Health behavior models in the age of mobile interventions: are our theories up to the task?
Open accessAbstract
Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.
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0 of 84 cited sources checked · 0 supported · 0 flagged
- Findingnone given
Most of the reviewed smoking and weight loss studies reported a theoretical basis for their mobile intervention.
“Most smoking and weight loss studies reported a theoretical basis for the mobile intervention”
Quote in the paperNot a substantial claim - Findingnone given
Many of the mobile health behavior interventions examined in this review were mainly one-way, consisting mostly of data input or informational output.
“Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output)”
Quote in the paperSubstantial claim - Findingnone given
Some of the reviewed mobile health behavior interventions used mobile technologies to deliver just-in-time, interactive, and adaptive interventions.
“but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions.”
Quote in the paperSubstantial claim - Findingown data
Most of the reviewed adherence and disease management studies did not report a theoretical basis for their mobile intervention.
“but most of the adherence and disease management studies did not.”
Quote in the paperSubstantial claim - Findingown data
Seven of the twelve weight loss, diet, and physical activity studies named a theoretical basis for their mobile intervention.
“Of the 12 studies summarized in Table 2, seven specified a theoretical basis for the mobile weight loss, diet, or physical activity intervention developed.”
Quote in the paperSubstantial claim
- Findingown data
Every one of the seven smoking cessation interventions was computer-tailored when treatment began.
“All of the interventions were computer-tailored at treatment initiation.”
Quote in the paperNot a substantial claim - Findingcitation
Except for the Rodgers intervention, the mobile smoking cessation interventions had a theoretical basis, citing social cognitive, self-regulation, transtheoretical, and cognitive-behavioral theories.
“With the exception of the Rodgers intervention [24, 29], these mobile smoking cessation interventions provided a theoretical basis, citing Social Cognitive Theory, Self-Regulation Theory, the Transtheoretical Model, and cognitive–behavioral theory.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [24] Rodgers, A., Corbett, T., Bramley, D., et al. (2005). Do u smoke after txt? Results of a randomized trial of smoking cessation using mobile phone text messaging. Tobacco Control, 14, 255–261. source
Not checked in the free run.
- cites [29] Free, C., Whittaker, R., Knight, R., Abramsky, T., Rodgers, A., & Roberts, G. (2009). Txt2stop: A pilot randomized controlled trial of mobile phone-based smoking cessation support. Tobacco Control, 18, 88–91. source
Not checked in the free run.
- Findingown data
The review identified seven studies that used mobile technology for smoking cessation interventions.
“Seven studies were identified that used mobile technology to deliver smoking cessation interventions.”
Quote in the paperNot a substantial claim - Findingown data
The review identified twelve studies reporting mobile health behavior interventions for weight loss, diet, and/or physical activity.
“We identified 12 studies reporting on mobile health behavior interventions for weight loss, diet, and/or physical activity.”
Quote in the paperNot a substantial claim - Findingown data
Twenty studies were identified for disease management: 16 in diabetes, three in asthma, and one in hypertension.
“we identified 20 studies: 16 in diabetes management, three in asthma management, and one in hypertension management (Table 4).”
Quote in the paperNot a substantial claim - Findingown data
The disease management interventions focused mainly on patient-entered data such as glucose readings, insulin doses, carbohydrate intake, exercise, lung function, or blood pressure, which a health professional reviewed to adjust regimens and give treatment recommendations.
“The interventions developed and evaluated in these studies focused primarily on patient input (e.g., blood glucose readings, insulin doses, carbohydrate intake, exercise, forced expiratory volume (FEV1), or blood pressure) which were reviewed by a health professional to provide periodic regimen adjustments and treatment recommendations.”
Quote in the paperSubstantial claim - Findingcitation
In an RCT of 210 overweight and obese adults, the PDA plus daily feedback condition achieved a mean percent weight loss of 6.5, compared with 4.8 for PDA monitoring only and 4.6 for paper-based monitoring.
“Compared to the monitoring only conditions which obtained a percent weight loss of 4.8 (PDA) and 4.6 (paper-based), the PDA plus daily feedback condition obtained a mean percent weight loss of 6.5.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] Burke, L. E., Conroy, M. B., Sereika, S. M., et al. (2011). The effect of electronic self-monitoring on weight loss and dietary intake: A randomized behavioral weight loss trial. Obesity, 19, 338–344. source
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- Findingcitation
Among the 20 disease management studies, only one named a theoretical basis, using social cognitive theory to send personalized SMS messages supporting diabetes management in children with type 1 diabetes.
“Across the 20 studies, only one [63] specified a theoretical basis for the intervention, using Social Cognitive Theory to provide personalized SMS messages to support diabetes management in children with type 1 diabetes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [63] Franklin, V. L., Waller, A., Pagliari, C., & Greene, S. A. (2006). A randomized controlled trial of Sweet Talk, a text messaging system to support young people with diabetes. Diabetes Med., 23, 1332–1338. source
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- Findingcitation
A similar pager dosing reminder program for HIV patients, with added texts, did not significantly improve adherence over a 9-month trial compared with usual care or peer support.
“This intervention, however, failed to produce significant improvements in adherence over the 9-month trial relative to usual care or peer support”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [55] Simoni, J. M., Huh, D., Frick, P. A., et al. (2009). Peer support and pager messaging to promote antiretroviral modifying therapy in Seattle: A randomized controlled trial. Journal of Acquired Immune Deficiency Syndromes, 52, 465–473. source
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- Findingown data
The review identified ten studies that evaluated mobile technology for improving treatment adherence.
“Ten studies were identified that evaluated the use of mobile technology to improve treatment adherence (Table 3).”
Quote in the paperNot a substantial claim - Findingown data
None of the appointment reminder studies provided a theoretical basis for the intervention.
“None of the studies provided a theoretical basis for the intervention provided.”
Quote in the paperSubstantial claim- Rests on an assumption: The absence of a reported theoretical basis in a study indicates that the intervention was not actually based on theory.
- Findingown data
All eight appointment reminder studies sent only one unadjusted message reminding the patient of the appointment, usually within a few days of the scheduled visit.
“Among the eight studies providing appointment reminders via mobile technologies, all provided only a single, unadjusted output reminding the patient of their appointment, usually timed to within a few days of the scheduled appointment.”
Quote in the paperSubstantial claim - Findingcitation
Among HIV patients with poor adherence, those randomly assigned to pager reminders showed significant medication adherence improvements at 2 and 12 weeks compared with a monitoring-only condition, based on electronic pill bottle data.
“Based on electronic pill bottle data (MEMS), those randomly assigned to the pager reminder condition had significant improvements in medication adherence compared to those in a medication monitoring only condition at 2 and 12 weeks.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [54] Safren, S. A., Hendriksen, E. S., Desousa, N., Boswell, S. L., & Mayer, K. H. (2003). Use of an on-line pager system to increase adherence to antiretroviral medications. AIDS Care, 15, 787–793. source
Not checked in the free run.
- Findingcitation
Three studies that compared SMS with traditional phone call reminders found no difference between the two modalities.
“Additionally, the three studies that compared SMS to traditional phone call reminders found no difference between these two modalities [57–59].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [57] Bos, A., Hoogstraten, J., & Prahl-Andersen, B. (2005). Failed appointments in an orthodontic clinic. BAm J Orthod Dentofacial Orthop, 127, 355–357. source
Not checked in the free run.
- cites [58] Leong, K. C., Leong, K. W., Mastura, I., et al. (2006). The use of text messaging to improve attendance in primary care: A randomized controlled trial. Family Practice, 23, 699–705. source
Not checked in the free run.
- cites [59] Chen, Z. W., Fang, L. Z., Chen, L. Y., & Dai, H. L. (2008). Comparison of an SMS text messaging and phone reminder to improve attendance at a health promotion center: A randomized controlled trial. Journal of Zhejiang University. Science B, 9, 34–38. source
Not checked in the free run.
- Findingcitation
Initial studies of mobile interventions for weight loss show modest but significant weight loss and related outcomes, consistent with other reviews.
“Consistent with other reviews [46], these initial studies of mobile interventions for weight loss show modest but significant weight loss and related outcomes.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [46] Krishna, S., & Boren, S. A. (2008). Diabetes self-management care via cell phone: A systematic review. J Diabetes Sci Technol, 2, 509–517. source
Not checked in the free run.
- Findingnone given
Despite their simple nature and lack of interactivity and theoretical basis, most appointment adherence interventions resulted in a significant increase in appointment adherence.
“Despite the simple nature of these appointment adherence interventions and their lack of interactivity and theoretical basis, most resulted in a significant increase in appointment adherence.”
Quote in the paperSubstantial claim - Findingnone given
Neither of the two medication adherence studies provided a theoretical basis for the intervention.
“Neither study provided a theoretical basis for the intervention provided.”
Quote in the paperSubstantial claim- Rests on an assumption: The absence of a reported theoretical basis in a study indicates that the intervention was not actually based on theory.
- Findingreasoning
Except for two PDA interventions that kept data local rather than sending it to a central server, any just-in-time adjustments in the disease management studies were made manually.
“Therefore, except for the two PDA interventions that did not transfer data to a central server [66, 67], any just-in-time intervention adjustments were made manually.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [66] Ma, Y., Olendzki, B. C., Chiriboga, D., et al. (2006). PDA-assisted low glycemic index dietary intervention for type 2 diabetes: A pilot study. Euro J Clin Nutr., 60, 1235–1243. source
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- cites [67] Forjuoh, S. N., Reis, M. D., Couchman, G. R., & Ory, M. G. (2008). Improving diabetes self-care with a PDA in ambulatory care. Telemed Health., 14, 273–279. source
Not checked in the free run.
- Findingnone given
Among 12 diabetes management studies with HbA1c as a primary outcome, ten found significant reductions in HbA1c.
“Among the 12 diabetes management studies with HbA1c as a primary outcome, ten found significant reductions in HbA1c.”
Quote in the paperSubstantial claim - Findingcitation
A number of reviewed studies used reminders via pager or mobile phone SMS to obtain blood glucose or to take medications.
“a number of the studies used reminders via pager or mobile phone SMS to obtain blood glucose [72, 75, 76] or to take medications [77].”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [72] Turner, J., Larsen, M., Tarassenko, L., Neil, A., & Farmer, A. (2009). Implementation of telehealth support for patients with type 2 diabetes using insulin treatment: An exploratory study. Inform Prim Care., 17, 47–53.
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- cites [75] Leu, M. G., Norris, T. E., Hummel, J., Isaac, M., & Brogan, M. W. (2005). A randomized controlled trial of an automated wireless messaging system for diabetes. Diabetes Technology and Therapeutics, 7, 710–718. source
Not checked in the free run.
- cites [76] Hanauer, D. A., Wentzell, K., Laffel, N., & Laffel, L. M. (2009). Computerized automated reminder diabetes system (CARDS): Email and SMS cell phone text messaging reminders to support diabetes management. Diabetes Technology and Therapeutics, 11, 99–106. source
Not checked in the free run.
- cites [77] Strandbygaard, U., Thomsen, S. F., & Backer, V. (2010). A daily SMS reminder increases adherence to asthma treatment: A three-month follow-up study. Respiratory Medicine, 104, 166–171. source
Not checked in the free run.
- Findingnone given
All three mobile asthma management interventions found significant improvements in the primary outcome.
“All three mobile asthma management interventions found significant improvements in the primary outcome.”
Quote in the paperNot a substantial claim - Findingreasoning
Early mobile health behavior interventions have only begun to leverage the output functionality of mobile phones.
