Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at, first published .
Acceptability of an mHealth App That Provides Harm Reduction Services Among People Who Inject Drugs: Survey Study

Acceptability of an mHealth App That Provides Harm Reduction Services Among People Who Inject Drugs: Survey Study

Acceptability of an mHealth App That Provides Harm Reduction Services Among People Who Inject Drugs: Survey Study

Original Paper

1Yale University School of Medicine, New Haven, CT, United States

2Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, United States

3Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States

4Centre of Excellence on Research on AIDS, University of Malaya, Kuala Lumpur, Malaysia

Corresponding Author:

Tyler Shelby, MPhil

Department of Epidemiology of Microbial Diseases

Yale University School of Public Health

60 College Street

New Haven, CT, 06510

United States

Phone: 1 6202284003


Background: Harm reduction services reduce the negative consequences of drug injection and are often embedded within syringe service programs (SSPs). However, people who inject drugs (PWID) suboptimally engage with such services because of stigma, fear, transportation restrictions, and limited hours of operation. Mobile health (mHealth) apps may provide an opportunity to overcome these barriers and extend the reach of SSPs beyond that of the traditional brick-and-mortar models.

Objective: This study aims to assess the prevalence of smartphone ownership, the level of comfort in providing the personal information required to use mHealth apps, and interest in using an mHealth app to access harm reduction services among PWID to guide the development of an app.

Methods: We administered a survey to 115 PWID who were enrolled via respondent-driven sampling from July 2018 to July 2019. We examined the extent to which PWID had access to smartphones; were comfortable in providing personal information such as name, email, and address; and expressed interest in various app-based services. We measured participant characteristics (demographics, health status, and behaviors) and used binary logistic and Poisson regressions to identify independent correlates of mHealth-related variables. The primary regression outcomes included summary scores for access, comfort, and interest. The secondary outcomes included binary survey responses for individual comfort or interest components.

Results: Most participants were White (74/105, 70.5%), male (78/115, 67.8%), and middle-aged (mean=41.7 years), and 67.9% (74/109) owned a smartphone. Participants reported high levels of comfort in providing personal information to use an mHealth app, including name (96/109, 88.1%), phone number (92/109, 84.4%), email (85/109, 77.9%), physical address (85/109, 77.9%), and linkage to medical records (72/109, 66.1%). Participants also reported strong interest in app-based services, including medication or sterile syringe delivery (100/110, 90.9%), lab or appointment scheduling (90/110, 81.8%), medication reminders (77/110, 70%), educational material (65/110, 59.1%), and group communication forums (64/110, 58.2%). Most participants were comfortable with the idea of home delivery of syringes (93/109, 85.3%). Homeless participants had lower access to smartphones (adjusted odds ratio [AOR] 0.15, 95% CI 0.05-0.46; P=.001), but no other participant characteristics were associated with primary outcomes. Among secondary outcomes, recent SSP use was positively associated with comfort with the home delivery of syringes (AOR 3.29, 95% CI 1.04-10.3 P=.04), and being older than 50 years was associated with an increased interest in educational materials (AOR 4.64, 95% CI 1.31-16.5; P=.02) and group communication forums (AOR 3.69, 95% CI 1.10-12.4; P=.04).

Conclusions: Our findings suggest that aside from those experiencing homelessness or unstable housing, PWID broadly have access to smartphones, are comfortable with sharing personal information, and express interest in a wide array of services within an app. Given the suboptimal access to and use of SSPs among PWID, an mHealth app has a high potential to address the harm reduction needs of this vulnerable population.

J Med Internet Res 2021;23(7):e25428




The current opioid epidemic has resulted in many adverse outcomes, including increases in hospitalizations attributed to opioid injection and increases in infections of hepatitis C virus (HCV) and HIV, endocarditis, soft tissue infections, and overdose death [1-4]. Reductions in HIV incidence have stalled as a consequence of the volatile opioid epidemic, and numerous outbreaks of HIV and HCV have been reported in both urban and rural settings [4,5]. Although the increasing rates of infection among people who inject drugs (PWID) are staggering, approximately 130 individuals die from overdose each day in the United States [6]. This ongoing crisis calls for further implementation and development of novel methods to reduce risk among PWID and increase the delivery of harm reduction services.

To reduce the incidence of such negative health outcomes, syringe service programs (SSPs) offer services including syringe exchange; the provision of clean injection supplies, fentanyl test kits, and bandages; naloxone (Narcan) training and kit provision; and linkage to internal or external programs for housing support, mental health counseling, primary care, and addiction treatment. Through such a wide range of services, SSPs have demonstrated efficacy in reducing rates of HIV transmission [7] and syringe sharing [8] and increasing HIV treatment and prevention cascades [9-11]. However, SSPs’ uptake and coverage are far from meeting recommended targets and provide consistent services to only a quarter of PWID [12]. Reasons for low uptake include (1) long geographical distance to SSPs [13-15], especially in rural areas where opioid use is rising substantially [15]; (2) limited SSP hours of operation [16]; (3) lack of public transportation [17]; (4) the limited power of SSP workforces [18]; and (5) perceived stigma among PWID and fear of arrest and police interference [8,19-22]. These reasons may largely be attributed to the operational style of current SSPs, that is, services and supplies are either provided at central SSP locations or delivered through mobile vans at selected sites during scheduled hours [23]. Although home delivery and contact-free SSPs have been suggested as alternatives to overcome these barriers, they remain underexplored [24,25].

