Abstract
Background:
There is a rising trend of alcohol addiction in our Indian society. Studies from high-income countries have demonstrated the effective usage of mobile phones in delivering psychosocial interventions in the treatment of substance disorders.
Aim:
To assess the effectiveness of Tele-Counseling as a mode of continuing care for patients with alcohol dependence syndrome.
Materials and Methods:
An open-label randomized control trial was conducted with 78 male participants. Patients with severe mental disorders or cognitive impairments were excluded. Participants received standard care treatments and were randomly assigned to either the Tele-Continuing Care (TCC) group or the Treatment as Usual (TAU) group. 1) ‘Telephone Continuing Care ’ group (TCC) who received pro-active contact and counseling through mobile phones from the treatment team on the 1st, 2nd, 4th, 8th, 12th, 16th, 20th, and 24th week after discharge. 2) The ‘Treatment-As-Usual’ group (TAU) received usual outpatient follow-up care. We contacted patients and their caregivers every month over mobile phones, belonging to both groups and collected information regarding drinking status up to one year after discharge. We compared abstinence rates, drinking percentage days, and treatment adherence rates. By including all the variables, we did logistic regression to predict relapse.
Results:
The mean age of participants was 41.38 ± 9.06 years, with the majority being married. A higher percentage of patients in the TAU group had higher education qualifications compared to the TCC group. Duration of treatment adherence was significantly (P = 0.017) longer in the TCC group than TAU group (159.83 (120) ±129.47 vs. 100.37 (60) ±112.95) days. Similarly, compliance with abstinence-maintaining medications was better (P = 0.027) in the TCC group than TAU group (142.85 (120) ±115.50 v/s 95.29 (52.5) ±104.42) days. Attendance to group therapy sessions also was better in the TCC group (P = 0.001) compared to the TAU group. However, there was no statistical significance between the two groups in terms of abstinence rate at the end of one year (TCC: 61.8% and TAU: 44.4%) and also the drinking percentage days at the end of one year (TCC 28.22% vs. TAU 38.76%). Logistic regression revealed that a Family History of Alcoholism and Poor Drug compliance were found to be significant predictors of relapse.
Conclusion:
As Tele-Counselling Care in our study showed partial effectiveness in improving the outcome measures, research should focus on improvising further to strengthen the Tele-Counselling model, especially in resource crunch Low- and Middle-Income countries and this could be included in the armamentarium of alcohol de-addiction program.
Keywords: Alcohol dependence syndrome and telephone continuing care, tele-counseling
Alcohol use disorders accounted for 9.6% of Disability Adjusted Life years (DALYs) caused by mental and substance use disorders.[1] They are the leading neuropsychiatric cause of disease burden among men in middle-income countries (WHO, 2008).[2,3] It has been a long time since alcohol use disorders have emerged as a major public health concern in India.[4]
There are various psychosocial interventions available to treat alcohol use disorders. A recent, well-done, meta-review on psychotherapies for managing alcohol use disorders found small to moderate effects for Motivational enhancement therapy, Cognitive-behaviour therapy, and a combination of therapies.[5] However, in Low and Middle-Income Countries (LMICs) countries like India, there seems to be a gross shortage of mental health professionals to deliver the needed psychosocial interventions (0.75 psychiatrists, 0.03 clinical psychologists, 0.05 psychiatric nurses, and 0.03 social workers per 100,000 of the population).[6] Patel (2007) argues that the ratio of mental health therapists per 100000 population in these countries is about 0.5% of that in high-income countries. Hence, we have to look for alternative ways to bridge this gap.[7]
Throughout the world mobile phones and the internet have penetrated the personal and social lives of people. According to the Telecom Regularity Authority of India report (press release No. 50/2017), there are over 1.1 billion subscribers to wireless telephones (mobile phones) with an overall wireless teledensity of 91.74%.[8] WHO recommends the use of electronic and mobile health technologies for the promotion of health and self-care.[8,9,10]
