Can Mobile Phone Apps Influence Peoples Health Behavior Change? An Evidence Review
J Med Internet Res. 2016 Nov; 18(eleven): e287.
Can Mobile Telephone Apps Influence People's Health Behavior Modify? An Evidence Review
Monitoring Editor: Gunther Eysenbach
Jing Zhao, MIPH, 
1 Becky Freeman, PhD,1 and Mu Li, PhD1
1Schoolhouse of Public Health, Sydney Medical Schoolhouse, The University of Sydney, Sydney, Commonwealth of australia
Jing Zhao, School of Public Health, Sydney Medical School, The Academy of Sydney, Edward Ford Building, A27, Sydney, 2006, Australia, Telephone: 61 2 9351 5996, Fax: 61 2 9351 5049, Electronic mail: ua.ude.yendys.inu@0105ahzj.
Jing Zhao
1School of Public Health, Sydney Medical Schoolhouse, The University of Sydney, Sydney, Australia
Becky Freeman
aneSchool of Public Wellness, Sydney Medical Schoolhouse, The University of Sydney, Sydney, Australia
Mu Li
1School of Public Health, Sydney Medical School, The Academy of Sydney, Sydney, Commonwealth of australia
Find articles by Mu Li
Received 2016 February 24; Revisions requested 2016 May 24; Revised 2016 Jul 7; Accepted 2016 Oct 12.
Abstract
Background
Globally, mobile phones have achieved wide reach at an unprecedented rate, and mobile telephone apps have get increasingly prevalent among users. The number of health-related apps that were published on the two leading platforms (iOS and Android) reached more than 100,000 in 2014. However, there is a lack of synthesized evidence regarding the effectiveness of mobile phone apps in irresolute people'due south health-related behaviors.
Objective
The aim was to examine the effectiveness of mobile telephone apps in achieving wellness-related behavior modify in a broader range of interventions and the quality of the reported studies.
Methods
We conducted a comprehensive bibliographic search of manufactures on health behavior change using mobile phone apps in peer-reviewed journals published between January 1, 2010 and June 1, 2015. Databases searched included Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Education Resource Information Centre (ERIC), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Articles published in the Periodical of Medical Cyberspace Enquiry during that aforementioned period were mitt-searched on the periodical's website. Behavior change mechanisms were coded and analyzed. The quality of each included study was assessed past the Cochrane Chance of Bias Assessment Tool.
Results
A full of 23 articles met the inclusion criteria, arranged nether 11 themes according to their target behaviors. All studies were conducted in loftier-income countries. Of these, 17 studies reported statistically significant effects in the management of targeted behavior change; 19 studies included in this analysis had a 65% or greater retention rate in the intervention grouping (range sixty%-100%); vi studies reported using behavior change theories with the theory of planned behavior beingness the almost commonly used (in 3 studies). Self-monitoring was the most mutual beliefs modify technique practical (in 12 studies). The studies suggest that some features improve the effectiveness of apps, such as less time consumption, user-friendly design, real-time feedback, individualized elements, detailed information, and health professional involvement. All studies were assessed as having some take a chance of bias.
Conclusions
Our results provide a snapshot of the current evidence of effectiveness for a range of health-related apps. Large sample, high-quality, adequately powered, randomized controlled trials are required. In light of the bias evident in the included studies, amend reporting of health-related app interventions is also required. The widespread adoption of mobile phones highlights a significant opportunity to impact health behaviors globally, peculiarly in depression- and centre-income countries.
