GoMo Health and the Center for BrainHealth focused on what matters most: meeting women where they are, making brain science easy to understand, and empowering them to take ownership of their mental and emotional well-being.
Received: 11 May 2018 | Revised: 19 July 2018 | Accepted: 23 July 2018 DOI: 10.1111/phn.12543
POPULATIONS AT RISK ACROSS THE LIFESPAN: POPULATION STUDIES
The feasibility and promise of mobile technology with community health worker reinforcement to reduce rural preterm birth
Mary E. Cramer PhD, RN, FAAN 1
| Elizabeth K. Mollard PhD, APRN‐NP 1
| Amy L.
Ford DNP, RN 1 | Kevin A. Kupzyk PhD 1 | Fernando A. Wilson PhD 2
1 University of Nebraska Medical Center College of Nursing, Omaha, Nebraska 2 University of Nebraska Medical Center College of Public Health, Omaha, Nebraska Correspondence Elizabeth Mollard, University of Nebraska Medical Center College of Nursing, 4101 Dewey Ave, Omaha, NE 68198. Email: elizabeth.mollard@unmc.edu. Funding information This study was supported by a grant from Blue Cross Blue Shield of Nebraska, Fund for Health Quality. The project was also supported by the National Institute of General Medical Sciences, 1U54GM115458‐01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Abstract Objectives : (1) Assess feasibility of a smartphone platform intervention combined with Community Health Worker (CHW) reinforcement in rural pregnant women; (2) Obtain data on the promise of the intervention on birth outcomes, patient activation, and medical care adherence; and (3) Explore financial implications of the intervention using return on investment (ROI). Sample : A total of 98 rural pregnant women were enrolled and assigned to interven‐ tion or control groups in this two‐group experimental design. Intervention : The intervention group received usual prenatal care plus a smartphone preloaded with a tailored prenatal platform with automated texting, chat function, and hyperlinks and weekly contact from the CHW. The control group received usual prenatal care and printed educational materials. Measurements : Demographics, health risk data, interaction with platform, medical records, hospital billing charges, Client Satisfaction Questionnaire‐8, satisfaction comments, and the Patient Activation Measure. Results : A total of 77 women completed the study. The intervention was well‐re‐ ceived, showed promise for improving birth outcomes, patient activation, and medi‐ cal care adherence. Financial analysis showed a positive ROI under two scenarios. Conclusions : Despite several practical issues, the study appears feasible. The inter‐ vention shows promise for extending prenatal care and improving birth outcomes in rural communities. Further research is needed with a larger and more at‐risk popula‐ tion to appreciate the impact of the intervention. KEYWORDS community‐based participatory research, community health workers, implementation science, mobile technology, premature birth, prenatal care, rural health, smartphone, text messaging
| 1 © 2018 Wiley Periodicals, Inc.
Public Health Nurs. 2018;1–9.
wileyonlinelibrary.com/journal/phn
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1 | INTRODUCTION
medical care adherence. The third aim was to explore the financial implications of the intervention 30‐day postdelivery using return on investment (ROI).
