Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Jul 1.
Published in final edited form as: Am J Transplant. 2016 Mar 14;16(7):2172–2180. doi: 10.1111/ajt.13701

A Randomized Controlled Trial of a Mobile Health Intervention to Promote Self-Management after Lung Transplantation

A DeVito Dabbs 1, MK Song 2, BA Myers 3, R Li 4, RP Hawkins 5, J M Pilewski 6, C A Bermudez 7, J Aubrecht 1, A Begey 1, M Connolly 1, M Alrawashdeh 1, MA Dew 6
PMCID: PMC4925283  NIHMSID: NIHMS748703  PMID: 26729617

Abstract

Lung transplant recipients are encouraged to perform self-management behaviors including: 1) monitoring health indicators, 2) adhering to their regimen, and 3) reporting abnormal health indicators to the transplant coordinator, yet performance is suboptimal. When hospital discharge was imminent, this two-group trial randomized 201 recipients to use either the-mHealth intervention (n=99), or usual care (n=102), to compare efficacy for promoting self-management behaviors (primary outcomes) and self-care agency, re-hospitalization and mortality (secondary outcomes) at home during the first year after transplant. The mHealth intervention group performed self-monitoring (OR=5.11, 95% CI 2.95–8.87, p<.001), adhered to medical regimen (OR=1.64, 95% CI 1.01–2.66, p=0.046) and reported abnormal health indicators (OR= 8.9, 95% CI 3.60–21.99, p<.001) more frequently than the usual care group. However, the two groups did not differ in re-hospitalization (OR= 0.78, 95% CI 0.36-1.66, p=.51) or mortality (HR= 1.71, 0.68-4.28, p=.25). The positive impact of the mHealth intervention on self-management behaviors suggests that the intervention holds promise and warrants further testing.

TRIAL REGISTRATION

ClinicalTrials.Gov NCT00818025

INTRODUCTION

Lung transplant recipients (LTRs) experience more transplant-related complications, higher health resource utilization, and higher mortality than recipients of other solid organs (1). Prevention and detection of early complications is known to reduce the likelihood of future impairments in lung function and, therefore, morbidity and mortality (2). The importance of self-management to promote better health outcomes after lung transplantation is well recognized (3-5). However, LTR find it difficult to perform these behaviors due to the complexity of the medical regimen (6-8). The connectivity, capabilities, and widespread use of mobile devices make mobile health (mHealth) technologies suitable for interventions that promote adherence, real-time data collection for self-monitoring, and self-management. Furthermore, in spite of the wide-spread use of smartphones and availability of health applications, few rigorous trials have been conducted on the efficacy of mHealth interventions for improving self-management in the real-world setting (9). A systematic review of the effectiveness of mHealth disease management interventions supports the need for high-quality, adequately powered trials (10). Thus, whether patients will adopt mHealth technologies, and if so, whether adoption improves self-management and health outcomes remains largely unknown.

Pocket PATH® is an mHealth intervention pilot-tested with LTR (12) to assist them in performing self-management behaviors. The intervention was guided by Orem’s theory of self-care (13), which purports relationships between self-care agency (i.e., perceived capability to engage in self-care), the performance of self-care behaviors, and ultimately health outcomes. The selection of intervention elements was based on the assessment of the needs and tasks of LTR as they attempted to perform self-management on their own at home (11), including: difficulties organizing daily measurements of health indicators, tracking trends in measurements over time, recognizing values that are outside normal ranges, and deciding when values should be reported to the transplant coordinator. We applied a user-centered design approach by involving LTR, the end-users, in the design and testing of Pocket PATH in order to improve usability, quality, and increase the likelihood that Pocket PATH would be adopted (11). While Pocket PATH uses a smartphone platform, its custom programming makes it different from other generic health apps for smartphones. For example, screens were designed with input from patients to record their daily measurements for the variety of health indicators that they are expected to self-monitor at home (e.g., spirometry, vital signs, symptoms). The data are displayed in graphical format to make it easy for patients to follow trends in their condition over time. The graphs include markings for the upper and lower limits of normal ranges for each indicator, to make it easy for patients to recognize worrisome changes in their measurements. Per the request of LTR, Pocket PATH also includes a decision-support feature that automatically sends a reminder to the patient to call the transplant coordinator whenever a value is recorded that should be reported immediately Once the proposed elements were finalized, we confirmed that self-monitoring, feedback and decision-support were consistent with recommended approaches for complex behavioral interventions (14, 15). An overview of Pocket PATH features and screen shots are provided as an on-line supplement.

We then conducted a pilot RCT with 30 LTR and found that patients in the Pocket PATH group performed self-management behaviors at higher rates and reported better health-related quality of life than patients in the usual care group (12). Based on preliminary work, we now report findings of a full-scale trial that evaluated the efficacy of Pocket PATH compared to usual care. Specifically, we examined the impact of Pocket PATH on primary outcomes of self-management behaviors at 2, 6, and 12-months following hospital discharge after transplant, including 1) performing self-monitoring, 2) adhering to the medical regimen, and 3) reporting critical health changes to the transplant coordinator (primary outcomes), and secondary outcomes, including: 1) self-care agency (perceived capability to engage in self-care) and 2) two health outcomes (re-hospitalization and mortality) over the 12 months post-hospital discharge.