Studies from high-income countries have demonstrated the effectiveness of mobile phones in delivering psychosocial interventions.[11] Brian et al. (2014)[12] introduced the term mobile health (mHealth) where digital technologies are used to improve mental health literacy, provide greater access to mental health services, strengthen outreach programs, and improve drug compliance. A study done in India found that 87.7% of patients attending a psychiatry department were using mobile phones. Those patients agreed to use their phones to get alerts for scheduled appointments, tablet reminders, reporting of side effects, and educational messages.[13]
Only a few studies have investigated this mobile technology utility in the context of substance disorders. Nandyal et al. (2019),[14] an Indian study used mobile phones to get data regarding abstinence status among alcohol-dependent patients who had received in-patient treatment, but no attempt was made to deliver any psychosocial interventions over the phone. A study from the United States used short message service (SMS) on 42 patients who had received in-patient detoxification for alcohol dependence. During the 8-week study, 57.14% of the participants replied to at least 50% of the SMS; however, low-risk consumption was achieved in 55.7% which was not different from the Treatment as-usual group.[15] A recent systematic review on the effectiveness of digital psychosocial interventions in Low- and Middle-Income Countries found 7 studies that showed moderate effectiveness compared to control interventions.[16]
Most of the studies with digital interventions were sms, WhatsApp messages, web-based surveys, and feedback. All of these appear impersonal with no contact with mental health therapists. Hence, we did this randomized control trial to assess the efficacy of providing counseling over mobile phones over a 6-month period and assessing the outcome for one year.
MATERIALS AND METHODS
Study population
This study was conducted in the de-addiction ward which is part of the psychiatry department of PSGIMSR, a tertiary teaching medical college hospital. Approval from the Institutional Human Ethics Committee (Proposal No. 12/177/Nov 22, 2012/PSGIMSR) was obtained before starting the study and the study period was from January 2013 to June 2014.
All consecutive hospitalized patients between the ages of 18 and 65 years, meeting the DSM IV criteria for alcohol dependence syndrome (when interviewed using Structured Clinical Interview, 1998 schedule for Diagnostic and Statistical Manual IV - SCID IV), were included in the study. Only patients who owned mobile phones were included in the study. Patients who had co-morbid severe mental disorders or stable cognitive impairments were excluded. We got written informed consent from all the participants and the family members.
All the patients received the standard care of treatments: Management of withdrawal symptoms with benzodiazepines, thiamine, and other vitamin supplementations, and psychological interventions (Motivation enhancement therapy, cue analysis and cue management, aversion therapy, relapse prevention strategies, group therapy, etc.).
At the time of discharge, participants were randomly assigned into the following 2 groups with 1:1 allocation as per a computer-generated randomization schedule as shown in Figure 1: 1) Tele-Continuing Care (TCC) and 2) Treatment as Usual (TAU). We did not make any attempts to blind the random allocation; participants, therapists, and assessors were aware of the treatment allocation.
Figure 1.
CONSORT Flow Diagram
Interventions
-
Tele-Continuing Care (TCC): The first author contacted the participants through their mobile phones, thrice in the first month and thereafter once a month till the end of 6 months. During each contact, the first author had a 10-minute session with the participants focussing on the following themes: a) Enquiring about abstinence and reinforcing it; b) Probing about ‘craving’ and practice of ‘relapse prevention strategies’; c) Alcohol-related health hazards that he had sustained; d) Compliance with medications; e) Attendance to group therapy and f) Recommending follow-up visits at the de-addiction clinic.
Patients were contacted at the timings as suggested by them and most of that was in the evening hours. If patients did not respond to the call, we attempted calling them on the next 2 successive days. Patients belonging to this group were also getting the usual standard of care as mentioned below.
Treatment As Usual (TAU): The usual standard care consists of consultation reviews at the hospital with the treating consultant psychiatrists and psychiatric residents. Counseling themes were on maintenance of abstinence and relapse prevention strategies; patients were also prescribed medications during these consultations. Group therapy occurred once a week and patients were encouraged to attend.
