Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention
2015; Lippincott Williams & Wilkins; Volume: 132; Issue: 12 Linguagem: Inglês
10.1161/cir.0000000000000232
ISSN1524-4539
AutoresLora E. Burke, Jun Ma, Kristen MJ Azar, Gary G. Bennett, Eric D. Peterson, Yaguang Zheng, William T. Riley, Janna Stephens, Svati H. Shah, Brian Suffoletto, Tanya N. Turan, Bonnie Spring, Julia Steinberger, Charlene C. Quinn,
Tópico(s)Nutritional Studies and Diet
ResumoHomeCirculationVol. 132, No. 12Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBCurrent Science on Consumer Use of Mobile Health for Cardiovascular Disease PreventionA Scientific Statement From the American Heart Association Lora E. Burke, PhD, MPH, FAHA, Chair, Jun Ma, MD, PhD, FAHA, Kristen M.J. Azar, MSN/MPH, BSN, RN, Gary G. Bennett, PhD, Eric D. Peterson, MD, Yaguang Zheng, PhD, MSN, RN, William Riley, PhD, Janna Stephens, BSN, PhD(c), RN, Svati H. Shah, MD, MHS, Brian Suffoletto, MD, MS, Tanya N. Turan, MD, FAHA, Bonnie Spring, PhD, FAHA, Julia Steinberger, MD, MS, FAHA and Charlene C. Quinn, PhD, RNon behalf of the American Heart Association Publications Committee of the Council on Epidemiology and Prevention, Behavior Change Committee of the Council on Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, Council on Quality of Care and Outcomes Research, and Stroke Council Lora E. BurkeLora E. Burke , Jun MaJun Ma , Kristen M.J. AzarKristen M.J. Azar , Gary G. BennettGary G. Bennett , Eric D. PetersonEric D. Peterson , Yaguang ZhengYaguang Zheng , William RileyWilliam Riley , Janna StephensJanna Stephens , Svati H. ShahSvati H. Shah , Brian SuffolettoBrian Suffoletto , Tanya N. TuranTanya N. Turan , Bonnie SpringBonnie Spring , Julia SteinbergerJulia Steinberger and Charlene C. QuinnCharlene C. Quinn and on behalf of the American Heart Association Publications Committee of the Council on Epidemiology and Prevention, Behavior Change Committee of the Council on Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, Council on Quality of Care and Outcomes Research, and Stroke Council Originally published13 Aug 2015https://doi.org/10.1161/CIR.0000000000000232Circulation. 2015;132:1157–1213is corrected byCorrectionOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2015: Previous Version 1 Although mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD.3In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life's Simple 7 metrics for improved cardiovascular health, 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood glucose, lipids, BP, body mass index) as the primary outcomes in the clinical trials testing mobile health (mHealth) interventions.eHealth, or digital health, is the use of emerging communication and information technologies, especially the Internet, to improve health and health care4 (Table 1). mHealth, a subsegment of eHealth, is the use of mobile computing and communication technologies (eg, mobile phones, wearable sensors) for health services and information.4,5 mHealth technology uses techniques and advanced concepts from an array of disciplines, for example, computer science, electrical and biomedical engineering, and medicine and health-related sciences.16 Mobile devices that permit collection of data in real time are increasingly ubiquitous, enabling researchers to assess multiple behaviors in various contexts and thus inform the development of interventions to prompt behavior change. Technology-supported behavioral health interventions are designed to engage individuals in health behaviors that prevent or manage illness, and they have led to fundamental changes in health practices.17 In addition to permitting more frequent and convenient community-based assessment of health parameters, these technology-mediated tools support the exchange of health information among consumers and between consumers and health providers, enable health decision making, and encourage positive health behaviors, including self-management and health promotion.18,19 Consequently, mHealth technologies are becoming more prevalent, and their use will continue to grow,20 consistent with the Institute of Medicine's call to increase the design and testing of health technologies.21Table 1. Glossary of Commonly Used mHealth TermseHealtheHealth, or digital health, is the use of emerging communication and information technologies, especially the use of the Internet, to improve health and health care.4mHealthA subsegment of eHealth, mHealth is the use of mobile computing and communication technologies (eg, mobile phones, wearable sensors) for health services and information.4,5SMSSMS is a text messaging service component of mobile devices. It uses standardized communications