MATERIALS AND METHODS

Design

The study was a randomized controlled trial comparing Pocket PATH and usual care. Outcomes were assessed over 12 months. The University of Pittsburgh IRB approved the study (PRO08070401).

Setting and Sample

Eligible LTR were over 18 years old, who underwent transplantation (January 2009–December 2012; followed through December 2013) at the University of Pittsburgh Medical Center (UPMC), and could read and speak English. LTR who had a previous transplant or were unable to perform their personal care were excluded. LTR were recruited during their transplant hospitalization after being transferred from the ICU. Seventy-five percent (n=201) of 294 eligible LTR agreed to participate (Figure 1). All LTR received clinical management according to the standard UPMC protocol for immunosuppression, infection prophylaxis, routine surveillance biopsies and follow-up evaluations as needed.

Figure 1. Flow Diagram of Participant Recruitment and Retention.

Figure 1

Open in a new tab

Data for survival status were available for 100% of subjects; data for clinical measures had intermittent missing as shown in Table 2.

Randomization and Blinding

A computer-generated randomization scheme (permuted block size of 10) was implemented to assign LTR to Pocket PATH or usual care (1:1 allocation) using sequential, security-lined envelopes prepared by the study statistician. Blinding of treatment condition to the interventionists and LTR was not possible because LTR in the Pocket PATH group were provided smartphones with custom programs and LTR in usual care were not. Investigators and post-randomization assessors were blind to group assignment.

Treatment Conditions

Usual Care

The usual care group received scripted discharge instructions from one of two interventionists and an instruction binder that emphasized the importance of performing daily self-management behaviors at home including, adhering to elements of the regimen, performing daily self-monitoring (e.g., using the paper-and-pencil logs to record daily health indicators), and reporting critical abnormal health indicators to the transplant coordinator based on pre-established parameters. Training sessions averaged 30 minutes.

Pocket PATH

In addition to the same discharge instructions that the usual care received, the Pocket PATH group received a smartphone with custom Pocket PATH programs to record daily health indicators, view graphical displays of trends and receive automatic feedback messages advising them to notify the transplant coordinator if health indicators were critical (outside the pre-established parameters). Training sessions averaged 60 minutes. A toll-free, tech-help line was available.

Data recorded using the Pocket PATH program were logged and graphed for LTR to view and automatically uploaded to the research project office daily via a secure cellular connection. Because we intended to assess LTR’ performance of self-management, including reporting critical health indicators immediately to the transplant coordinator), data recorded on the smartphones were not shared with the transplant team. LTR were told that the transplant team was responsible for managing all clinical care, and changes in clinical data should be reported to the transplant team. If LTR contacted the research staff about a clinical issue, they were instructed to contact the transplant team.

The full description of the components of our intervention fidelity framework, and how each element was monitored have been published elsewhere (16). The mean percentage of intervention elements delivered as intended was 98.6%. When asked at the end of the study, 100% of participants were satisfied with the treatment condition to which they were randomized.

Data Collection

Socio-demographic and clinical characteristics were collected before randomization by trained staff. The primary outcomes (self-management behaviors) were assessed at 2, 6, and 12-months, which coincided with routine post-transplant evaluations. The secondary outcome, self-care agency was assessed at 2, 6, and 12-months, and health outcomes were abstracted from the medical record and reported cumulatively for the 12-month follow-up period. Other than for scheduled data collections there were no other in person follow-up visits

Primary Outcome Measures

Self-monitoring

The Pocket PATH group entered values for health indicators using the device that automatically time-stamped the date. The usual care group recorded the date and values for health indicators on paper logs. Because the data collectors were blinded to group assignment they were instructed to ask all LTR if they had any paper logs to be photocopied at the 2, 6, and 12-month assessments. At the completion of the study, when all data were collected, two members of the project team independently calculated the percentage of days that LTRs performed self-monitoring by: (1) counting the number of days during each interval that values were recorded using the paper logs or Pocket PATH program (numerator) and (2) dividing the numerator by the number of days LTRs were at home (denominator). Performing this calculation adjusted for the days LTRs were re-hospitalized and not expected to self-monitor. For any discrepancies in calculations between independent raters, the process was repeated until there were no discrepancies in calculations.

Because the distribution of the percentage of days for self-monitoring was skewed, we categorized this outcome into an ordinal variable. We conducted sensitivity analyses using a variety of cut–points and results were consistent; therefore, <25%, 25% <50%, and ≥50% were selected as clinically meaningful and resulted in sufficient numbers in each category.

