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Mobile app-assisted self-care interventions are emerging promising tools to support self-care of patients with chronic diseases such as type 2 diabetes and hypertension. The effectiveness of such interventions requires further exploration for more supporting evidence.
A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to examine the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension in improving patient outcomes.
We followed the Cochrane Collaboration guidelines and searched MEDLINE, Cochrane Library, EMBASE, and CINAHL Plus for relevant studies published between January 2007 and January 2019. Primary outcomes included changes in hemoglobin A1c (HbA1c) levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Changes in other clinical-, behavioral-, knowledge-, and psychosocial-related outcomes were included as secondary outcomes. Primary outcomes and objective secondary outcomes that were reported in at least two trials were meta-analyzed; otherwise, a narrative synthesis was used for data analysis.
A total of 27 trials were identified and analyzed. For primary outcomes, the use of mobile app-assisted self-care interventions was associated with significant reductions in HbA1c levels (standardized mean difference [SMD] −0.44, 95% CI −0.59 to −0.29;
Mobile app-assisted self-care interventions can be effective tools for managing blood glucose and blood pressure, likely because their use facilitates remote management of health issues and data, provision of personalized self-care recommendations, patient–care provider communication, and decision making. More studies are required to further determine which combinations of intervention features are most effective in improving the control of the diseases. Moreover, evidence regarding the effects of these interventions on the behavioral, knowledge, and psychosocial outcomes of patients is still scarce, which warrants further examination.
Type 2 diabetes mellitus and hypertension are two common, serious medical conditions that can lead to the development of other disabling and life-threatening health problems such as stroke and heart attack. The two diseases are closely interlinked and frequently coexist. Globally, approximately 80% of type 2 diabetic patients have hypertension [
Diabetes and hypertension management requires lifelong self-care by patients, which can be demanding and overwhelming because patients are often unskilled or unaware of self-care and also lack the necessary knowledge, tools, and support [
However, the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension requires more supporting evidence and thus warrants a systematic review. First, previous reviews on the use of mobile app-assisted self-care interventions for diabetes [
We followed the Cochrane Collaboration guidelines for conducting this review [
MEDLINE, Cochrane Library, EMBASE, and CINAHL Plus were searched for relevant studies published between January 2007 and January 2019. According to some reviews [
Studies were included in the review if they (1) were RCTs, (2) examined the effects of mobile app-assisted self-care interventions relative to those of usual care on patient outcomes, (3) studied type 2 diabetic and/or hypertensive patients, and (4) were published in English-language, peer-reviewed journals. We excluded review articles, case reports, and studies that only provided an abstract.
Two researchers independently read the titles and abstracts of the citations identified in the literature search, excluded clearly irrelevant studies, and reviewed the full text of the remaining articles for inclusion. The reference lists of the included studies and relevant review papers were also examined to identify missed articles.
Two researchers independently extracted the following study characteristics from each included trial: authors, publication year, study location, disease studied, sample size, HbA1c/BP eligibility, mean age of participants, sex ratio, trial length, features of the interventions, and changes in patient outcomes from baseline to the end of the trial in both intervention and control groups. For an RCT with multiple intervention groups relevant to this review, we split the “shared” control group into two or more groups (with smaller sample size) to apply two or more pair-wise comparisons in the meta-analysis [
Primary outcomes included changes in HbA1c levels, systolic BP (SBP), and diastolic BP (DBP) at the end of the trial. Changes in other outcomes, including clinical (eg, fasting BG [FBG]), behavioral (eg, medication adherence), knowledge (eg, diabetes knowledge), and psychosocial (eg, distress) outcomes, were included as secondary outcomes.
Following the Cochrane Collaboration’s tool for risk of bias assessment [
Primary outcomes and objective secondary outcomes were meta-analyzed when they were reported in at least two trials. We pooled data across trials using random effects models and calculated the standardized mean difference (SMD) for each outcome. The
The quality of evidence for the primary and objective secondary outcomes was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system [
The study selection process (see
Flow diagram of the study selection process.
Summary of the characteristics of the 27 trials.