Measurements
At the time of recruitment, all the patients were interviewed by the first author using a semi-structured proforma to get information regarding their demographic characteristics, medical co-morbidities, and use of substances. We assessed the severity of dependence and motivation of the patients by using the Severity of Alcohol Dependence Questionnaire (SADQ)[17] and the University of Rhode Island Change Assessment Scale (URICA) respectively. The items of SADQ and URICA were translated from English to Tamil and back-translated to English by 2 independent residents and we used Tamil versions.
Outcome measures: We measured 3 primary outcomes: 1) Complete Abstinence: Patients did not consume any alcohol 2) Occasional drinking: Drinking intermittently, but not daily 3) Daily drinking: Drinking every day. The first author conducted semi-structured interviews with the patients and their caregivers, independently over their mobile phones, once a month for the first 6 months and then on the 9th month and 12th month to assess the drinking status. During each assessment, the drinking status was ascertained from the previous assessment time.
Our secondary outcome measure was on participants who had re-started drinking where we estimated the percentage of drinking days (Drinking days/Total number of follow-up days from recruitment).
Sample size calculation
To make a clinically meaningful difference, we assumed that Tele-Continuing care should improve the abstinence rate by at least 60%, and using the formula, 2× [SD] × (Zα+Zβ)/Mean difference,[18] we found that to observe a significant group difference with 80% power and an alpha of 0.05 (two-tailed), we needed a sample size of 34 participants in each group. Assuming the dropout rate to be 10%, we estimated that we would require 38 participants in each group.
Statistical analyses
We conducted all the statistical analyses using SPSS version 19.0 for Windows. We tested for normality of continuous variables by examining their histograms and with the Shapiro–Wilks test. Student t-test was used to compare means of age, duration of alcohol intake, duration of alcohol dependence, URICA score, and percentage of drinking days between the two groups. We used Mann Whitney U test to compare medians of age of onset of first drink, age of onset of daily drinking, SADQ score, duration of hospitalization, duration of treatment adherence, duration of medication compliance, and group therapy attendance. The Chi-square test was used to examine associations of the following variables between the 2 groups: education, marital status, income, number of caregivers having independent mobile phones, family history of alcoholism, history of withdrawal seizures, delirium tremens, and psychotic symptoms, URICA grade, abstinence maintaining medications and number of patients who were 1) Completely abstinent 2) Occasional drinking and 3) Daily drinking.
We did logistic regression by including all the above variables in the logistic model to see which variables would predict relapse. A significance level of P < 0.05 was used in the study.
RESULTS
Sample characteristics
We screened 88 patients; all were males and recruited 78 participants as shown in Figure 1. The mean age of the participants was 41.38 ± 9.06 years; most were married (88.5%) and living with their spouses. When only 26.9% had studied beyond the 10th standard, more patients in the TAU group had higher education qualifications than the TCC group [X2 (6) = 17.28, P = 0.008]. However, there were no differences in their occupation status and monthly family income. While all the participants had mobile phones, 70.5% of their primary caregivers independently owned mobile phones [Table 1].
Table 1.
