Depression is one of the most common mental health problems that affects the functional abilities of individuals (American Psychiatric Association, 2013). Depression has been identified as a major cause of global impairment, with an estimated global prevalence of 4.4%, with the trend increasing in low- and middle-income countries (World Health Organization [WHO], 2017). Although there are successful and low-cost approaches for depression prevention and treatment (Patel et al., 2016), many individuals with depression are underdiagnosed and receive less attention, particularly in resource-limited settings (Chisholm et al., 2016). Depression could worsen health outcomes when the treatment is not proper and timely (Ghio et al., 2014; Rotella & Mannucci, 2013).
Mobile health applications (apps) have been growing as a new frontier in mental health care delivery (Price et al., 2014). The advantages of mental health care apps have been evaluated to include their effects on treatment and prevention of mental health disorders, such as depression and anxiety (Grist et al., 2017; Hoermann et al., 2017; Kim et al., 2016). The use of mobile apps could solve conventional problems, such as cost and accessibility, and overcome regional barriers and reduce stigma of mental health care services (Firth et al., 2017). The latest estimates suggest that more than 10,000 mental health apps are available in the Apple iOS and Google app platforms (Aitken & Lyle, 2015; Neary & Schueller, 2018; Pohl, 2017; Torous et al., 2016). At some point in their lives, more than one half of mobile device users have downloaded at least one health-related app (Krebs & Duncan, 2015). However, many of the available apps for mental health care are not developed based on scientific evidence (Donker et al., 2013). There is lack of information about the quality or effectiveness of these apps, thus, the decision to download or purchase these apps is only made according to the marketplace rating, price, logo, or ease in identifying the attributes (Huang & Bashir, 2017).
The user-centered innovation approach proposes that the design of relevant and useful mobile health care apps must meet the needs and concerns of the target population. Effectiveness, reliability, and performance are key elements of the functionality of technology-based equipment and these criteria must be met to adapt to the needs of consumers, particularly in the field of mental health care (Norman & Draper, 1986). The objective of the current systematic review was to summarize the evidence regarding usability, acceptability, and adherence rates of mobile app interventions for the prevention or treatment of depression.
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used for this systematic review (Moher et al., 2009).
Search Strategies and Eligibility Criteria
A literature search was conducted in relevant databases, including PubMed, PsycINFO, and Embase for articles published between January 1, 2009, and August 1, 2019. The key terms used for searching included a combination of depression AND smartphone OR mobile AND application AND acceptability OR feasibility. The study was limited to articles published in Bahasa Indonesia or English. The Participants, Interventions, Comparators, Outcomes, and Study Design (PICOS) approach (Liberati et al., 2009) was used for criteria inclusion, as follows: (a) Participants: individuals with or without depression at recruitment, without the restriction of age, sex, or race/ethnicity; (b) Interventions: mobile phone-based prevention or treatment interventions; (c) Tests of feasibility relevant to the question, “Can this experiment be carried out?” (cultural adaptation, acceptability, usability, or adherence rate), as defined by Arain et al. (2010); and (d) Study design: Randomized or non-randomized study designs as defined by the Cochrane Collaboration (Higgins & Green, 2011). Systematic reviews were sought for relevant studies and prospective study protocols were included in the case of randomized controlled trials. Studies conducted in pregnant women or postpartum women with depression were excluded from the review.
Study Selection and Data Collection
All studies retrieved through the literature search were imported into Mendeley and duplicates were removed. Processes of identification, screening, eligibility, and inclusion followed the same procedure: two independent reviewers assessed the eligibility of studies. If there were any disagreements between the two independent reviewers on whether to include or exclude articles, they were resolved with the assistance of a third reviewer.
A standard form for data extraction was developed by the authors. The following information was extracted from the studies: first author, year of publication, and country of origin; participants' recruitment sources, inclusion/exclusion criteria for participants (i.e., age, sex, depressive status, and any clinically relevant information); names of the interventions, objectives (prevention or treatment), and duration and frequency; and main results regarding the feasibility (cultural adaptation, acceptability, usability, or adherence rate) of the intervention.