Keywords: review, mobile telephone apps, apps, beliefs change, intervention, mHealth
Introduction
Globally, mobile phone apps take become increasingly prevalent among users. By July 2015, Google Play, the largest app shop, had 1.6 million apps accessible for users. remains the second-largest app store, with 1.5 meg apps bachelor for download [1]. There has been a surge of wellness-related mobile telephone apps in recent years. The number of wellness-related apps released on the two leading platforms, iPhone operating organisation (iOS) and Android, had reached more than 100,000 in 2014 [2]. Traditionally, wellness care has been delivered through face-to-face interaction with clinicians. With this new engineering science at patients' and health care professionals' (HCPs) fingertips, people are irresolute the mode they interact. Apps used in health care settings have a number of functions, such every bit information and fourth dimension management, communications and consulting, patient management and monitoring, health record maintenance and access, reference and information gathering, and clinical determination making [iii]. Although several bug challenge the integration of apps into health care settings (eg, app design is primarily driven by commercial developers), their use has been widely expanded into clinical practice [4,5].
In 2014, the Earth Health Organization reported that noncommunicable diseases (NCDs) are the leading cause of death globally, responsible for 38 million (68%) of the world'south 56 meg deaths in 2012. More than than 40% of these deaths (16 meg) were premature and avoidable [6]. Simple interventions that subtract NCD risk factors could reduce premature deaths by one-half to two-thirds [vii]. Many of these risk factors, such as tobacco use, unhealthy diet, physical inactivity, stress, depression, harmful use of alcohol, overweight, and obesity, can be modified by behavioral change interventions [6]. Apps appear to be an ideal platform to deliver both uncomplicated and constructive interventions.
In addition to NCDs, health-related apps have the added potential to help a wide range of target audiences in a whole range of health issues [8]. For case, they can ameliorate contraceptive cognition of women [ix] or assist users to forestall nonspecific depression back pain [ten]. At that place are also apps designed as intervention tools to encourage healthy habits, such equally a sun protection app that provides real-time sun condom communication [xi]. Due to the possible positive implications for public health, there is an increasing interest from commercial companies, government agencies, public health organizations, and the general public to apply apps as a tool for health behavioral modify [12-fourteen].
Several reviews take examined the evidence of effectiveness of wellness-related apps when targeting one specific behavior, such as physical activity, or a specific condition, such as chronic pain [15-19]. Another report reviewed behavioral functionality of apps in health interventions without assessing the quality of the included studies [20]. The aims of this review are to examine the effectiveness of mobile telephone apps in achieving wellness-related beliefs modify across a broader range of health bug and to examine the quality of the reported studies.
Methods
Search Strategy
We searched titles, abstracts, and keywords of peer-reviewed manufactures published from Jan 1, 2010 to June 1, 2015. A comprehensive bibliographic search was conducted through Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Instruction Resource Information Center (ERIC), and Cumulative Index to Nursing and Centrolineal Health Literature (CINAHL) by using key search terms, such every bit mobile application, mobile app, smartphone, and information applied science, and using the qualifier "behavior modify" (see Multimedia Appendix ane for the full search strategy). In improver, the Journal of Medical Internet Research (JMIR) was hand-searched for the same period on the periodical'south website.
Study Option
We included manufactures if they were published in English language, in a peer-reviewed journal, after 2010, targeted at an adult population, and presented results from the analysis of principal or secondary outcomes. We only included randomized controlled trials (RCTs), example-control studies, and cohort studies that were designed for app-based interventions to improve any health-related behaviors. The exclusion criteria were quasi-experimental studies or qualitative studies; text bulletin, Spider web, e-mail, Twitter, social network services, or personal digital assistant-based health interventions; absenteeism of behavior change indicators or outcomes; an app was not the primary intervention tool; and articles focused mostly on app design and development. Conference abstracts, protocol papers, reviews, editorials, and commentary were too excluded.
The initial search returned 3353 articles: 1405 in Medline, 356 in Embase, 791 in CINAHL, 344 in PsycINFO, 296 in ERIC, 71 in PreMedline, 37 in Health Technology Assessment, and 53 in JMIR. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Figure 1), nosotros eliminated duplicates and screened the titles and abstracts, which narrowed the results to 868 articles. A total-text review reduced the sample to 88 manufactures; after applying the exclusion criteria, we further narrowed that to 55 articles, of which 32 were quasi-experiment studies or an app was not the primary intervention tool and they were later excluded. This left a final sample of 23 articles to exist included for the review. Studies excluded during the total-text review phase and their reasons for exclusion are listed in Multimedia Appendix ii. Data extraction from identified articles was completed by authors JZ and ML with disagreements resolved through word with author BF.
