Home-based digital counselling by frontline community workers for anxiety and depression in rural Pakistan: piloting mPareshan - a task-shifting primary mental health care intervention

Home-based digital counselling by frontline community workers for anxiety and depression in rural Pakistan: piloting mPareshan - a task-shifting primary mental health care intervention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Home-based digital counselling by frontline community workers for anxiety and depression in rural Pakistan: piloting mPareshan - a task-shifting primary mental health care intervention Fauziah Rabbani, Javeria Nafis, Samina Akhtar, Amna Siddiqui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5621643/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Aug, 2025 Read the published version in BMC Public Health → Version 1 posted 20 You are reading this latest preprint version Abstract Background There is limited evidence that interventions for improving mental well-being can be integrated sustainably into primary health care in Pakistan. We aimed to pilot ‘mPareshan digital intervention’ locally, adapted from WHO mhGAP and delivered by trained and supervised women lay workers to learn if it was feasible and possibly effective in reducing anxiety and depression prior to proposing implementation on a larger scale. Method Using Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9), a baseline household survey was conducted by independent data collectors to measure anxiety and depression. We trained 72 government Lady Health Workers (LHWs) and Lady Health Supervisors (LHSs) in District Badin, Sindh for 3 days to screen and counsel adult men and women (> 18 years) with mild and moderate symptoms of anxiety and depression. Supervised by LHSs, these screen positive participants (SPs) received 6 counselling sessions by LHWs through the mPareshan app during their routine household visits. The app had interactive audio/video psychoeducation features. Severe cases of anxiety and depression were referred to nearest available mental health service. Results Out of the 366 individuals surveyed at baseline, 98 participants (53 men and 45 women, mean age 43.2 years) screened positive for mild and moderate anxiety and depression and were eligible for the mPareshan digital counselling intervention. 6 SPs were lost to follow up for various reasons. Of the 92 SPs who completed all 6 counselling sessions, their mean PHQ-9 score declined from 7.5 (sd 3.1) before intervention to 2.6 (sd 2.2) after intervention. Mean GAD-7 score fell from 6.6 (sd 3.0) to 2.1 (sd 2.2) after 6 sessions. No significant association between sociodemographic variables (age, gender, education, and income levels) and anxiety and depression scores was noted. Conclusion Preliminary evidence of a meaningful improvement in anxiety and depression was found using this locally adapted digital counselling intervention delivered by lay health workers in a rural setting of Sindh, Pakistan. There is a need to test the effectiveness of this task-shifting mental health model in an appropriately powered randomised controlled trial. Trial Registration ACTRN12622000989741 at the Australian New Zealand Clinical Trial Registry (https//www.anzctr.org.au/Default.aspx). Anxiety depression Mental Health task-shifting primary health care Lady Health Workers digital counselling Figures Figure 1 Figure 2 Figure 3 Background Mental health issues account for 13% of the global disease burden [ 1 ]. In 2019, mental disorders contributed to 4.9% of the global Disability-Adjusted-Life-Years (DALYs) [ 2 ]. It is noteworthy that 82% of the global population suffering from mental disorders in 2019 were from the lower- and middle-income countries (LMICs) [ 1 ]. LMICs have an inequitably higher prevalence of mental health disorders, with a significant treatment gap [ 3 ]. Globally, anxiety and depression are the most common mental health disorders with 31% and 29% prevalence respectively [ 1 ]. The COVID-19 pandemic exacerbated this burden, increasing global anxiety and depression by more than 25% in 2020 [ 4 ]. Adequately treating these disorders remains a challenge worldwide [ 5 ]. However, the treatment gaps are higher in LMICs, where 80–95% of individuals with anxiety and depression do not receive appropriate mental health care due to limited availability of service providers [ 6 – 8 ]. Additional barriers in LMICs include the cost of mental healthcare, and the distance to reach mental health facilities [ 9 – 11 ]. In Pakistan, an LMIC, 4% of the total disease burden is attributed to mental health disorders [ 12 ]. With the current population at 240 million, Pakistan has one of the lowest psychiatrists to inhabitant ratio in the region [ 13 , 14 ]. There are only two trained psychiatrists per one million of population [ 15 ]. Reported prevalence rates for anxiety and depression range from 22 to 60% in Pakistan [ 16 – 18 ]. The pandemic has worsened the mental health crisis in Pakistan, with a rise in depression, anxiety, and stress [ 19 – 21 ]. The number of suicides being committed have also increased since the pandemic [ 22 , 23 ]. The growing prevalence of these disorders in Pakistan can be attributed to factors such as political turmoil, insecurity, natural disasters, social disturbances, and poverty [ 9 , 17 , 24 – 26 ]. Approximately 62% of Pakistan’s total population is living in rural areas [ 27 ]. Some studies have shown that rural areas in Pakistan have higher prevalence of mental disorders compared to urban areas [ 24 , 28 ]. The rural population faces poverty, unemployment, constrained resources, hindrance to healthcare access which create barriers for mental healthcare and a significant treatment gap [ 29 – 34 ]. There is increasing evidence linking poverty and mental ill-health [ 35 ]. Community Health Workers (CHWs) often serve as the first point of contact for individuals seeking healthcare in the developing world [ 36 ]. Utilizing CHWs in delivering mental healthcare through a task-sharing approach has proven to be an effective evidence-based implementation strategy for decreasing the mental health burden [ 37 , 38 ]. The use of digital innovation through mobile (mHealth) and technology-assisted applications and programmes has an immense capacity to promote and strengthen healthcare through these CHWs. These innovations utilize the ubiquity of cell phones to enhance the functionality of the health systems [ 39 ]. mHealth is cost-effective and offers extensive population-based outreach given its higher penetration and accessibility [ 40 – 42 ]. App-based interventions reduce barriers associated with traditional in-person interventions and offer efficient use of time by minimizing delays in initiating contact with the healthcare system [ 43 – 45 ]. mHealth programmes delivered by CHWs have proven to be effective in various socio-geographical settings, including LMICs [ 40 , 42 , 46 – 49 ]. These interventions have notably improved mental health outcomes, like anxiety and depression, in countries like the Dominican Republic, Brazil, Peru, Tanzania, India, Gambia and through web-based applications that eliminate geographic restrictions [ 46 , 47 , 50 – 54 ]. In Pakistan, women CHWs are already working in a government-mandated Lady Health Worker Programme (LHW-P) since 1994 [ 55 ]. They offer healthcare promotion, family planning, disease prevention and rehabilitation services to rural areas and urban slum residents [ 56 ]. The LHW-P covers about 85% of the rural population and receive 15–18 months of training to deliver these services [ 57 , 58 ]. These LHWs are local, well-respected, understand the norms and cultural sensitivities, and can be the agents of change in their communities. LHWs are supervised by Lady Health Supervisors (LHSs) who typically oversee 20–25 LHWs and have monthly meetings to ensure delivery of services by the LHWs in their communities [ 59 ]. Previous studies in Pakistan have utilized both the LHW-P and technology-assistance to implement interventions like community case management of childhood pneumonia and diarrhoea, and to alleviate perinatal depression [ 59 – 61 ]. If LHWs are additionally trained to provide mental health services, they can potentially address the mental health service gap in their communities and reduce the stigma associated with seeking mental health care [ 62 ]. The aim of this pilot study in rural Sindh, Pakistan was to gather preliminary evidence on the effectiveness of a home-based mHealth intervention (mPareshan) delivered by LHWs in reducing anxiety and depression. Methods Setting Badin is a rural coastal district in Pakistan’s southern province of Sindh with a total population of 1.8 million. The district has an average literacy rate (ability to read and write) of 24% with an approximate household size of 6 persons [ 63 ]. Badin has one of the highest suicide rates in Sindh with a poor mental health care infrastructure [ 64 ]. Comprised of 5 Talukas (administrative units) and 49 Union Councils, it is served by 1100 LHWs working under the supervision of 36 LHSs [ 58 ]. Each LHW typically caters to 100–150 households (HH) in their “catchment” area, which translates into 1000 people per area [ 59 ]. The mPareshan pilot study (2021–2023) was conducted in 5 Talukas of Badin using a non-randomized, pre- and post-test design with mixed methods of data collection. This paper is reporting on the quantitative data only, which was collected from household surveys. The complete protocol of the mPareshan study is available elsewhere [ 65 ]. Recruitment Trained data collectors conducted a baseline survey of residents in District Badin to screen for anxiety and depression symptoms using standardized psychometric scales of Patient Health Questionnaire-9 (PHQ-9) [ 66 ] and Generalized Anxiety Disorder-7 (GAD-7) [ 67 ]. Participants who screened positive (SP) for