Adhering to the regimen

Multiple reports (17-19), including five published meta-analyses (20-24), show that self-reported measures of adherence (alone or in combination with other sources, such as reports from collateral informants) are as likely to detect non-adherence as other measures (e.g., medication monitoring or biological measures). Data collectors, blinded to study group, administered collateral versions of the Health Habits Survey (25,26), a reliable and valid measure of adherence, that relies on reports of adherence by both LTR and their primary family caregivers at pre-determined assessment intervals (2, 6 and 12 months). The survey was used to assess adherence to all elements of the medical regimen (e.g., taking medications, attending clinic appointments, completing lab work). Ordinal response formatting was used to indicate how often each element was performed; the responses were then dichotomized to indicate whether or not the LTR met the minimal level of adherence for each element deemed acceptable by the transplant team (e.g., missed taking immunosuppression medications less than once a month). To arrive at a composite measure of overall adherence, we summed the number of elements (out of the total of nine) for which both the LTR and caregiver collateral reports agreed that criteria for acceptable levels of adherence to each element were met. If either the LTR or caregiver reported nonadherence to any element, it was not included in the final sum. The distribution for adherence was skewed, and thus adherence was dichotomized into high adherers (median of 8 and higher) and lower adherers (< 8).

Reporting critical health indicators

At the completion of the study, when all data were collected, two members of the research team independently calculated the percentage of critical values that were reported by first identifying the number of critical indicators recorded by reviewing the participants’ self-reported paper logs or Pocket PATH device logs and then calculating the percentage of critical indicators reported to the transplant coordinator by reviewing the transplant coordinators’ progress notes over the 12 months. Any discrepancies between independent data abstractors were resolved by rechecking source documents.

Secondary Outcome Measures

Self-Care agency

The Perception of Self-Care Agency (PSCA) (27) was used to assess LTR perceptions of their capability to engage in self-care. PCSA is a 53 item, Likert-type, self-report instrument with well-established psychometric properties (28-30). Possible scores range from 53 to 265; higher scores indicate higher self-care agency. Cronbach’s α for the PSCA in the current sample was 0.94.

Health outcomes

Re-hospitalization was defined as any unscheduled re-hospitalization (our program does not hospitalize LTRs for routine follow-up evaluation) and total days re-hospitalized over 12 months. All-cause mortality was determined using the post-transplant day of death or freedom from death at 12 months.

Statistical Analysis

Intention-to-treat (ITT) analysis was used for all outcomes. Because we had defined our primary outcomes a priori (i.e., the three self-management behaviors), we set the significance level at α = 0.05 for each, with no adjustment for multiple comparisons (31-33).

Sample size and power

We projected a sample size of 214 a priori based on the lowest effect size (d=0.44) found for the impact of Pocket PATH on self-management behaviors compared to usual care at the end of a 2-month pilot trial (12). However, when our projected recruitment deadline was reached (December 2012), we had enrolled a sample of 201 (i.e., 99 and 101 participants per group) to be followed for 12 months. Re-estimating power for the sample of 201 with our present repeated measures design, analyzed with ITT, showed that we had 80% power to detect between groups differences in primary outcomes as small as d=0.45 (2-tailed, significance level α = 0.05 subject to an estimate of 20% missing).

Baseline characteristics of the study groups were compared using chi-square tests or Fisher’s exact tests for categorical and Wilcoxon rank-sum tests for continuous variables. For repeatedly measured outcomes, we used generalized estimating equations (GEE) to examine the intervention effect, adjusting for time and baseline characteristics on which the study groups differed. The group by time interaction was included if it reached statistical significance (p<0.05). For the number of critical abnormal health indicators recorded, we adopted the negative binomial model for count data and set loge-transformed total participation days as offset. A repeated measure logistic regression model, which accounts for intra-person correlation, was used to model the probability of reporting critical abnormal indicators to the transplant team. We used a multivariable regression model to examine group differences in likelihood of re-hospitalization, and linear regression to examine total days of hospital stay during the 12-month period, adjusting for covariates. The multivariate Cox proportional hazards model was used to examine the intervention effect on survival. All analyses were conducted in SAS (version 9.4, SAS Institute, North Carolina).

Data and safety monitoring (DSM)

Safety concerns were important because we collected remote health data to assess the degree to which LTR performed self-monitoring; however, we were not responsible for managing their clinical care. Our DSM procedures for handling any critical abnormal health indicators to ensure safety, while keeping threats to study integrity to a minimum, have been published (34).

RESULTS

Sample characteristics

The sample was split nearly equally by gender with a mean age of 62 years (range of 51-67). The majority were married, white, educated beyond secondary school, unemployed, with computer experience. The two groups were balanced in socio-demographic and most clinical characteristics (Table 1), yet LTR in usual care were more likely to require re-intubation (p < .05) and longer hospital stays (p < .01) during the transplant hospitalization; therefore, these variables were considered imbalanced covariates in the multivariable regression analyses.

Table 1.

