A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management^,,

Abstract

The use of mobile and wireless technologies and wearable devices for improving health care processes and outcomes (mHealth) is promising for health promotion among patients with chronic diseases such as obesity and diabetes. This study comprehensively examined published mHealth intervention studies for obesity and diabetes treatment and management to assess their effectiveness and provide recommendations for future research. We systematically searched PubMed for mHealth-related studies on diabetes and obesity treatment and management published during 2000–2016. Relevant information was extracted and analyzed. Twenty-four studies met inclusion criteria and varied in terms of sample size, ethnicity, gender, and age of the participating patients and length of follow-up. The mHealth interventions were categorized into 3 types: mobile phone text messaging, wearable or portable monitoring devices, and applications running on smartphones. Primary outcomes included weight loss (an average loss ranging from −1.97 kg in 16 wk to −7.1 kg in 5 wk) or maintenance and blood glucose reduction (an average decrease of glycated hemoglobin ranging from −0.4% in 10 mo to −1.9% in 12 mo); main secondary outcomes included behavior changes and patient perceptions such as self-efficacy and acceptability of the intervention programs. More than 50% of studies reported positive effects of interventions based on primary outcomes. The duration or length of intervention ranged from 1 wk to 24 mo. However, most studies included small samples and short intervention periods and did not use rigorous data collection or analytic approaches. Although some studies suggest that mHealth interventions are effective and promising, most are pilot studies or have limitations in their study designs. There is an essential need for future studies that use larger study samples, longer intervention (≥ 6 mo) and follow-up periods (≥ 6 mo), and integrative and personalized innovative mobile technologies to provide comprehensive and sustainable support for patients and health service providers.

Introduction

Overweight and obesity are global public health problems because ∼40% of adults are overweight or obese worldwide. In the United States, the largest developed country, two-thirds of the population are overweight or obese (1). Obesity and diabetes increase the risk for many other diseases, such as hypertension, hyperlipidemia, and some cancers (2). The number of patients with type 2 diabetes (T2D)⁹ in the United States increased from 26 million in 2010 to 29 million in 2015 (3), and 14% of all health care cost was spent on diabetes treatment. Poor diabetes control is associated with low socioeconomic status. The prevalence of T2D has been increasing worldwide in the past 2 decades, especially at a particularly fast pace in some developing countries such as China and India (4–8). For example, the rate of diabetes has reached ~10% among adults in China, which is similar to that of US adults (7). It is projected that India will have 101 million patients with T2D, the largest number in the world, by 2030 (8).

Providing good health care services and preventing related health complications are critical for diabetic and obese patients, their families, and the society at large. Without effective prevention and management of diabetes and obesity, patients and their families will suffer. The society will also suffer from huge financial and other costs incurred during the care of those patients. However, there are many challenges in providing good health care to obese and diabetic patients and helping them control their weight and blood glucose (7, 9, 10), especially in developing countries with limited health care facilities and professionals. Treatment of obesity and diabetes is costly; requires long-term efforts from patients, their health providers, and other stakeholders; and is often ineffective because of complex factors, including many challenges that patients may face in their daily work and life.

Development of lower-cost, more effective methods for treatment and self-management of obesity and diabetes is greatly needed to reduce health care costs associated with obesity and diabetes while at the same time improving the quality of care and the life of patients (11, 12).

New advances in the use of mobile and wireless technologies and wearable devices for improving health care processes and outcomes (mHealth) provide promising options for low-cost, effective care (13) and health promotion for patients with chronic diseases such as obesity and diabetes. It can be an effective tool for patients by helping facilitate their interactions with health care providers, other patients, and family members.

mHealth has no standard definition yet. For the purpose of this study, we defined mHealth as health practice or services supported by mobile technologies and devices, including cell phones, wearable devices, and sensors as well as mobile applications running on smartphones (APPs). mHealth has been widely adopted to help manage diseases in various domains, such as HIV and AIDS, malaria, tuberculosis, diabetes, asthma, obesity, and smoking (13–17). However, despite the growing number of applications of mHealth, the effectiveness of mHealth APPs in improving health remains inconclusive, and the evidence is scattered. A systematic examination of related mHealth studies is needed to guide future mHealth research and practice.

The existing literature reviews of mHealth APPs mainly focus on the availability of commercial applications and use for patients (18). However, there are very few systematic reviews regarding the current APPs and their effectiveness as mHealth tools for obesity and diabetes treatment and management. To fill this gap and advance our understanding of existing research on mHealth in support of obesity and diabetes treatment and management, we conducted a systematic review of the related research and assessed the effectiveness of the mHealth interventions for obesity and diabetes treatment and management, identified gaps in the literature, and provided recommendations for future research.

Methods

This systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework (19) in terms of study selection, data collection, data analysis, and result reporting. Because of the high heterogeneity in study characteristics of the selected studies and limited number of comparable studies and quantitative results, quantitative meta-analysis could not be conducted in the present study.

Study selection

Database and search strategy.

To identify studies that have investigated the effectiveness of mHealth intervention programs for obesity and/or diabetes treatment and management, we searched PubMed for relevant articles published between 1 January 2000 and 31 August 2016. We limited the search for studies published since 2000 because, although smartphones originated in ∼2007, some mobile devices were available and tested in health promotion–related research before then. In the end, we found 24 studies that met our inclusion criteria, all published after 2008 (see Figure 1).