1.1 | Background Premature birth is a critical public health problem that causes a significant social and economic burden in the U.S. (Centers for Disease Control, 2017). Each year, 1 in 10 babies in the U.S. is born prematurely (before 37‐week gestation), leading to serious health problems, lengthy hospitalizations, lifelong disability, and death (American College of Obstetrics and Gynecology, 2016; March of Dimes, 2017). In the first year of life, the average health care expen‐ ditures for preterm infants are more than 10 times as high as those for uncomplicated newborns (March of Dimes, 2015). The financial burden from prematurity stretches into adulthood as babies born prematurely can experience lifelong problems including physical and intellectual disabilities, learning delays, vision or hearing loss, behav‐ ior problems, and neurological disorders (March of Dimes, 2017). Access to adequate prenatal health care is essential for reducing preterm births. However, many rural and underserved communities have limited access to care due to health care provider shortages and limited prenatal and social support services (National Conference of State Legislatures, 2016; Van Vleet & Paradise, 2015). Mobile tech‐ nology and community health workers (CHW) are two resources that can address rural and underserved barriers to health care by enhancing patient communication and extending provider outreach across distances (Abroms, Whittaker, Free, Mendel Van Alstyne, & Schindler‐Ruwisch, 2015; Singh et al., 2016; Thirumurthy & Lester, 2012). More than 65% of rural Americans own smartphones (Pew Research Center, 2018; Sterling, 2016), and 62% use their smart‐ phones to get information about health conditions (Smith, 2015). Another emerging resource for underserved areas are CHWs. CHWs are frontline public health workers, most commonly lay people with‐ out formal health care training, who have a close understanding of the community they serve. CHWs provide local, community‐based, culturally appropriate health counseling and education, and help patients connect with health and social support services (American Public Health Association, 2018; Hostetter & Klein, 2016; Kunz et al., 2017). Combining a smartphone intervention with CHW rein‐ forcement has the potential to increase health self‐care in pregnancy to reduce preterm birth in rural and underserved communities. 1.2 | Objectives The primary aim of this pilot study was to assess the feasibility of a tailored smartphone platform intervention (hereinafter re‐ ferred to as prenatal technology platform [PTP]) combined with CHW reinforcement among rural pregnant women. The HIPPA compliant PTP predeveloped by GoMo Health™ combined “tex‐ ting,” mobile websites, and other technologies to deliver targeted evidence‐based prenatal health information, instructional videos, and general wellness tips for pregnant women. The secondary aim was to obtain preliminary data on the appropriateness and prom‐ ise of the intervention on birth outcomes, patient activation, and
2 | METHODS
2.1 | Community‐based participatory research This research project used a community‐based participatory re‐ search approach that partnered academic researcher investigators from a large University with a 30‐member rural community advi‐ sory board formed for the purposes of this study. The community advisory board, named Central Nebraska Prenatal Advisory Board, included local rural health providers, hospital executives, pregnancy testing site directors, the rural regional public health department di‐ rector, a former state senator for the area, representatives from the state’s department of health, two Hispanic, Spanish‐speaking CHW, several directors of regional rural social service agencies, and two health care consumers who were new mothers (Cramer, Lazoritz, Shaffer, Palm & Ford, 2017). Community‐based participatory re‐ search is an essential tool for public health that utilizes community members to identify and address priority clinical problems and test evidence‐based solutions (Barkin, Schlundt, & Smith, 2013; Lasker & Weiss, 2003). Community‐based participatory research is an ideal method to ensure that priority health problems identified by com‐ munity members are understood by researchers and that health is viewed in a manner that is culturally and contextually relevant to people’s real‐life experiences (Barkin et al., 2013). 2.2 | Design, sample, & procedures This two‐group experimental design was approved by the research team’s University Institutional Review Board (IRB). Pregnant women ( n = 114) were recruited through referral by their health care provid‐ ers from five individual medical clinics in rural Midwestern counties. There was no formal training for providers to refer participants and the providers were not reimbursed or incentivized. After participants were referred to the study, enrollment was conducted at an in‐home visit conducted by the CHW where intervention participants were provided the preloaded smartphone. At 36 weeks, the CHW made a second home visit, collected conclusion data, and the smartphone. Inclusion criteria were rural women who planned to deliver at one of three rural hospitals, pregnancy at less than 24 weeks, and the ability to speak and read either Spanish or English. Exclusion criteria were women who required more than usual medical care (e.g., chronic hypertension, heart disease, uncontrolled diabetes) or those significantly predisposed to preterm birth (e.g., multiple gestation or history of preterm delivery). All ages could enroll, although minors had to obtain parental consent. Of those who met inclusion criteria, 98 enrolled and were assigned to the in‐ tervention or control group. Early recruits were assigned to the intervention group and later recruits to the control group due to
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time restraints on the use of the PTP. The study lasted 15 months, and participants were in the study from enrollment to 36 weeks gestation. Intervention participants received usual prenatal care plus a smartphone preloaded with the PTP and weekly contact from the CHW. Participants could use the smartphone for personal use during the study. The PTP sent weekly automated text messages containing evidence‐based prenatal self‐care information and hyperlinks with more in‐depth information (Figure 1). Messaging was personalized by name, trimester, health risks (e.g., smoking, alcohol or substance use, and prepregnancy BMI for overweight/obesity), and preferred language (English or Spanish). Additionally, the participants had ac‐ cess to the platform to message the CHW via “chat” and to access prenatal topics of their choosing. The CHW was a Hispanic, bilingual (English and Spanish) female who was currently working as a paid CHW for one of the local hos‐ pital systems. One member of our research team who is a women’s health nurse practitioner (WHNP) along with a clinic partner devel‐ oped a CHW prenatal health coaching curriculum to supplement the CHW’s preexisting training as a CHW. The CHW contacted partic‐ ipants weekly via “texting” and/or telephone call, in addition to re‐ sponding to participant initiated “chat” through the platform. The weekly CHW contact might include a variety of topics based on the participant’s need including appointment reminders, social service assistance, to discuss weekly text messages or hyperlinks from the platform, and to answer general questions. Control participants received two visits from the CHW, usual prenatal care as decided by their health care provider, an informa‐ tional prenatal packet including information on pregnancy trimester,
healthy eating, and smoking cessation, and a local social service directory.
