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An extraordinary increase in mobile phone ownership has revolutionized the opportunities to use mobile health approaches in lower- and middle-income countries (LMICs). Ecological momentary assessment and intervention (EMAI) uses mobile technology to gather data and deliver timely, personalized behavior change interventions in an individual’s natural setting. To our knowledge, there have been no previous trials of EMAI in sub-Saharan Africa.
To advance the evidence base for mobile health (mHealth) interventions in LMICs, we conduct a pilot randomized trial to assess the feasibility of EMAI and establish estimates of the potential effect of EMAI on a range of health-related behaviors in Rakai, Uganda.
This prospective, parallel-group, randomized pilot trial compared health behaviors between adult participants submitting ecological momentary assessment (EMA) data and receiving behaviorally responsive interventional health messaging (EMAI) with those submitting EMA data alone. Using a fully automated mobile phone app, participants submitted daily reports on 5 different health behaviors (fruit consumption, vegetable consumption, alcohol intake, cigarette smoking, and condomless sex with a non–long-term partner) during a 30-day period before randomization (P1). Participants were then block randomized to the control arm, continuing EMA reporting through exit, or the intervention arm, EMA reporting and behavioral health messaging receipt. Participants exited after 90 days of follow-up, divided into study periods 2 (P2: randomization + 29 days) and 3 (P3: 30 days postrandomization to exit). We used descriptive statistics to assess the feasibility of EMAI through the completeness of data and differences in reported behaviors between periods and study arms.
The study included 48 participants (24 per arm; 23/48, 48% women; median age 31 years). EMA data collection was feasible, with 85.5% (3777/4418) of the combined days reporting behavioral data. There was a decrease in the mean proportion of days when alcohol was consumed in both arms over time (control: P1, 9.6% of days to P2, 4.3% of days; intervention: P1, 7.2% of days to P3, 2.4% of days). Decreases in sex with a non–long-term partner without a condom were also reported in both arms (P1 to P3 control: 1.9% of days to 1% of days; intervention: 6.6% of days to 1.3% of days). An increase in vegetable consumption was found in the intervention (vegetable: 65.6% of days to 76.6% of days) but not in the control arm. Between arms, there was a significant difference in the change in reported vegetable consumption between P1 and P3 (control: 8% decrease in the mean proportion of days vegetables consumed; intervention: 11.1% increase;
Preliminary estimates suggest that EMAI may be a promising strategy for promoting behavior change across a range of behaviors. Larger trials examining the effectiveness of EMAI in LMICs are warranted.
ClinicalTrials.gov NCT04375423; https://www.clinicaltrials.gov/ct2/show/NCT04375423
To date, behavior change strategies in lower- and middle-income countries (LMICs) have failed to fully leverage the potential of mobile technology to promote optimal health outcomes. Although this may be partially because of historically limited technology access in these settings, an extraordinary increase in mobile technology ownership and use, facilitated by advances in lower-cost smartphones, has revolutionized the opportunities to use mobile health approaches in LMICs [
Ecological momentary assessment and intervention (EMAI) uses mobile technology to gather individual-level behavioral data and deliver timely, personalized behavior change interventions in an individual’s natural setting [
However, there is limited extant literature on the effectiveness of EMAI, particularly in LMICs. Several studies in high-income settings have demonstrated the preliminary effectiveness of EMAI using targeted, remote messages to improve mental health outcomes [
To advance the evidence base for mobile health interventions in LMICs, we conducted a pilot randomized trial to establish estimates of the potential effect of EMAI on a broad range of health-related behaviors in Rakai, Uganda. On the basis of extant EMAI literature, theory, and evidence [
The study was a prospective, parallel-group, randomized pilot trial in Rakai, Uganda. It sought to establish a preliminary estimate of the effect of EMAI on health behaviors between participants submitting EMA data and receiving behaviorally responsive interventional health messaging compared with those submitting EMA data alone.
The study sampled adult participants (aged 18-49 years) from the Rakai Community Cohort Study (RCCS), an open, population-based cohort running since 1994 [
In addition to the primary study outcome, the preliminary estimate of the effect of EMAI, as a pilot study, the secondary aim was to assess the feasibility of the data collection and intervention approach. To do so, we examined the indicators of data collection success by the study arm. All outcomes were assessed after the closure of the study.
