Smoking patterns and preferences for technology assisted smoking cessation interventions among adults with opioid and alcohol use disorders

Abstract

Background

Smoking remains a major public health burden among persons with opioid and/or alcohol use disorder.

Methods

A 49-item semi-structured survey was conducted among urban, inpatient detoxification program patients eliciting demographic and clinical characteristics, smoking profile, technology use patterns, and preferences for adopting technology-based smoking cessation interventions. Multivariate logistic regression models further evaluated the association between participant demographic and clinical characteristics and technology preferences.

Results

Participants were mostly male (91%), and admitted for detoxification for alcohol (47%), heroin (31%), or both alcohol and heroin (22%). Past 30-day smoking was reported by 78% of the sample. Mobile phone ownership was common (89%); with an average past-year turnover of 3 mobile phones and 3 phone numbers. Computer ownership was low (28%) and one third reported daily internet use (34%). Telephone (41%) and text message-based interventions (40%) were the most popular platforms to facilitate smoking cessation.

Conclusions

Despite concurrent AUD-OUD, most respondents had attempted to quit smoking in the last year and preferred telephone- and text message-based interventions to facilitate smoking cessation. High turnover of mobile phones, phone numbers, and limited access to computers pose barriers to dissemination of technology-based smoking cessation interventions in this vulnerable population.

Background

Smoking remains a major public health burden among persons with alcohol (AUDs) and opioid use disorders (OUDs).(Guydish et al., 2016) Populations with AUDs and OUDs have a two to four times higher rate of smoking than in the general public that is suggested to be attributed to the mesolimbic dopaminergic reward pathway among individuals with alcohol, opioid, and nicotine use disorders.(Söderpalm & Ericson, 2011) In addition, individuals with AUDs and OUDs report fewer quit attempts and are more likely to die from smoking-related causes than other substance-related illnesses.(Hurt et al., 1996; Novy, Hughes, & Callas, 2001; Weinberger, Platt, Jiang, & Goodwin, 2015) Given the difficulty in treating smoking cessation in this cohort, health care systems have turned to multi-modality interventions including pharmacotherapy, behavioral interventions, and technology-based interventions (TBIs) for smoking cessation. In addition, smoking cessation during enrollment in specialty addiction treatment is associated with greater abstinence from illicit substances and alcohol.(Lemon, Friedmann, & Stein, 2003; Weinberger et al., 2015) However, patients enrolled in specialty addiction treatment settings are seldom offered smoking cessation treatment.

Patient-level barriers to initiating smoking cessation among patients in addiction treatment include reduced self-efficacy to maintain sobriety from alcohol or illicit substances concurrent with smoking cessation, increased anxiety symptoms, and inability to quit smoking when exposed to other smokers.(Asher et al., 2003) Adoption of smoking cessation counseling and pharmacotherapy (e.g., nicotine replacement therapies, varenicline, bupropion) in addiction treatment programs remains inadequate and attributed to perceived lack of importance, limited staff training, and insufficient financial reimbursement for the delivery of cessation interventions.(Knudsen, Studts, Boyd, & Roman, 2010)

Technology-based smoking cessation interventions are uniquely positioned to reducing smoking for populations with OUDs and AUDs, and overcome administrative and clinical barriers. Recent studies have demonstrated the effectiveness of smartphone applications, text messaging, and web-based interventions in reducing smoking.(Buller, Borland, Bettinghaus, Shane, & Zimmerman, 2014; Civljak, Sheikh, Stead, & Car, 2010; Whittaker et al., 2012) Surveys among persons receiving treatment for substance use disorders (SUDs) in primary care, specialty addiction, and inpatient detoxification settings have described high rates of mobile phone, text messaging, smartphone application, and internet use comparable to the general population and attitudes favorable to integrating technology-based interventions (TBIs) to reduce alcohol and illicit substance use.(McClure, Acquavita, Harding, & Stitzer, 2013; Milward, Day, Wadsworth, Strang, & Lynskey, 2015; B Tofighi et al., 2016; Babak Tofighi et al., 2015; Tofighi B, 2018) However, smoking patterns and preferences for adopting technologies targeting smoking among inpatient detoxification patients remain unclear and represents a missed opportunity in expanding the reach of effective technology-based smoking cessation interventions for a wide array of patients in diverse clinical settings.