FIGURE 1.

Flow chart of the literature search and study selection procedures. RCT, randomized controlled trial.

The terms we used in the PubMed search included “cell phone and overweight,” “cell phone and obesity,” “cell phone and diabetes,” “smartphone and overweight,” “smartphone and obesity,” “smartphone and diabetes,” “mobile phone and overweight,” “mobile phone and obesity,” “mobile phone and diabetes,” “mHealth and overweight,” “mHealth and obesity,” “mHealth and diabetes,” “eHealth and overweight,” “eHealth and obesity,” “eHealth,” and “diabetes.” The search was limited to studies involving randomized trials (i.e., experimental but not observational studies), human studies, and publication in English. Search results were further screened manually by study title, abstract, and full text based on our study inclusion and exclusion criteria.

Study inclusion and exclusion criteria.

Inclusion criteria that we used were 1) participants in studies were diabetic or obese patients; 2) mobile devices, such as mobile phones and/or wearable monitoring devices, were used in the health intervention or care delivery; 3) diabetes- and obesity-related behaviors and outcomes were evaluated, for example, whether weight changes of obese patients or glycated hemoglobin (HbA1c) changes of diabetic patients were reported; 4) the study design was a randomized clinical trial (RCT) or quasi-experiment; and 5) the study was published in English.

Exclusion criteria included 1) review or commentary articles or studies not published in English; 2) proposed interventions that only used emails, web-based programs, or log books without interacting with patients and delivering an intervention to a mobile device; and 3) studies that involved patients with other chronic diseases, such as cardiac disease or cancer.

Data collection and analysis

Two coauthors and a research assistant extracted information from identified studies that met inclusion criteria, including study design, subjects, nature of the intervention, inclusion of control groups, and key research results. Extracted information was reviewed by other coauthors to verify accuracy. The methodological quality of the selected studies was assessed by using the Jadad scale (20), which has been recognized as a useful tool for evaluation of RCT quality (21). Jadad scores range from 0 (very poor) to 5 (rigorous) and consist of points for randomization (randomized = 1 point; table of random numbers or computer-generated randomization = additional 1 point), double blindness (double blind = 1 point; use masking such as identical placebo = additional 1 point), and follow-up (stating numbers of subjects withdrawn and the reasons for them in each group of a study = 1 point) (22).

The research designs used in previous RCT studies were highly heterogeneous with respect to intervention durations, outcome variables, and target populations, which prohibited quantitative analysis (i.e., meta-analysis). Thus, we present a narrative synthesis of the results of mHealth interventions for diabetes and obesity management reported by those studies.

Results

Main characteristics of the selected studies

Table 1 shows the main characteristics of the 24 selected studies. Among them, 15 studies (63%) were conducted in the United States, and the rest were conducted in 7 other countries, including Iran, Germany, South Korea, Italy, Finland, Spain, and Australia. Regarding study design, 16 studies (67%) were randomized controlled trials, and 8 (33%) were quasi-experiments.

TABLE 1.

Main study characteristics and findings from 24 studies that examined mHealth interventions for obesity and diabetes treatment and management¹

