App Descriptions Research Articles

Give it up! – a new mobile app and campaign supporting women to quit smoking in Poland – project rationale and app description

Give it up! – a new mobile app and campaign supporting women to quit smoking in Poland – project rationale and app description

Behavior change techniques in mobile applications for sedentary behavior.

ObjectiveMobile applications (apps) are increasingly being utilized in health behavior change interventions. To determine the presence of underlying behavior change mechanisms, apps for physical activity have been coded for behavior change techniques (BCTs). However, apps for sedentary behavior have yet to be assessed for BCTs. Thus, the purpose of the present study was to review apps designed to decrease sedentary time and determine the presence of BCTs.MethodsSystematic searches of the iTunes App and Google Play stores were completed using keyword searches. Two reviewers independently coded free (n = 36) and paid (n = 14) app descriptions using a taxonomy of 93 BCTs (December 2016–January 2017). A subsample (n = 4) of free apps were trialed for one week by the reviewers and coded for the presence of BCTs (February 2017).ResultsIn the free and paid app descriptions, only 10 of 93 BCTs were present with a mean of 2.42 BCTs (range 0–6) per app. The BCTs coded most frequently were “prompts/cues” (n = 43), “information about health consequences” (n = 31), and “self-monitoring of behavior” (n = 17). For the four free apps that were trialed, three additional BCTs were coded that were not coded in the descriptions: “graded tasks,” “focus on past successes,” and “behavior substitution.”ConclusionsThese sedentary behavior apps have fewer BCTs compared with physical activity apps and traditional (i.e., non-app) physical activity and healthy eating interventions. The present study sheds light on the behavior change potential of sedentary behavior apps and provides practical insight about coding for BCTs in apps.

Commercially Available Smartphone Apps to Support Postoperative Pain Self-Management: Scoping Review.

BackgroundRecently, the use of smartphones to deliver health-related content has experienced rapid growth, with more than 165,000 mobile health (mHealth) apps currently available in the digital marketplace. With 3 out of 4 Canadians currently owning a smartphone, mHealth apps offer opportunities to deliver accessible health-related knowledge and support. Many individuals experience pain after surgery, which can negatively impact their health-related quality of life, including sleep, emotional, and social functioning. Smartphone apps that provide remote real-time monitoring and symptom management have the potential to improve self-management skills in patients experiencing postoperative pain. Increased confidence and practice of self-management skills could contribute to decreased postoperative pain and reduce risk of developing persistent pain. Published reviews of general pain self-management apps demonstrate a lack of evidence-based content, theoretical grounding, and health care professional involvement. However, no review to date has focused on the app marketplace specific for individuals with postoperative pain.ObjectiveThe aim of this study was to characterize and critically appraise the content and functionality of commercially available postoperative pain self-management apps.MethodsAn electronic search and extraction was conducted between December 2016 and March 2017 of the official Canadian app stores for the three major smartphone operating systems (iPhone operating system [iOS], Android, and Windows). Stores were searched separately using predetermined search terms. Two authors screened apps based on information provided in the public app description. Metadata from all included apps were abstracted into a standard spreadsheet. Two authors verified the data with reference to the apps and downloaded apps themselves. The content and functionality of each app as it pertained to postoperative pain self-management was rated.ResultsA total of 10 apps met the inclusion criteria. All included apps were designed exclusively for the Android platform. Education was the most common self-management feature offered (8/10, 80%), with none of the apps offering features related to goal setting or social support. Overall, no single app was comprehensive in terms of pain self-management content. Five (50%) apps reported the involvement of a health care provider in their development. However, not a single app involved end users in their development, and none of the apps underwent scientific evaluation. Additionally, none of the apps were designed for use in pediatric patients.ConclusionsCurrently available postoperative pain apps for patients lack evidence-based content, goal setting, and social support functions. There is a need to develop and test comprehensive theory-based apps to support patients with pain self-management care following surgery.

Qualitative evaluation of mobile cancer apps with particular attention to the target group, content, and advertising.