mild and moderate symptoms of anxiety and depression were then recruited for the intervention delivered by LHWs through the mPareshan mobile application. Sociodemographic information including basic family and household information of the participants was also collected during the baseline survey. After completion of the intervention over the course of 6 months, an endline survey of the SPs was done to determine change in anxiety and depression scores. Survey answers were collected and electronically entered by data collectors using REDCap software on tablets. Sample size calculations and sampling strategy are detailed in the study protocol [ 65 ]. Outcome measures The main outcome measures were the PHQ-9 and GAD-7 scores. PHQ-9 screens for symptoms of depression. There are 9 items rated on a scale of 0 to 3. The maximum possible score is 27 and the minimum score is 0. Scores of 5, 10, 15, and 20 represent cut-off points for mild, moderate, moderately severe, and severe depression, respectively. PHQ-9 has 88% sensitivity and 88% specificity for detecting major depression at a cutoff score of 10 [ 66 ]. GAD-7 is used to screen symptoms of anxiety disorders. This scale has 7 items, and each item is rated on a sliding scale of 0–3 based on frequency of occurrence of the symptoms. The maximum possible score is 21 and the minimum score is 0. Scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. GAD-7 has 89% sensitivity and 82% specificity for detecting generalized anxiety disorder at a cut-off score of 10 [ 67 ]. Participants Inclusion criteria for SPs: aged 18 and over; residents of selected Talukas in Badin; showing ‘mild’ or ‘moderate’ symptoms of depression and anxiety based on PHQ-9 and GAD-7 scores. Exclusion criteria for SPs: undergoing any pharmacological treatment/therapy for mental health issues; exhibiting severe or moderately severe anxiety/depression symptoms with danger signs (self-harm, harm to others, suicidal ideation). The mPareshan App features The mPareshan App had three main segments: tracking, counselling, and referral (Fig. 1). The tracking segment was responsible for recording participant recruitment/retention and information related to participants’ consent. The referral segment identified potential danger signs such as suicidal ideation, self-harm, and harm to others, based on a two-week recall period. It then recommended appropriate referrals to the nearest mental health facility. In the absence of referral, the LHW directed the participant to the counselling segment, which offered the 6 counselling sessions. Counselling sessions (lasting around 20 minutes) involved imparting psychoeducation through audio and video clips, breathing exercises, and skill development for managing anxiety and depression symptoms. Each of the 6 sessions featured distinct content (Fig. 2). The initial two sessions raised awareness about the causes and signs of anxiety and depression. Sessions 3, 4, and 5 created awareness about coping skills, focusing on enjoyable pleasant activities and lifestyle adjustments. The final (6th ) session served as a recall of prior sessions. The Intervention SPs received these six counselling sessions over the course of 6 months. Prior to commencement of each 20-minutes counselling session, the LHW requested the SP to be seated in a comfortable, preferably less crowded place in the home. At the end of each session, the participant was instructed to practice breathing exercises as homework until the next session. The LHW ensured that the participant felt comfortable and consented to receiving the counselling. If the SP felt uncomfortable at any point in time, the session was discontinued. Completion of counselling segment redirected the LHW to the section on feedback where she checked all activities that were performed in the session and recorded her written comments. Once submitted to the server by the LHW, the session got locked and was passed on to her LHS for review. The LHS logged in from her portal to review all the feedback provided by LHW and submitted it to the Study Coordinator (SC) for a final check. The subsequent session got unlocked for the LHW after 15 days of completion of the previous session. These sessions coincided with LHWs’ scheduled monthly household visits in their catchment area. The content of the sessions was contextually designed to work in a rural setting and be culturally appropriate. Workflow of delivering the intervention through the mPareshan App is provided in Supplementary Material. Health worker training After the baseline survey which identified the HHs where SPs resided, the LHSs and LHWs catering to those HHs were mapped and given a customized 3-day training adapted from WHO mhGAP 2.0 guide [ 68 ]. The training adhered to a curriculum meticulously crafted by the research team and subsequently scrutinized by subject matter experts. This curriculum consisted of four modules: (i) Introduction, (ii) Essential Care and Practices, (iii) Anxiety and Depression, and (iv) Counselling Strategies. Training was delivered through presentations, role-plays, videos, and group discussions over three days. By conducting this initial training, the LHSs and LHWs were better equipped to understand and deliver the mPareshan intervention, as it primarily increased their mental health awareness [ 69 ]. In addition to mental health awareness, the LHWs responsible for delivering the intervention were also trained to use and configure Android tablets on which the mPareshan app was installed. LHWs practiced navigating the app and importing/exporting information to servers using dummy data. LHSs provided supportive supervision to LHWs, who in-turn delivered counselling sessions to SPs. Data Analysis Sociodemographic characteristics and point prevalence were analysed and presented using frequency percentages. Depending on distribution of PHQ-9 and GAD-7 data, we used paired t-tests or McNemar’s Chi-Square tests to evaluate change in scores of anxiety and depression from baseline to endline. One-way ANOVAs or Kruskal-Wallis Tests were used to determine correlation or difference in distribution of sociodemographic variables across anxiety and depression categories. Data was exported from REDCap software to Statistical Package for the Social Sciences (SPSS) Version 21 (IBM Corp). All data was entered, cleaned, coded, and analysed using SPSS. Results Point prevalence of anxiety & depression In February 2022, 366 participants were surveyed in a REDCap-based HH survey in District Badin, Sindh. This baseline survey population had a mean age of 42 years (sd 12.4) and consisted of 197 men (53.8%) and 169 women (46.2%). Their mean GAD-7 score was 2.5 (sd 4.1) and mean PHQ-9 score was 2.9 (sd 4.7). Among these, 276 (75%) had minimal anxiety and required no further assessment, while 7 (2%) were excluded from the pilot study because they had severe anxiety and were referred to next level of care. 83 (23%) had symptoms of mild and moderate anxiety (Fig. 3a). Among the same 366 participants, 271 (74%) had minimal depression, requiring no further assessment, while 7 (2%) were excluded due to having moderately severe and severe depression, and referred to a specialist. 88 (24%) had mild and moderate depression (Fig. 3b). Recruitment Of the 366 participants who were surveyed, 98 (26.8%) had mild and moderate anxiety and depression (Table 1 ) and were invited to take part in the 6-month digital counselling intervention. Table 2 shows the characteristics of the 98 SPs who participated in the intervention. Their mean age was 43.2 years (sd 11.5), with almost equal representation of men and women. Majority of them were illiterate and poor. Table 1 Participants eligible for intervention (Screen positives) Depression (PHQ9) Anxiety (GAD7) Minimal Mild Moderate Total Minimal 259 16 1 17 Mild 12 44 8 64 Moderate 0 5 12 17 Total 12 65 21 98 Table 2 Characteristics of individuals screening positive on the GAD-7 and PHQ-9 scale (n = 98) N (%) Age (years) 60 45 (45.9) 44 (44.9) 9 (9.2) Gender Male Female 53 (54.1) 45 (45.9) Marital Status Married Widowed Separated Never married 89 (90.8) 7 (7.1) 1 (1) 1 (1) Educational level No schooling Less than primary (can read and write) Till primary school Middle school (till 8th) Matriculation Intermediate Bachelors Masters Others 53 (54.1) 10 (10.2) 13 (13.3) 3 (3.1) 5 (5.1) 8 (8.2) 2 (2.0) 3 (3.1) 1 (1.0) Income (PKR) Less than 20,999 Between 21,999 and 30,999 Between 31,999 and 50,999 Greater than 51,999 None 78 (79.6) 9 (9.2) 7 (7.1) 1 (1) 3 (3.1) Relationship with household head Head Spouse Son/Daughter Son-in-Law/Daughter-in-Law Parent Brother/Sister 54 (55.1) 34 (34.7) 5 (5.1) 2 (2) 2 (2) 1 (1) Occupation Laborer Farmer Housewife Shopkeeper Salaried Job Unemployed Others 38 (38.8) 22 (22.4) 20 (20.4) 2 (2) 9 (9.2) 5 (5.1) 2 (2) Drug Use Yes No 47 (48.0) 51 (52.0) Health-seeking behaviour Visited faith healer in last 3 months 46 (46.9) Events in the last 3 months* Domestic violence/family conflict Death in household Traumatic brain injury 12 (12.2) 27 (27.6) 2 (2) History of chronic illness No history Hypertension Others Diabetes Cardiovascular disease 57 (58.2) 16 (16.3) 15 (15.3) 5 (5.1) 5 (5.1) *Independent events. Anxiety and depression scores before and after the six-month intervention All SPs (n = 98) were invited to complete 6 sessions of the mPareshan intervention. 