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Personally controlled health management systems (PCHMS), which may include a personal health record (PHR), health management tools, and information resources, have been advocated as a next-generation technology to improve health behaviors and outcomes. There have been successful trials of PCHMS in various health settings. However, there is mixed evidence for whether consumers will use these systems over the long term and whether they ultimately lead to improved health outcomes and behaviors.
The aim was to test whether use of a PCHMS by consumers can increase the uptake or updating of a written asthma action plan (AAP) among adults with asthma.
A 12-month parallel 2-group randomized controlled trial was conducted. Participants living with asthma were recruited nationally in Australia between April and August 2013, and randomized 1:1 to either the PCHMS group or control group (online static educational content). The primary outcome measure was possession of an up-to-date written AAP poststudy. Secondary measures included (1) utilizing the AAP; (2) planned or unplanned visits to a health care professional for asthma-related concerns; (3) severe asthma exacerbation, inadequately controlled asthma, or worsening of asthma that required a change in treatment; and (4) number of days lost from work or study due to asthma. Ancillary analyses examined reasons for adoption or nonadoption of the intervention. Outcome measures were collected by online questionnaire prestudy, monthly, and poststudy.
A total of 330 eligible participants were randomized into 1 of 2 arms (intervention: n=154; control: n=176). Access to the PCHMS was not associated with a significant difference in any of the primary or secondary outcomes. Most participants (80.5%, 124/154) did not access the intervention or accessed it only once.
Despite the intervention being effective in other preventive care settings, system use was negligible and outcome changes were not seen as a result. Consumers must perceive the need for assistance with a task and assign priority to the task supported by the eHealth intervention. Additionally, the cost of adopting the intervention (eg, additional effort, time spent learning the new system) must be lower than the benefit. Otherwise, there is high risk consumers will not adopt the eHealth intervention.
Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12612000716864; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=362714 (Archived by WebCite® at http://www.webcitation.org/6dMV6hg4A)
Personally controlled health management systems (PCHMS), which may include a personal health record (PHR), health management tools, and information resources, have been advocated as a next-generation technology to improve health behaviors and outcomes [
Lack of engagement in digital interventions is a common phenomenon [
In this study, we examine how effective a PCHMS is in encouraging adults with a chronic condition—asthma—to obtain a written asthma action plan (AAP) from their primary care practitioner over a 12-month period. We also explored the reasons that underlay the adoption or nonadoption of the intervention.
Asthma is a chronic condition [
When properly used, written AAPs are associated with fewer visits to the emergency department with an asthma exacerbation, fewer hospital admissions, better lung function, and an overall improvement of asthma symptoms [
This randomized controlled trial (RCT) is designed to test whether a PCHMS, tailored to help adults with asthma, would increase their rate of obtaining or updating a written AAP from a health care professional and whether this would lead to an improvement in asthma control.
Compared to participants allocated to the control group (ie, static online educational page), we hypothesized that those using a PCHMS are
More likely to obtain or update a written AAP;
More likely to make planned visits to a health care professional for asthma;
Less likely to make unplanned visits to a health care professional for asthma; and
Less likely to experience (1) severe asthma exacerbation, (2) inadequately controlled asthma, (3) worsening of asthma that requires a change in treatment, or (4) days lost from work or study due to asthma.
Details on participants, recruitment strategy, intervention description, data collection, ethical considerations, and study procedure are described in the study protocol [
In this parallel 2-arm RCT, participants were stratified by gender and level of asthma severity (intermittent vs persistent), and randomized 1:1 to have immediate access to the PCHMS or to control.
Participant recruitment took place between April and August 2013. All individuals who expressed an interest were assessed with a 5-minute online eligibility questionnaire. Eligible individuals were then invited to complete a 10- to 15-minute prestudy questionnaire. Participants in both arms continued to receive usual care from their health services and were surveyed monthly for asthma symptoms, asthma exacerbation, asthma control, and other competing priorities, and followed up with a 10- to 15-minute poststudy questionnaire between April and June 2014.
Eligible participants were adults (aged 18 years and older) living in Australia diagnosed with asthma, who had at least monthly access to the Internet and email, and had sufficient English language skills.
On completion of the prestudy questionnaire, participants who had been randomly allocated to the control arm were redirected to a static webpage with links to patient websites (eg, the Asthma Foundation, HealthInsite, myDR) that provided educational information on asthma. They were advised that they would be contacted to complete monthly surveys to elicit their asthma status during the study and would receive a follow-up questionnaire on conclusion of the study.