“These early mobile health behavior interventions also have only begun to leverage the output functionality of these phones.”
Quote in the paperNot a substantial claim - Findingcitation
Since 2008, three reviewed studies using Bluetooth or similar technology found significant reductions in HbA1c.
“Since 2008, three of the studies reviewed used this or similar technology and found significant reductions in HbA1c [71–73].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [71] Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008). WellDocTM mobile diabetes management randomized controlled trial: Change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technology and Therapeutics, 10, 160–168. source
Not checked in the free run.
- cites [72] Turner, J., Larsen, M., Tarassenko, L., Neil, A., & Farmer, A. (2009). Implementation of telehealth support for patients with type 2 diabetes using insulin treatment: An exploratory study. Inform Prim Care., 17, 47–53.
Not checked in the free run.
- cites [73] Cho, J. H., Lee, H. C., Lim, D. J., Kwon, H. S., & Yoon, K. H. (2009). Mobile communication using a mobile phone with a glucometer for glucose control in type 2 patients with diabetes: As effective as an Internet-based glucose monitoring system. Journal of Telemedicine and Telecare, 15, 77–82. source
Not checked in the free run.
- Findingreasoning
Most apparent from this review is the limited use of rapid two-way interaction of inputs and outputs that could be used to deliver just-in-time health behavior interventions.
“Most apparent from this review, however, is the limited use of the rapid two-way interaction of inputs and outputs that could be used to deliver just-in-time health behavior interventions.”
Quote in the paperSubstantial claim - Findingown data
The review reveals a paucity of discussion regarding the health behavior theories or models that provide the basis of the intervention.
“Our review of the studies of health behavior interventions delivered via mobile technologies reveals a paucity of discussion regarding the health behavior theories or models that provide the basis of the intervention.”
Quote in the paperSubstantial claim - Findingown data
Studies in the smoking and weight loss areas tended to use a theoretical model for their intervention, drawing predominately from Social Cognitive Theory or its variants.
“Studies in the smoking and weight loss areas tended to use a theoretical model for their intervention, drawing predominately from Social Cognitive Theory or its variants (e.g., self-efficacy).”
Quote in the paperNot a substantial claim - Findingreasoning
Most of the mobile interventions studied in the treatment adherence and disease management areas did not report a theoretical basis for intervention development.
“In contrast, most of the mobile interventions studied in the treatment adherence and disease management areas did not report a theoretical basis for intervention development”
Quote in the paperSubstantial claim - Findingcitation
Of the applications that predominately pushed out intervention messages, a few encouraged confirmation of receipt or some other reply response, but most did not.
“Of the applications that predominately pushed out intervention messages, a few encouraged confirmation of receipt or some other reply response (e.g., [43]), but most did not.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] Patrick, K., Raab, F., Adams, M. A., et al. (2009). A text message-based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11, e1. source
Not checked in the free run.
- Findingcitation
Among applications relying on input, many employed a user-initiated or unprompted input procedure despite a considerable ecological momentary assessment literature supporting the use of prompted inputs.
“Among applications relying on input, many employed a user-initiated or unprompted input procedure despite a considerable ecological momentary assessment literature supporting the use of prompted inputs [80].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [80] Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. source
Not checked in the free run.
- Findingnone given
Even among studies that provided a theoretical basis, very few attempted to evaluate any of the theoretical components hypothesized to be affected by the intervention.
“Even among those studies that provided a theoretical basis, very few attempted to evaluate any of the theoretical components hypothesized to be affected by the intervention.”
Quote in the paperSubstantial claim - Interpretationreasoning
Current health behavior theories seem inadequate for guiding the development of mobile interventions as these become more interactive and adaptive.
“Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive.”
Quote in the paperSubstantial claim- Rests on an assumption: Health behavior theories can improve the outcomes of mobile health behavior interventions.
- Premisecitation
As sensing technologies integrated with mobile phones develop, health behavior change interventions can be delivered based on psychophysiological state, social context, activity level, and behavior patterns, in addition to self-reports and time/location parameters.
“As sensing technologies integrated with the mobile phone via Bluetooth or other data transmission process continue to develop, health behavior change interventions can be delivered based not only on self-reports and time/location parameters but also on psychophysiological state, social context, activity level, and behavior patterns [10].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Pharow, P., Blobel, B., Ruotsalainen, P., Petersen, F., & Hovsto, A. (2009). Portable devices, sensors and networks: Wireless personalized eHealth services. Studies in Health Technology and Informatics, 150, 1012–1016.
Not checked in the free run.
- Interpretationreasoning
The predominantly linear and static nature of current health behavior models severely limits their ability to guide the dynamic, adaptive interventions that mobile technologies make possible.
“but the predominately linear and static nature of these models severely limits their ability to guide the dynamic, adaptive interventions possible via mobile technologies.”
Quote in the paperSubstantial claim - Premisecitation
Several named models-including the health belief model, theory of planned behavior, social cognitive theory, transtheoretical model, and self-determination theory-have served as the basis for many eHealth web and desktop/laptop computer interventions and have informed tailoring to individual baseline status.
“Models such as the Health Belief Model [12], Theory of Planned Behavior [13], Social Cognitive Theory [14], the Transtheoretical Model [15], and Self-Determination Theory [16] have served as the basis for many of the eHealth web and desktop/laptop computer interventions and have informed how interventions can be tailored to the individual’s baseline status.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [12] Becker, M. H. (Ed.) (1974). The health belief model and personal health behavior. Health Education Monographs, 2, 324–473.
Not checked in the free run.
- cites [13] Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion, 11, 87–98. source
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- cites [14] Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. source
Not checked in the free run.
- Premisecitation
Rich, complex, and frequent data inputs provide the potential to tailor health behavior interventions not only to baseline characteristics but also to frequently changing behaviors and environmental contexts.
“The availability of these rich, complex, and frequent data inputs provides the potential to deliver health behavior interventions tailored not only to the person’s baseline characteristics but also to his/her frequently changing behaviors and environmental contexts [11].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Patrick, K., Griswold, W. G., Raab, F., & Intille, S. S. (2008). Health and the mobile phone. American Journal of Preventive Medicine, 35, 177–181. source
Not checked in the free run.
- Premisenone given
Compared with Internet interventions delivered on desktop and laptop computers, mobile interventions can interact with individuals much more frequently and within the context of the behavior.
“Compared to Internet interventions delivered to desktop and laptop computers, mobile interventions have the capacity to interact with the individual with much greater frequency and in the context of the behavior.”
Quote in the paperSubstantial claim - Interpretationreasoning
Such interventions require health behavior models with dynamic, regulatory components to guide rapid adaptation using the individual's current and past behavior and situational context.
“Such interventions require health behavior models that have dynamic, regulatory system components to guide rapid intervention adaptation based on the individual’s current and past behavior and situational context.”
Quote in the paperNot a substantial claim - Premisecitation
Data entry burden is a persistent problem in disease management.
“Data entry burden is a persistent problem in disease management [69].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [69] Arsand, E., Tufano, J. T., Ralston, J. D., & Hjortdahl, P. (2008). Designing mobile dietary management support technologies for people with diabetes. Journal of Telemedicine and Telecare, 14, 329–332. source
Not checked in the free run.
- Premisecitation
In a study of adolescents on intensified insulin therapy, one quarter failed to send at least half of their four daily blood glucose values.
“In one study of adolescents on intensified insulin therapy, one quarter failed to send at least 50% of their four daily blood glucose values [70].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [70] Rami, B., Popow, C., Horn, W., Waldhoer, T., & Schober, E. (2006). Telemedical support to improve glycemic control in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics, 165, 701–705. source
Not checked in the free run.
- Premisecitation
A recent systematic review of mobile phone use for diabetes self-management found nine of ten studies reported significant improvement in HbA1c.
“This is consistent with a recent systematic review of mobile phone use for diabetes self-management in which nine of ten studies reviewed reported significant improvement in HbA1c [46].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [46] Krishna, S., & Boren, S. A. (2008). Diabetes self-management care via cell phone: A systematic review. J Diabetes Sci Technol, 2, 509–517. source
Not checked in the free run.
- Interpretationreasoning
Current health behavior models appear inadequate to answer many of the intervention development questions likely to arise as interventions better leverage the interactive capabilities of mobile technologies.
“these current models appear inadequate to answer many of the intervention development questions likely to arise as interventions better leverage the interactive capabilities of mobile technologies.”
Quote in the paperSubstantial claim - Interpretationcitation
This application illustrates the potential of control engineering to produce desired outcomes in systems with measurement error, model uncertainty, noise, and lagged effects.
“this application illustrates the potential of this approach to produce the desired outcome in systems that possess measurement error, model uncertainty, noise, and lagged effects.”
Quote in the paperSubstantial claim - Interpretationreasoning
Since the brain appears to regulate itself and other organs using feedback control processes, it seems reasonable that the brain also regulates human behavior via similar feedback control processes.
“Since the brain appears to regulate itself and other organs using feedback control processes, it seems reasonable that the brain also regulates human behavior via similar feedback control processes.”
Quote in the paperNot a substantial claim- Rests on an assumption: The brain regulates itself and other organs using feedback control processes.
- Interpretationreasoning
Current health behavior theories are particularly limited at informing just-in-time intervention adaptations.
“but they are particularly limited at informing just-in-time intervention adaptations.”
Quote in the paperSubstantial claim - Premisecitation
A differential equation drawn from the general principle of conservation of matter can be used to describe the relationship between smoking urge variables.
“A differential equation drawn from the general principle of conservation of matter can be used to describe the relationship between these variables [102]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [102] Ogunnaike, B. A., & Ray, W. H. (1994). Process dynamics, modeling, and control. New York: Oxford University Press.
Not checked in the free run.
- Interpretationnone given
Mobile devices can collect information in real time about the controlled, manipulated, and disturbance variables.
“Mobile devices can collect information in real time about the controlled, manipulated, and disturbance variables, as represented by sensor locations in Fig. 1.”
Quote in the paperSubstantial claim - Premisereasoning
These findings led to the Health Belief Model and the various health behavior theories and models that followed.
“These findings led to the Health Belief Model [85] and the various health behavior theories and models that followed.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [85] Rosenstock, I. M. (1974). Historical origins of the Health Belief Model. Health Education Monographs, 2, 328–335. source
Not checked in the free run.
- Interpretationreasoning
There are practical advantages of using dynamical systems to inform adaptive, time-intensive interventions delivered via mobile technologies.
“There are a number of practical advantages of using dynamical systems to inform adaptive, time-intensive interventions delivered via mobile technologies.”
Quote in the paperSubstantial claim - Interpretationcitation
Mobile devices allow the content of interventions to be adapted for each individual not only initially but also over time based on prior outcome data, prior responses, current context, and other variables.
“the content of interventions can be adapted for each individual not only initially but also over time based on his/her prior outcome data, prior responses to specific intervention outputs, current environmental and/or social context, and a range of other variables that might influence the optimal intervention based on the current state of the individual (i.e., ecological momentary interventions) [”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [90] Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 15, 1–39. source
Not checked in the free run.
- Premisecitation
The similarities between brain circuitry and control system engineering principles are so compelling that some brain processes are described via engineering feedback systems.
“The communalities between brain circuitry and control system engineering principles are so compelling that some brain processes are described via engineering feedback systems (e.g., [96]).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [96] Saper, C. B., Chou, T. C., & Scammell, T. E. (2001). The sleep switch: Hypothalamic control of sleep and wakefulness. Trends Neuroscience., 24, 726–731. source
Not checked in the free run.
