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The stepped-care approach, where people with early symptoms of depression are stepped up from low-intensity interventions to higher-level interventions as needed, has the potential to assist many people with mild depressive symptoms. Self-monitoring techniques assist people to understand their mental health symptoms by increasing their emotional self-awareness (ESA) and can be easily distributed on mobile phones at low cost. Increasing ESA is an important first step in psychotherapy and has the potential to intervene before mild depressive symptoms progress to major depressive disorder. In this secondary analysis we examined a mobile phone self-monitoring tool used by young people experiencing mild or more depressive symptoms to investigate the relationships between self-monitoring, ESA, and depression.
We tested two main hypotheses: (1) people who monitored their mood, stress, and coping strategies would have increased ESA from pretest to 6-week follow-up compared with an attention comparison group, and (2) an increase in ESA would predict a decrease in depressive symptoms.
We recruited patients aged 14 to 24 years from rural and metropolitan general practices. Eligible participants were identified as having mild or more mental health concerns by their general practitioner. Participants were randomly assigned to either the intervention group (where mood, stress, and daily activities were monitored) or the attention comparison group (where only daily activities were monitored), and both groups self-monitored for 2 to 4 weeks. Randomization was carried out electronically via random seed generation, by an in-house computer programmer; therefore, general practitioners, participants, and researchers were blinded to group allocation at randomization. Participants completed pretest, posttest, and 6-week follow-up measures of the Depression Anxiety Stress Scale and the ESA Scale. We estimated a parallel process latent growth curve model (LGCM) using Mplus to test the indirect effect of the intervention on depressive symptoms via the mediator ESA, and calculated 95% bias-corrected bootstrapping confidence intervals (CIs).
Of the 163 participants assessed for eligibility, 118 were randomly assigned and 114 were included in analyses (68 in the intervention group and 46 in the comparison group). A parallel process LGCM estimated the indirect effect of the intervention on depressive symptoms via ESA and was shown to be statistically significant based on the 95% bias-corrected bootstrapping CIs not containing zero (–6.366 to –0.029). The proportion of the maximum possible indirect effect estimated was κ2 =.54 (95% CI .426–.640).
This study supported the hypothesis that self-monitoring increases ESA, which in turn decreases depressive symptoms for young people with mild or more depressive symptoms. Mobile phone self-monitoring programs are ideally suited to first-step intervention programs for depression in the stepped-care approach, particularly when ESA is targeted as a mediating factor.
ClinicalTrials.gov NCT00794222; http://clinicaltrials.gov/ct2/show/NCT00794222 (Archived by WebCite at http://www.webcitation.org/65lldW34k)
Depression is a common, recurrent disorder and contributes to a substantial burden of disease [
The stepped-care approach, beginning with simple, inexpensive interventions (that are given before the onset of diagnosed major depressive disorder) with the ability to step up to higher-intensity programs as needed [
Methods for stepped-care interventions involving technology, such as computers, the Internet, or mobile phones, and simple methods such as self-monitoring techniques can engage young people and foster their involvement [
We developed and piloted a mobile phone self-monitoring program, the mobiletype program (Mobile Tracking of Young People’s Experiences) [
The contents of early intervention programs generally target a mechanism that predicts the outcome [
ESA may also provide a suitable framework for first-step intervention programs by assisting young people to become aware of their emotions in preparation for learning more adaptive coping strategies. Adolescence is an ideal target for first-step intervention of mental health problems, as young people begin to develop the ability to independently cope with everyday stresses and negative emotions during their teen years [
The overall aim of the current study was to investigate, in a randomized controlled trial, the utility of the mobiletype program as a first-step intervention program. The primary hypothesis was that young people who completed the mobiletype intervention program would have lower depressive symptoms than those who completed the attention comparison program. Using a mixed-methods model, we found that depressive symptoms significantly decreased over time for both intervention and attention comparison groups, and we found no significant difference in mental health symptoms between groups [