protocols to allow mobile phone devices to exchange short text messages. The terms text messaging and texting are used interchangeably to refer to both the medium and messages, and the term text message refers to the individual message sent.6MMSMMS is the next evolutionary step from SMS. MMS allows mobile phone users to exchange pictures with sound clips on their handsets or digital cameras.7AppApp is short for application, which is the same thing as a software program. Although an app may refer to a program for any hardware platform, it is most often used to describe programs for mobile devices such as smartphones and tablets.8WirelessBeing wireless means not using wires to send and receive electronic signals (ie, sending and receiving electronic signals by using radio waves).9Wi-FiWi-Fi is a wireless networking technology that allows computers and other devices to communicate over a wireless signal.10BluetoothThis wireless technology enables communication between Bluetooth-compatible devices. It is used for short-range connections between desktop and laptop computers, a mouse, digital cameras, scanners, cellular phones, and printers.11Operating systemAn operating system, or OS, is software that communicates with the hardware and allows other programs to run. Common mobile OSs include Android, iOS, and Windows Phone.12iOSiOS is a mobile OS developed by Apple. It was originally called the iPhone OS, but was renamed to the iOS in June 2009. The iOS currently runs on the iPhone, iPod Touch, and iPad.13Android OSAndroid OS is a Linux-based open-source platform for mobile cellular handsets developed by Google and the Open Handset Alliance. Android 1.0 was released in September 2008.14BandwidthIn computer networks, bandwidth is used as a synonym for data transfer rate, the amount of data that can be transmitted from one point to another in a given time period (usually a second). Network bandwidth is usually expressed in bits per second (bps); modern networks typically have speeds measured in the millions of bits per second (megabits per second or Mbps) or billions of bits per second (gigabits per second or Gbps).15MMS indicates multimedia messaging service; OS, operating system; and SMS, short messaging service.The ubiquity of mobile devices presents the opportunity to improve health outcomes through the delivery of state-of-the-art medical and health services with information and communication technologies.22 Because of their diverse capabilities and advanced computing features, smartphones are often considered pocket computers.16 In addition to these devices that can inform and communicate, there are wearable sensors that can be worn for short or extended periods and monitor activity or physiological changes (eg, exercise, heart rate, sleep). These sensors can provide data in real time or save the data to a device for later uploading and review.The US Food and Drug Administration has a public health responsibility to oversee the safety and effectiveness of medical devices. However, this applies only to applications (apps) that are accessory to regulated medical devices (eg, apps that diagnose a condition). Many mobile apps are not medical devices, meaning that they do not meet the definition of a device under section 201(h) of the Federal Food, Drug, and Cosmetic Act, and the US Food and Drug Administration does not regulate them. Some mobile apps may meet the definition of a medical device, but because they pose a lower risk to the public, the US Food and Drug Administration intends to exercise enforcement discretion over these devices. Most of the mHealth apps on the market at this time fit into these 2 categories.23,24Numerous innovations in health information technology are empowering individuals to assume a more active role in monitoring and managing their chronic conditions and therapeutic regimens, as well as their health and wellness.25 These advances are increasingly accepted by the public.26 Unlike the initial digital divide that placed computer use and Internet access beyond the reach of many older, disabled, and low-income individuals, mobile devices have been widely adopted across demographic and ethnic groups, especially those most in need of health behavior interventions.27,28 This trend is confirmed in the 2014 statistics from the Pew Research Center's Internet and American Life Project, which showed that 81% of households with an income above $75 000/y owned a smartphone, and nearly half (47%) of those with an annual household income below $30 000 owned a smartphone.29 The highest smartphone ownership was among Hispanic and blacks, at 61% and 59%, respectively. Of those with phones who use the Internet, 