Characteristics | Value | |
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2007-2009 | 2 (7) |
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2010-2012 | 4 (15) |
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2013-2015 | 10 (37) |
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2016-2019 | 11 (41) |
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North America | 13 (48) |
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Europe | 7 (26) |
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Asia | 5 (19) |
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Africa | 2 (7) |
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Type 2 diabetes | 19 (70) |
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Hypertension | 6 (22) |
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Type 2 diabetes and/or hypertension | 1 (4) |
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Coexisting type 2 diabetes and hypertension | 1 (4) |
Sample size, median (range) | 75 (14-250) | |
Mean age of participants in years, mean (range) | 57.3 (48.4-69.5) | |
Proportion of male participants in %, median (range) | 54 (28-76) | |
Trial length in months, median (range) | 6 (2-12) |
Details of the 27 trials.
Trial, publication year, study location | Trial length | Sample | HbA1c a/BPb eligibility | Intervention | Comparison treatment | |
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Anzaldo-Campos et al, 2016, Mexico | 10 months | IGc: n=102; CGd: n=100; mean age 52.0 years; male 38%; diabetes duration 8.3 years | HbA1c≥ 8% | A mobile app to facilitate self-monitoring of health-related data (eg, BGe and diet) and support from clinicians, nurses, and peer educators for care management | Usual care and the provision of educational classes and health evaluation in monthly medical group visits |
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Bender et al, 2017, US | 6 months | IG: n=22; CG: n=23; mean age 57.6 years; male 38%; diabetes duration not reported | No limit for HbA1c | A mobile app for behavior tracking, a Fitbit for steps monitoring, and social media for social support and education | Usual care and a Fitbit only for daily wear |
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Greenwood et al, 2015, US | 6 months | IG: n=45; CG: n=45; mean age 55.7 years; male 53%; diabetes duration 8.2 years | HbA1c: 7.5%-10.9% | A tablet-based app and a portal to support patients’ BG monitoring and diabetes education and enable certified diabetes educators’ access to patient data for telemonitoring | Usual care, booklets and referrals for diabetes education, and evaluation of patient self-reported glucose data by certified diabetes educators |
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Hansen et al, 2017, Denmark | 8 months | IG: n=83; CG: n=82; mean age 58 years; male 64%; diabetes duration 12.3 years | HbA1c> 7.5% | A tablet-based app to enable reporting of health-related data (eg, BG and BP) and monthly communication with HCPsf via video-conferencing | Usual care |
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Holmen et al (1), 2014, Norway | 12 months | IG: n=51; CG: n=25; mean age 57.7 years; male 64%; diabetes duration 10.6 years | HbA1c≥ 7.1% | A mobile phone-based system to enable vital sign monitoring, goal management, and motivational feedback | Usual care |
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Holmen et al (2), 2014, Norway | 12 months | IG: n=50; CG: n=25; mean age 56.9 years; male 53%; diabetes duration 9.5 years | HbA1c≥ 7.1% | A mobile phone-based system (to enable vital sign monitoring, goal management, and motivational feedback) and health counseling delivered by diabetes specialist nurses | Usual care |
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Hsu et al, 2016, US | 12 weeks | IG: n=20; CG: n=20; mean age 53.6 years; male sex not reported; diabetes duration 9.3 years | HbA1c: 9%-14% | A cloud-based diabetes management app supporting BG self-monitoring, insulin initiation/titration, shared decision making, and communication | Usual care with interim face-to-face visits and telephone/fax communication with educators, physicians, and/or nurses |
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Karhula et al, 2015, Finland | 12 months | IG: n=180; CG: n=70; mean age 66.3 years; male 56%; diabetes duration not reported | HbA1c> 6.5% | A mobile app for self-monitoring of health parameters (eg, BG and BP) and remote health coaching | Usual care |
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Kleinman et al, 2017, India | 6 months | IG: n=44; CG: n=46; mean age 48.4 years; male 70%; diabetes duration 9.2 years | HbA1c: 7.5%-12.5% | A smartphone app for patients and a web portal plus an app for HCPs for receiving reminders, data visualization, and providing care support to enhance self-care and collaborative care decisions | Usual care |