Inpatient detoxification programs offer an important access point to expand smoking cessation services for hard-to-reach populations with SUDs. The purpose of this study was to characterize nicotine use (i.e., electronic cigarette and combustible cigarette use), technology use patterns (i.e., mobile phone, text messaging, smart phone applications, computer, and internet use), and preferences for adopting TBIs to facilitate smoking cessation among patients enrolled in inpatient detoxification treatment and post-discharge.

Methods

Participants and Recruitment

Full details about the study have been reported previously.(Babak Tofighi et al., 2018) Briefly, study staff approached patients admitted to inpatient detoxification for alcohol and/or opioid use disorder(s) in 2015 in a safety-net tertiary referral center in the New York City Health & Hospitals system located in Manhattan, New York City. Convenience sampling facilitated recruitment of a quasi-representative sample of eligible participants (N=206) who were consented, invited to complete a 30 minute survey, and received a transportation voucher following survey administration. The study protocol was approved by the New York University School of Medicine Institutional Review Board and Bellevue Hospital Research Administration.

Our participants were categorized into alcohol, opioid, or concurrent alcohol and opioid users. Categorization was based on existing admission criteria for inpatient detoxification in Bellevue Hospital requiring the presentation of physiological dependence as evidence of tolerance and acute withdrawal symptoms. All patients met admission criteria for voluntarily accepting inpatient detoxification for OUD and/or AUD, documentation of psychoactive SUD, minimal risk of severe medical or psychiatric issues, and acute intoxication or withdrawal symptoms.

Survey Instrument

Trained research assistants administered the 49-item questionnaire via paper surveys that were then entered into REDCap data management software.(Harris et al., 2009) Survey items consisted of open-ended questions, binomial “Yes/No” items, 5-point Likert scales, and multiple-choice answers. Questions pertaining to demographic (i.e., age, gender, race/ethnicity, education, socioeconomic status) and clinical characteristics (i.e., substance use, medical and psychiatric history) were based on prior studies conducted by the study team in Bellevue Hospital’s office-based buprenorphine program and inpatient detoxification program.(Babak Tofighi et al., 2015; Babak Tofighi et al., 2018)

A 11-item survey assessed for smoking history, past quit attempts, and desire to quit.(Sherman et al., 2016) The Fagerström Test for Nicotine Dependence 6-item questionnaire assessed for nicotine dependence in smokers and is used clinically to determine appropriate cessation treatments.(Heatherton & Kozlowski, 1992) The single-item Motivation to Stop Scale was used to predict the desire to quit.(Kotz, Brown, & West, 2013) The last domain characterized technology use patterns (e.g., mobile phone and computer ownership, commonly accessed mobile phone features, internet use patterns). The survey also assessed preferences for in-person smoking cessation counseling with clinicians if participants remained uninterested in adopting any TBIs. Items were based on items adapted from the Pew Research Center’s Internet, Science, and Technology questionnaire(Fox & Fallows, 2014), and assessments of technology use patterns among patients with SUDs enrolled in primary care and specialty addiction treatment settings.(McClure et al., 2013; Babak Tofighi et al., 2015)

Data collection and statistical analysis

Descriptive statistics were used to assess for demographic, clinical, technology use characteristics, and smoking history. Open-ended responses underwent a line-by-line review to yield data clusters that were then coded using an a priori coding scheme pertaining to each domain. Mobile phone, text messaging, internet, and desktop computer use was assessed as “never” or “any” use in the preceding six months. Analyses were conducted using Stata statistical software (Version 12;StataCorp LP, College Station, TX, USA).

Results

Table 1 and Table 2 presents previously described baseline demographic and clinical characteristics of the study sample.(Babak Tofighi et al., 2018) Overall, respondents were mostly male (91%), unemployed or reliant on public assistance (51%), and Black (42%) or Hispanic (22%). The average daily standard drinks among participants requiring detoxification from alcohol was 22.2 drinks, and 13.3 bags of daily heroin use among respondents admitted for detoxification from opioids (see Table 2). Most respondents reported smartphone (66%) or basic cell phone ownership (20%), text messaging (96%), and an average past-year turnover of 3.3 (±2.98) mobile phones and 2.6(±2.36) phone numbers (see Table 3).(Babak Tofighi et al., 2018)

Table 1:

Demographic characteristics (N=206)

variable	% (n)
Age (years)
Mean (SD)	43.68 (±11.78)
Range	22–84
Gender
Male	91% (188)
Female	9% (18)
Race/Ethnicity
Black, Non Hispanic	42% (86)
White, Non Hispanic	34% (69)
Hispanic	22% (45)
Other, Non Hispanic	2.4% (5)
Education
Did not Finish High School	29% (60)
Completed high school or GED	39% (79)
Completed college or associate degree	23% (48)
Some college or associate degree	9% (19)
Employment
Full-time	17% (34)
Part-time	11% (23)
Unemployed	34% (70)
Public assistance (food stamps, welfare)	17% (34)
SSI or SSD	21% (43)
Retirement	1% (2)
Insurance
Uninsured	36% (74)
Medicaid	62% (127)
Medicare	1% (2)
Private	1% (3)
Recent incarceration (past year)	21% (44)
Residence
Own’ apartment (primary owner or rentee)	24% (50)
Family or friends	29% (59)
Halfway house	2% (5)
Homeless	45% (92)
Recent homelessness (past year)	64% (131)
Number of locations lived (past year)
Mean (SD)	4.11(11.91)
Range	001–99

Table 2:

Clinical characteristics (N=206)

variable	% (n)
% Smokers	77%(159)
Average daily cigarettes	14.7 (±8.92)
Age of first smoke	15.2 (±5.23)
FTND	4.38(±2.02)
Smoking frequency n=159
Everyday	84.2%(133)
Most days	7.0%(11)
Some days per week	7.0%(11)
Less than that	1.9%(3)
Primary Care Visit <12 months	58.7%(121)
Primary care provider counseling to quit smoking	76.0%(92/121)
Alcohol	67.5% (139)
Daily standard drinks of alcohol (mean, SD)	22.19(±14.98)
Age of onset (mean, SD)	16.20(+7.43)
Opioids	53%(109)
Injected opioids, past 30 days	52%(57)
Average daily number of bags used	13.34
Age of onset (mean, SD)	24.45(±8.45)
Co-occuring alcohol and opioid use	22%(46)
Benzodiazepine	23%(49)
Cannabis	5%(11)
Crack/Cocaine	14%(29)
Other	3%(6)

Table 3:

Technology use patterns (N=206)

variable	%(n)
Mobile phone ownership	86% (177)
Basic cell phone	20% (42)
Smart phone	66% (135)
Landline	3% (6)
Features used on mobile phone (n=206)
Text messaging	96% (169)
Internet	81% (143)
Camera	79% (139)
Smart phone applications	65% (135)
Video	64% (114)
Email	61% (107)
Social media	61% (108)
Barriers to phone ownership, mean (SD)
Phones owned in the last year	3.3 (2.98)
Phone numbers in the last year	2.6 (2.36)
Computer use in the last 12 months
Daily	29.61% (61)
>1/week	17.96% (37)
x1/week	6.31% (13)
x1/month	7.76% (16)
x1/few months	10.67% (22)
Never	27.66% (57)
Internet use in the last 12 months
Daily	51.45% (106)
>1/week	16% (33)
x1/week	5.82% (12)
x1/month	4.36% (9)
x1/few months	8.73% (18)
Never	12.62% (26)

Smoking history, past quit attempts, desire to quit

Among current smokers in our sample (76%, n=157), participants were categorized by those admitted for alcohol detoxification only 39% (n=61), detoxification for opioids 35% (n=55), and both alcohol and opioid detoxification 26% (n=41). The mean overall MTSS score was 4.5 (SD=± 1.98, range= 1–7) and did not vary between the three sub-groups (p=0.46). The mean number of daily cigarettes smoked was 14.8 (SD=± 8.93, range= 1–40) with no difference between sub-groups (p= 0.14). Regarding the FTND scores, the mean overall score was 4.39 (SD=± 2.03, range= 0–9) and did not demonstrate any difference between sub-groups (p= 0.41). Nearly half reported at least one past year quit attempt (45.2%, n=71) and did not vary between the alcohol only group (47.5%), concurrent alcohol and opioid detoxification group (43.9%), and opioid only group (43.6%) (p=0.90). Quit attempts were facilitated by nicotine replacement therapies (25%, n=51), e-cigarettes (17%, n=35), and brochures or pamphlets (7.4%, n=15). Only three participants utilized telephone quit lines (1.5%). Among respondents that reported a primary care visit in the last year (59%), most were counseled by their physicians to quit smoking (76%).