PRISMA 2009 flow diagram.
Data Drove and Analysis
The post-obit information was extracted and analyzed from each of the 23 articles: authors, research location and year of publication, study type, sample size, intervention duration, intervention tools with behavior change mechanisms, target behavior change, control group variables, measurement of behavior change indicators, and reported outcomes and significance levels. The search was kept wide with no specific target health behaviors in the search strategy. Based on the health behaviors identified, the articles were organized into eleven themes: mental health improvement or alcohol habit, physical activity, weight control and diet command, medication management, lifestyle improvement, diabetes direction, sun protection, hypertension management, cardiac rehabilitation, smoking cessation, family unit planning, and pain management. Apps were accounted constructive if they reported quantitative measures of successful behavior modify [21]. The characteristics of the studies meeting inclusion criteria are summarized in Multimedia Appendix three. For trial sample size, large samples usually meant at least 100 participants in each randomized group, moderate sample size was between 60 and 100 participants in each group, and small-scale sample size was less than threescore participants in each group [22,23]. According to the Cochrane Handbook for Systematic Reviews of Interventions [22], studies with retention over 80% are classified as having low attrition and studies with retention between 60% and 79% are classified as having moderate attrition. Influencing factors of completing app trials were evaluated to understand determinants of retention rates; features of effective apps were likewise examined.
Beliefs change mechanisms, including the apply of theory, techniques, and therapies, were extracted from each report. Beliefs change theories applied by the included studies were noted [24]. Behavior alter techniques used in the interventions were coded according to Abraham and Michie's taxonomy of beliefs change techniques (BCTs) [25]. Mental health or booze addiction apps were most probable to be based on a specific behavior therapy (see Multimedia Appendix 3).
Report Quality Cess
All included studies were appraised using the Cochrane Risk of Bias Assessment Tool [22]. This requires assessing each study against a set up of seven criteria: random sequence generation, allocation concealment, blinding of participants, blinding of result assessment, incomplete result data, selective reporting, and other bias. Depression risk of bias for abyss of follow-upward was defined past a cut-off of 80% complete follow-up [22] (see Multimedia Appendix iv).
Results
Characteristics of Included Studies
The 23 articles analyzed in this review were organized under 11 themes according to target behaviors. Of these, seven targeted mental wellness or booze addiction; 4 targeted increasing physical activity, weight control, and diet control; three aimed to meliorate medication management; 2 involved an intervention for lifestyle comeback; and 1 written report was identified in each of the following themes: diabetes direction, sun protection, hypertension management, cardiac rehabilitation, smoking cessation, family unit planning, and pain management. All studies were conducted in loftier-income countries, 10 in the United states, 3 in Australia, 2 in the United Kingdom and Sweden, respectively, and 1 each in South korea, Italy, New Zealand, Kingdom of spain, Switzerland, and the Netherlands. As defined by the inclusion criteria, all included studies used RCT design, except one example-control study [26]. There were 6 large sample studies [ten,11,27-30]. A 3-arm RCT study had the largest sample size (N=1932) [28], whereas 14 studies had a small sample size (ie, <threescore participants per group) [9,26,31-42]. Others had moderate sample sizes. The intervention duration ranged between 3 weeks [36] and viii months [27]. Of all the apps, only 6 studies evaluated commercially available apps [10,11,29,30,40,41] and i written report tested a publicly downloadable app adult by the Swedish authorities [28]; other apps were not publicly bachelor. Only one app, from Switzerland, was designed for people older than age 65 years [40]. All apps were designed in the English language language, with the exception of ane Castilian app [38]. In total,19 included in this analysis had more than 65% retention in the intervention group with a high of 100% [31,35,36] and a depression of sixty% [32]. Iii studies did not report retention rate [26,34,37] (see Multimedia Appendix three).