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Nagrebetsky et al, 2013, UK | 6 months | IG: n=7; CG: n=7; mean age 58 years; male 71%; diabetes duration 2.6 years | HbA1c: 8%-11% | A mobile phone-based telehealth platform (for self-monitoring of BG and self-titration of oral glucose-lowering medication) and monthly telephone calls (for lifestyle monitoring and change) | Usual care and monthly telephone calls for lifestyle monitoring and change |
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Orsama et al, 2013, Finland | 10 months | IG: n=24; CG: n=24; mean age 61.9 years; male 54%; diabetes duration not reported | HbA1c: 6.5%-11%; SBPg >140 mm Hg or DBPh >90 mm Hg | A diabetes lifestyle and self-care promotion program based on a mobile app to allow patients to report their conditions and receive system-generated feedback on health behaviors | Usual care, diabetes education, and HCP counseling |
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Quinn et al, 2008, US | 3 months | IG: n=13; CG: n=13; mean age 51.0 years; male 35%; diabetes duration 9.3 years | HbA1c ≥ 7.5% | A mobile phone-based software to provide real-time feedback on patient BG levels, display medication instructions, incorporate hypo- and hyperglycemia treatment algorithms, and request data for diabetes management | Usual care and instructions to patients about reporting BG levels to HCPs via phone calls or fax once every 2 weeks |
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Quinn et al, 2011, US (1) | 12 months | IG: n=23; CG: n=19; mean age 53 years; male 52%; diabetes duration 8.3 years | HbA1c ≥ 7.5% | A mobile app and patient care provider web portal to support patient self-monitoring and enable HCPs to receive health data shared by patients | Usual care |
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Quinn et al, 2011, US (2) | 12 months | IG: n=22; CG: n=19; mean age 53.5 years; male 46%; diabetes duration 7.8 years | HbA1c ≥ 7.5% | A mobile app and patient care provider web portal to support patient self-monitoring and allow HCPs to access unanalyzed patient data | Usual care |
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Quinn et al, 2011, US (3) | 12 months | IG: n=62; CG: n=18; mean age 52.3 years; male 50%; diabetes duration 8.5 years | HbA1c ≥ 7.5% | A mobile app and patient care provider web portal to support patient self-monitoring and allow HCPs to access analyzed patient data and evidence-based care guidelines | Usual care |
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Sun et al, 2019, China | 6 months | IG: n=44; CG: n=47; mean age 68.0 years; male 41%; diabetes duration 11.4 years | HbA1c: 7.0%-10.0% | A mobile app for self-monitoring of BG, diet, and physical activity; sharing of measurement records; and receiving HCP-provided care recommendations | Usual care |
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Takenga et al, 2014, Congo | 2 months | IG: n=20; CG: n=20; mean age 53.3 years; male 73%; diabetes duration not reported | No limit for HbA1c | A mobile system to support patients’ tracking of health conditions (eg, BG, BP, and body weight) and communication with HCPs | Usual care |
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Waki et al, 2014, Japan | 3 months | IG: n=27; CG: n=27; mean age 57.3 years; male 76%; diabetes duration 9.1 years | No limit for HbA1c | A smartphone-based system for self-monitoring of health conditions (eg, BG, BP, and diet), communication with HCPs, and receiving system’s auto-generated feedback | Usual care |
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Wayne et al, 2015, Canada | 6 months | IG: n=67; CG: n=64; mean age 53.2 years; male 28%; diabetes duration not reported | HbA1c ≥ 7.3% | A mobile phone-supported health coach program allowing patients to track their conditions (eg, BG, diet, physical activity, and mood) and communicate with HCPs | Usual care, exercise education, and health coach support in goal setting and progress monitoring through in-person meetings/telephones |
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Kim et al, 2016, US | 6 months | IG: n=52; CG: n=43; mean age 57.6 years; male 32%; hypertension duration not reported | No limit for BP | A mobile app (equipped with a BP monitoring device, electronic reminders, and a web-based disease management program for patient self-monitoring) and a reach-out program (delivered by nursing staff for education about medication, disease prevent, and chronic disease management) | Usual care and a reach-out program of the same type used in the IG |
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Lakshminarayan et al, 2018, US | 90 days | IG: n=34; CG: n=22; mean age 65.0 years; male 68%; hypertension duration not reported | No limit for BP | A smartphone app supporting BP self-monitoring, nurse-delivered education, and HCP-provided feedback | Usual care and education on hypertension management |