- Interpretationreasoning
Given the lag between intervention development, evaluation, and publication, the articles reviewed describe interventions developed a number of years ago.
“Given the lag between intervention development, evaluation, and eventual publication, the articles reviewed, although published as recently as the June 2010, describe interventions developed a number of years ago.”
Quote in the paperSubstantial claim - Interpretationreasoning
The limited interactivity of the mobile interventions reviewed may not reflect current status.
“Therefore, the limited interactivity of the mobile interventions reviewed may not reflect current status”
Quote in the paperNot a substantial claim - Premisecitation
Control engineering approaches have been proposed for adaptive interventions, for example to determine when and how much to change the frequency of family counseling to prevent conduct disorder in children.
“Control engineering approaches have been proposed for adaptive interventions to determine, for example, when and how much to change the frequency of family counseling to prevent the development of conduct disorder in children [93].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [93] Rivera, D. E., Pew, M. D., & Collins, L. M. (2007). Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence, 88(suppl. 2), S31–S40. source
Not checked in the free run.
- Premisecitation
Model estimation techniques such as system identification and functional data analysis can be used to estimate the coefficients from these data.
“Model estimation techniques such as system identification [103] and functional data analysis [104] can be used to estimate the Kd1, Kd 2, Ku1, and Ku2 coefficients from these data.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [103] Ljung, L. (1999). System identification: Theory for the user. Prentice Hall information and system sciences series. New York: Prentice Hall.
Not checked in the free run.
- cites [104] Ramsay, J., & Silverman, B. (2005). Functional data analysis. New York: Springer. source
Not checked in the free run.
- Premisecitation
Basic neuronal processes are time-intensive, adaptive interactions of excitatory and inhibitory synaptic processes that have been modeled at the single neuron level.
“Basic neuronal processes are time-intensive, adaptive interactions of excitatory and inhibitory synaptic processes that have been modeled at the single neuron level [95].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [95] Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008). The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 4, e1000092. source
Not checked in the free run.
- Interpretationnone given
Mobile technology applications also have the ability to adapt the timing of interventions based on these same data.
“mobile technology applications of health behavior interventions also have the ability to adapt the timing of the interventions based on these same data.”
Quote in the paperSubstantial claim - Premisecitation
A rich behavior change process-outcome literature describes dynamic interactions in face-to-face behavioral interventions that could be applied to behavioral interventions via mobile technologies.
“a rich behavior change process–outcome literature describes dynamic interactions in face-to-face behavioral interventions that could be applied to behavioral interventions via mobile technologies [91].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [91] Llewelyn, S., & Hardy, G. (2001). Process research in understanding and applying psychological therapies. The British Journal of Clinical Psychology, 40, 1–21. source
Not checked in the free run.
- Interpretationreasoning
A greater reliance on health behavior theories to guide mobile technology intervention development, even for apparently simple interventions, should result in interventions that address more comprehensively the potential mechanisms of behavior change, resulting in more effective interventions.
“Therefore, a greater reliance on health behavior theories to guide mobile technology intervention development, even for apparently simple interventions, should result in interventions that address more comprehensively the potential mechanisms of behavior change, resulting in more effective interventions.”
Quote in the paperSubstantial claim - Premisecitation
Interventions based on theories enriched by control system models provide engineers and health behavior researchers with a common language for collaborating on new mobile interventions.
“Interventions based on theories enriched by control system models also provide engineers and health behavior researchers with a common language for collaborating on new interventions delivered via mobile technologies [94].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [94] Navarro-Barrientos, J. E., Rivera, D. E., & Collins, L. M. (2010). A dynamical systems model for understanding behavioral interventions for weight loss. In S. K. Chai, J. J. Salerno, & P. L. Mabry (Eds.), 2010 international conference on social computing, behavioral modeling, and prediction (SBP 2010) (pp. 170–179). Heidelberg: Springer.
Not checked in the free run.
- Interpretationreasoning
Control system principles and procedures fit well with the rich longitudinal data and real-time intervention adaptations potentially available via mobile technologies.
“These control system principles and procedures fit well with the rich longitudinal data and real-time intervention adaptations potentially available via mobile technologies.”
Quote in the paperNot a substantial claim - Premisecitation
Control engineering approaches have been applied to the glucose-insulin closed-loop system.
“Control engineering approaches have been applied to the glucose–insulin closed-loop system [99]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [99] Bequette, B. W. (2009). Glucose clamp algorithms and insulin time–action profiles. J Diabetes Sci Technol., 3, 1005–1013. source
Not checked in the free run.
- Interpretationreasoning
The development of time-intensive, interactive, and adaptive health behavior interventions via mobile technologies demands more intra-individual dynamic regulatory processes than represented in current health behavior theories.
“The development of time-intensive, interactive, and adaptive health behavior interventions via mobile technologies demands more intra-individual dynamic regulatory processes than represented in our current health behavior theories.”
Quote in the paperSubstantial claim - Premisecitation
The National Institutes of Health currently funds a number of ongoing projects that use more intensive interactivity and just-in-time interventions than described in this review.
“the National Institutes of Health currently funds a number of ongoing projects that use more intensive interactivity and just-in-time interventions than described in this review [81].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [81] National Institutes of Health. (2010). http://projectreporter.nih.gov (reporter_searchresults.cfm?&new=1&icde=5933670&loc=2&CFID=17945345&CFTOKEN=13189329). Accessed 29 Oct 2010.
Not checked in the free run.
- Interpretationcitation
Control systems engineering may provide the dynamical system models needed to transform current health behavior theories into the dynamic theories required for time-intensive, interactive, and adaptive mobile health behavior interventions.
“Control systems engineering [92] may provide the dynamical system models needed to transform our current health behavior theories into the dynamic theories required for the time-intensive, interactive, and adaptive health behavior interventions delivered via mobile technologies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [92] Åström, K. J. & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. http://www.cds.caltech.edu/~murray/amwiki. source
Not checked in the free run.
- Interpretationreasoning
The opportunity via mobile devices to collect intensive context- and time-dependent data and to systematically vary intervention components enables researchers to test both intervention components and the theoretical concepts and dynamic models underlying them.
“The opportunity via mobile devices to collect intensive context- and time-dependent (longitudinal) data and to systematically vary intervention components enables researchers to test not only these components but also the theoretical concepts and dynamic models that underlie them.”
Quote in the paperSubstantial claim - Premisecitation
Mobile technology enables advanced control algorithms such as model predictive control that employ formal optimization methods to decide on current and future doses while satisfying clinical practice preferences and restrictions.
“Mobile technology is an enabler to advanced control algorithms such as model predictive control [105] that employ formal optimization methods to decide on current and future doses of intervention components while satisfying clinical practice preferences and restrictions [100].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [105] Camacho, E. F., & Bordons, C. (2004). Model predictive control (2nd ed.). New York: Springer.
Not checked in the free run.
- cites [100] Nandola, N., Rivera, D. E. (2010). A robust model predictive control formulation for hybrid systems with application to adaptive behavioral interventions. In American Control Conference, Baltimore, MD, pp. 6286–6292.
Not checked in the free run.
- Interpretationreasoning
With a dynamical systems model, it becomes possible to apply control systems engineering to develop algorithms that use real-time assessments and predicted responses to adaptively decide on timing and dose of intervention components.
“With a dynamical systems model, it becomes possible to apply control systems engineering to develop algorithms that use real-time assessments and predicted responses from the model to adaptively decide on the timing and dose of the intervention components.”
Quote in the paperSubstantial claim - Interpretationnone given
A control system framework can adapt to and consider variability of key controlling or explanatory variables.
“In addition to being sensitive to threshold (or mean) levels of key variables, a control system framework can adapt to and consider variability of key controlling or explanatory variables.”
Quote in the paperNot a substantial claim - Interpretationreasoning
For many health behaviors, maintaining a stable level or tighter range for some key variables may be as or more important than increasing or decreasing mean levels.
“For many health behaviors, maintaining a stable level or tighter range for some key variables may be as or more important than increasing or decreasing mean levels.”
Quote in the paperSubstantial claim
- May combine two claims. 38
- Classified as probably not a substantial claim. 27
May combine two claims.
Findingown dataThe seven smoking cessation studies came from three research groups and delivered interventions via mobile phone text messaging (SMS).
“the seven studies identified are from three research groups who developed and evaluated interventions delivered via mobile phone text messaging (aka short messaging service (SMS)).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationSocial cognitive theory was the main theoretical basis for the weight loss, diet, and physical activity interventions, though one study cited the theory of planned behavior.
“Consistent with the more general weight loss intervention literature, Social Cognitive Theory was the primary theoretical basis for these interventions although the Theory of Planned Behavior was cited by one study [38].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [38] Hurling, R., Catt, M., De Boni, M., et al. (2007). Using Internet and mobile phone technology to deliver an automated physical activity program: Randomized controlled trial. Journal of Medical Internet Research, 9, e7. source
Not checked in the free run.
May combine two claims.
FindingcitationIn a randomized evaluation, overweight adults in a mobile phone weight loss program lost significantly more weight (4.5 vs. 1.1 kg) and had a greater reduction in waist circumference (6.3 vs. 2.4 cm) after 1 year compared with controls.
“Compared to controls, those in the mobile phone intervention lost significantly more weight (4.5 vs. 1.1 kg) and had a greater reduction in waist circumference (6.3 vs. 2.4 cm) after 1 year in the program.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [40] Haapala, I., Barengo, N. C., Biggs, S., Surakka, L., & Manninen, P. (2009). Weight loss by mobile phone: A 1-year effectiveness study. Public Health Nutrition, 12, 2382–2391. source
Not checked in the free run.
May combine two claims.
Findingnone givenAll seven smoking cessation interventions also adjusted just-in-time when users sent text requests for help, which generated immediate replies with tips and strategies.
“All of these interventions also adjusted just-in-time via text message requests from users for assistance which generated immediate return text messages with tips and strategies to manage urges or withdrawal.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationThe smoking cessation interventions produced quit rates between 8% and 53%, with follow-up lasting from 4 weeks to 12 months.
“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.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [29] Free, C., Whittaker, R., Knight, R., Abramsky, T., Rodgers, A., & Roberts, G. (2009). Txt2stop: A pilot randomized controlled trial of mobile phone-based smoking cessation support. Tobacco Control, 18, 88–91. source
Not checked in the free run.
- cites [26] Whittaker, R., Maddison, R., McRobbie, H., et al. (2008). A multimedia mobile phone-based youth smoking cessation intervention: Findings from content development and piloting studies. Journal of Medical Internet Research, 10, e49. source
Not checked in the free run.
May combine two claims.
Findingown dataAmong the ten treatment adherence studies, eight aimed at medical appointment adherence and two at medication adherence, specifically for HIV treatment.
“Of these, eight targeted medical appointment adherence and two addressed medication adherence, specifically HIV treatment adherence.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationIn a 4-month randomized trial, a mobile phone text messaging weight loss program produced modest weight loss (2.88 kg) that was significantly greater than a print-only condition.
“Although weight loss in the text messaging condition was modest over 4 months (2.88 kg), it was significantly greater than in the print only condition.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] Patrick, K., Raab, F., Adams, M. A., et al. (2009). A text message-based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11, e1. source
Not checked in the free run.
May combine two claims.
FindingcitationOnly two appointment adherence studies had randomized controls; the rest used historical or nonrandomized controls.