34% mostly use their phones, rather than a desktop or laptop, to access online programs.30Mobile devices offer great promise for improving the health of the populace. Most smartphones include basic functionalities, for example, video streaming, e-mail, Internet access, and high-quality imaging. These developments in wireless technology and the shift to mobile devices are demanding a re-examination of technology as it currently exists within the healthcare infrastructure.16 However, the pace of science in evaluating these apps is incongruent with the business and industry sectors and the consumer demands. There are concerns that the health-promoting smartphone apps being developed fail to incorporate evidence-based content and that rigorous testing to provide efficacy data is trailing behind their adoption.31–34 However, a systematic review of the literature suggests a positive impact of consumer health informatics tools on select health conditions. For example, there were intermediate outcomes such as knowledge, adherence, self-management, and change in behaviors related to healthful eating, exercise, and physical activity but not obesity.35 Another review suggests that smartphone apps are useful tools at the point of care and in mobile clinical communication, as well as in remote patient monitoring and self-management of disease.36Recent articles have reviewed the latest technological advances in digital social networks related to health37 and wireless devices for cardiac monitoring.38 What is missing in the scientific literature is a report on the health-related mobile technologies focused specifically on CVD prevention. In particular, it is important to investigate the degree to which these CVD-focused technologies include best content and have been evaluated for their effectiveness. In the absence of such data, clinicians may be hesitant to recommend or endorse any program to their patients and thereby potentially miss an opportunity to improve their engagement in healthful behaviors.The aims of this scientific statement are to review the literature on mHealth tools available to the consumer in the prevention of CVD (eg, dietary self-monitoring apps, physical activity monitors, and BP monitors); to provide the current evidence on the use of the vast array of mobile devices such as use of mobile phones for communication and feedback, smartphone apps, wearable sensors, or physiological monitors that are readily available and promoted to the public for monitoring their health; and to provide recommendations for future research directions. The goal is to provide the clinician and researcher a review of the current evidence on using mHealth tools and devices when targeting behavior change, cardiovascular risk reduction, and improved cardiovascular health. This statement is divided into sections by the behaviors or health indicators included in the AHA's Life's Simple 7 program: achieving a healthful weight, improving physical activity, quitting smoking, achieving blood glucose control, and managing BP and lipids to achieve target levels. Within each section, the recent evidence for studies using mHealth approaches is reviewed, gaps are identified, and directions for future research are provided.Although the majority of studies reported the use of mobile devices, for example, basic mobile phones that support the use of text messaging (short message service [SMS]) or smartphones that provide Internet access, several reported interventions delivered via the Internet such as studies reporting on increased physical activity or BP management. The writing group made the decision to include these studies because there is an increasingly greater proportion of people accessing the Internet via mobile devices. As noted in a Pew report in February 2014, 68% of adults access the Internet with mobile devices.39 This figure has likely increased in the past year. Moreover, in some of the designated areas of cardiovascular risk, there were few studies reporting on the use of mHealth supported interventions.Review of the Scientific Literature on mHealth Tools Related to CVD PreventionSearch StrategyWe conducted a literature search that included the following terms: mHealth; mobile health; mobile phone; mobile device; mobile technology; mobile communication; mobile computer; mobile PC; cell phone; cellular phone; cellular telephone; handheld computer; handheld device; handheld technology; handheld PC; hand held computer; hand held device; hand held technology; hand held PC; tablet device; tablet computer; tablet technology; tablet PC; smartphone; smart phone; iPad; Kindle; Galaxy; iPhone; Blackberry; iPod; Bluetooth; short message