Baseline Characteristics for Total Sample and Per Treatment Group

All
(N = 201)		 Pocket PATH
(n = 99)		 Usual Care
(n =102)		 P Value
Socio-Demographics
 Gender, % (n) male 55.2 (111) 52.5 (52) 57.8 (59) 0.45
 Age, years, median (IQR) 62 (51,67) 62 (51,67) 62 (51,68) 0.82
 Marital status, % (n) married 71.6 (144) 74.7 (74) 68.6 (70) 0.34
 Race, % (n) white 91.0 (183) 92.9 (92) 89.2 (91) 0.36
 Education, % (n) ≥ high school 94.0 (189) 92.9 (92) 95.1 (97) 0.52
 Employment, % (n) yes 11.4 (23) 15.2 (15) 7.8 (8) 0.10
 Past computer use (n) % yes 81.6 (164) 83.8 (83) 79.4 (81) 0.42
Clinical Characteristics
 Lung disease, % (n) obstructive 49.8 (100) 54.5 (54) 45.1 (46) 0.18
 Procedure, % (n) single 18.4 (37) 19.2 (19) 17.6 (18) 0.78
 ICU days, median (IQR) 5 (3,12) 4 (3,11) 7 (3,14) 0.09
 Hospital days, median (IQR) 27 (19,44) 24 (16,38) 33 (21,49) 0.005 *
 Re-intubated, % (n) yes 23.4 (47) 17.2 (17) 29.4 (30) 0.04 *
 Discharge to home, % (n) 86.6 (174) 89.9 (89) 83.3 (85) 0.17
Open in a new tab
*

Imbalanced characteristics used as covariates in the outcome analyses

Primary outcomes

Self-monitoring

Adjusting for time and the covariates, the Pocket PATH group performed self-monitoring more frequently than usual care at all follow-up intervals (OR=5.11, 95% CI 2.95-8.87, p<.001) (Table 2). In both groups there was a significant time effect, and the percentage of the groups that had higher self-monitoring decreased overtime, including varying proportions of patients in each group who stopped performing self-monitoring at various time points. The frequency (%) of participants who did not self-monitor between 0-2 month, 2-6 month and 6-12 month were 8/88 (9%), 17/92 (18%) and 25/90 (28%), respectively, for the Pocket PATH group, and 46/96 (48%), 64/97 (66%) and 74/96 (77%) respectively for the control group.

Table 2.

Summary of Outcomes by Treatment Group and per Follow-up Interval

Outcome Time
(months)		 Data cut-
points £ 		Pocket PATH
(n = 99) 		Usual Care
(n = 102) 		Group Effect
(95% CI)‡ 		p
n frequency
(%)* or
median (IQR)† 		n frequency
(%)* or
median (IQR)†
Primary Outcomes
Self-monitoring* 0-2 mos <25%
25-<50%
≥50%		 88 19 (22%)
15 (17%)
54 (61%)		 96 65 (68%)
8 (8%)
23 (24%)		 5.11
(2.95, 8.87)		 <.001
3-6 mos <25%
25-<50%
≥50%		 92 31 (34%)
21 (23%)
40 (43%)		 97 70 (72%)
12 (12%)
15 (15%)
7-12 mos <25%
25-<50%
≥50%		 90 52 (58%)
16 (18%)
22 (24%)		 96 84 (88%)
5 (5%)
7 (7%)
Higher adherers* 0-2 mos ≥8 89 67 (75%) 93 61 (66%) 1.64
(1.02, 2.66)		 .05
3-6 mos ≥8 78 44 (56%) 92 43 (47%)
7-12 mos ≥8 74 31 (42%) 83 20 (24%)
Critical health
indicators reported† 		0-12 mos 53 100 (91.6,100) 24 77.5 (12.1,100) 8.90
(3.60,21.99)		 <.001
Secondary Outcomes
Self-Care Agency†
(possible scores
range 53 to 265)		 0-2 mos 88 230 (212,245) 93 227 (207,245) 1.67
(−4.35,7.68)		 0.59
3-6 mos 78 233 (213,248) 93 229 (209,246)
7-12 mos 74 238 (216,251) 83 232 (209,247)
At least one re-
hospitalization* 		0-12 mos Yes 99 80 (81%) 102 87 (85%) 0.78
(0.36, 1.66)		 0.51
Total re-hospital
days† 		0-12 mos 99 12 (3,32) 102 14.5 (4,47) −2.51
(−11.42,5.95)		 0.60
Mortality* 0-12 mos Yes 99 11 (11.1%) 102 8 (7.8%) 1.71
(0.68, 4.28)		 0.25
Open in a new tab
*

Summarized by frequency (%) per cut-points. £

†

Summarized by sample median (IQR).

‡

The group effect size (ES) estimates were based on appropriate multivariate regression models that adjust for imbalanced covariates. Because the outcomes follow different distributions, the group effects on the outcomes were summarized by different types of effect sizes (ES). Difference in mean was reported for self-care agency. Odds ratios (OR) were used to summarize intervention effects on self-monitoring, adherence, reporting critical health indicators, and re-hospitalizations, which are categorical outcomes that follow the binomial or ordinal distributions. Rate ratio (RR) estimate was presented for number of critical health indicators recorded, and hazard ratio (HR) was used for mortality. The difference in median was reported for total days.

Adhering to the regimen

Adjusting for time and the covariates, the Pocket PATH group was more likely to show high adherence than the usual care group (OR=1.64, 95% CI 1.01–2.66, p=0.046). In both groups there was a significant time effect, and the percentage of the groups that had higher adherence decreased overtime.

