Health behavior models in the age of mobile interventions: areour theories up to the task?

William T Riley, PhD1, Daniel E Rivera, PhD2, Audie A Atienza, PhD3, Wendy Nilsen, PhD4,Susannah M Allison, PhD5, and Robin Mermelstein, PhD6

1National Heart, Lung, and Blood Institute, NIH, 6701 Rockledge Dr, Room 10224, MSC 7936,Bethesda, MD 20892-7936, USA2School for Engineering of Matter, Transport, and Energy, Ira A. Fulton School of Engineering,Arizona State University, Tempe, AZ 85287-6106, USA3National Institute of Health, National Cancer Institute, 6130 Executive Boulevard, EPN 4082,Bethesda, MD 20892-7335, USA4Office of Behavioral and Social Science Research, NIH, 31 Center Dr., Room B1C19, MSC2027, Bethesda, MD 20892-2027, USA5National Institute of Mental Health, NIH, 6001 Executive Boulevard, Room 6226, MSC 9615,Bethesda, MD 20892-9615, USA6Department of Psychology and Public Health, Health Research and Policy Center, University ofIllinois at Chicago, 850 West Jackson Boulevard, Suite 400, Chicago, IL 60607, USA

AbstractMobile technologies are being used to deliver health behavior interventions. The study aims todetermine how health behavior theories are applied to mobile interventions. This is a review of thetheoretical basis and interactivity of mobile health behavior interventions. Many of the mobilehealth behavior interventions reviewed were predominately one way (i.e., mostly data input orinformational output), but some have leveraged mobile technologies to provide just-in-time,interactive, and adaptive interventions. Most smoking and weight loss studies reported atheoretical basis for the mobile intervention, but most of the adherence and disease managementstudies did not. Mobile health behavior intervention development could benefit from greaterapplication of health behavior theories. Current theories, however, appear inadequate to informmobile intervention development as these interventions become more interactive and adaptive.Dynamic feedback system theories of health behavior can be developed utilizing longitudinal datafrom mobile devices and control systems engineering models.

KeywordsMobile phones; Handheld computers; Health behavior interventions; Smoking cessation; Weightmanagement; Adherence; Chronic disease management; Health behavior theory; Dynamicalsystems; Control systems engineering

The development, evaluation, and dissemination of computerized health behaviorinterventions have expanded rapidly in the last decade. Advances in Internet-basedinfrastructure and accessibility promoted the migration of computerized health behavior

Correspondence to: W Riley, wiriley@mail.nih.gov.

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Published in final edited form as:Transl Behav Med. 2011 March 1; 1(1): 53–71. doi:10.1007/s13142-011-0021-7.

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interventions from prototype stand-alone software to robust, scalable, interactive, andtailored web-based programs. As a result, web-based health behavior interventions haveproliferated in recent years and appear to be an efficacious method for delivering healthbehavior interventions in a cost-effective manner [1–3].

The next evolution, or revolution, of computerized health interventions, mobile technologyand health (mHealth), appears to be underway. Mobile phones have achieved rapid and highpenetration. There are over 285 million wireless subscribers in the USA alone [4], and anestimated 67.6% of adults worldwide own cell phones [5]. Approximately 75% of US highschool students own a mobile phone [6]. In contrast to the initial Internet digital dividewhich limited the reach of computerized health behavior interventions for those in lowersocioeconomic groups, mobile phone use has been widely adopted across socioeconomicand demographic groups and appears greater among those populations most in need of theseinterventions [6, 7]. The penetration rates in developing countries, where wirelesstechnologies have leapfrogged the wired computer infrastructure, have producedconsiderable excitement in the global health community to reach and follow individuals whowere previously unreachable via traditional communication channels [8, 9].

Compared to Internet interventions delivered to desktop and laptop computers, mobileinterventions have the capacity to interact with the individual with much greater frequencyand in the context of the behavior. As sensing technologies integrated with the mobile phonevia Bluetooth or other data transmission process continue to develop, health behavior changeinterventions can be delivered based not only on self-reports and time/location parametersbut also on psychophysiological state, social context, activity level, and behavior patterns[10]. The availability of these rich, complex, and frequent data inputs provides the potentialto deliver health behavior interventions tailored not only to the person’s baselinecharacteristics but also to his/her frequently changing behaviors and environmental contexts[11].

To better leverage the potential of mobile technologies for health behavior interventions,health behavior theories and models need to be able to guide the development of complexinterventions that adapt rapidly over time in response to various inputs. Existing healthbehavior theories and models have served for many years as guides to interventiondevelopment and delivery, and these theories have been the basis not only for face-to-facecounseling interventions but also mass media and social marketing efforts. Models such asthe Health Belief Model [12], Theory of Planned Behavior [13], Social Cognitive Theory[14], the Transtheoretical Model [15], and Self-Determination Theory [16] have served asthe basis for many of the eHealth web and desktop/laptop computer interventions and haveinformed how interventions can be tailored to the individual’s baseline status.

As intervention developers take full advantage of mobile technologies, health behaviormodels will be required to guide not only tailored adjustments at intervention initiation butalso the dynamic process of frequent iterative intervention adjustments during the course ofintervention. The content and timing of a specific mobile phone intervention delivered viavoice, text, resident application, mobile web, or other modality can be driven by a range ofvariables including (a) the target behavior frequency, duration, or intensity; (b) the effect ofprior interventions on the target behavior; and (c) the current context of the individual (time,location, social environment, psychophysiological state, etc.). Such interventions requirehealth behavior models that have dynamic, regulatory system components to guide rapidintervention adaptation based on the individual’s current and past behavior and situationalcontext. Some have argued that current health behavior models are inadequate even for low-tech interventions [17], but the predominately linear and static nature of these models

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severely limits their ability to guide the dynamic, adaptive interventions possible via mobiletechnologies.

To evaluate the theoretical basis and adaptive nature of mobile interventions, the currentliterature on mobile technology health behavior interventions was reviewed. For this review,we define “mobile technology” as computer devices that are intended to be always on andcarried on the person throughout the day (i.e., during normal daily activities). Mobile phonesare the prototype device within this space, and most of the interventions reviewed usedmobile phone platforms, but precursors to current smart phones and text messaging systemssuch as personal digital assistants (PDAs) and two-way pagers also are consistent with thisdefinition. Excluded from this definition are devices that are portable (e.g., laptops,netbooks, iPads) but are not intended to be carried on the person throughout the day.Although technically mobile, we also excluded interventions that involved phone-basedcounseling, whether live or by interactive voice response (IVR), that could be deliveredsimilarly via landline (i.e., phone-based counseling via mobile phone). Prior to the adventand widespread use of multipurpose mobile devices, special purpose devices weredeveloped for the delivery of health behavior interventions [18, 19], but these pioneeringprototypes often lacked the portability, connectivity, input–output interface, and/orcomputing power of current multi-purpose devices and are not included in this review. Thisreview is also limited to the use of these mobile devices for delivering health behaviorinterventions, not assessments. There is a substantive literature on the use of mobile devicesfor health behavior assessment (e.g., ecological momentary assessment), and self-monitoringalone has been shown to be an effective health behavior intervention [20], but we excludedfrom this review mobile programs that involved assessment input only or that used mobiledevice output solely for the purpose of encouraging input (i.e., reinforcing recordingadherence).

For this review, a Medline search of “mobile phone,” “cell phone,” “text messaging,”“personal digital assistant,” “PDA,” “palmtop,” “handheld computer,” and “pager”published through June 2010 was conducted. Articles meeting the definition describedabove were selected, and additional articles were identified from references of these initiallyselected articles. A considerable number of these articles were proof-of-concept reports thatdescribed technical development, an ongoing study, or reported only usability/satisfactiondata. To insure review of fully functional mobile interventions, we narrowed the selectioncriteria to studies that reported some form of clinical outcome data, with or withoutrandomized controlled conditions. Nearly all of the articles identified meeting these criteriafell into one of four major health behavior areas: smoking, weight loss (including diet andexercise interventions), treatment adherence (i.e., adherence to medical recommendationsincluding both taking medication and attending appointments), and chronic diseasemanagement. Therefore, we limited this review to mobile health behavior interventions inthese four areas. Others have reviewed subsets of the research reviewed here for thepurposes of estimating effect sizes (e.g., [21, 22]). In contrast to meta-analytic reviews ofeffect size, the purpose of this review is to assess how theory has been employed in thedevelopment of mobile health behavior interventions and how the interactive and adaptivepotential of these interventions may require new dynamic systems theories of healthbehavior.

REVIEW OF MOBILE HEALTH BEHAVIOR INTERVENTIONSSmoking cessation

Seven studies were identified that used mobile technology to deliver smoking cessationinterventions. In Table 1 and subsequent tables of the other three areas reviewed, wesummarize for each study the basic study characteristics (e.g., sample, design), a brief

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description of the intervention, the theoretical basis of the intervention if reported, theinteractivity of the intervention, and the primary outcome. For interactivity, we specify if theintervention output was adjusted based on input provided by the user, if this adjustment wasmanual (e. g., healthcare professional reviewed data and determined intervention output) orautomatic (e.g., intervention output generated by computer algorithms), and if theadjustment occurred at intervention initiation (i.e., tailored or personalized based on initialassessment) or during the course of the intervention, or both. The concept of “just-in-time”of Intille et al. is used to characterize interventions that adjust based on data obtained duringthe course of the intervention [30]. As opposed to the term “real-time” which is commonlyis used in the mobile assessment literature to denote input occurring at the time of arecording prompt or the behavior being monitored, mobile interventions are often laggedover minutes, hours, or days as sufficient data are obtained to adapt the intervention, hencethe use of “just-in-time” to describe adaptations occurring during the course of theintervention.

As shown in Table 1, the seven studies identified are from three research groups whodeveloped and evaluated interventions delivered via mobile phone text messaging (aka shortmessaging service (SMS)). These interventions consisted primarily of text message outputalthough Whittaker et al. [26] also delivered mobile videos of role models and their quittingexperiences. All of the interventions were computer-tailored at treatment initiation. Theintervention developed by Riley and colleagues [23, 25] tailored both content and timing ofthe text messages to the times users indicated via an associated Internet site that urges andhigh-risk situations were likely to occur. All of these interventions also adjusted just-in-timevia text message requests from users for assistance which generated immediate return textmessages with tips and strategies to manage urges or withdrawal. The Brendryen [27, 28]and Riley interventions [23, 25] also adjusted the intervention based on smoking status (e.g.,different messages before vs. after quitting, resetting quit dates if relapse occurred).

With the exception of the Rodgers intervention [24, 29], these mobile smoking cessationinterventions provided a theoretical basis, citing Social Cognitive Theory, Self-RegulationTheory, the Transtheoretical Model, and cognitive–behavioral theory. These theoriesprimarily appear to have informed the content of the text messages which included skills tomanage urges and the facilitation of social support. The Transtheoretical Model was used toadapt the content of the text messages to the preparation, action, and maintenance stages ofthe quit process. In a separate report, Brendryen and colleagues [31] provided a detaileddescription of the intervention mapping process for the development of the Happy Endingintervention based on Self-Regulation Theory, Social Cognitive Theory, cognitive–behavioral therapy, motivational interviewing, and relapse prevention. In a stepwiseapproach, they performed a needs assessment and devised proximal change objectives basedon the self-observation, self-evaluation, and self-reaction processes of Self-RegulationTheory and the behavioral determinants posited by Social Cognitive Theory and relatedmodels. They generated theory-based methods and practical strategies to address thesechange objectives and developed the resulting intervention to deliver each of thesestrategies. This report provides a well-devised framework for the application of theory to amobile health behavior intervention.