service; SMS; pocket PC; pocketPC; PDA; personal digital assistant; Palm Pilot; Palmpilot; smartbook; mobile telephone; messaging service; MP3 player; pormedia player; podcast; email; e-mail; electronic mail; and electronic message. Search terms used within the technology or clinical topic (eg, diabetes mellitus) groups were divided with "or," and the search terms between the technology and clinical topic were connected with "and." Within each subsection, the key terms used in the search for a given clinical topic are identified. The search was limited to the past 10 years (2004–2014) and to studies reported in the English language. We limited our review to studies enrolling adults except for smoking cessation, for which we included adolescents. We included studies conducted in the United States and in developed countries. We also briefly discuss key systematic reviews or meta-analyses in each topic area, except in management of dyslipidemia.Use of mHealth to Improve Weight ManagementObesity causes or contributes to myriad physical and mental health conditions such as CVD, T2DM, and depression, which, either individually or collectively, represent the leading causes of morbidity and mortality in the United States.40–42 More than 35% of US adults >20 years are obese,43 and >1 in 4 Americans have multimorbidity,45 which is associated with high healthcare use and costs, functional impairment, poor quality of life, psychological distress, and premature death.46–50 Sustained weight loss of 3% to 5% can delay or possibly prevent T2DM51,52 and significantly improve CVD risk factors (eg, abnormal glucose, elevated BP).53–56 However, effective treatments for obesity that are accessible to consumers, affordable for diverse socioeconomic groups, and scalable at a population level are lacking.The 2013 obesity treatment guideline by the AHA, the American College of Cardiology (ACC), and The Obesity Society recommended that clinicians advise overweight and obese individuals who would benefit from weight loss to participate for ≥6 months in a comprehensive lifestyle program characterized by a combination of a reduced calorie intake, increased physical activity, and behavioral strategies.57 The guideline panelists found evidence of moderate strength supporting the efficacy of electronically delivered, comprehensive lifestyle programs that include personalized feedback from a trained interventionist, defined as programs delivered to participants by the Internet, e-mail, mobile texting, or similar electronic means. Therefore, it was recommended that electronically delivered interventions are an acceptable alternative to in-person interventions, although it was recognized that the former may result in smaller weight loss than the latter.Use of mHealth in Weight Management InterventionsThis review is limited to technology-supported lifestyle behavioral interventions for weight loss. Readers are referred to numerous systematic reviews of more traditional Internet-, e-mail–, and telephone-based lifestyle interventions for weight loss.58–63 Overall, weight management interventions have used a range of mobile technologies,60,64–68 including texting (SMS), smartphone applications, handheld personal digital assistants (PDAs), and interactive voice response (IVR) systems.66,69,70 Numerous network-connected devices have also been used,60,64 including e-scales and wireless physical activity monitoring devices.71 The use of mobile devices and their functionality (eg, SMS and multimedia messaging service, mobile Internet, and software apps) in weight loss interventions have improved exponentially in recent years. In this section, we focus on the latest evidence on mobile technology interventions for weight loss.With few exceptions,72 most interventions have used a single, predetermined technology and did not give participants the option of choosing between a single or multiple forms of technology simultaneously (which has become commonplace for commercial applications). Most technologies have been created in research settings, although at least 1 published study used a commercially available app.71 The majority of these trials were focused primarily on efficacy testing, and it was unclear whether these interventions used strategies designed to promote user engagement (eg, using established design principles, conducting usability testing, or undergoing iterative development and testing). Additionally, a key translational challenge is that many commercial apps have not been tested empirically, and many apps with empirical data are not commercially available.Review of Evidence for the Efficacy of mHealth-Based Weight Loss InterventionsWe conducted an electronic