There is strong evidence that home telehealth is at least as effective as usual care in significantly improving lung function and reducing asthma symptoms. There is moderate evidence that it tends to be more effective, but under-reporting of significance levels for between-group comparisons makes it difficult to establish clinical superiority. Patient characteristics will influence outcomes, as will the quality of conventional treatment. One study found that a home telehealth intervention was better at relieving symptoms than a usual care program in which patients received treatment from their general practitioner, but on par with a program that put them under a specialist’s eye.
There is also strong evidence that quality of life can be significantly improved with home telehealth. However, mere participation in a study, regardless of group assignment, often appears effective in bringing about some degree of improvement in quality of life. When additional benefit is gained from home telehealth, it generally consists of an increase in effect size rather than a change in its direction. There is moderate evidence that this increase can be significant. There is some cause to question the generalizability of this outcome, but the small number of studies measuring quality of life hinders definite conclusions.
There is strong evidence that home telehealth can be significantly more effective than usual care in improving medication adherence. At present, this finding appears to be reasonably replicable; 2 of 3 studies found a significant intervention effect.
We were unable to identify sufficient evidence on the effects of home telehealth on asthma knowledge or self-efficacy and cannot draw any conclusions on this point. A recent study did report that improvements in self-efficacy were no greater among home telehealth patients than among those treated with usual care. As this study was retrieved in a final scan of 2011-2012 literature and was not subject to the same level of analysis as the other studies included in this review, we will not attempt to extrapolate beyond the conclusions provided by its authors. See Ran et al. (2012) for more details.
Home telehealth, like any intensified regime of disease management, presents some risk of overtreatment. In one study, an increase in side effects associated with excessive medication use was found in the intervention group. This was not reported in any other studies, suggesting that the risk may be relatively minor.
High average levels of patient uptake (~65-95%) of home telehealth are achievable, as are high levels of patient satisfaction. However, findings suggest that uptake can vary significantly even within populations that have been selected for similar baseline characteristics: age, disease state, co-morbidities, and so on. Time-related declines in system use have been observed. It is also worth noting that several studies had difficulty recruiting patients, implying that enrolling patients in home telehealth program may be as challenging as preventing attrition.
An association between asthma control and uptake of remote monitoring interventions has been observed, but the nature of this relationship is unclear. It may be that high engagement in home-based telehealth increases the odds of improved control, or it may be that those with better control tend to have higher levels of uptake. It is also possible that the relationship is the result of a third factor that influences both intervention uptake and asthma control, such as motivation.
No information on patient cost or time savings was found.
Uptake and Use of Technology
Summary: Uptake and use of technology outcomes were reported in 2 studies (van der Meer et al., 2010; Prabhakaran et al., 2010). Findings showed moderate levels of patient engagement with substantial variability within populations and across time.
Study Details: Van der Meer et al. (2010) found that patients entered daily lung function scores (an optional component of the intervention) on an average of 107.8 days (CI 98-126) over the 12 months of the study. Authors also reported on ‘monitoring adherence’, defined as the percentage of weekly online Asthma Control Questionnaires (ACQ) completed by the intervention participants. This percentage was 88% in Month 1 but by Month 7 had declined to 60%, where it remained for the duration of the 12-month intervention. This resulted in a 12-month average monitoring adherence rate of 67%. After stratification by level of asthma control, it was found that well-controlled patients (ACQ scores < 0.75) had a monitoring adherence rate of 71%, vs. 68% for patients with partly controlled asthma (ACQ 0.75 to < 1.5) and 58% for those with uncontrolled asthma (ACQ ≥ 1.5).
Prabhakaran et al. (2010) reported that 92% of intervention group patients were satisfied with the SMS intervention. The mean patient response rate to text messages was given as 82%. However, the range in response rates (0-100%) suggests that uptake was highly variable.
Self-Management, Self-Efficacy, and Behaviour Change
Self-management, self-efficacy, and behaviour change outcomes were reported in 3 studies, all Level 2 evidence (Rasmussen et al., 2005; Strandbygaard et al., 2009; van der Meer et al., 2009).
Summary: Medication adherence was reported in all 3 studies. Study findings were inconsistent. There is strong evidence associating home telehealth interventions with significantly higher levels of medication adherence than usual care, but the generalizability of this effect is uncertain. Findings suggest that home telehealth has the potential to improve medication adherence, but that realization of this potential is dependent on contextual factors.
Study Details: In a 3-group study by Rasmussen et al. (2005), all groups saw significant improvement in medication compliance between baseline measurements and the 6-month follow up. ‘Good’ compliance, described as always or almost always using medication as prescribed, increased from 32% to 87% in the intervention group, which used home telehealth; from 25%-79% in Control Group 1, in which patients received treatment from an asthma specialist; and from 36% to 54% for Control Group 2, where received care from their general practitioners (p<.001 for all groups). The difference in improvement between the intervention group and Control Group 2 was significant (p<.001), as was the difference between Control Group1 and Control Group 2 (p<.001). In summary, both telehealth users and those under specialist care saw significantly greater improvements in medication compliance than those receiving care from a general practitioner.
In Strandbygaard et al. (2009), a significant difference in mean medication adherence at the study endpoint favoured the intervention group. Although the improvement seen in the intervention group over the course of the 2-month intervention failed to reach significance, medication adherence significantly declined in the control group. The absolute difference between the groups in mean adherence was a therefore statistically significant 17.8% (95% CI 3.2-32.3%; p<.019).
In van der Meer et al. (2009), no significant differences within or between groups were found for medication adherence.
