. 2021 Jan 27;23(2):133–145. doi: 10.1089/dia.2020.0383

Table 2.

Effectiveness of eHealth Interventions: Fact Sheet

Overall, the effectiveness of eHealth programs is moderate, but there is considerable variability across studies.^{8,17,28–33,36,38,39,62}

Program characteristics related to effectiveness:

▪ Studies can be hard to evaluate because of their use of different eHealth modalities, clinical targets and patient populations. Study sample sizes are small, and study quality is often low, with little information about the accuracy of the information collected; overall program effects and the fact that program components are rarely assessed, only total program effects.^{8,23,34,37,46}

▪ Use of eHealth systems that only monitor behavior or blood glucose levels are less helpful than systems that provide live or algorithm-based feedback that is actionable.^33,63

▪ Immediate/timely feedback is best.^2,63

▪ Built-in incentives for use, goal attainment, and “gamification” work best only in short term, if at all.^43,61

▪ To optimize ease of use, manual input of data should be minimized; instead fostering automated input and linkages to other devices, for example, CGM, accelerometers.^46,64

▪ There are mixed results on whether or not use of behavior change theory makes a difference with respect to outcomes.^34,49

▪ How long a program should last is unclear—it may depend on when a change in the clinical target can reasonably be expected; longer-term follow-up in studies is infrequent, so proximal goals are most frequently targeted.^{7,28,39,46,47,56}

▪ There is a need for clearly defined and assessed clinical targets^37,38—that is, what is the specific, accurately measured behavioral goal—what does the program ask the user to do differently?

▪ Many studies show statistically significant between-group differences even though the differences may not be clinically meaningful for the entire sample or for specific, often at-risk patient subgroups.^35,50

Effectiveness using different eHealth modalities

▪ Neither a single eHealth modality nor the sequence of use of eHealth modalities has been shown to be more or less “effective.”¹⁷

▪ Use of systems with multiple types of communication channels and components is best, while keeping complexity minimized—allows for user customization and choice.^{17,33,34,64,65}

Effectiveness using free-standing vs. linked programs

▪ Best outcomes are with integrated clinician/user contact,^37,38 but the exact number of contacts and their frequency has not been demonstrated for specific eHealth systems—this may be patient/clinic/goal-dependent.^5,39

▪ Systems with active clinician involvement have been shown to be more helpful than free-standing systems and user-only programs^{34,38,46,47,51,63,66,67}; although there may be differences in costs, there are little data to suggest whether different kinds of clinicians, for example, nurses, educators, physicians, affect eHealth efficacy differently; this too may be clinic/patient dependent.

▪ Although patient selection bias may be operative, there are some data to suggest that, up to a maximum, more frequent HCT/patient contact through the eHealth system leads to less attrition and better outcomes.^20,24,34,35

▪ Some data suggest the utility of inserting remote diabetes monitoring data directly into the EHR through the cloud. Unfortunately, there have been very few published evaluations of this tool and the range and type of data reported to date have been very limited.

Effectiveness related to user issues

▪ Findings from the eHealth literature are limited by the lack of diverse samples included; results generally indicate that the best eHealth outcomes occur among users with high baseline HbA1c who are younger and middle age, so that others might well need additional support and assistance.^37,38

▪ eHealth systems may have fewer benefits in advanced health systems where patient/clinician contact and communication are already extensive.^23,26,56

▪ It is important to include HCT and patient training in system use.^2,23

▪ Context matters and it is multilevel: effective eHealth programs focus on user needs, skills, culture, experience with technology, and literacy. Likewise, clinic context plays a big role: public vs. private health systems, leadership, culture, clinical style, competing clinical priorities, and experience with technology are major contextual factors.²

Effectiveness regarding specific eHealth targets

HbA1c:

▪ Results are mixed: only about half of studies show significant HbA1c reductions with eHealth interventions.^8,25,68

▪ Largest HbA1c reductions occur when baseline HbA1c levels are >8.0%.^35,38

▪ Reductions are often statistically significant but not necessarily clinically meaningful.³⁵

▪ Best results occur with multicomponent interventions that include education, monitoring and feedback.^34,35

▪ The inclusion of peer support does not necessarily lead to greater HbA1c improvement when using eHealth systems.^46,63,68

Medication use:

▪ Results of eHealth interventions are modest at best and usually include adults with type 2 diabetes (oral hypoglycemics, lipid-lowering or antihypertensive medications.²⁹

▪ Positive results tend to be short-term.²⁹

▪ Best approaches tend to be through text reminders or use of IVR two-way communication systems.⁶⁹

Obesity and weight loss:

▪ Results of eHealth weight loss interventions are poor.^24,70

▪ Best results tend to be around short-term weight loss; results for maintenance of weight loss over time are poor.^31,47,51

▪ Major problems are maintaining good user engagement, many studies include highly diverse user samples, whose variability in response makes evaluation difficult.^31,51,70