92 (93.9%) completed all 6 sessions. The rest were lost to follow-up for various reasons. The mean GAD-7 score for the 92 participants was 6.6 (sd 3.0) before intervention. After receiving the 6 sessions, the mean score dropped to 2.1 (sd 2.3) [t = 12.2 (p < 0.001)]. Similarly, the mean PHQ-9 score was 7.5 (sd 3.1) before the intervention, which reduced to 2.6 (sd 2.2) after the six sessions concluded [t = 14.1 (p < 0.001)]. The change in frequencies of GAD-7 and PHQ-9 categories (minimal, mild, moderate) were all significant with SPs having mild and moderate anxiety and depression transitioning to the minimal category. Table 3 Change in anxiety and depression scores after the intervention (n = 92) Pre-intervention Post-intervention Pre-post change in scores (paired t-test) Mean (SD) Mean Difference (SD) Test statistic, t (df) p-value GAD7 total score 6.6 (3.0) 2.1 (2.3) 4.5 (3.5) 12.2 (91) < 0.001 PHQ9 total score 7.5 (3.1) 2.6 (2.2) 4.9 (3.4) 14.1 (91) < 0.001 GAD7 categories N (%) Pre-post change in frequencies (McNemar's Chi-square Test) ꝉ Test statistic (χ2) p-value Minimal Anxiety 17 (18.5) 80 (87.0) 55.7 < 0.000 Mild Anxiety 59 (64.1) 10 (10.9) 39.1 < 0.000 Moderate Anxiety 16 (17.4) 2 (2.2) * < 0.000* PHQ9 categories Minimal Depression 11 (12.0) 78 (84.8) 65 < 0.000 Mild Depression 60 (65.2) 13 (14.1) 37.1 < 0.000 Moderate Depression 21 (22.8) 1 (1.1) * < 0.000* *Binomial distribution used. ꝉ McNemar’s Chi-square test used because categorical data is paired (dependent samples). Correlation of anxiety and depression scores with age, gender, education, and income levels At baseline, the SP’s age had no correlation with either their mean anxiety scores (R = 0.06, p = 0.57) or their mean depression scores (R=-0.05, p = 0.62). Their mean ages across the ‘ minimal, mild, and moderate’ anxiety and depression categories also did not differ (GAD-7: F = 0.32 (df = 2), p = 0.73; PHQ-9: F = 0.37 (df = 2), p = 0.69). SP’s age did not correlate with the change in anxiety scores after the intervention (R=-0.16, p = 0.12), or the change in depression scores (R=-0.05, p = 0.66). We found no difference between men and women’s anxiety and depression scores at baseline ([GAD7: Mean difference (SE)=-0.002 (0.60), t=-0.005, p = 0.99]; [PHQ9: Mean difference (SE) = 0.18 (0.63), t = 0.29, p = 0.77]). Likewise, there was no difference in terms of change in anxiety score during the intervention (p = 0.76), or the change in depression scores (p = 0.73) when stratified for gender. In terms of SP’s literacy levels, their baseline anxiety scores were not significantly different across their education levels (p = o.10), nor were their depression scores (p = 0.13). Similarly, anxiety scores were the same across different income category levels in SPs (p = 0.44), as were depression scores (p = 0.94). We also looked at whether the change in anxiety and depression scores after the intervention was associated with SP’s education and income levels. Neither the anxiety score change (F = 1.17, p = 0.33) nor the depression score change (F = 1.62, p = 0.14) differed across education levels. Likewise, SP’s income levels were also not associated with change in anxiety scores (F = 0.86, p = 0.50) or change in depression scores (F = 0.42, p = 0.79), after the intervention. Discussion This is the first example of frontline women lay workers in Pakistan delivering a home-based task-shifting mental health digital counselling intervention, to improve mental well-being in primary care. We have shown that the mPareshan intervention was able to make a meaningful reduction in psychiatric morbidity in a sample of adult rural population in Sindh, Pakistan. The preliminary findings of this study indicated that the intervention is effective in reducing anxiety and depression, with a drop of 4.4 points on the 7-item GAD scale and 4.9 points on the 9-item PHQ scale. Most participants who initially exhibited mild or moderate anxiety and depression shifted to minimal levels of symptomatic scores following the intervention. In our baseline household survey involving 366 adult participants, we observed a point prevalence of mild to moderate anxiety in 23% and mild to moderate depression in 24% of the sample. Other studies in Pakistan have used the PHQ-9 and GAD-7 to establish prevalence rates in other subsets of the population. This includes pregnant women in a rural, low-income subdistrict having around 35% mild to moderate depression [ 70 ]. In the 92 participants who completed the six sessions of the mPareshan intervention, the percentage with minimal anxiety and depression went up by 68.5% and 72.8%, respectively. The intervention was thus effective in reducing the anxiety and depression levels of the participants. Percentages of mild anxiety and depression categories went down by almost half while those with moderate anxiety and depression reduced significantly as well. The significant drop in mean anxiety and depression scores indicates the intervention’s efficacy in improving mental well-being in this sample of rural population in Pakistan. A recent systematic review looking at the effectiveness of non-specialist delivered digital interventions for mental health also shows that similar interventions have worked successfully in reducing mental health issues for service users [ 71 ]. Another systematic review that pooled the effect sizes of such digital mental health interventions in LMICs found that they were moderately to highly effective in reducing depression and anxiety symptoms [ 72 ]. We showed that the low-intensity nature of this intervention works in the real-life setting. With a weak referral system and scant specialized mental health resources, a frontline health worker delivered intervention like mPareshan can cater to most of the population who are showing symptoms of mild and moderate anxiety and depression. This preventative approach not only reduces symptoms but also minimizes the need for further psychiatric assessment among individuals who achieve minimal level of symptoms post-intervention. Other studies have also emphasized the efficacy of low-intensity digital interventions, in addressing mental healthcare delivery. These include low intensity mHealth interventions for maternal mental health in Spain [ 73 ], Qatar [ 74 ], as well as a low-intensity cognitive behavioural therapy intervention for adolescent mental health in England [ 75 ]. Despite a significant portion of our study population lacking formal education (54%) and falling into the lowest income category (80%), we did not find a significant association between education levels, income, and changes in anxiety and depression scores post-intervention. This emphasizes the universal applicability of the mPareshan intervention across all socio-economic strata, filling a critical gap in mental health care delivery in resource-constrained settings Men and women in our study responded equally well to the intervention, as there was no gender difference when comparing changes in anxiety and depression scores after the intervention. At the baseline, mean anxiety and depression scores were also similar between men and women. This finding contrasts with a Chinese rural cohort study which showed that women had higher prevalence and risks of depression and anxiety compared to men [ 76 ]. The use of LHWs in our intervention has shown to be an effective strategy for promoting mental health at the community level. Similar studies have demonstrated the positive impact of such CHWs in improving access to mental health services and enhancing community perceptions of mental health care [ 37 ]. Our findings support the use of LHWs not only as a means of delivering mental health interventions but also as a catalyst for broader community engagement and raising mental health awareness [ 62 ]. A noteworthy caveat of our intervention is its limited efficacy in addressing severe cases of anxiety and depression. However, the proportion of individuals with severe symptoms is relatively low (1–2%) in our study population, indicating that our intervention is addressing the needs of the majority who are viable candidates for this type of support. Another fundamental limitation of this pilot feasibility study is the absence of a comparison or control group. Conclusions In conclusion, we have found preliminary evidence of a meaningful improvement in anxiety and depression associated with a locally adapted digital counselling intervention delivered by lay health workers through routine primary health care in a rural setting of Sindh, Pakistan. The digital counselling intervention was integrated into the routine workload of the LHWs whose roles include maternal and child healthcare promotion activities, immunization drives etc. By leveraging existing community resources and adopting a low-intensity, scalable model, this intervention demonstrates significant potential to mitigate the burden of anxiety and depression in underserved populations. Future research should focus on long-term scalability through an appropriately powered randomized controlled trial to test if this task-shifting mental health intervention is effective compared to usual care. Integration into existing health systems to maximize impact and reach is also essential for the intervention’s sustainability and scalability. Abbreviations CHW Community Health Worker GAD-7 Generalized Anxiety Disorder-7 HH Household LHS Lady Health Supervisor LHW Lady Health Worker LHW-P Lady Health Worker Programme LMIC Lower and Middle-Income Country mhGAP Mental Health Gap Action Programme PHQ-9 Patient Health Questionnaire-9 REDCap Research Electronic Data Capture SC Study Coordinator SP Screen Positive WHO World Health Organization Declarations Ethics approval and consent to participate The study was approved by the Ethical Review Committee of Aga Khan University (ERC#2021-6570-20015). The study adheres to the tenets of the Declaration of Helsinki. All study participants provided informed consent which was given to them in their local language. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by a grant from the Brain & Mind Institute (BMI), Aga Khan University (Grant Brain & Mind-FR-11E-mPareshan App 83000). Authors' contributions All authors (FR, JN, SA, AS, ZM) made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas. Acknowledgements The authors would like to thank the overall research team members involved in the implementation of this trial. Special gratitude is expressed to the study participants that took part in this intervention and related quantitative assessments. References World Health Organization. World Mental Health Report: Transforming mental health for all. 2022. Collaborators. 