Medical apps are gaining importance rapidly. Also in the field of cancer care, apps are offered. Yet, so far little is known with respect to their quality. In a pilot phase we developed a rating tool based on formal and content-related criteria for the assessment of cancer apps. We used this instrument on cancer apps available in the App Store (iOS) concerning breast, prostate and colorectal cancer. The results were stratified according to target group, content and advertising. We assessed 41 mobile cancer apps. Six apps (14.63%) scored very high, fifteen apps (36.59%) high, seventeen apps (41.46%) were deficient, and three apps (7.32%) were insufficient. The largest group of apps represents those apps with the "deficient" rating. The very good to good apps had reliable sources, a concrete intent/ purpose in their app description, and a strict distinction of scientific content and advertisement. Apps with the predicates "deficient" or "insufficient" had particularly poor ratings, e.g. in the subscales "information on sources" and "data protection". Almost half of the tested apps were deficient or insufficient. In order to improve safety of patients using apps, some regulation seems mandatory. Putting apps under the legislation for medical products might be one way to better regulate and control quality. Second, efforts should focus on the development of checklists that make it easier for patients to search for suitable cancer apps.

Development of a Rating Tool for Mobile Cancer Apps: Information Analysis and Formal and Content-Related Evaluation of Selected Cancer Apps.

Mobile apps are offered in large numbers and have different qualities. The aim of this article was to develop a rating tool based on formal and content-related criteria for the assessment of cancer apps and to test its applicability on apps. After a thorough analysis of the literature, we developed a specific rating tool for cancer apps based on the MARS (mobile app rating system) and a rating tool for cancer websites. This instrument was applied to apps freely available in stores and focusing on some cancer topic. Ten apps were rated on the basis of 22 criteria. Sixty percent of the apps (6/10) were rated poor and insufficient. The rating by different scientists was homogenous. The good apps had reliable sources were regularly updated and had a concrete intent/purpose in their app description. In contrast, the apps that were rated poor had no distinction of scientific content and advertisement. In some cases, there was no imprint to identify the provider. As apps of poor quality can give misinformation and lead to wrong treatment decisions, efforts have to be made to increase usage of high-quality apps. Certification would help cancer patients to identify reliable apps, yet acceptance of a certification system must be backed up.

Mining domain knowledge from app descriptions

Domain analysis aims at gaining knowledge to a particular domain in the early stage of software development. A key challenge in domain analysis is to extract features automatically from related product artifacts. Compared with other kinds of artifacts, high volume of descriptions can be collected from App marketplaces (such as Google Play and Apple Store) easily when developing a new mobile application (App), so it is essential for the success of domain analysis to gain features and relationships from them using data analysis techniques. In this paper, we propose an approach to mine domain knowledge from App descriptions automatically, where the information of features in a single App description is firstly extracted and formally described by a Concern-based Description Model (CDM), which is based on predefined rules of feature extraction and a modified topic modeling method; then the overall knowledge in the domain is identified by classifying, clustering and merging the knowledge in the set of CDMs and topics, and the results are formalized by a Data-based Raw Domain Model (DRDM). Furthermore, we propose a quantified evaluation method for prioritizing the knowledge in DRDM. The proposed approach is validated by a series of experiments.

Automated Android application permission recommendation

The number of Android applications has increased rapidly as Android is becoming the dominant platform in the smartphone market. Security and privacy are key factors for an Android application to be successful. Android provides a permission mechanism to ensure security and privacy. This permission mechanism requires that developers declare the sensitive resources required by their applications. On installation or during runtime, users are required to agree with the permission request. However, in practice, there are numerous popular permission misuses, despite Android introducing official documents stating how to use these permissions properly. Some data mining techniques (e.g., association rule mining) have been proposed to help better recommend permissions required by an API. In this paper, based on popular techniques used to build recommendation systems, we propose two novel approaches to improve the effectiveness of the prior work. The first approach utilizes a collaborative filtering technique, which is inspired by the intuition that apps that have similar features — inferred from their APIs — usually share similar permissions. The second approach recommends permissions based on a text mining technique that uses a naive Bayes multinomial classification algorithm to build a prediction model by analyzing descriptions of apps. To evaluate these two approaches, we use 936 Android apps from F-Droid, which is a repository of free and open source Android applications. We find that our proposed approaches yield a significant improvement in terms of precision, recall, F1-score, and MAP of the top-k results over the baseline approach.

IOS Appstore-Based Phone Apps for Diabetes Management: Potential for Use in Medication Adherence.

