Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Using mobile technologies and personalisation to promote health behaviours

Current health behaviour interventions have limited reach and scalability, and lack ongoing support. Mobile technologies such as mobile applications (apps) and fitness trackers can deliver continuous support and be personalised to increase intervention efficacy and engagement. To date, little is known about the feasibility and efficacy of personalised mobile technologies in improving health behaviours. The overarching aim of this thesis is to evaluate the efficacy of mobile apps and fitness trackers for health behaviour change, specifically examine the role of personalisation and the feasibility of different personalisation approaches, and the impact of other modifiers such as the sudden environmental changes from COVID-19. To achieve this, the thesis used different methods: 1) a systematic review and meta-analysis to assess the efficacy of personalised mobile technologies on health behaviours and to identify personalisation moderators associated with higher efficacy, (2) a mixed-methods experimental trial to evaluate the feasibility of a personalised physical activity mobile app, and (3) a cross-sectional survey to analyse the feasibility of using mobile technologies to support health behaviours during COVID-19. Overall, the thesis found preliminary evidence of efficacy of personalised mobile apps and fitness trackers, and revealed the feasibility of different personalisation approaches, such as using system-captured data or dynamically including users’ preferences into personalisation algorithms. Under pandemic conditions, mobile technologies were also a feasible approach to support health behaviours; however, they should be more adaptable to changes in context and users’ circumstances. Future research should investigate the long-term effectiveness of mobile apps and fitness trackers, and identify intervention moderators associated with better health outcomes

Using mobile health interventions to promote physical activity: a mixed methods study

Thesis by publication.Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University NSW Australia" -- title page.Includes bibliographical references.Chapter 1. Introduction -- Chapter 2. Paper I - The use of social features in mobile health interventions to promote physical activity : a systematic review -- Chapter 3. Methods -- Chapter 4. Paper II - Efficacy of a mobile social networking intervention in promoting physical activity : quasi-experimental study -- Chapter 5. Paper III - Using a mobile social networking app to promote physical activity : a qualitative study of users' perspectives -- Chapter 6. Discussion and conclusion -- References -- Appendices.Mobile technologies (e.g. mobile applications, wearable trackers) and online social networks have emerged as potential facilitators of physical activity. To date, few studies have examined the integration of these technologies in an intervention, users' perceptions about them, and their combined efficacy on physical activity.This study adopted a mixed method design within a pre-post, one-arm quasi-experiment to evaluate the efficacy and acceptability of a mobile social networking application, connected with a wearable tracker, to promote physical activity. Quantitative results were analyzed using descriptive and inferential statistics. Interviews and focus groups were conducted before and after the intervention to explore users' perspectives.Fifty-five participants were enrolled in the study (mean age=23.6 years, 50.1% female). Quantitative analysis revealed a non-statistically significant increase in average daily step count between baseline and 6 months (mean change = 14.5 steps/day, P = 0.98, 95% confidence interval [-1136.5, 1107.5]). Post-hoc 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 = 0.008,95% confidence interval [837.9, 5211.8]. Qualitative analysis indicated users' preference for selfregulation techniques, social comparison with similar or existing connections, and personalization features.The study demonstrated the feasibility of a mobile social networking app, connected with a wearable tracker for physical activity promotion. A one-size-fits-all approach to behavior change was deemed insufficient by users, calling for the development of personalized interventions in future research.Mode of access: World wide web1 online resource (x, 100 pages) colour illustration

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)

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

BackgroundHeart 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. ObjectiveThis 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. MethodsFour 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). ResultsA 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. ConclusionsMobile 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 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. Author summary Technologies such as mobile apps or fitness trackers may play a key role in supporting healthy behaviors and deliver public health interventions during the COVID-19 pandemic. We conducted an international survey that asked people about their health behaviors, and their use of technologies before and during the pandemic. Sixty percent reported using a mobile app for health purposes; 38% used a fitness tracker. People who used mobile apps and fitness trackers during the pandemic were more active than people who did not. Women were more likely to use health apps than men, and people aged 45+ were more likely to use COVID-19 apps than people under 45. Differences in app usage based on sex and age indicate that tailored technologies are needed to support different groups. Participants revealed that they had to adapt their use of mobile apps to fit their needs during the highly restricted circumstances caused by COVID-19. Altogether, our findings provide new insights into how mobile apps and devices can deliver health support remotely during a pandemic, and highlight the need for these technologies to adapt to support people’s changing needs

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

ObjectivesTo 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.MethodsAn 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.ResultsParticipants 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 ConclusionsUse 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