Smartphone Interventions for Long-Term Health Management of Chronic Diseases: An Integrative Review

Abstract

Introduction: Long-term health management is challenging for the rapidly growing number of patients with chronic diseases. Smartphone interventions offer promising solutions. This article presents features of smartphone interventions for long-term chronic condition management, illustrating how these applications benefit patients with chronic diseases. Materials and Methods: Systematic searches for smartphone health interventions were conducted in five publication databases. Articles were included only if (1) the smartphone application (app) was exclusively developed for patients with chronic diseases and (2) the article incorporated a defined outcome measurement to evaluate the effects of the implemented intervention. Sixteen articles were included in the final review, including studies in diabetes, mental health problems, overweight, cancer, and chronic obstructive pulmonary disease. Results: These studies found that the smartphone intervention was a completely or at least partially effective tool to assist in managing some chronic diseases. With the help of health-related smartphone apps, patients with chronic conditions (1) felt secure in the knowledge that their illnesses were closely monitored, (2) participated in their own health management more effectively, and (3) felt that they had not been forgotten by their doctors and were taken good care of even outside the hospital/clinic. Conclusions: However, there are limited smartphone apps for the long-term health management of chronic diseases. More smartphone apps need to be developed to help people manage chronic diseases.

Introduction

The number of chronic diseases and the number of people with chronic illnesses are both increasing.¹ The life expectancy of people with chronic conditions has been prolonged because of improved treatments. One of the most serious challenges facing healthcare systems is empowering and engaging people with chronic diseases to manage their own health and/or therapeutic programs. With the rapid development of information science and technology, electronic health (e-health) has become an important tool for healthcare systems. The World Health Organization (WHO) defines e-health as the transfer of health resources and healthcare by electronic means.² E-health tools effectively communicate the right information needed by different audiences at the right time, in the right place, and in the best ways to guide healthcare and health promotion. The e-health tools can be interactive, interoperable, easy to use, engaging, adaptable, and accessible for diverse audiences.³

Mobile health (m-health) is a type of e-health, defined by WHO as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices.⁴ A 2011 WHO survey found that 83% of the 112 participating member countries reported at least one m-health initiative in their country.⁴ Three-quarters of WHO member countries reported four or more types of m-health initiatives.⁴ The types of m-health initiatives covered in the survey included communication between individual patients and health service providers, communication between healthcare professionals, intersectional communication in emergencies, health monitoring, and surveillance, and access to information for healthcare professionals at the point of care.⁴ Globally, the types of m-health initiatives most frequently reported were health call centers or telephone help lines (59%), emergency toll-free telephone services (55%), emergencies (54%), and mobile telemedicine (49%). The least frequently reported m-health initiatives were health surveys (26%), surveillance (26%), raising awareness (23%), and decision support systems (19%).⁴ By adding modern technology to healthcare systems, m-health empowers users to take advantage of health information and healthcare services at anytime and anywhere. Mobile technologies allow providers to help patients improve their health in real time, enabling them to personalize healthcare options and monitor progress.⁴ Mobile phones are widely used in the domain of m-health for their low cost and portability.⁵

M-health often uses and capitalizes on a mobile phone's core utility of voice and short messaging service (SMS) as well as more complex functionalities and applications (apps), including general packet radio service, third- and fourth-generation mobile telecommunications, global positioning system, and Bluetooth^® (Bluetooth SIG, Kirkland, WA) technology.² Studies have documented the effectiveness of mobile phone use in healthcare and clinical practice. Bluetooth, general packet radio service, wireless local area network, and third generation–based wireless networks have been used in management of chronic pulmonary patients in Turkey, and the real-time monitoring system improved classification accuracy and facilitated tracking of these patients.⁵ Some studies have highlighted the successful use of mobile phones to support telemedicine and remote healthcare in developing countries.⁶ Several studies have assessed the use of SMS in the management of behavior changes,^7,8 such as smoking,⁹ in the prevention of illness, such as acquired immunodeficiency syndrome,¹⁰ in the management of chronic diseases, such as diabetes,¹¹ and in the improvement in medication compliance and the quality of drug therapy.¹² Lester et al.¹³ confirmed in a randomized trial the effectiveness of mobile phone SMS in promoting antiretroviral treatment adherence in Kenya. In health education, Wangberg et al.¹⁴ used SMS to educate the parents of diabetic children every day and positively affected knowledge acquisition.