Interested participants attended an in-person visit at the study office, enrolling on completion of voluntary, written informed consent at the first in-person study visit. At the first study visit, participants were issued a password-protected smartphone programmed with the EMAI study app (emocha Health Inc), a phone charger, and a portable power bank. Participants were trained on the use of the smartphone and a fully automated study app and completed a paper-based enrollment questionnaire collecting participant demographic and behavioral data, recalling the 30 days before enrollment.
Communicating in Luganda, the primary language spoken in the region, the app collected EMA data on 5 behaviors of interest: fruit consumption, vegetable consumption, alcohol intake, cigarette smoking, and sex with a nonmarital or non–long-term partner without a condom. EMA behavioral data were submitted by the participant through the app (1) in response to a text message prompt twice per day, once at a random time and once at a fixed time asking about each of the behaviors since the last prompt-based report, (2) in response to a text message prompt sent each week, recalling behaviors throughout the week, and (3) through a participant-generated report sent within approximately 1 hour of engaging in any of the study behaviors of interest (an
For the first 30 days of the study, all participants sent EMA data, after which they were randomized to control or intervention conditions. Participants in the control arm continued to submit EMA data throughout the remainder of the study period. From randomization to study exit, intervention arm participants received health-related messages responsive to the behavioral data submitted in addition to continuing EMA data submissions. The messages, developed using participatory formative research including free-listing and sorting of proposed messages with a convenience sample of 8 Rakai residents to enhance appropriateness and relevance, provided positive reinforcement for reported healthy behaviors (eg, Living alcohol-free today is a step to a healthier future! Alcohol contributes to heart disease and liver cancer.) and encouragement to change in response to reported risk behaviors (eg, Alcohol abuse increases your risk of heart disease. Protect your heart, and stick to water or juice tomorrow). The participants received messages that were directly relevant to the responses they submitted. The specific message a participant received from the bank of possible messages (
Participants were compensated for their time (UGX 10,000; approximately US $3) and reimbursed for travel costs (UGX 5000-40,000; US $1.50-12) for each in-person study visit. Participants were given funds equivalent to 525 MBs of data monthly throughout the study and an incentive totaling UGX 100,000 (approximately US $30) in 3 increments at 30, 60, and 90 days for responding to ≥50% of data collection prompts. The study was approved by the Ugandan Virus Research Institute Research and Ethics Committee and the Johns Hopkins School of Medicine Institutional Review Board.
Study design.
Participants were assigned to the control or intervention study arm using block randomization with randomly varying block sizes of 4, 6, and 8 through blockr R package by Greg Snow. The study arm assignments were enclosed within opaque, consecutively numbered envelopes. At the day 30 visit, the study coordinator allocated the randomization assignment enclosed in the next consecutive envelope to each participant, activating the appropriate EMAI smartphone module. The assignments were not masked to the study participants or staff.
Participant characteristics and behaviors at enrollment were collected on the paper-based enrollment questionnaire. Occupation was measured using the last RCCS survey round. The exposure of interest, receiving intervention messages, was measured as a dichotomous variable, with all participants assigned to the intervention arm counted as exposed and all control arm participants as unexposed. The outcomes were examined separately for each of the 5 study behaviors of interest: (1) fruit consumption, (2) vegetable consumption, (3) alcohol use, (4) cigarette smoking, and (5) sex with a nonmarital or non–long-term partner without a condom.
Participants were assigned a
The total number of days in the study was counted from enrollment to the exit date. The number of event-contingent reports and prompt-driven behavioral report responses submitted was counted using the total number of database entries (submitted by the smartphone and received by the database) for each type of report mechanism. Each report included the behavioral information reported and the time and date it was submitted. A day was counted as missing behavioral data for a participant if there were no reports recorded in the database on a date between study enrollment and exit. The study follow-up was divided into 3 study periods: period 1 (baseline; P1: enrollment to the day before randomization), period 2 (P2: the day of randomization to 29 days postrandomization), and period 3 (P3: 30 days postrandomization to study exit).
Given the pilot nature of the study, we primarily used a descriptive approach to examine the study outcomes. Descriptive statistics were used to compare participant characteristics and data collection between the 2 study arms. We examined the comparability of participant characteristics by arm at baseline using chi-square and two-tailed Student
Between June 10, 2016, and March 1, 2017, 71 participants were screened for enrollment, of whom 58 were enrolled. Of 58 participants, 8 were excluded because of early failure of the study application and 2 dropped out at 15 and 79 days after enrollment. The complete analysis data set included 48 participants (
Participant flow diagram. EMA: ecological momentary assessment; EMAI: ecological momentary assessment and intervention.