Risk of Bias Assessment
The risk of bias assessment of all included studies was conducted independently by two reviewers. The quality of the reporting of feasibility studies was assessed based on selected, applicable, and adaptable items of the CONSORT 2010 Statement (Eldridge et al., 2016) and CONSORT-EHEALTH Statement (Eysenbach & CONSORT-EHEALTH Group, 2011). No risk of bias assessment was conducted for protocol studies.
Figure 1 summarizes the search and study selection procedure. After removing duplicates, 1,103 papers were identified. We screened the titles and abstracts, and subsequently retrieved the full-text reports of 34 studies. Based on the full-text examination, we included seven studies.
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Moher et al., 2009) flow diagram.
The characteristics of the included studies are presented in Table 1. There were four feasibility studies (Lüdtke et al., 2018; Schlosser et al., 2017; Similä et al., 2018; Takahashi et al., 2019), one qualitative study (Goodwin et al., 2016), one descriptive cross-sectional study (BinDhim et al., 2015), and one evaluation study (Lara et al., 2014). Five studies recruited adults with depression, one study recruited older adults, and one study recruited participants from the general population. The majority of studies had a small sample size (<100 participants) ranging from seven to 88 participants (BinDhim et al., 2015; Goodwin et al., 2016; Lüdtke et al., 2018; Schlosser et al., 2017; Similä et al., 2018; Takahashi et al., 2019), and one study had a large sample size (N = 17,318) (Lara et al., 2014). All studies used the Center for Epidemiologic Studies Depression Scale, Patient Health Questionnaire-9, and Geriatric Depression Scale to monitor the severity of depressive symptoms. Three studies were conducted in European countries, one in Japan, one in Australia, and one study was conducted in multiple countries. Five studies developed a mobile app for depression treatment, one study for screening of depression, and one study for prevention. All studies were determined to have a low risk of bias in all eight domains. For the type of interventions or apps themselves, many were developed with no cultural specifics or customization, such as Oiva (Similä et al., 2018); the motion picture-reproducing app (Takahashi et al., 2019); the North Lee Mental Health Services app (Goodwin et al., 2016); the free smartphone depression app (BinDhim et al., 2015); and the Help for Depression app (Lara et al., 2014). Only one study used a customized mobile app, namely the Personalized Real-Time Intervention for Motivational Enhancement for Depression app (Schlosser et al., 2017).
Characteristics of Included Studies
The completion rate of app use during the interventions was good (BinDhim et al., 2015; Goodwin et al., 2016; Lüdtke et al., 2018). The reported adherence rates were low, with <50% of participants using the apps frequently and many dropping out of the intervention (Lüdtke et al., 2018; Takahashi et al., 2019). Participants reported having an interest and willingness to use the mobile apps and continue to use it (BinDhim et al., 2015; Goodwin et al., 2016; Lara et al., 2014; Lüdtke et al., 2018; Schlosser et al., 2017; Similä et al., 2018). Although participants agreed that the apps were easy to use and helpful, satisfaction of using the mobile apps was low (Goodwin et al., 2016; Lara et al., 2014; Lüdtke et al., 2018; Takahashi et al., 2019).
The users of mobile apps for treatment or prevention of depression suggested usefulness and acceptability; however, mobile apps require design adjustments to facilitate their use, encourage retention, and improve adherence rates. The retention rates reported in the current review were good (>80%), relative to other studies that suggest the retention rates of computer-based psychological treatment for depression range from 10% to 70% (Richards & Richardson, 2012; Titov et al., 2010), and are consistent with previous reviews that suggest that the involvement of human support is important to improve engagement in digital health platforms (Richards & Richardson, 2012). Previous studies also suggest that guided interventions are more acceptable, have higher retention rates, and are more effective (Ebert et al., 2017).