Quit rates from the interventions evaluated in these studies range from 8% [29] to 53% [26]with a follow-up period ranging from 4 weeks to 12 months. A recent Cochrane review ofrandomized studies of mobile phone-based interventions for smoking cessation, whichincluded all of the randomized controlled trials (RCT) reviewed here, found a significantshort-term increase in self-reported quitting (relative risk= 2.18; 95% confidence interval1.80 to 2.65) but considerable heterogeneity and lack of sufficient evidence for long-term

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effects [32]. Although promising, the effects of mobile technologies for smoking cessationrequire more study, especially of the long-term effects of these interventions.

Weight loss, diet, and physical activityWe identified 12 studies reporting on mobile health behavior interventions for weight loss,diet, and/or physical activity. Four of these interventions were delivered via PDA, and eightwere delivered via mobile phone, predominately SMS. These interventions represent a rangeof interactivity from non-tailored weekly informational text messages [33] to real-time dietand exercise monitoring with multiple daily customized messages based on input [34]. Mostauto-adjusted the intervention based on computer algorithms, but the two interventionsencouraging walking in Chronic Obstructive Pulmonary Disease (COPD) patients weremanually adjusted by health professionals [35, 36]. The mobile weight loss programdeveloped by Bauer et al. [37] computer-generated text messages based on weekly inputfrom the overweight children in the study, but these messages were reviewed and modifiedby staff before sending. Diet, weight, and exercise data were provided via self-report for allinterventions except the Hurling et al. [38] study which used accelerometer data wirelesslytransmitted via Bluetooth to the mobile phone. Output from these interventions waspredominately text although tabular and graphic comparisons to targets/goals were providedby some interventions (e.g., [34, 39, 40]) and one intervention used a mobile phone programthat adjusted music tempo to encourage an appropriate walking pace [35].

Of the 12 studies summarized in Table 2, seven specified a theoretical basis for the mobileweight loss, diet, or physical activity intervention developed. Consistent with the moregeneral weight loss intervention literature, Social Cognitive Theory was the primarytheoretical basis for these interventions although the Theory of Planned Behavior was citedby one study [38]. In a well-controlled evaluation of a theoretically-based program, Haapalaand colleagues [40] randomly assigned overweight adults to control or a mobile phoneweight loss program. The intervention was based on Self-Efficacy Theory and a SystemsContingency Approach described by Hiltz [45] in which the amount, frequency, and type ofcomputer-mediated communication are hypothesized to influence learning effectiveness.These theories were combined in a contingency model of mobile phone weight loss thatcombines program factors (e.g., content), frequency of interaction with the program,exogenous variables, and changes in processes and outcomes. Text messages instructedusers in a staggered reduction of food intake and prompted daily weight reporting withimmediate tailored feedback. The program evaluated weight balance, calculated dailyenergy requirement, set daily weight goals and food consumption recommendations, andprovided feedback on the days remaining to the long-term weight target. Weight loss goalswere user defined, but typically averaged 2 kg/month. Weight was reported via mobilephone on average 4.4 times per week. Compared to controls, those in the mobile phoneintervention lost significantly more weight (4.5 vs. 1.1 kg) and had a greater reduction inwaist circumference (6.3 vs. 2.4 cm) after 1 year in the program.

As an example of interactivity in mobile weight management interventions, Patrick andcolleagues [43] reported on a 4-month, randomized trial of a mobile phone text messagingprogram for weight loss that, although not specified, appeared consistent with SocialCognitive Theory and that involved considerable interactivity. Text messages werepersonalized and varied not only in content but also in their interactive nature. Some textswere simple push reminders while others queried the user about their dietary behavior,obtained a reply, and then provided an intervention based on the reply. Although weight lossin the text messaging condition was modest over 4 months (2.88 kg), it was significantlygreater than in the print only condition.

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In a recently published study, Burke et al. [34] provided another example of interventioninteractivity via mobile device. They compared paper-based dietary and exercise monitoringto PDA-based monitoring and PDA-based monitoring plus daily feedback message in anRCT of 210 overweight and obese adults. The PDA monitoring program was based on theplatform developed by Beasley et al. [39], but the PDA monitoring plus feedback conditionadded daily messages of positive reinforcement and guidance for goal attainment tailored tothe input provided based on self-regulation theory. All three conditions also received 20group intervention sessions over a 6-month period. Compared to the monitoring onlyconditions which obtained a percent weight loss of 4.8 (PDA) and 4.6 (paper-based), thePDA plus daily feedback condition obtained a mean percent weight loss of 6.5.

Consistent with other reviews [46], these initial studies of mobile interventions for weightloss show modest but significant weight loss and related outcomes. Mobile interventions forweight loss, diet, and physical activity are poised to take advantage of a number of advancesin monitoring that could be leveraged to improve outcomes. Computerized dietarymonitoring has been used extensively in this field [47], and these self-monitoring procedureshave been adapted to mobile platforms. Recent research has begun to use cell phone camerasto assess dietary intake [48, 49] which may result in less underreporting of intake than fromself-monitoring. As mobile phones increasingly incorporate accelerometers, the potentialexists to use the automated data obtained from these accelerometers to monitor activitylevels and provide just-in-time interventions [50, 51]. These richer and potentially moreprecise measures of dietary intake and physical activity can provide the basis for robust andinteractive weight loss interventions delivered via mobile devices.

Treatment adherenceTen studies were identified that evaluated the use of mobile technology to improve treatmentadherence (Table 3). Of these, eight targeted medical appointment adherence and twoaddressed medication adherence, specifically HIV treatment adherence. All of theappointment adherence interventions were delivered via SMS while both of the medicationadherence interventions were delivered via two-way pager. Given the substantial use oftechnology for measuring medication adherence [52] and the availability of smart pillboxesfor improving adherence [53], it is surprising that only two studies have developed andevaluated a medication adherence intervention on a mobile platform, both using two-waypagers which have limited capabilities.

In 2003, Safren and colleagues [54] published the earliest evaluation of a mobile technologyidentified in this review. In HIV patients with poor adherence, they used a commercialsystem (Medimom) to enter patients’ drug regimens and transmit pager alerts to remindpatients to take their medications at the specified times. Based on electronic pill bottle data(MEMS), those randomly assigned to the pager reminder condition had significantimprovements in medication adherence compared to those in a medication monitoring onlycondition at 2 and 12 weeks. In a more recent study, Simoni and colleagues [55] developedand evaluated a similar pager dosing reminder program for HIV patients with additionaltexts providing education, adherence assessments, and entertainment. This intervention,however, failed to produce significant improvements in adherence over the 9-month trialrelative to usual care or peer support, possibly due to the mixed functions of the pagerprompt (i.e., prompt taking medications, provide education). Neither study provided atheoretical basis for the intervention provided. Both studies tailored the intervention atinitiation based on the medication regimen of each patient, but there were no adjustmentsmade during the course of the intervention.

Among the eight studies providing appointment reminders via mobile technologies, allprovided only a single, unadjusted output reminding the patient of their appointment, usually

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timed to within a few days of the scheduled appointment. The only two-way interaction wasin the Koshy et al. study [56] which provided the patient with the ability to text reply tocancel the appointment. None of the studies provided a theoretical basis for the interventionprovided.

Despite the simple nature of these appointment adherence interventions and their lack ofinteractivity and theoretical basis, most resulted in a significant increase in appointmentadherence. The only exception was Bos et al. [57], which had high appointment adherencerates to orthodontic visits across conditions. It is important to note, however, that only twostudies used randomized controls [58, 59]; the others used historical or nonrandomizedcontrols. Additionally, the three studies that compared SMS to traditional phone callreminders found no difference between these two modalities [57–59].

Disease managementIn addition to the treatment adherence research reviewed above, a number of treatmentadherence interventions are incorporated within a chronic disease management program.The use of mobile phones for disease management has been substantially reviewedelsewhere [46, 60–62], particularly in the area of diabetes management. Based on the criteriafor this review, we identified 20 studies: 16 in diabetes management, three in asthmamanagement, and one in hypertension management (Table 4). Across the 20 studies, onlyone [63] specified a theoretical basis for the intervention, using Social Cognitive Theory toprovide personalized SMS messages to support diabetes management in children with type 1diabetes. In part, this lack of theoretical basis may reflect the well-established practiceguidelines for managing type I and type 2 diabetes [64], but as the field extends theseinterventions beyond medication adjustments and integrates automated measures of physicalactivity, medication adherence, food intake, and other target variables into these systems[65], using health behavior theories to inform these interventions could improve outcomes.

The interventions developed and evaluated in these studies focused primarily on patientinput (e.g., blood glucose readings, insulin doses, carbohydrate intake, exercise, forcedexpiratory volume (FEV1), or blood pressure) which were reviewed by a health professionalto provide periodic regimen adjustments and treatment recommendations. Therefore, exceptfor the two PDA interventions that did not transfer data to a central server [66, 67], any just-in-time intervention adjustments were made manually. These manually adjustedinterventions are consistent with telemedicine models, but they have limited scalability dueto the provider burden required to review and deliver these interventions. Lanzola andcolleagues have proposed a multi-tier intervention model in which lower-tier diseasemanagement interventions are provided via automated treatment algorithms and higher tierinterventions involving the healthcare provider occur only if the lower-tier interventions areineffective [68].

Although advances in automating disease management intervention output are needed, thefield is leveraging advances in objective, real-time input. Data entry burden is a persistentproblem in disease management [69]. In one study of adolescents on intensified insulintherapy, one quarter failed to send at least 50% of their four daily blood glucose values [70].With the advent of Bluetooth-enabled glucometers, data entry burden is eliminated. Since2008, three of the studies reviewed used this or similar technology and found significantreductions in HbA1c [71–73]. Another study used Bluetooth-enabled blood pressure devicesto provide automated transmission for hypertension control in diabetes patients [74].

Although the automatic transmission of testing data eliminates data entry burden, it does noteliminate the responsibility of the patient to perform regular testing, and a number of thestudies used reminders via pager or mobile phone SMS to obtain blood glucose [72, 75, 76]

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or to take medications [77]. For example, Turner and colleagues provided blood glucosemeasurement reminders in an interactive manner with type 2 diabetes patients [72]. If noblood glucose data were automatically transmitted for 3 days, or if hypo- or hyper-glycemiapersisted, patients received additional reminders.

Outcomes from the mobile disease management studies were generally positive andstatistically significant. Among the 12 diabetes management studies with HbA1c as aprimary outcome, ten found significant reductions in HbA1c. This is consistent with a recentsystematic review of mobile phone use for diabetes self-management in which nine of tenstudies reviewed reported significant improvement in HbA1c [46]. All three mobile asthmamanagement interventions found significant improvements in the primary outcome. Forexample, in a small randomized controlled trial of no SMS vs. SMS daily reminders to takeasthma medication, Strandbygaard et al. [77] found that the percent adherence based onobjective inhaler dose counts decreased in the control condition (84.2% to 70.1%) butincreased in the intervention condition (77.9% to 81.5%) over the 12-week study period.Therefore, the ability of mobile disease management programs to assess patient statusfrequently and adjust treatment remotely appears to improve treatment outcomes and toserve as a basis for more comprehensive and automated mobile disease managementinterventions.

SUMMARY OF MOBILE HEALTH BEHAVIOR INTERVENTION FEATURESThe application of mobile technologies to health behavior interventions is a nascent butrapidly growing field that has only begun to leverage the full capabilities of mobile phonesand other mobile technologies. Most of the mobile interventions described in this reviewused text messaging (aka SMS) functionality of mobile phones. Text messages can bedelivered simply and inexpensively across mobile phone operating platforms and to a largepercentage of mobile phone users given the ubiquitous nature of this feature even on “lesssmart” mobile phones. As smart phone penetration rates increase and interoperabilitybetween operating platforms improves, however, additional features of mobile phones canbe used.

In addition to voice, text, and data inputs, researchers have begun to use video input viacamera features on these phones. Although video input has been primarily used fortelemedicine purposes [78, 79], health behavior researchers have begun to use cameras fordietary assessment [48]. Another significant set of input features are automated sensors,either resident on the smartphone (e.g., accelerometers and GPS) or connected viaBluetooth. Much of the mobile health behavior interventions to date have focused ontransmitting data from glucometers and spirometers, but an expanding variety of sensortechnologies can be used to assess physiological states, movement, location, and othervariables using mobile phones.

