who had answered incorrectly pre-search, answering correctly post-search (p = 0.10). Subjects using either debiasing user interface conducted fewer searches and accessed more documents compared to baseline (p < 0.001). In addition, the majority of subjects preferred using a debiasing interface over baseline. CONCLUSION: This study provides evidence that (i) debiasing strategies can be integrated into the user interface of a search engine; (ii) information interpretation behaviors can be to some extent debiased; and that (iii) attempts to debias information searching by consumers can influence their ability to answer health-related questions accurately, their confidence in these answers, as well as the strategies used to conduct searches and retrieve information.

OBJECTIVE: To test whether individuals experience cognitive biases whilst searching using information retrieval systems. Biases investigated are anchoring, order, exposure and reinforcement. DESIGN: A retrospective analysis and a prospective experiment were conducted to investigate whether cognitive biases affect the way that documentary evidence is interpreted while searching online. The retrospective analysis was conducted on the search and decision behaviors of 75 clinicians (44 doctors, 31 nurses), answering questions for 8 clinical scenarios within 80 minutes in a controlled setting. The prospective study was conducted on 227 undergraduate students, who used the same search engine to answer two of six randomly assigned consumer health questions. MEASUREMENTS: Frequencies of correct answers pre- and post- search, and confidence in answers were collected. The impact of reading a document on the final decision was measured by the population likelihood ratio (LR) of the frequency of reading the document and the frequency of obtaining a correct answer. Documents with a LR > 1 were most likely to be associated with a correct answer, and those with a LR < 1 were most likely to be associated with an incorrect answer to a question. Agreement between a subject and the evidence they read was estimated by a concurrence rate, which measured the frequency that subjects' answers agreed with the likelihood ratios of a group of documents, normalized for document order, time exposure or reinforcement through repeated access. Serial position curves were plotted for the relationship between subjects' pre-search confidence, document order, the number of times and length of time a document was accessed, and concurrence with post-search answers. Chi-square analyses tested for the presence of biases, and the Kolmogorov-Smirnov test checked for equality of distribution of evidence in the comparison populations. RESULTS: A person's prior belief (anchoring) has a significant impact on their post-search answer (retrospective: P < 0.001; prospective: P < 0.001). Documents accessed at different positions in a search session (order effect [retrospective: P = 0.76; prospective: P = 0.026]), and documents processed for different lengths of time (exposure effect [retrospective: P = 0.27; prospective: P = 0.0081]) also influenced decision post-search more than expected in the prospective experiment but not in the retrospective analysis. Reinforcement through repeated exposure to a document did not yield statistical differences in decision outcome post-search (retrospective: P = 0.31; prospective: P = 0.81). CONCLUSION: People may experience anchoring, exposure and order biases while searching for information, and these biases may influence the quality of decision making during and after the use of information retrieval systems.

Abstract This study aimed to develop a model for predicting the impact of information access using Web searches, on human decision making. Models were constructed using a database of search behaviors and decisions of 75 clinicians, who answered questions about eight scenarios within 80 minutes in a controlled setting at a university computer laboratory. Bayesian models were developed with and without bias factors to account for anchoring, primacy, recency, exposure, and reinforcement decision biases. Prior probabilities were estimated from the population prior, from a personal prior calculated from presearch answers and confidence ratings provided by the participants, from an overall measure of willingness to switch belief before and after searching, and from a willingness to switch belief calculated in each individual scenario. The optimal Bayes model predicted user answers in 73.3% (95% CI: 68.71 to 77.35%) of cases, and incorporated participants' willingness to switch belief before and after searching for each scenario, as well as the decision biases they encounter during the search journey. In most cases, it is possible to predict the impact of a sequence of documents retrieved by a Web search engine on a decision task without reference to the content or structure of the documents, but relying solely on a simple Bayesian model of belief revision.