Reporting critical abnormal health indicators

After adjusting for covariates, the estimated rate of recording critical abnormal health indicators in the Pocket PATH group was 3.10 times (95% CI 1.37–7.01, p = 0.007) that for usual care (Table 2). The estimated probability of reporting critical indicators to the transplant coordinator was 92.0% in Pocket PATH group compared to 56.4% in the usual care. The odds of reporting critical indicators in the Pocket PATH group was 8.9 times (95% CI 3.60–21.99, p<.001) that of the usual care group. Figure 2 displays the number of recorded critical indicators versus number of reported critical indicators to the transplant team.

Figure 2. Frequency of Critical Health Indicators Recorded versus Reported Per Treatment Group.

Figure 2

Open in a new tab

Each circle represents an individual recipient. The Pocket PATH group reported most of their critical indicators—as illustrated by the proximity of the circles to the diagonal line, which represents the ideal situation (i.e., a participant appropriately reports all recorded critical health indicators).

Secondary outcomes

Self-care agency and health outcomes

The two groups did not differ in levels of self-care agency. There were no statistically significant differences in the percentages of LTR who had at least one hospital re-admission or cumulative re-hospitalization days within the 12-month follow-up period between the groups. The risk of death was not different between the two groups. All-cause mortality over the 12-months was relatively low in both groups. Actuarial survival at 12 months was 88.9% for Pocket PATH and 92.2% for usual care.

DISCUSSION

Like other solid organ recipients, LTR are expected to perform self-management behaviors to promote better health outcomes after transplantation, however they face challenges trying to do so. We examined whether or not LTR would adopt Pocket PATH, an mHealth technology, and if so, whether adoption improved self-management and health outcomes.

Our results indicated that LTR were willing to adopt Pocket PATH. Moreover, the Pocket PATH group showed a higher likelihood for performing self-monitoring and adhering to the medical regimen. Furthermore, LTR were more likely to respond appropriately to decision-support feedback and report critical health indicators to transplant coordinators. This RCT lends empirical support for the potential benefits of Pocket PATH, an mHealth intervention to promote self-management, by reducing the inefficiency of paper-and-pencil methods for self-monitoring, combined with the success of graphical displays and automatic feedback to support timely reporting of health changes to providers.

The details of a given medical regimen or particular health indicators may differ between types of solid organ transplants, yet self-management behaviors are universal and known to promote better health outcomes across a variety of transplant populations (38,39). Although LTR were the first test population, Pocket PATH was designed to be easily adapted to meet the needs of patients with other chronic conditions, including recipients of other solid organ transplants. Its basic features for monitoring health indicators, displaying trends over time, calculating out-of-range values and guiding their decision-making about what and when to report to clinicians were designed for customization depending on the population of interest.

While our results demonstrate that Pocket PATH was superior to usual care in promoting self-management behaviors, the two groups did not differ in self-care agency and health outcomes. The lack of group differences in health outcomes may have been due to the decline in self-management behaviors over time. Such declines have been observed in other studies (35-37). Had the superior performance of self-management behaviors among the Pocket PATH group been sustained over the year, better health outcomes may have been achieved.

Although recipients in the Pocket PATH group were more likely to report critical health indicators than recipients in the usual care group, cumulative re-hospitalization days were similar between groups. Contrary to what one might expect, better reporting of presumably actionable events did not lead to an increased rate of re-admissions among recipients in the Pocket PATH compared to usual care. It is possible that timely reporting led to earlier detection of complications among recipients in the Pocket PATH group without increasing their need for re-hospitalization in the acute setting. Additional work is needed to examine the relationships between self-monitoring and causes of re-hospitalization.

Additionally, we were unable to detect significant intervention effects on survival, but there were relatively few deaths in both groups. Our survival rates were higher than the unadjusted survival rate of 79% at 1 year for adults who underwent lung transplantation in the recently reported era (38), most likely because we enrolled individuals at hospital discharge after perioperative deaths would have occurred and a year is a relatively short period to detect survival benefit.

Limitations

This trial was conducted in one transplant center. However, the characteristics of the sample are representative of those reported for other lung transplant populations (40). The groups were imbalanced in length of stay and need for re-intubation during the initial transplant hospitalization, suggesting that the usual care group included LTR who were more medically unstable, which may have influenced their ability to engage in self-management or increase their likelihood of complications. Although the analysis adjusted for the imbalance, a stratification scheme may be appropriate in the future.

Our reliance on a combination of self and collateral reports to measure adherence may be considered a limitation of the study (41). To overcome the concern that individuals tend to over report adherence, we implemented several evidence-based strategies to improve the accuracy and truthfulness of responses (17-19), including: 1) using a combination of collateral sources (collecting adherence data from both the LTR and their primary caregiver); 2) having a supportive health professional do the assessment; 3) using ordinal response formats (not dichotomous); 4) focusing the assessment on a particular behavior; and 5) using a measure that was reliable and valid (42).

The health outcomes we selected (re-hospitalization and survival) may not have been sensitive enough to detect the health impact of Pocket PATH. Other indicators of transplant health, such as reductions in infections, rejection episodes, bronchiolitis obliterans syndrome, health interventions and health resource utilization, would be important to assess in future studies. Finally, the follow-up period of our RCT was 12 months, which limited our ability to assess longer impact of Pocket PATH on self-management and health outcomes.