BackgroundCurrently, various phone apps have been developed to assist patients. Many of these apps are developed to assist patients in the self-management of chronic diseases such as diabetes. It is essential to analyze these various apps to understand the key features that would potentially be instrumental in helping patients successfully achieve goals in disease self-management.ObjectiveThe objective of this study was to conduct a review of all the available diabetes-related apps in the iOS App Store to evaluate which diabetic app is more interactive and offers a wide variety of operations such as monitoring glucose, water, carbohydrate intake, weight, body mass index (BMI), medication, blood pressure (BP) levels, reminders or push notifications, food database, charts, exercise management, email, sync between devices, syncing data directly to the prescribers, and other miscellaneous functions such as (Twitter integration, password protection, retina display, barcode scanner, apple watch functionality, and cloud syncing).MethodsData was gathered using the iOS App Store on an iPad. The search term “diabetes” resulted in 1209 results. Many of the results obtained were remotely related to diabetes and focused mainly on diet, exercise, emergency services, refill reminders, providing general diabetes information, and other nontherapeutic options. We reviewed each app description and only included apps that were meant for tracking blood glucose levels. All data were obtained in one sitting by one person on the same device, as we found that carrying out the search at different times or on different devices (iPhones) resulted in varying results. Apps that did not have a feature for tracking glucose levels were excluded from the study.ResultsThe search resulted in 1209 results; 85 apps were retained based on the inclusion criteria mentioned above. All the apps were reviewed for average customer ratings, number of reviews, price, and functions. Of all the apps surveyed, 18 apps with the highest number of user ratings were used for in-depth analysis. Of these 18 apps, 50% (9/18) also had a medication adherence function. Our analysis revealed that the Diabetes logbook used by the mySugr app was one of the best; it differentiated itself by introducing fun as a method of increasing adherence.ConclusionsA large variation was seen in patient ratings of app features. Many patient reviewers desired simplicity of app functions. Glucose level tracking and email features potentially helped patients and health care providers manage the disease more efficiently. However, none of the apps could sync data directly to the prescribers. Additional features such as graph customization, availability of data backup, and recording previous entries were also requested by many users. Thus, the use of apps in disease management and patient and health-care provider involvement in future app refinement and development should be encouraged.

Characterizing malicious Android apps by mining topic-specific data flow signatures

Context: State-of-the-art works on automated detection of Android malware have leveraged app descriptions to spot anomalies w.r.t the functionality implemented, or have used data flow information as a feature to discriminate malicious from benign apps. Although these works have yielded promising performance, we hypothesize that these performances can be improved by a better understanding of malicious behavior.Objective: To characterize malicious apps, we take into account both information on app descriptions, which are indicative of apps’ topics, and information on sensitive data flow, which can be relevant to discriminate malware from benign apps.Method: In this paper, we propose a topic-specific approach to malware comprehension based on app descriptions and data-flow information. First, we use an advanced topic model, adaptive LDA with GA, to cluster apps according to their descriptions. Then, we use information gain ratio of sensitive data flow information to build so-called “topic-specific data flow signatures”.Results: We conduct an empirical study on 3691 benign and 1612 malicious apps. We group them into 118 topics and generate topic-specific data flow signature. We verify the effectiveness of the topic-specific data flow signatures by comparing them with the overall data flow signature. In addition, we perform a deeper analysis on 25 representative topic-specific signatures and yield several implications.Conclusion: Topic-specific data flow signatures are efficient in highlighting the malicious behavior, and thus can help in characterizing malware.

Personalized app recommendation using spatio-temporal app usage log

This paper proposes a probabilistic method to recommend apps appropriate to current time and location of a user. The proposed method regards an app as a distribution of topics discovered from a large number of app descriptions. A user preference is then modeled, using spatio-temporal app usage log of a user, as a topic distribution that is affected by time and location. Since time and location can be regarded as two continuous random variables that are independent of each other, the proposed method is in contrast to conventional methods in that the conventional methods are based on a limited number of discrete contexts and assume that locations are dependent on time. Therefore, the proposed method captures user-specific contexts and is robust even in unseen time and location. Our experiments show that the proposed method outperforms two baseline methods in NDCG, which implies that the proposed method is effective in personalized app recommendation.

Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

Context: App stores provide a software development space and a market place that are both different from those to which we have become accustomed for traditional software development: The granularity is finer and there is a far greater source of information available for research and analysis. Information is available on price, customer rating and, through the data mining approach presented in this paper, the features claimed by app developers. These attributes make app stores ideal for empirical software engineering analysis.Objective: This paper11This paper is an extended version of our short paper at MSR 2012 [1]; a technical report is also available [2]. exploits App Store Analysis to understand the rich interplay between app customers and their developers.Method: We use data mining to extract app descriptions, price, rating, and popularity information from the Blackberry World App Store, and natural language processing to elicit each apps’ claimed features from its description.Results: The findings reveal that there are strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between app price and the number of features claimed for the app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher ratings than non-free apps, with a moderately high effect size (A^12=0.68). All data from our experiments and analysis are made available on-line to support further investigations.

Spam Mobile Apps

The increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users’ quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords, or similar apps being made available several times and across diverse categories. Market operators maintain antispam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% and 95% and recall varying between 38% and 98%. We further show that a limited number of features, in the range of 10--30, generated from app metadata is sufficient to achieve a satisfactory level of performance. On a set of 180,627 apps that were present at the app market during our crawl, our classifier predicts 2.7% of the apps as potential spam. Finally, we perform additional manual verification and show that human reviewers agree with 82% of our classifier predictions.

A Systematic Review of Apps using Mobile Criteria for Adolescent Pregnancy Prevention (mCAPP).

BackgroundAdolescents in the United States and globally represent a high-risk population for unintended pregnancy, which leads to high social, economic, and health costs. Access to smartphone apps is rapidly increasing among youth, but little is known about the strategies that apps employ to prevent pregnancy among adolescents and young adults. Further, there are no guidelines on best practices for adolescent and young adult pregnancy prevention through mobile apps.ObjectiveThis review developed a preliminary evaluation framework for the assessment of mobile apps for adolescent and young adult pregnancy prevention and used this framework to assess available apps in the Apple App Store and Google Play that targeted adolescents and young adults with family planning and pregnancy prevention support.MethodsWe developed an assessment rubric called Mobile Criteria for Adolescent Pregnancy Prevention (mCAPP) for data extraction using evidence-based and promising best practices from the literature. mCAPP comprises 4 domains: (1) app characteristics, (2) user interface features, (3) adolescent pregnancy prevention best practices, and (4) general sexual and reproductive health (SRH) features. For inclusion in the review, apps that advertised pregnancy prevention services and explicitly mentioned youth, were in English, and were free were systematically identified in the Apple App Store and Google Play in 2015. Screening, data extraction, and 4 interrater reliability checks were conducted by 2 reviewers. Each app was assessed for 92 facets of the mCAPP checklist.ResultsOur search returned 4043 app descriptions in the Apple App Store (462) and Google Play (3581). After screening for inclusion criteria, 22 unique apps were included in our analysis. Included apps targeted teens in primarily developed countries, and the most common user interface features were clinic and health service locators. While app strengths included provision of SRH education, description of modern contraceptives, and some use of evidence-based adolescent best practices, gaps remain in the implementation of the majority of adolescent best practices and user interface features. Of the 8 best practices for teen pregnancy prevention operationalized through mCAPP, the most commonly implemented best practice was the provision of information on how to use contraceptives to prevent pregnancy (15/22), followed by provision of accurate information on pregnancy risk of sexual behaviors (13/22); information on SRH communication, negotiation, or refusal skills (10/22); and the use of persuasive language around contraceptive use (9/22).ConclusionsThe quality and scope of apps for adolescent pregnancy prevention varies, indicating that developers and researchers may need a supportive framework. mCAPP can help researchers and developers consider mobile-relevant evidence-based best practices for adolescent SRH as they develop teen pregnancy prevention apps. Given the novelty of the mobile approach, further research is needed on the impact of mCAPP criteria via mobile channels on adolescent health knowledge, behaviors, and outcomes.

Mobile applications for obesity and weight management: current market characteristics.