In comparison with traditional mobile phones, smartphones provide mobile access to many computer functions such as Web browsing, e-mail, and social networks. They also contain powerful on-board computing capability, capacious memories, large screens, and open operating systems that encourage app development.¹⁵ Apps are software programs designed specifically to run on smartphones devices. By December 2008 there were over 10,000 third-party apps officially available for the iPhone^® (Apple^®, Cupertino, CA) alone.¹⁶ Mobile future, a broad-based coalition of businesses and nonprofit organizations in Washington, DC, reported that the number of smartphone apps downloaded increased massively, going up from 300 million apps downloaded in 2009 to 5 billion in 2010.¹⁷ By 2016, it is estimated that more than 44 billion apps will have been downloaded, which is equivalent to 6 app downloads for every person in the world.¹⁸ In the “health and fitness” category in Apple's App Store, developers have created thousands of downloadable apps for Apple's mobile devices.¹⁹

Some researchers have developed health-related smartphone apps to help people with chronic diseases manage their health or therapies. Researchers also have integrated smartphone apps into clinical practice, which is a new and potentially effective health promotion strategy. So far, applying smartphone interventions in healthcare is just at the exploratory stage. Few researchers have well-defined, disease-specific, and progress-relevant, targeted outcomes with comprehensive evaluation of efficacy. Standard, systematic, and widely used m-health strategies have the potential for improved, patient-centered management of chronic disease. Therefore, we performed this content analysis to identify the focus on chronic diseases of previous studies, how the interventions were performed, whether the outcomes turned out to be effective, and which aspects could be improved. Hopefully, this general picture we draw based on literature review will benefit future research and development in m-health care.

Materials and Methods

Data Sources

In order to find eligible intervention studies, we conducted a literature search for the use of smartphone apps in the management of chronic illness in the Pubmed, EBSCO, Springer, Elsevier, and ProQuest databases, although they have overlaps. The key words we searched were “smartphone application,” “smartphone app,” “smartphone apps,” and “mobile phone application.” We searched articles published between January 2000 and December 2012, when the search was completed. Finally, a snowball review was done, in which selected articles from the literature search were screened and checked for potential eligible studies. Based on the titles and abstracts, the first selection was conducted by two experienced reviewers, who excluded the articles that did not fulfill the inclusion criteria. The full articles were obtained for all studies that seemed to meet the necessary criteria. Two reviewers each checked and verified all the articles and decided about the final inclusion of the study.

Inclusion and Exclusion Criteria

A study was included if (1) a clinical trial determined whether the smartphone app was effective in managing the focus chronic disease and (2) clear and definite methods evaluated health-related outcomes. Studies were excluded if the intervention did not use third-party apps. Also, only original research articles were included in this review.

Results

This approach yielded 16 studies that met our inclusion/exclusion criteria. An overview of these 16 articles is described in Table 1. The contents of these 16 articles are presented in Table 2.

Table 1.