Mechanisms of Behavior Alter
Across the 23 studies, iii mechanisms were employed to promote beliefs alter: behavior modify theories, BCTs, and specific behavioral therapies. In full, 6 studies reported using behavior modify theories to underpin their app interventions [9,10,27-29,36]. The most unremarkably used theory was the theory of planned behavior [ix,ten,28], followed by social cognitive theory [29,36]. The pinnacle 3 most commonly used BCTs were cocky-monitoring (12 interventions) [10,27-29,38- 45], feedback provided on performance (eight interventions) [xi,28,29,36,37,41- 43], and tailoring messages (8 interventions) [x,26,30,36,38,41- 43]. Apps related to mental health or alcohol habit were usually based on a specific behavioral therapy, such as motivational enhancement therapy [35], behavioral activation therapy [33], and cognitive behavior therapy [34] (see Multimedia Appendix 3).
Quality of Selected Studies
The quality of reviewed studies is summarized in Multimedia Appendix 4. All 23 studies had some kind of take a chance of bias co-ordinate to the Cochrane Risk of Bias Assessment Tool. Only ix manufactures fairly reported random sequence generation. A figurer random number generator was used in 2 studies [ix,27]. The process of minimization, used to make pocket-sized groups like, was described in 3 studies [30,43,45]. A total of 11 studies explicitly stated that allocation was concealed (eg, using sequentially numbered opaque, sealed envelopes, central allotment) [nine,27-29,31-33,41-44]. Participants were blinded in one report, but the assessors had full knowledge of the assignments [36]. But 1 RCT written report of a smoking concession app was double-blinded to the 196 participants and assessors [45]. Assessors were blinded in another iv studies [9,28,35,38]. Due to the nature of using apps, subject blinding was often not possible beyond the interventions. The remaining studies were either non blinded or data was not explicitly provided in the reporting. We used a cutting-off of 80% completion for low risk of bias for abyss of follow-upward [22]. A total of 10 studies were at low chance of attrition bias [nine-11,31,35,36,38,42,43,45]. Only three studies did not outline the statistical analyses or dropout rate [26,34,37]. With regard to bias of selective outcome reporting, insufficient information was present in ane study [36] and a high risk of bias was present in v studies [thirty,37,38,40,44]. The quality cess of the reviewed studies is presented in Multimedia Appendix 4. The Cochrane risk of bias summary is reported in Figure 2.
Cochrane adventure of bias summary for wellness beliefs change trials.
Effectiveness of Apps and Features
Mental Health or Alcohol Habit
A full of 7 studies reported on app interventions focused on mental wellness or alcohol habit outcomes. Of these, 2 studies described 2 different apps [32,33] that targeted at developing coping skills for different degrees of depression. Watts et al [32] tested the effectiveness of an app delivering a cognitive behavior therapy-based program. At that place was a statistically significantly improvement on a depression test scale in both the app and figurer intervention groups at posttest, and no difference between the 2 groups over fourth dimension in follow-upward. In the other RCT study of a behavioral activation app addressing mild-to-moderate and major low conducted past Ly et al [33], it was found that the treatment worked significantly better for participants with a more astringent form of low. Ainsworth et al [31] reported that for patients with serious mental illness there was no meaning departure in quantitative feedback questionnaire scores, which was developed to assess the acceptability and feasibility between app and text message intervention groups, but at that place was significant comeback in the app group in two other measurements (less fourth dimension to complete cess and greater number of information points completed). In a study of a stress management app intervention delivered past oncology nurses, Villani et al [34] found there was a significant decrease in anxiety and significant improvement in affective change in terms of feet trait reduction and coping skill acquisition in the intervention group.
