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Assessment of Patient Medication Adherence Using Digital Health Technologies: A Research
Abstract
Patient medication adherence is critical for successful treatment outcomes and cost-effective healthcare delivery. Despite its importance, non-adherence remains a significant challenge. Digital health technologies offer promising solutions through real-time monitoring and personalized interventions. This paper reviews the effectiveness and challenges of digital tools for medication adherence, highlighting diverse interventions such as apps and wearables. Findings indicate varying effectiveness and challenges, including user engagement and privacy concerns. Digital health tech holds promise for improving adherence and health outcomes, but further research is needed for optimal integration into clinical practice.
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Background
Hypertension is one of the most prevalent long-term diseases seen in many countries, including Palestine. Patients with poorly controlled blood pressure are more likely to develop several complications. Therefore; it is imperative to control their blood pressure by improving their adherence to the treatment regimen.
Aim
The objective of this study was to evaluate the impact of using a mobile phone app on the level of adherence to treatment regimens among hypertensive patients in the Gaza Strip.
Methods and results
This study used an experimental design with a pre and post-intervention assessment. Using the Hill–Bone compliance to high blood pressure therapy scale, 191 participants completed the study: 94 in the control group and 97 in the intervention group. The intervention group used a phone app which reminds participants to take their medication, reminding them about their follow-up appointments and sending educational information about hypertension management. After 3 months of intervention, the level of adherence to treatment was reassessed. Results showed that participants in both groups showed a significant improvement in adherence levels, with higher improvements in the intervention group in the total score as well as all three domain scores: adherence to medication, diet and keeping appointments.
Conclusion
The use of a mobile phone app resulted in improvements in adherence to hypertension treatment. Thus, this study confirms the potential effectiveness of mobile technology in improving treatment adherence in hypertension and an opportunity to reduce cardiovascular mortality and morbidity. However, wider adoption has to be accompanied by ongoing evaluation and integration in public health systems.
Poor medication adherence may increase rates of loss to follow-up, disease relapse and drug resistance for individuals with active tuberculosis (TB). While TB programmes have historically used directly observed therapy (DOT) to address adherence, concerns have been raised about the patient burden, ethical limitations, effectiveness in improving treatment outcomes and long-term feasibility of DOT for health systems. Digital adherence technologies (DATs)—which include feature phone–based and smartphone-based technologies, digital pillboxes and ingestible sensors—may facilitate more patient-centric approaches for monitoring adherence, though available data are limited. Depending on the specific technology, DATs may help to remind patients to take their medications, facilitate digital observation of pill-taking, compile dosing histories and triage patients based on their level of adherence, which can facilitate provision of individualised care by TB programmes to patients with varied levels of risk. Research is needed to understand whether DATs are acceptable to patients and healthcare providers, accurate for measuring adherence, effective in improving treatment outcomes and impactful in improving health system efficiency. In this article, we describe the landscape of DATs that are being used in research or clinical practice by TB programmes and highlight priorities for research.
Background:
Optimal management of chronic diseases, such as type 2 diabetes and hypertension, often include prescription medications. Medication adherence (MA) is one component of self-management. Optimization through digital health-eHealth and mHealth-could enhance patient awareness and/or communication between the patient and provider.
Objective:
Medication adherence is a major issue that affects 50%-60% of chronically ill adults. Digital health refers to eHealth and mHealth, collectively, and as these technologies become more accessible, remote health delivery is increasingly available as an adjunct to improve medication adherence; communicate with patients and providers; and provide education to patients, families, and communities. The objective of this integrative review was to examine the types of digital health technologies that targeted medication adherence in the adult population with diabetes or hypertension.
Methods:
An integrative review was conducted using databases within EBSCOhost, PubMed, and Scopus. Eligible studies available as of September 2016 had to be written in English, had to contain digital health interventions to improve medication adherence to prescription medications in adults 18 years or older, and had to focus on diabetes or hypertension.