Full details of the Healthy.me Web-based PCHMS are described elsewhere [
An expert steering group was formed to tailor educational content for patients with asthma and to customize the interactive features of Healthy.me to deliver this content over the 12 months. Three asthma “journeys” were developed, providing evidence-based material to consumers about the written AAP. A usability study with 10 individuals was conducted and all major usability issues associated with the content and the intervention were addressed before commencing the study.
A review of online interventions found that those built on a theoretical framework demonstrated greater efficacy [
There are strong theoretical reasons why the PCHMS features drive behavioral change:
The online appointment booking service, embedded within health service information descriptions, allows consumers to turn information into action in keeping with the “cue to action” elements of the HBM [
Social features (eg, polls and forums), which allow individuals to connect with others and observe social norms on health behaviors, are designed according to principles of social cognitive theory [
PHRs, which facilitate self-management and self-awareness, are related to the principle of increasing self-efficacy.
The journey model, which allows stages of change described in a step-by-step manner, is congruent with the Theory of Transtheoretical Change [
All primary, secondary, and ancillary analyses are outlined in the published protocol [
The intention-to-treat principle was followed in the primary analysis. Missing values were managed by the last observation carried forward (LOCF) imputation procedure [
Binary logistic regression was employed to adjust for potential confounding factors or differences in baseline characteristics that were expected to be predictive of the outcome, including age, gender, past possession of a written AAP, smoking status, medications used for asthma, and past visits to a health care professional for asthma concerns [
A complete case analysis of secondary outcomes was also conducted using the data of those who completed the poststudy questionnaire and the Pearson chi-square test to identify any significant difference between intervention and control groups.
A comparison was made of the proportion of patients in the intervention and control groups who reported experiencing at least one of the following episodes during the study:
Severe asthma exacerbation (as indicated in the Official American Thoracic Society/European Respiratory Society Statement on Asthma Control and Exacerbations) [
Inadequate asthma control (as measured by Asthma Control Questionnaire [ACQ] score of ≥1.5 in that month) [
Worsening of asthma that required treatment changes (as measured by a decrease in ACQ score of ≥0.5 between 2 consecutive months) [
Missing one or more days from work or study due to asthma.
Ancillary analyses were conducted to examine reasons for adoption or nonadoption of the intervention. These were conducted using the data of those who completed the poststudy questionnaire or at least one monthly questionnaire. Participant engagement with the intervention was measured via system logs and their perception of the intervention was measured by the Technology Acceptance Model (TAM) instrument [
Recruitment was conducted over a period of 5 months between April and August 2013, during which 485 participants were assessed for eligibility (
Participant flowchart.
Baseline characteristics were similar for all allocated participants, participants lost to follow-up, and remaining participants (
Baseline characteristics of all participants and those lost to follow-up.
Baseline characteristic | All participants | Participants lost to follow-up | Remaining participants | |||
|
Control (n=176) | Intervention (n=154) | Control (n=79) | Intervention (n=98) | Control (n=97) | Intervention (n=56) |
Female, n (%) | 140 (79.5) | 124 (80.5) | 59 (75) | 80 (82) | 81 (84) | 44 (79) |
Age (years), mean (SD) | 39 (13) | 40 (14) | 37 (12) | 36 (12) | 41 (14) | 46 (14) |
Has written AAP (before study), n (%) | 71 (40.3) | 57 (37.0) | 28 (35) | 37 (38) | 43 (44) | 20 (36) |
Visited health care professional for asthma in past 12 months, n (%) | 142 (80.7) | 133 (86.4) | 71 (90) | 83 (85) | 71 (73) | 50 (89) |
Smoking status, n (%) | 16 (9.1) | 13 (8.4) | 10 (17) | 12 (12) | 6 (6) | 1 (2) |
Preventer use in the past 12 months, n (%)a | 87 (49.4) | 78 (50.6) | 34 (43) | 49 (50) | 53 (54) | 29 (52) |
Reliever use in the past 12 months, n (%)b | 169 (96.0) | 149 (96.8) | 76 (96) | 93 (95) | 93 (96) | 56 (100) |
Symptom controller use in the past 12 months, n (%)c | 7 (4.0) | 7 (4.5) | 4 (5) | 5 (5) | 3 (3) | 2 (4) |
Visit social networking sites (eg, Facebook, Twitter) several times a day, n (%) | 131 (74.4) | 99 (64.3) | 67 (85) | 66 (67) | 64 (66) | 33 (59) |
Never used the Internet to find health information, n (%) | 4 (2.3) | 8 (5.2) | 2 (3) | 6 (6) | 2 (2) | 2 (4) |
a Preventer use: Flixotide, Pulmicort, Qvar, Alvesco, Leukotriene, Singulair, Cromones, Intal, Tilade, Xolair (Omalizumab).
b Reliever use: Ventolin, Asmol, Epaq, Airomir, Bricanyl, Atrovent.
c Symptom controller use: Serevent, Oxis, Fovadile.