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JMIR Mental Health. 2016;3. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. 2017;16. Mohr DC, Tomasino KN, Lattie EG, Palac HL, Kwasny MJ, Weingardt K et al. Intellicare: An eclectic, skills-based app suite for the treatment of depression and anxiety. J Med Internet Res. 2017;19. Agarwal S, Perry HB, Long LA, Labrique AB. Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: Systematic review. Trop Med Int Health. 2015;20:1003–14. Van Straten A, Cuijpers P, Smits N. Effectiveness of a web-based self-help intervention for symptoms of depression, anxiety, and stress: Randomized controlled trial. J Med Internet Res. 2008;10:1–11. Iyawa GE, Langan-Martin J, Sevalie S, Masikara W. mHealth as Tools for Development in Mental Health. 2020. pp. 58–80. WHO Global Observatory for eHealth. mHealth: new horizons for health through mobile technologies: second global survey on eHealth. Geneva PP - Geneva: World Health Organization; 2011. Pham Q, Khatib Y, Stansfeld S, Fox S, Green T. Feasibility and Efficacy of an mHealth Game for Managing Anxiety: Flowy Randomized Controlled Pilot Trial and Design Evaluation. Games Health J. 2016;5:50–67. Miralles I, Granell C, Díaz-Sanahuja L, van Woensel W, Bretón-López J, Mira A et al. Smartphone apps for the treatment of mental disorders: Systematic review. JMIR Mhealth Uhealth. 2020;8. Menezes P, Quayle J, Claro HG, Da Silva S, Brandt LR, Diez-Canseco F, et al. Use of a mobile phone app to treat depression comorbid with hypertension or diabetes: A pilot study in Brazil and Peru. JMIR Ment Health. 2019;6:1–12. Caplan S, Sosa Lovera A, Reyna Liberato P. A feasibility study of a mental health mobile app in the Dominican Republic: The untold story. Int J Ment Health. 2018;47:311–45. Chandrashekar P. Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. Mhealth. 2018;4:6–6. Bechange S, Schmidt E, Ruddock A, Khan IK, Gillani M, Roca A et al. Understanding the role of lady health workers in improving access to eye health services in rural Pakistan – findings from a qualitative study. Archives Public Health. 2021;79. Rabbani F, Zahidie A. Recent strategies to improve community case management of diarrhea among children under five in developing countries. Diarrhea Treatment. Avid Science; 2016. pp. 2–25. Ali TM, Gul S. Community mental health services in Pakistan: Review study from Muslim world 2000–2015. Psychology, Community & Health. 2018;7. Aftab W, Piryani S, Rabbani F. Does supportive supervision intervention improve community health worker knowledge and practices for community management of childhood diarrhea and pneumonia? Lessons for scale-up from Nigraan and Nigraan Plus trials in Pakistan. Hum Resour Health. 2021;19:99. Rabbani F, Mukhi AAA, Perveen S, Gul X, Iqbal SP, Qazi SA et al. Improving community case management of diarrhoea and pneumonia in district Badin, Pakistan through a cluster randomised study–the NIGRAAN trial protocol. Implement Sci. 2014;9. Rahman A, Akhtar P, Hamdani SU, Atif N, Nazir H, Uddin I et al. Using technology to scale-up training and supervision of community health workers in the psychosocial management of perinatal depression: a non-inferiority, randomized controlled trial. Global Mental Health. 2019;6. Atif N, Nazir H, Sultan ZH, Rauf R, Waqas A, Malik A, et al. Technology-assisted peer therapy: a new way of delivering evidence-based psychological interventions. BMC Health Serv Res. 2022;22:1–12. Rabbani F, Akhtar S, Nafis J, Khan S, Siddiqi S, Merali Z. Addition of mental health to the lady health worker curriculum in Pakistan: now or never. Hum Resour Health. 2023;21:29. USAID. iMMAP. Pakistan Emergency Situation Analysis - Updated District Profile Badin, September 2014. 2014. Bhatti W. Incidence of suicide alarmingly high in South Asia: experts. The News International. 2022. https://www.thenews.com.pk/print/971854-incidence-of-suicide-alarmingly-high-in-south-asia-experts . Accessed 26 Jul 2023. Rabbani F, Nafis J, Akhtar S, Khan MS, Sayani S, Siddiqui A, et al. Technology-Assisted Mental Health Intervention Delivered by Frontline Workers at Community Doorsteps for Reducing Anxiety and Depression in Rural Pakistan: Protocol for the mPareshan Mixed Methods Implementation Trial. JMIR Res Protoc. 2024;13:e54272. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006;166. World Health Organization. mhGAP Intervention Guide Mental Health Gap Action Programme Version 2.0 for mental, neurological and substance use disorders in non-specialized health settings. World Health Organization; 2016. Akhtar S, Rabbani F, Nafis J, Merali Z. Where there is no specialist – Improving Mental Health Literacy of Frontline Community Health Workers in a Rural District of Pakistan: The mPareshan Project (Preprint). 2024. https://doi.org/10.21203/rs.3.rs-5571403/v1 Gallis JA, Maselko J, O’Donnell K, Song K, Saqib K, Turner EL, et al. Criterion-related validity and reliability of the Urdu version of the patient health questionnaire in a sample of community-based pregnant women in Pakistan. PeerJ. 2018;6:e5185. Mudiyanselage KWW, De Santis KK, Jörg F, Saleem M, Stewart R, Zeeb H, et al. The effectiveness of mental health interventions involving non-specialists and digital technology in low-and middle-income countries – a systematic review. BMC Public Health. 2024;24:77. Kim J, Aryee LMD, Bang H, Prajogo S, Choi YK, Hoch JS, et al. Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis. JMIR Ment Health. 2023;10:e43066. Jimenez-Barragan M, del Pino Gutierrez A, Garcia JC, Monistrol-Ruano O, Coll-Navarro E, Porta-Roda O, et al. Study protocol for improving mental health during pregnancy: a randomized controlled low-intensity m-health intervention by midwives at primary care centers. BMC Nurs. 2023;22:309. Naja S, Elyamani R, Chehab M, Ali Siddig Ahmed M, Babeker G, Lawand G et al. The impact of telemental health interventions on maternal mental health outcomes: a pilot randomized controlled trial during the COVID-19 pandemic. Health Psychol Behav Med. 2023;11. Turnbull M, Kirk H, Lincoln M, Peacock S, Howey L. A pilot evaluation of the role of a children’s wellbeing practitioner (CWP) in a child and adolescent mental health service (CAMHS). Clin Child Psychol Psychiatry. 2023;28:1150–9. Luo Z, Li Y, Hou Y, Liu X, Jiang J, Wang Y, et al. Gender-specific prevalence and associated factors of major depressive disorder and generalized anxiety disorder in a Chinese rural population: the Henan rural cohort study. BMC Public Health. 2019;19:1744. Additional Declarations No competing interests reported. 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ingredients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1BMCPublicHealth.png","url":"https://assets-eu.researchsquare.com/files/rs-5621643/v1/676ccdb085b3485297141d02.png"},{"id":71677501,"identity":"cb71d5dd-b452-416a-8e3f-c68ce7b80265","added_by":"auto","created_at":"2024-12-17 15:56:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":456987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContent overview of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003emPareshan\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emental health counselling sessions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2BMCPublicHealth.png","url":"https://assets-eu.researchsquare.com/files/rs-5621643/v1/df4ac44f940f0da1b719c178.png"},{"id":71678188,"identity":"a7719e16-fff8-4632-901d-942bc32ad2b5","added_by":"auto","created_at":"2024-12-17 16:04:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePoint prevalence of anxiety and depression (n=366)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3BMCPublicHealth.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5621643/v1/7e120b08c441b2beab77f317.jpg"},{"id":71679593,"identity":"0c0613ad-3642-4cfc-9e38-7ee828df926c","added_by":"auto","created_at":"2024-12-17 16:20:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5621643/v1/eb364b58-4f1f-413b-8fd6-02bc4ddd5cd6.pdf"},{"id":71677502,"identity":"0ed6fda4-187a-4f7d-9955-87f6e841647d","added_by":"auto","created_at":"2024-12-17 15:56:56","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2092123,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-5621643/v1/71e04a146f0aabb4784b3b4c.