Results:
Of the 337 located studies, 13 (3.9%) used a digital health intervention for medication adherence to prescribed medications for diabetes or hypertension and were assessed according to the Chronic Care Model.
Conclusions:
The 13 studies included in this review found no conclusive evidence of improved medication adherence using digital health interventions such as interactive voice response (IVR), short message service (SMS) text messaging, telemonitoring, and interactive software technology. Among the 13 studies were digital health interventions that foster medication adherence via one-way communication to the patient or two-way communication between the patient and health care provider for adjunct medication adherence strategies. More research is needed to determine which digital health interventions are most beneficial for individuals with diabetes or hypertension.
Background
Incomplete adherence to tuberculosis (TB) treatment increases the risk of delayed culture conversion with continued transmission in the community, as well as treatment failure, relapse, and development or amplification of drug resistance. We conducted a systematic review and meta-analysis of adherence interventions, including directly observed therapy (DOT), to determine which approaches lead to improved TB treatment outcomes.
Methods and findings
We systematically reviewed Medline as well as the references of published review articles for relevant studies of adherence to multidrug treatment of both drug-susceptible and drug-resistant TB through February 3, 2018. We included randomized controlled trials (RCTs) as well as prospective and retrospective cohort studies (CSs) with an internal or external control group that evaluated any adherence intervention and conducted a meta-analysis of their impact on TB treatment outcomes. Our search identified 7,729 articles, of which 129 met the inclusion criteria for quantitative analysis. Seven adherence categories were identified, including DOT offered by different providers and at various locations, reminders and tracers, incentives and enablers, patient education, digital technologies (short message services [SMSs] via mobile phones and video-observed therapy [VOT]), staff education, and combinations of these interventions. When compared with DOT alone, self-administered therapy (SAT) was associated with lower rates of treatment success (CS: risk ratio [RR] 0.81, 95% CI 0.73–0.89; RCT: RR 0.94, 95% CI 0.89–0.98), adherence (CS: RR 0.83, 95% CI 0.75–0.93), and sputum smear conversion (RCT: RR 0.92, 95% CI 0.87–0.98) as well as higher rates of development of drug resistance (CS: RR 4.19, 95% CI 2.34–7.49). When compared to DOT provided by healthcare providers, DOT provided by family members was associated with a lower rate of adherence (CS: RR 0.86, 95% CI 0.79–0.94). DOT delivery in the community versus at the clinic was associated with a higher rate of treatment success (CS: RR 1.08, 95% CI 1.01–1.15) and sputum conversion at the end of two months (CS: RR 1.05, 95% CI 1.02–1.08) as well as lower rates of treatment failure (CS: RR 0.56, 95% CI 0.33–0.95) and loss to follow-up (CS: RR 0.63, 95% CI 0.40–0.98). Medication monitors improved adherence and treatment success and VOT was comparable with DOT. SMS reminders led to a higher treatment completion rate in one RCT and were associated with higher rates of cure and sputum conversion when used in combination with medication monitors. TB treatment outcomes improved when patient education, healthcare provider education, incentives and enablers, psychological interventions, reminders and tracers, or mobile digital technologies were employed. Our findings are limited by the heterogeneity of the included studies and lack of standardized research methodology on adherence interventions.
Conclusion
TB treatment outcomes are improved with the use of adherence interventions, such as patient education and counseling, incentives and enablers, psychological interventions, reminders and tracers, and digital health technologies. Trained healthcare providers as well as community delivery provides patient-centered DOT options that both enhance adherence and improve treatment outcomes as compared to unsupervised, SAT alone.
Objective:
Forgetfulness is one of the main reasons of unintentional medication non-adherence. Adherence technologies that help people remember to take their medications on time often do not take into account the context of people's everyday lives. Existing evidence that highlights the effectiveness of remembering strategies that rely on contextual cues is largely based on research with older adults, and thus it is not clear whether it can be generalized to other populations or used to inform the design of wider adherence technologies that support medication self-management. Understanding how younger populations currently remember medications can inform the design of future adherence technologies that take advantage of existing contextual cues to support remembering.