The goal of this secondary analysis was to further examine the effects of a mobile phone self-monitoring program on ESA among young people with mild or more depressive symptoms as a first-step treatment of depression. Specifically, we were interested in the effects of self-monitoring on ESA and the association between ESA and depressive symptoms. We hypothesized that self-monitoring mood, stress, and coping strategies would increase young people’s awareness of their emotions, which would lead to a decrease in their symptoms of depression. We tested the following hypotheses: (1) young people in the intervention group would have an increase in ESA from pretest to 6-week follow-up compared with the attention comparison group, and (2) the increase in ESA would predict a decrease in depressive symptoms. We estimated a parallel process latent growth curve model (LGCM) [
The data presented here were from the mobiletype randomized controlled trial conducted from 2009 to 2011. This was a multicenter, multiregional, stratified (according to region), single-blind, attention-controlled study with balanced (1:1) individual randomization into parallel groups. This study was conducted in Victoria, Australia adhering to the reporting recommendations from the CONSORT statement [
All GPs in the Goulburn Valley Region and Albury-Wodonga Region were invited to participate in the study via the Regional Division of General Practice (support units that service clinical practices within a region). GPs in Melbourne were recruited via the local Divisions of General Practice. Clinics were targeted that listed an interest in adolescent health on the Melbourne General Practice Network (www.mgpn.com.au). Participating GPs were trained to use the mobiletype website and were provided with a study manual that included the study procedure, a variety of clinical supports (including referral details of adolescent-friendly allied health professionals and services), youth-friendly Internet, email, and phone support, and youth-focused psychoeducation handouts on a range of mental health problems (this information was also available on the mobiletype website), which were available for all participating GPs and patients. Continuing professional development quality assurance points were available to GPs for their participation in the study. Of the 103 GPs who agreed to participate, 35 actively participated in the study with at least one young person. These contributing GPs were from 26 different practices in the three recruitment areas: 12 in greater Melbourne, 7 in Albury-Wodonga, and 7 in the Goulburn Valley.
Young people meeting the following inclusion criteria were eligible to participate regardless of their reason for visiting the GPs. Participants were required to (1) be aged between 14 and 24 years, (2) speak proficient English, and (3) have a mild or more severe emotional or mental health issue as assessed by their GP or indicated by a score greater than 16 on the Kessler Psychological Distress Scale [
We used Version 4 of the mobiletype program as the intervention and attention comparison in this study, which was created in-house using Java Platform, Micro Edition by the Murdoch Childrens Research Institute. This program was written for use with multiple models of mobile phones and firmware. For this trial, participants were lent a study mobile phone with either the mobiletype intervention or a comparison program uploaded onto it. Data from the program was uploaded to a secure website constructed and hosted by the Murdoch Childrens Research Institute as well as encrypted and stored on the mobile phones.
Participants were prompted to complete a mobiletype entry by an auditory signal (beep) emitted from the mobile phone at random intervals in the blocks outlined in
The intervention group monitored themselves using the complete mobiletype program, which assessed 8 areas of functioning as developed in previous mobiletype studies [
Modules included in each block of the mobiletype comparison and intervention programs.
Module | Morning |
Noon |
Afternoon |
Evening |
|
|
|||||
Current activity | ✓ | ✓ | ✓ | ✓ | |
Stress | ✓ | ✓ | ✓ | ✓ | |
Mood | ✓ | ✓ | ✓ | ✓ | |
Alcohol use | ✓ | ||||
Cannabis use | ✓ | ||||
Sleep | ✓ | ||||
Diet | ✓ | ||||
Exercise | ✓ | ||||
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|||||
Current activity | ✓ | ✓ | ✓ | ✓ | |
Stress | |||||
Mood | |||||
Alcohol use | |||||
Cannabis use | |||||
Sleep | ✓ | ||||
Diet | ✓ | ||||
Exercise | ✓ |
The attention comparison program was designed to provide a data collection process similar to that in the intervention group by controlling for the amount of time spent engaged in the program condition and the overall research methodology and the attention given to them by health care professionals and research staff [
Data collected by the mobiletype program (intervention and comparison groups) on the mobile phone was sent via short message service to a secure website, where it was automatically collated. Each area of assessment was displayed in graphs (eg, daily mood graphs) or in tables (eg, daily alcohol intake). An individualized summary report of the data was written following structured prescriptive guidelines by the second author (registered psychologist), or the first author under the supervision of the second author.