Analyses of the primary outcome (possession of a written AAP at poststudy) was conducted by intention-to-treat using the data of all 330 allocated participants and the 153 participants who completed the poststudy questionnaire.
We did not apply the intention-to-treat principle to secondary and ancillary outcomes due to the availability of data for analyses. Analyses of secondary outcomes relating to use of the AAP and visits to a health care professional were conducted using the data of 153 participants who completed the poststudy questionnaire. Other study outcomes (ie, asthma exacerbation, asthma control, worsening of asthma, and loss days from work or study) were conducted using the data of 242 participants who completed at least one monthly questionnaire.
Analysis of the primary outcome is outlined in
Analysis of primary outcome by study group (for all participants and remaining participants).
Analysis | n | Has written AAP (poststudy) | Has written AAP (poststudy LOCFa) | |||||
|
|
n (%) | χ2 1 |
|
n (%) | χ2 1 |
|
|
|
|
|
0.6 | .43 |
|
0.4 | .52 | |
|
Control | 176 | 38 (22) |
|
|
66 (38) |
|
|
|
Intervention | 154 | 27 (18) |
|
|
64 (42) |
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|
|
|
|
|
|
|
|
|
|
|
Control | 97 | 38 (39) | 0.9 | .36 |
|
|
|
|
Intervention | 56 | 27 (48) |
|
|
|
|
|
a LOCF: last observation carried forward (imputation method to address missing data).
Binary logistic regression was adjusted for differences in baseline characteristics and potential confounding factors that might influence the primary outcome measure. Only one independent variable made a statistically significant contribution to the regression model: possession of written AAP at prestudy (χ2
22=299.6,
Analyses of secondary outcomes are presented in
Analyses of secondary outcomes by study group (for remaining participants).
Analysis | Participants, n (%) | χ2 1 |
|
||
|
Control | Intervention |
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|
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|
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|
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Used AAP more than once during study | 20 (21) | 11 (20) | 0.02 | .89 |
|
Visited health care professional for nonemergency asthma | 58 (60) | 36 (64) | 0.1 | .71 |
|
Visited health care professional for emergency/urgent asthma | 42 (43) | 24 (43) | 0.003 | .96 |
|
Visited emergency department for emergency/unplanned asthma | 15 (15) | 10 (18) | 0.03 | .88 |
|
Visited GP or respiratory physician for emergency/unplanned asthma | 35 (36) | 20 (36) | 0.002 | .96 |
|
|
|
|
|
|
|
Severe asthma exacerbation at least once during study | 62 (43) | 35 (36) | 0.8 | .37 |
|
Asthma inadequately controlled at least once during study (as measured by ACQ score ≥1.5) | 137 (94) | 87 (90) | 1.3 | .25 |
|
Worsening of asthma that requires a change in treatment (as measured by a decrease of ≥0.5 in ACQ score between 2 consecutive months) | 77 (53) | 44 (45) | 1.1 | .29 |
|
Lost days from work or school due to asthma during study | 61 (42) | 33 (34) | 1.3 | .26 |
Participant reasons for not obtaining or updating a written AAP during the study are outlined in
Reasons for not obtaining/updating a written AAP by study group.a
Reasons | Participants, n (%) | ||
|
Control (n=59) | Intervention (n=27) | |
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|
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|
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I did not know about the existence of AAPs | 18 (31) | 8 (30) |
|
I do not believe that a written AAP could be useful to me | 10 (17) | 4 (15) |
|
I lacked the motivation to get a written AAP | 9 (15) | 2 (7) |
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I do not know where to get it | 7 (12) | 1 (4) |
|
I think the written AAP could be difficult to use | 2 (3) | 3 (11) |
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|
|
|
|
I did not visit a doctor during the study | 13 (22) | 6 (22) |
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I lacked the time to get a written AAP | 10 (17) | 2 (7) |
|
I simply forgot | 9 (15) | 2 (7) |
|
It was inconvenient to get it | 1 (2) | 0 |
None of the above, please specifyb | 12 (20) | 9 (33) |
a Participants could select more than one reason.
b Reasons such as perceiving the plan to be “irrelevant,” lack of importance placed on asthma, or other life and health priorities which competed for their attention.
For those who provided further explanation in the poststudy questionnaires, a variety of reasons for not obtaining (or updating) a written AAP were offered.