Study ID2	Reference, year	Study design	Country	SS	G	3 y	mHealth type	Intervention period	Intervention	Control treatment	Key outcome(s)	Conclusions	Effective
1	Patrick et al. (16), 2009	RCT	United States	65	2	25–55	MPTM	16 wk	Personalized message 2–5 times/d, printed materials, and phone calls from a counselor 1 time/mo	Printed materials on weight control 1 time/mo	More weight loss than controls (−1.97 kg difference, 95% CI: −0.34, −3.60 kg), after adjusting for sex and age (P = 0.02).	Promote behavior changes that support weight loss in overweight adults.	Yes
2	Turner-McGrievy and Tate (23), 2011	RCT	United States	96	3	42.9 ± 11.2	APP	6 mo	A diet and PA-monitoring APP, and interaction with study counselors and other participants on Twitter	2 podcasts/wk and 2 minipodcasts/wk	1) Weight loss did not differ by group at 6 mo (P = 0.28). 2) Social support did not differ between groups (P = 0.08).	Mobile communication via Twitter and a monitoring APP without feedback did not enhance weight loss.	No
3	Shaw (24), 2012	RCT	United States	120	3	52 ± 15.5	MPTM	1 mo	General health message plus 1) messages about promoting success and rewarding oneself and 2) messages about preventing failure and avoiding temptations	General health message	1) The sustained weight loss rate between cases and controls was nearly the same at month 3 (P = 0.08). 2) The mean weight loss between controls was not different at month 3 (P > 0.05).	Text messaging shows promise in helping people sustain healthy behaviors.	No
4	Park and Kim (25), 2012	Q-E	South Korea	67	1	56.7 ± 5.6	MPTM	12 wk	Record WC, BW, BP, dietary patterns, and exercise weekly through cellular phone Recommendations for diet and exercise were sent to each patient by cellular phone	Only informed about the study	1) WC (P < 0.001), BW (P < 0.001), and TC (P < 0.001) significantly decreased in the intervention group and significantly increased in the control group. 2) BP and LDL-C significantly decreased in the intervention group ((P = 0.001, P = 0.025) but not significantly in the control group (P = 0.476, P = 0.104).	Tailored SMS improved health outcomes in postmenopausal women with abdominal obesity.	Yes
5	Burke et al. (26), 2012	RCT	United States	210	3	46.84	APP	24 mo	Feedback on their PDA or PDA only to help self-monitoring of diet and weight loss	Recorded diet by paper diary	The mean percentage weight loss at 24 mo was not different among groups (P = 0.33).	Mobile technology that offers feedback can play a role in improving weight loss.	No
6	Schiel et al. (27), 2012	Q-E	Germany	124	1	13.5 ± 2.8	WPMD	5 wk	A mobile phone to assess PA and eating habits	Pre-intervention	1) Significant weight reduction of 7.1 ± 3.0 kg (P < 0.01) and 2) significant PA and time spent in activities were associated with weight reduction (P = 0.04).	Mobile phones are highly accepted by children and adolescents and augment existing weight reduction and weight loss stabilization.	Yes
7	Sharifi et al. (28), 2013	Q-E	United States	31	1	6–12	MPTM	3 wk	Three weeks of text messages of obesity-related behaviors	Pre-intervention	1) Parents were generally enthusiastic about text messages to support healthy behaviors for their children. 2) Parents anticipated high responsiveness to messaging endorsed by their child’s doctor.	Text messaging is a promising medium in pediatric obesity-related behavior intervention. Parent perspectives could assist in the design of text-based interventions.	Yes
8	Allen et al. (29), 2013	RCT	United States	68	4	45 ± 11	APP	6 mo	1) Intensive counseling plus smartphone, 2) less intensive counseling plus smartphone, and 3) smartphone only to help self-monitoring	Intensive counseling intervention	Smartphone group tended to lose more weight (P = 0.89).	Smartphone application for self-monitoring is an adjunct to behavioral counseling.	No
9	Oliver et al. (30), 2013	Q-E	Spain	30	2	9–15	APP	1 wk	Record dietary and PA by PDA	Record dietary and PA with paper and pencil	1) PDAs produced more records than paper and pencil (P < 0.001). 2) Participants preferred a PDA over paper and pencil (P > 0.01).	A PDA is a reliable system for data collection.	Yes
10	Bond et al. (31), 2014	Q-E	United States	30	1	47.5 ± 13.5	APP	3 wk	Smartphone-based PA “breaks” were utilized to get participants active	Pre-intervention	Significant decreases in sedentary time and increases in light and moderate-to-vigorous PA (P < 0.05).	The smartphone APP is useful in PA promotion.	Yes
11	Nollen et al. (32), 2014	RCT	United States	51	2	9–14	APP	12 wk	Used a mobile APP to set real-time goals and self-monitor, provided tips, feedback, and positive reinforcement	Same content in a written manual	Increased fruit/vegetable intake (P = 0.08) and decreased sugar-sweetened beverage (P = 0.09) consumption.	Mobile APPs may help change diet behaviors, but larger-scale testing of similar programs is needed.	No
12	Partridge et al. (33), 2015	RCT	Australia	214	2	18–35	MPTM	12 wk	8 text messages/wk, 1 email/wk, 5 personalized coaching calls, a diet booklet, and access to resources and mobile phone APP on a website	Printed dietary and PA guidelines	2.2 kg (95% CI: 0.8, 3.6 kg) lighter (P < 0.05); consumed more vegetables (P = 0.009), fewer sugary soft drinks (P = 0.002), and fewer energy-dense takeout meals (P = 0.001); increased PA time (P = 0.003) compared with controls.	Text messaging was successful in preventing weight gain and improving lifestyle behaviors among overweight young adults.	Yes
