Nursing Intervention using smartphone technologies; a systematic review and meta-analysis

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1 Digital Healthcare Empowering Europeans R. Cornet et al. (Eds.) 2015 European Federation for Medical Informatics (EFMI). This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License. doi: / Nursing Intervention using smartphone technologies; a systematic review and meta-analysis Eunjoo Jeon a,b a, b, c, 1 and Hyeoun-Ae Park a College of Nursing, Seoul National University, Seoul, Korea b Systems Biomediecal Informatics Research Center, Seoul National University, Seoul, Korea c Research Institute of Nursing Science, Seoul National University, Seoul, Korea Abstract. We reviewed mobile technology-based interventions in nursing and computed effect size of the interventions. We searched eight databases (KoreaMED, KMBASE, KISS, NDSL, Medline, EMBASE, Cochrane central library and CINAHL) using three sets of terms: mobile application, mobile app, mobile phone or smartphone; health or healthcare; and nursing. The study design, mobile technology, sample size and clinical outcomes were extracted from each study. A total of 38 studies were selected for review. Seven and six studies were used in meta-analyses for weight and fasting plasma glucose changes respectively. We found that mobile interventions used in nursing have different characteristics compared to those in other disciplines. We also found that mobile interventions in nursing led to significant improvement in weight and glucose control. Keywords. Mobile Applications, Mobile Health Units, Nursing Research, Meta- Analysis. 1. Introduction With the recent popularization of wireless devices such as tablet PCs, PDAs, and smartphones, mobile healthcare (mhealth) has also become widespread. Mobile technology-based intervention involving automated message systems have been shown to improve health outcomes of the consumers. 1 Nowadays, mobile technology such as text message (SMS), photos and video message (MMS), telephone, and World Wide Web access is a part of people s daily lives. There have been several review studies on effect of mobile interventions. For example, Free et al., analysed 75 studies of mobile interventions delivered to health care consumers 1 and they found that mobile interventions bring about short-term benefits for asthma control, physical activity, and psychological support. Liang et al., analysed 22 studies of mobile interventions used for diabetes management and found a significant decrease in glucose level. 2 However, these reviews studies did not cover nursing. Since nursing focuses more on care than cure and views a patient in more holistic way, it is necessary to review studies on use of mobile interventions in nursing. With this background, we conducted a systematic review and meta-analyses to compute the effect size of mobile interventions in nursing. 1 Corresponding Author: Hyeoun-Ae Park, [email protected]

2 322 E. Jeon and H.-A. Park / Nursing Intervention Using Smartphone Technologies 2. Methods We conducted a systematic review according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Search strategy The study begins by searching the following literature databases: KoreaMED, KMBASE, KISS, and NDSL in Korea, and MEDLINE, EMBASE, COCHRANE CENTRAL, and CINAHL on Jul. 22 in We used combined keyword searches with Mobile AND (applications OR application OR apps OR app), mobile phone, smartphone and Health, Healthcare and Nursing. The searches were restricted to articles published either in Korean or English. The publication year was limited to after Inclusion and exclusion criteria We selected studies for review based on the following criteria: First, the study used personal digital assistants (PDA), Smartphone (e.g., iphone), handheld video-game consoles, and tablet PCs (eg., ipad). Even though the study used mobile devices, it is excluded if other functions of smartphone than making a phone call such as SMS, MMS, and WWW access were not used. Second, the study provided mobile interventions in healthcare. Third, the study is published by a nurse as either first or corresponding author or published in one of the nursing journals Study selection Two researchers independently reviewed 692 studies identified from literature search by applying selection criteria described above. First, we reviewed the titles and abstracts and this left 289 studies. Second, we reviewed the full text and this left 38 studies Data extraction The investigators collected data from each eligible article including study subject, type of health problem, technology used, duration, type of intervention, outcome measures, and statistical significance. Information of the study such as year of publication, study design, clinical area, and country were also collected from all eligible studies Data synthesis First, descriptive analysis was performed for 38 papers. We categorized the intervention studies by study characteristics such as language used, country, the design of study, aims of study, clinical area and mobile technology used. Second, randomized control trials (RCT) and quasi-experimental studies are selected and they were categorised by outcome measures such as health behaviour, clinical outcome, and system evaluation. Third, we conducted meta-analyses for clinical outcomes such as weight and fasting plasma glucose (FPG) with Comprehensive Meta-analysis program.

