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Personally controlled health management systems (PCHMS), which include a personal health record (PHR), health management tools, and consumer resources, represent the next stage in consumer eHealth systems. It is still unclear, however, what features contribute to an engaging and efficacious PCHMS.
To identify features in a Web-based PCHMS that are associated with consumer utilization of primary care and counselling services, and help-seeking rates for physical and emotional well-being concerns.
A one-group pre/posttest online prospective study was conducted on a university campus to measure use of a PCHMS for physical and emotional well-being needs during a university academic semester (July to November 2011). The PCHMS integrated an untethered personal health record (PHR) with well-being journeys, social forums, polls, diaries, and online messaging links with a health service provider, where journeys provide information for consumer participants to engage with clinicians and health services in an actionable way. 1985 students and staff aged 18 and above with access to the Internet were recruited online. Logistic regression, the Pearson product-moment correlation coefficient, and chi-square analyses were used to associate participants’ help-seeking behaviors and health service utilization with PCHMS usage among the 709 participants eligible for analysis.
A dose-response association was detected between the number of times a user logged into the PCHMS and the number of visits to a health care professional (
Frequent usage of a PCHMS was significantly associated with increased consumer health service utilization and help-seeking rates for emotional health matters in a university sample. Different bundles of PCHMS features were associated with physical and emotional well-being matters. PCHMS appears to be a promising mechanism to engage consumers in help-seeking or health service utilization for physical and emotional well-being matters.
Worldwide, governments have made multibillion dollar investments in eHealth to modernize health services delivery, with many questions still unanswered about the uptake, benefits, and cost-effectiveness of these investments [
PHRs have been advocated as the next generation tool that significantly improves consumers’ health behaviors and health outcomes [
However, a PCHMS often consists of multiple features, which refer to the functionalities available on the system. What are the features in a PCHMS that encourage consumers and patients to seek help or engage with health services for their well-being concerns? To date, it is still unclear what features contribute to an engaging and efficacious PCHMS.
Past studies have resulted in guidelines for the development of Internet interventions for consumer health [
In parallel, the idea of creating a “bundle” of actions has recently been advocated as a way to address system inertia to change [
For this reason, identifying features (or “bundles of features”) in a PCHMS that are associated with changes in consumers’ health behaviors remains a crucial area for research. In response, we designed an online prospective study to examine how a group of participants in a university setting used a PCHMS to manage their physical and emotional well-being. University students are known to experience elevated distress levels over an academic semester [
A one-group pre/posttest online prospective study was conducted over a university academic semester (July to November 2011). Inclusion criteria were (1) aged 18 or above, and (2) with access to the Internet and email at least on a monthly basis.
Students and staff were approached via email lists and advertisements in online print publications, which described the study and invited interested parties to use a PCHMS called
At baseline, demographic information (such as age and gender) was collected, as well as information about their use of social networking websites, use of the Internet to find health-related information, and visits to a health professional (including whether they visited prior to the study a health care professional, University Health Service, and the University Counselling and Psychological Services).
In the pre- and post-study questionnaires, measures 1-3 were administered and additional measures (4-5) were administered in the postintervention questionnaire: (more details on each measure are available in
This paper focuses on usage of PCHMS features with consumers’ health behaviors and thus only reports participants’ help-seeking behaviors and health service utilization rates collected at post-study.
A recent review by Danaher and Seeley [
In this study, we used simple website engagement measures to track participants’ activity on the website (ie, PCHMS login frequency and whether participants accessed, or did not access, each website feature). These measures were used to assess whether (1) there was a dose-response effect, that is, was the frequency of PCHMS login associated with rates of health service utilization and help-seeking behaviors, and whether (2) access to PCHMS feature(s) (ie, journey, personal health record, forum, poll, diary, and/or online appointment service) was associated with participants’ health service utilization and help-seeking behaviors for physical and/or emotional well-being.