13	Martin et al. (34), 2015	RCT	United States	40	2	44.4 ± 11.8	APP	12 wk	Prescribed personalized diet via a smartphone based on wireless body weight and step data	Health tips via smartphone	Weight loss was significantly greater than controls at week 12 (P < 0.001).	A smartphone APP may efficaciously promote clinically meaningful weight loss.	Yes
14	Pretlow et al. (35), 2015	Q-E	Unites States	43	1	10–21	APP	20 wk	Smartphone APP with health professionals’ support to lose weight	Pre-intervention	1) Significantly decreased %overBMI from baseline (P < 0.05). 2) Significant improvements of self-esteem, control over food, and a reduction in turning to food when stressed (P < 0.01). 3) Males, younger participants, and participants with higher levels of compliance achieved better weight loss (P = 0.02).	The food withdrawal approach, which can be delivered through a smartphone, was feasible to implement and was useful in helping reduce weight, particularly among boys.	Yes
15	Faridi et al. (36), 2008	RCT	United States	30	2	55.3 ± 8.7	MPTM	3 mo	Daily tailored text messages to enhance diabetic self-care behavior	Standard diabetes self-management	1) HbA1c levels changed but not significantly (P = 0.1534). 2) Self-efficacy scores improved significantly (P = 0.008).	Text messages may have a positive impact on some clinical outcomes and self-efficacy in patients with type 2 diabetes.	No
16	Hanauer et al. (37), 2009	RCT	United States	40	2	12–25	MPTM	3 mo	SMS reminders via cell phone	Electronic reminders via e-mail	Usage of SMS waned by month 3.	Maintaining interest about text messages for prolonged intervals remains a challenge.	No
17	Rossi et al. (38), 2009	Q-E	Italy	41	1	31.6 ± 11.9	APP	9 mo	Diary APP on mobile phones record blood glucose and quantify the total cholesterol intake, communicate with physician by SMS	Pre-intervention	Reduction in fasting blood glucose (P = 0.09), postprandial glucose (P = 0.13), and HbA1c levels (P = 0.27).	Mobile APPs represent a useful, safe, and easy-to-use tool to promote dietary freedom.	No
18	Quinn et al. (14), 2011	RCT	United States	163	4	18–64	MPTM	12 mo	Personalized messaging in response to blood glucose values, diabetes medications, and lifestyle behaviors by mobile phone	Usual care	1) HbA1c decreased over 12 mo (P < 0.001). 2) Appreciable differences in depression, diabetes symptoms, BP, and lipid concentrations were not observed between groups (P > 0.05).	Mobile coaching-based personal data analyzed and presented with evidence-based guidelines reduce HbA1c.	Yes
19	Luley et al. (39), 2011	RCT	Germany	70	2	57.8 ± 8	WPMD	6 mo	Telephone monitoring of PA and a low-calorie, low–glycemic index diet	Conventional low-fat diet and standard care	Weight loss, glucose, HbA1c, and antidiabetic drug intake were reduced (P < 0.001).	Telephone monitoring can effectively lower body weight, HbA1c, and antidiabetic drug use in patients with type 2 diabetes.	Yes
20	Goodarzi et al. (40), 2012	RCT	Iran	81	2	53.8 ± 10.32	MPTM	12 wk	4 messages/wk about exercise, diet, medication, and importance of self- monitoring blood glucose	Usual care and did not receive any educational message	Improved significantly in HbA1C (P < 0.024), LDL (P = 0.019), cholesterol (P = 0.002), BUN (P ≤ 0.001), microalbumin, knowledge (P ≤ 0.001), practice (P ≤ 0.001), and self-efficacy (P ≤ 0.001).	This SMS improved the management of type 2 diabetes by social and behavioral constructs, such as self-efficacy and knowledge.	Yes
21	Bell et al. (41), 2012	RCT	United States	65	2	58 ± 11	MPTM	6 mo	Self-care video messages by cell phone	Standard diabetes care	Decline in HbA1C over 12 mo (P < 0.002).	One-way, mobile phone–based video messages about diabetes self-care can improve HbA1C.	Yes
22	Zolfaghari et al. (42), 2012	Q-E	Iran	77	2	18–65	MPTM	3 mo	A short message every week on diet, exercise, taking diabetic medication, self-monitoring of blood glucose, and stress management	Counseling appointments via telephone	Between control and intervention groups, there was no significant difference in HbA1C (P = 0.489), diet adherence (P = 0.438), or physical exercise (P = 0.327), but medication adherence improved in the control group (P = 0.034).	SMSs of cellular phones may improve adherence to diabetes therapeutic regimen in patients with type 2 diabetes.	No
23	Orsama et al. (43), 2013	RCT	Finland	48	2	61.9 ± 7.8	WPMD	10 mo	Remote patient reporting and an automated telephone feedback system	Standard of care	Significant reduction in HbA1c (P < 0.03) and body weight (P < 0.03).	Automated feedback improves patient outcomes in patients with type 2 diabetes.	Yes
24	Pressman et al. (44), 2014	RCT	United States	225	2	18–75	WPMD	6 mo	Telemetry device at home to transmit glucose levels, BP readings, and weight measurements to the diabetes care manager weekly	Standard care	1) Self-efficacy (P = 0.319), fructosamine (P = 0.881), and HbA1c (P = 0.310) had no significant changes between groups. 2) LDL tended to decrease more in the intervention group (P = 0.045).	The use of a telemetric device to download information for care management was associated with improved control of LDL-C.	No