Overview of the 16 Articles Included

DISEASE, REFERENCE, DIAGNOSIS, NATIONALITY (PUBLICATION YEAR)	NUMBER OF SUBJECTS	AGE (YEARS)	SEX	EDUCATION	ELIGIBILITY CRITERIA OF PARTICIPANTS
Diabetes
Harris et al.,²⁰ diabetes, United States (2010)	Total n=14 Phase 1: n=6 Phase 2: n=8	Phase 1: 18∼65 Phase 2: 18∼70	NS	NS	1. Have access to the Web-based electronic medical record and case management 2. Phase 1: type 1 or type 2 diabetes 3. Phase 2: have completed a 3-month pilot feasibility trial of the wireless glucose meter uploads and mobile phone data displays
Curran et al.,²¹ diabetes, United Kingdom (2010)	n=6	NS	NS	NS	NS
Charpentier et al.,²² type 1 diabetes, France (2011)	Total n=162 G1: n=61 G2: n=60 G3: n=59	NS	NS	NS	1. Age >18 years old 2. Type 1 diabetes (>1 year) 3. On a basal-bolus insulin regimen (>6 months), either with MDI or with a pump 4. HbA1c ≥8% 5. Not in a diabetes educational program within 3 months before the study 6. Not required to receive follow-up more frequently than the quarterly visits
Rollo et al.,²³ type 2 diabetes, Australia (2011)	n=10	NS	NS	NS	1. Type 2 diabetes 2. BMI 24.1–47.9 kg/m² 3. Not eligible to participate in a large-scale trial of an automated telephone system for diabetes self-management
Quinn et al.,²⁴ type 2 diabetes, United States (2011)	Total n=163 Control group (G1): n=56 Intervention groups: G2, n=23; G3, n=22; G4, n=62	G1: 53.2±8.4 G2: 52.8±8.0 G3: 53.7±8.2 G4: 52±8.0	Male: 81 (49.7%) G1: 28 (50%) G2: 12 (52.2%) G3: 10 (45.5%) G4: 31 (50%)	High school/trade school or less: 49 (30.0%) College or associate: 63 (38.7%) Bachelor degree or higher: 51 (31.3%)	1. Physician diagnosis of type 2 diabetes for >6 months 2. HbA1c ≥7.5% within 3 months 3. Obese (BMI ≥30 kg/m²) 4. Mean PHQ-9: 5.2 (minimal to mild depression scores) 5. Hypertension: 103 (63.2%) 6. Hypercholesterolemia: 95 (58.3%) 7. Have access to the Web-based patient portal
Mental health problems
Morris et al.,²⁵ emotional disorder, United States (2010)	n=10	37±5.75	Female: 6 (60%)	NS	1. Have completed a Mayo Clinic Health Risk Assessment and have a stress level of 3 or above on a 5-point scale 2. Without extensive travel 3. No current involvement in psychological or psychiatric treatment
Burns et al.,²⁶ depression, United States (2011)	n=8	37.4±12.2	Female: 7 (87.5%)	At least a high school education, completing on average 14.3 years of education (SD 2.3)	1. Age ≥19 years old 2. Live in the United States 3. A diagnosis of major depressive disorder using the telephone-administered Mini-International Neuropsychiatric Interview 4. QIDS-C total score >11 5. PHQ-8 total score >10 6. Without sensory impairments, dementia, current participation in psychotherapy, initiation of an antidepressant medication in the past 10 days, or a severe psychiatric condition 7. Be within a cellular network range most of the day 8. Have an e-mail account, computer, broadband Internet access, and comfortable using the Internet and mobile phones
Rizvi et al.,²⁷ personality disorder, United States (2011)	n=22	33.86±10.27	Male: 4 (18.2%)	High school diploma or less: 13.6% Some college: 45.4% College degree: 31.8% Graduate or higher: 4.5%	1. Age ≥18 years old 2. Meet criteria for borderline personality disorder and substance use disorders 3. Aware of their diagnoses 4. In DBT treatment for at least 2 months and plan to remain in DBT for the next 30 days 5. Have been previously taught the DBT skill opposite action 6. Without other conditions that make them ineligible for DBT treatment in general
Reid et al.,²⁸ youth mental health problems, Australia (2011)	Total n=114 Intervention group: n=68 Comparison group: n=46	14–24	NS	NS	1. Age 14–24 years old 2. Have a mild or more severe emotional/mental health issue as assessed by their GP, or K10 Symptom score of >16 3. Not have a severe psychiatric or medical condition
Ly et al.,²⁹ depression, Sweden (2012)	Total n=120 Intervention group: n=60 Comparison group: n=60	NS	NS	NS	1. An ongoing primary diagnosis of major depression of mild to moderate character, not directly linked to another major primary psychiatric disorder 2. Medication allowed with the stable dose 3. Without suicidal intent or a history of misuse of alcohol or drugs 4. Have a smartphone and have access to the Internet
Obesity
Turner-McGrievy and Tate,³⁰ overweight, United States (2011)	Total n=96 G1 (Podcast-only): n=49 G2 (Podcast+Mobile): n=47	G1: 43.2±11.7 G2: 42.6±10.7	G1: 13 males (26.5%) G2: 11 males (23.4%)	College or less: G1, 19 (39%); G2, 24 (51%) Graduate degree: G1, 30 (61%); G2, 23 (49%)	1. BMI ≥32.6 kg/m² 2. Have a stable medical status and a controlled thyroid condition 3. Without an eating disorder or a history of myocardial infarction, stroke, or hypertensive or bone and joint issues 4. Not smoking, not in treatment for alcohol or drug dependency, not currently in a weight loss program, and not pregnant, breastfeeding, or planning on becoming pregnant within the next 6 months 5. Have access to and understand body weight scale for self-monitoring weight 6. Own one of four types of Internet-capable mobile devices: iPhone, iPod touch, BlackBerry, or an Android-based phone 7. Have access to the Internet and a computer 8. Be able to attend the three monitoring visits
Kirwan et al.,³¹ overweight, Australia (2012)	Total n=200 Intervention group: n=50 Control group: n=150	Intervention group: 39.3±12.8 Control group: 40.1±12.1	Male: Intervention group: 24 (48.0%) Control group: 72 (48%)	NS	1. Intervention participants from the 10,000 Steps program 2. Remain engaged with the intervention over a period of time (logged steps on at least one occasion in the 3 months pre-intervention) 3. Having access to an iPhone or iPod touch
Pellegrini et al.,³² weight loss, United States (2012)	n=96	NS	NS	NS	1. Age 18–60 years old 2. BMI 30.0–39.9 kg/m² 3. Interested in losing weight through diet and physical activity changes 4. Weight stable over the past 6 months 5. Not receiving concurrent weight loss treatment 6. Willing to self-monitor (on smartphone or paper) and wear an accelerometer daily for 6 months 7. Able to attend all assessments and group sessions 8. Have or be willing to learn basic technology usage skills
Cancer
Weaver et al.,³³ colon cancer, United Kingdom (2007)	n=6	54–76 (median, 64)	Male: 4 (66.7%)	NS	1. Stage II or III colon cancer patients with complete resection of the primary tumor 2. Treated with capecitabine+bevacizumab 3. Willing and capable of entering their own symptoms and temperature onto the phone twice a day
Klasnja et al.,³⁴ breast cancer, United States (2011)	n=9	57.6 (median, 57)	Female: 9 (100%)	Lower: 1 College degree: 5 Graduate degree: 3 (PhD, 1; Master, 2)	1. Undergo active treatment for cancer 2. Stage I, n=4; Stage II, n=1; Stage III, n=3; metastatic patient with Stage IV illness, n=1 3. Time of beginning the study: before surgery, n=2; in chemotherapy, n=2; in radiation therapy, n=2; begin aromatase inhibitor treatment, n=2; prepare for a reconstruction surgery, n=1 4. Have cancer for the first time (n=7); have a recurrence (n=2) 5. Own a home computer and a mobile phone, use computer and mobile phone several times a week or more often
COPD
Liu et al.,³⁵ COPD, Taiwan (2008)	Total n=48 Intervention group G1: n=24 Control group G2: n=24	G1: 71.4±1.7 G2: 72.8±1.3	Male G1: 24 (100%) G2: 24(100%)	NS	1. Age 40–80 years old 2. Moderate to severe COPD compliant with the criteria of the GOLD guidelines 3. Without an acute exacerbation 4. Had not received therapy with oral corticosteroids for ≥3 months before the study 5. Have a stable regimen of medications 6. Without undergoing an oxygen therapy 7. Without presence of illness that would influence the exercise training