These early mobile health behavior interventions also have only begun to leverage theoutput functionality of these phones. As with input, most of the output delivery to date hasbeen via text messaging. A few studies have used other output functions such as audio (e.g.,[35]) or video (e.g. [26]), but device-resident or wireless Internet applications onsmartphones provide a rich array of possible data and display outputs (e.g., progress charts,animation, videos, games) that could be used to deliver health behavior interventions.

Most apparent from this review, however, is the limited use of the rapid two-way interactionof inputs and outputs that could be used to deliver just-in-time health behavior interventions.Disease management and weight loss mobile health interventions have relied predominatelyon input while adherence and smoking cessation mobile health interventions have relied

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predominately on output. Among applications relying on input, many employed a user-initiated or unprompted input procedure despite a considerable ecological momentaryassessment literature supporting the use of prompted inputs [80]. Following input, someapplications provided tailored intervention responses, but others failed to deliver even aconfirmation message, despite the potential importance of this feature for encouragingcontinued input. Of the applications that predominately pushed out intervention messages, afew encouraged confirmation of receipt or some other reply response (e.g., [43]), but mostdid not. None of the adherence interventions made just-in-time adjustments, but most ofthese interventions were single prompt appointment reminders with no longitudinal basis foradjustment, and none obtained the input necessary to make treatment adjustments. Most ofthe smoking and weight loss mobile interventions provided some form of “just-in-time”response based on a prior input, but the frequency or intensity of interactivity variedsubstantially among these interventions from requests for additional text message assistancecommon among the smoking interventions (e.g., [23, 27]) to regular interventionadjustments based on dietary and exercise inputs [34, 43]. The disease managementinterventions also typically provided just-in-time intervention adjustments, but most reliedon manual adjustments by a health care professional to do so. Although more complexintervention adjustments might be best reserved for health professional judgment, standardtreatment algorithms can be used to automate, with 100% treatment fidelity, many of thesetreatment adjustments, greatly improving scalability, and reducing professional time andcosts.

Given the lag between intervention development, evaluation, and eventual publication, thearticles reviewed, although published as recently as the June 2010, describe interventionsdeveloped a number of years ago. Therefore, the limited interactivity of the mobileinterventions reviewed may not reflect current status, and the National Institutes of Healthcurrently funds a number of ongoing projects that use more intensive interactivity and just-in-time interventions than described in this review [81]. To insure that the mobileinterventions reviewed were mature enough for actual use, we limited our review to studiesthat subjected these interventions to some form of clinical outcome assessment. As a result,we did not report on recent mobile intervention development from proof-of-concept (e.g.,[82]), usability/feasibility (e.g., [83]), or ongoing research reports (e.g., [84]) in which morerecently developed interventions are described. Given the recent interest in and rapidprogression of mHealth interventions, reports on more intensively interactive mobile healthbehavior interventions are likely to be published in the coming years.

THEORETICAL BASIS FOR MOBILE HEALTH BEHAVIOR INTERVENTIONSAND NEW DIRECTIONS

Our review of the studies of health behavior interventions delivered via mobile technologiesreveals a paucity of discussion regarding the health behavior theories or models that providethe basis of the intervention. Even among those studies that provided a theoretical basis,very few attempted to evaluate any of the theoretical components hypothesized to beaffected by the intervention. Studies in the smoking and weight loss areas tended to use atheoretical model for their intervention, drawing predominately from Social CognitiveTheory or its variants (e.g., self-efficacy). Among these studies are extensive and thoughtfulexamples of using theory to guide mobile intervention development (e.g., [31, 40]). Incontrast, most of the mobile interventions studied in the treatment adherence and diseasemanagement areas did not report a theoretical basis for intervention development, but thismay be the result of reliance on evidence based clinical guidelines (e.g., diabetesmanagement) or on the simplicity of the intervention delivered (e.g., appointmentreminders).

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A simple reminder intervention, however, is an excellent example for the necessity of theoryto guide intervention. Text messaging reminders to attend appointments, take medications,exercise, etc. appear relatively straightforward and are consistent with the “cue to action”component of many health behavior theories, but the history of health behavior theoriescautions us about assuming that behavior change is straightforward or that innovative modesof delivery alone are sufficient to produce behavior change. In the 1950s, mobileneighborhood tuberculosis screenings were innovative, but the added salience andconvenience did not substantially increase screenings. Researchers studying uptake of theseneighborhood screenings learned that the perceived threat (severity and susceptibility) oftuberculosis, the perceived benefit of being screened, and the perceived barriers to gettingscreened contributed to a person obtaining these screenings. These findings led to the HealthBelief Model [85] and the various health behavior theories and models that followed.Therefore, a greater reliance on health behavior theories to guide mobile technologyintervention development, even for apparently simple interventions, should result ininterventions that address more comprehensively the potential mechanisms of behaviorchange, resulting in more effective interventions.

While mobile technology applications of health behavior interventions should be guided byour current health behavior models, it is important to acknowledge that these current modelsappear inadequate to answer many of the intervention development questions likely to ariseas interventions better leverage the interactive capabilities of mobile technologies. Somehave argued that these theories are inadequate even for traditional interventions [17], butthey are particularly limited at informing just-in-time intervention adaptations. Dunton andAtienza have noted that the increasing availability of time-intensive information (i.e.,longitudinal data obtained at an intensive frequency) allows for the intra-individual tailoringof interventions but that current health behavior theories are based on delineating between,not within-person differences [86]. Boorsboom and colleagues have argued that between-person theories do not imply, test, or support causal accounts valid at the individual level[87]. As a result, these theories and models have been used with considerable success totailor health behavior interventions based on pre-intervention factors, but typically have notbeen used to adapt the intervention to the individual over the course of the intervention [88,89]. As these interventions become deliverable on mobile devices, the content ofinterventions can be adapted for each individual not only initially but also over time basedon his/her prior outcome data, prior responses to specific intervention outputs, currentenvironmental and/or social context, and a range of other variables that might influence theoptimal intervention based on the current state of the individual (i.e., ecological momentaryinterventions) [90].

In addition to adapting the content of the intervention over time, mobile technologyapplications of health behavior interventions also have the ability to adapt the timing of theinterventions based on these same data. For example, following a prompt to exercise, howlong should the program wait before prompting again and how should the timing andcontent of the follow-up prompt be tailored based on prior pattern of responses to prompts?In this review, only a few studies adjusted the timing of the interventions, usually during thetransition from behavior change to maintenance (e.g., [23, 40]), but these timing adjustmentswere set a priori across all participants and were not dynamically determined at theindividual level.

The development of time-intensive, interactive, and adaptive health behavior interventionsvia mobile technologies demands more intra-individual dynamic regulatory processes thanrepresented in our current health behavior theories. These theories do not preclude thepossibility that some conceptual components change over time, and some concepts such asreciprocal determinism in Social Cognitive Theory are explicitly dynamic in nature [14].

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Moreover, a rich behavior change process–outcome literature describes dynamic interactionsin face-to-face behavioral interventions that could be applied to behavioral interventions viamobile technologies [91]. Adoption of dynamical system models for mobile health behaviorinterventions does not require that our current health behavior theories and models bediscarded, but the predominately static, linear nature of these theories appears to be a poorfit with the intra-individual dynamics of future mobile technology interventions.

Control systems engineering [92] may provide the dynamical system models needed totransform our current health behavior theories into the dynamic theories required for thetime-intensive, interactive, and adaptive health behavior interventions delivered via mobiletechnologies. Control systems engineering examines how to influence dynamical systems toachieve a desired outcome. These dynamical systems are multivariate, time-varyingprocesses, often nonlinear in nature, in which variables that can be manipulated (e.g., systeminput variables) lead to changes in outcomes of interest (e.g., system output variables)adjusting for exogenous effects (e.g., disturbance variables). Control engineering approacheshave been proposed for adaptive interventions to determine, for example, when and howmuch to change the frequency of family counseling to prevent the development of conductdisorder in children [93].

There are a number of practical advantages of using dynamical systems to inform adaptive,time-intensive interventions delivered via mobile technologies. Control systems engineeringprinciples and procedures are mature and provide a robust computational framework formodeling, simulation, and systematic decision making over time. These control systemprinciples and procedures fit well with the rich longitudinal data and real-time interventionadaptations potentially available via mobile technologies. Interventions based on theoriesenriched by control system models also provide engineers and health behavior researcherswith a common language for collaborating on new interventions delivered via mobiletechnologies [94].

Perhaps more importantly, these feedback control systems have the potential to reshapehealth behavior theories and improve our understanding of human behavior. Feedbackregulatory processes are core to neurobiology. Basic neuronal processes are time-intensive,adaptive interactions of excitatory and inhibitory synaptic processes that have been modeledat the single neuron level [95]. The communalities between brain circuitry and controlsystem engineering principles are so compelling that some brain processes are described viaengineering feedback systems (e.g., [96]). Since the brain appears to regulate itself and otherorgans using feedback control processes, it seems reasonable that the brain also regulateshuman behavior via similar feedback control processes. Optimal feedback control systemshave been applied to basic sensorimotor control [97] and to reinforcement learning models[98]. Therefore, the application of feedback control systems to current health behaviortheory concepts provides not only potential theoretical models that are amenable to the time-intensive, interactive nature of mobile health behavior interventions but also theoreticalmodels that are more consistent with the putative neurobiological and environmentalprocesses that regulate these behaviors.

One significant example of regulatory feedback for disease management processes is indiabetes where frequent adjustments to diet and insulin dose are determined based on bloodglucose levels. Control engineering approaches have been applied to the glucose–insulinclosed-loop system [99], and this application illustrates the potential of this approach toproduce the desired outcome in systems that possess measurement error, model uncertainty,noise, and lagged effects. This work could serve as a basis for developing mobiletechnologies for diabetes management that take advantage of advanced closed-loop controlsystem models to automate and better regulate blood glucose for insulin-dependent patients.

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Detailed descriptions of applications of control systems engineering to other health behaviorinterventions such as substance use have been described elsewhere [93, 100], but to illustratethe application of control systems engineering to mobile health behavior interventions, weprovide a simple model for intervening on smoking urges depicted in Fig. 1 whichrepresents an individual’s smoking behavior in terms of a production–inventory model[101]. Using a fluid analogy, smoking urge is accumulated as an inventory that is built up byan “inflow” of negative emotional stimuli (e.g., sadness, irritation) and external stimuli (e.g.,presence of others smoking, drinking coffee) and depleted by an urge “outflow” resultingfrom smoking events and the salutary effects of positive affect and dosages of interventioncomponents such as medications and psychosocial interventions. In a control systemsconceptualization, smoking urge (y(t)) is the controlled variable, while smoking activityrepresents a manipulated variable (u1(t)) whose action serves to reduce the urge to smoke.Environmental stimuli and positive and negative affect represent external disturbancevariables (d1(t), d2(t)) whose changes build up or deplete the smoking urge inventory. Thedose of intervention components represents additional manipulated variables (u2(t)) that canbe selected adaptively by a set of decision rules or mathematical algorithms (referred to asthe intervention controller in Fig. 1) based on assessed participant response. A differentialequation drawn from the general principle of conservation of matter can be used to describethe relationship between these variables [102]

where Kd1, Kd2, Ku1, and Ku2 represent coefficients that quantify the rate of change in urgebased on changes in manipulated variables (intervention dosages, smoking activity) anddisturbance variables (negative and positive affect, environmental stimuli). The inherentdynamics of smoking behavior in Fig. 1 constitutes a closed-loop feedback system in whichincreases in smoking urge precipitate a decision to smoke (the relationship between urge andsmoking activity is denoted by the “urge controller” box in Fig. 1).