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Home-based digital counselling by frontline community workers for anxiety and depression in rural Pakistan: piloting mPareshan - a task-shifting primary mental health care intervention","fulltext":[{"header":"Background","content":"\u003cp\u003eMental health issues account for 13% of the global disease burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2019, mental disorders contributed to 4.9% of the global Disability-Adjusted-Life-Years (DALYs) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is noteworthy that 82% of the global population suffering from mental disorders in 2019 were from the lower- and middle-income countries (LMICs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. LMICs have an inequitably higher prevalence of mental health disorders, with a significant treatment gap [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, anxiety and depression are the most common mental health disorders with 31% and 29% prevalence respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The COVID-19 pandemic exacerbated this burden, increasing global anxiety and depression by more than 25% in 2020 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Adequately treating these disorders remains a challenge worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the treatment gaps are higher in LMICs, where 80\u0026ndash;95% of individuals with anxiety and depression do not receive appropriate mental health care due to limited availability of service providers [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additional barriers in LMICs include the cost of mental healthcare, and the distance to reach mental health facilities [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Pakistan, an LMIC, 4% of the total disease burden is attributed to mental health disorders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. With the current population at 240\u0026nbsp;million, Pakistan has one of the lowest psychiatrists to inhabitant ratio in the region [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There are only two trained psychiatrists per one million of population [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Reported prevalence rates for anxiety and depression range from 22 to 60% in Pakistan [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The pandemic has worsened the mental health crisis in Pakistan, with a rise in depression, anxiety, and stress [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The number of suicides being committed have also increased since the pandemic [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe growing prevalence of these disorders in Pakistan can be attributed to factors such as political turmoil, insecurity, natural disasters, social disturbances, and poverty [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Approximately 62% of Pakistan\u0026rsquo;s total population is living in rural areas [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Some studies have shown that rural areas in Pakistan have higher prevalence of mental disorders compared to urban areas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The rural population faces poverty, unemployment, constrained resources, hindrance to healthcare access which create barriers for mental healthcare and a significant treatment gap [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. There is increasing evidence linking poverty and mental ill-health [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCommunity Health Workers (CHWs) often serve as the first point of contact for individuals seeking healthcare in the developing world [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Utilizing CHWs in delivering mental healthcare through a task-sharing approach has proven to be an effective evidence-based implementation strategy for decreasing the mental health burden [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The use of digital innovation through mobile (mHealth) and technology-assisted applications and programmes has an immense capacity to promote and strengthen healthcare through these CHWs. These innovations utilize the ubiquity of cell phones to enhance the functionality of the health systems [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. mHealth is cost-effective and offers extensive population-based outreach given its higher penetration and accessibility [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. App-based interventions reduce barriers associated with traditional in-person interventions and offer efficient use of time by minimizing delays in initiating contact with the healthcare system [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003emHealth programmes delivered by CHWs have proven to be effective in various socio-geographical settings, including LMICs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These interventions have notably improved mental health outcomes, like anxiety and depression, in countries like the Dominican Republic, Brazil, Peru, Tanzania, India, Gambia and through web-based applications that eliminate geographic restrictions [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51 CR52 CR53\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Pakistan, women CHWs are already working in a government-mandated Lady Health Worker Programme (LHW-P) since 1994 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. They offer healthcare promotion, family planning, disease prevention and rehabilitation services to rural areas and urban slum residents [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The LHW-P covers about 85% of the rural population and receive 15\u0026ndash;18 months of training to deliver these services [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These LHWs are local, well-respected, understand the norms and cultural sensitivities, and can be the agents of change in their communities. LHWs are supervised by Lady Health Supervisors (LHSs) who typically oversee 20\u0026ndash;25 LHWs and have monthly meetings to ensure delivery of services by the LHWs in their communities [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Previous studies in Pakistan have utilized both the LHW-P and technology-assistance to implement interventions like community case management of childhood pneumonia and diarrhoea, and to alleviate perinatal depression [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. If LHWs are additionally trained to provide mental health services, they can potentially address the mental health service gap in their communities and reduce the stigma associated with seeking mental health care [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this pilot study in rural Sindh, Pakistan was to gather preliminary evidence on the effectiveness of a home-based mHealth intervention (mPareshan) delivered by LHWs in reducing anxiety and depression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eSetting\u003c/h2\u003e\n\u003cp\u003eBadin is a rural coastal district in Pakistan\u0026rsquo;s southern province of Sindh with a total population of 1.8\u0026nbsp;million. The district has an average literacy rate (ability to read and write) of 24% with an approximate household size of 6 persons [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Badin has one of the highest suicide rates in Sindh with a poor mental health care infrastructure [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. Comprised of 5 Talukas (administrative units) and 49 Union Councils, it is served by 1100 LHWs working under the supervision of 36 LHSs [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. Each LHW typically caters to 100\u0026ndash;150 households (HH) in their \u0026ldquo;catchment\u0026rdquo; area, which translates into 1000 people per area [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. The mPareshan pilot study (2021\u0026ndash;2023) was conducted in 5 Talukas of Badin using a non-randomized, pre- and post-test design with mixed methods of data collection. This paper is reporting on the quantitative data only, which was collected from household surveys. The complete protocol of the mPareshan study is available elsewhere [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRecruitment\u003c/h3\u003e\n\u003cp\u003eTrained data collectors conducted a baseline survey of residents in District Badin to screen for anxiety and depression symptoms using standardized psychometric scales of Patient Health Questionnaire-9 (PHQ-9) [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] and Generalized Anxiety Disorder-7 (GAD-7) [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. Participants who screened positive (SP) for mild and moderate symptoms of anxiety and depression were then recruited for the intervention delivered by LHWs through the mPareshan mobile application. Sociodemographic information including basic family and household information of the participants was also collected during the baseline survey. After completion of the intervention over the course of 6 months, an endline survey of the SPs was done to determine change in anxiety and depression scores. Survey answers were collected and electronically entered by data collectors using REDCap software on tablets. Sample size calculations and sampling strategy are detailed in the study protocol [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eOutcome measures\u003c/h3\u003e\n\u003cp\u003eThe main outcome measures were the PHQ-9 and GAD-7 scores. PHQ-9 screens for symptoms of depression. There are 9 items rated on a scale of 0 to 3. The maximum possible score is 27 and the minimum score is 0. Scores of 5, 10, 15, and 20 represent cut-off points for mild, moderate, moderately severe, and severe depression, respectively. PHQ-9 has 88% sensitivity and 88% specificity for detecting major depression at a cutoff score of 10 [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. GAD-7 is used to screen symptoms of anxiety disorders. This scale has 7 items, and each item is rated on a sliding scale of 0\u0026ndash;3 based on frequency of occurrence of the symptoms. The maximum possible score is 21 and the minimum score is 0. Scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. GAD-7 has 89% sensitivity and 82% specificity for detecting generalized anxiety disorder at a cut-off score of 10 [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eInclusion criteria for SPs: aged 18 and over; residents of selected Talukas in Badin; showing \u0026lsquo;mild\u0026rsquo; or \u0026lsquo;moderate\u0026rsquo; symptoms of depression and anxiety based on PHQ-9 and GAD-7 scores. Exclusion criteria for SPs: undergoing any pharmacological treatment/therapy for mental health issues; exhibiting severe or moderately severe anxiety/depression symptoms with danger signs (self-harm, harm to others, suicidal ideation).\u003c/p\u003e\n\u003ch3\u003eThe mPareshan App features\u003c/h3\u003e\n\u003cp\u003eThe mPareshan App had three main segments: tracking, counselling, and referral (Fig.