Mobile-Health (mHealth) is the fastest-developing eHealth sector, with over 100 000 health applications (apps) currently available. Overweight/obesity is a problem of wide public concern that is potentially treatable/preventable through mHealth. This study describes the current weight-management app-market. Five app stores (Apple, Google, Amazon, Windows and Blackberry) in UK, US, Russia, Japan and Germany, Italy, France, China, Australia and Canada were searched for keywords: 'weight', 'calorie', 'weight-loss', 'slimming', 'diet', 'dietitian' and 'overweight' in January/February 2016 using App-Annie software. The 10 most downloaded apps in the lifetime of an app were recorded. Developers' lists and the app descriptions were searched to identify any professional input with keywords 'professional', 'dietitian' and 'nutritionist'. A total of 28 905 relevant apps were identified as follows: Apple iTunes=8559 (4634, 54% paid), Google Play=1762 (597, 33.9% paid), Amazon App=13569 (4821, 35.5% paid), Windows=2419 (819, 17% paid) and Blackberry=2596 (940, 36% paid). The 28 905 identified apps focused mainly on physical activity (34%), diet (31%), and recording/monitoring of exercise, calorie intake and body weight (23%). Only 17 apps (0.05%) were developed with identifiable professional input. Apps on weight management are widely available and very popular but currently lack professional content expertise. Encouraging app development based on evidence-based online approaches would assure content quality, allowing healthcare professionals to recommend their use.

Using Behavior Change Techniques to Guide Selections of Mobile Applications to Promote Fluid Consumption

To determine the extent to which validated techniques for behavior change have been infused in commercially available fluid consumption applications (apps). Coders evaluated behavior change techniques represented in online descriptions for 50 fluid consumption apps and the latest version of each app. Apps incorporated a limited range of behavior change techniques (<20% of taxonomy). The number of techniques varied by operating system but not as a function of whether apps were free or paid. Limitations include the lack of experimental evidence establishing the efficacy of these apps. Patients with urolithiasis can choose from many apps to support the recommended increase in fluid intake. Apps for iOS devices incorporate more behavior change techniques compared to apps for the Android operating system. Free apps are likely to expose patients to a similar number of techniques as paid apps. Physicians and patients should screen app descriptions for features to promote self-monitoring and provide feedback on discrepancies between behavior and a fluid consumption goal.

A quality review of smartphone applications for the management of pain.

Smartphone applications (apps) are recent innovations that have not been studied extensively. The lack of regulatory body assessing the content of existing apps means that their quality is often unknown. This review aims to assess the quality of smartphone apps that claim to provide information and treatment for pain conditions. It assesses the degree to which apps adhere to evidence-based practices in psychological research for pain management and which stand the best chance of being effective for consumers. Another aim is to identify potential apps health-care professionals may wish to recommend to clients. Pain management apps on the official iPhone and Android stores were searched in January 2014. Those containing a psychological component in the app description were downloaded and rated for quality using a checklist devised by two researchers. The checklist was based on cognitive behavioural therapy (CBT) guidelines since the latter is the most effective intervention for computerized programs. A total of 195 apps met inclusion criteria. Although CBT is a promising alternative to traditional psychological interventions, only six apps endorsed theoretical reference to CBT principles. Existing apps are often constructed by lay people or software developers, with little input from health-care professionals. Pain apps sometimes promise a solution to pain without a consideration of app content. The development of evidence-based apps and rigorous evaluation of any long-term outcomes are important in enhancing understanding of the potential of these apps.

Smartphone Applications for Mental Health.

Many adolescents and adults do not seek treatment for mental health symptoms. Smartphone applications (apps) may assist individuals with mental health concerns in alleviating symptoms or increasing understanding. This study seeks to characterize apps readily available to smartphone users seeking mental health information and/or support. Ten key terms were searched in the Apple iTunes and Google Play stores: mental health, depression, anxiety, schizophrenia, bipolar, trauma, trauma in schools, post traumatic stress disorder (PTSD), child trauma, and bullying. A content analysis of the first 20 application descriptions retrieved per category was conducted. Out of 300 nonduplicate applications, 208 (70%) were relevant to search topic, mental health or stress. The most common purported purpose for the apps was symptom relief (41%; n = 85) and general mental health education (18%; n = 37). The most frequently mentioned approaches to improving mental health were those that may benefit only milder symptoms such as relaxation (21%; n = 43). Most app descriptions did not include information to substantiate stated effectiveness of the application (59%; n = 123) and had no mention of privacy or security (89%; n = 185). Due to uncertainty of the helpfulness of readily available mental health applications, clinicians working with mental health patients should inquire about and provide guidance on application use, and patients should have access to ways to assess the potential utility of these applications. Strategic policy and research developments are likely needed to equip patients with applications for mental health, which are patient centered and evidence based.

Characteristics of Chinese m-Health Applications for Diabetes Self-Management.