2.3 | Measures Information from participants was collected via two CHW home vis‐ its, the PTP, medical records, and hospital billing charge data. Our technology vendor, GoMo Health ™ , collected the mobile engagement data (hits, SMS, etc.). The project coordinator, a WHNP, gathered the collected data on birth outcomes from hospital delivery notes and financial information from hospital finance departments, both obtained through data transfer agreements between the university and the hospitals. Medical adherence data (e.g., clinic visits, missed appointments, and prenatal care information) was collected by the outpatient clinic nurses. The CHW collected PAM, CSQ, baseline data on health risks (smoking, obesity, substances, demographics) from participants during the home visit at enrollment and 36 weeks. Additionally, the CHW collected data necessary for individualization of the PTP intervention (i.e., language, trimester, risk factors) to be communicated to GoMo Health ™ for participants enrolled in the in‐ tervention group. Aim 1 study measures for feasibility included the subcategories of (1) patient satisfaction with intervention, (2) enrollment, (3) fidel‐ ity, and (4) data collection. Patient satisfaction data were collected using the Client Satisfaction Questionnaire (CSQ‐8). The CSQ‐8 is an 8‐question, self‐report measure designed to evaluate satisfac‐ tion and value of care. Scores range from 8 to 32, with higher scores indicating greater satisfaction. The CSQ‐8 has high‐internal consis‐ tency, good reliability, and validity and demonstrates a correlation
FIGURE 1 (a) Participants receive automated texts with hyperlinks for additional information. (b) Hyperlinks contain evidence‐based prenatal self‐care information. (c) The PTP includes the ability to chat with the CHW, information about the study, and access to the evidence‐based prenatal self‐care topics
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3 | RESULTS
between satisfaction and patient outcomes (Attkisson & Zwick, 1982; Nguyen, Attkisson, & Stegner, 1983). Participants were also asked to provide comments on their satisfaction or dissatisfaction with the intervention upon completion of the study. Enrollment was examined for efficiency (time of recruitment and problems encountered) and attrition. Problems and solutions rele‐ vant to the intervention were identified from monthly community board meetings and research team meetings. Data on intervention fidelity came from our weekly research team meetings to discuss is‐ sues regarding delivery, receipt, and enactment of the intervention. Information on data collection was examined in terms of ease, time required, and missing data, and this information came from inter‐ views with those who collected the study data (i.e., clinic nurses, CHW, project coordinators, health economist). Aim 2 primary birth outcome measures were preterm (<37 weeks gestation) and low birth weight (<2,500 g), which were collected from hospital delivery notes. Secondary outcome measures were adherence to medical appointments (ratio of kept‐to‐scheduled ap‐ pointments) collected from clinic medical records, and the Patient Activation Measures (PAM). The PAM is a 13‐item questionnaire having strong psychometric properties measuring patient activa‐ tion. Patient activation refers to an individual’s knowledge, skills, and confidence in self‐care management which has been associated with better health outcomes and the ability to make a positive change in one’s health status (Hibbard, Mahoney, Stockard, & Tusler, 2005). Based on scores from 0 to 100, the PAM assigns patients to levels from 1 (least activated) to 4 (most activated). PAM scores are closely associated with health care costs (Hibbard, Greene, Sacks, Overton, & Parrotta, 2016). The CHW administered the PAM as a retrospec‐ tive pre‐post survey at the 36‐week home visit. Aim 3 measures for health care cost‐effectiveness were col‐ lected from hospital billing and financial departments for each par‐ ticipants’ (i.e., mother and baby) total charges. Data also included primary payer, primary payment, secondary payer, secondary pay‐ ment, patient payments, total charges, primary diagnosis, and diag‐ nosis description. 