The potential of mobile technologies to generate the intensive longitudinal data setsreflecting the behavior of a smoker over time and observed in different settings and contextsis important for building a dynamical system model such as the individual smoking behaviormodel illustrated above. Mobile devices can collect information in real time about thecontrolled, manipulated, and disturbance variables, as represented by sensor locations in Fig.1. Model estimation techniques such as system identification [103] and functional dataanalysis [104] can be used to estimate the Kd1, Kd 2, Ku1, and Ku2 coefficients from thesedata. These modeling techniques also can be applied to reverse-engineer the functionalrelationship underlying the urge controller which reflects the actions of an individualsmoker. With a dynamical systems model, it becomes possible to apply control systemsengineering to develop algorithms that use real-time assessments and predicted responsesfrom the model to adaptively decide on the timing and dose of the intervention components.Mobile technology is an enabler to advanced control algorithms such as model predictivecontrol [105] that employ formal optimization methods to decide on current and future dosesof intervention components while satisfying clinical practice preferences and restrictions[100]. The use of these control technologies in a smoking cessation intervention as shown inFig. 1 parallels the closed-loop control system for diabetes management describedpreviously. The optimization process leads to specific intervention decisions (u2(t)) forproviding behavioral strategies and/or medication prompts to replace smoking as a means ofreducing smoking urge.

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In addition to being sensitive to threshold (or mean) levels of key variables, a control systemframework can adapt to and consider variability of key controlling or explanatory variables.Most theories of health behavior change to date have focused on mean levels of predictivevariables, but with the advent of newer data technologies, we can develop models that focuson lability or stability of key controlling factors. For many health behaviors, maintaining astable level or tighter range for some key variables may be as or more important thanincreasing or decreasing mean levels.

Novel dynamic health behavior theories and their application to health behaviorinterventions via mobile technologies will require empirical validation. Adaptive treatmentmethodologies has been applied to interventions that adjust over the course of weeks ormonths, not hours or days, but these methodologies can be applied to mobile technologyinterventions [106]. Other methodological considerations for adaptive interactivetechnologies applied to eHealth also may be appropriate to consider for mHealthinterventions [107, 108]. The opportunity via mobile devices to collect intensive context-and time-dependent (longitudinal) data and to systematically vary intervention componentsenables researchers to test not only these components but also the theoretical concepts anddynamic models that underlie them.

The application of mobile technologies to health behavior interventions is an exciting andrapidly growing field. The ability to provide frequent, time-intensive interventions in thecontext of the behavior holds considerable promise but also poses many challenges to ourcurrent health behavior theories and models. To meet these challenges, our current healthbehavior theories and models need to expand from elucidating between-person differencesto explaining within-person changes over time and to evolve to incorporate dynamicfeedback control systems to “close the loop.” Health behavior interventions delivered viamobile technologies offer not only the impetus to transform our current theories into moredynamic feedback control models but also the potential to provide the intensive longitudinaldata necessary to test and improve our theoretical intervention models. To date, only asubset of mobile health behavior interventions have been theory-based, but a theory-driveniterative model of mobile intervention development holds promise for improving not onlyour mobile health behavior interventions but also our theoretical and empiricalunderstanding of health behavior change.

References1. Webb TL, Joseph J, Yardley L, Michie S. Using the Internet to promote health behavior change: A

systematic review and meta-analysis of the impact of theoretical basis, use of behavior changetechniques, and mode of delivery on efficacy. Journal of Medical Internet Research. 2010; 12:e4.[PubMed: 20164043]

2. Tate DF, Finkelstein EA, Khavjou O, Gustafson A. Cost effectiveness of Internet interventions:Review and recommendations. Annals of Behavioral Medicine. 2009; 38:40–45. [PubMed:19834778]

3. Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review ofeHealth interventions for physical activity and dietary behavior change. American Journal ofPreventive Medicine. 2007; 33:336–345. [PubMed: 17888860]

4. CTIA. 2009. http://www.ctia.org/advocacy/research/index.cfm/AID/103235. International Communications Union. 2010.

http://www.itu.int/ITU-d/ict/publications/idi/2010/Material/MIS_2010_Summary_E.pdf6. Pew Internet &American Life Project. 2010.

http://www.pewInternet.org/~/media//Files/Reports/2010/PIP_Mobile_Access_2010.pdf7. Leena K, Tomi L, Arja R. Intensity of mobile phone use and health compromising behaviours: How

is information and communication technology connected to health-related lifestyle in adolescence?Journal of Adolescence. 2005; 28:35–47. [PubMed: 15683633]

Riley et al. Page 13

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

8. Kosaraju A, Barrigan CR, Poropatich RK, Casscells SW. Use of mobile phones as a tool for UnitedStates health diplomacy abroad. Telemed J E Health. 2010; 16:218–222. [PubMed: 20156128]

9. Kaplan WA. Can the ubiquitous power of mobile phones be used to improve health outcomes indeveloping countries? Global Healthcare. 2006; 2:9–22.

10. Pharow P, Blobel B, Ruotsalainen P, Petersen F, Hovsto A. Portable devices, sensors andnetworks: Wireless personalized eHealth services. Studies in Health Technology and Informatics.2009; 150:1012–1016. [PubMed: 19745466]

11. Patrick K, Griswold WG, Raab F, Intille SS. Health and the mobile phone. American Journal ofPreventive Medicine. 2008; 35:177–181. [PubMed: 18550322]

12. Becker, MH., editor. Health Education Monographs. Vol. 2. 1974. The health belief model andpersonal health behavior; p. 324-473.

13. Godin G, Kok G. The theory of planned behavior: A review of its applications to health-relatedbehaviors. American Journal of Health Promotion. 1996; 11:87–98. [PubMed: 10163601]

14. Bandura A. Social cognitive theory: An agentic perspective. Annual Review of Psychology. 2001;52:1–26.

15. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. AmericanJournal of Health Promotion. 1997; 12:38–48. [PubMed: 10170434]

16. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, socialdevelopment, and well-being. Amer Psychol. 2000; 55:68–78. [PubMed: 11392867]

17. Ogden J. Some problems with social cognition models: A pragmatic and conceptual analysis.Health Psychology. 2003; 22:424–428. [PubMed: 12940399]

18. Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted therapy for obesity: A newfrontier for behavior therapy. J Cons Clin Psychol. 1985; 53:698–703.

19. Prue DM, Riley A, Orlandi MA, Jerome A. Development of a computer-assisted smokingcessation program: A preliminary report. J Adv Med. 1990; 3:131–139.

20. Brownell KD. Adherence to dietary regimens. 2: Components of effective interventions.Behavioral Medicine. 1995; 20:155–164. [PubMed: 7620227]

21. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobiletelephone short-message service. American Journal of Preventive Medicine. 2009; 36:165–173.[PubMed: 19135907]

22. Krishna S, Boren SA. Healthcare via cell phone: A systematic review. Telemed e-Health. 2009;15:231–240.

23. Obermayer JL, Riley WT, Asif O, Jean-Mary J. College smoking-cessation using cell phone textmessaging. J Amer Coll Health. 2004; 53:71–78. [PubMed: 15495883]

24. Rodgers A, Corbett T, Bramley D, et al. Do u smoke after txt? Results of a randomized trial ofsmoking cessation using mobile phone text messaging. Tobacco Control. 2005; 14:255–261.[PubMed: 16046689]

25. Riley WT, Obermayer J, Jean-Mary J. Internet and mobile phone text messaging intervention forcollege smokers. J Amer Coll Health. 2008; 57:245–248. [PubMed: 18809542]

26. Whittaker R, Maddison R, McRobbie H, et al. A multimedia mobile phone-based youth smokingcessation intervention: Findings from content development and piloting studies. Journal ofMedical Internet Research. 2008; 10:e49. [PubMed: 19033148]

27. Brendryen H, Kraft P. Happy Ending: A randomized controlled trial of a digital multi-mediasmoking cessation intervention. Addiction. 2008; 103:478–484. [PubMed: 18269367]

28. Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through Internetand cell phone without nicotine replacement (happy ending): Randomized controlled trial. Journalof Medical Internet Research. 2008; 10:e51. [PubMed: 19087949]

29. Free C, Whittaker R, Knight R, Abramsky T, Rodgers A, Roberts G. Txt2stop: A pilot randomizedcontrolled trial of mobile phone-based smoking cessation support. Tobacco Control. 2009; 18:88–91. [PubMed: 19318534]

30. Intille, SS.; Kukla, C.; Farzanfar, R.; Bakr, W. Just-in-time technology to encourage incremental,dietary behavior change. AMIA 2003 symposium proceedings; p. 874

Riley et al. Page 14

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

31. Brendryen H, Kraft P, Schaalma H. Looking inside the black box: Using intervention mapping todescribe the development of an automated smoking cessation intervention happy ending. TheJournal of Smoking Cessation. 2010; 5:29–56.

32. Whittaker R, Borland R, Bullen C, Lin RB, McRobbie H, Rodgers A. Mobile phone-basedinterventions for smoking cessation. Cochrane Database of Systematic Reviews. 2009;4:CD006611.

33. Joo N, Kim B. Mobile phone short message service messaging for behaviour modification in acommunity-based weight loss programme in Korea. Journal of Telemedicine and Telecare. 2007;13:416–420. [PubMed: 18078554]

34. Burke LE, Conroy MB, Sereika SM, et al. The effect of electronic self-monitoring on weight lossand dietary intake: A randomized behavioral weight loss trial. Obesity. 2011; 19:338–344.[PubMed: 20847736]

35. Liu WT, Wang CH, Lin HC, et al. Efficacy of a cell phone-based exercise programme for COPD.The European Respiratory Journal. 2008; 32:651–659. [PubMed: 18508824]

36. Nguyen HQ, Gill DP, Wolpin S, Steele BG, Benditt JO. Pilot study of a cell phone-based exercisepersistence intervention post-rehabilitation for COPD. Int J COPD. 2009; 4:301–313.

37. Bauer S, de Niet J, Timman R, Kordy H. Enhancement of care through self-monitoring andtailored feedback via text messaging and their use in the treatment of childhood overweight.Patient Education and Counseling. 2010; 79:315–319. [PubMed: 20418046]

38. Hurling R, Catt M, De Boni M, et al. Using Internet and mobile phone technology to deliver anautomated physical activity program: Randomized controlled trial. Journal of Medical InternetResearch. 2007; 9:e7. [PubMed: 17478409]

39. Beasley JM, Riley WT, Davis A, Singh J. Evaluation of a PDA-based dietary assessment andintervention program: A randomized controlled trial. Journal of the American College of Nutrition.2008; 27:280–286. [PubMed: 18689560]

40. Haapala I, Barengo NC, Biggs S, Surakka L, Manninen P. Weight loss by mobile phone: A 1-yeareffectiveness study. Public Health Nutrition. 2009; 12:2382–2391. [PubMed: 19323865]

41. Atienza AA, King AC, Oliveira BM, Ahn DK, Gardner CD. Using hand-held computertechnologies to improve dietary intake. American Journal of Preventive Medicine. 2008; 34:514–518. [PubMed: 18471588]

42. King AC, Ahn DK, Oliveira BM, Atienza AA, Castro CM, Gardner CD. Promoting physicalactivity through hand-held computer technology. American Journal of Preventive Medicine. 2008;34:138–142. [PubMed: 18201644]

43. Patrick K, Raab F, Adams MA, et al. A text message-based intervention for weight loss:Randomized controlled trial. Journal of Medical Internet Research. 2009; 11:e1. [PubMed:19141433]

44. Fjeldsoe BS, Miller YD, Marshall AL. MobileMums: A randomized controlled trial of an SMS-based physical activity intervention. Annals of Behavioral Medicine. 2010; 39:101–111. [PubMed:20174902]

45. Hiltz SR. Productivity enhancement from computer-mediated communication: A systemscontingency approach. Communications of the ACM. 1988; 31:1438–1454.