BMI, body mass index; COPD, chronic obstructive pulmonary disease; DBT, dialectical behavior therapy; G, group; GOLD, Global Initiative for Chronic Obstructive Pulmonary Disease; GP, general practitioner; HbA1c, glycated hemoglobin; MDI, multiple daily insulin injections; NS, not stated; PHQ, Patient Health Questionnaire; QIDS-C, Quick Inventory of Depression Symptoms—Clinician Rated.

Table 2.

Summary of Key Elements of Smartphone Intervention Studies

DISEASE, REFERENCE, INTERVENTION DURATION, BASELINE	INTERVENTION	ITEMS OF ASSESSMENT	TIME OF ASSESSMENT	METHOD OR INSTRUMENT OF ASSESSMENT	OUTCOMES
Diabetes
Harris et al.,²⁰ (—), (—)	HealthReachMobile is a suite of mobile phone applications designed to help patients with diabetes understand day-to-day trends in their blood glucose and communicate with care providers between office visits. In the 1st phase, participants were observed and interviewed while using the prototypes under simulated conditions. A modified think-aloud design protocol was used to elicit participants' perspectives.	1. The wireless upload and automated messaging features 2. The prototype's design strengths, weaknesses, and potential usefulness	—	A modified think-aloud design protocol and interview	1. The wireless upload process was convenient and easy. 2. Value of the system was tied to real-world relationships. 3. Automated messages were not useful as designed. 4. Graphical and tabular feedback on the phone was useful.
	In the 2nd phase, participants who had completed a 3-month pilot feasibility trial were asked about their overall experience.	1. Overall experience2. The confusion and suggestions for modifications	3 months	Interview
Curran et al.,²¹ 2 weeks, (—)	INNSULIN is a Java application for mobile phones. The user selects the food and how many servings he or she will eat, the activity he or she does, and how long he or she does it. The network then calculates the correct dose and displays results to the user, telling the user how much insulin he or she intends to inject.	1. User interface2. Test the remote sending of data over a 3G network to a centralized server	Post-intervention		1. The intensive insulinotherapy approach is of value and has the ability to store large amounts of past glucose results.2. The neural network approach has implications for future intelligent insulin pumps.3. The diabetes results were sent in real time from a phone to a secure Web site where the consultant endocrinologist can view the insulin levels of each patient.
Charpentier et al.,²² 6 months, not significantly different (p>0.05)	G1: usual quarterly follow-upG2: home use of a smartphone recommending insulin doses with quarterly visitsG3: use the smartphone with short teleconsultations every 2 weeks but no visiting until end point	1. HbA1c	Pre-intervention/post-intervention	Laboratory examination	G3<G1 (p<0.05)
		2. Hypoglycemic episodes		Reported by participants	Not significantly different (p>0.05)
		3. Medical time spent for hospital		—	Not significantly different (p>0.05)
		4. Telephone consultation		—	Not significantly different (p>0.05)
		5. Hospital visits		Medical record	G1 and G2 are about 5 h more than G3 (p<0.05)
Rollo et al.,²³ 3 days, (—)	The Nutricam program was installed on three mobile phones (K800i; Sony Ericsson Mobile Communications AB, Sweden), and the camera resolution was set to 1 megapixel (1280×960 pixels). The food diary was used as the reference method. At each eating occasion, subjects recorded their intake over a 3-day period using both Nutricam and a written food diary. The photograph of food items and a voice recording that explained the contents of the photograph were sent to a Web site and analyzed by a dietitian.	Evaluation of the Nutricam	Post–intervention	Interview	Individual differences in energy intake between the two records varied from 6.7% to 29.7%. On average, energy intake was significantly under-recorded using the Nutricam. In total, 144 Nutricam entries were received.
		Usability and acceptability			All subjects reported that the mobile phone system was easy to use. The level of detail provided in the voice recording and food items obscured in photographs reduced the quality of the mobile phone records. The system was considered an acceptable alternative to written records and has the potential to be used by adults with type 2 diabetes for monitoring dietary intake by a dietitian.
Quinn et al.,²⁴ 1 year, G4 had higher baseline HbA1c than G1 (9.9% versus 9.2%, p=0.04); others not significantly different (p>0.05)	The intervention was a patient-coaching system and provider clinical decision support. The patient-coaching system included a mobile diabetes management software application and a Web portal. The patient Web portal augmented the mobile software application and consisted of a secure messaging center. The provider portal had different views of patient data on the basis of study group assignment: G1, UC; G2, CO; G3, CPP (allowed providers to access unanalyzed patient data); G4, CPDS (allowed providers to access analyzed patient data linked to standards of care and evidence-based guidelines).	1. HbA1c	Pre-intervention and 3rd, 6th, 9th, 12th month	Bayer DCA 2000	1. G4 decreased 1.9%, G1 decreased 0.7% (p<0.001)2. G2<G1 (p=0.02)3. CO and CPP mean HbA1c levels also decreased over 12 months. Both had greater 12-month HbA1c reductions than UC (CO, p=0.02; CPP, p=0.045).
			Pre-intervention and 3rd, 6th, and 9th months	Collect from patients' medical charts	G4 had a significantly greater decrease than G1 (p<0.001).G2 and G3 were similar to G4 (p>0.05 for both comparisons).
		2. Depressive symptoms	Pre-intervention/post-intervention	PHQ-9, 17-item Diabetes Distress Scale	Not significantly different (p>0.05)
		3. Patient-reported symptoms associated with diabetes		9-item version of the Self-Completion Patient Outcome Instrument	Not significantly different (p>0.05)
		4. Clinical measurement related to diabetes complications (blood pressure, lipid levels)		Obtained from provider medical office records	Not significantly different (p>0.05)
		5. Hypoglycemic events, hospitalization, and emergency room visits		Quarterly telephone calls to patients	Not significantly different (p>0.05)