The current review also highlights the ease of use of mobile apps based on users' perceptions. However, most included studies were conducted in developed countries, in which individuals may have higher levels of digital literacy, and thus are increasingly exposed to information technologies (Huawei Technologies Co. Ltd., 2017). These conditions may differ from populations in developing countries with minimal services coverage and basic infrastructure. Previous literature has highlighted the gaps in eHealth technology that can lead to greater disparities in health care, which especially affect socially disadvantaged individuals, a problem that can be compounded by lower levels of health literacy, willingness to use technology-intensive interventions, and the capacity to use technology (Latulippe et al., 2017). Considering that the use of mobile apps in the field of health care is linked to digital literacy, it is important to address and improve levels of digital literacy before development of such apps.
Despite good usability, adherence rates of mobile apps were low. Caring for individuals with mental health issues could become problematic if specialized in-person resources are not available, thus making it impossible to refer the most severe cases requiring urgent treatment (e.g., individuals displaying suicidal ideation). One solution is to study alternative intervention models that operate according to the Blended-Concept (Erbe et al., 2017), which combines intensive face-to-face care with automated, technology-based, and minimally guided activities that can be used to reinforce in-person sessions. Even more so, these components can be supplemented by staggered (or stepped) care approaches, in which the degree and intensity of human support received are tailored to individual needs (Ebert et al., 2017). Blended care is proposed as an effective and cost-effective way of organizing mental health care (Wentzel et al., 2016), and it could be a solution to increasing the adherence rates of technology-based interventions. Future studies should consider mobile apps with more attractive designs to improve user retention, and incorporate larger samples with a control group.
Mobile apps for the prevention or treatment of depression may be a viable way for adult populations to cope with inadequate mental health care services in mid- and low-income countries (WHO, 2009). However, the current systematic review did not yield any studies that included research from developing countries or in children or teenagers, and only adult participants were involved in cultural adaptation of the single interventions (Schlosser et al., 2017). There is lack of research conducted in socioeconomically impoverished countries that may affect acceptance and adherence to mobile apps for prevention or treatment of depression. Involving children or adolescents in further research conducted in developing countries is also needed. Future studies should also consider the ethical and methodological challenges that are involved in the inclusion of different age groups in developing countries. In addition, future studies are required to evaluate costs and benefits of mobile app intervention to prevent or treat depression.
The current systematic review has several limitations, such as the fact that this review did not include a grey literature search and only included studies published in English. Thus, this review reflects a small portion of linguistic diversity, which could affect bias in the selection process.
Although development of mobile apps is growing rapidly, adherence rates of those apps are still low. Acceptability of mobile apps for mental health care delivery is needed for continued use of the apps to have significant impacts on health outcomes. However, caring for individuals with mental health problems may have specific challenges, such as the need for patients to have direct, face-to-face consultation with health care providers to relieve anxiety or to have more confidence in finding information or possible solutions. Future studies need to pay more attention to adherence rates of users for extended periods of time, and not only during the interval of intervention.