Methods:
We conducted three surveys with a total of over a thousand participants to investigate remembering strategies used by different populations: women who take oral contraception, parents and carers who give antibiotics to their children, and older adults who take medications for chronic conditions.
Results:
Regardless of the population or the type of regimen, relying on contextual cues-routine events, locations, and meaningful objects-is a common and often effective strategy; combinations of two or more types of cues are more effective than relying on a single cue.
Conclusions:
To effectively support remembering, adherence technologies should help users recognize contextual cues they already have at their disposal and reinforce relevant cues available in their environment. We show that, given the latest developments in technology, such support is already feasible.
Context:
Medication non-adherence is a commonly observed problem in the self-administration of treatment, regardless of the disease type. Text messaging reminders, as electronic reminders, provide an opportunity to improve medication adherence. In this study, we aimed to provide evidence addressing the question of whether text message reminders were effective in improving patients' adherence to medication.
Evidence acquisition:
We carried out a systematic literature search, using the five electronic bibliographic databases: PubMed, Embase, PsycINFO, CINAHL, and the Cochrane central register of controlled trials. Studies were included on the basis of whether they examined the benefits and effects of short-message service (SMS) interventions on medication adherence.
Results:
The results of this systematic review indicated that text messaging interventions have improved patients' medication adherence rate (85%, 29.34). Included in the review, those who had problems with adherence, or those whom text messaging was most helpful had HIV, asthma, diabetes, schizophrenia and heart disease (73.5%). The period of intervention varied from 1 week to 14 months. The most common study design was randomized controlled trials (RCTs) (66%) carried out in the developed countries.
Conclusions:
This study demonstrated the potential of mobile phone text messaging for medication non-adherence problem solving.
Smartphone-based devices and applications (SBDAs) with cost effectiveness and remote sensing are the most promising and effective means of delivering mobile healthcare (mHealthcare). Several SBDAs have been commercialized for the personalized monitoring and/or management of basic physiological parameters, such as blood pressure, weight, body analysis, pulse rate, electrocardiograph, blood glucose, blood glucose saturation, sleeping and physical activity. With advances in Bluetooth technology, software, cloud computing and remote sensing, SBDAs provide real-time on-site analysis and telemedicine opportunities in remote areas. This scenario is of utmost importance for developing countries, where the number of smartphone users is about 70% of 6.8 billion cell phone subscribers worldwide with limited access to basic healthcare service. The technology platform facilitates patient-doctor communication and the patients to effectively manage and keep track of their medical conditions. Besides tremendous healthcare cost savings, SBDAs are very critical for the monitoring and effective management of emerging epidemics and food contamination outbreaks. The next decade will witness pioneering advances and increasing applications of SBDAs in this exponentially growing field of mHealthcare. This article provides a critical review of commercial SBDAs that are being widely used for personalized healthcare monitoring and management.
We evaluated the feasibility and acceptability of a 2-week-long ecological momentary intervention (EMI), delivered via personal digital assistants (PDAs), to improve treatment adherence in bipolar disorder. EMIs use mobile technology to deliver treatment as clients engage in their typical daily routines, in their usual settings. Overall, participants (N = 14) stated that EMI sessions were helpful, user-friendly, and engaging, and reported satisfaction with the timing and burden of sessions, as well as the method of delivery. All participants completed the study, and all PDAs were returned undamaged. On average, participants completed 92% of EMI sessions. Although this study was not designed to assess efficacy, depression scores decreased significantly over the study period and data suggest relatively high rates of treatment adherence; missed medication was reported 3% of the time and three participants reported missing a total of six mental health appointments. Negative feedback largely involved technical and logistical issues, many of which are easily addressable. These preliminary findings add to the growing body of literature indicating that mobile-technology-assisted interventions are feasible to implement and acceptable to patients with serious mental illnesses.
Background
Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.