46. Krishna S, Boren SA. Diabetes self-management care via cell phone: A systematic review. JDiabetes Sci Technol. 2008; 2:509–517. [PubMed: 19885219]

47. Ngo J, Engelen A, Molag M, Roesle J, Garcia-Segovia P, Serra-Majem L. A review of the use ofinformation and communication technologies for dietary assessment. British J Nutr. 2009;101:S102–S112.

48. Boushey CJ, Kerr DA, Wright J, Lutes KD, Ebert DS, Delp EJ. Use of technology in children’sdietary assessment. Euro J Clin Nutr. 2009; 63:S50–S57.

49. Six BL, Schap TE, Zhu FM, Mariappan A, Bosch M, Delp EJ, et al. Evidence-based developmentof a mobile telephone food record. Journal of the American Dietetic Association. 2010; 110:74–79. [PubMed: 20102830]

50. Hynes M, Wang H, Kilmartin L. Off-the-shelf mobile handset environments for deployingaccelerometer based gait and activity analysis algorithms. Conf Proc IEEE Eng Med Biol Soc.2009; 2009:5187–5190. [PubMed: 19964383]

Riley et al. Page 15

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

51. Bexelius C, Lof M, Sandin S, Trolle Lagerros Y, Forsum E, Litton JE. Measure of physical activityusing cell phones: Validation using criterion measures. J Med Internet Res. 2010; 29:e2. [PubMed:20118036]

52. Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trialsand clinical practice. Clinical Therapeutics. 1999; 21:1074–1090. [PubMed: 10440628]

53. Naditz A. Medication compliance—helping patients through technology: Modern “smart”pillboxes keep memory-short patients on their medical regimen. Telemed J E Health. 2008;14:875–880. [PubMed: 19062349]

54. Safren SA, Hendriksen ES, Desousa N, Boswell SL, Mayer KH. Use of an on-line pager system toincrease adherence to antiretroviral medications. AIDS Care. 2003; 15:787–793. [PubMed:14617500]

55. Simoni JM, Huh D, Frick PA, et al. Peer support and pager messaging to promote antiretroviralmodifying therapy in Seattle: A randomized controlled trial. Journal of Acquired ImmuneDeficiency Syndromes. 2009; 52:465–473. [PubMed: 19911481]

56. Koshy E, Car J, Majeed A. Effectiveness of mobile-phone short messaging service (SMS)reminders for ophthalmology outpatient appointments: Observational study. BMC Ophthalmology.2008; 8:9. [PubMed: 18513438]

57. Bos A, Hoogstraten J, Prahl-Andersen B. Failed appointments in an orthodontic clinic. BAm JOrthod Dentofacial Orthop. 2005; 127:355–357.

58. Leong KC, Leong KW, Mastura I, et al. The use of text messaging to improve attendance inprimary care: A randomized controlled trial. Family Practice. 2006; 23:699–705. [PubMed:16916871]

59. Chen ZW, Fang LZ, Chen LY, Dai HL. Comparison of an SMS text messaging and phonereminder to improve attendance at a health promotion center: A randomized controlled trial.Journal of Zhejiang University Science B. 2008; 9:34–38. [PubMed: 18196610]

60. Blake H. Mobile phone technology in chronic disease management. Nursing Standard. 2008;23:43–46. [PubMed: 19093357]

61. Russel-Minda E, Jutai J, Speechly M, Bradley K, Chudyk A, Petrella R. Health technologies formonitoring and managing diabetes: A systematic review. J Diabetes Sci Technol. 2009; 3:1460–1471. [PubMed: 20144402]

62. Skinner C, Finkelstein J. Using cell phones for chronic disease prevention and management. AMIAAnnual Symposium Proceedings. 2008; 6:1137. [PubMed: 18999018]

63. Franklin VL, Waller A, Pagliari C, Greene SA. A randomized controlled trial of Sweet Talk, a textmessaging system to support young people with diabetes. Diabetes Med. 2006; 23:1332–1338.

64. American Diabetes Association. Executive summary: Standards of medical care in diabetes—2009.Diabetes Care. 2009; 32:S6–S12. [PubMed: 19118288]

65. Hedtke PA. Can wireless technology enable new diabetes management tools. J Diabetes SciTechnol. 2008; 2:127–130. [PubMed: 19885187]

66. Ma Y, Olendzki BC, Chiriboga D, et al. PDA-assisted low glycemic index dietary intervention fortype 2 diabetes: A pilot study. Euro J Clin Nutr. 2006; 60:1235–1243.

67. Forjuoh SN, Reis MD, Couchman GR, Ory MG. Improving diabetes self-care with a PDA inambulatory care. Telemed Health. 2008; 14:273–279.

68. Lanzola G, Capozzi D, D’Annunzio G, Ferrari P, Bellazzi R, Larizza C. Going mobile with amulti-access service for the management of diabetes patients. J Diabetes Sci Technol. 2007;1:730–737. [PubMed: 19885142]

69. Arsand E, Tufano JT, Ralston JD, Hjortdahl P. Designing mobile dietary management supporttechnologies for people with diabetes. Journal of Telemedicine and Telecare. 2008; 14:329–332.[PubMed: 18852310]

70. Rami B, Popow C, Horn W, Waldhoer T, Schober E. Telemedical support to improve glycemiccontrol in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics. 2006;165:701–705. [PubMed: 16670859]

71. Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc™ mobilediabetes management randomized controlled trial: Change in clinical and behavioral outcomes and

Riley et al. Page 16

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

patient and physician satisfaction. Diabetes Technology and Therapeutics. 2008; 10:160–168.[PubMed: 18473689]

72. Turner J, Larsen M, Tarassenko L, Neil A, Farmer A. Implementation of telehealth support forpatients with type 2 diabetes using insulin treatment: An exploratory study. Inform Prim Care.2009; 17:47–53. [PubMed: 19490773]

73. Cho JH, Lee HC, Lim DJ, Kwon HS, Yoon KH. Mobile communication using a mobile phone witha glucometer for glucose control in type 2 patients with diabetes: As effective as an Internet-basedglucose monitoring system. Journal of Telemedicine and Telecare. 2009; 15:77–82. [PubMed:19246607]

74. Logan AG, McIsaac WJ, Tisler A, et al. Mobile phone-based remote patient monitoring system formanagement of hypertension in diabetic patients. Amer J Hypertension. 2007; 20:942–948.

75. Leu MG, Norris TE, Hummel J, Isaac M, Brogan MW. A randomized controlled trial of anautomated wireless messaging system for diabetes. Diabetes Technology and Therapeutics. 2005;7:710–718. [PubMed: 16241873]

76. Hanauer DA, Wentzell K, Laffel N, Laffel LM. Computerized automated reminder diabetes system(CARDS): Email and SMS cell phone text messaging reminders to support diabetes management.Diabetes Technology and Therapeutics. 2009; 11:99–106. [PubMed: 19848576]

77. Strandbygaard U, Thomsen SF, Backer V. A daily SMS reminder increases adherence to asthmatreatment: A three-month follow-up study. Respiratory Medicine. 2010; 104:166–171. [PubMed:19854632]

78. Chung P, Yu T, Scheinfeld N. Using cellphones for teledermatology, a preliminary study.Dermatology Online Journal. 2007; 13:2. [PubMed: 18328196]

79. Tang FH, Law MY, Lee AC, Chan LW. A mobile phone integrated health care delivery system formedical images. Journal of Digital Imaging. 2004; 17:217–225. [PubMed: 15534754]

80. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annual Review ofClinical Psychology. 2008; 4:1–32.

81. National Institutes of Health. [Accessed 29 Oct 2010] 2010. http://projectreporter.nih.gov(reporter_searchresults.cfm?&new=1&icde=5933670&loc=2&CFID=17945345&CFTOKEN=13189329)

82. Skevofilakas M, Mougiakakou SG, Zarkogianni K, et al. A communication and informationtechnology infrastructure for real-time monitoring and management of type 1 diabetes patients.Conf Proc IEEE Eng Med Biol Soc. 2007; 2007:3685–3686. [PubMed: 18002797]

83. Holtz B, Whitten P. Managing asthma with mobile phones: A feasibility study. Telemed J EHealth. 2009; 15:907–909. [PubMed: 19919198]

84. Ryan D, Pinnock H, Lee AJ, et al. The CYMPLA trial. Mobile phone-based structured interventionto achieve asthma control in patients with uncontrolled persistent asthma: A pragmatic randomizedcontrolled trial. Primary Care Respiratory Journal. 2009; 18:343–345.

85. Rosenstock IM. Historical origins of the Health Belief Model. Health Education Monographs.1974; 2:328–335.

86. Dunton, Atienza. The need for time-intensive information in healthful eating and physical activityresearch: A timely topic. JADA. 2009; 109(1):30–35.

87. Boorsboom D, Mellenbergh GJ, van Heerden J. The theoretical status of latent variables.Psychological Review. 2003; 110:202–219.

88. Neville LM, O’Hara B, Milat AJ. Computer-tailored dietary behaviour change interventions: Asystematic review. Health Education Research. 2009; 24:699–720. [PubMed: 19286893]

89. Lustria ML, Cortese J, Noar SM, Glueckauf RL. Computer-tailored health interventions deliveredover the Web: Review and analysis of key components. Patient Education and Counseling. 2009;74:156–173. [PubMed: 18947966]

90. Heron KE, Smyth JM. Ecological momentary interventions: Incorporating mobile technology intopsychosocial and health behaviour treatments. British Journal of Health Psychology. 2010; 15:1–39. [PubMed: 19646331]

91. Llewelyn S, Hardy G. Process research in understanding and applying psychological therapies. TheBritish Journal of Clinical Psychology. 2001; 40:1–21. [PubMed: 11317944]

Riley et al. Page 17

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

92. Åström, KJ.; Murray, RM. Feedback systems: An introduction for scientists and engineers. 2008.http://www.cds.caltech.edu/~murray/amwiki

93. Rivera DE, Pew MD, Collins LM. Using engineering control principles to inform the design ofadaptive interventions: A conceptual introduction. Drug and Alcohol Dependence. 2007; 88(suppl2):S31–S40. [PubMed: 17169503]

94. Navarro-Barrientos, JE.; Rivera, DE.; Collins, LM. A dynamical systems model for understandingbehavioral interventions for weight loss. In: Chai, SK.; Salerno, JJ.; Mabry, PL., editors. 2010international conference on social computing, behavioral modeling, and prediction (SBP 2010).Heidelberg: Springer; 2010. p. 170-179.

95. Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K. The dynamic brain: From spikingneurons to neural masses and cortical fields. PLoS Computational Biology. 2008; 4:e1000092.[PubMed: 18769680]

96. Saper CB, Chou TC, Scammell TE. The sleep switch: Hypothalamic control of sleep andwakefulness. Trends Neuroscience. 2001; 24:726–731.

97. Todorov E. Optimality principles in sensorimotor control. Nature Neuroscience. 2004; 7:907–915.98. Lewis FL, Vamvoudakis KG. Reinforcement learning for partially observable dynamic processes:

Adaptive dynamic programming using measured output data. IEEE Transactions on Systems,Man, and Cybernetics Part B, Cybernetics. 2010; 41:14–25.

99. Bequette BW. Glucose clamp algorithms and insulin time–action profiles. J Diabetes Sci Technol.2009; 3:1005–1013. [PubMed: 20144413]

100. Nandola, N.; Rivera, DE. A robust model predictive control formulation for hybrid systems withapplication to adaptive behavioral interventions. American Control Conference; Baltimore, MD.2010. p. 6286-6292.

101. Schwartz JD, Rivera DE. A process control approach to tactical inventory management inproduction-inventory systems. Int J Production Economics. 2010; 125:111–124.

102. Ogunnaike, BA.; Ray, WH. Process dynamics, modeling, and control. New York: OxfordUniversity Press; 1994.

103. Ljung, L. Prentice Hall information and system sciences series. New York: Prentice Hall; 1999.System identification: Theory for the user.