¹APP, application run on a smartphone; BP, blood pressure; BW, body weight; BUN, blood urea nitrogen; G, number of groups; HbA1c, glycated hemoglobin; ID, identifier; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; LDL-C, LDL cholesterol; MPTM, mobile phone text messaging; PA, physical activity; PDA, personal digital assistant; Q-E, quasi-experiment; RCT, randomized controlled trial; SMS, short-message service; SS, sample size; TC, total cholesterol; WC, waist circumference; WPMD, wearable or portable monitoring device; %overBMI, BMI − BMI at 50th percentile for age and sex/BMI at 50th percentile × 100.

²Study IDs indicate the 1st to 24th study.

³Unless otherwise indicated, values are ranges or means ± SDs.

⁴Mean.

Most studies included small samples. Sample sizes of the selected studies ranged from 15 to 124 subjects/intervention or control group, with 8 studies (33%) with no groups of >30 subjects/group, 11 studies (46%) with 30–60 subjects/group, and 5 studies (21%) with >60 subjects/group. Two studies (8%) recruited only female subjects, whereas 22 (92%) recruited both male and female subjects (Table 2).

TABLE 2.

Summary of characteristics of 24 studies that examined mHealth interventions for obesity and diabetes treatment and management¹

Category	Number of studies (%)	Study ID2
Country/setting
United States	15 (63)	1–3, 5, 7, 8, 10, 11, 13, 14–16, 18, 21, 24
Iran	2 (8)	20, 22
Germany	2 (8)	6, 19
Spain	1 (4)	9
Australia	1 (4)	12
Finland	1 (4)	23
Italy	1 (4)	17
South Korea	1 (4)	4
Targeted disease outcome
Overweight/obesity	14 (58)	1–14
Diabetes	10 (42)	15–24
Study type
RCT	16 (67)	1–3, 5, 8, 11–13, 15, 16, 18–24
Q-E	8 (33)	4, 6, 7, 9, 10, 14, 17, 22
mHealth type
MPTM	11 (46)	1, 3–4, 7, 12, 15, 16, 18, 20–22
APP	9 (33)	2, 5, 8–11, 13, 14, 17
WPMD	4 (31)	6, 19, 23–24
Intervention time/duration
<3 mo	13 (54)	1, 3, 6, 7, 9–13, 15, 16, 20, 22
3–6 mo	7 (29)	2, 4, 8, 14, 19, 21, 24
>6 mo	4 (17)	5, 17, 18, 23
Sample size per group
≤30	8 (33)	8–13, 15, 16, 23
>30–60	11 (46)	1–3, 7, 14, 17–22
>60	5 (21)	4–6, 12, 24
Age group
Child	6 (25)	6, 7, 9, 11, 14, 16
Adult	18 (75)	1–5, 8, 10, 12, 13, 15, 17–24
Subject sex
Female	2 (8)	4, 10
Male and female	22 (92)	1–3, 5–9, 11–24

¹APP, application run on a smartphone; ID, identifier; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; MPTM, mobile phone text messaging; Q-E, quasi-experiment; RCT, randomized controlled trial; WPMD, wearable or portable monitoring device.

²See Table 1 for the related references for each study.

Types of mHealth interventions

As reported in Tables 1 and 2, based on the nature of specific mHealth technologies investigated by the selected studies, we categorized the mHealth interventions into 3 types: 1) mobile phone text messaging (MPTM), which uses text messages as the primary mode of communication between patients and health care providers; 2) an APP, which uses smartphones to deliver patient education or help patients self-manage their conditions; and 3) wearable or portable monitoring devices (WPMDs), which offer patient data collection over a wireless connection and can monitor patients’ physiological status. This classification is made based on several considerations including simplicity, understandability to a nontechnical audience, and technological complexity involved in interventions, but there could be other ways to classify. For example, from a system perspective, text messaging is an APP running on a mobile phone. Wearable devices are hardware and are associated with software.

Regarding tested mHealth intervention approaches, about half (13 studies, 54%) used MPTM, 6 (25%) used WPMDs, and 5 (21%) used APPs. Intervention durations ranged widely from only 1 wk to 2 y, although most had a short duration. Specifically, more than half (13 studies, 54%) had an intervention <3 mo (i.e., 12 wk), 7 (29%) had an intervention between 3 and 6 mo, and only 4 studies (17%) had an intervention >6 mo.

This study found that MPTM and APP were largely used to facilitate behavior changes in patients with obesity or diabetes by providing patients with knowledge and tips for weight or blood sugar control, providing reminders about some activities to control them, providing social support, and collecting patient physiological data, such as body weight and amount of physical activity (PA), for self-monitoring and disease management (23, 26). MPTM was mainly used for providing knowledge and tips on diet, PA, and drugs, whereas APPs played versatile roles in disease control, such as providing feedback to help with positive behavior changes and serving as data collection platforms.

In contrast, our review suggested that WPMD were used exclusively for data collection (patient monitoring). The interventions transmitted to mobile devices based on the awareness of patient status through patient monitoring included 1) information related to controlling one’s weight and diabetes; 2) reminders about diet, PA, or medication; 3) feedback on food consumption, PA, anthropometry, or laboratory test results; and 4) supportive remarks to encourage and motivate patients to continue making positive behavior changes.

Interestingly, the use of MPTM was more common in short-term studies and obesity interventions, whereas APPs and WPMD were more popular in long-term studies and for obesity and diabetes interventions, respectively (Table 3).

TABLE 3.

The types and specific functions of mHealth interventions for obesity and diabetes treatment and management¹

	Study ID2
	Overweight/obesity														Diabetes
Type and specific function provided by interventions	13	24	33	44	55	63	73	84	93	103	113	123	133	144	153	163	175	185	194	203	214	223	233	244
MPTM
Knowledge/tips
Diet	X		X	X								X			X					X	X	X
PA			X	X								X			X					X	X	X
Drug																				X	X	X
Reminder
Glucose test															X	X						X
PA															X
Social support6							X													X	X	X
Surveillance
Diet			X	X
PA			X	X											X
BW/WC				X
BP				X
Blood sugar															X
Feedback
Diet				X
PA				X
APP
Knowledge/tips
Diet													X				X	X
Drug																	X	X
Reminder
Weighed self								X			X
PA										X	X			X
Glucose test																	X
Social support		X						X										X
Data collection
Diet		X			X			X	X				X				X	X
PA		X			X			X	X
BW													X
Blood sugar																	X	X
Feedback
Diet					X			X			X							X
PA					X			X			X							X
Blood sugar																		X
WPMD
Surveillance
Diet						X
PA						X													X				X
BW																			X				X	X
Blood sugar																							X	X
BP																							X

¹APP, application run on a smartphone; BP, blood pressure; BW, body weight; ID, identifier; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; MPTM, mobile phone text messaging; PA, physical activity; WC, waist circumference; WPMD, wearable or portable monitoring device.