In total, 3 RCT studies aimed to lower alcohol consumption amongst adults. Gonzalez et al [35] demonstrated that an app based on motivational enhancement theory resulted in a significant increase in the percent of days abstinent among participants with booze employ disorder over the vi-week study catamenia when compared to controls. In the Gustafson et al [27] study, significantly fewer risky drinking days were accomplished in self-determination theory-based app intervention group than the patients in control grouping. Gajecki et al [28] showed that an app based on theory of planned behavior did not seem to affect alcohol consumption among university students.
Increasing Physical Activity, Weight Control, and Diet Control
In total, 4 studies implemented and described app interventions intended to improve physical activity, weight control, and diet command. Rabbi et al [36] found that participants who used an app based on gimmicky behavioral science theories walked significantly more than the control group after iii weeks; further, the users rated the app'due south personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals. Laing et al [29] demonstrated that ane of the well-nigh popular commercially available weight loss apps, MyFitnessPal, which is based on social cognitive theory, was not effective in helping overweight patients lose weight in a clinical setting over a 6-month menses. 1 case-control report [26] identified significantly decreased weight, fat mass, and body mass index (BMI) in the intervention group compared to controls. Carter et al [43] compared an app intervention grouping (created on an testify-based behavioral approach) to two other control groups, i using a paper-based food diary and the other using an online food diary. Over the half-dozen-calendar month study menstruum, adherence to the trial was statistically significantly higher in the mobile phone app group compared with the online website grouping and the newspaper diary grouping. Farther, the mean weight change, BMI change, and body fat change were highest in the app intervention grouping.
Medication Direction
In total, 3 RCT studies evaluated the effectiveness of apps to improve medication adherence. In an antiretroviral therapy study, Perera et al [37] compared 2 randomized groups using dissimilar versions of the same app (an augmented version and standard version) in a 3-month study. There was a significantly higher level of self-reported adherence and decreased viral load amid the augmented app group compared to the standard version grouping. An RCT evaluating an app designed to aid elderly Castilian patients reduce nonadherence and medication errors when taking multiple medications reported that app users had significantly better adherence, fewer missed doses, and a pregnant reduction in medication errors in patients with initial college rates of errors [38]. In a study of adherence to antidepressant medications amid college students, Hammond et al [39] establish that in that location was a potent trend suggesting that the use of a medication reminder app was beneficial in increasing antidepressant medication adherence.
Lifestyle Comeback
Only 2 studies measured lifestyle changes in users of 2 commercially available apps. Ane trial [30] measured changes in health-related behaviors, sleep problems, and fatigue in airline pilots. It institute that the intervention arm had a significant improvement in reducing the level of fatigue, improving slumber quality, increasing strenuous physical activeness, and changing snacking behavior measures. The other lifestyle study was a iii-arm trial to promote walking [40] that included 2 app groups, 1 using social motivation strategies and the other employing an individual motivation strategy, and a brochure-based command group. The ii intervention groups both showed pregnant improvements in full walking fourth dimension.
Other Themes
Every bit shown in Multimedia Appendix iii, only a small number of studies were found under the themes of diabetes direction, dominicus protection, hypertension management, cardiac rehabilitation smoking cessation, family planning, and pain direction. Kirwan et al [41] found a freely available app supplemented with text message feedback could significantly ameliorate glycemic control between baseline and 9-month follow-upwards for patients with type 1 diabetes compared to the control group. I of the first evaluation studies of a commercially bachelor sun protection app [11] showed that merely 1/7 lord's day protection behaviors, wearing wide-brimmed hats, was practiced more past intervention than control participants. In a study comparing an app designed for hypertension direction with traditional care [42], the intervention group participants accomplished a significant decrease in systolic blood pressure at 12 weeks compared to control participants. Varnfield et al [44] institute that the intervention group had significantly higher uptake, adherence, and completion of a cardiac rehabilitation program than the control group. A report of an innovative app addressing heavy smoking showed promising quit rates compared to an app that followed standard United states Clinical Practice Guidelines [45]. Gilliam et al [9] noted that young women had a significantly college knowledge of family planning and increased interest in longer-term contraception methods after using an app-based on the theory of planned behavior. In a three-arm RCT for back pain management [10], users of the app showed significant improvement compared to the control grouping in every comparing of the disquisitional physical, behavioral, and worksite upshot measures at iv-month follow-upward.