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Logan et al, 2012, Canada | 12 months | IG: n=55; CG: n=55; mean age 62.9 years; male 56%; hypertension duration not reported | SBP ≥130 mm Hg | A smartphone app (for BP telemonitoring and self-care) and a booklet (with information about self-management, treatments, and therapy goals) | Usual care and a booklet of the same type used in the IG |
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Márquez Contreras et al, 2019, Spain | 12 months | IG: n=73; CG: n=75; mean age 57.5 years; male sex 48%; hypertension duration not reported | No limit for BP | A smartphone app to promote education about hypertension and provide patients with reminders of appointments and medication | Usual care |
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Moore et al, 2014, US | 12 weeks | IG: n=20; CG: n=22; mean age 50.0 years; male 60%; hypertension duration not reported | BP: 140/90-180/120 mm Hg | A tablet-based app, virtual visits, instant messaging, and a nurse health coach to facilitate self-monitoring of BP and medication intake; visualization of information on actions, outcomes, and medication adjustment; and discussion about care management and goal settings | Usual care together with office visits, phone calls, and emails with HCPs for discussing care management, goal settings, and medication adjustment |
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Sarfo et al, 2019, Ghana | 9 months | IG: n=30; CG: n=30; mean age 55 years; male 65%; hypertension duration not reported | SBP ≥140 mm Hg | A smartphone app for monitoring and reporting of BP and medication intake, provision of motivational text messages generated based on patients’ medication adherence, and sharing of patients’ health reports with clinicians | Usual care and text messages about healthy lifestyle management and clinicians’ monthly review of patients’ BP |
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Or and Tao, 2016, Hong Kong SAR, China | 3 months | IG: n=33; CG: n=30; mean age 69.5 years; male 32%; diabetes duration 12.5 years; hypertension duration 10.2 years | No limit for HbA1c and BP | A tablet-based self-monitoring app allowing automated recording and monitoring of BG and B |
Usual care |
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Yoo et al, 2009, Korea | 3 months | IG: n=57; CG: n=54; mean age 58.2 years; male 59%; diabetes duration 6.6 years; hypertension duration 3.7 years | HbA1c: 6.5%-10%; BP >130/80 mm Hg | An internet-enabled, cellphone-based system coupled with a BG measuring device, an automatic BP monitor, a body weight scale, and a database providing reminders, health recommendations, and data sharing for self-care | Usual care |
aHbA1c: hemoglobin A1c.
bBP: blood pressure.
cIG: intervention group.
dCG: control group.
eBG: blood glucose.
fHCP: health care provider.
gSBP: systolic blood pressure.
hDBP: diastolic blood pressure.
Risk of bias of the 27 trials.
Risk of bias for each trial.
The meta-analysis results showed that mobile app-assisted self-care interventions were associated with significant reductions in HbA1c levels (SMD −0.44, 95% CI −0.59 to −0.29,
Results of meta-analysis and Grading of Recommendations Assessment, Development and Evaluation assessments for hemoglobin A1c levels, systolic blood pressure, and diastolic blood pressure.
Outcomes | Trials included | Sample size | SMDa (95% CI) |
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Egger test | Quality of evidence (GRADE)b | ||
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HbA1cc levels | 21 | 1671 | −0.44 (−0.59 to −0.29) | <.001 | 50 | 1.15 | .26 | |
SBPf | 16 | 1433 | −0.17 (−0.31 to −0.03) | .02 | 41 | 0.52 | .61 | |
DBPg | 14 | 1292 | −0.17 (−0.30 to −0.03) | .02 | 25 | 0.09 | .93 |
aSMD: standardized mean difference.
bGRADE: Grading of Recommendations Assessment, Development and Evaluation.
cHbA1c: hemoglobin A1c.
dDowngraded by one level for indirectness (surrogate outcome).
eDowngraded by one level for inconsistency (moderate heterogeneity level,
fSBP: systolic blood pressure.
gDBP: diastolic blood pressure.
Forest plots for hemoglobin A1c (top), systolic blood pressure (middle), and diastolic blood pressure (bottom).
The analysis of the HbA1c outcome by disease type was not applicable because the outcome was only examined in diabetic patients and not hypertensive patients in the 27 included trials. The results of the subgroup analysis for SBP indicated that mobile app-assisted interventions led to significant reductions in SBP in hypertensive patients (SMD −0.28, 95% CI −0.51 to −0.04,
The presence of BP monitoring, automated feedback, personalized goal setting, reminders, education materials, and data visualization features yielded significant reductions in SBP while the reductions were not significant for interventions that did not have these features, although the differences between the subgroups were not statistically significant. The presence of diet- and physical activity–monitoring features was not associated with reductions in SBP. For other features, changes in SBP were found to be similar between the subgroups.