Participants feeling comfortable with a verbal plan or their own experience in self-management:
My doctor and I have discussed this in detail. No written plan required.
I am familiar with the steps in a written plan and follow these principles; however, I don’t require an actual hard copy of one, as I am confident in my self-management. Also, the last doctor who tried to force one upon me did not even try to understand my asthma or lifestyle, rather insulting me rude [rudely] and thinking that a generic plan (that included medicine that I do not respond to) was the only way to go. I’m sure he was an exception, but I’m honestly fine with the way I manage my asthma, and when I ask GPs about my medication, it’s rare that there’s anything new going on.
The poor experience they had with previous written plans / past health care professionals:
I got one a while ago and it was a tick and flick from a drug company and I felt it was useless—gave me nothing more than I know now.
I have never received one for me though I am a severe asthmatic. My child has received one for whenever he is sick and ends up in hospital. But we have no action plan for either of us for what to do on a normal day and we are feeling unwell with signs and symptoms of asthma.
Discouragement by health care professionals:
It’s never been offered by doctor.
Doctor told me not to bother.
Competing priorities experienced during the study:
I really didn’t use it very much—not really enough to comment. On a personal note, during the last 12 months, I have been going through a process of appointments and getting my son diagnosed for autism and then ongoing therapies/appointments. I also have 2 other children and am expecting a third plus working part time so I have found adding this extra facet into my life almost impossible. It certainly has nothing against the resource. I have simply been too busy to put the time in and for that I apologize.
The lack of importance participants placed on asthma:
Complacency—I should I know but having been asthmatic all my life I don’t give it the importance I should.
I guess I always think it will never get worse...which I know is wrong.
A belief that a written AAP is “irrelevant” to their condition:
Look, for someone who has just been diagnosed with asthma or someone quite young, it’s probably great. But for someone like me who has had asthma for over 40 years, has an informal plan of what to do (ie, I know when I need to be on the preventative, what causes it, when I need Ventolin, what to do if I’m having too much Ventolin, etc), it’s not very helpful.
I had one, but because my asthma triggers and symptoms and signs change so often they quickly become out of date.
Inadequacies of the intervention or asthma content:
Do I have to log in? It would be better if access was open.
I already understand my asthma. I thought this might contribute to that understanding, but I think it was aimed at a much younger/newer to asthma participant.
Participants in both groups were asked to report monthly on their life priorities and the importance they placed on their health and asthma. On a scale from 1 to 10 (where 1 was highest priority and 10 was lowest), participants on average rated health moderately highly (control: mean 3.5, SD 1.9; intervention: mean 3.3, SD 2.1). However, the priority they placed on asthma was not as high (control: mean 4.3, SD 2.2; intervention: mean 3.8, SD 2.3). In fact, asthma was often not a health issue reported by participants that caused them the most concern on a monthly basis.
The average number of life priorities reported by participants was similar in both groups (control: mean 3.0, SD 1.3; intervention: mean 2.7, SD 1.5). These priorities ranged from issues related to work, family/relationship, and money. Health was not always mentioned in this list of priorities. On average, participants reported approximately 2 health issues per month (control: mean 1.9, SD 1.0; intervention: mean 1.5, SD 0.9). These issues are related to a range of bodily systems (eg, cardiovascular, musculoskeletal, psychological, neurological) and not only restricted to the respiratory system.
Participant usage and perceptions of the Healthy.me intervention are outlined in
Usage frequency of Healthy.me (n=154 participants).
Usage frequency (times) | Participants, n (%) |
0 | 30 (19.5) |
1 | 94 (61.0) |
2-5 | 27 (17.5) |
6-10 | 2 (1.3) |
>10 | 1 (0.6) |
Perception of the intervention as measured by the Technology Acceptance Model (n=56).a
Perception of intervention | Mean (SD) | |
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|
Healthy.me was easy to use | 4.9 (1.5) |
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I find it was easy to get Healthy.me to do what I wanted it to do | 4.8 (1.4) |
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It was easy to become confident with using Healthy.me | 4.9 (1.4) |
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Managing my asthma through Healthy.me will be beneficial to me | 4.7 (1.2) |
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The advantages of using Healthy.me to manage my asthma will outweigh the disadvantages | 4.9 (1.2) |
|
Overall, using Healthy.me will help me improve my asthma in general | 4.8 (1.2) |
a Participants were allocated to PCHMS and completed the poststudy questionnaire.
b Likert scale 1 to 7, where 1=strongly disagree, 4=neutral, 7=strongly agree.
