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Although several studies have investigated the effects of mobile health (mHealth) interventions on depression among people living with HIV, few studies have explored mediators of mHealth-based interventions to improve mental health in people living with HIV. Identifying influential mediators may enhance and refine effective components of mHealth interventions to improve mental health of people living with HIV.
This study aimed to examine mediating factors of the effects of a mHealth intervention,
This study used data from a randomized controlled trial of a mHealth intervention among people living with HIV with elevated depressive symptoms in Guangzhou, China. A total of 300 patients were assigned to receive either the mHealth intervention (n=150) or a waitlist control group (n=150) through computer-generated block randomization. Depressive symptoms, coping, and HIV-related stigma were measured at baseline, 3-, 6-, and 9-month follow-ups. The latent growth curve model was used to examine the effects of the intervention on depressive symptoms via potential mediators. Mediating effects were estimated using bias-corrected 95% bootstrapped CIs (BCIs) with resampling of 5000.
Enhanced positive coping and reduced HIV-related stigma served as effective treatment mediators in the mHealth intervention. Specially, there was a significant indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of positive coping (beta=–2.86; 95% BCI –4.78 to –0.94). The indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of HIV-related stigma was also statistically significant (beta=–1.71; 95% BCI –3.03 to –0.40). These findings indicated that enhancement of positive coping and reduction of HIV-related stigma were important mediating factors of the mHealth intervention in reducing depression among people living with HIV.
This study revealed the underlying mediators of a mHealth intervention to reduce depression among people living with HIV using latent growth curve model and 4 time-point longitudinal measurement data. The study results underscored the importance of improving positive coping skills and mitigating HIV-related stigma in mHealth interventions to reduce depression among people living with HIV.
Depression is highly prevalent among people living with HIV (PLWH) [
Literature has shown that interventions such as cognitive behavioral stress management (CBSM) are effective at reducing depression in various populations including PLWH [
Previous studies have indicated that factors such as coping and stigma may be strong predictors of depression. Coping strategies have been defined as individuals’ emotional, cognitive, and behavioral attempts in response to stressful events [
However, the few studies that explored intervention mediators of depression reduction were mostly conducted within interventions that occurred in clinic settings and were delivered face-to-face. To the best of the knowledge of the authors, no study has been done based on mobile health (mHealth) interventions [
In addition, existing studies mostly used qualitative analysis or pre-post measurements to examine mediators of interventions for depression reduction; longitudinal studies with multiple waves are lacking [
To bridge the gap in the existing literature, this study aimed to examine mediating factors of the effects of an mHealth intervention, Run4Love, designed for depression reduction among PLWH based on CBSM with 4 time-point measurement data. We hypothesized that both coping and HIV-related stigma would play important roles in mediating the effects of the Run4Love mHealth intervention. Specifically, we hypothesized that the intervention would improve positive coping and decrease HIV-related stigma, which in turn would lead to reduced depression among PLWH.
This study used data from a randomized controlled trial (RCT) of an mHealth intervention, Run4Love, for depression reduction among PLWH in China. This study is a secondary analysis of the Run4Love RCT (ChiCTR-IPR-17012606) [
Patients were eligible to participate if they were aged 18 years or older, HIV seropositive, having elevated depressive symptoms (CES-D≥16), willing to provide hair samples, and using WeChat, the most popular application for instant communication in China [
Participants in the intervention group participated in a 3-month mHealth intervention delivered by the enhanced WeChat platform, consisting of the adapted CBSM course and regular physical activity promotion [
The Run4Love intervention also included 5 phone calls from research staff at the first week and 1, 2, 5, and 8 months after enrollment. The purpose of the phone calls was to confirm participation and proper use of the platform at the first week and to offer social support, facilitate intervention implementation, identify barriers to adherence, and provide feedback on mental health in subsequent months. All calls had a script for reference. The calls lasted for an average of 10 min during the first week and 15 min during subsequent weeks. The intervention is described in detail elsewhere [
Participants in the control group received a brochure on nutrition and healthy living in addition to usual care for HIV treatment. Moreover, they were offered to receive the Run4Love intervention as soon as the study ended (ie, 9 months after enrollment).
Depressive symptoms were assessed by the CES-D with good reliability and validity [
The 20-item Simplified Ways of Coping Questionnaire (SWCQ) was used to assess coping with proven reliability and validity [
HIV-related stigma was measured by 14 items derived from the HIV Stigma Scale with good reliability and validity among Chinese PLWH [
Demographic variables included age, gender, educational level, sexual orientation, marital status, employment status, family monthly income, and duration since HIV diagnosis.