Of the 48 participants, 23 (48%) were female, with a median age of 31 (IQR 25-38) years. Less than one-third of the participants worked in agriculture (14/48, 29%), with 17% (8/48) working in trade, 23% (11/48) teachers, and 31% (15/48) in other occupations. In the 30 days before study enrollment, nearly all participants (47/48, 98%) reported eating a vegetable on at least one day, 90% (43/48) consumed fruit, whereas 35% (17/48) consumed alcohol, 17% (8/48) reported sex with a nonmarital or non–long-term partner, and 13% (3/48) smoked a cigarette. There were no significant differences in participant characteristics or behaviors at enrollment between the study arms (
Participant characteristics at enrollment by study arm.
Participant characteristics | Control (n=24) | Intervention (n=24) | Total (N=48) | |||||||
Female, n (%) | 12 (50) | 11 (46) | 23 (48) | .77 | ||||||
Age at enrollment (years), mean (SD) | 32.7 (7.1) | 30.1 (6.7) | 31.4 (7.0) | .10 | ||||||
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.94 | |||||||||
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Some secondary | 7 (29) | 8 (33) | 15 (31) |
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Secondary | 10 (42) | 9 (38) | 19 (40) |
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University, technical or vocational | 7 (29) | 7 (29) | 14 (29) |
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Yes, owns a cell phone, n (%) | 24 (100) | 24 (100) | 48 (100) | N/Ab | ||||||
Yes, feels comfortable using a phone to send text messages, n (%) | 24 (100) | 22 (92) | 46 (96) | .15 | ||||||
Yes, ever used a smartphone app, n (%) | 14 (58) | 12 (50) | 26 (54) | .56 | ||||||
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.53 | |||||||||
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Agrarian | 6 (25) | 8 (33) | 14 (29) |
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Trader | 5 (20) | 3 (12) | 8 (16) |
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Teacher | 4 (17) | 7 (29) | 11 (23) |
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Other | 9 (38) | 6 (25) | 15 (31) |
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Smoked cigarette at least one day, n (%) | 3 (13) | 0 (0) | 3 (13) | .07 | |||||
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Among smokers, days smoked at least one cigarette, mean (SD) | 20 (13.1) | N/A | N/A | N/A | |||||
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Drank alcoholic beverage at least one day, n (%) | 8 (33) | 9 (38) | 17 (35) | .76 | |||||
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Among drinkers, days drank at least one alcoholic beverage, mean (SD) | 2.1 (1.3) | 1.6 (0.7) | N/A | .29 | |||||
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Ate vegetables at least one day, n (%) | 22 (92) | 21 (88) | 43 (90) | .64 | |||||
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Among those who ate vegetables, days ate at least one vegetable, mean (SD) | 7.2 (5.4) | 6.7 (7.9) | N/A | .80 | |||||
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Ate fruit at least one day, n (%) | 23 (96) | 24 (100) | 47 (98) | .31 | |||||
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Among those who ate fruit, days ate at least one fruit, mean (SD) | 13.6 (9.0) | 12.5 (8.2) | N/A | .67 | |||||
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Had sex with nonmarital or non–long-term partner without using a condom at least once, n (%) | 5 (21) | 3 (13) | 8 (17) | .44 | |||||
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Times had sex with a nonmarital or non–long-term partner without a condom, among those reporting sex, mean (SD) | 2.4 (1.9) | 2.3 (0.6) | N/A | .96 |
aTwo-sided
bN/A: not applicable.
The mean total number of days of follow-up was 92 (minimum 90 and maximum 94). Comparing study arms, there were no significant differences in time in study, data submission types (event-contingent or prompt-based responses), or proportion of study days without data submitted (
Over the study periods, the reported engagement in any of the behaviors varied. All 48 participants reported eating fruits and vegetables on at least one day during each of the 3 study periods, except for 1 participant who did not report eating vegetables on any day during study period 2. Ever consuming alcohol was reported by approximately half of the participants or fewer across the periods (control arm period 1: 13 participants, period 2: 9 participants, and period 3: 8 participants; intervention arm periods 1 and 2: 12 participants, period 3: 7 participants). Far fewer participants reported ever having sex with a non–long-term partner without a condom (control arm period 1: 4 participants, periods 2 and 3: 2 participants; intervention arm periods 1 and 2: 4 participants and period 3: 5 participants) or ever smoking cigarettes (control arm period 1: 7 participants, period 2: 3 participants, and period 3: 4 participants; intervention arm period 1: 3 participants, period 2: 1 participant, and period 3: 0 participants).