Access to the Healthy.me PCHMS did not improve the rate of possession of written AAPs, planned visits to health services for nonemergency asthma management, asthma status, control, or work and study productivity. These results are in stark contrast to earlier trials of the same PCHMS, which showed significant improvements in outcomes associated with consumer behavior change, including influenza vaccination [
High attrition rates are common in eHealth intervention studies [
In decision theoretic terms, the expected utility of any eHealth intervention is a product of the utility or benefit of each individual interaction with the system to the user and the number of times the interaction takes place [
In this trial, some participants suggested that they saw little benefit in using the system, either because they or their health professionals saw little value in having an AAP, because asthma management was not a major priority in their life compared to other competing priorities, or that they have already developed their own strategies to manage the condition and needed no further assistance. Perhaps as suggested by some participants, the intervention would be more helpful for those who are newly diagnosed with asthma.
A systematic review of PHRs used for chronic conditions found that unless a system clearly assisted consumers in self-management tasks, they were unlikely to be successful [
Recent systematic reviews concluded that although there is evidence that some digital interventions are associated with positive asthma self-management outcomes [
Our own earlier trials of this intervention focused on supporting preventive health tasks. A trial aimed at encouraging influenza vaccination demonstrated a significant doubling in vaccination rates, most likely because the system allowed easy and immediate access to booking a vaccination with a primary care center, for a condition where seasonality and acting in a timely manner is important [
Although the lack of uptake of eHealth interventions is a widely known phenomenon [
Although current evidence advocates the importance of having a theoretical basis to direct behavioral changes, it is equally important to consider whether such theories can be used to minimize participant attrition. For example, identifying early on those who are truly uninterested and focus instead on those who are likely to continue could potentially reduce participant attrition [
Studies have confirmed once again that implementation uptake is often the biggest challenge in any eHealth project, both for consumers and clinicians. Trials that focus on implementation of asthma interventions are emerging in clinical settings [
Rather than attempting to “perfect” the design of an intervention to exist on its own, interventions should be designed for the context. When designing an intervention for consumers and patients, it is important to identify early on whether the intervention should focus on task support or on belief change. Moreover, research should focus on how we can design consumer eHealth interventions that are integrated in health care settings and/or how such interventions would function in the consumer circle of care (eg, caregivers).
Study strengths include nationwide recruitment, use of recommended care indicators for outcome measures, and triangulation of participant feedback with quantitative results.
Notable limitations of this study include the gender and age distribution of participants, the attrition rate, and the use of self-reported data. The majority of participants were female in their late thirties / early forties and it is possible this population sample behaved differently than a more representative sample.
Participants had a higher rate of AAP possession than reported in other studies. As a result, as a cohort, they may already be better engaged and confident in their self-management and less likely to benefit from the intervention compared to the population average, reducing the potential effect size. Further, because the outcome measure was focused on having an up-to-date written AAP that was updated by a clinician (eg, once a year), we may have missed some participants as the study duration was only 12 months. Future studies should consider extending the trial period to more than 12 months.
Our primary recruitment strategy is online, which has a number of limitations, such as high rates of attrition. More effective recruitment could potentially result when it is channeled through influencers such as health care providers or with the encouragement of caregivers who help patients to deal with issues every day. However, this is an intervention designed primarily for consumers, to be delivered online, thus it is important that there is a direct channel to recruit consumers who are already online.
Consumers are increasingly turning to the Internet and social media for health advice, yet we still do not fully understand why some online interventions work and others do not. In this study, participant goals were poorly aligned with the clinical goals of the system despite there being clear evidence underpinning these latter clinical goals. It may be that a different approach is required in the domain of asthma management in adults, at least as far as AAPs are concerned, focusing not so much on task support as on belief change. More generally, researchers should not feel discouraged to publish negative findings because in failure many significant lessons can be learned.
Use of Health Belief Model (HBM) in Asthma Patient Journeys.
Utilization of Personally Controlled Health Management System (PCHMS).
asthma action plan
Asthma Control Questionnaire
Health Belief Model
last observation carried forward
personally controlled health management system
personal health record
randomized controlled trial
Technology Assessment Model
The authors thank Sara Morgan from the Asthma Foundation New South Wales for her assistance in designing the intervention content and Nathan Mortimer for his assistance in forum moderation. The authors also thank Jingbo Liu, Vitaliy Kim, Farshid Anvari, and Jay Liu for their contributions to software development, and the study participants for their time and feedback. This research is supported by grants received from the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Informatics and EHealth (1032664). The funding body did not have a role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.
The University of New South Wales, Enrico Coiera, and Annie Lau could benefit from the commercial exploitation of the Healthy.me platform or its technologies.