2.4 | Analytic strategy Chi‐square ( χ 2 ) and likelihood ratio tests (LRT) were used to examine differences between the intervention and control groups for demo‐ graphics, risk factors, insurance, and emergency room use. We used t tests to analyze differences in prepregnancy BMI, weight gain dur‐ ing pregnancy, age, and weeks pregnant. The Mann–Whitney U test (in place of t tests due to nonnormal distributions) compared birth weight and weeks’ gestation between the control and intervention groups. The LRT tested differences between the two groups for (1) low birth weight versus normal birth weight, and (2) preterm versus full term gestation. Results were analyzed with SPSS 23. Due to the small sample size and the pilot nature of this study, results were not significant at the 5% level; therefore, analysis results are reported as descriptive statistics and trends.
3.1 | Demographic information Of 98 enrolled participants, 21 did not complete the program. The reasons for noncompletion included four miscarriages, 1 “opt‐out,” and 16 who moved or switched health care out of the area with‐ out notification and for whom no final birth data were available. Thus, the final sample for analysis was n = 77 ( n = 41/52 interven‐ tion [79%]; n = 36/46 control [78%]). The sample was mostly white race, married with spouse present, educated with some college, em‐ ployed, and had insurance. Nearly half were Hispanic/Latino ethnic‐ ity, and about one‐third spoke a primary language other than English another language (mostly Spanish and some Somali). Only four par‐ ticipants smoked, and one reported using alcohol (all in the inter‐ vention group). About two‐thirds were overweight, and one‐quarter were obese. Prepregnancy BMIs for the intervention and control ( M = 27.46 and M = 27.49, respectively) were nearly identical.
3.2 | Feasibility
3.2.1 | Recruitment Efficiency
Recruitment was slower than anticipated due to 2–4‐day lag times between clinic referral and the CHW enrollment home visit. Three months into the study less than half of the total sample was en‐ rolled. The community board proposed adding a $50 diaper incen‐ tive (half at enrollment and half at close) to increase enrollment. The IRB approval for change of protocol took 6–7 weeks for ap‐ proval which further reduced efficiency. The diaper incentive change increased total referrals, but not enrollment. We had dif‐ ficulty recruiting the high‐risk population of teens and undocu‐ mented Hispanic women, a priority population as identified by the community board. To increase enrollment of this population, the community board proposed (1) translating intervention materials into Spanish and (2) seeking a waiver of parental consent for mi‐ nors. The IRB approved translation but only agreed to parental waivers for 17–18‐year olds living alone. These changes led to a slight increase in recruitment ( n = 10) of Spanish‐speaking only participants but did not increase the number of minors in the study. All referred patients under 17 years of age refused to obtain their parents’ approvals, and several undocumented minors could not obtain parental consent because they were living in the U.S. without parents. Undocumented women especially those who were 17–19 years expressed discomfort or hesitancy signing what they perceived to be “government forms” (e.g., consents). Many of the undocumented pregnant women recruited from Africa or Central America chose not to participate due to language or read‐ ing barriers. The final problem with recruitment was maintaining provider interest in the study. The researchers and community board worked to maintain provider interest, but as recruitment slowed so did provider interest and support.