Future directions

Since this trial began, smartphones have become ubiquitous and a variety of mobile ‘apps’ are available to help patients track aspects of their health. Although existing health applications may help patients track values over time, to our knowledge none are specifically designed to assist patients to perform the range of self-management behaviors expected after transplant. Additionally, typical health apps are not designed with decision-support (i.e., Pocket PATH is programmed to send automatic reminders when values meet critical threshold). Furthermore, unlike Pocket PATH, few (if any) mobile apps have been subjected to randomized, controlled trials and tested with actual patients in real-world settings. Some patients may be using health applications but their efficacy is unknown.

The moderators and mediators that may explain the mechanisms of the effects of Pocket PATH and other mHealth technologies on health outcomes need to be further explored (43,44). While mHealth technologies like Pocket PATH have the potential to become powerful, innovative medical tools, we need to explore ways to sustain self-management behaviors overtime and incorporate the use of mHealth technologies into clinical care. Future work should aim to strengthen mHealth interventions in several ways: 1) explore the benefits of adding more frequent messages to provide positive reinforcement for patients who are performing well and reminders for patients whose performance has declined; 2) explore ways to install mHealth programs on the mobile devices the patients may already be using to assist with acceptance and reduce costs; 3) explore the benefits of data sharing and communicating between patients and clinicians to promote self-management; 4) identify opportunities for clinicians to incorporate the use of data from mHealth technologies into routine care of patients, such as accessing data when patients are being evaluated for unexpected problems, or reviewing data with patients during routine clinical visits; and 5) expand the evaluation of Pocket PATH to examine its efficacy in other transplant populations. As the use of mHealth technologies such as Pocket PATH continue to evolve, ongoing evaluation of their acceptance and efficacy is warranted for their promise of improving health outcomes to be fulfilled (9).

Supplementary Material

Supp Info

ACKNOWLEDGMENTS

We acknowledge the contributions of the nursing staff of 9D UPMC-Presbyterian and the interdisciplinary clinicians of the UPMC Cardiothoracic Transplantation Program, particularly the transplant coordinators. We also thank the lung transplant recipients and families whose participation made this study possible. This study was funded by the NIH, NINR (R01NR010711-DeVito Dabbs, PI).

Abbreviations

GEE

generalized estimating equations

LTR

lung transplant recipients

mHealth

mobile health

Pocket PATH

Pocket Personal Assistant for Tracking Health

Footnotes

DISCLAIMER

The funding organization played no role in the collection of data, analysis, interpretation, or the right to approve or disapprove publication of the finished manuscript.

DISCLOSURE

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Supporting Information

Additional Supporting Information may be found in the online version of this article.

Pocket PATH®: Personal Assistant for Tracking Health: Overview, Design and Testing, and Features