3 E. Jeon and H.-A. Park / Nursing Intervention Using Smartphone Technologies Results 3.1. Study characteristics The characteristics of the 38 studies are presented in Table 1. The 37 studies of them are written in English and only one in Korean. Most of studies are quasi-experimental study (n=20; 52.6%) that evaluated clinical outcomes. There are 13 system development studies (34.2%) that evaluated only system performance. Regarding the aim of study, the most studies (n=20; 52.6%) are for the chronic disease selfmanagement. SMS is the most widely used functions (n= 20). However, sensing technology is adopted only by three studies. There are eight intervention studies targeting obesity management and seven diabetes management. Table 1. Summary of study characteristics (n=38). Study characteristics Categories Number of studies Study design Randomized Controlled trial 5 Quasi-experimental 20 Development study 13 Aims of study Education 5 Chronic disease self-management 20 Symptom management 5 Health promotion 7 Hospital Information System for 1 Healthcare provider Target problem Diabetes 7 Obesity 8 Breast cancer 2 Chronic pain 2 COPD 2 Metabolic syndrome 1 Asthma 1 Hematopoietic stem cell transplant 1 Tuberculosis 1 Mobile technology * SMS 20 Mobile app 11 Multi-media 6 Internet access using mobile 9 Sensing technology 3 Outcome measurement * Health behavior change 15 Health outcome 22 Feasibility of system 20 * Multiple counts are allowed 3.2. Outcome measure of studies Most of studies (n=22) measured clinical outcome such as weight, waist circumference, body mass index, FPG, haemoglobin A1c, blood pressure, and cholesterol. The most frequently measured clinical outcomes are weight and fasting plasma glucose. Twenty studies reported system evaluation outcome such as users intention to use and satisfaction with the system. Fifteen studies measured health behaviour changes such as exercise frequency, diet intake, self-efficacy, and knowledge.

4 324 E. Jeon and H.-A. Park / Nursing Intervention Using Smartphone Technologies 3.3. Computing effect size Seven studies (with 268 participants in total) were used in a meta-analysis for computing effect size of weight. 3-9 We found a slightly positive effect of mobile intervention on weight reduction (Hedges g: -0.23, 95% CI: to -0.03) (Figure 1). Weight reduction outcomes were not heterogeneous (I2<0.001, T2<0.001). Six studies (with 291 participants in total) were used in a meta-analysis for computing effect size of fasting plasma glucose. 3,7,10-13 We also found a slightly positive effect on FPG reduction (Hedges g: -0.35, 95% CI: to -0.16) (Figure 2). FPG reduction outcomes were not heterogeneous (I2=7.62, T2=0.01). Figure 1. Combined effect size of weight control using mobile interventions in nursing. Figure 2. Combined effect size of fasting plasma glucose control using mobile interventions in nursing. 4. Discussion We found that mobile interventions used in nursing are mainly for self-management of chronic diseases compared to other healthcare disciplines. Symptom management by healthcare providers was most widely used in other healthcare disciplines. 1 This finding reflects difference between nursing and other healthcare discipline which nursing values empowering patients for their self-management. Even though we were able to compute effect sizes of weight and FPG and showed that there is a significant difference between mobile interventions and traditional interventions in this study. We would like to recommend further studies on estimating effect sizes of other outcome measures than weight and FPG. There have been studies

5 E. Jeon and H.-A. Park / Nursing Intervention Using Smartphone Technologies 325 showing positive effect of web-based interventions on clinical outcomes, it would be interesting to compare effect of mobile-based intervention studies with web-based intervention studies. There were still a lot of studies only evaluated feasibility of the mobile interventions in nursing, the effect size of mobile interventions will be estimated more accurately with more studies with clinical outcome measures. Even though we found positive effects of mobile interventions on some clinical outcomes using meta-analyses, it is difficult for us to explain how such clinical outcomes were occurred. We would like to recommend a further study examining how such clinical outcome were changed including cognitive factors and behavioral change. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No ). References 1. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS medicine. 2013;10(1):e Liang X, Wang Q, Yang X, Cao J, Chen J, Mo X, et al. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabetic medicine : a journal of the British Diabetic Association. 2011;28(4): Lee CY, Lee H, Jeon KM, Hong YM, Park SH. Self management program for obesity control among middle aged women in Korea: A pilot study. Japan Journal of Nursing Science. 2011;8(1): Allen JK, Stephens J, Dennison Himmelfarb CR, Stewart KJ, Hauck S. Randomized controlled pilot study testing use of smartphone technology for obesity treatment. Journal of obesity. 2013; Faghanipour S, Hajikazemi E, Nikpour S, Shariatpanahi Sa-S, Hosseini AF. Mobile phone short message service (SMS) for weight management in iranian overweight and obese women: a pilot study. International journal of telemedicine and applications. 2013;2013:5. 6. Shaw RJ, Bosworth HB, Silva SS, Lipkus IM, Davis LL, Ronald SS, et al. Mobile health messages help sustain recent weight loss. The American journal of medicine. 2013;126(11): Park M-J, Kim H-S. Evaluation of mobile phone and Internet intervention on waist circumference and blood pressure in post-menopausal women with abdominal obesity. International journal of medical informatics. 2012;81(6): Park M-J, Kim H-S, Kim K-S. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. International journal of medical informatics. 2009;78(10): Bouhaidar CM, Deshazo JP, Puri P, Gray P, Robins JLW, Salyer J. Text messaging as adjunct to community-based weight management program. Computers Informatics Nursing. 2013;31(10): Kim S-I, Kim H-S. Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. International journal of medical informatics. 2008;77(6): Yoon K-H, Kim H-S. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Research and Clinical Practice. 2008;79(2): Jung H, Lee B, Lee J-E, Kwon Y-H, Song H. Efficacy of a programme for workers with metabolic syndrome based on an e-health system in the workplace: a pilot study. Journal of telemedicine and telecare. 2012;18(6): Kim HS, Jeong HS. A nurse short message service by cellular phone in type 2 diabetic patients for six months. Journal of clinical nursing. 2007;16(6):