Study data collection indicators by study arm.
Data indicator | Control | Intervention | Effect sizeb (95% CI) | ||||||||
Total study days, mean (range) | 92.0 (90-94) | 92.1 (90-94) | −0.28 (46) | .78 | −0.08 (−0.66 to 0.50) | ||||||
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Period 1: baseline (study day 1 to day before randomization) | 30.6 (29-33) | 30.8 (29-33) | −0.57 (46) | .57 | −0.16 (−0.71 to 0.38) | |||||
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Period 2 (randomization to 29 days postrandomization) | 30 (30-30) | 30 (30-30) | N/Ac | N/A | N/A | |||||
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Period 3 (30 days postrandomization to final study day) | 31.4 (28-33) | 31.3 (28-33) | 0.23 (46) | .82 | 0.07 (−0.48 to 0.61) | |||||
Total event-contingent reports, mean (SD) | 108.6 (67.5) | 99.8 (46.4) | 0.53 (46) | .60 | 0.15 (−0.44 to 0.75) | ||||||
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Period 1: baseline (study day 1 to day before randomization) | 47.5 (35.0) | 43.3 (26.7) | 0.47 (46) | .63 | 0.14 (−0.45 to 0.72) | |||||
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Period 2 (randomization to 30 days after randomization) | 30.0 (22.4) | 27.9 (14.7) | 0.38 (46) | .71 | 0.11 (−0.54 to 0.76) | |||||
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Period 3 (30 days after randomization to final study day) | 31.1 (23.0) | 28.9 (13.8) | 0.46 (46) | .65 | 0.13 (−0.48 to 0.75) | |||||
Total responses submitted to prompts, mean (SD) | 92.2 (28.6) | 96.9 (23.2) | −0.63 (46) | .53 | −0.18 (−0.78 to 0.42) | ||||||
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Period 1: baseline (study day 1 to day before randomization) | 20.4 (6.9) | 21.5 (5.0) | −0.62 (46) | .54 | −0.18 (−0.78 to 0.42) | |||||
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Period 2 (randomization to 30 days after randomization) | 35.4 (14.5) | 38.3 (9.0) | −0.85 (46) | .40 | −0.24 (−0.82 to 0.33) | |||||
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Period 3 (30 days after randomization to final study day) | 36.4 (12.8) | 37.1 (12.2) | −0.18 (46) | .85 | −0.05 (−0.64 to 0.53) | |||||
Total days without behavior reported, mean (SD) | 14.2 (10.7) | 12.4 (9.3) | 0.62 (46) | .54 | 0.18 (−0.45 to 0.81) | ||||||
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Period 1 (study day 1 to day before randomization) | 3.1 (2.6) | 3.4 (2.9) | −0.36 (46) | .72 | −0.10 (−0.70 to 0.49) | |||||
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Period 2 (randomization to 30 days after randomization) | 3.7 (3.2) | 3.0 (3.1) | 0.73 (46) | .47 | 0.21 (−0.44 to 0.86) | |||||
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Period 3 (30 days after randomization to final study day) | 7.4 (7.1) | 6.0 (6.2) | 0.74 (46) | .47 | 0.21 (−0.40 to 0.82) | |||||
Proportion of study days without behavior report (%), mean (SD) | 15.4 (11.5) | 13.5 (10.1) | 0.61 (46) | .54 | 0.18 (−0.45 to 0.81) | ||||||
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Fruit | 79.0 (0.2) | 78.6 (0.2) | 0.06 (46) | .95 | 0.02 (−0.61 to 0.65) | |||||
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Vegetable | 57.8 (0.2) | 65.6 (0.3) | −0.99 (46) | .33 | −0.28 (−0.93 to 0.36) | |||||
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Alcohol | 9.6 (0.2) | 7.2 (0.1) | 0.51 (46) | .61 | 0.15 (−0.44 to 0.73) | |||||
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Sex with non–long-term partner without a condom | 1.9 (0.04) | 6.6 (0.1) | −1.74 (46) | .09 | −0.50 (−0.96 to −0.05) | |||||
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Smoking | 6.5 (0.2) | 1.4 (0.03) | 1.26 (46) | .22 | 0.36 (−0.14 to 0.86) |
aTwo-sided
bCohen
cN/A: not applicable.