Mental health problems
Morris et al.,²⁵ 4 weeks (—)	Participants reported their moods several times a day on a Mood Map and activated mobile therapies as needed via their mobile phones. During weekly open-ended interviews, participants discussed their feeling of using the device and responded to longitudinal views of their data.	1. The way the phone application used for self-reflection and coping stressful situations2. Other ways the application had been shaped to nuances of their lives	1st, 2nd, 3rd, and 4th week	Open-ended interviews	1. Participants quickly grasped the Mood Mapping and therapeutic concepts and applied them creatively in order to help themselves and empathize with others.2. Participants reported changes over the course of the study in their mood patterns and coping skills.
Burns et al.,²⁶ 8 weeks, (—)	Mobilyze! is a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Phase 1 (Data Collection): Contextual data were acquired from a collection of 38 sensors or more depending on the number of proximal Bluetooth devices or open applications on the mobile phone. Phase 2 (Learners): Participants were periodically prompted to self-report their states using ecological momentary assessment on the phone. Phase 3 (Action Components): Every 5 min, the mobile phone application sent current, unlabeled sensor values to the backend. The learner used these sensor readings to make predictions and infer the participant's state without input from the participant.	1. Accuracy rates self-reported	Pre-intervention/4th week, post-intervention	1. Learners predicting categorical contextual states	1. Promising accuracy rates (60–91%)
		2. Depressive symptoms:• Changes in major depressive disorder diagnosis• Evaluator-rated depression symptom severity• Self-reported depressive symptom severity		2. Instruments:• Telephone-administered Mini-International Neuropsychiatric Interview• QIDS-C• PHQ-9	2. Results:• Participants became less likely to meet criteria for major depressive disorder diagnosis (p=0.03).• Evaluator-rated depressive symptoms improved (p<0.001).• Self-reported depressive symptoms improved (p<0.001).
		3. General anxiety symptom severity		3. Generalized Anxiety Disorder 7-item scale	3. Comorbid anxiety symptoms decreased (p<0.001).
		4. Participants' feedback of the intervention components		4. Evaluation items:• A semistructured interview• Online self-reports at each assessment• Coaches recorded feedback received during the coaching sessions.	4. The most common problems for which participants sought technical support were loss of connectivity, shortness of battery life, and phone freezing during use, but they were satisfied with the phone application.
Rizvi et al.,²⁷ 10–14 days, (—)	Participants were given the DBT Coach program installed on a Nokia Series 60 platform smartphone and instructed to use the DBT Coach and Follow-Up DBT Coach as often as they wanted during this time. Participants were instructed to complete a brief questionnaire on the phone each day and call the research assistant if they experienced any technical problems with the phone. Participants returned the phone and completed post-intervention assessment.	1. Emotional intensity and urges	1. Every day	1. Substances (0–10 scale)2. Likert scale (1–5)	1. Participants' emotional intensity and urges decreased.2. Participants reported a decrease in overall depression symptomatology and psychological distress.3. DBT Coach was perceived as usable and helpful to participants.4. Participants reported significant gains in their confidence about effectively applying the skill of OA.5. Coaching calls to the DBT individual therapist did not significantly change.6. DBT individual therapists reported an increase in their clients using OA.
		2. Satisfaction and usability	2. Post-intervention	Face-valid 16-item instrument
		3. Confidence in executing various components of the skill of OA	3. Pre- and post-intervention	Behavioral Confidence Questionnaire
		4. Severity of depression		Beck Depression Inventory
		5. Effect of their clients		Therapist Questionnaire
Reid et al.,²⁸ 6 weeks, not significantly different (p>0.05)	Both groups self-monitored for 2–4 weeks and reviewed the monitoring data with their GP. Participants completed pre-, post-, and 6-week post-test measures. The intervention group used the complete mobile-type program, which assessed eight areas of functioning. The comparison group used an abbreviated version of the mobile-type program.	1. ESA	Pre-intervention/post-intervention, 6th week	ESA scale	The mobile-type intervention program increased young people's ESA between pre- and post-test, but this effect was not significant until 6 weeks after completion of the program.
		2. Depression
		3. Anxiety		Depression, Anxiety, Stress Scale	1. Not significantly different (p>0.05) among pre-test, post-test, and week 6.2. However, there was a substantial decrease in each of the depression, anxiety, and stress scores from pre-test to 6 weeks post-test, and this decrease was a medium-size effect for each scale.
		4. Stress
Ly et al.,²⁹ 8 weeks, (—)	Control group: brief online education and recommend use of a smartphone application that is not directly aimed at depressionIntervention group: smartphone-based behavioral activation intervention (consists of a Web-based psychoeducation and a smartphone application), with minimal therapist contact	1. Self-assessed depression	Pre-intervention/post-intervention	Beck Depression Inventory-II, PHQ-9	Important implications:1. Smartphones may serve an important role in healthcare. Adding applications to facilitate health into the smartphone platform may be perceived by the user to be a natural step, and thus can reach out to more people.2. The using of a smartphone application could serve as the therapist's prolonged arm into the daily life of the patient. Smartphone applications may lead to improved compliance compared with our earlier Internet treatments.
		2. Severity of anxiety		Beck Anxiety Inventory
		3. Quality of life		Quality of Life Inventory
		4. Psychological flexibility		Acceptance and Action Questionnaire-II
		5. Health-related costs		Trimbos and Institute of Medical Technology Assessment Cost Questionnaire for Psychiatry