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Due to time constraints of the study, patients recruited early in the study were enrolled in the intervention group, and those re‐ cruited later were enrolled in the control group. Despite nonrandom assignment, both the intervention and control groups were virtually equivalent on all demographic variables. Attrition Of N = 98 enrolled in our study, there were n = 77 who had com‐ plete birth data with which to measure birth outcomes. Those who had birth outcome data did not differ in attrition from those with‐ out complete data (control M = 21.3%; intervention M = 23.1%) ( p = 0.83). The two groups were similar on most demographic vari‐ ables except that those without complete data were significantly more likely to be uninsured or on government insurance ( p = 0.009); smoke ( p = 0.013); and be Hispanic/Latino ( p = 0.006). The loss of five loaned smartphones was also associated with attrition. 3.2.2 | Acceptability Fifty‐one participants completed the CSQ‐8 ( N = 16 control and N = 35 intervention). Intervention participants were satisfied and scored higher on the CSQ‐8 ( M = 3.59, SD = 0.3) than the control ( M = 3.22, SD = 0.7). Intervention participants also rated themselves higher on every CSQ‐8 item. The largest improvements were on Item 3: “ To what extent has our program met your needs ?” (intervention M = 3.46 vs. control M = 2.88) and Item 5: “ How satisfied are you with the amount of help you received ?” (intervention M = 3.63 vs. control M = 3.13). Thirty‐two respondents from the intervention group of‐ fered comments about the PTP and all were deemed positive by the
research team. Participants stated that they valued the information provided through the PTP (e.g., “ the personalized text messages were very helpful, and I learned different info that I had no clue about”, “the advice and text messages were a good reminder on topics that you may not think of”, and “learned about nutrition and resources that I was able to share with other moms ”). Participants liked the personalized as‐ pects of the program and individualized contacts with the CHW via technology (e.g., “ Knowing that I could call/text her,” “[I liked] personal contact with the representative ”). Several commented on the ease of the PTP (e.g., “ very easy to follow,” “I liked getting texts every week,” “it was convenient to get the reminders”) . Finally, some suggested having more text messages and contacts with the CHW (e.g., “ more access to the nurse [sic], “would like more text messages and more meetings”) .
3.2.3 | Fidelity Patient delivery and receipt
We measured the number of PTP (1) chats, (2) hyperlink hits, and (3) participant phone calls with the CHW. Overall, participants were highly engaged and receptive to the PTP. There were 241 chats among 41 participants. Most were initiated by participants (i.e., in‐ coming) during the first few months following enrollment. English‐ speaking participants were more engaged than Spanish‐speaking only participants; however, anecdotally, the CHW believed the lower participation rate was because most Spanish‐only speaking participants worked at the local meat packing plant where phone use was discouraged. There were many outgoing chats (initiated by the CHW) and most were focused on technology‐related is‐ sues or medical appointment reminders. Further evidence of
TABLE 1 Outcomes comparison between control and intervention groups
Group
Mean (SD)
Range
N
Weeks gestation Control
39.13 (1.6)
37 40 77 37 40 77
33.5–41.9
Intervention 39.43 (1.1)
36–41
Total
39.29 (1.4) 7.46 (1.1)
33.5–41.9
Birth weight
Control
4–9.3
Intervention 7.34 (0.9)
4.8–9.9
Total
7.4 (1)
4–9.9
N (%) Control
N (%) Intervention Total
Preterm or full term
Preterm Full term
2 (5.4%)
1 (2.5%)
3
35 (94.6%)
39 (97.5%)
74 77
Total Low
37
40
Low birth weight
1 (2.7%)
1 (2.5%)
2
Normal
36 (97.3%)
39 (97.5%)
75 77
Total
37
40 21 35 56 35 37 72
PAM increase
Control
0.15 (0.3) 0.19 (0.2) 0.18 (0.2) 95.06 (6.8)
−0.2 to 0.8 −0.2 to 0.9 −0.2 to 0.9 78.6–100 73.3–100 73.3–100
Intervention
Total
Percent adher‐ ence to medical visits
Control
Intervention 93.85 (8.3)
Total
94.44 (7.5)
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participant receptivity to the intervention was the percentage of hyperlink hits that occurred primarily during the first months after enrollment (Figure 1). Again, English‐only speaking participants were more likely to hit the hyperlinks. Finally, the CHW had fre‐ quent telephone calls ( n = 210) with the 41 participants, and the majority ( N = 203 calls) involved medical appointment reminders. The average number of calls was 5.83 per participant, with a range of 1–10 per participant. The CHW successfully resolved 97% of participant issues by phone and the focus was mainly on preparing participants for office visits. Enactment of intervention Three issues affected intervention enactment (1) loaning smart‐ phones to participants, (2) issues with clinical partners, and (3) a placebo effect. We learned during the study that most participants (86%) already owned a smartphone, had unlimited texting (86%), and at‐home Wi‐Fi (65%). Thus, participants said that it had been inconvenient