The advent of online software distribution channels like Apple Inc.'s App Store and Google Inc.'s Google Play has offered developers a single, low cost, and powerful distribution mechanism. These online stores help users discover apps as well as leave a review. Ratings and reviews add value to both the developer and potential new users by providing a crowd-sourced indicator of app quality. Hence, for developers it is important to get positive reviews and high ratings to ensure that an app has a viable future. But, what exactly do users say on these app stores? And more importantly, what is the experience that compels a user to leave either a positive or a negative rating? Our analysis of 8.7 million reviews from 17,330 apps shows that users tend to leave short, yet informative reviews, and the rating as well as the category influences the length of a review. In this preliminary study, we found that users will leave longer messages when they rate an app poorly, and the depth of feedback in certain categories is significantly higher than for other

Digital phenotyping is a promising method for advancing scalable detection and prediction methods in mental health research and practice. However, little is known about how digital phenotyping data are used to make inferences about youth mental health. We conducted a scoping review of 35 studies to better understand how passive sensing (e.g., Global Positioning System, microphone etc) and electronic usage data (e.g., social media use, device activity etc) collected via smartphones are used in detecting and predicting depression and/or anxiety in young people between 12 and 25 years-of-age. GPS and/or Wifi association logs and accelerometers were the most used sensors, although a wide variety of low-level features were extracted and computed (e.g., transition frequency, time spent in specific locations, uniformity of movement). Mobility and sociability patterns were explored in more studies compared to other behaviours such as sleep, phone use, and circadian movement. Studies used machine learning, statistical regression, and correlation analyses to examine relationships between variables. Results were mixed, but machine learning indicated that models using feature combinations (e.g., mobility, sociability, and sleep features) were better able to predict and detect symptoms of youth anxiety and/or depression when compared to models using single features (e.g., transition frequency). There was inconsistent reporting of age, gender, attrition, and phone characteristics (e.g., operating system, models), and all studies were assessed to have moderate to high risk of bias. To increase translation potential for clinical practice, we recommend the development of a standardised reporting framework to improve transparency and replicability of methodology.

Online software distribution channels such as Apple Inc.'s App Store and Google Inc.'s Google Play provide a platform for third-party app distribution. These online stores feature a public review system, allowing users to express opinions regarding purchased apps. These reviews can influence product-purchasing decisions via polarised sentiment (1 to 5 stars) and user expressed opinion. For developers, reviews are a user-facing crowd-sourced indicator of app quality. Hence, high ratings and positive reviews affect the viability of an app's commercial feasibility. However, it is less clear what information is contained within these reviews, and more importantly, if an analysis of these reviews can inform developers of design priorities as opposed to just influencing purchasing decisions. We analysed 8.7 million reviews from 17,330 apps on the App Store and found that the most frequently used words in user reviews lean toward expressions of sentiment despite employment of only approximately 37% of the words within the English language dictionary. Furthermore, the range of words used to express negative opinions is significantly higher than when positive sentiments are expressed.

Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence–driven adaptive trial—Vibe Up. Objective: We evaluated the effectiveness of recruitment via Facebook and Instagram compared to traditional methods for a treatment trial and compared different recruitment methods’ retention rates. With recruitment coinciding with COVID-19 lockdowns across Australia, we also compared the cost-effectiveness of social media recruitment during and after lockdowns. Methods: Recruitment was completed for 2 pilot trials and 6 minitrials from June 2021 to May 2022. To recruit participants, paid social media advertising on Facebook and Instagram was used, alongside mailing lists of university networks and student organizations or services, media releases, announcements during classes and events, study posters or flyers on university campuses, and health professional networks. Recruitment data, including engagement metrics collected by Meta (Facebook and Instagram), advertising costs, and Qualtrics data on recruitment methods and survey completion rates, were analyzed using RStudio with R (version 3.6.3; R Foundation for Statistical Computing). Results: In total, 1314 eligible participants (aged 22.79, SD 4.71 years; 1079, 82.1% female) were recruited to 2 pilot trials and 6 minitrials. The vast majority were recruited via Facebook and Instagram advertising (n=1203; 92%). Pairwise comparisons revealed that the lead institution’s website was more effective in recruiting eligible participants than Facebook (z=3.47; P=.003) and Instagram (z=4.23; P<.001). No differences were found between recruitment methods in retaining participants at baseline, at midpoint, and at study completion. Wilcoxon tests found significant differences between lockdown (pilot 1 and pilot 2) and postlockdown (minitrials 1-6) on costs incurred per link click (lockdown: median Aus $0.35 [US $0.22], IQR Aus $0.27-$0.47 [US $0.17-$0.29]; postlockdown: median Aus $1.00 [US $0.62], IQR Aus $0.70-$1.47 [US $0.44-$0.92]; W=9087; P<.001) and the amount spent per hour to reach the target sample size (lockdown: median Aus $4.75 [US $2.95], IQR Aus $1.94-6.34 [US $1.22-$3.97]; postlockdown: median Aus $13.29 [US $8.26], IQR Aus $4.70-25.31 [US $2.95-$15.87]; W=16044; P<.001). Conclusions: Social media advertising via Facebook and Instagram was the most successful strategy for recruiting distressed tertiary students into this artificial intelligence–driven adaptive trial, providing evidence for the use of this recruitment method for this type of trial in digital mental health research. No recruitment method stood out in terms of participant retention. Perhaps a reflection of the added distress experienced by young people, social media recruitment during the COVID-19 lockdown period was more cost-effective. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621001092886; https://tinyurl.com/39f2pdmd; Australian New Zealand Clinical Trials Registry ACTRN12621001223820; https://tinyurl.com/bdhkvucv

Background: Assistive technology is becoming increasingly accessible and affordable for supporting people with dementia and their care partners living at home, with strong potential for technology-based prompting to assist with initiation and tracking of complex, multistep activities of daily living. However, there is limited direct comparison of different prompt features to guide optimal technology design. Objective: Across 3 experiments, we investigated the features of tablet-based prompts that best support people with dementia to complete activities of daily living at home, measuring prompt effectiveness and gaining feedback from people with dementia and their care partners about their experiences. Methods: Across experiments, we developed a specialized iPad app to enable data collection with people with dementia at home over an extended experimental period. In experiment 1, we varied the prompts in a 3 (visual type: text instruction, iconic image, and photographic image) × 3 (audio type: no sound, symbolic sound, and verbal instruction) experimental design using repeated measures across multiple testing sessions involving single-step activities. In experiment 2, we tested the most effective prompt breakdown for complex multistep tasks comparing 3 conditions (1-prompt, 3-prompt, and 7-prompt conditions). In experiment 3, we compared initiation and maintenance alerts that involved either an auditory tone or an auditory tone combined with a verbal instruction. Throughout, we asked people with dementia and their care partners to reflect on the usefulness of prompting technology in their everyday lives and what could be developed to better meet their needs. Results: First, our results showed that audible verbal instructions were more useful for task completion than either tone-based or visual prompts. Second, a more granular breakdown of tasks was generally more useful and increased independent use, but this varied across individuals. Third, while a voice or text maintenance alert enabled people with dementia to persist with a multistep task for longer when it was more frequent, task initiation still frequently required support from a care partner. Conclusions: These findings can help inform developers of assistive technology about the design features that promote the usefulness of home prompting systems for people with dementia as well as the preferences and insights of people with dementia and their care partners regarding assistive technology design.