		6. Cost-effectiveness		EQ-5D
		7. Activity related to depressive symptoms		Observation
Obesity
Turner-McGrievy and Tate,³⁰ 6 months, not significantly different (p>0.05)	Both groups received two podcasts per week for 3 months and two minipodcasts per week for 3–6 months. The podcast+mobile group also used a diet and physical activity monitoring application on their mobile device and interacted with study counselors and other participants on Twitter.	1. Weight loss	Pre-intervention, 3rd month, 6th month	Measurement	Not significantly different (p>0.05)
		2. Days/week of reported diet monitoring	3rd month, 6th month	Reported by participants	Not significantly different (p>0.05)
		3. Method of monitoring			G1: use Web or paperG2: use an application p<0.05
		4. Downloads per episode			G1<G2 (p<0.05)
		5. Source of social support			G1: friendsG2: online sources p<0.05
Kirwan et al.,³¹ 3 months, not significantly different (p>0.05)	The iStepLog application was designed to allow members of the 10,000 Steps program to record their daily physical activity levels on their mobile device and synchronize this information with their online Step Log. Only the intervention group received the iStepLog application and used it on their own iPhone or iPod touch. The control group was selected retrospectively, with the primary investigator blinded to the study period data and a strict protocol adopted.	1. Total number of steps	Post-intervention	The iStepLog application compared with a Web browser	1. The intervention group logged steps on an average of 62 days, compared with 41 days in the control group.2. Intervention participants used the application 71.22% of the time to log their steps.3. The intervention may assist in maintaining participant engagement and behavior change.4. The use of the application was associated with an increased likelihood to log steps daily and an increased likelihood to log greater than 10,000 steps on each entry.5. Over 80% of respondents reported either agreeing or strongly agreeing with each item.
		2. Total number of days' steps
		3. Time of using the iStepLog application
		4. Usability and usefulness of the iStepLog application		Usability and usefulness questionnaire
Pellegrini et al.,³² 1 year, (—)	G1: STNDG2: TECHG3: SELFSTND and TECH will attend eight group sessions and receive regular coaching calls during the first 6 months of the intervention; SELF will receive the Group Lifestyle Balance Program DVDs and will not receive coaching calls. During months 1–6, TECH will use a specially designed smartphone application to monitor dietary intake, body weight, and objectively measured physical activity (obtained from a Bluetooth-enabled accelerometer). STND and SELF will self-monitor on paper diaries.	1. Weight loss	3rd, 6th, 12th month	A calibrated beam balance scale	ENGAGED is an innovative weight loss intervention that integrates theory with emerging mobile technologies. We hypothesize that TECH, compared with STND and SELF, will result in greater weight loss by virtue of improved behavioral adherence and goal achievement.
		2. Self-monitoring adherence		Paper diaries for keeping track (STND and SELF) or smartphone application (TECH)
		3. Diet and activity goal attainment
Cancer
Weaver et al.,³³ 4 weeks, (—)	4 participants: weeks 1–2, only the Web site; weeks 3–4: the Web site and the mobile application5 participants: weeks 1–2, the Web site and the mobile application; weeks 3–4, only the Web site	Use feelings of patients	Pre-intervention/2nd, 4th week	Qualitative interview	Health Weaver Mobile helps patients to access and capture care-related information and to share information with clinicians during clinic visits. The enhanced ability to manage information helps patients to manage their care and to feel more confident in their ability to stay in control of their information and their health.
Klasnja et al.,³⁴ 6 weeks, (—)	Patients entered symptom data into a mobile phone software twice daily. These data were transmitted to a dedicated server. If incoming readings gave rise to concern, amber or red alerts were generated. The study nurse contacted the patient to initiate appropriate management. After completing the twice-daily questionnaire, patients received self-care advice related to their symptoms on the phone.Data alerts: If the data were not entered for more than 24 h, a red alert was generated. If an amber alert was generated for four out of five consecutive 12-h time slots, it was escalated to a red alert.	1. Use feelings of patients	6th week	Qualitative interview	1. Using the software is a very positive experience, and it is simple to use.2. The symptoms were being very closely monitored.3. Hospital staff's responses were very quick.4. The nurses' contacting made patients feel that they were being less “bothersome.”5. Patients felt involved and responsible for their care management plan.
		2. Reasons for alerts		Data were collected on the server.	91 alerts: 54 red and 37 amberRed alerts:1. Data alerts: 54% (29/54)2. “Symptom-generated,” 46% (25/54): temperature rising, 15 (60%); diarrhea, 8 (32%); hand–foot syndrome, 2 (8%)
COPD
Liu et al.,³⁵ 1 year, not significantly different (p>0.05)	G1: Daily endurance exercise training was taken with cell phone assistance. During the first 3 months, walking at 80% of their maximal capacity with the tempo of music from a software installed on a cell phone was monitored. The level of walking endurance was readjusted monthly according to the result of ISWT.G2: The same protocol was followed, and daily walking exercise was done at home.	1. ISWT distance	Pre-intervention/1st, 2nd, 3rd, 12th month	ISWT	Significantly different (p<0.05)
		2. Duration of endurance walking		SF-12 scoring	Significantly different (p<0.05)
		3. Breathlessness		Borg scale	Significantly different (p<0.05)
		4. Inspiratory capacity		Specific instrument	Significantly different (p<0.05)
		5. Acute exacerbations and hospitalizations		Medical record	Significantly different (p<0.05)
		6. Quality of life		SF-12 quality-of-life questionnaire	Significantly different (p<0.05)
		7. Hypoglycemic events, hospitalization, and emergency room visits		Quarterly telephone calls to patients	Not significantly different (p>0.05)