There was a decrease in the mean proportion of days when alcohol was consumed in both the control and intervention arms. In the control arm, a decrease was observed between periods 1 and 2 (9.6% of days to 4.3% of days), whereas it was observed between periods 1 and 3 in the intervention arm (7.2% of days to 2.4% of days;
Mean proportion of days participants report behaviors by study period and arm (n=24 per arm).
Reported behavior | Control (%), mean (95% CI) | Intervention (%), mean (95% CI) | |||||
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Period 1 | Period 2 | Period 3 | Period 1 | Period 2 | Period 3 | |
Fruit | 79 (71.9 to 86) | 80 (71.3 to 88.8) | 82 (73.7 to 90.3) | 78.6 (70.2 to 87.1) | 82.6 (73.5 to 91.8) | 87.0 (79.3 to 94.7) | |
Vegetable | 57.8 (48.2 to 67.5) | 55.6 (44.2 to 66.9) | 49.9 (37.7 to 62) | 65.6 (54.1 to 77) | 68 (57.3 to 78.6) | 76.6 (67 to 86.2) | |
Alcohol | 9.6 (1.3 to 17.8) | 4.3 (1 to 7.6) | 4.2 (1.6 to 6.8) | 7.2 (2.9 to 11.6) | 5 (−0.1 to 10.1) | 2.4 (−0.2 to 4.9) | |
Sex with non–long-term partner without a condom | 1.9 (0.5 to 3.3) | 1 (−0.7 to 2.7) | 1 (−0.1 to 2.1) | 6.6 (1.8 to 11.4) | 2.1 (0.3 to 3.8) | 1.3 (0.2 to 2.3) | |
Smoking | 6.5 (−0.9 to 13.9) | 5.1 (−2.6 to 12.8) | 5.8 (−2.7 to 14.3) | 1.4 (−0.1 to 3) | 0.3 (−0.3 to 1) | 0a |
a95% CI values are not applicable.
Mean proportion of days on which behavior occurred by participant according to study arm and period (n=24 per arm).
The comparison of study arms showed a significant difference in the change in reported vegetable consumption between periods 1 and 3 (control: 8% decrease in the mean proportion of days vegetables were consumed; intervention: 11.1% increase in the mean proportion of days vegetables were consumed;
Mean difference in proportion of days participants reported behavior between periods (later period minus earlier period, positive number indicates increase in behavior over time, and negative number indicates decrease in behavior over time; n=24 per arm).
Reported behavior | Control (%) | Intervention (%) | Difference of differences (intervention−control; %) | ||
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Fruit | 1.01 | 3.99 | 2.98 | .49 |
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Vegetable | −2.28 | 2.41 | 4.69 | .47 |
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Alcohol | −5.28 | −2.24 | 3.04 | .24 |
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Sex with non–long-term partner without a condom | −0.94 | −4.53 | −3.59 | .12 |
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Smoking | −1.36 | −1.12 | 0.24 | .90 |
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Fruit | 1.98 | 4.38 | 2.4 | .52 |
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Vegetable | −5.71 | 8.64 | 14.35 | .002 |
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Alcohol | −0.05 | −2.61 | −2.56 | .21 |
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Sex with non–long-term partner without a condom | −0.02 | −0.8 | −0.78 | .42 |
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Smoking | 0.71 | −0.31 | −1.02 | .07 |
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Fruit | 2.99 | 8.37 | 5.38 | .18 |
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Vegetable | −7.99 | 11.05 | 19.04 | .01 |
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Alcohol | −5.33 | −4.86 | 0.47 | .89 |
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Sex with non–long-term partner without a condom | −0.96 | −5.33 | −4.37 | .07 |
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Smoking | −0.65 | −1.43 | −0.78 | .69 |
aTwo-sided
This pilot study found that EMAI was feasible and may influence a range of participant behaviors. EMA alone may also affect the reported behaviors. To our knowledge, this is the first study of EMAI in sub-Saharan Africa. It provides a foundation on which further research on EMAI in transferable settings can be framed.