“It is important to note, however, that only two studies used randomized controls [58, 59]; the others used historical or nonrandomized controls.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [58] Leong, K. C., Leong, K. W., Mastura, I., et al. (2006). The use of text messaging to improve attendance in primary care: A randomized controlled trial. Family Practice, 23, 699–705. source
Not checked in the free run.
- cites [59] Chen, Z. W., Fang, L. Z., Chen, L. Y., & Dai, H. L. (2008). Comparison of an SMS text messaging and phone reminder to improve attendance at a health promotion center: A randomized controlled trial. Journal of Zhejiang University. Science B, 9, 34–38. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationVideo input has been primarily used for telemedicine, but health behavior researchers have begun to use cameras for dietary assessment.
“Although video input has been primarily used for telemedicine purposes [78, 79], health behavior researchers have begun to use cameras for dietary assessment [48].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [78] Chung, P., Yu, T., & Scheinfeld, N. (2007). Using cellphones for teledermatology, a preliminary study. Dermatology Online Journal, 13, 2. source
Not checked in the free run.
- cites [79] Tang, F. H., Law, M. Y., Lee, A. C., & Chan, L. W. (2004). A mobile phone integrated health care delivery system for medical images. Journal of Digital Imaging, 17, 217–225. source
Not checked in the free run.
- cites [48] Boushey, C. J., Kerr, D. A., Wright, J., Lutes, K. D., Ebert, D. S., & Delp, E. J. (2009). Use of technology in children’s dietary assessment. Euro J Clin Nutr., 63, S50–S57. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenResearchers have begun to use video input via camera features on mobile phones.
“In addition to voice, text, and data inputs, researchers have begun to use video input via camera features on these phones.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningOutcomes from the mobile disease management studies were generally positive and statistically significant.
“Outcomes from the mobile disease management studies were generally positive and statistically significant.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenMuch of the mobile health behavior interventions to date have focused on transmitting data from glucometers and spirometers.
“Much of the mobile health behavior interventions to date have focused on transmitting data from glucometers and spirometers”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationA few studies have used other output functions such as audio or video.
“A few studies have used other output functions such as audio (e.g., [35]) or video (e.g. [26])”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [35] Liu, W. T., Wang, C. H., Lin, H. C., et al. (2008). Efficacy of a cell phone-based exercise programme for COPD. The European Respiratory Journal, 32, 651–659. source
Not checked in the free run.
- cites [26] Whittaker, R., Maddison, R., McRobbie, H., et al. (2008). A multimedia mobile phone-based youth smoking cessation intervention: Findings from content development and piloting studies. Journal of Medical Internet Research, 10, e49. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningMost of the mobile interventions described in this review used text messaging (SMS) functionality.
“Most of the mobile interventions described in this review used text messaging (aka SMS) functionality of mobile phones.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenMost output delivery to date has been via text messaging.
“As with input, most of the output delivery to date has been via text messaging.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenFollowing input, some applications provided tailored intervention responses, but others failed to deliver even a confirmation message.
“Following input, some applications provided tailored intervention responses, but others failed to deliver even a confirmation message”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationMost 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 varied substantially.
“Most of the 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 varied substantially among these interventions”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [23] Obermayer, J. L., Riley, W. T., Asif, O., & Jean-Mary, J. (2004). College smoking-cessation using cell phone text messaging. J Amer Coll Health, 53, 71–78. source
Not checked in the free run.
- cites [27] Brendryen, H., & Kraft, P. (2008). Happy Ending: A randomized controlled trial of a digital multi-media smoking cessation intervention. Addiction, 103, 478–484. source
Not checked in the free run.
- cites [34] Burke, L. E., Conroy, M. B., Sereika, S. M., et al. (2011). The effect of electronic self-monitoring on weight loss and dietary intake: A randomized behavioral weight loss trial. Obesity, 19, 338–344. source
Not checked in the free run.
- cites [43] Patrick, K., Raab, F., Adams, M. A., et al. (2009). A text message-based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11, e1. source
Not checked in the free run.
May combine two claims.
FindingcitationIn this review, only a few studies adjusted the timing of interventions, usually during the transition from behavior change to maintenance, but these adjustments were set a priori across all participants and were not dynamically determined at the individual level.
“In this review, only a few studies adjusted the timing of the interventions, usually during the transition from behavior change to maintenance (e.g., [23, 40]), but these timing adjustments were set a priori across all participants and were not dynamically determined at the individual level.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [23] Obermayer, J. L., Riley, W. T., Asif, O., & Jean-Mary, J. (2004). College smoking-cessation using cell phone text messaging. J Amer Coll Health, 53, 71–78. source
Not checked in the free run.
- cites [40] Haapala, I., Barengo, N. C., Biggs, S., Surakka, L., & Manninen, P. (2009). Weight loss by mobile phone: A 1-year effectiveness study. Public Health Nutrition, 12, 2382–2391. source
Not checked in the free run.
May combine two claims.
Findingown dataNone of the adherence interventions made just-in-time adjustments; most were single prompt appointment reminders with no longitudinal basis for adjustment; none obtained the input necessary to make treatment adjustments.
“None of the adherence interventions made just-in-time adjustments, but most of these interventions were single prompt appointment reminders with no longitudinal basis for adjustment, and none obtained the input necessary to make treatment adjustments.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationThese theories and models have been used with considerable success to tailor health behavior interventions based on pre-intervention factors, but typically have not been used to adapt the intervention to the individual over the course of the intervention.
“these theories and models have been used with considerable success to tailor health behavior interventions based on pre-intervention factors, but typically have not been used to adapt the intervention to the individual over the course of the intervention [88, 89].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [88] Neville, L. M., O'Hara, B., & Milat, A. J. (2009). Computer-tailored dietary behaviour change interventions: A systematic review. Health Education Research, 24, 699–720. source
Not checked in the free run.
- cites [89] Lustria, M. L., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer-tailored health interventions delivered over the Web: Review and analysis of key components. Patient Education and Counseling, 74, 156–173. source
Not checked in the free run.
May combine two claims.
Findingnone givenDisease management and weight loss mobile health interventions have relied predominately on input, while adherence and smoking cessation interventions have relied predominately on output.
“Disease management and weight loss mobile health interventions have relied predominately on input while adherence and smoking cessation mobile health interventions have relied predominately on output.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenThe disease management interventions typically provided just-in-time intervention adjustments, but most relied on manual adjustments by a health care professional.
“The disease management interventions also typically provided just-in-time intervention adjustments, but most relied on manual adjustments by a health care professional to do so.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone givenTo date, only a subset of mobile health behavior interventions have been theory-based.
“To date, only a subset of mobile health behavior interventions have been theory-based”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningMost theories of health behavior change to date have focused on mean levels of predictive variables, but with newer data technologies, models can be developed that focus on lability or stability of key controlling factors.
“Most theories of health behavior change to date have focused on mean levels of predictive variables, but with the advent of newer data technologies, we can develop models that focus on lability or stability of key controlling factors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationWeb-based health behavior interventions have proliferated and appear to be an efficacious and cost-effective way to deliver health behavior interventions.
“web-based health behavior interventions have proliferated in recent years and appear to be an efficacious method for delivering health behavior interventions in a cost-effective manner [1–3].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [1] Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12, e4. source
Not checked in the free run.
- cites [2] Tate, D. F., Finkelstein, E. A., Khavjou, O., & Gustafson, A. (2009). Cost effectiveness of Internet interventions: Review and recommendations. Annals of Behavioral Medicine, 38, 40–45. source
Not checked in the free run.
- cites [3] Norman, G. J., Zabinski, M. F., Adams, M. A., Rosenberg, D. E., Yaroch, A. L., & Atienza, A. A. (2007). A review of eHealth interventions for physical activity and dietary behavior change. American Journal of Preventive Medicine, 33, 336–345. source
Not checked in the free run.
May combine two claims.
PremisecitationA Cochrane review of randomized mobile phone-based smoking cessation interventions found a significant short-term increase in self-reported quitting (relative risk 2.18; 95% CI 1.80-2.65) but considerable heterogeneity and insufficient evidence for long-term effects.
“A recent Cochrane review of randomized studies of mobile phone-based interventions for smoking cessation, which included all of the randomized controlled trials (RCT) reviewed here, found a significant short-term increase in self-reported quitting (relative risk= 2.18; 95% confidence interval 1.80 to 2.65) but considerable heterogeneity and lack of sufficient evidence for long-term effects [32].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Whittaker, R., Borland, R., Bullen, C., Lin, R. B., McRobbie, H., & Rodgers, A. (2009). Mobile phone-based interventions for smoking cessation. Cochrane Database of Systematic Reviews, 4, CD006611.
Not checked in the free run.
May combine two claims.
PremisecitationMobile phone use has been widely adopted across socioeconomic and demographic groups and appears greater among populations most in need of health behavior interventions.
“mobile phone use has been widely adopted across socioeconomic and demographic groups and appears greater among those populations most in need of these interventions [6, 7].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [6] Pew Internet &American Life Project. (2010). http://www.pewInternet.org/~/media//Files/Reports/2010/PIP_Mobile_Access_2010.pdf
Not checked in the free run.
- cites [7] Leena, K., Tomi, L., & Arja, R. (2005). 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, 28, 35–47. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationLanzola and colleagues proposed a multi-tier model: lower tiers use automated treatment algorithms for disease management, while higher tiers involve a healthcare provider only when lower-tier interventions fail.
“Lanzola and colleagues have proposed a multi-tier intervention model in which lower-tier disease management interventions are provided via automated treatment algorithms and higher tier interventions involving the healthcare provider occur only if the lower-tier interventions are ineffective [68].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [68] Lanzola, G., Capozzi, D., D’Annunzio, G., Ferrari, P., Bellazzi, R., & Larizza, C. (2007). Going mobile with a multi-access service for the management of diabetes patients. J Diabetes Sci Technol., 1, 730–737. source
Not checked in the free run.
May combine two claims.
InterpretationreasoningManually adjusted disease management interventions fit telemedicine models but scale poorly because providers must review data and deliver the interventions.
“These manually adjusted interventions are consistent with telemedicine models, but they have limited scalability due to the provider burden required to review and deliver these interventions.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe ability of mobile disease management programs to assess patient status frequently and adjust treatment remotely appears to improve treatment outcomes and to serve as a basis for more comprehensive and automated mobile disease management interventions.
“Therefore, the ability of mobile disease management programs to assess patient status frequently and adjust treatment remotely appears to improve treatment outcomes and to serve as a basis for more comprehensive and automated mobile disease management interventions.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationIn a small randomized controlled trial, Strandbygaard et al. found that percent adherence based on objective inhaler dose counts decreased in the control condition (84.2% to 70.1%) but increased in the intervention condition (77.9% to 81.5%) over 12 weeks.
“Strandbygaard et al. [77] found that the percent adherence based on objective inhaler dose counts decreased in the control condition (84.2% to 70.1%) but increased in the intervention condition (77.9% to 81.5%) over the 12-week study period.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [77] Strandbygaard, U., Thomsen, S. F., & Backer, V. (2010). A daily SMS reminder increases adherence to asthma treatment: A three-month follow-up study. Respiratory Medicine, 104, 166–171. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenDevice-resident or wireless Internet applications on smartphones provide a rich array of possible data and display outputs that could be used to deliver health behavior interventions.
“device-resident or wireless Internet applications on smartphones 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.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAutomatic transmission of testing data eliminates data entry burden but does not eliminate the patient's responsibility to perform regular testing.
“Although the automatic transmission of testing data eliminates data entry burden, it does not eliminate the responsibility of the patient to perform regular testing”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningAn expanding variety of sensor technologies can be used to assess physiological states, movement, location, and other variables using mobile phones.