Methods
In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.
Results
A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.
Conclusions
Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.
Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 being increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are currently being investigated for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and scopes for clinical adoption.
Technology has redefined the way patients and providers communicate and obtain health information. The realm of digital health encompasses a diverse set of technologies, including mobile health, health information technology, wearable devices, telehealth and telemedicine, and personalized medicine. These technologies have begun to improve care delivery without the traditional constraints of distance, location, and time. A growing body of evidence supports the use of digital health technology for improving patient education and implementation of skills and behaviors integral to lifestyle medicine. Patient education can now be delivered in standard formats (eg, articles, written messages) as well a wide array of multimedia (video, audio, interactive games, etc), which may be more appropriate for certain topics and learning styles. In addition, patient engagement in their care plays an important role in improving health outcomes. Despite digital health technology development often outpacing its research, there is sufficient evidence to support the use of many current technologies in clinical practice. Digital health tools will continue to grow in their ability to cost-effectively monitor and encourage healthy behaviors at scale, and better methods of evaluation will likely increase clinician confidence in their use.
Background:
Medication adherence is frequently suboptimal in adults with chronic diseases, resulting in negative consequences. Traditional interventions to improve adherence are complex and not widely effective. Mobile applications may be a scalable means to support medication adherence.
Objective:
To investigate the effect of mobile apps on medication adherence in adults with chronic diseases.
Methods:
MEDLINE, EMBASE, CINAHL Plus, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials evaluating the effectiveness of any mobile application (app) intervention directed at patients with chronic disease to improve medication adherence in comparison with usual care. A random-effects model was used to pool the outcome data. Risk of bias and quality of study were assessed per Cochrane guidelines.
Results:
Fourteen studies were included in this systematic review involving 1,785 participants, 940 of whom were randomized to a mobile app intervention group and 845 to the usual care group. The meta-analysis showed that the use of mobile apps was associated with a significant improvement in patient adherence to medication (Cohen's d = 0.40, 95% CI = 0.27-0.52; P < 0.001), with a low quality of GRADE evidence. There was no evidence of publication bias (Egger's test; P = 0.81) or substantial heterogeneity (I2 = 29%). In the sensitivity analysis, our findings remained robust to change in inclusion criteria based on study quality (Cohen's d = 0.43, 95% CI = 0.33-0.54; P < 0.001). The included apps incorporated 9 features, sorted from high to low based on relative weights (RW): documentation (RW = 0.254), medication reminder (RW = 0.204), data sharing (RW = 0.148), feedback message (RW = 0.104), clinical decision support (RW = 0.097), education (RW = 0.081), customization (RW = 0.049), data statistics (RW = 0.041), and appointment reminder (RW = 0.041). In the subgroup analysis, the effect was not sensitive to study characteristics or app features (0.37 ≤ P ≤ 0.95). App acceptability was reported by participants in the intervention group in 8 studies: 144 of 156 participants (91.7%) were satisfied with all aspects of the apps.
Conclusions:
Compared with conventional care, mobile apps are effective interventions to help improve medication adherence in adults with chronic diseases. Although promising, these results should be interpreted with caution given the low level of evidence and short intervention duration. Future research will not only need to identify ideal app features and the costs to providers but also need to improve the apps to make them user friendly, secure, and effective based on patient-centered theory.
Disclosures:
Funding for this study was provided by Chongqing Science and Technology Bureau (No. cstc2017shmsA130115). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have no conflicts of interest to disclose.
Background:
Poor hypertension control can increase the risk of cardiovascular, renal and kidney diseases, and increase the social and economic burden of the disease. This study aimed to explore the lifestyle and self-management skills (medication adherence, self-monitoring, self-efficacy) determinants of hypertension control in a sample of hypertensive Australians.
Research design and method:
In a cross-sectional design, a total of 233 hypertensive adults completed a survey. Hypertension control was categorised as good control if hypertensive individuals managed to reduce their blood pressure (BP) < 140/90 mmHg, otherwise it was categorised as poor control. Data were analysed using bivariate analysis and hierarchical logistic regression.