The feasibility of EMAI in a pilot study context in Rakai, Uganda, was supported in this study through high participant retention in both arms, yielding comparable study groups without adjustment and consistent submission of EMA data. The 85.5% (3777/4418) of the combined study follow-up days with behavioral data in this study is consistent with or better than data collection feasibility in other studies in high-income settings [
Remote data collection and messaging may have some effect on behavior over a relatively short period. Descriptive comparisons of daily behavioral reports between the approximately 30-day study periods within arms suggest that alcohol consumption, sex with a non–long-term partner without a condom, fruit consumption, and smoking may be influenced by EMA or EMAI and that vegetable consumption may be influenced by EMAI. Although the study expected behavior change to be associated with intervention messaging, we hypothesize that daily reporting increased participant awareness of health risk behaviors, which may have changed their practices or reporting. This is consistent with theoretical and interventional extant self-monitoring literature, supporting that reactivity associated with improved self-awareness, particularly for routinized behaviors, can lead to behavior change [
Future research should not only expand beyond a pilot context to determine more robust estimates of the EMAI effect but should also examine differential pathways of change in raising awareness of risk behaviors compared with provision of feedback on positive, routinized behaviors to better understand the potential of both EMA and EMAI, beyond measurement. Although behavioral change may be rapid with EMAI, it is also necessary to examine the sustainability of change beyond the study’s relatively short 90-day follow-up. Although our study used smartphones to allow for geospatial data collection in addition to behavioral data exchange, any phone with SMS or Unstructured Supplementary Service Data capabilities could support the key elements of the study monitoring and intervention. Research examining the effect of EMAI using more basic phones could broaden the reach of future EMAI work by allowing interventions to operate on any type of phone currently owned by members of the population of interest.
Throughout the study period, the direction of the intervention arm trends in the mean proportion of days when behaviors were reported was consistent: increasing for fruit and vegetable consumption and decreasing for alcohol consumption, cigarette smoking, and sex with a nonmarital or non–long-term partner without a condom. There was more variation in the trends in the control arm. Although only vegetable consumption showed significant differences in change over time between arms, the direction and, with the exception of alcohol, the magnitude of the change in behaviors between study arms are consistent with the study hypotheses. This supports that remote intervention messaging warrants further study to promote behavioral change. Remote data collection and intervention may be particularly important in LMIC contexts such as Rakia, Uganda, where regular follow-up of people is difficult because of high population mobility and poor infrastructure. Similarly, in the era of COVID-19, human interaction carries risks that may exceed small to moderate, but otherwise important, behavior change benefits. Research to further establish the effectiveness of EMAI in these settings may be of critical importance.
The findings of this pilot trial were not designed to be generalizable beyond the study’s target population, including participants with at least a secondary level of education or a 90-day follow-up period. However, as preliminary estimates, they offer insight into the potential of EMAI and warrant further exploration. Behaviors were self-reported. It is not possible to differentiate actual changes in behavior from changes in reported behaviors influenced by social desirability bias, potentially reinforced by intervention messages, or other facts. However, although at different magnitudes, changes were observed in both the control and intervention arms of the trial. Furthermore, for sensitive behaviors such as condom use, self-report is the best available standard, with questions asking about recent experiences considered to be more valid and reliable than longer recall periods [
The study enrolled current RCCS participants who are accustomed to participating in trials. They may respond differently to interventions than research-naïve participants. Given the pilot nature of the study, explanations for reported behavioral changes other than the effect of intervention messaging cannot be ruled out. Particularly given block randomization and the study limit of 10 simultaneous active participants, seasonality in vegetable access, for example, could have influenced the decrease observed in vegetable consumption in the control arm.
Preliminary estimates from this pilot trial suggest that EMAI may be an effective strategy to promote behavior change across a range of behaviors. Larger trials examining the effectiveness of EMA alone and EMAI with responsive messaging in LMICs are warranted. Cost-effectiveness work is also recommended to establish the comparative potential of EMAI with more traditional approaches, leveraging increasingly accessible mobile technology in low-resource settings.
Bank of possible messages sent in response to reported behaviors (English translations).
CONSORT-eHEALTH checklist V1.6.1.
ecological momentary assessment
ecological momentary assessment and intervention
lower- and middle-income country
Rakai Community Cohort Study
The study was funded by the Johns Hopkins Center for Global Health and the National Institutes of Health NIAID R01 AI143333. AGT was supported by the National Institutes of Health, NHLBI T32HL007055.
LWC, IM, and MKG conceptualized and designed the study. The study procedures and data collection were implemented by IM, AA, CK, JM, AGT, EB, and GN. Study analyses were completed by LKB, MKG, AA, and LWC. LKB wrote the manuscript. All other authors have reviewed and substantively contributed to the manuscript revisions.
emocha Mobile Health Inc developed the application used in this study. LWC is entitled to royalties on certain nonresearch revenues generated by this company and owns company equity. Specific to this study, LWC received no royalties or compensation from emocha Mobile Health Inc. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. None of the other study team members had known competing interests.