to carry two phones (both theirs and the study phone). Participants sometimes forgot to carry the study phone and check intervention messages. Some of the provided phones transmitted errant spam messages from old phone numbers causing participants to turn off their phones for a week or more. The spam problem was resolved after several weeks. Issues with clinical partners included getting provider buy in and changes in clinical practice after initiation of the study. Although we had the support of the community advisory board, and many clinical providers not all providers were motivated to refer patients. The diaper incentive, which was unsuccessful at promoting enroll‐ ment of participants, did increase referrals, making us believe that further incentives for patients or even for clinicians may have in‐ creased motivation to refer patients to participate in the study. An additional issue was when clinic partners altered their “usual med‐ ical care” during the study by (1) hiring a CHW and/or (2) changing clinical practices (e.g., greater attention to missed appointments, increasing patient education). These changes may have skewed re‐ sults of the study design and impacted the control group which was recruited last and most likely to benefit from the deviation from usual medical care that had been established before the study. Finally, there may have been a placebo effect among the con‐ trol group based on control group participants who commented on the CSQ‐8 that they enjoyed the “program,” the “home visits,” and the “information” provided during the study. It is possible that these comments reflect that at least some in the control group may have believed the informational packets and two CHW home visits for data collection constituted the study “treatment.”
3.2.4 | Promise of intervention Since pilot studies are not designed to assess evidence of benefit for‐ mally and are, in general, underpowered to achieve statistical significance at the 5% level (Lancaster, Dodd, & Williamson, 2004; Lee, Whitehead, Jacques, & Julious, 2014; Thabane et al., 2010), as was the case in our study. We analyzed descriptive statistics to report on the appropriate‐ ness and promise of the intervention for improving outcomes (Table 1). In this study, the intervention group had more full‐term deliveries (97.5%) than the control group (94.6%). The intervention group also had slightly longer average weeks gestation ( M = 39.43, SD = 1.1) versus the control group ( M = 39.13, SD = 1.6). The intervention group had slightly more normal birth weight infants (97.5%) versus the control group (97.3%). We also measured whether the intervention would result in greater pre‐post PAM scores, and results showed that the intervention group had a greater increase in PAM scores than the control group. Individual analysis of the PAM showed that two items showed the biggest im‐ provement for the intervention group: Item 5: “I am confident that I can tell whether I need to go to the doctor or whether I can take care of a health problem myself”; and Item 12: “I am confident I can figure out solutions when new problems arise with my health.” 3.2.5 | Financial impact of intervention The financial impact analysis was to determine whether the PTP intervention was cost‐effective and could produce health care cost savings for the intervention group relative to the control group. To undertake this analysis, we compiled data on participants’ use of hospital services, including hospital setting (e.g., inpatient vs. emer‐ gency), dates of admission and discharge, age, the primary source of payment, payment amounts, total hospital charges, and primary clinical diagnosis and its description. Three hospital partners for this study provided data for study participants in both the intervention and control groups. The financial analyses were undertaken from the perspective of a health care provider. Program costs consisted of the following components: the PTP service fee, the CHW, and provision of a smartphone to participants for the duration of the study. The per member per month cost of the PTP for this study was $3.00 which included the ability to chat within the platform. The cost of the CHW was a function of the pro‐ portion of full‐time equivalence (FTE) allocated to the program and the average wage plus benefits of a CHW. The duties of the CHW include receiving 2 weeks training to utilize the PTP in addition to 0.3 FTE of CHW time allocated toward participant outreach after its implementation. The hourly cost of the CHW used in our study was
TABLE 2 Return on investment analysis by estimated inpatient cost savings per participant and option based on $520 cost savings per participant
Intervention cost per participant
Return on investment (%)
Option
Intervention description
A B
Mobile Technology service only Mobile Technology service, CHW training, and CHW program management
$27.00
1,859%
$251.25
90%