The pretest, posttest, and 6-week follow-up questionnaire packages included the Depression Anxiety Stress Scale (DASS) [
We anticipated recruitment of 200 participants from 10 general practices. This sample size was based on Cohen’s [
Participants were randomly assigned to either (1) the mobiletype monitoring intervention group or (2) the mobiletype attention comparison group; both groups also received medical care as usual. A database was set up by an in-house computer programmer with identification numbers for 100 Melbourne, 50 Goulburn Valley, and 50 Albury-Wodonga participants. Each number was attached to a link that downloaded either the intervention or comparison program directly to the mobile phone. This process was blinded; the intervention and comparison program could not be differentiated when downloading the program. The programmer used a random seed generator to allocate each program to the 200 identification numbers at the individual level and stratified according to area (Melbourne, Goulburn Valley, and Albury-Wodonga). This programmer was not involved in any data collection or analysis. A research assistant downloaded each program by selecting the next consecutive link for the next study mobile phone and was blinded to the allocation, as he knew only the identification number and area to load onto study mobile phones (eg, Melbourne01, Melbourne02). Mobile phones and identification numbers were allocated to consecutively recruited participants. The researchers, participants, and GPs were blinded to randomization pretest. GPs and participants became aware of the group allocation at the posttest when the summary reports were reviewed. This study had approval from the Human Research Ethics Committee of the Royal Children’s Hospital, Melbourne (RCH HREC: 28113), and was registered in ClinicalTrials.gov (Reference: NCT00794222).
In addition to providing treatment as usual, GPs screened their patients for eligibility to the study; organized an appointment for willing participants with a research assistant using an online booking form or a faxed referral form, or by phone; and completed a pretest questionnaire for each participant. Participants then met with a mobiletype research assistant, generally within 5 days of referral, to learn the study process, complete consent forms and the pretest questionnaire package, familiarize themselves with the mobiletype program and the other features of the phone, and complete a practice entry of the mobiletype program. Participants were provided with a study manual that described the research procedure and offered trouble-shooting tips.
All participants borrowed a Sony Ericsson 7501i (Sony Limited Australia, North Ryde, NSW, Australia) mobile phone containing the mobiletype program for the study period. Information regarding the development and testing of the mobiletype program has been previously published [
On completing the monitoring period, participants reviewed the self-monitoring data with their GP on the mobiletype website. Young people completed a posttest assessment immediately following this appointment, and again at 6 weeks and 6 months after this review (6-month posttests not included in the current analysis). GPs completed a posttest questionnaire immediately after the appointment. Questionnaires were completed online, over the phone with a researcher, or via a mailed hardcopy questionnaire. Participants were given a A$20 gift card for each posttest questionnaire completed (maximum of A$60 for all questionnaires completed).
We conducted all analyses on an intention-to-treat basis using all available data from participants included at randomization. Data were assumed to be missing at random [
Recently, structural equation modeling has modernized Baron and Kenny’s well-known mediation model [
A recent development in statistical analysis has led to the capacity of estimating effect sizes for mediation models [
We conducted a secondary analysis to determine whether the intervention group had a decrease in rumination compared with the comparison group. A mixed model analysis was conducted over time and between groups using SPSS Version 17.0.0 (IBM Corporation, Somers, NY, USA) with the MIXED procedure. As with the LGCM, survey time was entered as a continuous variable in weeks (0, 3, and 9). The mixed model employed the more conservative restricted maximum likelihood estimation and unstructured covariance matrix.
We collected data collection between April 16, 2009 and January 28, 2011. As seen in
In total, 67% of participants (76/114) completed all questionnaires and 85% of participants (97/114) completed questionnaires at two or more time points. We conducted
Flow diagram of the study process.
We found no statistically significant differences in demographic characteristics between the intervention and comparison groups on any pretest measures listed in
Participants in the intervention group completed an average of 3.3 (SD 1.42, range 1–8) mobiletype entries each day and completed the program from 1 to 34 days with a mean of 17.7 (SD 6.69) days completed. In the comparison group, participants completed an average of 4 (SD 1.77, range 1–12) mobiletype entries per day, and completed the program for 8 to 25 days with a mean of 16.8 (SD 4.03) days completed. The minimum dose of the program was completion of at least two mobiletype entries a day for at least 14 days. As
General demographics of participants in the comparison and intervention groups (n = 114).
