The Use of Mobile Technologies to Promote Physical Activity and Reduce Sedentary Behaviors in the Middle East and North Africa Region: Systematic Review and Meta-Analysis.

BACKGROUND: The Middle East and North Africa (MENA) region faces unique challenges in promoting physical activity and reducing sedentary behaviors, as the prevalence of insufficient physical activity is higher than the global average. Mobile technologies present a promising approach to delivering behavioral interventions; however, little is known about the effectiveness and user perspectives on these technologies in the MENA region. OBJECTIVE: This study aims to evaluate the effectiveness of mobile interventions targeting physical activity and sedentary behaviors in the MENA region and explore users' perspectives on these interventions as well as any other outcomes that might influence users' adoption and use of mobile technologies (eg, appropriateness and cultural fit). METHODS: A systematic search of 5 databases (MEDLINE, Embase, CINAHL, Scopus, and Global Index Medicus) was performed. Any primary studies (participants of all ages regardless of medical condition) conducted in the MENA region that investigated the use of mobile technologies and reported any measures of physical activity, sedentary behaviors, or user perceptions were included. We conducted a narrative synthesis of all studies and a meta-analysis of randomized controlled trials (RCTs). The Cochrane risk-of-bias tool was used to assess the quality of the included RCTs; quality assessment of the rest of the included studies was completed using the relevant Joanna Briggs Institute critical appraisal tools. RESULTS: In total, 27 articles describing 22 interventions (n=10, 37% RCTs) and 4 (15%) nonexperimental studies were included (n=6141, 46% women). Half (11/22, 50%) of the interventions included mobile apps, whereas the other half examined SMS. The main app functions were goal setting and self-monitoring of activity, whereas SMS interventions were primarily used to deliver educational content. Users in experimental studies described several benefits of the interventions (eg, gaining knowledge and receiving reminders to be active). Engagement with the interventions was poorly reported; few studies (8/27, 30%) examined users' perspectives on the appropriateness or cultural fit of the interventions. Nonexperimental studies examined users' perspectives on mobile apps and fitness trackers, reporting several barriers to their use, such as perceived lack of usefulness, loss of interest, and technical issues. The meta-analysis of RCTs showed a positive effect of mobile interventions on physical activity outcomes (standardized mean difference=0.45, 95% CI 0.17-0.73); several sensitivity analyses showed similar results. The trim-and-fill method showed possible publication bias. Only 20% (2/10) of the RCTs measured sedentary behaviors; both reported positive changes. CONCLUSIONS: The use of mobile interventions for physical activity and sedentary behaviors in the MENA region is in its early stages, with preliminary evidence of effectiveness. Policy makers and researchers should invest in high-quality studies to evaluate long-term effectiveness, intervention engagement, and implementation outcomes, which can inform the design of culturally and socially appropriate interventions for countries in the MENA region. TRIAL REGISTRATION: PROSPERO CRD42023392699; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=392699.

A personalized mobile app for physical activity: An experimental mixed-methods study.

Objectives: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users' preferences, amongst university students. Methods: We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18-34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted. Results: Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112). Conclusions: Incorporating users' preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies.

BACKGROUND: Heart failure self-management is essential to avoid decompensation and readmissions. Mobile apps seem promising in supporting heart failure self-management, and there has been a rapid growth in publications in this area. However, to date, systematic reviews have mostly focused on remote monitoring interventions using nonapp types of mobile technologies to transmit data to health care providers, rarely focusing on supporting patient self-management of heart failure. OBJECTIVE: This study aims to systematically review the evidence on the effect of heart failure self-management apps on health outcomes, patient-reported outcomes, and patient experience. METHODS: Four databases (PubMed, Embase, CINAHL, and PsycINFO) were searched for studies examining interventions that comprised a mobile app targeting heart failure self-management and reported any health-related outcomes or patient-reported outcomes or perspectives published from 2008 to December 2021. The studies were independently screened. The risk of bias was appraised using Cochrane tools. We performed a narrative synthesis of the results. The protocol was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42020158041). RESULTS: A total of 28 articles (randomized controlled trials [RCTs]: n=10, 36%), assessing 23 apps, and a total of 1397 participants were included. The most common app features were weight monitoring (19/23, 83%), symptom monitoring (18/23, 78%), and vital sign monitoring (15/23, 65%). Only 26% (6/23) of the apps provided all guideline-defined core components of heart failure self-management programs: education, symptom monitoring, medication support, and physical activity support. RCTs were small, involving altogether 717 participants, had ≤6 months of follow-up, and outcomes were predominantly self-reported. Approximately 20% (2/10) of RCTs reported a significant improvement in their primary outcomes: heart failure knowledge (P=.002) and self-care (P=.004). One of the RCTs found a significant reduction in readmissions (P=.02), and 20% (2/10) of RCTs reported higher unplanned clinic visits. Other experimental studies also found significant improvements in knowledge, self-care, and readmissions, among others. Less than half of the studies involved patients and clinicians in the design of apps. Engagement with the intervention was poorly reported, with only 11% (3/28) of studies quantifying app engagement metrics such as frequency of use over the study duration. The most desirable app features were automated self-monitoring and feedback, personalization, communication with clinicians, and data sharing and integration. CONCLUSIONS: Mobile apps may improve heart failure self-management; however, more robust evaluation studies are needed to analyze key end points for heart failure. On the basis of the results of this review, we provide a road map for future studies in this area.