“an expanding variety of sensor technologies can be used to assess physiological states, movement, location, and other variables using mobile phones.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe application of feedback control systems to current health behavior theory concepts provides potential theoretical models that are amenable to the time-intensive, interactive nature of mobile health behavior interventions and more consistent with putative neurobiological and environmental processes.
“Therefore, the application of feedback control systems to current health behavior theory concepts provides not only potential theoretical models that are amenable to the time-intensive, interactive nature of mobile health behavior interventions but also theoretical models that are more consistent with the putative neurobiological and environmental processes that regulate these behaviors.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationBoorsboom and colleagues have argued that between-person theories do not imply, test, or support causal accounts valid at the individual level.
“Boorsboom and colleagues have argued that between-person theories do not imply, test, or support causal accounts valid at the individual level [87].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [87] Boorsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 202–219. source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThis work could serve as a basis for developing mobile technologies for diabetes management that use advanced closed-loop control system models to automate and better regulate blood glucose for insulin-dependent patients.
“This work could serve as a basis for developing mobile technologies for diabetes management that take advantage of advanced closed-loop control system models to automate and better regulate blood glucose for insulin-dependent patients.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationnone givenText 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.
“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”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAdoption of dynamical system models for mobile health behavior interventions does not require discarding current health behavior theories, but the predominately static, linear nature of these theories appears to be a poor fit with the intra-individual dynamics of future mobile technology interventions.
“Adoption of dynamical system models for mobile health behavior interventions does not require that our current health behavior theories and models be discarded, but the predominately static, linear nature of these theories appears to be a poor fit with the intra-individual dynamics of future mobile technology interventions.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe history of health behavior theories cautions against assuming that behavior change is straightforward or that innovative modes of delivery alone are sufficient to produce behavior change.
“the history of health behavior theories cautions us about assuming that behavior change is straightforward or that innovative modes of delivery alone are sufficient to produce behavior change.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Interpretationnone givenThe potential of mobile technologies to generate intensive longitudinal data sets reflecting a smoker's behavior over time and in different settings is important for building a dynamical system model.
“The potential of mobile technologies to generate the intensive longitudinal data sets reflecting the behavior of a smoker over time and observed in different settings and contexts is important for building a dynamical system model such as the individual smoking behavior model illustrated above.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationOptimal feedback control systems have been applied to basic sensorimotor control and to reinforcement learning models.
“Optimal feedback control systems have been applied to basic sensorimotor control [97] and to reinforcement learning models [98].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [97] Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience, 7, 907–915. source
Not checked in the free run.
- cites [98] Lewis, F. L., & Vamvoudakis, K. G. (2010). Reinforcement learning for partially observable dynamic processes: Adaptive dynamic programming using measured output data. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 41, 14–25. source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationDunton and Atienza have noted that increasing availability of time-intensive information allows for intra-individual tailoring of interventions but that current health behavior theories are based on delineating between-person, not within-person, differences.
“Dunton and Atienza have noted that the increasing availability of time-intensive information (i.e., longitudinal data obtained at an intensive frequency) allows for the intra-individual tailoring of interventions but that current health behavior theories are based on delineating between, not within-person differences [86].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [86] Dunton and Atienza. (2009). The need for time-intensive information in healthful eating and physical activity research: A timely topic. JADA, 109(1), 30–35. source
Not checked in the free run.
May combine two claims.
InterpretationreasoningFeedback control systems have the potential to reshape health behavior theories and improve understanding of human behavior.
“these feedback control systems have the potential to reshape health behavior theories and improve our understanding of human behavior.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningStandard treatment algorithms can be used to automate many treatment adjustments with complete treatment fidelity, greatly improving scalability and reducing professional time and costs.
“standard treatment algorithms can be used to automate, with 100% treatment fidelity, many of these treatment adjustments, greatly improving scalability, and reducing professional time and costs.”
Quote in the paperNot a substantial claim- Rests on an assumption: Standard treatment algorithms can automate many treatment adjustments with complete treatment fidelity.
May combine two claims.
PremisecitationCurrent health behavior theories do not preclude the possibility that some conceptual components change over time, and some concepts such as reciprocal determinism in Social Cognitive Theory are explicitly dynamic.
“These theories do not preclude the possibility that some conceptual components change over time, and some concepts such as reciprocal determinism in Social Cognitive Theory are explicitly dynamic in nature [14].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. source
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
PremisecitationSome have argued that these theories are inadequate even for traditional interventions.
“Some have argued that these theories are inadequate even for traditional interventions [17]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Ogden, J. (2003). Some problems with social cognition models: A pragmatic and conceptual analysis. Health Psychology, 22, 424–428. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe lack of reported theoretical basis in treatment adherence and disease management may be the result of reliance on evidence-based clinical guidelines or on the simplicity of the intervention delivered.
“but this may be the result of reliance on evidence based clinical guidelines (e.g., diabetes management) or on the simplicity of the intervention delivered (e.g., appointment reminders).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningAs a result, the review did not report on recent mobile intervention development from proof-of-concept, usability/feasibility, or ongoing research reports.
“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])”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [82] Skevofilakas, M., Mougiakakou, S. G., Zarkogianni, K., et al. (2007). A communication and information technology infrastructure for real-time monitoring and management of type 1 diabetes patients. Conf Proc IEEE Eng Med Biol Soc., 2007, 3685–3686.
Not checked in the free run.
- cites [83] Holtz, B., & Whitten, P. (2009). Managing asthma with mobile phones: A feasibility study. Telemed J E Health., 15, 907–909. source
Not checked in the free run.
- cites [84] Ryan, D., Pinnock, H., Lee, A. J., et al. (2009). The CYMPLA trial. Mobile phone-based structured intervention to achieve asthma control in patients with uncontrolled persistent asthma: A pragmatic randomized controlled trial. Primary Care Respiratory Journal, 18, 343–345. source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningTo ensure that the mobile interventions reviewed were mature enough for actual use, the review was limited to studies that subjected these interventions to some form of clinical outcome assessment.
“To insure that the mobile interventions reviewed were mature enough for actual use, we limited our review to studies that subjected these interventions to some form of clinical outcome assessment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe use of these control technologies in a smoking cessation intervention parallels the closed-loop control system for diabetes management.
“The use of these control technologies in a smoking cessation intervention as shown in Fig. 1 parallels the closed-loop control system for diabetes management described previously.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationnone givenNovel dynamic health behavior theories and their application to mobile health behavior interventions will require empirical validation.
“Novel dynamic health behavior theories and their application to health behavior interventions via mobile technologies will require empirical validation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningThe optimization process leads to specific intervention decisions for providing behavioral strategies and/or medication prompts to replace smoking as a means of reducing smoking urge.
“The optimization process leads to specific intervention decisions (u2(t)) for providing behavioral strategies and/or medication prompts to replace smoking as a means of reducing smoking urge.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningHealth behavior interventions delivered via mobile technologies offer both the impetus to transform current theories into more dynamic feedback control models and the potential to provide intensive longitudinal data necessary to test and improve theoretical intervention models.
“Health behavior interventions delivered via mobile technologies offer not only the impetus to transform our current theories into more dynamic feedback control models but also the potential to provide the intensive longitudinal data necessary to test and improve our theoretical intervention models.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationcitationOther methodological considerations for adaptive interactive technologies applied to eHealth may be appropriate to consider for mHealth interventions.
“Other methodological considerations for adaptive interactive technologies applied to eHealth also may be appropriate to consider for mHealth interventions [107, 108].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [107] O’Grady, L., Witteman, H., Bender, J. L., Urowitz, S., Wiljer, D., & Jaded, A. R. (2009). Measuring the impact of a moving target: Towards a dynamic framework for evaluating collaborative adaptive interactive technologies. Journal of Medical Internet Research, 11, e20. source
Not checked in the free run.
- cites [108] Resnicow, K., Strecher, V., Couper, M., et al. (2010). Methodologic and design issues in patient-centered e-health research. American Journal of Preventive Medicine, 38, 98–102. source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenThe ability to provide frequent, time-intensive interventions in the context of the behavior holds considerable promise but also poses many challenges to current health behavior theories and models.
“The ability to provide frequent, time-intensive interventions in the context of the behavior holds considerable promise but also poses many challenges to our current health behavior theories and models.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAdaptive treatment methodologies have been applied to interventions that adjust over weeks or months, not hours or days, but these methodologies can be applied to mobile technology interventions.
“Adaptive treatment methodologies has been applied to interventions that adjust over the course of weeks or months, not hours or days, but these methodologies can be applied to mobile technology interventions [106].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [106] Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196. source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningA theory-driven iterative model of mobile intervention development holds promise for improving mobile health behavior interventions and theoretical and empirical understanding of health behavior change.
“a theory-driven iterative model of mobile intervention development holds promise for improving not only our mobile health behavior interventions but also our theoretical and empirical understanding of health behavior change.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThese modeling techniques can be applied to reverse-engineer the functional relationship underlying the urge controller.
“These modeling techniques also can be applied to reverse-engineer the functional relationship underlying the urge controller which reflects the actions of an individual smoker.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The absence of a reported theoretical basis in a study indicates that the intervention was not actually based on theory.
“Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not.”
The paper relies on this without support.
Health behavior theories can improve the outcomes of mobile health behavior interventions.
“Mobile health behavior intervention development could benefit from greater application of health behavior theories.”
The paper relies on this without support.
The reviewed studies are representative of mobile health behavior interventions in the four areas covered by the review.
“Nearly all of the articles identified meeting these criteria fell into one of four major health behavior areas: smoking, weight loss (including diet and exercise interventions), treatment adherence (i.e., adherence to medical recommendations including both taking medication and attending appointments), and chronic disease management.”
The paper relies on this without support.
Bluetooth-enabled glucometers eliminate the need for patients to manually enter data.
“With the advent of Bluetooth-enabled glucometers, data entry burden is eliminated.”
The paper relies on this without support.
Text messages can be delivered simply and inexpensively across mobile phone operating platforms and to a large percentage of mobile phone users.
“Text messages can be delivered simply and inexpensively across mobile phone operating platforms and to a large percentage of mobile phone users given the ubiquitous nature of this feature even on “less smart” mobile phones.”
The paper relies on this without support.
Standard treatment algorithms can automate many treatment adjustments with complete treatment fidelity.
“standard treatment algorithms can be used to automate, with 100% treatment fidelity, many of these treatment adjustments”
The paper relies on this without support.
In the 1950s, mobile neighborhood tuberculosis screenings did not substantially increase screenings despite added salience and convenience.
“In the 1950s, mobile neighborhood tuberculosis screenings were innovative, but the added salience and convenience did not substantially increase screenings.”
The paper relies on this without support.
Perceived threat, perceived benefit, and perceived barriers contributed to whether a person obtained tuberculosis screenings.
“the perceived threat (severity and susceptibility) of tuberculosis, the perceived benefit of being screened, and the perceived barriers to getting screened contributed to a person obtaining these screenings.”
The paper relies on this without support.
Control systems engineering principles and procedures are mature and provide a robust computational framework for modeling, simulation, and systematic decision making over time.
“Control systems engineering principles and procedures are mature and provide a robust computational framework for modeling, simulation, and systematic decision making over time.”
The paper relies on this without support.
Feedback regulatory processes are central to neurobiology.
“Feedback regulatory processes are core to neurobiology.”
The paper relies on this without support.
The brain regulates itself and other organs using feedback control processes.