Suggested Features of Effective App Interventions
Identifying features that heighten intervention effectiveness can inform the evolution of app-based intervention to produce greater health behavior modify and support evaluation of complex interventions. The reviewed studies revealed some of import features that could be useful in informing future app intervention design. For instance, the MyFitnessPal app incorporates cocky-monitoring, goal setting, feedback, and social networking features, all deemed critical functions in physical activeness and dietary interventions, and it has received the highest possible rating (5/5 stars) from app store reviewers [29]. However, participants in the MyFitnessPal app trial simply had minimal alter in trunk weight with no departure betwixt groups. This may be because participants plant calorie counting took also much time [29]. This finding is consequent with a previous systematic review suggesting that the amount of participant fourth dimension required is an important consideration for physical activity and health eating interventions [46].
Another example is that despite receiving no grooming on how to use the app, the usage of the diabetes management app was high among participants, and there was significantly improved glycemic control in the intervention group between baseline and follow-up at 9 months compared to the control group. This may be attributed to a number of of import features of this study, such as the user-friendly blueprint, usefulness of the information, usability of the app, and additional weekly personalized text-message feedback from a health care professional [41]. 1 important feature of the trial improving airline pilots' health-related behavior and sleep was the tailored communication, supplemented by additional background information bachelor on the website [30].
Discussion
In total, 17 studies reviewed reported statistically meaning effects in the targeted behavior alter, and only one app seemed to have had a negative effect among men with an alcohol use disorder [28]. In i study, behavior alter to increase meditation adherence did not achieve statistical significance [39]. In total, 10 studies used active comparators that were shown to be besides effective; although the intervention groups did not outperform their comparator, the effectiveness of these apps should be considered. For example, in a study to ameliorate patients' coping skills with depression, mobile phone apps and computer groups were both associated with statistically significant benefits at posttest assessment [32]. Interventions including an agile comparator could ensure that all patients who agree to participate in the trial will non exist knowingly disadvantaged [47]. Farther, this could provide some insight to the app developers for the preferred mode of delivery between apps and existing alternatives, like Web-based or text bulletin-based interventions.
In total, 14 studies had quite small sample sizes, and their findings must be interpreted with caution. Additionally, the long-term sustainability of effects is largely unknown. Trials of larger sample size and longer intervention duration or follow-upwards fourth dimension are warranted to assess effectiveness of mobile phone app interventions. The quality of the included studies in terms of loftier run a risk of bias in selection, performance, detection, or attrition, and the quality of reporting of the interventions in some of the articles besides calls for more than rigorous study design and reporting.
With respect to the mechanisms of behavior alter, it is important to use theory to inform intervention design as well as specifying BCTs [48,49]. It is apparent that interventions based on behavior change theory are more constructive than those lacking a theoretical basis [48-50]. In our review, only 6 studies explicitly reported using behavior change theories to underpin their app interventions [9,ten,27-29,36]. In total, 21 studies explicitly reported BCTs were incorporated; the other 2 studies [33,35] did not mention any BCT used in the intervention. However, it seemed that the number of BCTs used did not predict effectiveness. For instance, the smoking cessation app report reported that applied 5 BCTs—self-monitoring, goal setting, self-tracking, social support, and existence motivated—did not significantly improve outcomes in smoking cessation compared to the control group [45], whereas the hurting management app with iii BCTs showed significant improvement compared to the control group in every comparing [10]. In our review, the most unremarkably adopted BCT (in 12 studies) was self-monitoring, but results were mixed in terms of how constructive this technique was in changing behavior. This finding may exist a consequence of different BCTs targeting different aspects of the behavior alter process.