literature search using Medline (PubMed), CINAHL, and PsychInfo in June 2014 and extended to 2004. Search terms for this topic included the following: overweight, obese, obesity, body mass, adiposity, adipose, weight loss, and weight gain. Only original studies with human subjects with a primary outcome of weight loss and published in English were included. Of 184 references identified, 169 were excluded on the basis of a review of the title (n=19), abstract (n=121), and full text (n=29). Fourteen references were eligible for this review, including 10 studies conducted among US adults and 2 among adults outside the United States.Table 2 includes the studies reviewed and provides details on study design, intervention, sample characteristics, and primary outcomes. Five of the 8 US randomized, controlled trials (RCTs)74–76,83,84 reported significantly more weight loss in the intervention group than in the control or comparison group. The testing and use of mobile technologies varied a great deal, and combinations of mHealth components and tools were often very specific to a particular study. Five investigators used text messaging, also referred to as SMS,73,74,79,82,83 in studies that ranged from 8 weeks to 1 year in duration. Patrick et al74 permitted the participant to set the frequency of the SMS (2–5 times a day) and found a significant difference in weight loss between the 2 groups at 4 months, whereas Napolitano et al83 observed better weight loss in the Facebook plus SMS than the Facebook alone group at 8 weeks. Only 1 study,79 which used SMS and multimedia messaging service 4 times a day in addition to a monthly e-newsletter in a 12-month study, did not observe a significant difference in weight loss compared with a monthly e-newsletter control group. Two of the SMS studies were conducted outside the United States. Carter et al82 observed greater weight loss at 6 months in the group receiving SMS compared with the Web site plus Internet forum or paper diary plus Internet forum groups, whereas Haapala et al73 demonstrated similar results in a study that compared SMS with a wait-list control group at 12 months. Although none of the US studies using SMS reported positive findings beyond 9 months, the Finnish study73 showed that an SMS intervention could result in significantly greater weight loss than no intervention for up to 12 months.Table 2. Description of Studies Using mHealth for Weight Loss or Weight MaintenanceStudy Cited, Design, Outcome, Setting, CountrySample Characteristics, Group Size, Baseline BMI, Study RetentionStudy Groups and ComponentsTechnology UsedIntervention Duration, No. of Intervention Contacts, Intervention Adherence, InterventionistPrimary Outcome: Mean Weight Loss (kg, kg/m2, or % Change)Haapala et al,73 2009Design: 2-group RCTOutcome: wtΔ and waist circumference ΔSetting: CommunityCountry: FinlandN=125Int1: n=62Int2: n=63Women: 77.4%Mean age (SD):Int1: 38.1 (4.7) yInt2: 38.0 (4.7) yBMI:Int1: 30.6 (2.7) kg/m2Int2: 30.4 (2.8) kg/m2Retention:Int1: 73%Int2: 65%Int1: SMS (for personalized feedback) and study Web site (for tracking and information)Diet: Cut down on unnecessary food intake and alcoholPA: Increase daily physical activityBehavior: Self-monitoring and reporting of wt via SMS or study Web siteInt2: Wait list controlNo InterventionMobile phone, SMS, study Web siteDuration: 1 yContacts:Int1: Real time when participants reported wt via text messagingInt2: No intervention contactIntervention adherence:Mean No. (SD) of Ps reporting wt via SMS or study Web site per week:3 mo:Int1: 8.2 (4.0)6 mo:Int1: 5.7 (4.6)9 mo:Int1: 3.7 (3.5)12 mo:Int1: 3.1 (3.5)Interventionist:Int1: AutomatedInt2: NAITT (LOCF)12 mo: wtΔ, kg, M (SD):Int1: −3.1 (4.9)Int2: −0.7 (4.7)P=0.008Waist circumference Δ, cm, M (SD):Int1: −4.5 (5.3)Int2: −1.6 (4.5)P=0.002Patrick et al,74 2009Design: 2-group RCTOutcome: wtΔSetting: CommunityCountry: United StatesN=78Int1: n=39Int2: n=39Mean age (SD): 44.9 (7.7) y Women: 80%White: 75%Black: 17%BMI:Int1: 32.8 (4.3) kg/m2Int2: 33.5 (4.5) kg/m2Retention:Int1: 67%Int2: 67%Int1: Mobile phone wt loss programDiet goal: 500-kcal/d reductionPA: Increase from baselineBehavior: Self-monitoring weekly wt using mobile phone; time/frequency of tailored SMS set by Ps (2–5 times/d), monthly phone calls by coachInt2: MailDiet: No interventionPA: No interventionBehavior: Monthly mailings (healthful eating, PA, and wt loss)Mobile phone SMS and MMSDuration: 4 moContacts:Int1: Daily SMS and MMS, frequency set by PsInt2: 4 monthly mailingsIntervention adherence:Int1: 100% adherence to responding to all messages requesting a reply; by week 16, ≈66%.Int2: NRInterventionist:Int1: Health coach+automatedInt2: NALOCF imputation4 mo: wtΔ, kg, M (SE):Int1: −2.10 (0.51)Int2: −0.40 (0.51)P=0.03Completers only:Int1: −2.46 (0.64)Int2: −0.47 (0.64)P=0.04Turner-McGrievy et al,75 2009Design: 2-group RCTOutcome: wtΔSetting: CommunityCountry: United StatesN=78Int1: n=41Int2: n=37Mean age (SD):Int1: 37.7 (11.8) yInt2: 39.6 (12.2) yWomen:Int1: 68%Int2: 81%White:Int1: 85%Int2: 78%BMI:Int1: 31.8 (3.2) kg/m2Int2: 31.4 (4.1) kg/m2Retention:Int1: 90%Int2: 92%Int1: Social cognitive theory–based wt loss podcastDiet: Increase fruit and vegetable intake, decrease fat intakePA: Increase from baselineBehavior: Encourage tracking wt, calories, and exerciseInt2: Non–theory-based wt loss podcastDiet: Avoid overeatingPA: NRBehavior: NRPodcast via MP3 player or computer for Int1 and Int2Duration: 12 wkContacts:Int1: 2 podcasts/wk (mean length, 15 min)Int2: Same as Int1 (mean length, 18 min)Intervention adherence:Mean (SD) No. of podcasts listened to (n =24):Int1: 17.5 (8.1)Int2: 16.6 (7.5)P<0.67Interventionist:Int1: AutomatedInt2: AutomatedITT (BOCF)12 wk: wtΔ, kg, M (SD):Int1: −2.9 (3.5)Int2: −0.3 (2.1)P<0.001BMI Δ, kg/m2, M (SD):Int1: −1.0 (1.2)Int2: −0.1 (0.7)P<0.001Shuger et al,76 2011Design: 3-group RCTOutcome: wtSetting: CommunityCountry: United StatesN=197Int1: n=49Int2: n=49Int3: n=49Int4: n=50Mean age (SD): 46.9 (10.8) yWomen: 81.7%White: 66.8%Black: 32.1%BMI:Int1: 33.0 (5.0) kg/m2Int2: 33.2 (5.4) kg/m2Int3: 33.1 (4.8) kg/m2Int4: 33.7 (5.5) kg/m2Retention:At 4 mo: 70%At 9 mo: 62%Int1: Group-based behavioral wt loss program+armbandDiet: adopt healthful eating patternPA: Increase PA+armbandBehavior: Self-monitoring of daily meal, lifestyle activity, and emotion/mood+weekly weigh-in and coach-directed sessions for wt loss support and maintenanceInt2: Armband aloneDiet: adopt healthful eating patternPA: Increase PA+armbandBehavior: self-monitoring of daily meal, lifestyle activity, and emotion/mood+real-time feedback on energy expenditure, minutes spent in moderate and vigorous PA, and steps/dInt3: Group-based behavioral wt loss program aloneDiet: Same as Int1+emphasis on wt lossPA: Same as Int1Behavior: Same as Int 1+weekly weigh-in and coach-directed sessions for wt loss support and maintenanceInt4: Self-directed wt loss program following an evidence-based manualDiet: Adopt healthful eating patternPA: Increase PABehavior: Self-monitoring of daily meal, lifestyle activity, and emotion/moodBodymedia armband with a real-time wrist watch display and a personalized wt management solutions Web accountDuration: 9 moContacts:Int1: Same as Int2 and Int3Int2: Real time when participants uploaded armband and recorded daily energy intake and body wt to the Web siteInt3: 14 weekly group sessions during the first 4 mo; 6 1-on-1 phone counseling sessions during the final 5 moInt4: 1 Self-directed wt loss manualIntervention adherence: NRInterventionist:Int1: Health coach+automatedInt2: AutomatedInt3: Health coachInt4: NAITT (how handled missing data NR)Baseline:Wt, kg, M (SE):Int1: 100.32 (2.97)Int2: 101.15 (2.95)Int3: 101.84 (2.95)Int4: 102.22 (2.97)NSD among 4 groups4 mo:Wt, kg, M (SE):Int1: 96.83 (2.99)Int2: 98.48 (2.97)Int3: 100.74 (2.99)Int4: 101.23 (3.03)P=NR9 mo:Wt, kg, M (SE):Int1: 93.73 (2.99)Int2: 97.60 (2.99)Int3: 99.98 (3.00)Int4: 101.32 (3.05)Int1 vs Int4: P =0.04Int2 or Int3 vs Int4: P=NRBurke et al,77 2012; Burke et al,78 2011Design: 3-group RCTOutcome: % wtΔ at 6 and 24 moSetting: Community/academic centerCountry: United StatesN=210Int1: n=68Int2: n=70Int3: n=72Mean age (SD): 46.8 (9.0) yWomen: 84.8%White: 78.1%Median BMI (IQR): 33.09 (6.89) kg/m2Retention:Int1: 86.8%Int2: 84.3%Int3: 86.1%Int1: PDA onlyDiet: 1200–1800/d calorie goal based on wt and sex; ≤25% of total calories from fatPA: Increase by 30 min semiannually to 180 min by 6 moBehavior: Self-monitoring with PDAInt2: PDA with daily tailored feedback messageDiet: Same as Int1PA: Same as Int1Behavior: Self-monitoring with PDA and receiving automated daily feedback on calories or fat intake.Int3: Paper diaryDiet: Same as Int1PA: Same as Int1Behavior: Self-monitoring with paper diary and a nutritional reference bookPDA with dietary and PA self-monitoring program, daily remotely delivered feedback message in real time to Int2 groupDuration: 24 moContacts:Int1: Weekly group sessions for months 1–4, biweekly for months 5–12, and monthly for months 13–18, 1 session during the last 6 moInt2: Same as Int1Int3: Same as Int1Intervention adherence:≥30% adherent to dietary self-monitoring at 18 moInt1: 19%–20%Int2: 19%–20%Int3: 8%Interventionist:Int1: Dietitians and exercise physiologistsInt2: Dietitians and exercise physiologists+automatedInt3: Dietit
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