Preferences for technology-based smoking cessation interventions

Preferred smoking cessation strategies included text messaging (40%), telephone calls (39%), and counseling with clinic providers (35%). The single most important platform to support smoking cessation included telephone calls (25%), counseling with clinic providers (27%), and text messaging (20%) (see Table 4). Following bivariate analysis of demographic characteristics and preferences for technology-based smoking cessation interventions, younger participants (18–29 yo) reported significantly higher interest in utilizing online forums (p=0.036) and email (p<0.001) compared to older participants. Recently incarcerated respondents demonstrated increased interest in telephone (p=0.004), smartphone application (p=0.003), social media (p=0.006), and email (p<.001) assisted smoking cessation interventions compared to non-incarcerated participants.

Table 4:

Smoking cessation prior strategies and preferences (N=206)

variable	%(n)
Past year smoking cessation strategies
Nicotine replacement therapies	25 (51)
Prescribed medications (e.g., buproprion, varenicline)	4.4 (9)
E-cigarettes	17 (35)
Telephone quit line	1.5 (3)
Smart phone application or text messaging tool	NA (0)
Brochures or pamphlets	7.4 (15)
Self-guided without any assistance	6.9 (14)
Preferred smoking cessation strategy
Phone Call	39.3 (81)
Text Messaages	40.3 (83)
Smart phone Apss	19.9 (41)
Online Forums	13.1 (27)
Social Media	14.6 (30)
E-mail	18.9 (39)
Website	17.0 (35)
Brochures	20.4 (42)
Favorite smoking cessation strategy (Ranked )
1. Phone Call	25.3 (40)
2.Text Messaages	20.3 (32)
3. Brochures	10.8 (17)
4. Website	5.1 (8)
5. E-mail	4.4 (7)
6. Smart phone Apss	4.4 (7)
7. Social Media	3.2 (5)

Discussion

Dissemination of smoking cessation interventions for patients enrolled in specialty addiction treatment remains suboptimal.(Abraham, Bagwell-Adams, & Jayawardhana, 2017; Apollonio, Philipps, & Bero, 2012; Asher et al., 2003) This cross-sectional survey describes smoking patterns, prior quit attempts, and preferences for adopting TBIs to enhance smoking cessation among inpatient detoxification patients admitted for AUD and/or OUD. This study of primarily male, non-Caucasian, low-income alcohol and/or opioid users described high rates of smoking (77%) as well as past year quit attempts (45.2%), neither of which differed between respondents admitted for AUD and/or OUD.

Consistent with prior studies assessing TBI preferences among participants with SUDs, mobile phone interventions (e.g., telephone calls, text messaging, smartphone applications) were preferred to email, social media, and web-based interventions to facilitate smoking cessation.(McClure et al., 2013; Milward et al., 2015; Babak Tofighi et al., 2015) However, we found comparable rates of interest in adopting mHealth tools among older respondents versus younger participants, non-Caucasian, undomiciled, less than high school, and unemployed respondents compared to less marginalized participants. Importantly, recently incarcerated individuals described significantly higher rates of acceptability for mHealth, social media, and e-mail based interventions to facilitate smoking cessation. These compelling findings may inform larger-scale adoption of mobile phone-based smoking cessation interventions for demographically diverse patient sub-groups completing inpatient detoxification, whose care often remains episodic and fragmented.

Smoking patterns and past year quit attempts

The high rates of smoking among AUD, OUD, and combined AUD-OUD sub-groups may be attributed to nicotine-induced upregulation of μ-opioid receptors and enhanced GABAergic interneuron activity in the midbrain.(Dani & Harris, 2005) Subjective findings among human participants exposed to alcohol and nicotine suggest reductions in the sedating effects of alcohol while alcohol exacerbates the pleasurable effects of nicotine.(PERKINS, 1997) Importantly, the prevalence of tobacco use in persons with AUD ranges from 50–90%, and 84–94% among individuals with OUD.(Guydish et al., 2016; Yardley, Mirbaba, & Ray, 2015) Studies suggest no significant differences in tobacco use, the number of cigarettes smoked daily, as well as the severity of nicotine dependence per the Fagerström’s Test for Nicotine Dependence between participants with AUD, OUD, and concurrent AUD/OUD.(Apollonio et al., 2012; Guydish et al., 2016; Lemon et al., 2003; Pomerleau, Carton, Lutzke, Flessland, & Pomerleau, 1994)