“Since the brain appears to regulate itself and other organs using feedback control processes”
The authors state this as an assumption.
Smoking urge can be modeled as an inventory built up by negative emotional and external stimuli and depleted by smoking and positive affect or intervention components.
“Using a fluid analogy, smoking urge is accumulated as an inventory that is built up by an “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” resulting from smoking events and the salutary effects of positive affect and dosages of intervention components such as medications and”
The authors state this as an assumption.
The inherent dynamics of smoking behavior form a closed-loop feedback system.
“The inherent dynamics of smoking behavior in Fig. 1 constitutes a closed-loop feedback system”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mobile technology interventions informed by strategies, such as health behavior theory, hold the potential for more comprehensive mechanisms of behavior change, fostering a health care approach that is both impactful and sustainable.
“Furthermore, mobile technology interventions informed by strategies, such as health behavior theory, hold the potential for more comprehensive mechanisms of behavior change, fostering a health care approach that is both impactful and sustainable [27].”
From Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis (2025) · cites this paper as [27]
The ‘Theoretical Basis for Mobile Health Behavior Interventions and New Directions’ section says that theory-guided mobile interventions should address behavior-change mechanisms more comprehensively and become more effective. The ‘Summary of Mobile Health Behavior Intervention Features’ also discusses potential scalability and cost savings, but the paper does not establish that this approach to health care is sustainable. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA growing number of investigators have recognized that current theoretical models are inadequate to inform the development of BITs that can increasingly interact dynamically and adaptively with users.
“However, a growing number of investigators have recognized that current theoretical models are inadequate to inform the development of BITs that can increasingly interact dynamically and adaptively with users (77).”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [77]
Behavioral theories have classically been how behavioral sciences describe how interventions, individuals, and context interact but are fraught with well-documented problems such as lack of falsification, lack of information about subtle dynamics, and being so abstract that they are difficult to apply in practice.
“Behavioral theories have classically been how behavioral sciences describe how interventions, individuals, and context interact but these are fraught with well-documented problems such as lack of falsification, lack of information about subtle dynamics, and they are often so abstract that they are difficult to apply in practice [36, 37].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [37]
Mobile phone-delivered interventions facilitate frequent engagement and allow users to apply promoted skills and behaviors in real time to real-life experiences.
“Interventions delivered via mobile phones facilitate frequent engagement as users go about their daily lives [12,13] and afford users the opportunity to apply the skills and behaviors promoted by the intervention directly in real time to their real life experiences [14].”
From Feasibility of "CopeSmart": A Telemental Health App for Adolescents (2015) · cites this paper as [13]
CBT is suitable for implementation in digital health interventions.
“Moreover, and with regard to its structure, it is suitable for implementation in digital health interventions [37-40].”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [40]
Existing psychological models offer little information on how to design and implement BITs to ensure that they are useful and usable.
“While these models are useful in describing the types of behavioral and cognitive changes required of users in their environments to achieve clinical targets, they offer little information on how to design and implement BITs to ensure that they are useful and usable [1].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [1]
Existing psychological models, such as the Theory of Planned Behavior or Social Cognitive Theory, are useful in describing the types of behavioral and cognitive changes required of users in their environments to achieve clinical targets.
“While these models are useful in describing the types of behavioral and cognitive changes required of users in their environments to achieve clinical targets, they offer little information on how to design and implement BITs to ensure that they are useful and usable [1].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [1]
A growing number of investigators have commented on the lack of models informing the design of behavioral intervention technologies (BITs).
“A growing number of investigators have commented on the lack of models informing the design of behavioral intervention technologies (BITs) [1].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [1]
Such a framework would help avoid the all-too-common process of developing BITs that ignore psychological or engineering principles or that rely entirely on developer intuition.
“This would help avoid the all-too-common process of developing BITs that ignore psychological or engineering principles or that rely entirely on developer intuition [1,3].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [1]
Much of the development of BITs to date has been informed primarily through the application of behavioral and psychological theories and by developer intuition.
“Much of the development of BITs to date has been informed primarily through the application of behavioral and psychological theories and by developer intuition [1].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [1]
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Mobile app rating scale: a new tool for assessing the quality of health mobile apps
Stoyanov, S. R., Hides, L., Kavanagh, D. J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3422 · Cited by 2,233 · see citing works
“The use of mobile apps for health and well being promotion has grown exponentially in recent years [1].”
BackgroundNahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
“sary to develop efficacious JITAIs and decide whether a JITAI is warranted in a particular setting [10].”
BackgroundLeventhal, H., Phillips, L. A., & Burns, E. (2016) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9782-2 · Cited by 1,350 · see citing works
Mobile Health Technology Evaluation
Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., et al. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.03.017 · Cited by 900 · see citing works
“This is a multi-step process that: (1) defines the classification problem; (2) divides the annotated data set into training and validation data sets; (3) decides what machine-learning classifiers will be tested (e.”
BackgroundSelf-citationMohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2013.03.008 · Cited by 693 · see citing works
“users [77].”
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Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015) · Health Psychology · doi:10.1037/hea0000305 · Cited by 671 · see citing works
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“Furthermore, many smartphone apps are not based on behavioural change theories or guidelines (where these could have made a significant, positive difference, if implemented) [30,44].”
BackgroundEmpirical Studies on Usability of mHealth Apps: A Systematic Literature Review
Zapata, B. C., Fernández-Alemán, J. L., Idri, A., & Toval, A. (2015) · Journal of Medical Systems · doi:10.1007/s10916-014-0182-2 · Cited by 585 · see citing works
“Furthermore, mobile technologies have a high adaptability that allows them to be practiced in many different fields [58, 59].”
BackgroundMapping mHealth research: a decade of evolution
Fiordelli, M., Diviani, N., & Schulz, P. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2430 · Cited by 570 · see citing works
“6% of adults worldwide own a mobile phone [2,10], making it the most equitable communication technology [1].”
BackgroundMethodInfluentialNahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015) · Health Psychology · doi:10.1037/hea0000306 · Cited by 568 · see citing works
“Advances in mobile technology offer many opportunities for delivering interventions that accommodate an individual’s immediate needs (Riley et al., 2011).”
BackgroundMohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3077 · Cited by 510 · see citing works
“While these models are useful in describing the types of behavioral and cognitive changes required of users in their environments to achieve clinical targets, they offer little information on how to design and implement BITs to ensure that they are useful and usable [1].”
BackgroundInfluentialCan smartphone apps increase physical activity? Systematic review and meta-analysis
Romeo, A., Edney, S., Plotnikoff, R., Curtis, R., Ryan, J., Sanders, I., Crozier, A., & Maher, C. (2019) · Journal of Medical Internet Research · doi:10.2196/12053 · Cited by 487 · see citing works
“They are equipped with advanced technological features, most notably their connection to the internet, global positioning system, and inbuilt accelerometers [9], and offer the capacity to create individualized and interactive apps that collect real-time data [10].”
Apps to promote physical activity among adults: a review and content analysis
Middelweerd, A., Mollee, J. S., van der Wal, C. N., Brug, J., & te Velde, S. J. (2014) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-014-0097-9 · Cited by 546 · see citing works
“The application of smartphones and apps in health behavior interventions are growing rapidly, however little has been published about the interventions using the new technology to provide real-time feedback [25].”
BackgroundBehavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity
Conroy, D. E., Yang, C., & Maher, J. P. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.01.010 · Cited by 466 · see citing works
Increasing Physical Activity With Mobile Devices: A Meta-Analysis
Fanning, J., Mullen, S. P., & McAuley, E. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2171 · Cited by 457 · see citing works
“It is clear that some of the unique qualities of these devices would be attractive features for physical activity interventions allowing scientists to: collect objective and self-report measures of activity in real time; provide feedback and support at the point of decision; provide interactive, immersive, and individ…”
BackgroundUsing smartphones and health apps to change and manage health behaviors: a population-based survey
Ernsting, C., Dombrowski, S. U., Oedekoven, M., O´Sullivan, J. L., Kanzler, M., Kuhlmey, A., & Gellert, P. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6838 · Cited by 400 · see citing works
“A systematic review of mobile health interventions based primarily on randomized controlled trials by Riley et al [2], who investigated the effectiveness and theoretical background of mobile interventions such as apps for smartphones or tablets.”
BackgroundMethodInfluentialMummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D., & Sutton, S. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5927 · Cited by 388 · see citing works
“Researchers may draw from behavioral theories, such as social cognitive theory [52], which are frequently used in academia to guide mobile health interventions [2].”
BackgroundEffectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews
Kitsiou, S., Paré, G., Jaana, M., & Gerber, B. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173160 · Cited by 390 · see citing works
Howells, A., Ivtzan, I., & Eiroa-Orosa, F. J. (2016) · Journal of Happiness Studies · doi:10.1007/s10902-014-9589-1 · Cited by 407 · see citing works
“Acknowledging this distinction, Riley et al. (2011) argue that mobile interventions require their own guiding theory that captures their dynamic nature and that which sets them apart from other forms of intervention.”
BackgroundEvaluating mobile phone applications for health behaviour change: A systematic review
McKay, F. H., Cheng, C. C., Wright, A., Shill, J., Stephens, H., & Uccellini, M. (2016) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x16673538 · Cited by 367 · see citing works
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333 · see citing works
“Moreover, and with regard to its structure, it is suitable for implementation in digital health interventions [37-40].”
BackgroundMobile Applications for Weight Management
Azar, K. M. J., Lesser, L. I., Laing, B. Y., Stephens, J., Aurora, M. S., Burke, L. E., & Palaniappan, L. P. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.07.005 · Cited by 334 · see citing works
Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review
Harst, L., Lantzsch, H., & Scheibe, M. (2019) · Journal of Medical Internet Research · doi:10.2196/13117 · Cited by 320 · see citing works
“Such theories should, according to the authors, include features and attributes of the technology as well as characteristics of the end users [15].”
BackgroundDireito, A., Dale, L. P., Shields, E., Dobson, R., Whittaker, R., & Maddison, R. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-646 · Cited by 308 · see citing works
“While theoretically grounded mHealth behaviour interventions increase the likelihood of achieving behaviour change, it has been suggested that current theories are inadequate to guide mHealth interventions, which need to be more interactive and dynamic [16].”
BackgroundKaner, E. F., Beyer, F. R., Garnett, C., Crane, D., Brown, J., Muirhead, C., Redmore, J., O'Donnell, A., Newham, J. J., de Vocht, F., et al. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011479.pub2 · Cited by 308 · see citing works
Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research
Hekler, E. B., Klasnja, P., Froehlich, J. E., & Buman, M. P. (2013) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466452 · Cited by 328 · see citing works
“, see [61] for a discussion about this).”
BackgroundBig data analytics for preventive medicine
Razzak, I., Imran, M. A., & Xu, G. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04095-y · Cited by 332 · see citing works
“Moreover, the rapid growth of cellular networks provides opportunity to be in touch with patient [13].”
BackgroundVillinger, K., Wahl, D. R., Boeing, H., Schupp, H. T., & Renner, B. (2019) · Obesity Reviews · doi:10.1111/obr.12903 · Cited by 290 · see citing works
“persuasive technology might be essential ingredients of effective focussed mobile interventions.(12,13,121,122,135,136) Hence, while mobile phones are a promising platform for accessible and cost‐effective interventions, further development and innovation are required to”
BackgroundClawson, J., Pater, J. A., Miller, A., Mynatt, E. D., & Mamykina, L. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807554 · Cited by 312 · see citing works
There’s an App for That: Content Analysis of Paid Health and Fitness Apps
West, J. H., Hall, P. C., Hanson, C. L., Barnes, M. D., Giraud-Carrier, C., & Barrett, J. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1977 · Cited by 314 · see citing works
“There is growing interest and inquiry in understanding how mobile devices may influence health behavior [10].”