Socio-demographic details
| Characteristics | Tele- Continuing Care (TCC) n=40 | Treatment As Usual (TAU) n=38 | Statistics |
|---|---|---|---|
| Age (years) | 41.38±8.72 | 41.39±9.54 | t(76)=−0.01, P=0.64 |
| Marital Status Married Single |
36 (90%) 4 (10%) |
33 (86.8%) 5 (13.2%) |
X2(2)=0.19, P=0.66 |
| Education Illiterate Primary school Middle school High school Diploma Undergraduate/Post-Graduate |
1 (2.5%) 9 (22.5%) 10 (25%) 11 (27.5%) 9 (22.5%) - |
1 (2.6%) 2 (5.3%) 14 (36.8%) 9 (23.7%) 3 (7.9%) 9 (23.7%) |
X2(6)=17.28, P=0.008 |
| Occupation Unskilled Skilled Self-employed Professional |
2 (5%) 21 (52.5%) 16 (40%) 1 (2.5%) |
1 (2.6%) 15 (39.4%) 18 (47.4%) 4 (10.5%) |
X2(5)=3.58, P=0.61 |
| Income (In Rupees/month) <5000 5000–10000 10000–20000 >20000 |
3 (7.5%) 21 (52.5%) 6 (15%) 10 (25%) |
3 (7.9%) 16 (42.1%) 5 (31.2%) 14 (36.8%) |
X2(3)=1.38, P=0.71 |
| Number of caregivers having independent phone | 27 (67.5%) | 28 (73.7%) | X2(5)=3.58, P=0.55 |
History of alcohol use and current treatment
Overall in our study sample the age at first drink was around 21 years and they were drinking daily from 30 years of age. In 76.9% of the participants, one or more of their family members drank heavily. On assessing the severity, we found that the majority (47.4%) had severe dependence, and the majority in our sample were in the action stage (55.1%) of motivation. Patients stayed in the hospital for a mean duration of 14.51 ± 6.6 days and most of them received medications to remain abstinent. There were no statistical differences between the two groups in terms of age at first drink, age at which they started drinking daily, severity of dependence, stage of motivation, duration of hospital stay, and the medications for maintaining abstinence. [Table 2]
Table 2.
Features related to alcohol use and treatment
| Characteristics | Tele- Continuing Care (TCC) n=40 | Treatment As Usual (TAU) n=38 | Statistics |
|---|---|---|---|
| Age (years) | |||
| First drink | 20.70 (18.5)±5.89 | 21.34 (20)±5.70 | U=662, P=0.32 |
| Onset of daily drinking | 2970±6.01 | 30.97±7.06 | t(76)=-o.86, P=0.39 |
| Duration of Alcohol dependence | |||
| (years) | 20.63±9.74 | 20.05±9.31 | t(76)=0.27, P=0.79 |
| History of | |||
| Withdrawal seizures | 5 (12.5%) | 7 (18.4%) | X2(1)=0.52, P=0.47 |
| Delirium tremens | 14 (35%) | 7 (18.4%) | X2(1)=2.72, P=0.099 |
| Psychotic symptoms | 3 (7.5%) | 1 (2.6%) | X2(1)=0.95, P=0.33 |
| Family history of excessive alcohol use | 32 (80%) | 28 (73.7%) | X2(1)=0.44, P=0.51 |
| SADQ score | 30.08 (29.5)±10.25 | 29.94 (29.5)±11.31 | U=738, P=0.82 |
| URICA score | 11.71±2.71 | 11.58±2.84 | t(76)=0.21, P=0.83 |
| Stage of motivation | |||
| Precontemplation | 5 (12.5%) | 4 (10.5%) | X2(2)=0.24, P=0.89 |
| Contemplation | 14 (35%) | 12 (31.6%) | |
| Action | 21 (52.5%) | 22 (57.9%) | |
| Duration of hospitalization (Days) | 13.58 (14)±6.22 | 15.50 (16.5)±6.85 | U=612, P=0.14 |
| Medication for maintaining abstinence | |||
| No medication | 18 (45%) | 14 (36.8%) | X2(6)=1.65, P=0.95 |
| Disulfiram | 17 (42.5%) | 18 (47.4%) | |
| Baclofen | 4 (10%) | 5 (13.2%) | |
| Acamprosate | 2 (5%) | 2 (53%) | |
| Naltrexone | 1 (2.5%) | 1 (2.6%) | |
Drop out
During the first 6 months of the intervention phase, 2 participants (5%) in the TCC group and 7 participants (18.4%) in the TAU group dropped out (X2 (1) = 3.44, P = 0.06). Complete follow-up data for one year were available for 34 participants (85%) in TCC and 27 participants (71.1%) in TAU (X2 (1) =2.22, P = 0.14).
Drinking outcomes in TCC and TAU groups
Our primary outcomes, complete abstinence, occasional drinking, and daily drinking did not differ between the 2 groups. [Figure 2]
Figure 2.