²See Table 1 for the related references for each study.

³Length of intervention period: <3 mo.

⁴Length of intervention period: 3–6 mo.

⁵Length of intervention period: >6 mo.

⁶Patient-patient and patient–health care giver communication.

Table 4 shows that among 24 studies, 17 (71%) were pilot studies, indicating that those studies were at the early stage of their research. Among 11 studies (46%) that provided incentives to participants, such as money or mobile devices, 8 (73%) had intervention periods >3 mo. The average dropout rate across all studies that reported this information was 17.4%. Obesity intervention studies had a higher average drop-out rate (19.6%) than those on diabetes interventions (13.9%).

TABLE 4.

The implementation of mHealth interventions for obesity and diabetes treatment and management¹

	Study ID2
	Overweight/obesity														Diabetes
Implementation of mHealth intervention	13	24	33	44	55	63	73	84	93	103	113	123	133	144	153	163	175	185	194	203	214	223	233	244
Was a pilot study	Y	Y	Y	N	N	Y	Y	Y	Y	Y	Y	N	Y	Y	Y	Y	Y	N	N	N	N	Y	Y	Y
Provided incentive to subjects6	N	Y	N	N	Y	N	N	N	Y	N	N	Y	N	Y	N	Y	N	Y	Y	N	Y	N	Y	Y
Subject drop-out rate, %	17	10	15	23	14	NA	NA	35	NA	14	27	14	10	37	0	11	18	23	6	19	8	NA	NA	12

¹ID, identifier; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; N, no; NA, not available; Y, yes.

²See Table 1 for the related references for each study.

³Length of intervention period: <3 mo.

⁴Length of intervention period: 3–6 mo.

⁵Length of intervention period: >6 mo.

⁶In this row, N indicates no or not available.

Applications of mHealth interventions by disease (obesity and/or diabetes)

Out of the 24 studies reviewed, 14 mHealth interventions (58%) were for overweight or obesity treatment and management, whereas 10 (42%) were for diabetes. APPs were used mostly in obesity control (8 for obesity compared with 2 for diabetes). MPTM interventions were equally used with 5 MPTM interventions for both obesity and diabetes treatment and management, respectively. As a new source for data collection, WPMD were less prevalent than the other 2 types of mHealth interventions (1 for obesity, 3 for diabetes).

Findings of the effects of mHealth interventions

Qualitative findings.

For the 14 studies on mHealth interventions for overweight or obesity management, 9 (64%) reported positive results (16, 25, 27, 28, 30, 31, 33–35), 6 studies (43%) reported weight loss or waist circumference decrease (16, 25, 27, 34, 35), 5 studies (36%) reported behavior changes (16, 28, 31, 33, 35), and 2 studies (14%) reported secondary outcomes, such as acceptability, biochemical tests, or blood pressure with statistically significant improvement (25, 31). Shorter-term interventions tended to get more positive results in weight-control studies than did the longer-term interventions (Table 5).

TABLE 5.

The effects of mHealth interventions on various outcomes related to obesity and diabetes, by type of mHealth intervention¹

	Study ID2
	Overweight/obesity														Diabetes
Outcomes examined	13	24	33	44	55	63	73	84	93	103	113	123	133	144	153	163	175	185	194	203	214	223	233	244
MPTM
Weight loss	X			X								X	X
Weight maintenance
Waist circumference				X									X
Biochemical test				X														X		X	X
Self-efficacy
Acceptability							X
Behavior												X
Blood pressure													X
Symptoms
APP
Weight loss														X
Self-efficacy		X
Acceptability
Behavior									X	X				X
Biochemical test
WPMD
Weight loss						X													X				X
Biochemical test						X													X				X
Behaviors						X
Medicine dose																			X
Blood pressure

¹All outcomes (see Table 1 for details) denoted with an X were statistically significant (P ≤ 0.05). APP, application run on a smartphone; ID, identifier; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; MPTM, mobile phone text messaging; WPMD, wearable or portable monitoring device.

²See Table 1 for the related references for each study.

³Length of intervention period: <3 mo.

⁴Length of intervention period: 3–6 mo.

⁵Length of intervention period: >6 mo.

For the 10 studies on mHealth interventions for diabetes treatment and management, 5 (50%) reported statistically significantly improved results in primary outcomes or clinical biochemical analysis, such as blood glucose, HbA1c, and blood lipids (14, 39, 40, 41, 43). Two WPMD intervention studies reported positive results, but the only APP intervention appeared to have no effect on controlling diabetes. It is notable that behavior changes after receiving interventions did not reach statistical significance in any diabetes control studies (Table 5).

Quantitative findings.

As shown in Table 6, 6 studies on mHealth interventions for obesity management reveal significant body weight loss (16, 25, 27, 34, 35); the highest average body weight loss was 7.1 kg (pre- and postintervention comparison) (27); the highest proportion of weight loss after receiving mobile interventions was 9.4% higher than that at baseline (34). Some research reported that a mobile intervention reduced waist circumference by 3.0 cm (25), reduced sedentary time by 47.2 min/d (31), increased completion rate of self-registrations of diet and PAs by nearly 20% (30), and increased light PA time by 31 min/d and moderate-to-vigorous PA time by 16.3 min/d (31).