\u0026nbsp;1). The tracking segment was responsible for recording participant recruitment/retention and information related to participants\u0026rsquo; consent. The referral segment identified potential danger signs such as suicidal ideation, self-harm, and harm to others, based on a two-week recall period. It then recommended appropriate referrals to the nearest mental health facility. In the absence of referral, the LHW directed the participant to the counselling segment, which offered the 6 counselling sessions.\u003c/p\u003e\n\u003cp\u003eCounselling sessions (lasting around 20 minutes) involved imparting psychoeducation through audio and video clips, breathing exercises, and skill development for managing anxiety and depression symptoms. Each of the 6 sessions featured distinct content (Fig.\u0026nbsp;2). The initial two sessions raised awareness about the causes and signs of anxiety and depression. Sessions 3, 4, and 5 created awareness about coping skills, focusing on enjoyable pleasant activities and lifestyle adjustments. The final (6th ) session served as a recall of prior sessions.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eThe Intervention\u003c/h2\u003e\n\u003cp\u003eSPs received these six counselling sessions over the course of 6 months. Prior to commencement of each 20-minutes counselling session, the LHW requested the SP to be seated in a comfortable, preferably less crowded place in the home. At the end of each session, the participant was instructed to practice breathing exercises as homework until the next session. The LHW ensured that the participant felt comfortable and consented to receiving the counselling. If the SP felt uncomfortable at any point in time, the session was discontinued. Completion of counselling segment redirected the LHW to the section on feedback where she checked all activities that were performed in the session and recorded her written comments. Once submitted to the server by the LHW, the session got locked and was passed on to her LHS for review. The LHS logged in from her portal to review all the feedback provided by LHW and submitted it to the Study Coordinator (SC) for a final check. The subsequent session got unlocked for the LHW after 15 days of completion of the previous session. These sessions coincided with LHWs\u0026rsquo; scheduled monthly household visits in their catchment area. The content of the sessions was contextually designed to work in a rural setting and be culturally appropriate. Workflow of delivering the intervention through the mPareshan App is provided in Supplementary Material.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eHealth worker training\u003c/h3\u003e\n\u003cp\u003eAfter the baseline survey which identified the HHs where SPs resided, the LHSs and LHWs catering to those HHs were mapped and given a customized 3-day training adapted from WHO mhGAP 2.0 guide [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. The training adhered to a curriculum meticulously crafted by the research team and subsequently scrutinized by subject matter experts. This curriculum consisted of four modules: (i) Introduction, (ii) Essential Care and Practices, (iii) Anxiety and Depression, and (iv) Counselling Strategies. Training was delivered through presentations, role-plays, videos, and group discussions over three days. By conducting this initial training, the LHSs and LHWs were better equipped to understand and deliver the mPareshan intervention, as it primarily increased their mental health awareness [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. In addition to mental health awareness, the LHWs responsible for delivering the intervention were also trained to use and configure Android tablets on which the mPareshan app was installed. LHWs practiced navigating the app and importing/exporting information to servers using dummy data. LHSs provided supportive supervision to LHWs, who in-turn delivered counselling sessions to SPs.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eSociodemographic characteristics and point prevalence were analysed and presented using frequency percentages. Depending on distribution of PHQ-9 and GAD-7 data, we used paired t-tests or McNemar\u0026rsquo;s Chi-Square tests to evaluate change in scores of anxiety and depression from baseline to endline. One-way ANOVAs or Kruskal-Wallis Tests were used to determine correlation or difference in distribution of sociodemographic variables across anxiety and depression categories. Data was exported from REDCap software to Statistical Package for the Social Sciences (SPSS) Version 21 (IBM Corp). All data was entered, cleaned, coded, and analysed using SPSS.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePoint prevalence of anxiety \u0026amp; depression\u003c/h2\u003e\n \u003cp\u003eIn February 2022, 366 participants were surveyed in a REDCap-based HH survey in District Badin, Sindh. This baseline survey population had a mean age of 42 years (sd 12.4) and consisted of 197 men (53.8%) and 169 women (46.2%). Their mean GAD-7 score was 2.5 (sd 4.1) and mean PHQ-9 score was 2.9 (sd 4.7). Among these, 276 (75%) had minimal anxiety and required no further assessment, while 7 (2%) were excluded from the pilot study because they had severe anxiety and were referred to next level of care. 83 (23%) had symptoms of mild and moderate anxiety (Fig. 3a). Among the same 366 participants, 271 (74%) had minimal depression, requiring no further assessment, while 7 (2%) were excluded due to having moderately severe and severe depression, and referred to a specialist. 88 (24%) had mild and moderate depression (Fig. 3b).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eRecruitment\u003c/h2\u003e\n \u003cp\u003eOf the 366 participants who were surveyed, 98 (26.8%) had mild and moderate anxiety and depression (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and were invited to take part in the 6-month digital counselling intervention. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the characteristics of the 98 SPs who participated in the intervention. Their mean age was 43.2 years (sd 11.5), with almost equal representation of men and women. Majority of them were illiterate and poor.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipants eligible for intervention (Screen positives)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eDepression (PHQ9)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety (GAD7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e64\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of individuals screening positive on the GAD-7 and PHQ-9 scale (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAge (years)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n \u003cp\u003e41\u0026ndash;60\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (45.9)\u003c/p\u003e\n \u003cp\u003e44 (44.9)\u003c/p\u003e\n \u003cp\u003e9 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53 (54.1)\u003c/p\u003e\n \u003cp\u003e45 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMarital Status\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89 (90.8)\u003c/p\u003e\n \u003cp\u003e7 (7.1)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEducational level\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eNo schooling\u003c/p\u003e\n \u003cp\u003eLess than primary (can read and write)\u003c/p\u003e\n \u003cp\u003eTill primary school\u003c/p\u003e\n \u003cp\u003eMiddle school (till 8th)\u003c/p\u003e\n \u003cp\u003eMatriculation\u003c/p\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53 (54.1)\u003c/p\u003e\n \u003cp\u003e10 (10.2)\u003c/p\u003e\n \u003cp\u003e13 (13.3)\u003c/p\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003cp\u003e8 (8.2)\u003c/p\u003e\n \u003cp\u003e2 (2.0)\u003c/p\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIncome (PKR)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eLess than 20,999\u003c/p\u003e\n \u003cp\u003eBetween 21,999 and 30,999\u003c/p\u003e\n \u003cp\u003eBetween 31,999 and 50,999\u003c/p\u003e\n \u003cp\u003eGreater than 51,999\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78 (79.6)\u003c/p\u003e\n \u003cp\u003e9 (9.2)\u003c/p\u003e\n \u003cp\u003e7 (7.1)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRelationship with household head\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eHead\u003c/p\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003cp\u003eSon/Daughter\u003c/p\u003e\n \u003cp\u003eSon-in-Law/Daughter-in-Law\u003c/p\u003e\n \u003cp\u003eParent\u003c/p\u003e\n \u003cp\u003eBrother/Sister\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54 (55.1)\u003c/p\u003e\n \u003cp\u003e34 (34.7)\u003c/p\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOccupation\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eLaborer\u003c/p\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003cp\u003eShopkeeper\u003c/p\u003e\n \u003cp\u003eSalaried Job\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (38.8)\u003c/p\u003e\n \u003cp\u003e22 (22.4)\u003c/p\u003e\n \u003cp\u003e20 (20.4)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003cp\u003e9 (9.2)\u003c/p\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDrug Use\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (48.0)\u003c/p\u003e\n \u003cp\u003e51 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHealth-seeking behaviour\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eVisited faith healer in last 3 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEvents in the last 3 months*\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eDomestic violence/family conflict\u003c/p\u003e\n \u003cp\u003eDeath in household\u003c/p\u003e\n \u003cp\u003eTraumatic brain injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (12.2)\u003c/p\u003e\n \u003cp\u003e27 (27.6)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHistory of chronic illness\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eNo history\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (58.2)\u003c/p\u003e\n \u003cp\u003e16 (16.3)\u003c/p\u003e\n \u003cp\u003e15 (15.3)\u003c/p\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e*Independent events.