- Aitken, M. & Lyle, J. (2015). Patient adoption of mHealth, report by the IMS Institute for Health-care Informatics. https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/patient-adoption-of-mhealth.pdf
- American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). doi:10.1176/appi.books.9780890425596 [CrossRef]
- Arain, M., Campbell, M. J., Cooper, C. L. & Lancaster, G. A. (2010). What is a pilot or feasibility study? A review of current practice and editorial policy. BMC Medical Research Methodology, 10, 67 doi:10.1186/1471-2288-10-67 [CrossRef] PMID:20637084
- BinDhim, N. F., Shaman, A. M., Trevena, L., Basyouni, M. H., Pont, L. G. & Alhawassi, T. M. (2015). Depression screening via a smartphone app: Cross-country user characteristics and feasibility. Journal of the American Medical Informatics Association, 22, 29–34 doi:10.1136/amiajnl-2014-002840 [CrossRef] PMID:25326599
- Chisholm, D., Sweeny, K., Sheehan, P., Rasmussen, B., Smit, F., Cuijpers, P. & Saxena, S. (2016). Scaling-up treatment of depression and anxiety: A global return on investment analysis. The Lancet. Psychiatry, 3(5), 415–424 doi:10.1016/S2215-0366(16)30024-4 [CrossRef] PMID:27083119
- Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R. & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: A systematic review. Journal of Medical Internet Research, 15(11), e247 doi:10.2196/jmir.2791 [CrossRef] PMID:24240579
- Ebert, D. D., Cuijpers, P., Muñoz, R. F. & Baumeister, H. (2017). Prevention of mental health disorders using internet- and mobile-based interventions: A narrative review and recommendations for future research. Frontiers in Psychiatry, 8, 116 doi:10.3389/fpsyt.2017.00116 [CrossRef] PMID:28848454
- Eldridge, S. M., Chan, C. L., Campbell, M. J., Bond, C. M., Hopewell, S., Thabane, L. & Lancaster, G. A.the PAFS consensus group. (2016). CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. BMJ (Clinical Research Ed.), 355, i5239 doi:10.1136/bmj.i5239 [CrossRef] PMID:27777223
- Erbe, D., Eichert, H. C., Riper, H. & Ebert, D. D. (2017). Blending face-to-face and internet-based interventions for the treatment of mental disorders in adults: Systematic review. Journal of Medical Internet Research, 19, e306 doi:10.2196/jmir.6588 [CrossRef] PMID:28916506
- Eysenbach, G.CONSORT-EHEALTH Group. (2011). CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. Journal of Medical Internet Research, 13(4), e126 doi:10.2196/jmir.1923 [CrossRef] PMID:22209829
- Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S. & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287–298 doi:10.1002/wps.20472 [CrossRef] PMID:28941113
- Ghio, L., Gotelli, S., Marcenaro, M., Amore, M. & Natta, W. (2014). Duration of untreated illness and outcomes in unipolar depression: A systematic review and meta-analysis. Journal of Affective Disorders, 152–154, 45–51. doi:10.1016/j.jad.2013.10.002 [CrossRef] PMID:24183486
- Goodwin, J., Cummins, J., Behan, L. & O'Brien, S. M. (2016). Development of a mental health smartphone app: Perspectives of mental health service users. Journal of Mental Health (Abingdon, England), 25(5), 434–440 doi:10.3109/09638237.2015.1124392 [CrossRef] PMID:26732242
- Grist, R., Porter, J. & Stallard, P. (2017). Mental health mobile apps for preadolescents and adolescents: A systematic review. Journal of Medical Internet Research, 19(5), e176 doi:10.2196/jmir.7332 [CrossRef]
- Higgins, J. P. T. & Green, S. (2011). Cochrane handbook for systematic reviews of interventions (version 5.1.0). http://www.handbook.cochrane.org
- Hoermann, S., McCabe, K. L., Milne, D. N. & Calvo, R. A. (2017). Application of synchronous text-based dialogue systems in mental health interventions: Systematic review. Journal of Medical Internet Research, 19(8), e267 doi:10.2196/jmir.7023 [CrossRef] PMID:28784594
- Huang, H. Y. & Bashir, M. (2017). Users' adoption of mental health apps: Examining the impact of information cues. Journal of Medical Internet Research, 5, e83 doi:10.2196/mhealth.6827 [CrossRef] PMID:28659256
- Huawei Technologies Co. Ltd. (2017). Harnessing the power of connectivity. https://www.huawei.com/minisite/gci/assets/files/gci_2017_whitepaper_en.pdf?v=20191217v2