A dash indicates the information was not provided.

3G, third generation; CO, coach only; CPDS, coach–patient portal with decision support (allowed providers to access analyzed patient data linked to standards of care and evidence-based guidelines); CPP, coach–patient portal; DBT, dialectical behavior therapy; ESA, emotional self-awareness; G, group; GP, general practitioner; HbA1c, glycated hemoglobin; ISWT, incremental shuttle walking test; OA, opposite action; PHQ, Patient Health Questionnaire; QIDS-C, Quick Inventory of Depression Symptoms—Clinician Rated; SELF, self-guided behavioral weight loss; SF-12, Short Form-12; STND, standard behavioral weight loss; TECH, technology-supported behavioral weight loss; UC, usual care.

Overview of the Included Articles

The 16 articles evaluated smartphone intervention to help people with five types of chronic diseases: diabetes (five articles), mental health problems (five articles), overweight (three articles), cancer (two articles), and chronic obstructive pulmonary disease (one article). Clinical trials in these studies determined whether the smartphone app was effective in managing the specific chronic disease. These 16 studies were performed in the United States (eight articles), Australia (three articles), the United Kingdom (two articles), Taiwan (one article), France (one article), and Sweden (one article). Sample sizes ranged from 6 to 163 participants. The mean participant age ranged from 18 to 73 years, and the duration of the intervention ranged from 3 days to 1 year. The intervention designs, items of assessment, time of assessment, methods or instruments of assessment, and outcomes are described in detail in Table 2.

In summary, except for the study protocol about the behavioral activation–based guided self-help treatment administered through a smartphone app, the findings of the other 15 reviewed studies demonstrate that smartphone interventions for long-term health management of people with chronic diseases can be effective, at least as demonstrated by the primary outcomes. There are interventions that did not demonstrate the desired positive effects in all assessment parameters.

Discussion

Target Diseases

One of the important factors that lead to a successful usage of the apps is to choose the fit and matched disease. Usually, the type of disease being able to use the health-related smartphone apps is the first concern for the researchers; then, how an app might be applied according to the related disease/condition is the next concern. These 16 studies focused on chronic illness: diabetes, mental health problems, overweight, cancer, and chronic obstructive pulmonary disease. Some illnesses are even associated with one another; for example, overweight and obesity are associated with several chronic diseases, including type 2 diabetes, hypertension, cardiovascular disease, arthritis, hyperlipidemia, and asthma.³⁶ Except for the nonsignificant results in a study of depression, the other 15 studies demonstrated that health-related smartphone apps were effective in monitoring patients' symptoms, supporting patients, or helping manage the chronic illness in some aspects.