Results:
55% of the participants had poor BP control. A significantly higher frequency of poor BP self-monitoring was observed in individuals with poor control compared to their counterparts. Poor self-monitoring and a sedentary lifestyle were associated with higher odds of poor control (OR: 5.33, 95% CI: 1.78 - 15.93; p < 0.01, and OR: 4.69, 95% CI: 1.00 - 22.25; p < 0.05, respectively). No significant association was observed between other variables and hypertension control.
Conclusions:
Successful BP control for hypertensive individuals may require interventions and strategies that assist with improving BP self-monitoring skills and increasing physical activity.
Introduction:
Smart watches have the potential to support health in everyday living by: enabling self-monitoring of personal activity; obtaining feedback based on activity measures; allowing for in-situ surveys to identify patterns of behavior; and supporting bi-directional communication with health care providers and family members. However, smart watches are an emerging technology and research with these devices is at a nascent stage.
Methods:
We conducted a systematic review of smart watch studies that engaged people in their use by searching PubMed, Embase, IEEE XPlore and ACM Digital libraries. Participant demographics, device features, watch applications and methods, and technical challenges were abstracted from included studies.
Results:
Seventy-three studies were returned in the search. Seventeen studies published were included. Included studies were published from 2014-2016, with the exception of one published in 2011. Most studies employed the use of consumer-grade smart watches (14/17, 82%). Patient-related studies focused on activity monitoring, heart rate monitoring, speech therapy adherence, diabetes self-management, and detection of seizures, tremors, scratching, eating, and medication-taking behaviors. Most patient-related studies enrolled participants with few exclusion criteria to validate smart watch function (10/17, 58%). Only studies that focused on Parkinson's disease, epilepsy, and diabetes management enrolled persons living with targeted conditions. One study focused on nursing work in the ICU and one focused on CPR training for laypeople.
Conclusion:
Consumer-grade smart watches have penetrated the health research space rapidly since 2014. Smart watch technical function, acceptability, and effectiveness in supporting health must be validated in larger field studies that enroll actual participants living with the conditions these devices target.
Mobile phones are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features, enhanced preferences and diverse capabilities. Their sophisticated sensors and complex software applications make the mobile healthcare (m-health) based applications more feasible and innovative. In a number of scenarios user-friendliness, convenience and effectiveness of these systems have been acknowledged by both patients as well as healthcare providers. M-health technology employs advanced concepts and techniques from multidisciplinary fields of electrical engineering, computer science, biomedical engineering and medicine which benefit the innovations of these fields towards healthcare systems. This paper deals with two important aspects of current mobile phone based sensor applications in healthcare. Firstly, critical review of advanced applications such as; vital sign monitoring, blood glucose monitoring and in-built camera based smartphone sensor applications. Secondly, investigating challenges and critical issues related to the use of smartphones in healthcare including; reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy. It was found that the mobile based applications have been widely developed in recent years with fast growing deployment by healthcare professionals and patients. However, despite the advantages of smartphones in patient monitoring, education, and management there are some critical issues and challenges related to security and privacy of data, acceptability, reliability and cost that need to be addressed.
Asthma is one of the most common long-term conditions worldwide, which places considerable pressure on patients, communities and health systems. The major international clinical guidelines now recommend the inclusion of self management programmes in the routine management of patients with asthma. These programmes have been associated with improved outcomes in patients with asthma. However, the implementation of self management programmes in clinical practice, and their uptake by patients, is still poor. Recent developments in mobile technology, such as smartphone and tablet computer apps, could help develop a platform for the delivery of self management interventions that are highly customisable, low-cost and easily accessible.
To assess the effectiveness, cost-effectiveness and feasibility of using smartphone and tablet apps to facilitate the self management of individuals with asthma.