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAnxiety and depression scores before and after the six-month intervention\u003c/h2\u003e\n \u003cp\u003eAll SPs (n\u0026thinsp;=\u0026thinsp;98) were invited to complete 6 sessions of the mPareshan intervention. 92 (93.9%) completed all 6 sessions. The rest were lost to follow-up for various reasons. The mean GAD-7 score for the 92 participants was 6.6 (sd 3.0) before intervention. After receiving the 6 sessions, the mean score dropped to 2.1 (sd 2.3) [t\u0026thinsp;=\u0026thinsp;12.2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. Similarly, the mean PHQ-9 score was 7.5 (sd 3.1) before the intervention, which reduced to 2.6 (sd 2.2) after the six sessions concluded [t\u0026thinsp;=\u0026thinsp;14.1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. The change in frequencies of GAD-7 and PHQ-9 categories (minimal, mild, moderate) were all significant with SPs having mild and moderate anxiety and depression transitioning to the minimal category.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChange in anxiety and depression scores after the intervention (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-intervention\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-intervention\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePre-post change in scores (paired t-test)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Difference (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest statistic, t (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD7 total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9 total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.1 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGAD7 categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-post change in frequencies (McNemar\u0026apos;s Chi-square Test) ꝉ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest statistic (\u0026chi;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ9 categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.000*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*Binomial distribution used.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eꝉ McNemar\u0026rsquo;s Chi-square test used because categorical data is paired (dependent samples).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation of anxiety and depression scores with age, gender, education, and income levels\u003c/h2\u003e\n \u003cp\u003eAt baseline, the SP\u0026rsquo;s age had no correlation with either their mean anxiety scores (R\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.57) or their mean depression scores (R=-0.05, p\u0026thinsp;=\u0026thinsp;0.62). Their mean ages across the \u0026lsquo;\u003cem\u003eminimal, mild, and moderate\u0026rsquo;\u003c/em\u003e anxiety and depression categories also did not differ (GAD-7: F\u0026thinsp;=\u0026thinsp;0.32 (df\u0026thinsp;=\u0026thinsp;2), p\u0026thinsp;=\u0026thinsp;0.73; PHQ-9: F\u0026thinsp;=\u0026thinsp;0.37 (df\u0026thinsp;=\u0026thinsp;2), p\u0026thinsp;=\u0026thinsp;0.69). SP\u0026rsquo;s age did not correlate with the change in anxiety scores after the intervention (R=-0.16, p\u0026thinsp;=\u0026thinsp;0.12), or the change in depression scores (R=-0.05, p\u0026thinsp;=\u0026thinsp;0.66). We found no difference between men and women\u0026rsquo;s anxiety and depression scores at baseline ([GAD7: Mean difference (SE)=-0.002 (0.60), t=-0.005, p\u0026thinsp;=\u0026thinsp;0.99]; [PHQ9: Mean difference (SE)\u0026thinsp;=\u0026thinsp;0.18 (0.63), t\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.77]). Likewise, there was no difference in terms of change in anxiety score during the intervention (p\u0026thinsp;=\u0026thinsp;0.76), or the change in depression scores (p\u0026thinsp;=\u0026thinsp;0.73) when stratified for gender. In terms of SP\u0026rsquo;s literacy levels, their baseline anxiety scores were not significantly different across their education levels (p\u0026thinsp;=\u0026thinsp;o.10), nor were their depression scores (p\u0026thinsp;=\u0026thinsp;0.13). Similarly, anxiety scores were the same across different income category levels in SPs (p\u0026thinsp;=\u0026thinsp;0.44), as were depression scores (p\u0026thinsp;=\u0026thinsp;0.94). We also looked at whether the change in anxiety and depression scores after the intervention was associated with SP\u0026rsquo;s education and income levels. Neither the anxiety score change (F\u0026thinsp;=\u0026thinsp;1.17, p\u0026thinsp;=\u0026thinsp;0.33) nor the depression score change (F\u0026thinsp;=\u0026thinsp;1.62, p\u0026thinsp;=\u0026thinsp;0.14) differed across education levels. Likewise, SP\u0026rsquo;s income levels were also not associated with change in anxiety scores (F\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.50) or change in depression scores (F\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;0.79), after the intervention.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first example of frontline women lay workers in Pakistan delivering a home-based task-shifting mental health digital counselling intervention, to improve mental well-being in primary care. We have shown that the \u003cem\u003emPareshan\u003c/em\u003e intervention was able to make a meaningful reduction in psychiatric morbidity in a sample of adult rural population in Sindh, Pakistan. The preliminary findings of this study indicated that the intervention is effective in reducing anxiety and depression, with a drop of 4.4 points on the 7-item GAD scale and 4.9 points on the 9-item PHQ scale. Most participants who initially exhibited mild or moderate anxiety and depression shifted to minimal levels of symptomatic scores following the intervention.\u003c/p\u003e \u003cp\u003eIn our baseline household survey involving 366 adult participants, we observed a point prevalence of mild to moderate anxiety in 23% and mild to moderate depression in 24% of the sample. Other studies in Pakistan have used the PHQ-9 and GAD-7 to establish prevalence rates in other subsets of the population. This includes pregnant women in a rural, low-income subdistrict having around 35% mild to moderate depression [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the 92 participants who completed the six sessions of the mPareshan intervention, the percentage with minimal anxiety and depression went up by 68.5% and 72.8%, respectively. The intervention was thus effective in reducing the anxiety and depression levels of the participants. Percentages of mild anxiety and depression categories went down by almost half while those with moderate anxiety and depression reduced significantly as well. The significant drop in mean anxiety and depression scores indicates the intervention\u0026rsquo;s efficacy in improving mental well-being in this sample of rural population in Pakistan. A recent systematic review looking at the effectiveness of non-specialist delivered digital interventions for mental health also shows that similar interventions have worked successfully in reducing mental health issues for service users [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Another systematic review that pooled the effect sizes of such digital mental health interventions in LMICs found that they were moderately to highly effective in reducing depression and anxiety symptoms [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe showed that the low-intensity nature of this intervention works in the real-life setting. With a weak referral system and scant specialized mental health resources, a frontline health worker delivered intervention like mPareshan can cater to most of the population who are showing symptoms of mild and moderate anxiety and depression. This preventative approach not only reduces symptoms but also minimizes the need for further psychiatric assessment among individuals who achieve minimal level of symptoms post-intervention. Other studies have also emphasized the efficacy of low-intensity digital interventions, in addressing mental healthcare delivery. These include low intensity mHealth interventions for maternal mental health in Spain [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], Qatar [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], as well as a low-intensity cognitive behavioural therapy intervention for adolescent mental health in England [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite a significant portion of our study population lacking formal education (54%) and falling into the lowest income category (80%), we did not find a significant association between education levels, income, and changes in anxiety and depression scores post-intervention. This emphasizes the universal applicability of the \u003cem\u003emPareshan\u003c/em\u003e intervention across all socio-economic strata, filling a critical gap in mental health care delivery in resource-constrained settings\u003c/p\u003e \u003cp\u003eMen and women in our study responded equally well to the intervention, as there was no gender difference when comparing changes in anxiety and depression scores after the intervention. At the baseline, mean anxiety and depression scores were also similar between men and women. This finding contrasts with a Chinese rural cohort study which showed that women had higher prevalence and risks of depression and anxiety compared to men [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of LHWs in our intervention has shown to be an effective strategy for promoting mental health at the community level. Similar studies have demonstrated the positive impact of such CHWs in improving access to mental health services and enhancing community perceptions of mental health care [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our findings support the use of LHWs not only as a means of delivering mental health interventions but also as a catalyst for broader community engagement and raising mental health awareness [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA noteworthy caveat of our intervention is its limited efficacy in addressing severe cases of anxiety and depression. However, the proportion of individuals with severe symptoms is relatively low (1\u0026ndash;2%) in our study population, indicating that our intervention is addressing the needs of the majority who are viable candidates for this type of support. Another fundamental limitation of this pilot feasibility study is the absence of a comparison or control group.