Heron, K. E., Everhart, R. S., McHale, S. M., & Smyth, J. M. (2017) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsx078 · Cited by 283 · see citing works
“As has been discussed in the EMA literature more broadly, existing psychological and health behavior theories are not always well-suited for EMA approaches (Riley et al., 2011).”
BackgroundSawesi, S., Rashrash, M., Phalakornkule, K., Carpenter, J. S., & Jones, J. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.4514 · Cited by 253 · see citing works
“This finding supports the results of a previous study showing the majority of mobile-based interventions used for improving medication adherence and disease management were developed without a theoretical basis [202].”
ResultClinical Child and Family Psychology Review
(2013) · Clinical Child and Family Psychology Review · doi:10.1007/10567.1573-2827 · Cited by 267 · see citing works
Arora, S., Peters, A. L., Burner, E., Lam, C. N., & Menchine, M. (2014) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2013.10.012 · Cited by 273 · see citing works
Mobile App-Based Health Promotion Programs: A Systematic Review of the Literature
Lee, M., Lee, H., Kim, Y., Kim, J.-H., Cho, M., Jang, J., & Jang, H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15122838 · Cited by 237 · see citing works
Anxiety: There is an app for that. A systematic review of anxiety apps: SUCALA et al.
Sucala, M., Cuijpers, P., Muench, F., Cardoș, R., Soflau, R., Dobrean, A., Achimas-Cadariu, P., & David, D. (2017) · Depression and Anxiety · doi:10.1002/da.22654 · Cited by 232 · see citing works
“that one of the advantages of using mobile technologies in behavioral health is that their ability to closelymonitor symptoms canhelp expand our current theoretical models beyond the between-person differences towithin-person changes over time (Riley et al., 2011).”
BackgroundAnnual Research Review: Ecological momentary assessment studies in child psychology and psychiatry
Russell, M. A., & Gajos, J. M. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13204 · Cited by 236 · see citing works
“A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript present (Riley et al., 2011).”
BackgroundPreventive Health Behavior Change Text Message Interventions: A Meta-analysis
Armanasco, A. A., Miller, Y. D., Fjeldsoe, B. S., & Marshall, A. L. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.10.042 · Cited by 210 · see citing works
Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management
Piette, J. D., List, J. M., Rana, G., Townsend, W. A., Striplin, D., & Heisler, M. (2015) · Circulation · doi:10.1161/circulationaha.114.008723 · Cited by 223 · see citing works
Digital Behaviour Change Interventions to Break and Form Habits
Pinder, C., Vermeulen, J., Cowan, B. R., & Beale, R. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3196830 · Cited by 235 · see citing works
“This limits their application to DBCIs that can adapt rapidly to their users and to changing inputs [Riley et al. 2011].”
BackgroundInfluentialAgile science: creating useful products for behavior change in the real world
Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., & Martin, C. A. (2016) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0395-7 · Cited by 208 · see citing works
“Behavioral theories have classically been how behavioral sciences describe how interventions, individuals, and context interact but these are fraught with well-documented problems such as lack of falsification, lack of information about subtle dynamics, and they are often so abstract that they are difficult to apply i…”
BackgroundSelf-citationPark, L. G., Howie‐Esquivel, J., Chung, M. L., & Dracup, K. (2013) · Patient Education and Counseling · doi:10.1016/j.pec.2013.10.027 · Cited by 213 · see citing works
“Finally, more advanced health behavior theories and models that account for the interactive and adaptive nature of mHealth interventions will need to be developed and tested [43].”
BackgroundDahlke, D. V., Fair, K. N., Hong, Y. A., Beaudoin, C. E., Pulczinski, J. C., & Ory, M. G. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3861 · Cited by 188 · see citing works
“This study’s findings are consistent with prior research that asserts that mHealth interventions could benefit from increased use of behavior and communications theories in their design [10-16, 20, 21 ].”
ResultSpruijt‐Metz, D., Hekler, E. B., Saranummi, N., Intille, S. S., Korhonen, I., Nilsen, W., Rivera, D. E., Spring, B. J., Michie, S., Asch, D. A., et al. (2015) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0324-1 · Cited by 210 · see citing works
Self-citationPilot evaluation of the text4baby mobile health program
Evans, W. D., Wallace, J. L., & Snider, J. W. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-1031 · Cited by 207 · see citing works
“; licensee BioMed Central L Commons Attribution License (http://creativec reproduction in any medium, provided the or widespread adoption and rapid advancement in the capacities of smartphones (mobile phones with an operating system capable of running applications), more technically advanced interventions, including m…”
BackgroundBeatty, A. L., Fukuoka, Y., & Whooley, M. A. (2013) · Journal of the American Heart Association · doi:10.1161/jaha.113.000568 · Cited by 200 · see citing works
“Mobile health provides the opportunity to improve access to health promotion interventions and has the unique advantage of being able to influence health behaviors in real-time.(11) Of smartphone users, 86% have used their mobile phone to access just-in-time information in the past month.”
BackgroundMomentary assessment of affect, physical feeling states, and physical activity in children.
Dunton, G. F., Huh, J., Leventhal, A. M., Riggs, N. R., Hedeker, D., Spruijt‐Metz, D., & Pentz, M. A. (2013) · Health Psychology · doi:10.1037/a0032640 · Cited by 202 · see citing works
“The explanatory value of theoretical health behavior models could be enhanced by taking into account multilevel interactions between enduring person-level factors and moment-tomoment level fluctuations in affective and physical feeling states to predict variability in the performance of a health behavior across the da…”
BackgroundTaj, F., Klein, M. C. A., & van Halteren, A. (2019) · JMIR mhealth and uhealth · doi:10.2196/13311 · Cited by 170 · see citing works
“Reference Title 45 [37] Health behavior models in the age of mobile interventions: are our theories up to the task?”
BackgroundBuckingham, S. A., Williams, A. J., Morrissey, K., Price, L., & Harrison, J. (2019) · Digital Health · doi:10.1177/2055207619839883 · Cited by 182 · see citing works
“It has been suggested that new behaviour change models and theories may be needed to account for the interactive, dynamic and adaptive nature of mHealth interventions.(86) Long-term impact and acceptability of mHealth technology is still unclear.”
BackgroundChen, M., & Lin, N. (2018) · Internet Research · doi:10.1108/intr-03-2017-0099 · Cited by 200 · see citing works
Lentferink, A., Oldenhuis, H. K. E., de Groot, M., Polstra, L., Velthuijsen, H., & van Gemert‐Pijnen, J. E. W. C. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7288 · Cited by 162 · see citing works
“As advocated by Riley et al [88], applying traditional health behavior change models might not be the best fit for healthy lifestyle interventions via technology due to their interactive and adaptive character.”
mHealth approaches to child obesity prevention: successes, unique challenges, and next directions
Tate, E. B., Spruijt‐Metz, D., O’Reilly, G. A., Jordan‐Marsh, M., Gotsis, M., Pentz, M. A., & Dunton, G. F. (2013) · Translational Behavioral Medicine · doi:10.1007/s13142-013-0222-3 · Cited by 172 · see citing works
“mHealth tools have been successfully used as data collection devices, assessment tools, platforms for delivering interventions, devices for self-monitoring of behavior, and real-time surveillance techniques (see [11, 13–21]).”
MethodFu, H., McMahon, S. K., Gross, C. R., Adam, T. J., & Wyman, J. F. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.06.016 · Cited by 173 · see citing works
“A recent review of health apps showed that most health apps are not consistently grounded in a theoretical basis since their designs were mostly information input or output functions [51].”
BackgroundDynamic Models of Behavior for Just-in-Time Adaptive Interventions
Spruijt‐Metz, D., & Nilsen, W. (2014) · IEEE Pervasive Computing · doi:10.1109/mprv.2014.46 · Cited by 188 · see citing works
Self-citationBuller, D. B., Borland, R., Bettinghaus, E. P., Shane, J. H., & Zimmerman, D. E. (2014) · Telemedicine and e-Health · doi:10.1089/tmj.2013.0169 · Cited by 178 · see citing works
Issues in mHealth: Findings From Key Informant Interviews
Whittaker, R. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1989 · Cited by 173 · see citing works
Advancing Models and Theories for Digital Behavior Change Interventions
Hekler, E. B., Michie, S., Pavel, M., Rivera, D. E., Collins, L. M., Jimison, H. B., Garnett, C., Parral, S., & Spruijt-Metz, D. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.013 · Cited by 174 · see citing works
“Any given context can be specified as a point in that space; and (iii) suggest cross-79 disciplinary methods to facilitate advancing the concept of multidimensional generalization 80 space for DBCIs.”
BackgroundInfluentialSelf-citationDeveloping and Pretesting a Text Messaging Program for Health Behavior Change: Recommended Steps
Abroms, L. C., Whittaker, R., Free, C., Alstyne, J. M. V., & Schindler-Ruwisch, J. M. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4917 · Cited by 155 · see citing works
“Some theoretical constructs may be especially well-suited to mobile communication, such as the Health Belief Model’s construct of cue to action [27] and the use of implementation intentions [28], which prompt the setting of goals around if-then statements (ie, “If I finish work, then I will head to the gym for a worko…”
Jacob, C., Sezgın, E., Sánchez-Vázquez, A., & Ivory, C. J. (2022) · JMIR mhealth and uhealth · doi:10.2196/36284 · Cited by 172 · see citing works
Orr, J. A., & King, R. J. (2015) · Health Psychology Review · doi:10.1080/17437199.2015.1022847 · Cited by 174 · see citing works
“This may be because bidirectional communication establishes a dynamic feedback loop that facilitates ongoing and evolving sharing of information that can strengthen the likelihood of health behaviour change (Riley et al., 2011).”
BackgroundAn Adaptive Physical Activity Intervention for Overweight Adults: A Randomized Controlled Trial
Adams, M. A., Sallis, J. F., Norman, G. J., Hovell, M. F., Hekler, E. B., & Perata, E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082901 · Cited by 167 · see citing works
“Few behavioral interventions have approached lifestyle change engineering from this theoretical perspective [35].”
BackgroundSingle-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions
Dallery, J., Cassidy, R. N., & Raiff, B. R. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2227 · Cited by 168 · see citing works
“Indeed, we know very little about theory-derived mechanisms by which these interventions affect health-related behavior [83].”
BackgroundSlater, H., Campbell, J. M., Stinson, J. N., Burley, M. M., & Briggs, A. M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8888 · Cited by 155 · see citing works
“Although the use of mHealth technologies, including mobile apps, to support self-management of NCDs has also grown substantially [10], the evaluation of their quality, safety, and outcomes indicate that significant evidence-practice and policy-practice gaps remain [1,11,12].”
Behavior Change Techniques in Popular Alcohol Reduction Apps
Crane, D., Garnett, C., Brown, J., West, R., & Michie, S. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4060 · Cited by 177 · see citing works
“Mobile phone apps for mobile health (mHealth) enable disease monitoring, management, and education; improve health behavior assessment; and facilitate more frequent user-to-user or practitioner-to-user communication [6-8].”