PCHMS Web logs were analyzed to determine whether participants accessed (or did not access) any of the features at any time during the study. Some of these website engagement measures have previously been used to measure user engagement of PHR systems [
The dose-response phenomenon tested in this study is related to the
Personal Health Record (PHR) for self-recording of medical test results, medications, scheduled appointments, and personnel looking after one’s health (see
Online appointment booking with the University Health Service (primary care) and the UNSW Counselling and Psychological Services (sent via email using the “Book now” button in the PCHMS).
Diary for participants to write down their thoughts about their health. By default, the diary is private. However, participants can select to share their diary with all participants enrolled in the PCHMS.
Social communication spaces, which support interaction across the continuum of care between fellow participants and clinicians. Features include the poll system and forums moderated by clinicians. Poll system in which participants answer simple health questions (eg, how much sleep did you get last night?), where they can view and compare their response with other participants’ aggregated answers in graph format (
Journeys that provide information for consumer participants to engage with clinicians and health services in an actionable way. Participants in this study had access to four well-being journeys for physical and emotional well-being: “Stay Healthy”, “Stressed out?”, “Feeling Anxious about the Exams?”, and “My Emotional Well-being Program”.
The four well-being journeys for physical and emotional well-being were designed and developed in consultation with University Counselling and Psychological Services psychologists and University Health Service primary care physicians, utilizing evidence-based consumer education material routinely used at UNSW to promote physical and emotional well-being. Written in youth-friendly language, using evidence-based mental health, psychoeducational, and psychosocial material, the journeys consisted of skills-focused content delivered online, as well as well-being workshops that participants could attend in-person at the University Counselling and Psychological Services. Participants could learn about mindfulness meditation, anxiety management, time management, and stress management at these workshops.
Journeys were delivered via the PCHMS at four pivotal time-points during a university academic semester (ie, beginning of semester, 4 weeks into semester, after mid-semester break, and before exams) to address physical and emotional well-being concerns likely to be concerning participants at each time-point. Participants were alerted with an email when a new journey became available on the PCHMS. These journeys provided task specific knowledge in an actionable way. For example, as participants read the journey for advice on physical or emotional well-being, they could immediately:
book an appointment with a university primary care physician or a psychologist from the journey page,
register to attend a well-being workshop,
post a question on a forum to seek advice from fellow participants or a clinician (primary care physician or a psychologist), or
send themselves an email reminder to do so later.
A pilot study was conducted in a controlled setting with 15 university staff and students of different ages, gender, and familiarity with computers to test the intervention, the measures, and the research design. Substantive usability issues were resolved before recruiting participants in their real-life setting.
Analysis was conducted on an intention-to-treat basis. Sequential logistic regression analyses were undertaken to prospectively examine the crude and adjusted odds ratios (ORs) for participants’ health service utilization and help-seeking behaviors for physical and emotional well-being matters [
Participants’ health service utilization rates (ie, visits to a health professional, University Health Service, or the University Counselling and Psychological Services), and their help-seeking behaviors for physical or emotional well-being matters were compared at different PCHMS login frequency thresholds (zero logins, once only, two to five times, six to 10 times, more than 10 times). The rationale for selecting these login frequency cutoffs is based on using heuristics to ensure important login frequency thresholds are covered (ie, zero, once only, and ≥ a high login frequency threshold) and that there are sufficient data points in each frequency threshold to conduct analyses.
Between group analyses were conducted using chi-square analysis. Participants’ pre-study characteristics (namely use of the Internet to find health information, use of social networking websites, visits to a health care professional in the past 6 months, and their self-rated well-being ratings classified as over or below 50 at pre-study) were compared between different PCHMS login frequencies using chi-square to assess whether these characteristics were associated with PCHMS usage levels. Descriptive analyses were conducted on participants’ reasons for
Data analysis was performed using IBM SPSS Statistics 20 [
Personal Health Record on Healthy.me (University of New South Wales, 2009-2013).