REFERENCES

  • 1.OPTN. OPTN/SRTR 2010 Annual data report Division of Transplantation, Healthcare Systems Bureau, Department of Health and Human Services, Health Resources and Services Administration. Available at: http://srtr.transplant.hrsa.gov/annual_reports/2012/pdf/2012_SRTR_ADR.pdf (Accessed Sebtember 1, 2015)
  • 2.Alvarez A, Lama R, Algar J, et al. Predicting mortality after lung transplantation. Transplant P. 2001;33:1630–31. doi: 10.1016/s0041-1345(00)02621-x. [DOI] [PubMed] [Google Scholar]
  • 3.Schuurmans MM, Benden C, Inci I. Practical approach to early postoperative management of lung transplant recipients. Swiss Med Wkly. 2013;143:w13773. doi: 10.4414/smw.2013.13773. [DOI] [PubMed] [Google Scholar]
  • 4.Kugler C, Fuehner T, Dierich M, DeWall C, Haverich A, Simon A, Welte T, Gottlieb J. Effect of adherence to home spirometry on bronchiolitis obliterans and graft survival after lung transplantation. Transplantation. 2009;88:129–134. doi: 10.1097/TP.0b013e3181aad129. [DOI] [PubMed] [Google Scholar]
  • 5.Finkelstein SM, Lindgren BR, Robiner W, Lindquist R, Hertz M, Carlin BP, VanWormer A. A randomized controlled trial comparing health and quality of life of lung transplant recipients following nurse and computer-based triage utilizing home spirometry monitoring. Telemed J E Health. 2013;19:897–903. doi: 10.1089/tmj.2013.0049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Dew MA, Dimartini AF, De Vito Dabbs A, Zomak R, De Geest S, Dobbels F, Myaskovsky L, Switzer GE, Unruh M, Steel JL, Kormos RL, McCurry KR. Adherence to the medical regimen during the first two years after lung transplantation. Transplantation. 2008;85:193–202. doi: 10.1097/TP.0b013e318160135f. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Kugler C, Gottlieb J, Dierich M, Haverich A, Strueber M, Welte T, Simon A. Significance of patient self-monitoring for long-term outcomes after lung transplantation. Clin Transplant. 2010;24:709–716. doi: 10.1111/j.1399-0012.2009.01197.x. [DOI] [PubMed] [Google Scholar]
  • 8.Bosma OH, Vermeulen KM, Verschuuren EA, Erasmus ME, van der Bij W. Adherence to immunosuppression in adult lung transplant recipients: prevalence and risk factors. J Heart Lung Transplant. 2011;30:1275–1280. doi: 10.1016/j.healun.2011.05.007. [DOI] [PubMed] [Google Scholar]
  • 9.Collins F. How to Fulfill the True promise of “mHealth”. Sci Am. 2012;307(1):16. doi: 10.1038/scientificamerican0712-16. http://www.scientificamerican.com/article/real-promise-mobile-health-apps/ Published online: 19 June 2012 | doi:10.1038/scientificamerican0712-16. Accessed March 29, 2014. [DOI] [PubMed] [Google Scholar]
  • 10.Free C, Phillips G, Galli L, et al. The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review. PLoS Med. 2013;10(1):e1001362. doi: 10.1371/journal.pmed.1001362. doi:10.1371/journal.pmed.1001362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.De Vito Dabbs A, Myers BA, Mc Curry KR, Dunbar-Jacob J, Hawkins RP, Begey A, Dew MA. User-centered design and interactive health technologies for patients. Comput Inform Nurs. 2009;27:175–183. doi: 10.1097/NCN.0b013e31819f7c7c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.DeVito Dabbs A, Dew MA, Myers B, Begey A, Hawkins R, Ren D, Dunbar-Jacob J, Oconnell E, McCurry KR. Evaluation of a hand-held, computer-based intervention to promote early self-care behaviors after lung transplant. Clin Transplant. 2009;23:537–545. doi: 10.1111/j.1399-0012.2009.00992.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychology. 2008;27(3):379–387. doi: 10.1037/0278-6133.27.3.379. [DOI] [PubMed] [Google Scholar]
  • 14.Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: The new medical research council guidance. British Medical Journal (Clinical Research Ed.) 2008;337:a1655. doi: 10.1136/bmj.a1655. Published online 2008 Sep 29. doi: 10.1136/bmj.a1655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Michie S, Hardeman W, Fanshawe T, Prevost AT, Taylor L, Kinmonth AL. Investigating theoretical explanations for behaviour change: The case study of ProActive. Psychology and Health. 2008;23:25–39. doi: 10.1080/08870440701670588. [DOI] [PubMed] [Google Scholar]
  • 16.DeVito Dabbs AJ, Song MK, Myers BM, Hawkins RP, Aubrecht J, Begey A, Connolly, Li R, Pilewski JM, Bermudez CA, Dew MA. Clinical trials of health IT interventions intended for patient use: Unique issues and considerations. Clinical Trials: Journal of the Society for Clinical Trials. 2013;10(6):896–906. doi: 10.1177/1740774513493149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Hays RD, DiMatteo MR. Key issues and suggestions for patient compliance assessment: Sources of information, focus of measures, and nature of response options. J Compliance Health Care. 1987;2:37–53. [Google Scholar]
  • 18.Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353:487–497. doi: 10.1056/NEJMra050100. [DOI] [PubMed] [Google Scholar]
  • 19.Morisky DE, DiMatteo MR. Improving the measurement of self-reported medication nonadherence: response to authors. J Clin Epidemiol. 2011;64:255–263. doi: 10.1016/j.jclinepi.2010.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care. 2002;40:794–811. doi: 10.1097/00005650-200209000-00009. [DOI] [PubMed] [Google Scholar]
  • 21.DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research. Med Care. 2004;42:200–209. doi: 10.1097/01.mlr.0000114908.90348.f9. [DOI] [PubMed] [Google Scholar]
  • 22.Dew MA, DiMartini AF, DeVito Dabbs A, Myaskovsky L, Steel J, Unruh M, Switzer GE, Zomak R, Kormos RL, Greenhouse JB. Rates and risk factors for nonadherence to the medical regimen after adult solid organ transplantation. Transplantation. 2007;83:858–873. doi: 10.1097/01.tp.0000258599.65257.a6. [DOI] [PubMed] [Google Scholar]