Characteristic | Comparison group | Intervention group |
|
|
Total numbera, n (%) | 49 (41.5%) | 69 (58.5.0%) | .08 | |
14 days completedb, n (%) | 28 (59.6%) | 36 (52.9%) | .33 | |
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Melbourne | 14 (30.4%) | 28 (42.6%) | .27 | |
Goulburn Valley | 21 (45.7%) | 21 (29.4%) | ||
Albury-Wodonga | 11 (23.9%) | 19 (27.9%) | ||
Male participants, n (%) | 17 (37.0%) | 15 (22.1%) | .08 | |
Age (years), mean (SD) | 17.4 (3.2) | 18.5 (3.2) | .06 | |
Ethnic identificationc, n (%) | 4 (9.1%) | 10 (22.7%) | .37 | |
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Employed | 7 (15.2%) | 18 (26.5%) | .21 | |
Unemployed | 4 (8.7%) | 9 (13.2%) | ||
Student | 35 (76.1%) | 41 (60.3%) | ||
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Ever had alcohol | 38 (86.4%) | 59 (88.1%) | .79 | |
Ever been drunk | 31 (70.5%) | 53 (79.1%) | .30 | |
Ever had a cigarette | 25 (56.8%) | 38 (56.7%) | .99 | |
Ever tried marijuana | 18 (40.9%) | 33 (49.3%) | .39 | |
Ever tried otherd drugs | 10 (22.7%) | 26 (38.8%) | .08 | |
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Depression | 19.4 (10.8) | 20.4 (11.0) | .63 | |
Anxiety | 11.0 (8.0) | 14.1 (9.7) | .08 | |
Stress | 16.9 (7.9) | 20.3 (8.9) | .04 |
a Binomial test on number at randomization (n = 118).
b Completed mobiletype entries at least twice daily for 14 days.
c Observed means (n = 111).
d Sedatives, tranquilizers, amphetamines, analgesics, inhalants, cocaine, LSD, and heroin.
e Depression Anxiety Stress Scale.
Descriptive statistics for the intervention and comparison groups’ scoresa on depression and emotional self-awareness at pretest, posttest, and 6-week follow-up.
Comparison group | Intervention group | ||||||
nb | Mean (SD) | 95% CI | nb | Mean (SD) | 95% CI | ||
Depression | |||||||
Pretest | 44 | 19.4 (10.9) | 16.1–22.7 | 67 | 20.4 (11.0) | 17.8–23.1 | |
Posttest | 33 | 15.2 (8.9) | 12.1–18.3 | 50 | 16.3 (10.8) | 13.3–19.4 | |
6-week follow-up | 36 | 12.5 (11.8) | 8.5–16.5 | 50 | 13.5 (10.5) | 10.5–16.5 | |
Emotional self-awareness | |||||||
Pretest | 44 | 61.1 (11.9) | 57.4–64.7 | 67 | 61.6 (12.1) | 58.7–64.6 | |
Posttest | 32 | 63.1 (11.1) | 59.1–67.1 | 46 | 64.7 (10.9) | 60.9–67.4 | |
6-week follow-up | 35 | 62.2 (11.6) | 58.2–66.1 | 47 | 68.9 (11.2) | 65.5–72.1 | |
Rumination | |||||||
Pretest | 44 | 12.8 (3.16) | 11.9–13.8 | 67 | 14.0 (3.43) | 13.2–14.9 | |
Posttest | 33 | 12.2 (3.57) | 10.9–13.4 | 46 | 12.4 (3.57) | 11.3–13.4 | |
6-week follow-up | 35 | 11.2 (3.67) | 10.0–12.5 | 48 | 11.7 (3.62) | 10.7–12.8 |
a Observed scores.
b Number of participants used to calculate the mean, standard deviation (SD), and 95% confidence interval (CI).
The path diagram in
All possible pathways were calculated between group and the four latent variables as illustrated in
The indirect effect of group on the slope of depression via the slope of ESA reported in
There was a negative relationship between changes in ESA and changes in depressive symptoms for both groups; accordingly, an increase in ESA was associated with a decrease in depressive symptoms as seen in
Coefficients and bias-corrected bootstrapping confidence intervals (CIs) of the parallel process latent growth curve model.
Effect | Point estimate | 95% Bootstrap CI | |
|
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Slope of ESA | –0.902a | –6.209 to –0.052 | |
Intercept of depression | –0.052 | –0.126 to 0.012 | |
Intercept of ESA | –0.026 | –0.315 to 0.027 | |
Group | 0.587 | –0.114 to 5.072 | |
|
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Intercept of ESA | –0.044 | –0.083 to 0.162 | |
Intercept of depression | –0.003 | –0.038 to 0.063 | |
Group | 0.676a | 0.019 to 1.231 | |
|
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ESA | 0.439 | –3.904 to 4.562 | |
Depression | 1.018 | –2.980 to 5.208 | |
|
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Via the slope of ESA | –0.610a | –5.596 to –0.003 | |
Via the intercept of ESA | –0.012 | –0.526 to 0.105 | |
Total indirect effect | –0.621b | –6.269 to –0.036 |
a Confidence interval does not contain zero.
