Rupa Nallamothu PH 162 MPH Internship 2021 Assignment 1: Annotated Bibliography During my internship, I will be writing a systematic review on time lags in the mHealth translation process. Time lags refer to the time taken between health research and its translation into health improvements in clinical settings. In fields, such as mobile health(mHealth), the identification of time lags can remove barriers in the delivery of services.1 Although there were 51, 476 mHealth apps in the Apple app store at the end of 2020, the majority of these apps are not evidence based.2 This lack of availability of evidence based apps can be attributed to time lags in the translation process. The objective of this review is to identify and summarize the usage patterns and time lags in evidence based apps. Appendix A: Annotated Bibliographies Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510-520. doi:10.1258/jrsm.2011.110180 Both scientific literature and policy tend to assume that speedy translation of research into practice is beneficial. Although some time lags are necessary in order to ensure the safety and efficacy of interventions, we should try to minimize time lags because delays can be seen as a waste of resources. The reduction of time lags increases the rates of return of interventions. Generally, it takes an average of 17 years for 14% of new scientific discoveries to be used in clinical practice. The purpose of this study is to review the description and quantification of time lags in the health research translation process in scientific literature. The authors of this study found that researchers must agree on models, definitions, and measures in order to understand time lags. This study was relevant because the authors developed a conceptual model of interventions in research to application in clinical settings. This model helped me gain an understanding of the estimation of time lags in the translation process. This knowledge will help me conduct a systematic review on time lags in mHealth during this internship. Hanney SR, Castle-Clarke S, Grant J, et al. How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy, and practice. Health Res Policy Syst. 2015;13:1. Published 2015 Jan 1. doi:10.1186/1478-4505-13-1 Time lags refer to the time taken between biomedical/health research and its translation into health improvements in a clinical setting. The reduction of time lag can increase the rate of return to interventions. Although there is value in analysis of time lags, ways to measure time lags are underdeveloped. The purpose of this study is to develop appropriate methods to measure and understand time lags. The authors of this study conducted a literature review to show disagreements in the measurement of time lags in research. After identifying gaps in understanding time lags, they developed a new approach to quantify time lags. Also, they found a way to show and measure different elements in time lags. This study is relevant because it provides tools for conducting case studies on time lags. This information is useful because I will be identifying calibration points in studies in order to determine time lags in the publication of the research and the development of applications in app marketplaces. Shah AC, Badawy SM. Telemedicine in Pediatrics: Systematic Review of Randomized Controlled Trials. JMIR Pediatr Parent. 2021;4(1):e22696. Published 2021 Feb 24. doi:10.2196/22696 Telemedicine describes the use of technology in health services for patients and families. mHealth can be a form of telemedicine. Recently, there has been an increase in the use of telemedicine to deliver services to medically underserved and lowincome areas. The purpose of this review is to evaluate recent evidence of the feasibility and accessibility of telemedicine services and the satifastication of providers and patients with these services. The authors excluded studies that were not randomized controlled trials, older than 10 years, and that did not use a study intervention related to telemedicine or assessed telemedicine as a form of pediatric care in this review. They found that telemedicine interventions resulted in outcomes that were similar to or better than the outcomes of control groups. This study is important because it has important background information about the field of 1 Rupa Nallamothu PH 162 MPH Internship 2021 Assignment 1: Annotated Bibliography telemedicine. It shows that interventions using telemedicine are feasible. Through reading this article, I was able to gain a deeper understanding of the field of telemedicine in order to be able to understand articles that I will include in my review. Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. NPJ Digit Med. 2021;4(1):20. Published 2021 Feb 11. doi:10.1038/s41746-021-00386-8 Since the number of digital mental health interventions are increasing rapidly, the interest in the scientific community in these interventions has grown as well. An analysis of usage of mental health apps show these interventions have a median 15-day retention of only 3.9%. This low amount of user engagement might affect the effectiveness of apps. Apps that have a greater amount of engagement have been associated with greater reductions in depression and anxiety symptoms in users than apps that lack engagement features. The purpose of this review is to determine the effect of persuasive design and behavioral economics techniques on user engagement and clinical outcomes. The authors found that behavioral and cognitive techniques were the most common in apps. They found that persuasive design features often increased the efficacy of apps. This study is relevant because they used a data extraction template to gather information about study identifiers, study characteristics, intervention characteristics, techniques to increase engagement, engagement measures, efficacy measures, app content, app modality, accessory interventions, and usability features. In order to assess apps in my systematic review, I based my electronic data extraction form on this template because I felt that this template was very comprehensive. Carlo AD, Hosseini Ghomi R, Renn BN, Areán PA. By the numbers: ratings and utilization of behavioral health mobile applications. NPJ Digit Med. 2019;2:54. Published 2019 Jun 17. doi:10.1038/s41746-019-0129-6 There are a large number(greater than 10,000) of behavioral health applications on the Apple and Android marketplaces. The majority of these apps have not been evaluated or regulated, so there is a lack of understanding about the usage patterns of these apps. The purpose of this study is to identify the usage patterns of the most downloaded apps for mental and behavioral health disorders and compare their ratings in different frameworks. The authors found that the most downloaded apps focus on relaxation, mindfulness, and meditation skills. The ratings of these apps are very different based on rating frameworks. They concluded that there is a need for evidence based apps which integrate consumer preferences, up-to-date health app guidance for clinicians and consumers, and standardized quality and effectiveness measures for apps. This review is relevant because it provides information about evaluation frameworks for mHealth applications. In order to obtain information about