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

In the paper

“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.”

What the sources say

• Not checked yetcites [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
• Disputed

  Split into 2 atoms — 0 confirmed, 0 partially supported, 2 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Checked by a reasoning model — may be wrong.

  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. sourceExperimental — accuracy not yet measured

In the paper

“Other methodological considerations for adaptive interactive technologies applied to eHealth also may be appropriate to consider for mHealth interventions [107, 108].”

What the sources say

• Supported

  The Results describe flexible, iterative, user- and context-focused approaches to evaluating adaptive, interactive eHealth technologies. The Discussion’s “Technology” section explicitly includes mobile phones among the devices to consider when evaluating platform portability. Together, these passages support the cautious suggestion that such considerations may be relevant to mHealth interventions; the paper does not claim to test their application to mHealth. (LLM, conf=0.88)

  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
• Supported

  The Introduction describes methodological approaches for studying how, why, and for whom e-health interventions work, and explicitly includes interventions delivered by PDAs and cell phones. The Conclusion recommends applying these design and analytic approaches to future e-health research. This supports the cautious suggestion that the considerations may also be relevant to mHealth interventions. (LLM, conf=0.90)

  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

In the paper

“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)).”

In the paper

“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].”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“Of these, eight targeted medical appointment adherence and two addressed medication adherence, specifically HIV treatment adherence.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“It is important to note, however, that only two studies used randomized controls [58, 59]; the others used historical or nonrandomized controls.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“Although video input has been primarily used for telemedicine purposes [78, 79], health behavior researchers have begun to use cameras for dietary assessment [48].”

What the sources say

• Not checked yetcites [78] Chung, P., Yu, T., & Scheinfeld, N. (2007). Using cellphones for teledermatology, a preliminary study. Dermatology Online Journal, 13, 2. source
• Not checked yetcites [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 yetcites [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

In the paper

“In addition to voice, text, and data inputs, researchers have begun to use video input via camera features on these phones.”

In the paper

“Outcomes from the mobile disease management studies were generally positive and statistically significant.”

In the paper

“Much of the mobile health behavior interventions to date have focused on transmitting data from glucometers and spirometers”

In the paper

“A few studies have used other output functions such as audio (e.g., [35]) or video (e.g. [26])”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“Most of the mobile interventions described in this review used text messaging (aka SMS) functionality of mobile phones.”

In the paper

“As with input, most of the output delivery to date has been via text messaging.”

In the paper

“Following input, some applications provided tailored intervention responses, but others failed to deliver even a confirmation message”

In the paper

“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”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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 yetcites [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

In the paper

“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.”

In the paper

“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].”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

In the paper

“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.”

In the paper

“To date, only a subset of mobile health behavior interventions have been theory-based”

In the paper

“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.”

In the paper

“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].”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

“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].”

What the sources say

• Not checked yetcites [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.

In the paper

“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].”

What the sources say

• Not checked yetcites [6] Pew Internet &American Life Project. (2010). http://www.pewInternet.org/~/media//Files/Reports/2010/PIP_Mobile_Access_2010.pdf
• Not checked yetcites [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

In the paper

“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].”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“Although the automatic transmission of testing data eliminates data entry burden, it does not eliminate the responsibility of the patient to perform regular testing”

In the paper

“an expanding variety of sensor technologies can be used to assess physiological states, movement, location, and other variables using mobile phones.”

In the paper

“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.”

In the paper

“Boorsboom and colleagues have argued that between-person theories do not imply, test, or support causal accounts valid at the individual level [87].”

What the sources say

• Not checked yetcites [87] Boorsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 202–219. source

In the paper

“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.”

In the paper

“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”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“Optimal feedback control systems have been applied to basic sensorimotor control [97] and to reinforcement learning models [98].”

What the sources say

• Not checked yetcites [97] Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience, 7, 907–915. source
• Not checked yetcites [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

In the paper

“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].”

What the sources say

• Not checked yetcites [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

In the paper

“these feedback control systems have the potential to reshape health behavior theories and improve our understanding of human behavior.”

In the paper

“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.”

In the paper

“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].”

What the sources say

• Not checked yetcites [14] Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. source

In the paper

“Some have argued that these theories are inadequate even for traditional interventions [17]”

What the sources say

• Not checked yetcites [17] Ogden, J. (2003). Some problems with social cognition models: A pragmatic and conceptual analysis. Health Psychology, 22, 424–428. source

In the paper

“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).”

In the paper

“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])”

What the sources say

• Not checked yetcites [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 yetcites [83] Holtz, B., & Whitten, P. (2009). Managing asthma with mobile phones: A feasibility study. Telemed J E Health., 15, 907–909. source
• Not checked yetcites [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

In the paper

“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.”

In the paper

“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.”

In the paper

“Novel dynamic health behavior theories and their application to health behavior interventions via mobile technologies will require empirical validation.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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].”

What the sources say

• Not checked yetcites [106] Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196. source

In the paper

“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.”

In the paper

“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.”