TABLE 6.

Key quantitative outcomes with statistical significance of 24 studies that examined mHealth interventions for obesity and diabetes treatment and management¹

mHealth methods, targeted outcomes, and study ID2	Outcome	Key results	P
Patient education
PA
1	BW, kg	DI − C = −1.97	0.02
4	WC, cm	DI, A − B = −3.0, DC, A − B = −0.9	<0.001
	BW, kg	DI, A − B = −2.0, DC, A − B = −0.7	<0.001
12	BW, kg	DI − C = −2.2	<0.05
20	HbA1c, %	DI, A − B = −0.89, DC, A − B = 0.35	<0.024
21	HbA1c, %	DI − C = −0.2	<0.002
Diet
4, 12, 20, 21	↑	↑
13	BW, %	DI, A − B = −9.4, DC, A − B = 0.6	<0.001
18	HbA1c, %	DI, A − B = −1.9, DC, A − B = −0.7	<0.001
Diabetes medicine
18, 20, 21	↑	↑
Surveillance
PA
4	↑	↑
6	BW, kg	DI − C = −7.1	<0.01
9	%CSD	DI − C = 19.4	0.009
%CSPA	DI − C = 24.11	0.001
19	BG, mmol/L	DI, A − B = −1.0	<0.001
HbA1c, %	DI, A − B = −0.8	<0.001
23	HbA1c, %	DI, A − B = −0.4, DC, A − B = 0.036	<0.03
Diet
4, 9, 18, 23	↑	↑
6	BW, kg	DI − C = −7.1	<0.01
BG
18, 23	↑	↑
BW
19	↑	↑
Feedback to subjects
PA
4, 18	↑	↑
Diet
4, 13, 18	↑	↑
BW
13	↑	↑
BG
18	↑	↑
Behavioral reminder
PA
10	ST, min	DI, A − B = −47.2	<0.05
	LPA, min	DI, A − B = +31.0	<0.05
MVPA, min	DI, A − B = +16.3	<0.05
14	%overBMI	DI, A − B = 7.1	<0.05
BG test
21	↑	↑
Social support3
18, 20, 21	↑	↑

¹BG, blood glucose; BW, body weight; D_C, _{A − B}, difference between before and after intervention in the control group; D_{I, A − B}, difference between after and before intervention in the intervention group; D_{I − C}, difference between the intervention group and the control group; HbA1c, glycated hemoglobin; ID, identifier; LPA, light physical activity; mHealth, use of mobile and wireless technologies and wearable devices for improving the health care processes and outcomes; MVPA, moderate-to-vigorous physical activity; PA, physical activity; ST, sedentary time; WC, waist circumference; %CSD, percentage of completed self-register of diet; %CSPA, percentage of completed self-register of PA; %overBMI, BMI − BMI at 50th percentile for age and sex/BMI at 50th percentile × 100; ↑, see the result with the same study number above.

²See Table 1 for the related references for each study.

³Patient-patient and patient–health care giver communication.

Among studies on diabetes, the mHealth interventions used in 5 studies resulted in decreased HbA1c (14, 39, 40, 41, 43). The greatest percentage reduction of HbA1c was ∼1% (40), and blood glucose reduction was ∼1 mmol/L (39).

Discussion

The growing global obesity and diabetes epidemic affects both developed and developing counties, many of which have limited resources to help patients fight the related consequences (45, 46). At present a large number of people worldwide suffer from the epidemic, with >40% of adults being overweight or obese globally. The number of obese and diabetic patients will continue to increase at least in the near future. Effective and sustainable intervention programs are needed to improve patients’ health and reduce care cost. The use of mobile and wireless technologies (i.e., mHealth interventions), attributable to the pervasiveness and ubiquity of mobile, handheld devices, to support obesity and diabetes treatment and management, including long-term care and self-management by patients, may transform health service delivery across the globe (13).

Some earlier literature reviews documented the dramatic increase of mHealth use (18, 47–49). For example, there were >1000 commercial APPs for diabetes care in the Google Play Store (for Android) and 605 in the Apple App store (for iOS) in 2013 (18). Previous reviews evaluated the usability, feasibility, and acceptability or patient preferences of mHealth interventions (49, 50). However, few existing reviews have assessed the impact of mHealth on disease-specific clinical outcomes (50, 51). In addition, previous reviews focused on certain types of mHealth tools only (e.g., mobile phone messaging applications) (47, 48, 50, 51) or focused only on specific populations, mHealth intervention methods (e.g., only on phone text messages), or outcomes (e.g., only on obesity or BMI) (52–54).

To fill the research gaps and help guide future research, our systematic review evaluated the whole spectrum of effects of mHealth interventions for obesity and diabetes treatment and management. We categorized mHealth intervention methods into 3 types, namely MPTM, APPs, and WPMDs, based on the type of information and communication technologies used. MPTM plays an important role in providing knowledge outreach, activity reminders, tools for social support, feedback for behavior changes and maintenance, and sometimes serves as an indirect channel for data collection and monitoring. MPTM has been more easily and widely used than the 2 other types of mHealth interventions because it is easy to implement, but it is largely limited to patient education. APPs have been playing versatile roles in interventions, whereas WPMDs mainly support health-related data collection.