- Kim, J., Lim, S., Min, Y. H., Shin, Y. W., Lee, B., Sohn, G., Jung, K. H., Lee, J. H., Son, B. H., Ahn, S. H., Shin, S. Y. & Lee, J. W. (2016). Depression screening using daily mental-health ratings from a smartphone application for breast cancer patients. Journal of Medical Internet Research, 18(8), e216 doi:10.2196/jmir.5598 [CrossRef] PMID:27492880
- Krebs, P. & Duncan, D. T. (2015). Health app use among US mobile phone owners: A national survey. Journal of Medical Internet Research, 3, e101 doi:10.2196/mhealth.4924 [CrossRef] PMID:26537656
- Lara, M. A., Tiburcio, M., Aguilar Abrego, A. & Sánchez-Solís, A. (2014). A four-year experience with a web-based self-help intervention for depressive symptoms in Mexico. Revista Panamericana de Salud Pública, 35(5–6), 399–406 PMID:25211568
- Latulippe, K., Hamel, C. & Giroux, D. (2017). Social health inequalities and eHealth: A literature review with qualitative synthesis of theoretical and empirical studies. Journal of Medical Internet Research, 19(4), e136 doi:10.2196/jmir.6731 [CrossRef] PMID:28450271
- Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J. & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Medicine, 6(7), e1000100 doi:10.1371/journal.pmed.1000100 [CrossRef] PMID:19621070
- Lüdtke, T., Pult, L. K., Schröder, J., Moritz, S. & Bücker, L. (2018). A randomized controlled trial on a smartphone self-help application (Be Good to Yourself) to reduce depressive symptoms. Psychiatry Research, 269, 753–762 doi:10.1016/j.psychres.2018.08.113 [CrossRef] PMID:30273901
- Moher, S., Liberati, A., Tetzlaff, J. & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. British Medical Journal, 339, b2535 doi:10.1136/bmj.b2535 [CrossRef]
- Neary, M. & Schueller, S. M. (2018). State of the field of mental health apps. Cognitive and Behavioral Practice, 25(4), 531–537 doi:10.1016/j.cbpra.2018.01.002 [CrossRef]
- Norman, D. & Draper, S. W. (1986). User cantered system design: New perspectives on human-computer interaction. Lawrence Erlbaum.
- Patel, U., Sobowale, K., Fan, J., Liu, N., Kuwabara, S., Lei, Z., Sherer, R. & Van Voorhees, B. (2016). Cultural considerations for the adaptation of an internet-based intervention for depression prevention in Mainland China. International Journal of Adolescent Medicine and Health, 29(5). doi:10.1515/ijamh-2015-0099 [CrossRef] PMID:26926858
- Pohl, M. (2017). 325,000 mobile health apps available in 2017 – Android now the leading mHealth platform. https://research2guidance.com/325000-mobile-health-apps-available-in-2017
- Price, M., Yuen, E. K., Goetter, E. M., Herbert, J. D., Forman, E. M., Acierno, R. & Ruggiero, K. J. (2014). mHealth: A mechanism to deliver more accessible, more effective mental health care. Clinical Psychology & Psychotherapy, 21, 427–436 doi:10.1002/cpp.1855 [CrossRef] PMID:23918764
- Richards, D. & Richardson, T. (2012). Computer-based psychological treatments for depression: A systematic review and meta-analysis. Clinical Psychology Review, 32(4), 329–342 doi:10.1016/j.cpr.2012.02.004 [CrossRef] PMID:22466510
- Rotella, F. & Mannucci, E. (2013). Depression as a risk factor for diabetes: A meta-analysis of longitudinal studies. The Journal of Clinical Psychiatry, 74(1), 31–37 doi:10.4088/JCP.12r07922 [CrossRef] PMID:23419223
- Schlosser, D. A., Campellone, T. R., Truong, B., Anguera, J. A., Vergani, S., Vinogradov, S. & Arean, P. (2017). The feasibility, acceptability, and outcomes of PRIME-D: A novel mobile intervention treatment for depression. Depression and Anxiety, 34(6), 546–554 doi:10.1002/da.22624 [CrossRef] PMID:28419621
- Similä, H., Immonen, H., Tervola, J. K., Enwald, H. & Korpelainen, R. (2018). Feasibility of mobile mental wellness training for older adults. Geriatric Nursing, 39(5), 499–505 doi:10.1016/j.gerinurse.2018.02.001 [CrossRef]
- Takahashi, K., Takada, K. & Hirao, K. (2019). Feasibility and preliminary efficacy of a smartphone application intervention for subthreshold depression. Early Intervention in Psychiatry, 13(1), 133–136 doi:10.1111/eip.12540 [CrossRef] PMID:29356332
- Titov, N., Andrews, G., Schwencke, G., Robinson, E., Peters, L. & Spence, J. (2010). Randomized controlled trial of internet cognitive behavioural treatment for social phobia with and without motivational enhancement strategies. The Australian and New Zealand Journal of Psychiatry, 44(10), 938–945 doi:10.3109/00048674.2010.493859 [CrossRef] PMID:20932208