104. Ramsay, J.; Silverman, B. Functional data analysis. New York: Springer; 2005.105. Camacho, EF.; Bordons, C. Model predictive control. 2. New York: Springer; 2004.106. Collins LM, Murphy SA, Bierman KL. A conceptual framework for adaptive preventive

interventions. Prevention Science. 2004; 5:185–196. [PubMed: 15470938]107. O’Grady L, Witteman H, Bender JL, Urowitz S, Wiljer D, Jaded AR. Measuring the impact of a

moving target: Towards a dynamic framework for evaluating collaborative adaptive interactivetechnologies. Journal of Medical Internet Research. 2009; 11:e20. [PubMed: 19632973]

108. Resnicow K, Strecher V, Couper M, et al. Methodologic and design issues in patient-centered e-health research. American Journal of Preventive Medicine. 2010; 38:98–102. [PubMed:20117564]

109. Downer SR, Meara JG, Da Costa AC. Use of SMS text messaging to improve outpatientadherence. MJA. 2005; 183:366–368. [PubMed: 16201955]

110. Downer SR, Meara JG, Da Costa AC, Sethuraman K. SMS text messaging improves outpatientattendance. Australian Health Review. 2006; 30:389–396. [PubMed: 16879098]

111. Foley J, O’Neill M. Use of mobile short message service (SMS) as a reminder: The effect onpatient attendance. European Archives of Paediatric Dentistry. 2009; 10:15–18. [PubMed:19254521]

112. da Costa TM, Salomao PL, Martha AS, Pisa IT, Sigulem D. The impact of short message servicetext messages sent as appointment reminders to patients’ cell phones at outpatient clinics in SaoPaulo, Brazil. International Journal of Medical Informatics. 2010; 79:65–70. [PubMed:19783204]

113. Kwon HS, Cho JH, Kim HS, et al. Development of web-based diabetic patient managementsystem using short message service (SMS). Diabetes Research and Clinical Practice. 2004;66S:S133–S137. [PubMed: 15563964]

Riley et al. Page 18

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

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-PA Author Manuscript

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-PA Author Manuscript

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-PA Author Manuscript

114. Ostojic V, Cvoriscec B, Ostojic SB, Reznikoff D, Stipic-Markovic A, Tudjman Z. Improvingasthma control through telemedicine: A study of short-message service. Telemed J E Health.2005; 11:28–35. [PubMed: 15785218]

115. Kim HS, Yoo YS, Shim HS. Effects of an Internet-based intervention on plasma glucose levels inpatients with type 2 diabetes. Journal of Nursing Care Quality. 2005; 20:335–340. [PubMed:16177585]

116. Kim HS, Kim NC, Ahn SH. Impact of a nurse short message service intervention for patients withdiabetes. Journal of Nursing Care Quality. 2006; 21:266–271. [PubMed: 16816608]

117. Kim HS, Jeong HS. A nurse short message service by cellular phone in type-2 diabetic patientsfor six months. Journal of Clinical Nursing. 2007; 16:1082–1087. [PubMed: 17518883]

118. Benhamou PY, Melki V, Boizel R, et al. One-year efficacy and safety of Web-based follow-upusing cellular phone in type 1 diabetic patients under insulin pump therapy: The PumpNet study.Diabetes Metabol. 2007; 33:220–226.

119. Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12months. Diabetes Research and Clinical Practice. 2008; 79:256–261. [PubMed: 17988756]

120. Newton KH, Wiltshire EJ, Elley CR. Pedometers and text messaging to increase physical activity:Randomized controlled trial of adolescents with type 1 diabetes. Diabetes Care. 2009; 32:813–815. [PubMed: 19228863]

121. Prabhakaran L, Chee WY, Chua KC, Abisheganaden J, Wong WM. The use of text messaging toimprove asthma control: A pilot study using mobile phone short messaging service (SMS).Journal of Telemedicine and Telecare. 2010; 16:286–290. [PubMed: 20576744]

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Implications

Practice

Mobile technologies are rapidly evolving as a method for delivering health behaviorinterventions that can be tailored to the individual throughout the intervention, but thecontent and timing of these interventions have not been consistently grounded in healthbehavior theories, so practitioners need to consider the theoretical and empirical basis ofmobile health behavior interventions.

Policy

Investment in the development of mobile health behavior interventions needs to bebalanced with investment in theoretically grounded content development and evaluationprocedures that are responsive to this rapidly evolving area.

Research

In addition to the responsive evaluation of mobile health behavior interventions,researchers need to utilize these applications to test and advance more dynamic healthbehavior theories, taking advantage of control systems engineering and other dynamicfeedback models to advance new theories that can be better applied to the intensiveadaptability possible from mobile health behavior interventions.

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Fig. 1.Illustration of control systems dynamical model for smoking urge

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Riley et al. Page 22

Tabl

e 1

Smok

ing

cess

atio

n m

obile

inte

rven

tion

stud

ies

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

Obe

rmay

er e

tal

. [23

]N

=46,

age

18–

25-y

ear s

mok

ers;

pre–

post

SMS

for m

anag

ing

urge

s tim

ed a

ndta

ilore

d to

like

ly h

igh-

risk

situ

atio

ns a

ndto

stag

e of

cha

nge;

add

ition

al m

essa

ges

prov

ided

by

text

requ

est

Self-

Reg

ulat

ion;

Tra

nsth

eore

tical

Mod

elO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e (b

io-v

alid

ated

)of

17%

at 6

wee

ks

Rod

gers

et a

l.[2

4]N

=1,7

05 a

dult

smok

ers;

RC

T of

SMS

mes

sage

s vs.

no tr

eatm

ent

SMS

with

mot

ivat

iona

l sup

port,

qui

tin

form

atio

n, a

nd d

istra

ctio

n (g

ener

alin

tere

st in

fo);

addi

tiona

l mes

sage

spr

ovid

ed b

y te

xt re

ques

t

Non

e re

porte

dO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e of

28%

for

inte

rven

tion

vs. 1

3% fo

rco

ntro

l at 6

wee

ks

Rile

y et

al.

[25]

N=3

1, a

ge 1

8–25

-yea

r sm

oker

s;pr

e–po

stSa

me

as O

berm

ayer

et a

l. [2

3]Se

lf-R

egul

atio

n; T

rans

theo

retic

al M

odel

Out

put a

uto-

adju

sted

initi

ally

and

just

-in-

time

Qui

t rat

e (b

io-v

alid

ated

)of

42%

at 6

wee

ks

Whi

ttake

r et a

l.[2

6]N

=17,

age

16–

45-y

ear s

mok

ers;

pre–

post

Vid

eos o

f rol

e m

odel

qui

tting

exp

erie

nces

inte

rspe

rsed

with

SM

S m

essa

ges

prov

idin

g qu

ittin

g as

sist

ance

Soci

al C

ogni

tive

Theo

ry; S

ocia

l Mar

ketin

gO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e of

53%

at 4

wee

ks

Bre

ndry

en a

ndK

raft

[27]

N=3

96 a

dult

smok

ers;

RC

T of

inte

rven

tion

vs. s

elf-

help

boo

klet

IVR

and

SM

S m

essa

ges a

s par

t of m

ulti-

chan

nel s

mok

ing

cess

atio

n in

terv

entio

nta

ilore

d to

smok

ing

stat

us; m

ost a

lso

rece

ived

NR

T

Self-

Reg

ulat

ion;

Soc

ial C

ogni

tive

Theo

ry;

Cog

nitiv

e–B

ehav

iora

l Mod

elO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e of

38%

for

inte

rven

tion

vs. 2

4% fo

rse

lf-he

lp b

ookl

et a

t 12

mon

ths

Bre

ndry

en e

t al.

[28]

N=2

90 a

dult

smok

ers;

RC

T of

inte

rven

tion

vs. s

elf-

help

boo

klet

Sam

e as

Bre

ndry

en a

nd K

raft

[27]

Self-

Reg

ulat

ion;

Soc

ial C

ogni

tive

Theo

ry;

Cog

nitiv

e–B

ehav

iora

lO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e of

20%

for

inte

rven

tion

vs. 1

0% fo

rse

lf-he

lp b

ookl

et a

t 12

mon

ths

Free

et a

l. [2

9]N

=200

adu

lt sm

oker

s; R

CT

ofin

terv

entio

n vs

. gen

eric

text

mes

sage

s

Sam

e as

Rod

gers

et a

l. w

ith S

MS

text

sad

apte

d fo

r UK

par

ticip

ants

Non

e re

porte

dO

utpu

t aut

o-ad

just

edin

itial

ly a

nd ju

st-in

-tim

e

Qui

t rat

e (b

io-v

alid

ated

)of

8%

for i

nter

vent

ion

vs.

6% fo

r con

trol a

t 6m

onth

s

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Riley et al. Page 23

Tabl

e 2

Wei

ght l

oss (

diet

, phy

sica

l act

ivity

) mob

ile in

terv

entio

n st

udie

s

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

Joo

and

Kim

,[3

3]N

=927

adu

lt pu

blic

hea

lthce

nter

pat

ient

s; p

re–p

ost

Wee

kly

info

rmat

iona

l SM

S m

essa

ges r

egar

ding

diet

and

exe

rcis

eN

one

repo

rted

Out

put n

ot a

djus

ted

Wei

ght l

oss o

f 1.6

kg

at 1

2w

eeks

Hur

ling

et a

l.[3

8]N

=77

norm

al a

nd o

verw

eigh

tad

ults

; RC

T of

inte

rven

tion

vs.

exer

cise

adv

ice

alon

e

Web

-bas

ed p

rogr

am fo

r man

agin

g pe

rcei

ved

barr

iers

and

sche

dulin

g w

eekl

y ex

erci

se;

rem

inde

rs v

ia e

mai

l or S

MS

mes

sagi

ng;

acce

lero

met

er d

ata

obta

ined

and

tran

smitt

ed v

iam

obile

pho

ne fo

r rea

l-tim

e w

eb fe

edba

ck

Theo

ry o

f Pla

nned

Beh

avio

r; C

ompo

nent

s of

othe

r the

orie

s

Out

put a

uto-

adju

sted

initi

ally

Phys

ical

act

ivity

incr

ease

of 1

2 M

ET m

in fo

rin

terv

entio

n vs

. 4 M

ET m

info

r con

trol a

t 9 w

eeks

Atie

nza

et a

l.[4

1]N

=36

heal

thy

adul

ts; R

CT

ofin

terv

entio

n vs

. prin

t mat

eria

lsPD

A p

rogr

am a

sses

sing

veg

etab

le a

nd w

hole

grai

n in

take

and

pro

vidi

ng b

ehav

iora

l stra

tegi

esto

mot

ivat

e ch

ange

s to

diet

ary

inta

ke

Self-

Reg

ulat

ion

Theo

ry;

Soci

al C

ogni

tive

Theo

ryO

utpu

t man

ually

adj

uste

d in

itial

lySe

rvin

gs o

f veg

etab

les o

rgr

ain

fiber

per

1,0

00 k

cal o

f1

for i

nter

vent

ion

vs. 0

for

cont

rols

at 8

wee

ks

Kin

g et

al.

