Concept: App Store
In March 2015, Apple Inc announced ResearchKit, a novel open-source framework intended to help medical researchers to easily create apps for medical studies. With the announcement of this framework, Apple presented 5 apps built in a beta phase based on this framework.
There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality.
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.
Although sleep apps are among the most popular commercially available health apps, little is known about how well these apps are grounded in behavioral theory. Three-hundred and sixty-nine apps were initially identified using the term “sleep” from the Google play store and Apple iTunes in September 2015. The final sample consisted of 35 apps that met the following inclusion criteria: 1) Stand-alone functionality; 2) Sleep tracker or monitor apps ranked by 100 + users; 3) Sleep Alarm apps ranked by 1000 + users; and 4) English language. A coding instrument was developed to assess the presence of 19 theoretical constructs. All 35 apps were downloaded and coded. The inter-rater reliability between coders was 0.996. A “1” was assigned if a construct was present in the app and “0” if it was not. Mean scores were calculated across all apps, and comparisons were made between total scores and app ratings using R. The mean behavior construct scores (BCS) across all apps was 34% (5% - 84%). Behavioral constructs for realistic goal setting (86%), time management (77%), and self-monitoring (66%) were most common. Although a positive association was observed between BCS and user ratings, this was not found to be statistically significant (p > 0.05). The mean persuasive technology score was 42% (20% to 80%), with higher scores for paid compared to free apps (p < 0.05). While the overall behavior construct scores were low, an opportunity exists to develop or modify existing apps to support sustainable sleep hygiene practices.
BACKGROUND: Smartphone use is growing exponentially and will soon become the only mobile phone handset for about 6 billion users. Smartphones are ideal marketing targets as consumers can be reached anytime, anywhere. Smartphone application (app) stores are global shops that sell apps to users all around the world. Although smartphone stores have a wide collection of health-related apps they also have a wide set of harmful apps. In this study, the availability of ‘pro-smoking’ apps in two of the largest smartphone app stores (Apple App store and Android Market) was examined. METHOD: In February 2012, we searched the Apple App Store and Android Market for pro-smoking apps, using the keywords Smoke, Cigarette, Cigar, Smoking and Tobacco. We excluded apps that were not tobacco-related and then assessed the tobacco-related apps against our inclusion criteria. RESULT: 107 pro-smoking apps were identified and classified into six categories based on functionality. 42 of these apps were from the Android Market and downloaded by over 6 million users. Some apps have explicit images of cigarette brands. CONCLUSIONS: Tobacco products are being promoted in the new ‘smartphone app’ medium which has global reach, a huge consumer base of various age groups and underdeveloped regulation. The paper also provides two examples of app store responses to country-specific laws and regulations that could be used to control the harmful contents in the app stores for individual countries.
Although the Health & Fitness category of the Apple App Store features hundreds of calorie counting apps, the extent to which popular calorie counting apps include health behavior theory is unknown.
Most psychological experimentation takes place in laboratories aiming to maximize experimental control; however, this creates artificial environments that are not representative of real-life situations. Since cognitive processes usually take place in noisy environments, they should also be tested in these contexts. The recent advent of smartphone technology provides an ideal medium for such testing. In order to examine the feasibility of mobile devices (MD) in psychological research in general, and laterality research in particular, we developed a MD version of the widely used speech laterality test, the consonant-vowel dichotic listening (DL) paradigm, for use with iPhones/iPods. First, we evaluated the retest reliability and concurrent validity of the DL paradigm in its MD version in two samples tested in controlled, laboratory settings (Experiment 1). Second, we explored its ecological validity by collecting data from the general population by means of a free release of the MD version (iDichotic) to the iTunes App Store (Experiment 2). The results of Experiment 1 indicated high reliability (r(ICC) = 0.78) and validity (r(ICC) = 0.76-0.82) of the MD version, which consistently showed the expected right ear advantage (REA). When tested in real-life settings (Experiment 2), participants (N = 167) also showed a significant REA. Importantly, the size of the REA was not dependent on whether the participants chose to listen to the syllables in their native language or not. Together, these results establish the current MD version as a valid and reliable method for administering the DL paradigm both in experimentally controlled as well as uncontrolled settings. Furthermore, the present findings support the feasibility of using smartphones in conducting large-scale field experiments.
The delivery of mobile health (mHealth) services is acceptable to mental health consumers. However, despite the benefits of accessibility, cost-effectiveness, anonymity, and ability to tailor content to individual needs, consumer engagement remains a hurdle for uptake and continued use. This may be unsurprising as few studies have examined app content from the consumer perspective or assessed consumer preferences for the content of apps for mental health management. An opportunity to examine consumer perspectives exists in using naturally generated data that is publically available in the Google Play and Apple app stores. Whereas commercial developers routinely use this data, to date there has been no in-depth evaluation within scientific research.
Researchers have largely turned to commercial app stores, randomized trials, and systematic reviews to make sense of the mHealth landscape. Few studies have approached understanding by collecting information from target end users. The end user perspective is critical as end user interest in and use of mHealth technologies will ultimately drive the efficacy of these tools.
Abstract The purpose of this investigation was to analyse the concurrent validity and reliability of an iPhone app (called: My Jump) for measuring vertical jump performance. Twenty recreationally active healthy men (age: 22.1 ± 3.6 years) completed five maximal countermovement jumps, which were evaluated using a force platform (time in the air method) and a specially designed iPhone app. My jump was developed to calculate the jump height from flight time using the high-speed video recording facility on the iPhone 5 s. Jump heights of the 100 jumps measured, for both devices, were compared using the intraclass correlation coefficient, Pearson product moment correlation coefficient ®, Cronbach’s alpha (α), coefficient of variation and Bland-Altman plots. There was almost perfect agreement between the force platform and My Jump for the countermovement jump height (intraclass correlation coefficient = 0.997, P < 0.001; Bland-Altman bias = 1.1 ± 0.5 cm, P < 0.001). In comparison with the force platform, My Jump showed good validity for the CMJ height (r = 0.995, P < 0.001). The results of the present study showed that CMJ height can be easily, accurately and reliably evaluated using a specially developed iPhone 5 s app.