Further, the presence of BG monitoring, automated feedback, and personalized goal-setting features was associated with reductions in DBP while the reductions were not significant for interventions that did not have these features, although the differences between the subgroups were not statistically significant. Diet monitoring, body weight monitoring, and data visualization were not associated with reductions in DBP. For other features, changes in DBP were found to be similar between the subgroups.
Results of subgroup analysis by intervention feature in relation to reductions in hemoglobin A1c levels, systolic blood pressure, and diastolic blood pressure.
Features | HbA1ca reduction | SBPb reduction | DBPc reduction | |
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BGd | — e | Δf | •g |
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BPh | Δ | • | Δ |
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Body weight | Δ | Δ | ×i |
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Medication | • | Δ | Δ |
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Diet | Δ | × | × |
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Physical activity | Δ | × | Δ |
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Mood | — | — | — |
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Automated feedback | Δ | • | • |
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Medication adjustment aid | Δ | — | — |
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Personalized goal setting | × | • | • |
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Reminders | Δ | • | Δ |
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• | — | — | |
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Δ | • | Δ | |
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Δ | • | × |
aHbA1c: hemoglobin A1c.
bSBP: systolic blood pressure.
cDBP: diastolic blood pressure.
dBG: blood glucose.
e—: Subgroup analysis was not performed for the feature because there were fewer than two trials in one of the subgroups.
fΔ: Similar changes were found between the two subgroups (presence of the feature vs absence of the feature).
g•: Presence of the feature was related to a more favorable effect on the outcome.
hBP: blood pressure.
i×: Absence of the feature was related to a more favorable effect on the outcome.
jHCP: health care provider.
A total of 8 objective secondary outcomes were meta-analyzed (
Results of meta-analysis and Grading of Recommendations Assessment, Development and Evaluation assessments for objective secondary outcomes.
Outcomes | Trials included | Sample size | SMDa (95% CI) |
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Egger test | Quality of evidence (GRADEb) | ||
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FBGc | 6 | 416 | −0.29 (−0.49 to −0.10) | .004 | 2 | 2.27 | .09 | |
Waist circumference | 4 | 433 | −0.23 (−0.43 to −0.04) | .02 | 0 | 0.60 | .61 | |
Body weight | 9 | 682 | −0.09 (−0.24 to 0.07) | .97 | 0 | 0.02 | .98 | |
BMI | 6 | 575 | −0.06 (−0.23 to 0.12) | .53 | 12 | 3.36 | .03 | |
Total cholesterol | 7 | 777 | −0.18 (−0.37 to 0.02) | .07 | 35 | 0.23 | .83 | |
LDLf cholesterol | 7 | 734 | −0.08 (−0.23 to 0.07) | .29 | 0 | 0.06 | .95 | |
HDLg cholesterol | 7 | 743 | −0.10 (−0.28 to 0.07) | .24 | 18 | 1.26 | .26 | |
Triglycerides | 7 | 720 | −0.13 (−0.29 to 0.02) | .09 | 0 | 0.21 | .84 |
aSMD: standardized mean difference.
bGRADE: Grading of Recommendations Assessment, Development and Evaluation.
cFBG: fasting blood glucose.
dDowngraded by one level for indirectness (surrogate outcome).
eDowngraded by one level for publication bias.
fLDL: low-density lipoprotein.
gHDL: high-density lipoprotein.
A total of 42 secondary outcomes were narratively synthesized (
Narrative synthesis results of the effects of mobile app-assisted self-care interventions.