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$18.75/hour including benefits, which resulted in $1,500 in train‐ ing or approximately $37.50 per intervention participant. Similarly, participant outreach by the CHW was estimated to cost $213.75 per intervention participant. Finally, the average cost of providing a smartphone to participants was estimated at $20 per month. Based on these data, we performed return on investment (ROI) analysis stratified by two scenarios, where the provision of a smart‐ phone was not included (due to the high participant ownership of smartphone). Option #1 assumed that the only intervention cost was the PTP, which was calculated to be $27 per participant. Option #2 incorporated the CHW training in addition to the PTP as well as the CHW cost of participant outreach totaling $251.25 per participant (Table 2). We calculated average differences in inpatient costs between intervention and control groups in addition to cost differences after adjusting for outliers (highest 5% of charges) and selected pa‐ tient characteristics (patient age in years, primary payer (Medicaid, Blue Cross/Blue Shield, other commercial insurance, self‐pay), and cesarean vs. vaginal delivery). Adjustments were based on multi‐ variable regression analysis. The average overall difference in costs totaled $1,079 savings for intervention participants compared to the controls. Excluding outlier patients, the average difference in costs was $1,547 savings between intervention and control groups. This decreased to a $529 savings for the intervention group after adjusting for age, primary payer, and cesarean section. Therefore, we estimated ROI by this range of cost savings, that is, $529, $1,079, and $1,547. This range provides information on the sensitivity of ROI to substantial differences in program health care cost savings. Utilizing the above data on inpatient cost in addition to the two op‐ tions described above concerning anticipated program costs per par‐ ticipant, returns are highest for option #1 (1859%) because of the low per member per month cost of providing the PTP with chat enabled. However, even after including all cost components (PTP, CHW train‐ ing, and CHW participant outreach), ROI is estimated at 90% showing either option presents a good ROI and increase in health care savings.
effectiveness. Intervention participants showed higher increases in their patient activation (PAM), and they were satisfied with interven‐ tion participation. Participants were actively engaged with the CHW and used the PTP as evidenced by the number of hyperlinks, chats, and phone calls. Participants valued the personalized aspects of the intervention, and all offered positive comments on the intervention. Further research is needed to determine the extent of the promise for the use of both smartphones and CHWs toward improving birth outcomes. Our study showed that the intervention had significant cost savings. The ROI analysis suggests that an intervention that utilizes the PTP with CHW reinforcement is likely to be cost‐effective and financially sustainable. Depending on the scenario, the ROI for the intervention ranged from 90% to over 1859%. While the reasons for these cost savings are not clear, possible reasons could include the improved patient activation scores associated with the inter‐ vention group. Research shows that patient activation is associated with reduced health care costs (Hibbard et al., 2016) and has been shown to accurately predict the use of costly services in patients up to 4 years (Hibbard, Green, Shi, Mittler, & Scanlon, 2015). Based on nonresponses to some PAM questions regarding “health problems,” and even though emerging research shows increased PAM scores may associate with improved pregnancy experience and increased vagi‐ nal delivery rate (Ledford et al., 2018), further research is needed on the PAM as a measurement tool in pregnant patients. Additionally, more research is required to establish the link between mobile tech‐ nology and CHW reinforcement on patient activation in a prenatal population. Our feasibility findings contribute to a better understanding of intervention delivery and sampling issues as well as the appro‐ priateness of impact measures in rural populations—all of which are essential for larger scale studies (Thabane et al., 2010; Conn, Algase, Rawl, Zerwic, & Wyman, 2011). Our study outcome results show promising trends for the intervention to improve patient communication and promote self‐care competence during preg‐ nancy. These findings are consistent with recent research show‐ ing mobile phones to be an emerging health technology that can positively modify health behaviors (Abroms et al., 2015; Muench & Baumel, 2017; Singh et al., 2016). Additional research is needed that includes more at‐risk participants who can benefit from the intervention and a larger sample to draw conclusions about inter‐ vention effectiveness and financial implications compared to usual prenatal care. 4.1 | Limitations The small sample size and the low‐risk population were signifi‐ cant limitations, although appropriate for a pilot feasibility study ((Lancaster et al., 2004; Lee et al., 2014; Thabane et al., 2010). An unexpected limitation was the study bias introduced when clinic staff changed clinical practices, and some clinics hiring CHWs unre‐ lated to our study. The limitations of our financial analysis are based on restricting the cost data to hospital billing charges.