We searched the Cochrane Airways Group Register (CAGR), the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, CINAHL, Global Health Library, Compendex/Inspec/Referex, IEEEXplore, ACM Digital Library, CiteSeer(x) and CAB abstracts via Web of Knowledge. We also searched registers of current and ongoing trials and the grey literature. We checked the reference lists of all primary studies and review articles for additional references. We searched for studies published from 2000 onwards. The latest search was run in June 2013.
We included parallel randomised controlled trials (RCTs) that compared self management interventions for patients with clinician-diagnosed asthma delivered via smartphone apps to self management interventions delivered via traditional methods (e.g. paper-based asthma diaries).
We used standard methods expected by the Cochrane Collaboration. Our primary outcomes were symptom scores; frequency of healthcare visits due to asthma exacerbations or complications and health-related quality of life.
We included two RCTs with a total of 408 participants. We found no cluster RCTs, controlled before and after studies or interrupted time series studies that met the inclusion criteria for this systematic review. Both RCTs evaluated the effect of a mobile phone-based asthma self management intervention on asthma control by comparing it to traditional, paper-based asthma self management. One study allowed participants to keep daily entries of their asthma symptoms, asthma medication usage, peak flow readings and peak flow variability on their mobile phone, from which their level of asthma control was calculated remotely and displayed together with the corresponding asthma self management recommendations. In the other study, participants recorded the same readings twice daily, and they received immediate self management feedback in the form of a three-colour traffic light display on their phones. Participants falling into the amber zone of their action plan twice, or into the red zone once, received a phone call from an asthma nurse who enquired about the reasons for their uncontrolled asthma.We did not conduct a meta-analysis of the data extracted due to the considerable degree of heterogeneity between these studies. Instead we adopted a narrative synthesis approach. Overall, the results were inconclusive and we judged the evidence to have a GRADE rating of low quality because further evidence is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. In addition, there was not enough information in one of the included studies to assess the risk of bias for the majority of the domains. Although the other included study was methodologically rigorous, it was not possible to blind participants or personnel in the study. Moreover, there are concerns in both studies in relation to attrition bias and other sources of bias.One study showed that the use of a smartphone app for the delivery of an asthma self management programme had no statistically significant effect on asthma symptom scores (mean difference (MD) 0.01, 95% confidence interval (CI) -0.23 to 0.25), asthma-related quality of life (MD of mean scores 0.02, 95% CI -0.35 to 0.39), unscheduled visits to the emergency department (OR 7.20, 95% CI 0.37 to 140.76) or frequency of hospital admissions (odds ratio (OR) 3.07, 95% CI 0.32 to 29.83). The other included study found that the use of a smartphone app resulted in higher asthma-related quality of life scores at six-month follow-up (MD 5.50, 95% CI 1.48 to 9.52 for the physical component score of the SF-12 questionnaire; MD 6.00, 95% CI 2.51 to 9.49 for the mental component score of the SF-12 questionnaire), improved lung function (PEFR) at four (MD 27.80, 95% CI 4.51 to 51.09), five (MD 31.40, 95% CI 8.51 to 54.29) and six months (MD 39.20, 95% CI 16.58 to 61.82), and reduced visits to the emergency department due to asthma-related complications (OR 0.20, 95% CI 0.04 to 0.99). Both studies failed to find any statistical differences in terms of adherence to the intervention and occurrence of other asthma-related complications.
The current evidence base is not sufficient to advise clinical practitioners, policy-makers and the general public with regards to the use of smartphone and tablet computer apps for the delivery of asthma self management programmes. In order to understand the efficacy of apps as standalone interventions, future research should attempt to minimise the differential clinical management of patients between control and intervention groups. Those studies evaluating apps as part of complex, multicomponent interventions, should attempt to tease out the relative contribution of each intervention component. Consideration of the theoretical constructs used to inform the development of the intervention would help to achieve this goal. Finally, researchers should also take into account: the role of ancillary components in moderating the observed effects, the seasonal nature of asthma and long-term adherence to self management practices.
Future of hypertension
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