Drinking outcomes in TCC and TAU groups
Complete abstinence rates gradually declined during the one-year follow-up in both groups, but at the end of one year, the rate of abstinence was higher in the TCC group (61.8%) when compared to the TAU group (44.4%). However, this was not statistically significant (as shown in Table 3).
Table 3.
Comparison of outcome in Tele-Continuing Care (TCC) and Treatment as Usual groups (TAU)
| Characteristics | Tele- Continuing Care (TCC) n=40 | Treatment As Usual (TAU) n=38 | Statistics |
|---|---|---|---|
| Duration of treatment adherence (Days) | |||
| Mean (Median)±SD | 159.83 (120)±129.47 | 100.37 (60)±112.95 | U=522.5, P=0.017 |
| Compliance with medication (Days) | |||
| Mean (Median)±SD | 142.85 (120)±115.50 | 95.29 (52.5)±104.42 | U=540, P=0.027 |
| No. of Group therapy sessions n (%) | |||
| 0 | 11 (27.5%) | 26 (68.4%) | X2(2)=13.6, P=0.001 |
| 1 | 16 (40%) | 5 (13.2%) | |
| ≥2 | 13 (32.5%) | 7 18.4%) | |
| Drinking status | |||
| 1st month | n=40 | n=38 | |
| Complete abstinent | 33 (82.5%) | 31 (81.6%) | X2(2)=3.83, P=0.15 |
| Occasional drinking | 0 | 3 (7.9%) | |
| Daily drinking | 7 (17.5%) | 4 (10.5%) | |
| Drinking days (%) among relapsers | 40.46 (40)±22.14 | 45.66 (43.3)±30.09 | t(12)=-o.37, P=0.72 |
| 2nd month | n=40 | n=38 | |
| Complete abstinent | 32 (80%) | 27 (71.1%) | X2(2)=2.17, P=0.34 |
| Occasional drinking | 1 (2.5%) | 4 (10.5%) | |
| Daily drinking | 7 (17.5%) | 7 (18.4%) | |
| Drinking days (%) among relapsers | 47.6 (60)±28.16 | 38.08 (26.65±30.18 | t(19)=0.74, P=0.47 |
| 3rd month | n=40 | n=37 | |
| Complete abstinent | 29 (72.5%) | 24 (64.9%) | X2(2)=1.03, P=0.59 |
| Occasional drinking | 4 (10%) | 3 (8.1%) | |
| Daily drinking | 7 (175%) | 10 (27%) | |
| Drinking days (%) among relapsers | 36.4 (26.6)±29.05 | 35.71 (21.1)±31.49 | U=98.5, P=0.81 |
| 4th month | n=39 | n=35 | |
| Complete abstinent | 28 (71.8%) | 22 (62.9%) | X2(2)=3.63, P=0.16 |
| Occasional drinking | 4 (10.3%) | 1 (2.9%) | |
| Daily drinking | 7 (17.9%) | 12 (34.3%) | |
| Drinking days (%) among relapsers | 44.79 (42.9)±26.08 | 31.06 (23.3)±25.86 | U=73, P=0.14 |
| 5th month | n=39 | n=33 | |
| Complete abstinent | 25 (64.1%) | 21 (63.6%) | X2(2)=0.52, P=0.77 |
| Occasional drinking | 4 (10.3%) | 2 (6.1%) | |
| Daily drinking | 10 (25.6%) | 10 (30.3%) | |
| Drinking days (%) among relapsers | 41.07 (36.7)±27.38 | 31.96 (26.7)±26.5 | U=107.5, P=0.23 |
| 6th month | n=38 | n=31 | |
| Complete abstinent | 27 (71.1%) | 18 (58.1%) | X2(2)=1.30, P=0.52 |
| Occasional drinking | 2 (5.3%) | 2 (6.5%) | |
| Daily drinking | 9 (23.7%) | 11 (35.5%) | |
| Drinking days (%) among relapsers | 47.06 (39.65)±24.28 | 3314 (24.70)±27.63 | U=90.5, P=0.08 |
| 9th month | n=34 | n=27 | |
| Complete abstinent | 19 (55.9%) | 13 (48.1%) | X2(2)=4.62, P=0.099 |
| Occasional drinking | 10 (29.4%) | 4 (148%) | |
| Daily drinking | 5 (14.7%) | 10 (37%) | |
| Drinking days (%) among relapsers | 29.56 (23)±22.48 | 35.2 (24.65)±30.07 | U=153, P=0.82 |
| 12th month | n=34 | n=27 | |
| Complete abstinent | 21 (61.8%) | 12 (44.4%) | X2(2)=4.23, P=0.12 |
| Occasional drinking | 9 (26.5%) | 6 (22.2%) | |
| Daily drinking | 4 (11.8%) | 9 (333%) | |
| Drinking days (%) among relapsers | 28.22 (25.8)±18.49 | 38.76 (24.7)±32.95 | U=162, P=0.63 |
Drinking day (%)=No. of drinking days/Total no. of follow-up days
Among patients who relapsed into drinking, the mean drinking percentage days ranged from 19.29% to 43.06% days of the follow-up period and there was no difference between the two groups.
Treatment adherence in TCC and TAU groups