All analyses were conducted based on intention-to-treat principle. Baseline characteristics were compared between the intervention and control groups using
The latent growth curve model (LGCM) was used to examine mediating factors of the effects of the mHealth intervention on depressive symptoms among PLWH using 4 time-point measurement data. As an extension of structural equation modeling (SEM), LGCM allowed simultaneous analysis of multiple time points, thus potentially providing more accurate estimation of changes over time [
A stepwise approach was used to examine the mediating effects, which were widely used with longitudinal data [
Second, conditional LGCM was conducted to examine the effects of the intervention on the outcome and potential mediators. Intervention condition (ie, mHealth intervention vs control) was explored as a predictor of changes in depressive symptoms, coping, and HIV-related stigma across time (ie, predicting the slope factor). A dummy variable was created to represent group assignment. The mHealth intervention group was coded as 1 and the control group as 0. A significant path from the intervention condition to the slope of the variable of interest would indicate a significantly larger change in that variable over time in intervention group than in control group.
Third, a longitudinal mediation model was used to investigate whether the mHealth intervention was effective in reducing depressive symptoms via the potential mediators by examining changes in the slopes of the outcome (ie, depressive symptoms) and mediators (ie, coping and HIV-related stigma). The mediating effects were estimated using bias-corrected 95% bootstrapped CIs (BCIs) with resampling of 5000 [
All LGCMs were conducted using maximum likelihood estimation. Model fit was assessed using the comparative fit index (CFI) and the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and the relative chi-square ratio (chi-square/df). A LGCM model with a good model fit met the following criteria: CFI>0.90, RMSEA<0.08, SRMR<0.08, and relative chi-square ratio<3.0 [
A total of 1555 PLWH were assessed for eligibility, among whom 1255 were excluded or withdrawn before enrollment. A total of 1017 were excluded because of lower CES-D scores (ie, CES-D<16), and 538 were further screened. Among the 538 participants, 164 declined to participate; 24 refused eligibility interview, and 50 were excluded because of other reasons such as currently taking psychotropic medication, participating in other studies, or unable to read because of eye problems. The RCT included 300 participants, with 150 in the intervention group and 150 in the control group. Details of the recruitment process are described in the study protocol [
Descriptive statistics for baseline data are presented in
Participants’ characteristics for the intervention and control groups at baseline.
Characteristics | Total (n=300) | Intervention (n=150) | Control (n=150) | |
Age (years), median (interquartile range, IQR) | 27.5 (24.5-31.3) | 27.4 (24.3-31.1) | 27.8 (24.6-32.2) | .40a |
Male, n (%) | 277 (92.3) | 142 (94.7) | 135 (90.0) | .19b |
Educational level > high school, n (%) | 182 (60.7) | 98 (65.3) | 84 (56.0) | .12b |
Homosexual/bisexual/uncertain, n (%) | 245 (81.7) | 130 (86.7) | 115 (76.7) | .04b |
Married, n (%) | 38 (12.7) | 18(12.0) | 20 (13.3) | .73b |
Employed, n (%) | 251 (83.7) | 123 (82.0) | 128 (85.3) | .53b |
Family monthly income ≥7000 (yuan), n (%) | 124 (41.3) | 68 (45.3) | 56 (37.3) | .20b |
Duration since HIV diagnosis (years), median (IQR) | 1.7 (0.6-3.7) | 1.7 (0.6-4.0) | 1.8 (0.6-3.9) | .62c |
Center for Epidemiological Studies Depression Scale, mean (SD) | 24.1 (6.6) | 23.9 (6.4) | 24.3 (6.9) | .68a |
SWCQd, positive coping, mean (SD) | 18.4 (5.8) | 18.4 (5.5) | 18.3 (6.2) | .92a |
SWCQ, negative coping, mean (SD) | 11.8 (3.9) | 11.8 (3.9) | 11.8 (3.9) | .94a |
HIV Stigma Scale, mean (SD) | 37.5 (7.6) | 37.1 (7.7) | 38.0 (7.5) | .31a |
aBased on
bBased on chi-square test, the Fisher exact
cBased on Wilcoxon rank-sum test.
dSWCQ: Simplified Ways of Coping Questionnaire.
Repeated measurements of depressive symptoms and potential mediators in the Run4Love randomized controlled trial.
Variables, group | Baseline, mean (SD) | 3-month follow-up, mean (SD) | 6-month follow-up, mean (SD) | 9-month follow-up, mean (SD) | |
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Intervention | 23.93 (6.39) | 17.87 (9.44) | 17.60 (10.06) | 17.86 (10.72) |
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Control | 24.25 (6.86) | 23.85 (10.11) | 24.11 (11.42) | 23.43 (11.45) |
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Intervention | 18.39 (5.45) | 20.79 (7.33) | 21.03 (7.48) | 20.95 (7.75) |
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Control | 18.32 (6.15) | 17.70 (5.88) | 17.38 (6.59) | 18.31 (6.41) |
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Intervention | 11.78 (3.85) | 11.12 (4.26) | 11.33 (4.38) | 11.71 (4.09) |
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Control | 11.75 (3.88) | 11.43 (3.71) | 11.32 (4.14) | 11.87 (4.09) |
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Intervention | 37.10 (7.67) | 34.28 (9.19) | 34.30 (8.52) | 33.98 (9.01) |
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Control | 37.99 (7.54) | 37.50 (8.27) | 37.35 (9.92) | 37.79 (9.99) |
Measurements of depressive symptoms, coping, and HIV-related stigma over time. Error bars indicate 95% confidence intervals.