Outcomes | Number of trials | ||||
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Favoring interventiona | Showing no significant difference between intervention and controlb | Favoring controlc | ||
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Postprandial BGd | 1 [ |
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Right brachial-ankle pulse wave velocity |
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1 [ |
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Left brachial-ankle pulse wave velocity |
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1 [ |
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Adiponectin | 1 [ |
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High-sensitivity C-reactive protein |
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1 [ |
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Interleukin-6 |
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1 [ |
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Homeostatic model assessment of insulin resistance |
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1 [ |
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Waist/hip ratio |
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1 [ |
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Creatinine |
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1 [ |
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Medication dose |
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1 [ |
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Insulin dose |
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1 [ |
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Quality of life |
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6 [ |
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Diabetes symptoms |
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3 [ |
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Lifestyle-/health-related activity |
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4 [ |
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Adherence to medication | 1 [ |
5 [ |
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Adherence to physical activities | 1 [ |
6 [ |
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Adherence to healthy diet | 1 [ |
4 [ |
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Frequency of carbohydrate spacing | 1 [ |
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Frequency of smoking |
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1 [ |
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Frequency of drinking |
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1 [ |
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Frequency of communicating with physicians |
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1 [ |
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Adherence to BG monitoring | 2 [ |
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Adherence to foot care | 1 [ |
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Diabetes knowledge | 1 [ |
3 [ |
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Hypertension knowledge |
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3 [ |
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Satisfaction with diabetes treatment | 1 [ |
1 [ |
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Satisfaction with life |
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1 [ |
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Ability to interact with health organizations and HCPse |
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2 [ |
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Ability to monitor the conditions and having insights into living with the conditions |
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2 [ |
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Self-efficacy for medication taking/coping with diseases |
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4 [ |
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Emotional well-being |
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2 [ |
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Positive emotion |
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1 [ |
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Negative emotion |
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1 [ |
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Distress |
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4 [ |
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Depression |
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7 [ |
1 [ |
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Anxiety |
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2 [ |
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Comfort with self-monitoring |
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1 [ |
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Self-autonomous regulation |
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1 [ |
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Determination about not allowing illnesses to control life |
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2 [ |
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Positive and active engagement in life |
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2 [ |
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Feeling of having the skills to manage disease | 1 [ |
1 [ |
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Feeling of having social support |
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2 [ |
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aSignificant improvement in the outcome at the end of the trial in the intervention group compared with the control group.
bNo significant difference in the outcome at the end of the trial between the intervention and control groups.
cSignificant deterioration in the outcome at the end of the trial in the intervention group compared with the control group.
dBG: blood glucose.
eHCP: health care provider.
This systematic review identified 27 trials that examined the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension.
Overall, our review showed that the use of mobile app-assisted self-care interventions led to significant reductions in HbA1c levels, SBP, and DBP—the fundamental clinical parameters in diabetic and hypertensive patients. For HbA1c levels, we observed an SMD of −0.44 and an absolute MD of −0.49%. The effect size was clinically meaningful and similar to that reported in previous reviews that examined other similar types of health technology (ie, SMD −0.30 to −0.40 [
The subgroup analysis of BP by disease type showed that among hypertensive patients, the effect size for SBP (SMD −0.28, absolute MD −4.20 mm Hg) could be regarded as clinically important and was similar to that found in previous reviews that studied hypertensive patients (absolute MD −3.74 to −4.71 mm Hg) [
All of the reviewed interventions had more than one feature, and our subgroup analysis revealed that the effects of the features on patient outcomes varied, as follows. The presence of medication-, BG-, and BP-monitoring features were favorable in reducing HbA1c levels, SBP, and DBP. Such result could be because patients already had a belief that the behaviors of monitoring of medication, BG, and BP were more immediately relevant to the control of the diseases, and, thereby, with the support of the features, patients’ engagement in the behaviors was further developed. Also, because the features could enable the tracking and organization of the health parameters in a more structured and systematic manner [
With respect to secondary outcomes, our meta-analyses indicated that mobile app-assisted self-care interventions had significant lowering effects on FBG and waist circumference. No significant differences were observed in body weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides between the intervention and control groups, probably because the design of the interventions was less targeted for these health indexes. Our narrative synthesis indicated that in a small number of trials, the interventions were helpful in improving several clinical, behavioral, knowledge, and psychosocial outcomes. According to these trials, it appeared that such interventions have a potential to engage patients in disease management, including maintaining a healthy lifestyle, improving self-care knowledge, and addressing psychosocial needs. On the other hand, there were trials that examined these outcomes that did not show positive effects. In fact, two trials demonstrated negative effects of the mobile app-assisted self-care interventions on depression and medication dose. Given the mixed results yielded from only a small set of studies, to further understand the impacts of the interventions on these outcomes in the self-care of the diseases, more research is needed.