The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey.

OBJECTIVES: To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups. METHODS: An online cross-sectional survey was conducted during June-September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher's exact tests. Three open-ended questions were included to elicit participants' views; thematic analysis was conducted. RESULTS: Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18-44 (46.1%), more people aged 60+ (74.5%) and more people aged 45-60 (57.6%) used a COVID-19 related app (P < .001). Qualitative data suggest people viewed technologies (especially social media) as a 'double-edged sword': helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19. CONCLUSIONS: Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.

Smartphone-Delivered Ecological Momentary Interventions Based on Ecological Momentary Assessments to Promote Health Behaviors: Systematic Review and Adapted Checklist for Reporting Ecological Momentary Assessment and Intervention Studies.

BACKGROUND: Healthy behaviors are crucial for maintaining a person's health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. OBJECTIVE: This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. METHODS: We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. RESULTS: We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs. CONCLUSIONS: The use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots). TRIAL REGISTRATION: PROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739.

Implementation of Electronic Medical Records in Mental Health Settings: Scoping Review.

BACKGROUND: The success of electronic medical records (EMRs) is dependent on implementation features, such as usability and fit with clinical processes. The use of EMRs in mental health settings brings additional and specific challenges owing to the personal, detailed, narrative, and exploratory nature of the assessment, diagnosis, and treatment in this field. Understanding the determinants of successful EMR implementation is imperative to guide the future design, implementation, and investment of EMRs in the mental health field. OBJECTIVE: We intended to explore evidence on effective EMR implementation for mental health settings and provide recommendations to support the design, adoption, usability, and outcomes. METHODS: The scoping review combined two search strategies that focused on clinician-facing EMRs, one for primary studies in mental health settings and one for reviews of peer-reviewed literature in any health setting. Three databases (Medline, EMBASE, and PsycINFO) were searched from January 2010 to June 2020 using keywords to describe EMRs, settings, and impacts. The Proctor framework for implementation outcomes was used to guide data extraction and synthesis. Constructs in this framework include adoption, acceptability, appropriateness, feasibility, fidelity, cost, penetration, and sustainability. Quality assessment was conducted using a modified Hawker appraisal tool and the Joanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. RESULTS: This review included 23 studies, namely 12 primary studies in mental health settings and 11 reviews. Overall, the results suggested that adoption of EMRs was impacted by financial, technical, and organizational factors, as well as clinician perceptions of appropriateness and acceptability. EMRs were perceived as acceptable and appropriate by clinicians if the system did not interrupt workflow and improved documentation completeness and accuracy. Clinicians were more likely to value EMRs if they supported quality of care, were fit for purpose, did not interfere with the clinician-patient relationship, and were operated with readily available technical support. Evidence on the feasibility of the implemented EMRs was mixed; the primary studies and reviews found mixed impacts on documentation quality and time; one primary study found downward trends in adverse events, whereas a review found improvements in care quality. Five papers provided information on implementation outcomes such as cost and fidelity, and none reported on the penetration and sustainability of EMRs. CONCLUSIONS: The body of evidence relating to EMR implementation in mental health settings is limited. Implementation of EMRs could benefit from methods used in general health settings such as co-designing the software and tailoring EMRs to clinical needs and workflows to improve usability and acceptance. Studies in mental health and general health settings rarely focused on long-term implementation outcomes such as penetration and sustainability. Future evaluations of EMRs in all settings should consider long-term impacts to address current knowledge gaps.

Replication studies in the clinical decision support literature-frequency, fidelity, and impact.