Retention charge per unit is defined equally the proportion of participants who remained in the written report to completion. Despite the potential convenience and benefits to app users, only ten studies in our review achieved a high retentiveness rate (>80%) in intervention group [9-xi,31,35,36,38,42,43,45]. The My Repast Mate app [43] is a weight loss intervention with a high retentivity charge per unit; twoscore of 43 (93%) participants returned for follow-up at 6 months. Compared with other similar apps, the key features of the My Meal Mate app are expert-designed, tailored content and weekly supportive text letters. Similarly, the FitBack app had a loftier retention rate of 92% (183/199) and too tailored content to users' preferences and interests; participants achieved greater improvement in all concrete, behavioral, and worksite issue measures than the control group [10]. Varnfield et al [44] had a 77% (46/sixty) completion rate in the habitation care cardiac rehabilitation app intervention grouping, which was approximately thirty% more the control group. The involvement of experts who provided weekly scheduled phone consultations with informed, personalized feedback on progress co-ordinate to participants' goals likely contributed to this relatively higher level of participant retention. In a poststudy survey, users rated MyBehavior's personalized suggestions more than positively than the nonpersonalized and generic suggestions [36]. Personalization and adaptation in real time appear to exist cardinal elements in engaging a diverse grouping of participants [51]. This is reinforced past Tang et al [52], who found that immature adults highly valued the personalized features of a weight loss app. These studies support that tailored information, real-time feedback, and expert consultation are the app functions that might be most acceptable and useful to participants. In turn, it is probable that these features could result in maintaining higher retention rates and enhancing intervention effectiveness. Further, our findings likewise bespeak that apps with a elementary interface and that make better use of app design and technology may reduce the time required for users to participate in the intervention and meliorate retentivity. Identifying features that may enhance intervention effectiveness could inform the evolution of health behavior alter apps and back up the evaluation of complex interventions.
Implications for Future Research and Practice
Mobile phone apps are seen as a potential low-price manner to deliver health interventions to both full general and at-adventure populations. Many such apps exist; even so, rigorous research to test their effectiveness and acceptability is lacking. In that location were 7 publicly bachelor apps that were used in the reviewed studies [10,11,28-xxx,40,41]. Despite their apparent popularity, public and commercial apps take not been comprehensively evaluated to date; they are currently being used without a thorough understanding of their associated risks and benefits [53]. There is a gap betwixt app concept, commitment, and translation into health behavior change.
The Cochrane Risk of Bias Assessment is a adept tool to assess the quality of intervention trials. However, in our findings, the "blinding of participants and personnel" was poor; but one report [45] was double-blinded due to the unique nature of app interventions. The quality of mHealth testify reporting could exist improved through the use of recently published guidelines to aid better agreement and synthesizing findings. The Consolidated Standards for Reporting Trials (Consort) provides a 22-particular checklist for reporting Web-based and mHealth RCTs [54]. The mHealth Evidence Reporting and Assessment (mERA) checklist could also aid quality improvement of mHealth intervention reporting [55]. Additionally, the Transparent Reporting of Evaluations with Nonrandomized Designs (Tendency) argument could help to amend the reporting quality of nonrandomized evaluations of public health interventions [56]. In this review, only 4 studies described "blinding of issue assessment" [nine,28,35,38]. It might be possible to blind outcome assessors, those doing data analysis, or those administering co-interventions, which is i of the 22 essential items recommended in the CONSORT checklist [54]. It is important for researchers to adopt these guidelines vigilantly for better reporting and advice of inquiry results.