The subject dropout rate was higher in studies related to controlling overweight and obesity than those related to controlling diabetes (25, 29, 32, 35–37, 39, 41, 44), suggesting that interventions may be more difficult to adopt and less well perceived by patients with overweight and obesity than by diabetic patients because diabetes is likely viewed by patients as a more severe condition than obesity. For both types of studies, incentives were important to maintain participants’ adherence to interventions, which highlights the importance of investigating the self-sustainability of such mHealth interventions in future studies.

According to behavior change theories, such as the Knowledge-Attitude-Practice Model (55), the Health Belief Model (56), Social Cognitive Theory (57), and the Stages of Change Model (57), mHealth interventions can serve multiple functions in the self-management of obesity or diabetes (58). Our review suggests that the majority of the mHealth interventions targeted improving nutrition-related behaviors, including diet and PA, and this was particularly true for obesity management. Moreover, existing mHealth applications for diabetes treatment and management have been expanded to nonnutrition aspects, for example, WPTM is used to provide personalized guidelines for medication and lifestyle behaviors (14), and WPMDs are used to transmit glucose, blood pressure, and weight information to care providers to improve care management (44).

Our review results show that the effects of mHealth interventions are heterogeneous across studies, which may be partially because of the different outcomes targeted and assessed. The selected studies assessed a wide range of health outcomes, although most studies focused only on 1 or 2 outcomes. The primary and direct outcomes were weight loss, weight maintenance, and waist circumference reduction for obesity studies, and blood glucose and HbA1c control and reduction for diabetes studies. The most important secondary outcomes of both types of studies were health behaviors, such as PA and diet. Some studies also assessed other clinical measurements and management actions, such as blood pressure, medicine dose, self-efficacy, social support, and acceptability of mHealth (25, 28, 38, 40–42). We found the mHealth interventions were more effective if they targeted PA-related outcomes than other outcomes, with 11 of 18 studies that targeted PA reporting positive effects on increased PA and decreased body weight and waist circumference. Second to PA interventions were the interventions targeting diet-related outcomes, with 9 of 16 studies that targeted diet outcomes reporting improved diet. Our results suggest that mHealth intervention effectiveness is outcome-, context-, and intervention-dependent. Findings may vary even when similar mHealth tools are used (e.g., MPTM or an APP) in different intervention designs and settings.

Despite the limitations of the 24 studies, such as short intervention durations, >50% of the studies reported some desirable, positive effects on obesity and diabetes control based on the primary outcomes (14, 16, 25, 27, 28, 30, 31, 33–35, 39–41, 43). This is encouraging for future related research and interventions. Theoretically, behavior changes should precede changes in anthropometry and laboratory examination outcomes because obesity and diabetes are lifestyle-related diseases. However, the studies included in this review reported that mobile interventions were less effective in changing behavior than they were in changing anthropometry or laboratory results (14, 16, 25, 34, 40, 41). This finding may be attributable to the limitations of the published studies and the challenges of evaluating health behaviors and changes in those behaviors. For example, evaluations of PA and eating behaviors in previous studies were typically based on self-reported questionnaires that included only subjective questions. There is a need for more objective and precise measurements of behavior changes in future research.

Although more than half of the 24 studies demonstrated some positive effects of mHealth interventions on obesity or diabetes control, our findings related to the effectiveness of mHealth should be interpreted with caution. This is also because of the limitations of the published related studies in study design and implementation. First, sample sizes of most available studies were small. Only 5 of 24 studies had >60 subjects in the intervention and control groups (25–27, 33, 44). Second, the intervention period in more than half of the studies was <3 mo. Such short-term interventions showed more positive effects than long-term interventions (>6 mo). Some studies revealed that long-term effects of mHealth technologies were difficult to maintain in obesity and diabetes interventions for a number of reasons. For example, if an intervention is long enough to cover holiday seasons, changes in diet and PA patterns during the holidays may affect the intervention (26, 35, 54), the burden of long-time adherence to self-monitoring may also potentially affect long-term effects (26, 35, 54), and personal contact and coaching in addition to self-monitoring may be needed to achieve the desired long-term effects (54). Third, most of the studies were carried out in developed countries such as the United States; therefore, the findings may not be generalizable to developing countries. There are some limitations of our study. Because of the highly heterogeneous characteristics of the selected studies, a quantitative meta-analysis was not possible. Despite these limitations, this study provides a broad overview of mHealth applications for the treatment and management of obesity and diabetes and sheds some light on future research.

In conclusion, a growing body of research has investigated some mHealth interventions for the management and treatment of obesity and diabetes across countries, with most conducted in the United States. Although the preliminary evidence collected from existing research is mixed, mHealth interventions are likely a promising means to promote behavior changes among patients with chronic diseases by providing them with health information and timely suggestions for improving health behaviors, providing them with feedback and social support, helping them collect health data, and showing data to patients and their care providers. In the future, more research with rigorous and innovative study designs and intervention strategies should be conducted. In addition, studies with large sample sizes and long-term interventions and follow-ups are needed to help assess the effectiveness of mHealth intervention programs and their impact on multiple health-related outcomes more thoroughly and objectively.