- Torous, J. B., Chan, S. R., Yellowlees, P. M. & Boland, R. (2016). To use or not? Evaluating ASPECTS of smartphone apps and mobile technology for clinical care in psychiatry. The Journal of Clinical Psychiatry, 77, e734–e738 doi:10.4088/JCP.15com10619 [CrossRef] PMID:27136691
- Wentzel, J., van der Vaart, R., Bohlmeijer, E. T. & van Gemert-Pijnen, J. E. W. C. (2016). Mixing online and face-to-face therapy: How to benefit from blended care in mental health care. Journal of Medical Internet Research Mental Health, 3(1), e9 doi:10.2196/mental.4534 [CrossRef] PMID:26860537
- World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. https://apps.who.int/iris/bitstream/handle/10665/254610/WHOMSD-MER-2017.2-eng.pdf
Characteristics of Included Studies
|Author (Year)||Country||Sample||Study Design||App/Intervention||Results|
|Similä et al. (2018)||Finland||Older adults; N = 7||Mixed-methods approach||Oiva is a stand-alone mobile app for mental wellness trainingAim: treatment||Group leaders (social instructor and occupational therapist) were not familiar with Oiva beforehand, but they were interested in the opportunity to test it as part of their group activities. Overall user experience of the Oiva app was positive and two older users in particular got help managing their depression and anxiety.|
|Lüdtke et al. (2018)||Germany||Adults with depression; age 18 to 65 years; N= 88||Randomized controlled trial||Self-help smartphone app called Be Good to Yourself
Aim: treatment; 4-week intervention||The completion rate was 84%; only 39% of participants in the intervention group actually used the app frequently (i.e., several times per week). The majority of participants evaluated the self-help app as positive and intended to use the app in the future. Qualitative data: “Be Good to Yourself has helped me identify some stress triggers in my life” and “Awesome idea that can help change lives. Helped my depression and anxiety.”|
|Schlosser et al. (2017)||United States||Adults with depression; N= 36||Mixed-methods approach||Personalized Real-Time Intervention for Motivational Enhancement for Depression (PRIME-D)
Aim: treatment; 12-week intervention||79% of participants stated that PRIME-D was helping them achieve their goals, 79% stated that they planned to continue using PRIME-D, and 83% stated that they would recommend PRIME-D to others. Completion rate was approximately 80%.|
|Takahashi et al. (2017)||Japan||Adults with subthreshold depression; N = 22||Open-label, single-arm pre-post test||Motion picture-reproducing app
Aim: treatment; 5-week intervention||50% adherence rate; mean time for app use over 5 weeks was 124.23 (SD= 81.94) min/week. 91% (20/22) of participants agreed that the app was easy to use; 55% of participants were satisfied.|
|Goodwin et al. (2016)||Republic of Ireland||Patients with depression, age 18 to 49 years; N= 8||Qualitative descriptive||North Lee Mental Health Services (NLMHS) app
Aim: treatment||All participants expressed interest in using the NLMHS app; easy-to-use app that could increase access to services, and allow service users to take an active role in their care.|
|BinDhim et al. (2015)||Multiple countries||8,241 participants from 66 countries, aged >18 years, who downloaded the app, 25.7% had previously been diagnosed with depression, 59.1% had completed high school||Cross-sectional||Free smartphone depression app
Aim: screening||Average number of completions was 5.3. Average number of times the app was launched by each participant was 3.2. A large number of people from different countries were searching for, and willing to use, a depression screening app.|
|Lara et al. (2014)||Australia||Adults; N= 17,318||Mixed methods||Help for Depression (HDep) app
Aim: prevention||Participation dropped from 65.1% (Module 1) to 33.3% in Module 7. All seven modules were rated very high for “helpfulness/usefulness,” with mean scores all >4 on a 1 to 5 scale. 97.5% of participants said it had an enormous influence on helping them identify and transform negative thoughts.|