I of the primary benefits of apps is their potential for incredibly loftier reach. With mobile phone employ reaching near saturation among some populations, particularly young adults, and the high rates of consumer acceptability, app effectiveness inquiry must also consider full app achieve. This aspect of health beliefs change apps has not been assessed, with most studies being exceptionally small in scale. Apps that offer fifty-fifty a small wellness benefit could still be a valuable public health intervention if the population-level reach is high enough. Merely, encouragingly, we identified some registered large-calibration clinical trial protocols of app-based interventions, suggesting that the electric current limited scientific prove may be eased in coming years [57-60].
All identified studies were conducted in high-income countries, which could exist partly due to our search criteria limiting publications in English simply. However, information technology is also possible that a pregnant need for app research on health behavior change in lower- and center-income countries is being neglected. The brunt of NCDs, such as heart illness, diabetes, cancer, and mental disorders, is loftier in depression- and middle-income countries and is predicted to grow [4]. Mobile phones accept slap-up potential to reach populations that previously had restricted access to interventions or health care information [61]. Apps have too created new opportunities and possibilities to accomplish populations who were largely unreachable via traditional health care channels [62]. mHealth interventions have a positive bear on on some chronic diseases in developing countries [63] and text messaging has been recognized as a successful tool to improve beliefs modify outcomes [13,15]. In comparison with text messaging just, mobile phone apps offering more than active appointment in wellness intendance and improved convenience at substantially lower cost. However, the electric current evidence base of operations for the utilise of app-based interventions in developing countries remains pocket-sized [64]. The widespread adoption of mobile phones highlights a pregnant opportunity to impact health behaviors globally, particularly in low- and centre-income countries.
Limitations
Limitations of this review are worth noting. The search terms are restricted to health behavior change, and we focused generally on medicine- and health scientific discipline-related databases, which may have excluded publications in other areas. Although iPhone and Android app stores debuted in June 2007 [65], they have experienced exponential growth in popularity since 2010; some relevant articles published earlier January 2010 could take been missed. The included studies were all conducted in high-income countries where the health care systems are different from many depression- and heart-income countries, which limits the ability to draw generalizable conclusions [66]. The inclusion of studies targeted at the adult population could too confine interpretations about whether app-based interventions tin can influence behavior alter amidst younger users.
Conclusions
To our knowledge, no previous study has completed a comprehensive thematic literature review of mobile telephone apps for wellness behavior change. Although a majority of the studies reviewed reported statistically significant effects in targeted behavior change, adequately powered and relatively longer duration RCTs are still required to determine the effectiveness of app-based interventions. Further inquiry should focus on conducting evaluation research in low- and heart-income countries. Moreover, these results highlight the need for better reporting of wellness-related app interventions. Collaborations between researchers, HCPs, app developers, and policy makers could enhance the process of delivering and testing bear witness-based apps to improve health outcomes.
Acknowledgments
We thank Angela Webber for providing critical comments.
Abbreviations
| BCT | behavior change technique |
| BMI | body mass Alphabetize |
| Espoused | Consolidated Standards for Reporting Trials |
| HCP | wellness care professional |
| iOS | iPhone operating organisation |
| JMIR | Journal of Medical Net Research |
| PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| RCT | randomized command trial |
| NCD | noncommunicable illness |
| Tendency | Transparent Reporting of Evaluations with Nonrandomized Designs |
Multimedia Appendix 1
Search strategy.
Multimedia Appendix 2
Studies excluded during total text review.
Multimedia Appendix 3
Characteristics of selected studies.
Multimedia Appendix 4
Study Quality Assessment.
Footnotes
Contributed by
Authors' Contributions: JZ, ML, and BF contributed to the pattern of the review protocol. Authors ML and JZ completed data extraction of relevant articles with disagreements resolved through give-and-take with author BF. JZ drafted the paper; ML and BF reviewed the manuscript and contributed to subsequent drafts. All authors read and approved the final review.
Conflicts of Interest: None declared.
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Articles from Journal of Medical Cyberspace Research are provided here courtesy of Gunther Eysenbach
steinkewerelf1974.blogspot.com
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295827/
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