Conditional LGCM supported the beneficial effects of the intervention on the outcome and potential mediators.
Conditional latent growth curve modeling examining the effects of the mobile health intervention on the outcome and potential mediators. Continuous lines with arrows indicate statistically significant paths. Dotted lines indicate nonsignificant paths. The first and second factor loadings of the latent slope of all models were set to 1, the third and fourth factor loadings of the latent slope of all models were freely estimated. Group: intervention or control group; DS: depressive symptoms; HS: HIV-related stigma; PC: positive coping; NC: negative coping.
Model fit indices of all latent growth curve models.
Model | CFIa | RMSEAb | SRMRc | Relative chi-square ratio (df) |
Reference | >0.90 | <0.08 | <0.08 | <3.0 |
LGCMd for depressive symptoms | 1.00 | 0.04 | 0.02 | 1.4 (7) |
LGCM for positive coping | 1.00 | 0.00 | 0.03 | 0.8 (7) |
LGCM for negative coping | 1.00 | 0.02 | 0.04 | 1.1 (7) |
LGCM for HIV-related stigma | 1.00 | 0.00 | 0.02 | 0.8 (7) |
Final LGCM | 0.98 | 0.05 | 0.04 | 1.7 (56) |
aCFI: comparative fit index.
bRMSEA: root mean square error of approximation.
cSRMR: standardized root mean square residual.
dLGCM: latent growth curve model.
The path diagram of LGCM in
The results indicated a significantly indirect effect of the intervention on the slope of depressive symptoms via the slope of positive coping (beta=2.275×(−1.257)=−2.86; 95% BCI −4.78 to −0.94). This means that the mHealth intervention significantly improved participants’ positive coping over time, which in turn significantly reduced depressive symptoms of the participants over time. Similarly, there was also a significantly indirect effect of the intervention on the slope of depressive symptoms via the slope of HIV-related stigma (beta=−1.962)×0.873=−1.71; 95% BCI −3.03 to −0.40), indicating significant intervention effects in reducing HIV-related stigma, which in turn significantly reduced depressive symptoms of the participants over time.
The direct effect of the intervention on the slope of depressive symptoms was not statistically significant (beta=−0.06; 95% BCI −2.15 to 2.03) when the mediators were added, indicating no direct effect of the intervention on depressive symptoms of the participants. Therefore, the effects of the mHealth intervention Run4Love in reducing depressive symptoms of the participants might be largely explained by the indirect effects of the intervention on enhancing positive coping and reducing HIV-related stigma among PLWH.
Latent growth curve modeling examining mediating effects between the mobile health intervention and changes in depressive symptoms. Continuous lines with arrows indicate statistically significant paths. Dotted lines indicate nonsignificant paths. The first and second factor loadings of the latent slope of the model were set to 1, the third and fourth factor loadings of the latent slope of the model were freely estimated. Group: intervention or control group; PC: positive coping; HS: HIV-related stigma; DS: depressive symptoms.
Coefficients and bootstrapping CIs of the final parallel-process latent growth curve modeling.
Effect | Estimate | 95% BCIa | Standardized estimate | |
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Slope of positive coping | −1.26b | −1.89 to −0.62 | −0.59 |
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Slope of HIV-related stigma | 0.87b | 0.48 to 1.27 | 0.52 |
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Group | −0.06 | −2.15 to 2.03 | −0.00 |
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Intercept of positive coping | −0.22 | −0.47 to 0.04 | −0.15 |
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Intercept of HIV-related stigma | 0.08 | −0.06 to 0.22 | 0.08 |
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Group | 2.28b | 1.06 to 3.50 | 0.37 |
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Intercept of depressive symptoms | 0.20b | 0.04 to 0.36 | 0.37 |
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Intercept of HIV-related stigma | −0.10 | −0.22 to 0.02 | −0.21 |
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Group | −1.96b | −3.22 to−0.70 | −0.25 |
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Intercept of positive coping | −0.35b | −0.57 to−0.13 | −0.40 |
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Intercept of depressive symptoms | −0.14b | −0.28 to−0.01 | −0.21 |
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Via the slope of positive coping | −2.86b | −4.78 to−0.94 | −0.22 |
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Via the slope of HIV-related stigma | −1.71b | −3.03 to−0.40 | −0.13 |
Total indirect effect | −4.57b | −7.01 to −2.14 | −0.35 |
aBCI: bootstrapped CIs.
bCI does not contain zero.