Duration of treatment adherence was significantly longer in the TCC group than TAU group (159.83 (120) ±129.47 vs. 100.37 (60) ±112.95) days. Similarly, compliance with abstinence-maintaining medications was better in the TCC group than in the TAU group (142.85 (120) ±115.50 vs. 95.29 (52.5) ±104.42) days. Attendance to group therapy sessions also was better in the TCC group (as shown in Table 3).
We did multiple logistic regressions to identify factors that could predict the risk of relapse. A family history of alcoholism and drug compliance was found to be statistically significant in predicting the relapse risk. A positive family history of alcoholism in one member was found to have a trend towards significance in predicting relapse whereas a positive family history in more than one family member was found to have a higher risk of relapse with a statistical significance of P value of 0.013 and odds ratio of 23.813. Poor drug compliance was another factor significantly predicting relapse with a P value of 0.0001.
DISCUSSION
During the one-year follow-up, we did not find any differences in complete abstinence rates, drinking status, and percentage of drinking days between the 2 groups; however, the duration of treatment adherence and medication compliance were significantly longer in the TCC group than TAU group.
During the one-year follow-up, we found that the complete abstinence rate declined gradually from 82.1% to 52.5%. At the time of admission for de-addiction treatment, all the patients were drinking alcohol every day. During the 12-month follow-up, even among patients who relapsed, the mean drinking percentage days ranged from 19.29% to 43.06%.
A recent Cochrane review on brief interventions for alcohol use disorder found that participants who received brief interventions consumed less alcohol than minimal or no intervention participants after 1 year. There was a minimal but statistically significant effect of a brief intervention on the frequency of binges per week and drinking days per week. Longer counseling duration did not have much additional effect, according to this systematic review.[19] We also found a good outcome in our study participants of both groups because of proactive contact from our side: 10-minute counseling in the TCC group or even just asking about their drinking status in the TAU group.
There are a few studies from India, that have suggested that brief interventions be an efficacious modality for individuals with problematic alcohol use, including in primary healthcare settings, community settings, and the workplace. However, the characteristics of patients in these studies reveal that they were having harmful use rather than dependence.[20] A study used trained health assistants as lay counselors and achieved 36% remission (AUDIT score being less than 8) and 41% stopped drinking in the 2-week assessment period. In this study also participants were harmful drinkers without any medical emergencies and the treatments were delivered face to face.[21]
In our study, the duration of treatment adherence and medication compliance was about 2 months longer in the TCC group than TAU group. However, this did not translate into any better drinking outcomes in the TCC group. We speculate various reasons for this. Our sample size being smaller could have resulted in a Type II error, failing to detect the difference. In the TAU group also we contacted the participants over the phone to enquire about their outcome which itself could have been perceived as a support and improved their motivation to remain abstinent.