OBJECTIVE: To assess the frequency, fidelity, and impact of replication studies in the clinical decision support system (CDSS) literature. MATERIALS AND METHODS: A PRISMA-compliant review identified CDSS replications across 28 health and biomedical informatics journals. Included articles were assessed for fidelity to the original study using 5 categories: Identical, Substitutable, In-class, Augmented, and Out-of-class; and 7 IMPISCO domains: Investigators (I), Method (M), Population (P), Intervention (I), Setting (S), Comparator (C), and Outcome (O). A fidelity score and heat map were generated using the ratings. RESULTS: From 4063 publications matching search criteria for CDSS research, only 12/4063 (0.3%) were ultimately identified as replications. Six articles replicated but could not reproduce the results of the Han et al (2005) CPOE study showing mortality increase and, over time, changed from truth testing to generalizing this result. Other replications successfully tested variants of CDSS technology (2/12) or validated measurement instruments (4/12). DISCUSSION: A replication rate of 3 in a thousand studies is low even by the low rates in other disciplines. Several new reporting methods were developed for this study, including the IMPISCO framework, fidelity scores, and fidelity heat maps. A reporting structure for clearly identifying replication research is also proposed. CONCLUSION: There is an urgent need to better characterize which core CDSS principles require replication, identify past replication data, and conduct missing replication studies. Attention to replication should improve the efficiency and effectiveness of CDSS research and avoiding potentially harmful trial and error technology deployment.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression.

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.

Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression.

OBJECTIVE: To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed and seven additional databases, from 2007 to 2020. STUDY SELECTION: Randomised controlled trials in adults (18-65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted. DATA EXTRACTION AND SYNTHESIS: We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses. MAIN OUTCOME MEASURES: Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents. RESULTS: Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I2=69%, T 2=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression. CONCLUSION: Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.

A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study.

BACKGROUND: Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. OBJECTIVE: This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features. METHODS: This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups-underweight-normal and overweight-obese BMI-using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis. RESULTS: In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants' BMI decreased by 0.34 kg/m2 (P<.001), with a loss of 0.86 kg/m2 in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app's self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention. CONCLUSIONS: This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs.

Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners.

OBJECTIVE: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. MATERIALS AND METHODS: Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. RESULTS: Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. CONCLUSIONS: AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.

The Personalization of Conversational Agents in Health Care: Systematic Review.

BACKGROUND: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. OBJECTIVE: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. METHODS: We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. RESULTS: The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. CONCLUSIONS: Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.

A network model of activities in primary care consultations.

OBJECTIVE: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. MATERIALS AND METHODS: This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. RESULTS: Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient's present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. DISCUSSION: Primary care consultations do not appear to follow a classic linear model of defined information seeking activities; rather, they are fragmented, highly interdependent, and can be reactively triggered. CONCLUSION: The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries.

Context-Aware Systems for Chronic Disease Patients: Scoping Review.

BACKGROUND: Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work. OBJECTIVE: This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures. METHODS: A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients' health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted. RESULTS: The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results. CONCLUSIONS: The use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.

Efficacy of a Mobile Social Networking Intervention in Promoting Physical Activity: Quasi-Experimental Study.

BACKGROUND: Technological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity; yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. OBJECTIVE: This study aimed to pilot test an intervention composed of a social networking mobile app, connected with a wearable tracker, and investigate its efficacy in improving physical activity, as well as explore participant engagement and the usability of the app. METHODS: This was a pre-post quasi-experimental study with 1 arm, where participants were subjected to the intervention for a 6-month period. The primary outcome measure was the difference in daily step count between baseline and 6 months. Secondary outcome measures included engagement with the intervention and system usability. Descriptive and inferential statistical tests were conducted; posthoc subgroup analyses were carried out for participants with different levels of steps at baseline, app usage, and social features usage. RESULTS: A total of 55 participants were enrolled in the study; the mean age was 23.6 years and 28 (51%) were female. There was a nonstatistically significant increase in the average daily step count between baseline and 6 months (mean change=14.5 steps/day, P=.98, 95% CI -1136.5 to 1107.5). Subgroup analysis comparing the higher and lower physical activity groups at baseline showed that the latter had a statistically significantly higher increase in their daily step count (group difference in mean change from baseline to 6 months=3025 steps per day, P=.008, 95% CI 837.9-5211.8). At 6 months, the retention rate was 82% (45/55); app usage decreased over time. The mean system usability score was 60.1 (SD 19.2). CONCLUSIONS: This study showed the preliminary efficacy of a mobile social networking intervention, integrated with a wearable tracker to promote physical activity, particularly for less physically active subgroups of the population. Future research should explore how to address challenges faced by physically inactive people to provide tailored advices. In addition, users' perspectives should be explored to shed light on factors that might influence their engagement with the intervention.

Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users' Perspectives.