Our study is among the first to examine mediating factors of the effects of an mHealth intervention designed for depression reduction among PLWH. In addition, this study is the first study that has utilized latent growth curve modeling for examining mediators in an mHealth intervention study using 4 time-point measurement data among PLWH. We found that enhancement of positive coping and reduction of HIV-related stigma were important mediating factors of the mHealth intervention in reducing depression among PLWH.
Previous studies using qualitative analysis have demonstrated a mediating effect of positive coping on achieving depression reduction in face-to-face interventions [
One reason for the significant enhancement of positive coping among PLWH might be that the Run4Love mHealth intervention was adapted from evidence-based CBSM program, with an important component of training in coping skills [
One advantage of the Run4Love mHealth intervention over traditional face-to-face interventions may be that the materials (eg, audio clips and essays) can be repeated, read, or heard at any time or location of participants’ choice. The tracking and monitoring functions of the Run4Love intervention can provide timely feedback to both researchers and participants, such as whether and for how long each participant read or listened to the materials sent via the mHealth platform. Instead of recalling what is learned in face-to-face sessions or seeking clinicians’ suggestions in traditional interventions, participants in mHealth interventions are able to read, listen to, and review materials related to positive coping skills whenever they encounter challenges in their daily lives [
The results of this study also revealed that depression reduction in the Run4Love mHealth intervention was significantly mediated by reduction of HIV-related stigma. Previous studies found that face-to-face cognitive behavioral interventions were effective in reducing HIV-related stigma and depression in PLWH [
Although the specific features of the Run4Love intervention that might explain the significant reduction in HIV-related stigma are not clear, 2 possible elements might be (1) the important component of HIV stigma reduction messages in the CBSM and (2) the additional 5 phone calls from research staff that occurred throughout the study. The phone calls may have served as additional social support for the participants as research staff helped to facilitate participation, improve intervention adherence, and provide feedback and guidance on participants’ mental health status. Previous research indicates that social support is an important factor in reducing HIV-related stigma [
There were several limitations in this study. First, although our study focused on positive coping and HIV-related stigma, other factors such as stress, self-efficacy, physical activity, patient satisfaction, guidance (working alliance), emotion regulation skills, and expectations might also serve as potential mediating factors for depression reduction among PLWH in the Run4Love mHealth intervention. Social support that the research staff may have provided with the phone calls might also serve as potential mediators, but we did not measure social support in this study. Future studies should further assess and explore the potential mediating effects of these factors in mHealth interventions. Second, data in this study were self-reported, which might introduce recall and social desirability biases. More objective measures such as biomarkers could be incorporated in the future studies. Third, as the data in this study were collected in an urban setting, caution should be exercised when generalizing the results to other places such as rural areas. Fourth, as the temporal sequence could not be identified between the mediators and outcome in this study, a causal relationship between the mediators and outcome cannot be confirmed. Fifth, selection bias in enrollment may limit the generalizability of the findings. Sixth, mediators only statistically narrow the mechanisms of change and might not necessarily be congruent [
In conclusion, this study revealed several mediating effects of an mHealth intervention using latent growth curve modeling and 4 time-point longitudinal measurement data. Positive coping and HIV-related stigma were important mediating factors of the Run4Love mHealth intervention in reducing depression among PLWH. The study’s findings provided empirical evidence for future research to enhance positive coping and reduce HIV-related stigma in mHealth interventions to reduce depression among PLWH. In addition, future interventions and policies aimed at reducing depression among PLWH should be designed with specific features that address positive coping and stigma to maximize intervention efficacy.
CONSORT-EHEALTH checklist (V 1.6.1).
bootstrapped CI
cognitive behavioral stress management
Center for Epidemiological Studies Depression Scale
comparative fit index
latent growth curve model
mobile health
people living with HIV
randomized controlled trial
root mean square error of approximation
structural equation modeling
standardized root mean square residual
Simplified Ways of Coping Questionnaire
This study was supported by the National Natural Science Foundation of China (grant no 71573290) and China Medical Board open competition funding (grant no 17–271). The funders provided grants to implement this program but had no role in the design of the study, data collection, analysis, interpretation of the data, and preparation of the manuscript.
YG designed the study. WC, LL, and CL were important collaborators. MZ, YL, ZX, and JQ analyzed the data. MZ wrote the first draft of the manuscript. YG and AMW made significant revisions of the manuscript. MZ, YL, CZ, ZX, JQ, and YZ reviewed data analysis and revised the manuscript. All authors reviewed and approved the final manuscript for publication.
None declared.