To examine the features and types of health information provided in Chinese diabetes mobile applications (apps) for patients' self-management. Through multiple rounds of screening, we identified a total of 95 relevant iOS (Apple, Cupertino, CA) and Android™ (Google, Mountain View, CA) apps and examined each app's features and health information types based on each app's description in the app stores. We used a 15-feature algorithm to evaluate the apps' abilities for supporting diabetic patients' self-management, based on U.S. national standards for diabetes self-management. We also adapted the health information wants framework to analyze the types of information that the apps provided for diabetic patients. Diabetes education was the most common feature, provided by 75% of the apps. Blood glucose checking was enabled by 65% of the apps. Diet management, insulin checking, and physical activity monitoring were enabled by 53%, 49%, and 44% of the apps, respectively. Only a small percentage of the apps enabled psychosocial support (29%) or tracking of blood pressure (14%), cholesterol (14%), or body mass index (11%). None of the apps provided all seven types of information posited by the health information wants framework. Only a small percentage of the apps provided information about psychosocial support (29%), healthcare providers (24%), or healthcare facilities (24%). Information about complementary and alternative medicine was the least likely type of information provided in the apps, with only 7% of the apps providing this type of information. Our findings have important implications for improving the quality of Chinese diabetes mobile apps to facilitate patients' self-management.

There’s An App For That! Evaluation Of Free Aerobic Exercise Apps

PURPOSE: An increasingly common mode of daily exercise is the use of exercise apps via smartphone, but few studies have examined how these apps might help to promote physical activity. The purpose of this study was to evaluate written descriptions of free aerobic exercise apps and the potential for promoting physical activity. METHODS: In February 2013, study investigators identified 63 free exercise apps on iTunes that included aerobic exercise. Written descriptions posted by the app developers were evaluated by two investigators using the enabling and reinforcing components of the Precede-Proceed health promotion model as a guide. Six factors (three enabling and three reinforcing) were evaluated by the investigators. One point was recorded for each of the factors incorporated into a particular app. Total points were determined and average scores are presented as means±SD. RESULTS: Out of the 63 apps, 19 included only aerobic exercise and 44 included a variety of exercise routines, including aerobic exercise. Enabling scores ranged from 0 to 3, with an average score of 1.8±0.9. Fifty (79.4 %) apps included videos or pictures, while 44 apps (69.8 %) included written instructions on how to perform exercises. Twenty-two (34.9 %) apps included the ability to track daily exercise. Reinforcing scores ranged from 0 to 3, with an average score of 0.7±0.8. Twenty-five (39.7 %) apps interfaced with a social networking site, while 15 (23.8 %) included rewards for daily exercise. Only 6 (9.5 %) provided feedback and support from a personal trainer or other individuals following adoption of an exercise program. Total scores ranged from 0 to 5, with an average of 2.5±1.1. None of the apps included all 6 factors. CONCLUSION: It will be important for exercise professionals to collaborate with app developers, so new exercise apps include ways to encourage and reinforce daily exercise. Additional research is needed on how exercise apps can be used to promote physical activity.

Clinical Presentation of Tinnitus and a Review of, and Evidence Base for, Tinnitus Applications

A lack of evidence base presently exists, to validate the efficacy of mobile technology applications (apps) for tinnitus—highlighting a need for research. We reviewed tinnitus apps available via two popular smartphone operating systems: the Apple iOS and Google Android platforms. A March (2016) search using platforms available in the Austral-Asia region yielded over 260 tinnitus-related apps. Search parameters required apps to employ an English language format and feature the keyword “tinnitus” in the app name or description. Those apps retained for consideration (257) enabled 4 primary themes to emerge, featuring apps offering: (1) sound therapy approaches, (2) information, tips or assessment, (3) health promotion or alternative approaches, and (4) miscellaneous, non-therapeutic apps. Themes enabled further organization of related app characteristics into sub-groups. Numerous apps (44.3%) presented sound menus, ordered into a virtual library of presumably beneficial listening options. To place tinnitus apps in the context of current therapeutic considerations, an overview of the clinical presentation of tinnitus, and approaches aimed at mediating the perception of tinnitus and affective responses, precedes the review. We suggest future research addresses: the relative benefit(s) of one app over another, efficacy of tinnitus apps as a therapeutic option, long-term outcomes, and generalizability across populations.