Parallel process latent growth curve model of the group effect on the growth of depressive symptoms via the growth of emotional self-awareness (ESA). Unstandardized estimates reported; boldface lines represent statistically significant pathways. aTime interval from pretest by week. DEPpre, post, 6-week, ESApre, post,6-week = the observed score of the Depression Anxiety Stress Scale depression subscale and ESA scale at pretest, posttest, and 6-week follow-up, respectively; Group = intervention program condition; iDEP = latent intercept of depression; iESA = latent intercept of ESA; sDEP = latent slope of depression; sESA = latent slope of ESA.
The relationship between self-monitoring, the slope of depressive symptoms, and the slope of emotional self-awareness (ESA). The points represent individuals' estimated slopes and the lines represent the line of best fit with 95% confidence intervals.
The unstandardized indirect effect size can be interpreted on the DASS depression subscale (on a scale from 0 to 42), indicating that the intervention group is estimated to have a linear decrease in depressive symptoms of .688 per week (95% CI –.962 to –.487) indirectly through mediation of the linear slope of ESA when compared with the comparison group. The proportion of the maximum possible indirect effect has similar properties to, and can be interpreted on a similar scale to, Cohen's
We conducted a secondary analysis to determine whether rumination decreased over time between groups. The mixed model analysis of the Ruminative Response Scale brooding subscale showed a significant main effect of time (β = –0.16,
The current study examined the use of a mobile phone self-monitoring program on ESA with young people who had mild or more depressive symptoms, and supported the hypothesis that self-monitoring mood, stress, and coping strategies increases awareness of emotions. The second hypothesis that an increase in ESA would predict a decrease in depressive symptoms was also supported. Based on Preacher and Kelley’s proportion of the maximum possible indirect effect [
This study supports previous research suggesting that simple self-monitoring techniques effectively increase self-awareness, in this case, awareness of one’s own emotions [
Self-monitoring techniques may provide an alternative to watchful waiting as a first-step intervention in the stepped-care approach. Mobile phones are ideally suited to this purpose, as the mobiletype program can be downloaded to patients’ own mobile phones to help young people understand and manage mild depressive symptoms. Detailed information about patients’ mental health in recent weeks is then uploaded to GPs in an easy-to-read format, saving time in appointments and allowing progression to more intensive second-step interventions when needed. Young people often do not recognize mental health problems [
Our secondary hypothesis that the intervention program participants would have a decrease in rumination when compared with those in the comparison program was not supported. Further research is needed to determine whether there is an inverse relationship between rumination and ESA. Nevertheless, rumination decreased over time as did depressive symptoms [
Primary care is a particularly difficult setting in which to conduct randomized controlled studies [
To our knowledge, this is the first randomized controlled trial examining the use of a mobile phone self-monitoring program as an intervention tool for young people with depressive symptoms. Self-monitoring was shown to effectively decrease depression via the mechanism of ESA, suggesting that self-monitoring programs that focus on increasing ESA may provide a useful framework for first-step care in depression. The program provided GPs with information about a young person’s daily activities and can be used to detect early signs of mental health problems, such as elevated negative mood, stress and causes of stress, maladaptive coping strategies, isolation from peers, diet, and exercise, as well as other risk and protective factors. The mobile phone self-monitoring program has the advantage of being low cost, quick, and easy to use.
In summary, mobile phones are well suited to first-step interventions, providing an alternative to watchful waiting and allowing young people to provide accurate information to their GPs about their mood and stress [
Emotional self-awareness scale.
Indirect effect size estimates of the the parallel process latent growth curve model for group on depressive symptoms via the pathway of emotional self-awareness.
Depression Anxiety Stress Scale
emotional self-awareness
general practitioner
latent growth curve model
This study was supported by research grants from the Telstra Foundation and The Shepherd Foundation, and the Victorian Government’s Operational Infrastructure Support Program supported infrastructure. Sylvia Kauer received an Ian Scott PhD scholarship funded by Australian Rotary Health. Be.Interactive provided telecommunication support. Telstra Corporation provided prepaid SIM cards and telecommunication support. Sony Ericsson provided 40 mobile phones for use in this study. The Statistical Mediation and Moderation Analysis discussion forum on Facebook, led by Kristopher J Preacher and Andrew F Hayes, provided support for the mediation analyses conducted.
None declared.