Importantly, more persons with AUDs and OUDs die from smoking than alcohol or illicit substance-related illnesses, and this represents a missed opportunity to expand access to evidence-based smoking cessation interventions among patients utilizing specialty addiction treatment settings.(Hser, McCarthy, & Anglin, 1994) Past year quit attempts in all three sub-groups (AUD 47.5%, OUD 43.6%, AUD/OUD 43.9%) were comparable to the general population (40.3%).(Rodu & Plurphanswat, 2017) Among participants with a primary care visit in the preceding year (58.7%), most received smoking cessation counseling by their provider (76%). These findings contrast with the limited availability of smoking cessation counseling (40%) and pharmacotherapies for smoking cessation in specialty addiction treatment programs.(Abraham et al., 2017) Barriers to implementation of smoking cessation in specialty addiction treatment settings include limited training, resources, organization, and cultural norms.(Knudsen, 2017)

Technology use patterns

TBIs (e.g., mobile phone, computer, and web-based interventions) offer an effective, ubiquitous, and patient-centered approach to expanding the reach of effective behavior change interventions (e.g., cognitive behavioral therapy, motivational enhancement therapy) for individuals with substance use disorders.(Choo, Ranney, Wong, & Mello, 2012; McClure et al., 2013; Babak Tofighi et al., 2015) However, in our sample, few participants utilized technology-based platforms to improve adherence with effective pharmacotherapies and/or behavior change interventions for smoking cessation. Most respondents preferred text messages, telephone, and/or smartphone application-based cessation tools to web-based platforms (i.e., online forums, web-based interventions, social media). Indeed, mobile phone-based interventions have demonstrated significantly improved rates of smoking cessation.(Buller et al., 2014; Palmer et al., 2018) However, when tailored to user preferences, web-based platforms offer a useful platform to reduce smoking, especially when combined with pharmacotherapy and mobile phone tools.(Civljak et al., 2010) Several multi-site randomized controlled trials have now established the effectiveness of web-, smartphone-, and text message-based delivery of behavior change models (e.g., cognitive behavioral therapy, motivational enhancement therapy, community reinforcement approach) to reduce smoking, alcohol, and illicit substance use. [9,11,34,36]

Although text messaging and telephone calls remained the most popular TBIs, preferences varied between participant sub-groups. For instance, younger participants (i.e. 18–29 years old) were more likely to adopt online forums and e-mail compared to their older counterparts. Online forums and email-base smoking cessation tools have demonstrated high utilization and increased quit attempts among younger age groups.(Cremers et al., 2014) Among recently incarcerated respondents, there was a stronger preference for telephone, smartphone application, social media, and e-mail based interventions compared to their non-incarcerated counterparts. These promising findings offer novel approaches to reduce the burden of smoking among criminal justice involved populations, who are 2–4 times more likely to smoke compared to non-criminal justice involved individuals.(Bailey, Okechukwu, Kawachi, & Williams, 2015; Kennedy, Sharapova, Beasley, & Hsia, 2016) While smoking bans in prisons and jails may yield temporary benefits, sustained smoking cessation post-release remains poor (5%) and emphasizes the need for more ubiquitous and low-cost approaches to ensure improved clinical outcomes.(Clarke et al., 2013)

Lastly, older participants demonstrated comparable rates of interest in adopting mHealth tools as their younger counterparts. These findings are similar to previous studies demonstrating that while older participants may be less comfortable with emerging TBIs, brief tutorials and trouble-shooting with older patient populations may bridge potential gaps in TBI adoption.(Heart & Kalderon, 2013)

Limitations

Our results should be interpreted with caution. The cost of integrating TBIs in often over utilized and under-resourced specialty addiction and primary care settings must be taken into consideration. Considering the rapid penetration of smartphones among vulnerable patient sub-groups,(Fox & Fallows, 2014) preferences for integrating smartphone applications or phone-delivered web-based interventions may have outpaced the findings presented from this survey. Findings from this cross-sectional study may be subject to recall bias, social desirability bias, and limits any potential causal inferences. The higher percentage of males in this cohort limits its generalizability to women with concurrent smoking and AUD-OUD. The small sample size of the study may have limited our capacity to detect differences between the three patient sub-groups (i.e., AUD versus OUD versus AUD-OUD). Lastly, questions pertaining to technology use patterns and preferences were not validated and efforts were made to mitigate measurement error by simplifying survey items and extending training and piloting of survey items with study staff.

Conclusions

Despite concurrent AUD-OUD, most participants described numerous past year smoking cessation attempts. Telephone- and text message-based interventions were the most popular platforms to facilitate smoking cessation. However, further studies are needed to assess systems-, provider-, and patient-level barriers for adopting technology-based smoking cessation interventions among populations with alcohol and illicit substance use disorders.