[42]

N=3

7, a

ge ≥

50 y

ears

unde

ract

ive

adul

ts; R

CT

ofin

terv

entio

n vs

. prin

t mat

eria

ls

PDA

pro

gram

ass

essi

ng p

hysi

cal a

ctiv

ities

,ve

geta

ble

and

who

le g

rain

inta

ke, a

ndpr

ovid

ing

beha

vior

al st

rate

gies

to m

otiv

ate

chan

ges t

o di

etar

y in

take

Self-

Reg

ulat

ion

Theo

ry;

Soci

al C

ogni

tive

Theo

ryO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eIn

crea

se o

f mod

erat

e+p

hysi

cal a

ctiv

ity o

f 178

min

/wee

k fo

r int

erve

ntio

nvs

. dec

reas

e of

80

min

/w

eek

for c

ontro

l at 8

wee

ks

Bea

sley

et a

l.[3

9]N

=174

ove

rwei

ght o

r obe

sead

ults

; RC

T of

inte

rven

tion

vs.

pape

r dia

ry

PDA

die

tary

self-

mon

itorin

g pr

ogra

m w

ithfe

edba

ck c

ompa

red

to p

erso

naliz

ed ta

rget

s for

very

low

fat d

iet r

ecom

men

datio

ns

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eTo

tal f

at in

take

dec

reas

e of

31 g

/day

for i

nter

vent

ion

vs. 2

2 g/

day

for c

ontro

l at 3

wee

ks

Liu

et a

l. [3

5]N

=48

adul

t CO

PD p

atie

nts;

RC

T of

inte

rven

tion

vs. n

otre

atm

ent

Mus

ic te

mpo

del

iver

ed v

ia m

obile

pho

nepr

ogra

m a

djus

ted

base

d on

ISW

T to

enc

oura

gea

wak

ing

pace

at 8

0% m

ax e

ffor

t

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d in

itial

lyan

d ju

st-in

-tim

eIS

WT

dist

ance

incr

ease

of

68.4

m fo

r int

erve

ntio

nfr

om b

asel

ine

to 1

2 w

eeks

Ngu

yen

et a

l.[3

6]N

=17

adul

t CO

PD p

atie

nts;

RC

T of

mob

ile c

oach

ed v

s.m

obile

mon

itorin

g al

one

Dai

ly lo

g vi

a SM

S of

exe

rcis

e an

d sy

mpt

oms;

Wee

kly

SMS

mes

sage

s rei

nfor

cing

logg

edex

erci

se b

y nu

rse

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eIn

crem

enta

l cyc

le te

stch

ange

of +

1.3

W fo

rm

onito

ring

vs. −

5.5

W fo

rco

ache

d

Haa

pala

et a

l.[4

0]N

=125

ove

rwei

ght a

dults

; RC

Tof

inte

rven

tion

vs. n

o tre

atm

ent

Con

tinge

ncy

prog

ram

via

SM

S th

at in

stru

cted

user

s in

a st

agge

red

redu

ctio

n of

food

inta

keba

sed

on w

eigh

t, da

ily e

nerg

y re

quire

men

ts,

and

shor

t- an

d lo

ng-te

rm w

eigh

t goa

ls

Self-

Effic

acy

Theo

ry;

Syst

ems C

ontin

genc

yA

ppro

ach

Out

put a

uto-

adju

sted

initi

ally

and

just

-in-ti

me

Wei

ght l

oss o

f 4.5

kg

for

inte

rven

tion

vs. 1

.1 k

g fo

rco

ntro

l at 1

2 m

onth

s

Patri

ck e

t al.

[43]

N=6

5 ov

erw

eigh

t adu

lts; R

CT

of in

terv

entio

n vs

. prin

tin

form

atio

n

Tailo

red

SMS

mes

sage

s tha

t enc

oura

ged

goal

setti

ng, s

elf-

mon

itorin

g, a

nd a

ddre

ssed

bar

riers

to w

eigh

t los

s

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eW

eigh

t los

s of 2

.9 k

g fo

rin

terv

entio

n vs

. 0.9

kg

for

prin

t inf

orm

atio

n at

4m

onth

s

Fjel

dsoe

et a

l.[4

4]N

=88

post

nata

l wom

en; R

CT

ofin

terv

entio

n vs

. ini

tial

beha

vior

al c

ouns

elin

g on

ly

Tailo

red

SMS

mes

sage

s enc

oura

ging

phy

sica

lac

tivity

sent

3–5

tim

es p

er w

eek

prov

ided

inco

njun

ctio

n w

ith tw

o be

havi

oral

cou

nsel

ing

sess

ions

; wee

kly

SMS

goal

che

ck w

ithre

spon

se

Soci

al C

ogni

tive

Theo

ryO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eW

alki

ng fo

r exe

rcis

edu

ratio

n in

crea

se o

f 6.7

min

for i

nter

vent

ion

vs. 0

.3m

in fo

r con

trol a

t 13

wee

ks

Bur

ke e

t al.

[34]

N=2

10 o

verw

eigh

t or o

bese

adul

ts; R

CT

of P

DA

+ ta

ilore

dIn

add

ition

to 2

0 gr

oup

sess

ions

ove

r 6 m

onth

s,PD

A p

rogr

am o

f sel

f-m

onito

ring

and

sum

mar

you

tput

of d

ieta

ry in

take

(sam

e di

etar

y

Self-

Reg

ulat

ion

Theo

ryO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eW

eigh

t los

s of 6

.5%

for

PDA

+ ta

ilore

d fe

edba

ck,

4.8%

for P

DA

alo

ne, a

nd

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Riley et al. Page 24

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

feed

back

, PD

A m

onito

ring,

and

pape

r-ba

sed

mon

itorin

gm

onito

ring

prog

ram

as B

easl

ey e

t al.

[39]

) and

exer

cise

act

ivity

with

or w

ithou

t cus

tom

tailo

red

mul

tiple

dai

ly m

essa

ges b

ased

on

inpu

t

4.6%

for p

aper

-bas

eddi

etar

y m

onito

ring

at 6

mon

ths

Bau

er e

t al.

[37]

N=4

0 ov

erw

eigh

t chi

ldre

n; p

re–

post

Wee

kly

text

mes

sage

que

stio

ns o

n m

aint

aini

ngdi

et a

nd p

hysi

cal a

ctiv

ity fo

llow

ing

12-w

eek

grou

p in

terv

entio

n. F

eedb

ack

base

d on

resp

onse

s pro

vide

d re

info

rcem

ent,

prom

oted

soci

al su

ppor

t, re

min

ded

skill

s lea

rned

dur

ing

treat

men

t, an

d m

otiv

ated

par

ticip

ants

(rev

iew

edan

d m

odifi

ed b

y st

aff b

efor

e se

ndin

g)

Cog

nitiv

e–B

ehav

iora

lO

utpu

t (se

mi)

auto

-adj

uste

din

itial

ly a

nd ju

st-in

-tim

eB

MI s

tand

ard

devi

atio

nsc

ore

redu

ctio

n of

0.0

7fr

om e

nd o

f gro

upin

terv

entio

n to

12

mon

ths

ISW

T In

term

itten

t Shu

ttle

Wal

k Te

st

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

Riley et al. Page 25

Tabl

e 3

Adh

eren

ce m

obile

inte

rven

tion

stud

ies

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

Safr

en e

t al.

[54]

N=8

2 H

IV +

pat

ient

s; R

CT

of in

terv

entio

nvs

. mon

itorin

g on

lyTa

ilore

d do

se re

min

ders

via

two-

way

pag

er, a

long

with

oth

erre

min

ders

(e.g

., m

eals

,ap

poin

tmen

ts)

Non

e re

porte

dO

utpu

t man

ually

adju

sted

initi

ally

Dos

e ad

here

nce

of 6

4% fo

r int

erve

ntio

nvs

. 52%

for c

ontro

l at 1

2 w

eeks

Bos

et a

l. [5

7]N

=343

orth

odon

tic p

atie

nts;

non

rand

omiz

edco

mpa

rison

of t

ext m

essa

ge, p

hone

, mai

l, an

dno

rem

inde

r

SMS

appo

intm

ent r

emin

der

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

ttend

ance

of 9

0.6%

for

mai

l, 90

.4%

for p

hone

, 82.

4% fo

r SM

S,an

d 83

.7%

for n

o re

min

der o

ver 3

wee

ks

Dow

ner e

t al.

[109

]N

=2,8

64 g

ener

al m

edic

al o

utpa

tient

s with

mob

ile p

hone

s; h

isto

ric c

ontro

l of

inte

rven

tion

vs. n

o re

min

der

SMS

appo

intm

ent r

emin

der

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

dher

ence

of 8

5.8%

for

inte

rven

tion

vs. 7

6.6%

for c

ontro

l ove

r 1m

onth

Dow

ner e

t al.

[110

]N

=45,

110

gene

ral m

edic

al o

utpa

tient

s;hi

stor

ic c

ontro

l of i

nter

vent

ion

vs. n

ore

min

der

SMS

appo

intm

ent r

emin

der

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

dher

ence

of 9

0.2%

for

inte

rven

tion

vs. 8

0.5%

for c

ontro

l ove

r 3m

onth

s

Leon

g et

al.

[58]

N=9

93 p

rimar

y ca

re o

utpa

tient

s; R

CT

ofSM

S, p

hone

or n

o re

min

der

SMS

appo

intm

ent r

emin

der

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

dher

ence

of 5

9.6%

for

SMS,

59.

0% fo

r pho

ne, a

nd 4

8.1%

for

cont

rol o

ver 3

mon

ths

Kos

hy e

t al.

[56]

N=9

,512

oph

thal

mol

ogy

outp

atie

nts;

nonr

ando

miz

ed c

ompa

rison

of i

nter

vent

ion

vs. n

o re

min

der

SMS

appo

intm

ent r

emin

der w

ithab

ility

to te

xt re

ply

to c

ance

lap

poin

tmen

t

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

dher

ence

of 8

8.8%

for S

MS

vs. 8

1.9%

for c

ontro

l ove

r 6 m

onth

s

Che

n et

al.

[59]

N=1

,859

pre

vent

ive

scre

enin

g pa

tient

s; R

CT

of S

MS,

pho

ne o

r no

rem

inde

rSM

S ap

poin

tmen

t rem

inde

rN

one

repo

rted

Out

put n

ot a

djus

ted

App

oint

men

t adh

eren

ce o

f 87.

5% fo

rSM

S, 8

8.3%

for p

hone

, and

80.

5% fo

rco

ntro

l ove

r 2 m

onth

s

Sim

oni e

t al.

[55]

N=2

24 H

IV +

pat

ient

s; R

CT

of p

ager

+ p

eer

supp

ort,

page

r onl

y, p

eer s

uppo

rt on

ly, o

rus

ual c

are

Tailo

red

dose

rem

inde

rs v

ia tw

o-w

ay p

ager

alo

ng w

ith e

duca

tiona

l,en

terta

inm

ent,

and

adhe

renc

eas

sess

men

t tex

ts

Non

e re

porte

dO

utpu

t man

ually

adju

sted

initi

ally

Dos

e ad

here

nce

of 5

0.1%

for p

ager

+ p

eer

supp

ort,

41.8

% fo

r pag

er, 4

7.0%

for p

eer

supp

ort a

nd 4

4.4%

for u

sual

car

e at

3m

onth

s

Fole

y et

al.

[111

]N

=276

ped

iatri

c de

ntal

pat

ient

s; h

isto

rical

cont

rol o

f int

erve

ntio

n vs

. no

rem

inde

rSM

S ap

poin

tmen

t rem

inde

rN

one

repo

rted

Out

put n

ot a

djus

ted

App

oint

men

t adh

eren

ce o

f 89.

6% fo

r SM

Svs

. 76.

1% fo

r con

trol o

ver 1

mon

th

Da

Cos

ta e

t al.

[112

]N

=7,8

90 g

ener

al o

utpa

tient

app

oint

men

ts;

nonr

ando

miz

ed c

ompa

rison

of i

nter

vent

ion

vs. n

o re

min

der

SMS

appo

intm

ent r

emin

der

Non

e re

porte

dO

utpu

t not

adj

uste

dA

ppoi

ntm

ent a

dher

ence

of 8

0.6%

for S

MS

vs. 7

4.4%

for c

ontro

l ove

r 11

mon

ths

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

Riley et al. Page 26

Tabl

e 4

Dis

ease

man

agem

ent m

obile

inte

rven

tion

stud

ies

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

Kw

on e

t al.