Poll on Healthy.me (University of New South Wales, 2009-2013).
A total of 1985 participants met inclusion criteria and were recruited into the study. All completed the pre-study questionnaire. Of those, 709 completed the post-study questionnaire (
Baseline characteristics of eligible participants are presented in
Overall, 50% (358/709) of participants visited a health care professional (for themselves or others) for a physical well-being concern and 13% (95/709) for emotional well-being during the study (
Baseline characteristics of study participants who completed both pre-study and post-study questionnaires.
Characteristics | Total |
|
Mean age, years (SD) |
|
25.2 (9.41) |
Female gender (%) |
|
427 (60.2%) |
University student |
|
625 (88.1%) |
Non-medicine faculty a |
|
570 (80.4%) |
Patient at University Health Service (prior to study) |
|
148 (20.9%) |
Visited UNSW Counselling and Psychological Service (prior to study) |
|
83 (11.7%) |
|
|
|
|
Several times a day | 434 (61.2%) |
|
Several times a week | 183 (25.8%) |
|
Several times a month | 29 (4.1%) |
|
Less often | 39 (5.5%) |
|
I do not use social networking websites | 24 (3.4%) |
|
|
|
|
Several times a week | 79 (11.1%) |
|
Few times a month | 161 (22.7%) |
|
Less often | 93 (13.1%) |
|
Never | 38 (5.4%) |
|
|
|
|
None | 188 (26.5%) |
|
Once only | 173 (24.4%) |
|
Two to three times | 238 (33.6%) |
|
More often | 110 (15.5%) |
aFaculty refers to the School or the Faculty that a participant is from, regardless of whether he/she is a student or a staff member.
Participants’ health service utilization, help-seeking behaviors, and experiences of physical and emotional well-being concerns during the study.
Number |
||
|
||
|
I experienced a physical well-being concern during study | 479 (67.6%) |
|
I experienced an emotional well-being concern during study | 422 (59.5%) |
|
||
|
I encountered someone with physical well-being concerns during study | 400 (56.4%) |
|
I encountered someone with well-being concerns during study | 365 (51.5%) |
|
||
|
I visited a health care professional for only physical well-being concerns (for self or others) | 276 (38.9%) |
|
I visited a health care professional for only emotional well-being concerns (for self or others) | 13 (1.8%) |
|
I visited a health care professional for both physical and emotional well-being concerns (for self or others) | 82 (11.6%) |
|
||
I sought advice on physical well-being (for myself) | 370 (52.2%) | |
I sought advice on physical well-being (for others) | 88 (12.4%) | |
I sought advice on emotional well-being (for myself) | 201 (28.3%) | |
I sought advice on emotional well-being (for others) | 75 (10.6%) | |
|
||
|
There was a need for physical well-being assistance (for self or others), but I did not seek help | 109 (15.4%) |
|
There was a need for emotional well-being assistance (for self or others), but I did not seek help | 221 (31.2%) |
|
|
|
Physical well-being | 2.2 (0.87) | |
Emotional well-being | 2.2 (0.82) |
aConfidence: 1=not confident, 2=quite confident, 3=confident, 4=very confident
Health service utilization and help-seeking behaviors according to different usage levels of PCHMS.
|
% (95% CI) | ||||
No. of PCHMS logins a | Visited health professional b | Visited University Health Service c | Visited University Counselling and Psychological Services d | Sought help for physical well-being e | Sought help for emotional well-being f |
0 (n=136) | 44 (36 to 53) | 16 (11 to 23) | 7 (4 to 13) | 47 (39 to 55) | 31 (24 to 39) |
1 (n=287) | 57 (51 to 62) | 16 (13 to 21) | 4 (2 to 6) | 53 (47 to 58) | 29 (24 to 34) |
2 to 5 (n=165) | 61 (53 to 68) | 19 (14 to 26) | 6 (3 to 10) | 62 (54 to 69) | 33 (27 to 41) |
6 to 10 (n=61) | 54 (42 to 66) | 21 (13 to 33) | 5 (2 to 13) | 51 (39 to 63) | 26 (17 to 38) |
≥ 10 (n=59) | 67 (53 to 77) | 24 (15 to 36) | 14 (7 to 25) | 63 (50 to 74) | 49 (37 to 62) |
a1 participant was excluded as his/her no. of logins is recorded as >4000. Among the 708 participants included in this analysis, the mean of login frequency is 4.3, standard deviation is 19.05, and the maximum number of logins is 456.