BACKGROUND: Despite many health benefits of physical activity, nearly a third of the world's adult population is insufficiently active. Technological interventions, such as mobile apps, wearable trackers, and Web-based social networks, offer great promise in promoting physical activity, but little is known about users' acceptability and long-term engagement with these interventions. OBJECTIVE: The aim of this study was to understand users' perspectives regarding a mobile social networking intervention to promote physical activity. METHODS: Participants, mostly university students and staff, were recruited using purposive sampling techniques. Participants were enrolled in a 6-month feasibility study where they were provided with a wearable physical activity tracker (Fitbit Flex 2) and a wireless scale (Fitbit Aria) integrated with a social networking mobile app (named "fit.healthy.me"). We conducted semistructured, in-depth qualitative interviews and focus groups pre- and postintervention, which were recorded and transcribed verbatim. The data were analyzed in Nvivo 11 using thematic analysis techniques. RESULTS: In this study, 55 participants were enrolled; 51% (28/55) were females, and the mean age was 23.6 (SD 4.6) years. The following 3 types of factors emerged from the data as influencing engagement with the intervention and physical activity: individual (self-monitoring of behavior, goal setting, and feedback on behavior), social (social comparison, similarity and familiarity between users, and participation from other users in the network), and technological. In addition, automation and personalization were observed as enhancing the delivery of both individual and social aspects. Technological limitations were mentioned as potential barriers to long-term usage. CONCLUSIONS: Self-regulatory techniques and social factors are important to consider when designing a physical activity intervention, but a one-size-fits-all approach is unlikely to satisfy different users' preferences. Future research should adopt innovative research designs to test interventions that can adapt and respond to users' needs and preferences throughout time.

Conversational agents in healthcare: a systematic review.

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.

The use of social features in mobile health interventions to promote physical activity: a systematic review.

Mobile health (mHealth) technologies have increasingly been used in interventions to promote physical activity (PA), yet, they often have high attrition rates. Integrating social features into mHealth has the potential to engage users; however, little is known about the efficacy and user engagement of such interventions. Thus, the aim of this systematic review was to characterize and evaluate the impact of interventions integrating social features in mHealth interventions to promote PA. During database screening, studies were included if they involved people who were exposed to a mHealth intervention with social features, to promote PA. We conducted a narrative synthesis of included studies and a meta-analysis of randomized controlled trials (RCTs). Nineteen studies were included: 4 RCTs, 10 quasi-experimental, and 5 non-experimental studies. Most experimental studies had retention rates above 80%, except two. Social features were often used to provide social support or comparison. The meta-analysis found a non-significant effect on PA outcomes [standardized difference in means = 0.957, 95% confidence interval -1.09 to 3.00]. Users' preferences of social features were mixed: some felt more motivated by social support and competition, while others expressed concerns about comparison, indicating that a one-size-fits-all approach is insufficient. In summary, this is an emerging area of research, with limited evidence suggesting that social features may increase user engagement. However, due to the quasi-experimental and multi-component nature of most studies, it is difficult to determine the specific impact of social features, suggesting the need for more robust studies to assess the impact of different intervention components.

Use of a mobile social networking intervention for weight management: a mixed-methods study protocol.

INTRODUCTION: Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and healthcare costs. Information and communication technologies are increasingly being used in interventions to promote behaviour change in diet and physical activity. In particular, social networking platforms seem promising for the delivery of weight control interventions.We intend to pilot test an intervention involving the use of a social networking mobile application and tracking devices (Fitbit Flex 2 and Fitbit Aria scale) to promote the social comparison of weight and physical activity, in order to evaluate whether mechanisms of social influence lead to changes in those outcomes over the course of the study. METHODS AND ANALYSIS: Mixed-methods study involving semi-structured interviews and a pre-post quasi-experimental pilot with one arm, where healthy participants in different body mass index (BMI) categories, aged between 19 and 35 years old, will be subjected to a social networking intervention over a 6-month period. The primary outcome is the average difference in weight before and after the intervention. Secondary outcomes include BMI, number of steps per day, engagement with the intervention, social support and system usability. Semi-structured interviews will assess participants' expectations and perceptions regarding the intervention. ETHICS AND DISSEMINATION: Ethics approval was granted by Macquarie University's Human Research Ethics Committee for Medical Sciences on 3 November 2016 (ethics reference number 5201600716).The social network will be moderated by a researcher with clinical expertise, who will monitor and respond to concerns raised by participants. Monitoring will involve daily observation of measures collected by the fitness tracker and the wireless scale, as well as continuous supervision of forum interactions and posts. Additionally, a protocol is in place to monitor for participant misbehaviour and direct participants-in-need to appropriate sources of help.