[113

]N

=185

adu

lt di

abet

es p

atie

nts;

pre–

post

Glu

cose

read

ings

reco

rded

via

SM

S,re

view

ed b

y pr

ovid

er w

ho se

ntre

com

men

datio

ns v

ia S

MS

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eH

bA1c

dec

reas

ed 7

.5%

to7.

0% a

t 3 m

onth

s

Leu

et a

l. [7

5]N

=50

adul

t dia

bete

s pat

ient

s;R

CT

of p

ager

rem

inde

rs v

s.ro

utin

e ca

re

Two-

way

pag

er re

min

ders

for v

ario

usdi

abet

es se

lf-m

anag

emen

t act

iviti

es(c

usto

miz

ed)

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d at

bas

elin

eH

bA1c

dec

reas

ed 8

.5%

to8.

2% fo

r int

erve

ntio

n vs

.8.

5% to

7.9

% fo

r con

trols

at

3 to

6 m

onth

s

Ost

ojic

et a

l. [1

14]

N=2

6 ad

ult a

sthm

a pa

tient

s; R

CT

of te

xt v

s. no

text

mes

sage

reco

rdin

g

Dai

ly S

MS

reco

rdin

g of

pea

k flo

wre

view

ed b

y pr

ovid

er w

ho se

nt w

eekl

ytre

atm

ent a

djus

tmen

t mes

sage

s via

text

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eFE

V1

incr

ease

d fr

om77

.63%

to 8

1.25

% fo

rin

terv

entio

n vs

. 78.

88%

to78

.25%

for c

ontro

ls a

t 16

wee

ks

Kim

et a

l. [1

15]

N=4

2 ad

ult t

ype

2 di

abet

espa

tient

s; p

re–p

ost

SMS

mes

sage

s sen

t by

prov

ider

s bas

edon

revi

ew o

f dat

a pr

ovid

ed b

y pa

tient

via

Inte

rnet

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eFa

stin

g pl

asm

a gl

ucos

ede

crea

sed

from

174

to 1

45.4

m/d

L at

12

wee

ks

Kim

et a

l. [1

16]

N=4

5 ad

ult t

ype

2 di

abet

espa

tient

s; p

re–p

ost

Sam

e as

Kim

et a

l. [1

16]

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eH

bA1c

dec

reas

ed 8

.1%

to7.

0% a

t 12

wee

ks

Fran

klin

et a

l. [6

3]N

=92,

age

8–1

8-ye

ar ty

pe 1

diab

etes

pat

ient

s; R

CT

ofco

nven

tiona

l ins

ulin

ther

apy,

conv

entio

nal t

hera

py w

ith S

MS,

and

inte

nsiv

e th

erap

y w

ith S

MS

Pers

onal

ized

dai

ly S

MS

mes

sage

s to

supp

ort d

iabe

tes m

anag

emen

tSo

cial

Cog

nitiv

e Th

eory

Out

put a

uto-

adju

sted

at i

nitia

tion

HbA

1c d

ecre

ased

10.

0 to

9.2

for i

nten

sive

ther

apy

+SM

S; n

o ch

ange

in o

ther

grou

ps

Ram

i et a

l. [7

0]N

=36,

age

10–

19-y

ear t

ype

1di

abet

es p

atie

nts;

rand

omiz

edcr

osso

ver d

esig

n of

inte

rven

tion

vs. u

sual

car

e pa

per d

iary

reco

rdin

g

SMS

reco

rdin

g of

blo

od g

luco

se, i

nsul

indo

se, a

nd c

arbo

hydr

ate

inta

ke w

ithw

eekl

y fe

edba

ck (a

utom

ated

if O

K,

gene

rate

d by

dia

beto

logi

sts i

fad

just

men

ts n

eede

d)

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eH

bA1c

cha

nge

of 9

.1%

to8.

9% (i

nter

vent

ion)

to 9

.2%

(con

trol);

and

8.9

% to

9.9

%(c

ontro

l) to

8.9

%(in

terv

entio

n) a

t 3 a

nd 6

mon

ths

Ma

et a

l. [6

6]N

=15

adul

t typ

e 2

diab

etes

patie

nts;

pre

–pos

tPD

A d

ieta

ry m

onito

ring

with

gly

cem

icin

dex

feed

back

use

d to

aug

men

t a si

x-co

ntac

t dia

bete

s man

agem

ent

inte

rven

tion

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed ju

st-in

-tim

eH

bA1c

dec

reas

e fr

om 8

.0%

to 7

.5%

at 6

mon

ths

Kim

and

Jeon

g,[1

17]

N=5

1 ad

ult d

iabe

tes p

atie

nts;

pre

–po

stSa

me

as K

im e

t al.

[115

]N

one

repo

rted

Out

put m

anua

lly a

djus

ted

just

-in-ti

me

HbA

1c d

ecre

ased

from

8.1%

to 7

.0%

for

inte

rven

tion

and

7.6%

to7.

7% fo

r con

trol a

t 6 m

onth

s

Ben

ham

ou e

t al.

[118

]N

=30

adul

t typ

e 1

diab

etes

patie

nts;

rand

omiz

ed c

ross

over

of

SMS

feed

back

vs.

self-

mon

itorin

gon

ly

Prov

ider

s ack

now

ledg

ed re

ceip

t of

wee

kly

trans

mitt

ed g

luco

se lo

gs a

ndpr

ovid

ed re

com

men

datio

ns

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eH

bA1c

cha

nge

of 8

.3%

to8.

2% fo

r int

erve

ntio

n an

d8.

2% to

8.3

% a

t 6 m

onth

s

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

NIH

-PA Author Manuscript

Riley et al. Page 27

Ref

eren

ceSt

udy

char

acte

rist

ics

Inte

rven

tion

desc

ript

ion

The

oret

ical

bas

isIn

tera

ctiv

ityO

utco

me

Forju

oh e

t al.

[67]

N=4

3 ad

ult t

ype

2 di

abet

espa

tient

s; p

re–p

ost

PDA

reco

rdin

g of

blo

od g

luco

se,

med

icat

ions

, mea

ls, e

xerc

ise,

etc

., w

ithsu

mm

ary

outp

ut

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed ju

st-in

-tim

eH

bA1c

redu

ctio

n fr

om 9

.7%

to 8

.0%

at 6

mon

ths

Loga

n et

al.

[74]

N=3

3 ad

ult h

yper

tens

ive

type

2di

abet

es p

atie

nts;

pre

–pos

tM

obile

pho

ne o

btai

ned

and

trans

mitt

edB

P m

easu

rem

ents

; sum

mar

y da

taav

aila

ble

and

aler

ts se

nt to

pat

ient

s if B

Pou

t of r

ange

(tak

e ad

ditio

nal m

easu

res,

cont

act p

hysi

cian

)

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed ju

st-in

-tim

e24

-h a

mbu

lato

ry sy

stol

ic B

Pde

crea

sed

from

141

to 1

32ov

er 2

wee

ks

Qui

nn e

t al.

[71]

N=3

0 ty

pe 2

dia

bete

s pat

ient

s;R

CT

of a

utom

ated

glu

cose

trans

mis

sion

via

pho

ne v

s. fa

xing

or c

allin

g in

glu

cose

logs

eve

ry 2

wee

ks

Mob

ile p

hone

wire

less

ly o

btai

ned

and

trans

mitt

ed g

luco

se re

adin

gs w

ith re

al-

time

feed

back

and

life

styl

ere

com

men

datio

ns p

rovi

ded

via

mob

ileph

one

appl

icat

ion

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eH

bA1c

dec

reas

e of

9.5

1%to

7.4

8% fo

r int

erve

ntio

n vs

.9.

05%

to 8

.37%

at 3

mon

ths

Yoo

n et

al.

[119

]N

=51;

RC

T of

inte

rven

tion

vs.

usua

l car

eSa

me

as K

im e

t al.

[115

]N

one

repo

rted

Out

put m

anua

lly a

djus

ted

just

-in-ti

me

HbA

1c c

hang

e of

8.0

9% to

6.77

% fo

r int

erve

ntio

n vs

.7.

59 to

8.4

0 fo

r con

trol a

t 12

mon

ths

New

ton

et a

l.[1

20]

N=7

8, a

ge 1

1–18

-yea

r dia

bete

spa

tient

s; R

CT

of m

obile

inte

rven

tion

vs. u

sual

car

e

Wee

kly

SMS

rem

inde

rs to

wea

rpe

dom

eter

and

be

activ

eN

one

repo

rted

Out

put n

ot a

djus

ted

Med

ian

step

cou

ntde

crea

sed

by 2

2 st

eps f

orin

terv

entio

n vs

. 840

step

sfo

r con

trol a

t 12

wee

ks

Turn

er e

t al.

[72]

N=2

3 ty

pe 2

dia

bete

s pat

ient

s;pr

e–po

stM

obile

pho

ne w

irele

ssly

obt

aine

d an

dtra

nsm

itted

glu

cose

read

ings

; rea

l-tim

efe

edba

ck o

f inp

ut a

nd se

mi-a

utom

ated

mes

sage

s for

self-

man

agem

ent

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eH

bA1c

dec

reas

e fr

om 9

.5%

to 9

.0%

at 3

mon

ths

Han

auer

et a

l. [7

6]N

=40,

age

12–

25-y

ear d

iabe

tes

patie

nts;

RC

T of

SM

S vs

. em

ail

rem

inde

rs

Cus

tom

ized

sche

dule

for S

MS

rem

inde

rs to

obt

ain

bloo

d gl

ucos

ere

adin

gs. R

eadi

ng su

bmitt

ed v

ia S

MS

resu

lted

in p

ositi

ve fe

edba

ck if

in ra

nge,

and

inst

ruct

ions

if o

ut o

f ran

ge

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eH

bA1c

cha

nge

of 8

.8%

to8.

7% fo

r int

erve

ntio

n an

d8.

6% to

8.8

% fo

r con

trol a

t3

mon

ths

Cho

et a

l. [7

3]N

=69

type

2 d

iabe

tes p

atie

nts;

RC

T of

pho

ne v

s. in

tern

etdi

abet

es m

onito

ring

and

man

agem

ent

Com

bina

tion

mob

ile p

hone

and

gluc

omet

er tr

ansm

ittin

g gl

ucos

e re

adin

gto

pro

vide

r who

sent

trea

tmen

tad

just

men

ts v

ia S

MS

Non

e re

porte

dO

utpu

t man

ually

adj

uste

d ju

st-in

-tim

eH

bA1c

dec

reas

e of

8.3

% to

7.1%

for p

hone

pro

gram

and

7.6%

to 6

.9%

for

Inte

rnet

pro

gram

Stra

ndby

gaar

d et

al. [

77]

N=5

4 ad

ult a

sthm

a pa

tient

s; R

CT

of S

MS

vs. n

o SM

SD

aily

SM

S re

min

der t

o ta

ke a

sthm

am

edic

atio

nN

one

repo

rted

Out

put n

ot a

djus

ted

Inha

ler d

ose

adhe

renc

e fr

om77

.9%

to 8

1.5%

for

inte

rven

tion

vs. 8

4.2%

to70

.1%

for c

ontro

l at 1

2w

eeks

Prab

haka

ran

et a

l.[1

21]

N=1

20 a

dult

asth

ma

patie

nts;

RC

T of

SM

S vs

. no

SMS

Dai

ly×2

wee

ks, t

hen

wee

kly

SMS

mes

sage

s to

obta

in sy

mpt

om a

ndm

edic

atio

n ad

here

nce

data

; wee

kly

SMS

feed

back

on

prog

ress

Non

e re

porte

dO

utpu

t aut

o-ad

just

ed in

itial

ly a

ndju

st-in

-tim

eA

sthm

a C

ontro

l Tes

tim

prov

emen

t (to

scor

e ≥

20)

in 6

2% o

f SM

S vs

. 49%

of

cont

rols

BP b

lood

pre

ssur

e

Transl Behav Med. Author manuscript; available in PMC 2011 July 25.