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we have found preliminary evidence of a meaningful improvement in anxiety and depression associated with a locally adapted digital counselling intervention delivered by lay health workers through routine primary health care in a rural setting of Sindh, Pakistan. The digital counselling intervention was integrated into the routine workload of the LHWs whose roles include maternal and child healthcare promotion activities, immunization drives etc. By leveraging existing community resources and adopting a low-intensity, scalable model, this intervention demonstrates significant potential to mitigate the burden of anxiety and depression in underserved populations. Future research should focus on long-term scalability through an appropriately powered randomized controlled trial to test if this task-shifting mental health intervention is effective compared to usual care. Integration into existing health systems to maximize impact and reach is also essential for the intervention\u0026rsquo;s sustainability and scalability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Health Worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAD-7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Anxiety Disorder-7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHousehold\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLady Health Supervisor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLady Health Worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLHW-P\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLady Health Worker Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLower and Middle-Income Country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emhGAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMental Health Gap Action Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREDCap\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResearch Electronic Data Capture\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStudy Coordinator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScreen Positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Ethical Review Committee of Aga Khan University (ERC#2021-6570-20015). The study adheres to the tenets of the Declaration of Helsinki. All study participants provided informed consent which was given to them in their local language.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by a grant from the Brain \u0026amp; Mind Institute (BMI), Aga Khan University (Grant Brain \u0026amp; Mind-FR-11E-mPareshan App 83000).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors (FR, JN, SA, AS, ZM) made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank the overall research team members involved in the implementation of this trial. Special gratitude is expressed to the study participants that took part in this intervention and related quantitative assessments.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World Mental Health Report: Transforming mental health for all. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9:137\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaved A, Lee C, Zakaria H, Buenaventura RD, Cetkovich-Bakmas M, Duailibi K et al. Reducing the stigma of mental health disorders with a focus on low- and middle-income countries. Asian J Psychiatry. 2021;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. 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BMC Public Health. 2019;19:1744.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, depression, Mental Health, task-shifting, primary health care, Lady Health Workers, digital counselling","lastPublishedDoi":"10.21203/rs.3.rs-5621643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5621643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is limited evidence that interventions for improving mental well-being can be integrated sustainably into primary health care in Pakistan. We aimed to pilot \u0026lsquo;mPareshan digital intervention\u0026rsquo; locally, adapted from WHO mhGAP and delivered by trained and supervised women lay workers to learn if it was feasible and possibly effective in reducing anxiety and depression prior to proposing implementation on a larger scale.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eUsing Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9), a baseline household survey was conducted by independent data collectors to measure anxiety and depression. We trained 72 government Lady Health Workers (LHWs) and Lady Health Supervisors (LHSs) in District Badin, Sindh for 3 days to screen and counsel adult men and women (\u0026gt;\u0026thinsp;18 years) with mild and moderate symptoms of anxiety and depression. Supervised by LHSs, these screen positive participants (SPs) received 6 counselling sessions by LHWs through the mPareshan app during their routine household visits. The app had interactive audio/video psychoeducation features. Severe cases of anxiety and depression were referred to nearest available mental health service.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of the 366 individuals surveyed at baseline, 98 participants (53 men and 45 women, mean age 43.2 years) screened positive for mild and moderate anxiety and depression and were eligible for the mPareshan digital counselling intervention. 6 SPs were lost to follow up for various reasons. Of the 92 SPs who completed all 6 counselling sessions, their mean PHQ-9 score declined from 7.5 (sd 3.1) before intervention to 2.6 (sd 2.2) after intervention. Mean GAD-7 score fell from 6.6 (sd 3.0) to 2.1 (sd 2.2) after 6 sessions. No significant association between sociodemographic variables (age, gender, education, and income levels) and anxiety and depression scores was noted.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePreliminary evidence of a meaningful improvement in anxiety and depression was found using this locally adapted digital counselling intervention delivered by lay health workers in a rural setting of Sindh, Pakistan. There is a need to test the effectiveness of this task-shifting mental health model in an appropriately powered randomised controlled trial.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eACTRN12622000989741 at the Australian New Zealand Clinical Trial Registry (https//www.anzctr.org.au/Default.aspx).\u003c/p\u003e","manuscriptTitle":"Home-based digital counselling by frontline community workers for anxiety and depression in rural Pakistan: piloting mPareshan - a task-shifting primary mental health care intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 15:48:51","doi":"10.21203/rs.3.rs-5621643/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-13T11:17:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-11T11:35:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-09T05:56:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-05T10:18:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-03T06:07:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291243234480097196622932989408350615206","date":"2024-12-28T10:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191488852094100461962471611061358049650","date":"2024-12-26T15:31:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191623500212507691766211177187000973924","date":"2024-12-26T10:18:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-24T09:04:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71969927278963091219173905407821453751","date":"2024-12-24T08:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283319457306886918130283059017979116504","date":"2024-12-24T06:36:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185805242461524725793785506811311564305","date":"2024-12-24T04:37:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109927406791556810467040768277393557680","date":"2024-12-24T04:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135354628379586449637250693895745649408","date":"2024-12-23T15:51:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224955320028733411581016835309714831616","date":"2024-12-23T11:30:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253279298713156247037387711121841149669","date":"2024-12-23T10:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-23T09:04:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-13T01:15:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-13T01:14:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-12-11T06:45:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7fae2a8-e689-4526-b3c4-eb8477aad575","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T09:59:19+00:00","versionOfRecord":{"articleIdentity":"rs-5621643","link":"https://doi.org/10.1186/s12889-025-23665-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-08-08 15:56:54","publishedOnDateReadable":"August 8th, 2025"},"versionCreatedAt":"2024-12-17 15:48:51","video":"","vorDoi":"10.1186/s12889-025-23665-5","vorDoiUrl":"https://doi.org/10.1186/s12889-025-23665-5","workflowStages":[]},"version":"v1","identity":"rs-5621643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5621643","identity":"rs-5621643","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}