  • 23.Dew MA, DiMartini AF, Steel J, DeVito Dabbs A, Myaskovsky L, Unruh M, Greenhouse J. Meta-analysis of risk for relapse to substance use after transplantation of the liver or other solid organs. Liver Transpl. 2008;14:159–172. doi: 10.1002/lt.21278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dew MA, DeVito Dabbs A, Myaskovsky L, Shyu S, Shellmer DA, DiMartini AF, Steel J, Unruh M, Switzer GE, Shapiro R, Greenhouse JB. Meta-Analysis of medical regimen adherence outcomes in pediatric solid organ transplantation. Transplantation. 2009;88:736–746. doi: 10.1097/TP.0b013e3181b2a0e0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Simmons RG, Abress L, Anderson CR. Quality of life after kidney transplantation: A prospective, randomized comparison of cyclosporine and conventional Immunosuppressive therapy. Transplantation. 1988;45:415–420. doi: 10.1097/00007890-198802000-00034. [DOI] [PubMed] [Google Scholar]
  • 26.Dew MA, Kormos RL, Roth LH, Murali S, DiMartini A, Griffith BP. Early post-transplant medical compliance and mental health predict physical morbidity and mortality one to three years after heart transplantation. J Heart Lung Transplant. 1999;18:549–562. doi: 10.1016/s1053-2498(98)00044-8. [DOI] [PubMed] [Google Scholar]
  • 27.Sousa VDHS, Miller EH, Carroll MA. Measures of diabetes self-care agency, diabetes self-efficacy and diabetes self-management for insulin-treated individuals with type 2-diabetes. J Clinical Nurs. 2008:18. doi: 10.1111/j.1365-2702.2008.02729.x. [DOI] [PubMed] [Google Scholar]
  • 28.Hanson B, Bickel L. Development and testing on perception of self-care agency. In: Riehl-Sisca J, editor. The Science and Art of Self-Care. Appleton-Century-Crofts; Norwalk, Connecticut: 1985. pp. 271–278. [Google Scholar]
  • 29.Cleveland S. Perceived self-care agency: A lisrel factor analysis of Bickels and Hanson’s questionnaire. Nurs Res. 1989;38:59. [PubMed] [Google Scholar]
  • 30.Weaver M. Perceived self-care agency: A lisrel factor analysis of Bickel and Hanson’s Questionnaire. Nurs Res. 1987;36:381–387. [PubMed] [Google Scholar]
  • 31.Sankoh AJ, D’Agostino RB, Huque MF. Efficacy endpoint selection and multiplicity adjustment methods in clinical trials with inherent multiple endpoint issues. Statist Med. 2003;22:3133–3150. doi: 10.1002/sim.1557. [DOI] [PubMed] [Google Scholar]
  • 32.Proschan MA, Waclawiw MA. Practical guidelines for multiplicity adjustment in clinical trials. Control Clin Trials. 2000;21:527–539. doi: 10.1016/s0197-2456(00)00106-9. [DOI] [PubMed] [Google Scholar]
  • 33.Rothman K. No adjustments are needed for multiple comparisons. Epidemiology. 1990;1:43–46. [PubMed] [Google Scholar]
  • 34.Kovach KA, Aubrecht JA, Dew MA, Myers B, DeVito Dabbs A. Data safety and monitoring for research involving remote health monitoring. Telemed J E Health. 2011;17:574–579. doi: 10.1089/tmj.2010.0219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Beasley JM, Riley WT, Davis A, Singh J. Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial. J Am Coll Nutr. 2008;27:280–286. doi: 10.1080/07315724.2008.10719701. [DOI] [PubMed] [Google Scholar]
  • 36.Burke LE, Styn MA, Sereika SM, Conroy MB, Ye L, Glanz K, Sevick MA, Ewing LJ. Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med. 2012;43:20–26. doi: 10.1016/j.amepre.2012.03.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Glanz K, Murphy S, Moylan J, Evensen D, Curb JD. Improving dietary self-monitoring and adherence with hand-held computers: a pilot study. Am J Health Promot. 2006;20:165–170. doi: 10.4278/0890-1171-20.3.165. [DOI] [PubMed] [Google Scholar]
  • 38.Barlow J, Wright C, Sheasby J, Turner A, Hainsworth J. Self-management approaches for people with chronic conditions: a review. Patient Educat Couns. 2002;48(2):177–87. doi: 10.1016/s0738-3991(02)00032-0. [DOI] [PubMed] [Google Scholar]
  • 39.Schulman-Green D, Jaser S, Martin F, et al. Processes of self-management in chronic illness. Image J Nurs Sch. 2012;44(2):136–44. doi: 10.1111/j.1547-5069.2012.01444.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Yusen RD, Christie JD, Edwards LB, Kucheryavaya AY, Benden C, Dipchand AI, Dobbels F, Kirk R, Lund LH, Rahmel AO, Stehlik J, International Society for Heart and Lung Transplantation The Registry of the International Society for Heart and Lung Transplantation: Thirtieth adult lung and heart-lung transplant report--2013; focus theme: age. J Heart Lung Transplant. 2013;32:965–978. doi: 10.1016/j.healun.2013.08.007. [DOI] [PubMed] [Google Scholar]
  • 41.Dunbar-Jacob J, Sereika SM, Houze M, Luyster FS, Callan JA. Accuracy of measures of medicaiton adherence in a choleterol-lowing regimen. West J Nurs Res. 2012;34(5):587–597. doi: 10.1177/0193945912439251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Stirratt MJ, Dunbar-Jacob J, Crane HM, et al. Self-report measures of medication adherence behavior: recommendations on optimal use. Translational Behavioral Medicine. 2015;5(4):470–482. doi: 10.1007/s13142-015-0315-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Dimick C. The empowered patient. Preparing for a new patient interaction. J AHIMA. 2010;81:26–31. [PubMed] [Google Scholar]
  • 44.Steinhubl SR, Muse ED, Topol EJ. Can mobile health technologies transform health care? JAMA. 2013;310:2395–2396. doi: 10.1001/jama.2013.281078. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp Info
NIHMS748703-supplement-Supp_Info.doc (983KB, doc)

RESOURCES