BackgroundPark, L. G., Howie‐Esquivel, J., & Dracup, K. (2014) · Journal of Advanced Nursing · doi:10.1111/jan.12400 · Cited by 166 · see citing works
“Other health behavioural theories may be developed that take into account the time-intensive, interactive and adaptive nature of mHealth interventions that demand more intra-individual dynamic regulatory processes (Riley et al. 2011).”
BackgroundHilliard, M. E., Hahn, A. L., Ridge, A. K., Eakin, M. N., & Riekert, K. A. (2014) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3599 · Cited by 140 · see citing works
“To ensure that the app features are integrated in a way that is consistent with behavioral theory and evidence [47], psychologists and behavioral scientists should be consulted [20]; additionally, they could advise on research design and evaluation.”
BackgroundEcological momentary interventions for depression and anxiety
Schueller, S. M., Aguilera, A., & Mohr, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22649 · Cited by 168 · see citing works
“Furthermore, behavior change theories, guided by findings from RCTs, are insufficient to guide such decisions, as most do not account for highly interactive interventions, high levels of intra-individual tailoring, and high frequency longitudinal data (Riley et al., 2011).”
BackgroundBae, S. W., Chung, T. A., Ferreira, D., Dey, A. K., & Suffoletto, B. P. (2017) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.11.039 · Cited by 151 · see citing works
Curtis, K., Lahiri, S., & Brown, K. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3857 · Cited by 144 · see citing works
“Certain BCTs, such as self-monitoring which have been shown to be effective for adult and childhood weight management [15-17], are optimized through this medium and continue to increase in their sophistication [7].”
Kerr, D. A., Harray, A. J., Pollard, C. M., Dhaliwal, S. S., Delp, E. J., Howat, P. A., Pickering, M. R., Ahmad, Z., Meng, X., Pratt, I., et al. (2016) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-016-0376-8 · Cited by 147 · see citing works
“A criticism of the design of technology-based behavioural interventions is the lack of behaviour change models to inform them [43, 44].”
BackgroundText messaging interventions for adolescent and young adult substance use: a meta-analysis
Mason, M., Ola, B., Zaharakis, N., & Zhang, J. (2015) · Prevention Science · doi:10.1007/s11121-014-0498-7 · Cited by 160 · see citing works
Cole-Lewis, H., Ezeanochie, N., & Turgiss, J. (2019) · JMIR Formative Research · doi:10.2196/14052 · Cited by 155 · see citing works
Kaplan, R. M., & Stone, A. A. (2013) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143736 · Cited by 144 · see citing works
Moller, A. C., Merchant, G., Conroy, D. E., West, R., Hekler, E., Kugler, K. C., & Michie, S. (2017) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9818-7 · Cited by 151 · see citing works
“have been developed on the basis of group differences and cross-sectional designs rather than on the basis of change within individuals (Davies et al., 2014; Michie et al., 2014; Riley et al., 2011).”
BackgroundAdopting the sensemaking perspective for chronic disease self-management
Mamykina, L., Smaldone, A. M., & Henry, S. B. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.06.006 · Cited by 140 · see citing works
“Over the years, researchers in biomedical informatics adopted a number of theories of health behaviors to the design of informatics interventions for health in wellness [17].”
BackgroundInfluentialTutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions
Hekler, E. B., Rivera, D. E., Martín, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E. V., Klasnja, P., Adams, M. A., & Buman, M. P. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8622 · Cited by 137 · see citing works
“As articulated elsewhere, advancements in digital technologies are rapidly converging to enable a new era in the understanding of human behavior [13,18,138].”
BackgroundSelf-citationText Messaging to Motivate Walking in Older African Americans
Kim, B. H., & Glanz, K. (2012) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2012.09.050 · Cited by 138 · see citing works
Review of Smartphone Applications for the Treatment of Eating Disorders
Juarascio, A. S., Manasse, S. M., Goldstein, S. P., Forman, E. M., & Butryn, M. L. (2015) · European Eating Disorders Review · doi:10.1002/erv.2327 · Cited by 143 · see citing works
“There is a call within the field of health behavior change for developers to begin utilizing varying methods of both input and output functionalities to personalize therapeutic apps and increase the likelihood of behavior change (Riley et al., 2011).”
Perski, O., Blandford, A., Ubhi, H. K., West, R., & Michie, S. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0422-8 · Cited by 123 · see citing works
“A frequently mentioned justification for using smartphone apps to deliver complex behaviour change interventions is that these are capable of delivering support as and when required, or “just-in-time” [5, 6].”
BackgroundSmartphones in the secondary prevention of cardiovascular disease: a systematic review.
Hamilton, S. J., Mills, B., Birch, E. M., & Thompson, S. C. (2018) · BMC Cardiovascular Disorders · doi:10.1186/s12872-018-0764-x · Cited by 128 · see citing works
μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch
Intille, S., Haynes, C., Maniar, D., Ponnada, A., & Manjourides, J. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971717 · Cited by 130 · see citing works
“Understanding the dynamics of behavior throughout weeks, days, and even minutes may be critical to developing better ubicomp technologies, advancing the science of behavior change, and developing new “just-in-time” interventions that take advantage of moment-to-moment tailoring [31, 40, 43].”
BackgroundMobile Phone Interventions for the Secondary Prevention of Cardiovascular Disease
Park, L. G., Beatty, A. L., Stafford, Z., & Whooley, M. (2016) · Progress in cardiovascular diseases · doi:10.1016/j.pcad.2016.03.002 · Cited by 126 · see citing works
Jacob, C., Sánchez-Vázquez, A., & Ivory, C. J. (2020) · JMIR mhealth and uhealth · doi:10.2196/18072 · Cited by 111 · see citing works
“a move to multidimensional theories that go beyond usability and consider the surrounding contexts and implementation issues [127,128,131-134].”
BackgroundAllison, S. M., Bauermeister, J. A., Bull, S. S., Lightfoot, M. A., Mustanski, B. S., Shegog, R., & Levine, D. (2012) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2012.06.012 · Cited by 127 · see citing works
Self-citationPromoting Father Involvement for Child and Family Health
Allport, B. S., Johnson, S. B., Aqil, A. R., Labrique, A. B., Nelson, T. J., Angela, K. C., Carabas, Y., & Marcell, A. V. (2018) · Academic Pediatrics · doi:10.1016/j.acap.2018.03.011 · Cited by 151 · see citing works
Developing Embodied Conversational Agents for Coaching People in a Healthy Lifestyle: Scoping Review
Kramer, L. L., ter Stal, S., Mulder, B. C., de Vet, E., & van Velsen, L. (2020) · Journal of Medical Internet Research · doi:10.2196/14058 · Cited by 121 · see citing works
Domin, A., Spruijt‐Metz, D., Theisen, D., Ouzzahra, Y., & Vögele, C. (2021) · JMIR mhealth and uhealth · doi:10.2196/24308 · Cited by 118 · see citing works
“A data extraction form was developed specifically for this review and served as a basis for Tables S1-S10 presented in Multimedia Appendix 1 [2,22,24-30,35-37,46-181].”
BackgroundMethodYeager, C. M., & Benight, C. C. (2018) · mHealth · doi:10.21037/mhealth.2018.08.04 · Cited by 115 · see citing works
“In general, models that focus primarily on the relationship between engagement and clinical outcomes fail to consider the dynamic, multi-dimensional aspects of engagement (77).”
BackgroundLeon, N., Surender, R., Bobrow, K., Muller, J., & Farmer, A. (2015) · BMC Family Practice · doi:10.1186/s12875-015-0289-7 · Cited by 126 · see citing works
Baumel, A., & Yom-Tov, E. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/ibx037 · Cited by 128 · see citing works
Harnessing the power of technology for the treatment and prevention of eating disorders
Bauer, S., & Moessner, M. (2013) · International Journal of Eating Disorders · doi:10.1002/eat.22109 · Cited by 128 · see citing works
Designing the Future of Children’s Mental Health Services
Lyon, A. R., Dopp, A. R., Brewer, S. K., Kientz, J. A., & Munson, S. A. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01038-x · Cited by 117 · see citing works
Danaher, B. G., Brendryen, H., Seeley, J. R., Tyler, M. S., & Woolley, T. (2015) · Internet Interventions · doi:10.1016/j.invent.2015.01.002 · Cited by 102 · see citing works
“First, intervention content can change based on data obtained during the course of the intervention, as in delivery of textmessagesthatarerelevanttoaparticipant'srecentsuccess/problems in managing eating (Intille et al., 2003) or quitting tobacco (Riley et al., 2011; Ybarra et al., 2012).”
BackgroundMethodInfluentialGiunti, G., Kool, J., Rivera-Romero, O., & Zubiete, E. D. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8996 · Cited by 107 · see citing works
“The use of mobile software apps for health and well-being promotion has grown in recent years [13,14].”
BackgroundSystematic review and meta-analysis of internet interventions for smoking cessation among adults
Graham, A., Carpenter, K., Cha, S., Cole, S., Jacobs, M., Raskob, M., & Cole-Lewis, H. (2016) · Substance Abuse and Rehabilitation · doi:10.2147/sar.s101660 · Cited by 114 · see citing works
“For the purposes of this review, we exclude mHealth interventions, such as text messaging or interventions delivered solely through a mobile device.(33) mHealth interventions were excluded in order to decrease heterogeneity in the interventions assessed in this review and to be consistent with previous reviews of Inte…”
Lu, C., Hu, Y., Xie, J., Fu, Q. J., Leigh, I., Governor, S., & Wang, G. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9145 · Cited by 114 · see citing works
“This will decrease the digital gap in the Chinese population [32].”
BackgroundA biopsy of Breast Cancer mobile applications: state of the practice review
Giunti, G., Giunta, D. H., Fernández, E., Bender, J. L., & Fernández-Luque, L. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.10.022 · Cited by 98 · see citing works
Zhou, W., Chen, M., Yuan, J., & Sun, Y. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.03.018 · Cited by 107 · see citing works
Jones, D. J., Forehand, R., Cuellar, J., Parent, J. M., Honeycutt, A., Khavjou, O., Gonzalez, M., Anton, M. T., & Newey, G. A. (2013) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2013.822308 · Cited by 114 · see citing works
“Fourth, although the integration of technology into services research has proceeded largely without theory (see Riley et al., 2011; Ritterband, Thorndike, Cox, Kovatchev, & Gonder-Frederick, 2009, for reviews), our aims and hypotheses were firmly grounded in a theoretical framework.”
Mummah, S. A., Robinson, T. N., Mathur, M. B., Farzinkhou, S., Sutton, S. R., & Gardner, C. D. (2017) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-017-0563-2 · Cited by 103 · see citing works
- Authors
- Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., & Mermelstein, R.
- Affiliations
- National Institutes of Health (US)
- Arizona State University (US)
- University of Illinois Chicago (US)
- Corresponding author
- Riley, William T
- ORCID
- Venue
- Translational Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-02-24
- Volume
- 1
- Issue
- 1
- Pages
- 53-71
- ISSN
- 1613-9860 (ISSN-L)
- 1869-6716
- PMC
- PMC3142960
- OpenAlex
- W2086310388
- Cited by
- 978
- References
- 131
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 39 · 2023: 43 · 2022: 48 · 2021: 82 · 2020: 74 · 2019: 97 · 2018: 98 · 2017: 109 · 2016: 98 · 2015: 75 · 2014: 76 · 2013: 61 · 2012: 31
- FWCI
- 9.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Translational Behavioral Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Drug Abuse (R21DA024266, K25DA021173)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health intervention, health behavior theory, just-in-time adaptive interventions, interactive intervention, smoking cessation, weight loss, medication adherence, disease management, control engineering
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