Predicting the favorable factors influencing the outcome measures was always a challenging tasks considering high rate of relapse in alcohol dependence. In our study, age, education, income, occupation, and marital status did influence the abstinence in contrast to previous Indian studies,[7,14] which showed lower socio economic status having higher predicting risk of relapse.
Positive family history of alcoholism was found to be significant predictor of relapse. 84.4% of relapsed in our study had positive family history of alcoholism. This association of family history of alcoholism in predicting relapse was also reflected in previous Indian outcome studies.[14] Similarly, drug compliance was also found to be predicting maintenance of abstinence (P = 0.0001). This association was also consistent with previous Indian studies.[14]
Mobile Health interventions in substance use have been studied earlier and there is a renewed interest due to the COVID-19 pandemic. Interventions can be through basic functions like talking, texting messages, and sending educational materials using highly sophisticated apps. Nandyal et al. (2019)[14] used mobile phones to follow up and assess the drinking status of 54 patients who received de-addiction treatment at their center; patients were not receiving any psychosocial interventions over their phones. They found that only 27.5% were abstinent throughout the 6 months and the remaining had lapses or relapses. In a randomized controlled trial done in the United States, Godley et al.[22] found similar abstinence percentage days in the telephone-based continuing care (87.39%) and the usual continuing care (84.64%) at the end of 6 months.
Mobile apps have used self-monitoring to promote the reduction of alcohol use,[16,23,24,25,26] whereas one app used a game designed to train clients to overcome alcohol attention bias.[27] It was found that participants did not use apps regularly and were not shown to reduce alcohol consumption.[28] Interventions that involve some face-to-face contact are more efficacious than those that are delivered only electronically.[29] In our study even in the intervention arm, during each counseling session, the patient was recommended to attend consultations with their therapists.
In our study, 41% of patients (TCC: 45%, TAU: 36.8%) didn’t receive disulfiram or anti-craving medication as either patients were not willing or the treating clinician didn’t offer this.
We had a good retention of patients in this program in both groups and this also contributed to the proactive contact over their mobile phones. During the 6 months intervention phase, 88.5% of patients attended our phone calls (TCC - 95% and TAU - 81.6%) and we were able to get outcome data for 78.2% of the original sample (TCC - 85% and TAU - 71.1%) at the end of one year.
Limitations
We didn’t have any objective measure of alcohol consumption or abstinence status. There is a possibility that patients would have given false data regarding their abstinence status because of stigma and desirable positive outcomes. However, in all the patients, we were able to cross-check the drinking status with their caregivers independently; the treatment adherence data was taken from the medical records.
Being a non-blind study, investigator bias cannot be ruled out completely. The participants were recruited from the in-patient de-addiction unit where patients with moderate to severe dependence were admitted. Hence the findings cannot be generalized to patients in the community or patients with less dependence.
CONCLUSION
Continuing care is very essential while treating patients with alcohol-related problems. Brief counseling through mobile phones, as a means of continuing care, resulted in better medication compliance and treatment adherence than treatment as usual. As Tele-Counselling Care in our study showed partial effectiveness in improving the outcome measures, research should focus on improvising further to strengthen the Tele-Counselling model, especially in resource crunch Low and Middle-Income Countries, and this could be included in the armamentarium of alcohol de-addiction programs.
Authors’ contributions
Concept, Design, Literature search, Data acquisition, Data analysis, Manuscript preparation, editing and review, Guarantor: PR
Concept, Design, Literature search, Data analysis, Manuscript preparation, editing, and review: RG
Manuscript preparation, editing, and review: SR
Data availability statement
The data supporting the findings of this study are available with corresponding author and can be produced on request.
Ethical statement
An approval from the Institutional Human Ethics Committee (Proposal No.12/177/Nov 22, 2012/PSGIMSR) was obtained before starting the study.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
We are thankful to all the participants of the study.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study are available with corresponding author and can be produced on request.