apps, the authors utilized PrioriData, a mobile app market research firm. Since I was looking for companies to help me with data extraction, this article helped me learn about a company that could be a partner in my research. Carlo AD, Hosseini Ghomi R, Renn BN, Strong MA, Areán PA. Assessment of Real-World Use of Behavioral Health Mobile Applications by a Novel Stickiness Metric. JAMA Netw Open. 2020;3(8):e2011978. Published 2020 Aug 3. doi:10.1001/jamanetworkopen.2020.11978 Although the amount of digital health treatments for individuals with behavioral health problems are significantly increasing, patient engagement can be challenging. The purpose of this study is to describe usage patterns of mobile behavioral health apps and identify the most continually assessed characteristics of them. The authors of this study excluded studies that were not designed to treat a behavioral health disorder and did not promote a specific behavioral health treatment or technique from this review. Also they did not include apps that were available in both of the leading marketplaces( the Apple iOS or Google Play marketplaces ), fewer than 10 000 total global downloads, and fewer than 1000 total global monthly active users. The authors found that most downloaded apps did not have the most continually assessed characteristics. This study is relevant because it provides measures to assess the utilization of apps, such as benchmarks for total downloads and amount of monthly users. I will use these measures to extract information about the monthly and daily rates of utilization of apps in my systematic review. 2 Rupa Nallamothu PH 162 MPH Internship 2021 Assignment 1: Annotated Bibliography Lee JA, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. 2018;18(1):12. Published 2018 Feb 20. doi:10.1186/s12911-018-0591-0 Although mobile health (mHealth) applications have been used to improve self-management in patients who have chronic diseases, there is a lack of evidence of the effectiveness of these interventions in adults with chronic diseases. The purpose of this review is to determine if the use of mobile health applications in interventions improves health outcomes for adults with chronic illnesses. This study only explored randomized control trials(RCTs). After the authors searched databases, such as PubMed, CINAHL, EMBASE, the Cochrane Library, PsycINFO, and Web of Science, they extracted data from these studies into an electronic collection sheet. The authors found that the majority (10/12) of the mHealth interventions resulted in significant improvements in health outcomes. This study is relevant because it provides important information about the efficacy of mHealth interventions. Since this study found that these interventions can enhance self-management for patients with chronic conditions, this study shows that mHealth interventions can provide benefits to patients. Also, this article has a clear definition of mhealth interventions. This definition furthered my understanding of the field of mHealth. Jake-Schoffman DE, Silfee VJ, Waring ME, et al. Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps. JMIR Mhealth Uhealth. 2017;5(12):e190. Published 2017 Dec 18. doi:10.2196/mhealth.8758 Since the pace of traditional academic research methods is slow and less flexible when compared to that of commercial app development, this delay might result in large time lags in the dissemination of evidence based health apps into commercial markets where the general public has access to them. The production of an app for public use requires a large amount of resources, such as time and funding. Researchers may not have access to these resources. Therefore, an efficient and cost effective alternative might be leveraging commercial apps and gathering evidence for them. The purpose of this study is to summarize ways to evaluate the content, usability, and efficacy of commercially available health apps. The authors of this study concluded that evidence from efficacy testing, content analyses, and usability testing can increase the use of commercial apps in clinical practice. This study is relevant because it addresses methods to determine the quality of commercial apps. During this internship, I will be including and analyzing commercial apps in my systematic review. Therefore, I might use methods from this study to evaluate commercial apps. Qu C, Sas C, Daudén Roquet C, Doherty G. Functionality of Top-Rated Mobile Apps for Depression: Systematic Search and Evaluation [published correction appears in JMIR Ment Health. 2020 Feb 21;7(2):e18042]. JMIR Ment Health. 2020;7(1):e15321. Published 2020 Jan 24. doi:10.2196/15321 Recently many mobile apps that claim to help people with the challenges of living with depression have increased in app marketplaces. However, the population of interest and the functionality of these apps is unclear. The purpose of this study is to explore key features of apps with good ratings for depression in order to inform the design of other apps for depression. The authors of this review extracted information from the top-rated iPhone OS (iOS) and Android mobile apps for depression in the app marketplaces. They found that a majority of apps specify a particular evidence base for their intervention (18/29, 62%), while a smaller amount of them stated that they received clinical input into their design (12/29, 41%). This article provides useful information about how to conduct a systematic analysis to review selected apps and gather data from app marketplaces. As I begin to analyze and select apps from the app marketplaces during this internship, this study will provide guidelines for me. Watts S, Mackenzie A, Thomas C, et al. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry. 2013;13:49. Published 2013 Feb 7. doi:10.1186/1471-244X-13-49 Cognitive behavior therapy (CBT) is effective in the treatment of depression. The delivery of CBT through the internet(iCBT) has been shown to be as effective as receiving this therapy in a face to face encounter and cost effective. Since a mobile phone has the ability to record and label situations where or when the patient feels anxious, it can be used to deliver CBT. People have reported that the use of a mobile application is convenient and acceptable. The aim of this study is to establish the efficacy of The 3 Rupa Nallamothu PH 162 MPH Internship 2021 Assignment 1: Annotated Bibliography Sadness Program, which is a validated computer program, when it is delivered through a mobile application. The authors found that the mobile program resulted in statistically significant benefits in the Patient Health Questionnaire-9 test. This article is relevant because it provides information about the effectiveness of a mobile intervention and validates this form of delivery. Since I will focus on this delivery of interventions, this article was useful in furthering my understanding of the effectiveness of mHealth interventions. This study shows that mHealth interventions can be effective. References 1. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510-520. doi:10.1258/jrsm.2011.110180 2. Healthcare apps available Apple App Store 2020 | Statista. Statista. https://www.statista.com/statistics/779910/health-apps-available-ios-worldwide/. Published 2021. Accessed April 15, 2021. 4