Target Population

After finding apps to match the disease of interest to the researcher, the characteristics of potential app users is the next concern. Which patients are qualified to use the apps? The majority of the studies involved in this review paid special attention to the type of patients who might most effectively use the app. This literature review shows that the criteria are as follows: First, the patients should be able to understand, read and write. Second, the patients should have a smartphone and be able to use smartphone apps. For some health-related smartphone apps, the patients should have access to the Internet and be comfortable using a computer. Third, the patients are willing to and capable of using the health-related smartphone apps to manage their chronic diseases. Also, the patients' positive attitude toward using health-related smartphone apps is of great importance.

Contents of the App

What specifically the smartphone apps can help to manage is the third concern a researcher needs to consider. App contents often include symptom management, treatment management, and daily activity management. For example, in regard to symptom management, one study focused on the management of chemotherapy-associated side effects of patients with cancer. Five studies in this review focused on symptoms management of patients with mental health problems. In regard to treatment and daily activity management, five studies in this review focused on the specific requirements to help diabetes patients manage blood glucose, insulin dose adjustment, and dietary intake. Three studies in this review focused on the daily activities of overweight patients to help them manage calorie and fat intake and physical activities. One study focused on exercise for patients with chronic obstructive pulmonary disease to increase their endurance walking at home and inspiratory capacity. One study tried to provide health information to support the education needs of cancer patients.

Evaluation of Smartphone Interventions

The evaluations of the apps in managing the chronic illnesses are different but also share common aspects. The acceptability and the effectiveness of the apps are often evaluated. For the aspect of acceptability, the dropout rate and the user experience indicate the feasibility and the applicability of the app. Qualitative interviews evaluate the participants' user experience with the intervention app. Summarizing the studies included, we found that the effectiveness of interventions was tested by different means, such as the management of symptoms, related knowledge of the chronic diseases, quality of life, psychosocial health, times of hospital visits, inpatient days, and the cost of healthcare.

Possible Improvement in Smartphone Apps

Use of smartphone apps in long-term health management of people with chronic diseases is still at the beginning stage. We think considerable improvement is possible in the future. For example, it is very important to have a group of talented and qualified professional staff to implement the smartphone intervention and respond to the needs proposed by patients. As such, the pre-intervention training for project staff team members is of great importance. One study found that the research nurse and ward nurses felt competent and confident in monitoring the patients' data via the computer and offering advice to participants after their implementation training.³³

The Internet can effectively distribute psychological treatment programs to improve mental health.^37,38 There is a drive to use online technology to expand patient knowledge and support patient–physician communication to improve chronic illness management and treatment outcomes. Nine studies in the review documented that apps combined with Internet use are more effective. Because some symptoms require intervention as soon as they occur, the Internet can help doctors or nurses to give patients advice as early as possible. As countries are increasingly empowered by smartphones with Internet connection, it is also possible to develop health-related smartphone apps connected to the Internet. However, some current Internet-delivered interventions and Internet-based treatment apps have limitations with respect to the security of patients' information.

Every smartphone app mainly focuses on only part of the management of the chronic illness. For example, researchers developed INNSULIN,²¹ which tells the diabetes patient how much insulin he or she intends to inject. However, the management of diabetes includes many aspects, such as dietary intake, which is the object of obesity management and blood glucose control. So a comprehensive smartphone app is needed to help patients manage the multiple facets of their condition. Some chronic diseases are associated with several other chronic diseases. It is necessary and promising to develop apps that can combine management of different chronic diseases. Then the doctors can choose the specific module of the app personalized to the patient.

Health-related smartphone apps have been developed, but development of more sophisticated, versatile products and the expansion of apps in healthcare systems could be of enormous benefit. If people with chronic diseases find self-management and education accessible, easy, and effective with health-related smartphone apps, they can actively participate in managing their illness and improve their health by using apps.

Conclusions

Recognizing the communication and support needs for people with chronic diseases, our review highlights the importance of future research on interventions with health-related smartphone apps. With the help of the apps on mobile devices, participants in these studies with chronic diseases felt secure in the knowledge that their illnesses were being closely monitored. They were participating in their own health management more effectively. They also felt that they had not been forgotten by their doctors and were taken good care of even outside the hospital/clinic.

We believe that with the development of smartphones, apps, and the Internet, integrating apps to facilitate health into the platform of smartphones may be effectively used by more and more caregivers and patients. Smartphone apps aimed at supporting healthcare can play an important role. Although the smartphone apps tested in the reviewed studies may be limited in terms of content, they reveal promising outcomes in long-term management of chronic diseases. The use of health-related smartphone apps could serve as the therapist's extended arm into the daily life of the patients. It is highly possible that smartphone interventions for other health problems will be tested in future comparative effectiveness studies.

Footnotes

Acknowledgments

This research was supported by the Science and Technology Commission of Shanghai Municipality, China. The project number is 12410707900.

Disclosure Statement

No competing financial interests exist.

C.Y. is the Principal Investigator of this research project who was responsible for the entire study design. J.W., C.W., and Y.W. performed the literature review and the drafting of manuscript. N.Y., A.Y., and Y.S. performed critical revisions of the manuscript.

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