4 | DISCUSSION
We conducted a pilot feasibility study using the PTP intervention that leveraged smartphone technology and CHW reinforcement to promote prenatal self‐care among rural women. Feasibility studies help research‐ ers determine whether a study is likely to be delivered successfully, while considering the practical aspects and challenges of the project. Despite numerous study difficulties with recruitment and enrollment and the change in clinical practices and potential placebo effect that may have impacted our control group, the PTP appeared to our research team to be a feasible and cost‐effective intervention. Considering some of the below recommendations, we believe that this study could be conducted on a larger, fully powered scale. The intervention showed promise for improving health out‐ comes in participants but needs a larger sample size to determine
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Board and who actively participated in the planning and implemen‐ tation of this community‐based participatory pilot study by assisting with data collection, analysis, and dissemination.
4.2 | Conclusion and recommendations Our feasibility findings help inform implementation science re‐ searchers on rural recruitment, sampling, and fidelity using mobile technology with CHW reinforcement as a clinical intervention in rural populations, where is it essential to promote adoption and integration of evidence‐based practices into provider practices (Thabane et al., 2010). This study provides data for a larger scale research study. Future studies should consider a four‐group de‐ sign (CHW, CHW +PTP, PTP, Control) to separate the effects of the mobile technology from the CHW. We recommend that any future study using the PTP as a clinical intervention be tested using the patient’s smartphone. Most young adults already own a smartphone and have an unlimited text message plan, so testing the PTP on their equipment would not add unnecessary financial burdens and would increase ease of access to the platform and increase engagement. Should participants relocate or change providers midstudy, the PTP would follow them and help reduce study attrition while improving continuity of care. It is advisable that future studies operate within a longer time frame (2–3 years) and that testing be done in a larger population pool with more high‐risk patients who are likely to ben‐ efit from the intervention. For investigators using a community‐based participatory ap‐ proach, it is vital to ensure control over study protocols to avoid bias that can inadvertently be introduced by clinical partners who may be interested in improving patient care by altering professional prac‐ tices. Researchers must assure that community partners understand study bias, and how altering usual medical care during a research study alters study findings. It is also advisable that recruitment and consent occur at the clinic visit where patients are more easily ap‐ proached about study participation as opposed to in‐home visits by the CHW. To mitigate barriers of clinic staff time, we recommend that 1) clinic nurses screen for eligible patients and 2) a clinical CHW, social worker, or staff meet with the patient at the clinic in another office to consent, gather baseline data, orient patients to the tech‐ nology, and provide recruitment incentives. Our findings underscore the need to develop recruitment strat‐ egies including incentives, enroll during clinic visits, negotiate with the IRB on parental consent waivers for minors, and format text messages using multilingual audio services for those who cannot read. Appropriate outcomes measures include birth weight and ges‐ tation; however, a larger sample and study time frame are required to adequately test the intervention’s impact on these outcomes. Finally, further research should focus on the intervention’s effect on patient activation and link to cost‐effectiveness since activated patients have better self‐management, functioning, and use fewer health services over time compared to less activated patients and our intervention especially showed promise in this area. ACKNOWLEDGEMENTS The authors wish to acknowledge Stephen Lazoritz, MD and Kenton Shaffer, MD who co‐chaired the Central Nebraska Prenatal Advisory
CONFLICT OF INTEREST The authors report no conflict of interest.
ORCID Mary E. Cramer
https://orcid.org/0000-0002-1510-9477
http://orcid.org/0000-0003-0221-3459
Elizabeth K. Mollard
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