Our review suggests several implications for research. First, limited RCTs have emphasized behavioral, knowledge, and psychosocial aspects as primary outcomes in their examination. Further RCTs should focus more on these outcomes to obtain better understanding of whether or not, how, and to what extent mobile app-assisted health interventions change the health, self-care behaviors, and health technology adoption behaviors of patients; expand their knowledge base about health decision making and care; and influence their feelings about and attitudes toward technology-based self-care. Second, although the associations between each intervention feature and improvements in patient outcomes have been examined, information about the appropriate/optimal combinations of the features is important and limited. Future studies should further examine which combinations of intervention features are more effective for patients and disease self-care. Perhaps, the design can exploit artificial intelligence techniques to identify patients’ needs and then combine and present appropriate features tailored to those needs. In addition, our results also indicated that some features, including personalized goal setting; data visualization; and monitoring of diet, physical activity, and body weight, were not always associated with (more) improvements in patient outcomes. Further studies are required to determine the possible reasons for these observations, such as variations in patients’ acceptance and adoption of the features, engagement in the self-care activities that the features intended to support, or perceptions of the design and efficacy of the features. Design of the technology as well as patients’ attitudes toward and acceptance of the technology determine whether the technology could demonstrate its benefits and impacts [
Our review also provides recommendations for the design and development of mobile app-assisted self-care interventions. First, our results suggest that mobile app-assisted self-care interventions should incorporate features including logging, personalized feedback, communication with HCPs, education, and data visualization in the design and implementation phases of the interventions. This suggestion is consistent with that of Greenwood et al [
Our review has several strengths. It provides evidence regarding the effects of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension on patient outcomes. In addition to HbA1c levels and BP, several relevant outcomes that were scarcely examined in previous reviews are also analyzed in our review. Our review also provides an evidence-based review of the features of such interventions and their associations with improvements in glycemic and BP control. Our study has limitations. First, although type 2 diabetes and hypertension overlap in population and are closely interlinked, combining the two diseases into one systematic review may cause high heterogeneity. In this study, subgroup analysis by disease type was only conducted for primary outcomes to understand the effects of the intervention in different disease population. The effects of the intervention on the secondary outcomes should be interpreted with caution due to the variability in disease type. Second, the reported effects should be interpreted with caution because control patients in some of the reviewed trials received enhanced usual care, including additional education or phone call communications with their HCPs. Third, 42 patient outcomes were examined using narrative synthesis by simply counting their statistical significance. The effect sizes and significant levels of these outcomes were not obtained. Fourth, publication bias was detected when BMI was the examined outcome. Fifth, only English language articles were included in our review, which may have introduced language and publication bias. Finally, the review lacked an a priori and published protocol.
For type 2 diabetic and/or hypertensive patients, performing self-care and maintaining a healthy lifestyle are necessary but also challenging. The use of mobile app-assisted self-care interventions appears to be effective in improving glycemic and BP management and control; however, this effectiveness was not consistent in some other outcomes. Hence, further investigations on the effects of the interventions on other outcomes are warranted. Moreover, it will be valuable to determine which combinations of features of such interventions are most effective in achieving improvements in the desired outcomes, as it can guide the optimal design of such interventions.
Features identified in the mobile app-assisted self-care interventions of the 27 trials.
Key features of the mobile app-assisted self-care interventions.
Effects of each intervention feature on hemoglobin A1c reduction.
Effects of each intervention feature on systolic blood pressure reduction.
Effects of each intervention feature on diastolic blood pressure reduction.
blood glucose
blood pressure
diastolic blood pressure
fasting blood glucose
Grading of Recommendations Assessment, Development and Evaluation
hemoglobin A1c
health care provider
high-density lipoprotein
low-density lipoprotein
mean difference
randomized controlled trial
systolic blood pressure
standardized mean difference
We are grateful to Ms Min Jiang for citation screening and data extraction. The review was conducted with the support of the Department of Industrial and Manufacturing Systems Engineering at the University of Hong Kong.
KL and CO designed the systematic review and developed the study protocol. KL and ZX screened the studies identified in the databases and performed data extraction. KL performed the data analysis. KL, CO, ZX contributed to the writing of the manuscript.
None declared.