Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review

Background Adherence to chronic disease management is critical to achieving improved health outcomes, quality of life, and cost-effective health care. As the burden of chronic diseases continues to grow globally, so does the impact of non-adherence. Mobile technologies are increasingly being used in health care and public health practice (mHealth) for patient communication, monitoring, and education, and to facilitate adherence to chronic diseases management. Objective We conducted a systematic review of the literature to evaluate the effectiveness of mHealth in supporting the adherence of patients to chronic diseases management (“mAdherence”), and the usability, feasibility, and acceptability of mAdherence tools and platforms in chronic disease management among patients and health care providers. Methods We searched PubMed, Embase, and EBSCO databases for studies that assessed the role of mAdherence in chronic disease management of diabetes mellitus, cardiovascular disease, and chronic lung diseases from 1980 through May 2014. Outcomes of interest included effect of mHealth on patient adherence to chronic diseases management, disease-specific clinical outcomes after intervention, and the usability, feasibility, and acceptability of mAdherence tools and platforms in chronic disease management among target end-users. Results In all, 107 articles met all inclusion criteria. Short message service was the most commonly used mAdherence tool in 40.2% (43/107) of studies. Usability, feasibility, and acceptability or patient preferences for mAdherence interventions were assessed in 57.9% (62/107) of studies and found to be generally high. A total of 27 studies employed randomized controlled trial (RCT) methods to assess impact on adherence behaviors, and significant improvements were observed in 15 of those studies (56%). Of the 41 RCTs that measured effects on disease-specific clinical outcomes, significant improvements between groups were reported in 16 studies (39%). Conclusions There is potential for mHealth tools to better facilitate adherence to chronic disease management, but the evidence supporting its current effectiveness is mixed. Further research should focus on understanding and improving how mHealth tools can overcome specific barriers to adherence.


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The diabetes social support interview (alpha-reliability 0.72-0.97). Patients were asked to continue with their usual care, which included a visit to their primary diabetes HCP every 3 months.
Patients were asked to continue with usual care and use Glucose Buddy.
• Quality of life.

No significant difference.
No significant change over time was found in either group in relation to selfefficacy, self-care activities, and quality of life. • Participation rates.

mHealth tool used
• Number of transmissions between groups.
• Child and parent self-reporting of diabetes knowledge and satisfaction. Participants received counseling on nature of disease, risk factors, importance of BG monitoring, and reinforcement of diet, exercise, medications, etc., over a telephone call.
Patients received SMS messages with information on diet, exercise, medication intake, BG monitoring, and stress management over their mobile phones -around 6 messages per week.
Self-reported adherence was measured by self-care diabetes questionnaire, including information on medication, diet, and physical exercise adherence.

No significant difference.
There was no significant difference in diet, physical exercise, and medication intake adherence in either group. After cardiac events, patients were discharged with a prescription of aspirin 75 mg and clopidogrel, and were provided with educational sessions highlighting the importance of adherence to recommendations.

Cardiovascular diseases (n = 5)
After cardiac events, patients received standard care and received a daily personalized SMS.
One-month self-reported aspirin adherence and controlled aspirin adherence using platelet function testing. • Patients with moderate to severe COPD, average age 71-73 years.
Patients followed the same exercise protocol at home as the intervention group, but without the mobile phone program.
Music software with an individualized tempo was installed on patients' mobile phones. Patients participated in endurance exercise training by walking at a speed following the music. The mobile phone recorded the duration of music played, equal to the duration of walking.
Adherence to and compliance with homebased training exercise program by monitoring the frequency of performance and the duration of the endurance walking program every week.

Significant difference.
Higher proportion of patients in the mobile phone group (92%) were adherent to the home-based exercise program compared to the control group (38%) (P < 0.01). • Participants in the SMS group showed a significantly higher follow-up adherence rate compared to traditional and control groups: 60% vs. 54% vs. 28%, respectively (P = 0.003).

Reference mHealth tool used
• Medication compliance rate among those who completed the follow-up visit did not differ significantly between the three groups (P = 0.113), but rates were higher in the SMS group (80%) than the traditional (74%) and control (50%) groups. • There were no differences between groups over time in maximal workload, 6minute walk distance, or HRQL.

mHealth tool used
• MOBILE-Self-Monitored increased total steps/day, whereas MOBILE-Coached logged fewer steps over 6 months (P = 0.04). Adherence to asthma preventer inhalers was assessed at 6, 12, 18 weeks and at 6 and 9 months. Significant difference.

Reference mHealth tool used
• Average selfreported adherence over all time points was significantly higher in the intervention group (57.8%) compared to the control group (43.2%) (P = 0.003).

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The proportions with average adherence of 80% or more for the control group was 7 of 66 (10.6%) and for the intervention group 15 of 58 (25.9%). The difference between the two groups was 15.3% (P = 0.034). • Adoles cents and adults with poorly controlled asthma.

Reference mHealth tool used
Patients in the control (paper) group were asked to keep a paper diary, recording the same data as the intervention group (symptoms, medication use, and peak flow readings) twice daily.
Patients in the mobile phone group were provided with the t+ Asthma application, which enabled twice-daily recording and transmission of symptoms, medication use, and peak flow. Incursion into the red or amber zones triggered contact by an asthma nurse. Both patients and clinicians were able to access the patient data.
Asthma control was measured using the ACQ.
No significant difference.

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There was no significant difference in asthma control or selfefficacy between the two groups. • Small differences between groups were noted; P values were not provided.

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The intervention group had more than 1 day fewer asthma symptoms in the previous 2 weeks vs. baseline and vs. the control group at both 1 and 3 months post baseline. • SMS group (n = 12).
• Pa tients with clinical history of asthma, ages 18-45 years.
Patients received prescription medications for last 4 weeks of study period, but did not receive any SMS reminders about medication intake.
Patients received the prescribed medications as well as daily SMS messages reminding them to take their asthma medication.
Medication adherence was measured based on medicine dose-count at the end of 4 weeks.
Significant difference.

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The adherence rate increased from 77.9% to 81.5% (P = 0.52) in SMS group, but significantly reduced in the control group, from 84.2% to 70.1% (P = 0.01).
• At the end of 4week period, the difference in adherence rate between the two groups was 17.8% (95% CI 3.2-32.3%, P = 0.019). A difference (P = 0.04) between the monitoring and the control phase was observed for the diabetes medication only.