Other: Asthma Knowledge, Inhalation Technique, and Use of Personal Action Plans
Other self-management, self-efficacy, and behaviour change outcomes included use of personal action plans, inhalation technique, and asthma knowledge. As only 1 study reported on each, no synthesis can be attempted. A summary of findings follows.
In a 3-group study by Rasmussen et al. (2005), self-reported use of personal action plans increased significantly in the intervention group (2% at baseline to 88% at 6 months; p<.001) and in Control Group 1 (3% to 66%, p<.001), but not in Control Group 2. This is perhaps unsurprising; those in the intervention group and Control Group 1 were encouraged to use electronic and paper action plans, respectively, whereas Control Group 2 appears to have received no support in this respect.
Van der Meer et al. (2009) reported on asthma knowledge and inhalation technique. Improvements were seen in both intervention and control groups. Within-group significance levels were not reported. Although improvements in both outcomes were slightly greater in the control group, the differences were not significant.
Recent Developments: A scan of material from 2011-2012, a time period not covered by our initial searches, found 1 additional article that addressed self-management, self-efficacy, and behaviour change outcomes (Ryan et al., 2012). In this randomized controlled study, members of the intervention group received feedback on asthma management through a mobile phone. No significant differences between groups were found in self-efficacy or asthma knowledge. As this study was not subject to the same level of analysis as the other studies included in this review, we will not attempt further analysis. See Ryan et al. (2012) for more details.
Clinical Outcomes, Symptoms, and Health Status
Clinical outcomes and/or symptoms and health status outcomes were reported in 5 studies, all Level 2 evidence (Rasmussen et al., 2005; van der Meer et al., 2009, 2010; Willems et al., 2007, 2008). Asthma symptoms and lung function were the most frequent indicators measured.
Summary: Asthma symptoms were measured in 3 studies (Rasmussen et al., 2005; van der Meer et al., 2009, 2010; Willems et al., 2007, 2008). While there is strong evidence associating home telehealth with significant improvements in asthma symptoms, findings are inconsistent. The frequency with which these improvements are significantly greater than those seen with usual care is also unclear.
Study Details: Two Level 2 studies reported significant improvements in asthma symptoms over the course of the intervention (Rasmussen et al., 2005; van der Meer et al., 2009, 2010). However, another Level 2 study found no significant changes (Willems et al., 2007, 2008). Furthermore, though improvements in van der Meer et al. (2009, 2010) were greater in the intervention group than in the control, significance levels were not reported.
Interestingly, 1 study identified an increase in side effects in the intervention group, presumably stemming from increased medication use (Rasmussen et al., 2005).
Summary: Measures of lung function were made in 4 studies (Prabhakaran et al., 2010; Rasmussen et al., 2005; Strandbygaard et al., 2009; van der Meer et al., 2009, 2010). There is moderate evidence that home telehealth is significantly more effective than usual care in improving lung function, and strong evidence that it is at least equivalent. Unfortunately, under-reporting of significance levels makes it difficult to quantify the extent of this improvement.
Study Details: Between-group comparisons favoured the intervention group in all studies. However, only 1 study reported a p-value <0.05 (Rasmussen et al., 2005). Additional details can be seen in the table below (Table C.1.3.1: Patient Outcomes – Clinical Outcomes, Symptoms, and Health Status).
|And on the qualitative side . . .In Pinnock et al. (2007), having an ongoing record of symptoms and peak flow was seen as useful by both clinicians and patients, allowing them to understand the triggers for their asthma and manage their treatment accordingly. Newly diagnosed patients were appreciative of the mobile intervention in the early phases of self-monitoring: it made them more aware of their symptoms and they took comfort in the knowledge that they were being monitored.|
Quality of Life
Summary: Quality of life was measured in 3 studies, all Level 2 evidence (Rasmussen et al., 2005; van der Meer et al., 2009; Willems et al., 2008). There is strong evidence associating home telehealth with improvements in quality of life. Quantifying the degree of this improvement is challenging, as 2 of 3 studies, though reporting improvement, did not give significance levels. Evidence of significantly greater improvement than that seen in usual care is moderate and of questionable generalizability.
Study Details: Rasmussen et al. (2005) reported significantly higher odds of improvement in the intervention group, whereas Willems et al. (2008) did not find a significant difference between groups. Significance levels were not reported in van der Meer et al. (2009), although results favoured the intervention group.
In Willems et al. (2008), 3 different quality of life measures were used: the EQ-5D, a descriptive instrument with a visual analogue scale; the SF-6D, a modified version of the SF-36; and pediatric and adult versions of the Asthma-specific Quality of Life Questionnaire (AQLQ). Although there were significant improvement over time for both intervention and control groups with all 3 measures, no significant differences between groups were found.
The AQLQ was also used in van der Meer et al. (2009) and Rasmussen et al. (2005). In van der Meer et al. (2009), pre-/post-scores improved more in the intervention group than in the control group (0.56 (95% CI 0.43-0.68) vs. 0.18 (95% CI 0.05-0.31). Patients in the intervention group also showed clinically relevant improvement (≥0.5) in scores more often than those in the usual care group (54% vs. 27%). Significance levels were not reported.
Rasmussen et al. (2005) found that scores on the AQLQ improved significantly more in the intervention group than in either of 2 control groups. Odds of improvement of ≥0.5 on the AQLQ were 33% in the intervention group vs. 18% and 19% in Control Groups 1 and 2, respectively.
Cost and Time Savings
No cost or time savings outcomes were reported.