bVisited health professional during study: χ2
4=11.80,
cVisited University Health Service during study: χ2
4=2.79,
dVisited UNSW Counselling and Psychological Service during study: χ2
4=10.26,
eSought help for physical well-being during study: χ2
4=8.94,
fSought help for emotional well-being during study: χ2
4=10.70,
Participant flowchart in the study.
In absolute terms, participants who logged into the PCHMS more than 10 times were 22 percentage points more than those who never logged in to visit a health care professional during the study: χ2
4=11.80,
Visits to the University Health Service did not differ significantly between different PCHMS login frequency thresholds: χ2
4=2.79,
Overall, 54% (386/709) of participants sought formal or informal help (for themselves or others) for physical well-being and 32% (225/709) for emotional well-being during the study (
Help-seeking behaviors for physical well-being matters did not differ significantly between different PCHMS login frequencies: χ2
4=8.94,
Reasons for
Different groups of system features were correlated with different consumer behaviors:
Formal or informal help-seeking behaviors for
Help-seeking for
Full details of the logistic regression models are summarized in
Health service utilization rates and help-seeking behaviors between different PCHMS login frequency thresholds.
Reasons for
Reason | Number a
|
No time / inconvenience | 56 (51.4%) |
I didn’t know (or still don’t know) what seems to be the problem | 34 (31.2%) |
Cost | 32 (29.4%) |
Not well enough (or motivated) to seek help | 27 (24.8%) |
I didn’t think anyone (or anything) can help | 22 (20.2%) |
I didn’t know how to seek help | 16 (14.7%) |
Fear of what others may think | 14 (12.8%) |
Fear of confrontation and learning about the health issue | 14 (12.8%) |
Previous unsatisfactory contacts with health care professionals | 9 (8.3%) |
Stigma or cultural attitudes | 8 (7.3%) |
Other | 7 (6.4%) |
aParticipants who experienced a physical well-being concern during study but did not seek help. Participants can select more than one reason.
Reasons for
Reason | Number a
|
No time / inconvenience | 94 (42.5%) |
Fear of confrontation and learning about the health issue | 31 (40.0%) |
I didn’t think anyone (or anything) can help | 80 (36.2%) |
I didn’t know (or still don’t know) what seems to be the problem | 59 (26.7%) |
Not well enough (or motivated) to seek help | 54 (24.4%) |
Cost | 46 (20.8%) |
Fear of what others may think | 46 (20.8%) |
I didn’t know how to seek help | 39 (17.6%) |
Stigma or cultural attitudes | 33 (14.9%) |
Other | 27 (12.2%) |
Previous unsatisfactory contacts with health care professionals | 19 (8.6%) |
aParticipants who experienced an emotional well-being concern during study but did not seek help. Participants can select more than one reason.
PCHMS features associated with health service utilization and help-seeking behaviors.
This is the first study that shows a dose-response effect of using a PCHMS on consumers’ health service utilization (primary care and counselling) and help-seeking behaviors for emotional well-being. To our knowledge, this is also the first study that contributes an understanding of which bundles of PCHMS features are associated with consumer help-seeking and health service utilization behaviors.
The nature of this study only allows associational inferences to be drawn and specifically, we cannot say that it was the usage of these features that drove user behaviors. An alternate reading of our results is that those individuals who are most likely to use health services are also the population most likely to be drawn to use a PCHMS. Both causal readings are of interest, and it is also likely that both probably are to some extent in operation in the results reported here. As the pre-study characteristics and well-being ratings of users were uniformly distributed across different PCHMS login frequency thresholds, we could not in this study detect differences in users to explain differences in their behaviors, suggesting the dose-response reading of our results is more likely the explanation. Untangling these two alternate readings will undoubtedly be resolved with further research.
The dose-response effect observed in this study may be explained by the availability heuristic, which describes the “situation in which people assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind.” [
A PCHMS in practice offers a “bundle” of eHealth services and features including but not limited to a PHR. However, no studies to date have examined which bundles of features might motivate consumers’ health behaviors. Past studies that examined user engagement investigated the whole website [
While help-seeking for physical well-being was only correlated with use of the personal health record, a bundle of PCHMS features were correlated with emotional well-being help-seeking. Providing an environment that allows self-reflection (diary), social feedback (poll), and reducing the barriers to engage with health services (online appointment booking) appeared to work in combination for emotional well-being help-seeking. As suggested by Coiera, one reason such bundles might work is that they are programmatic, bringing together components that reinforce each other’s value and use [
There are strong theoretical reasons why the features tested in this study could drive behavioral change:
Our findings are in line with the emerging body of literature that associates eHealth interventions with consumers’ health behaviors (such as personalization, tailoring, and behavioral feedback [
Our findings also extend previous studies that describe models, guidelines, and definitions on Internet interventions for consumers’ health behaviors [
Attrition is a significant concern in consumer eHealth research [
Key strengths include the large number of participants, a multifaceted PCHMS with connectivity to health service providers that model many of the generic PHR systems and the use of PCHMS usage metrics to associate with consumers’ health behaviors. Some limitations include:
University setting: Participants in a university setting may have been more motivated and willing to try new technologies to manage their health than the general population [
Self-reports and self-entry functionality: The study relied on self-reports by participants, which have been shown to be acceptable in studies of help-seeking, health service utilization, and mental health-related studies among students [
Causality vs association: Although findings in this study are limited by its cross-sectional nature and we could attribute no causal relationships, our findings concur with Couper and colleagues’ study, which found that website engagement was significantly associated with consumers’ health behaviors [
PCHMS engagement measures: studies have reported numerous metrics for measuring user engagement with a website, such as number of website visits, time spent on a site, and number of features used [
Our online prospective study provides evidence that PCHMS usage is associated with consumers’ utilization of health services and help-seeking behaviors for emotional well-being concerns. The features in this PCHMS are sufficiently general to be applicable to a variety of help-seeking and preventative health tasks.
While there is evidence that Web interventions can trigger significant consumer health behaviors, the empirical and theoretical basis for developing PCHMS features in general is still weak. Abandoning an eHealth application is a common and significant phenomenon. Asking participants to engage in
Details on questionnaire measures.
Details of logistic regression results for each outcome measure.
health belief model
odds ratio
personally controlled health management system
personal health record
randomized controlled trial
relative risk
social cognitive theory
transtheoretical model
University of New South Wales
The authors thank Farshid Anvari, Jingbo Liu, Vitaliy Kim, and Jay Liu for their contributions to software development, and the staff and students at UNSW who assisted or participated in the study.
This research is supported in part by grants received from the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Informatics and E-Health (1032664), and the HCF Health and Medical Research Foundation. The funding bodies 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.
Study design: AL, JP, AA1, JC, STL, EC. Journey design: AA1, JC, AL. Data collection: AL, AA1, JC. Data analyses: AL, AA2, EC. First draft: AL. Draft revision: AL, EC, JP, STL, AA1, AA2, JC.
The University of New South Wales and some of the researchers (EC, AL) at the Centre for Health Informatics involved in this project could benefit from commercialization of the PCHMS.