With regard to improving the accessibility and coverage of harm reduction services, a mobile health (mHealth) app has great potential to provide PWID with better access to health care and harm reduction services while also protecting their privacy and offering them a better sense of control of their environment. PWID increasingly have access to smartphones [26], and mHealth interventions have demonstrated significant potential to positively impact a variety of health conditions, including obesity prevention [27], physical activity and healthy eating promotion [28,29], cerebral stroke detection and management [30], and diabetes management [31,32], among others. However, the variety and quantity of mHealth apps for HIV care and prevention are still very limited. Only 18 were available in 2018 [33], and these apps mainly focus only on 2 functionalities: (1) self-management and self-monitoring tools for increasing opioid users’ adherence to medication [33-36] and (2) tools that can improve linkage and retention in HIV care among people with HIV [37,38]. None of the existing HIV-related apps have combined essential functionalities that are desired among individuals who seek HIV care and prevention, including scheduling appointments; viewing medication logs, lab reports, and current pharmacy information; tracking nutrition and fitness; and exchanging social support with other users, along with links to local resources and health information and support for self-managing stress and depression [33,37].

Study Objectives

Given the potential of mHealth apps to increase effectiveness and access to HIV care and harm reduction, we sought to better understand its feasibility and acceptance among a group of individuals who have substantial risk for HIV, that is, PWID. In addition, we sought to understand whether any participant characteristics were associated with feasibility and acceptability or with particular services that could be offered in an mHealth platform. Herein, we present and discuss our findings, focusing on the following three dimensions of feasibility and acceptability: access, comfort, and interest.


From June 2018 to June 2019, 115 PWID were screened for eligibility in this study at the New Haven Syringe Service Program (NHSSP). The eligibility criteria were as follows: (1) being 18 years or older; (2) being able to understand, speak, and read English or Spanish; (3) self-reporting as an active injection drug user within the past 60 days; and (4) having at least one injection partner. Respondent-driven sampling (RDS) was used for recruitment, and the original seeds were recruited from clientele who use the NHSSP. Seeds were recruited using flyers distributed at the central and mobile distribution locations of the NHSSP.

Once enrolled, seeds were asked to complete a cross-sectional egocentric survey in-person at the NHSSP. An iPad (Apple Inc) was used to display survey questions which were hosted on Qualtrics (SAP) to the participants, and the study staff conducting the interview selected the respondents’ answers. Upon completion of the survey, each seed was compensated US $20 for their time. Following the interview, RDS was used to recruit subsequent waves of participants from within the seed’s current injection network (ie, individuals who had injected drugs with the participant within the past 2 months) using the same aforementioned eligibility criteria. Participants were allowed to recruit injection partners that were either currently engaged with or not engaged with the SSP. This allowed our sample to expand beyond the limits of the SSP clientele. Each seed was given US $10 for each successful network referral, and each referee was given US $20 upon completion of the survey. Once these network referees completed the survey, they were also asked to recruit their own injection network partners following the same RDS protocol. This resulted in subsequent waves of recruitment and expansion of the participant sample beyond the network of the original seed. All study protocols were approved by the Yale Human Subjections Committee, and a certificate of confidentiality was obtained from the National Institutes of Health to further protect participant information.


Access to Mobile Devices

We dichotomously assessed access to mobile devices by asking participants if they had access to a cellphone without an internet connection, a cellphone with an internet connection (smartphone), a tablet, or a computer. The main item of interest in this survey was a smartphone, which would be required for the hypothetical service app.

Comfort Levels

We assessed comfort levels associated with using an app and providing personal information by asking participants whether they would be willing or unwilling to provide various personal information to use the app, including their name, phone number, email, address, alternative address, and linkage to medical records. Participants also indicated their comfort level with home delivery of syringes, rated on a 5-point Likert scale, with 1 being very comfortable and 5 being very uncomfortable; a grouped outcome variable for this item was created in which very comfortable and comfortable responses were coded as 1 and all other responses were coded as 0. A summary score for comfort was created by adding up participant responses for each item, thus transforming it to a 0-7 scale, with higher scores indicating a higher level of comfort.


We assessed interest in potential mHealth app services by asking participants to rate the usefulness of various services, including delivery of medications or syringes, scheduling appointments with providers and labs, setting medication reminders, accessing educational material about health and safe injection practices, and accessing group communication forums with peers. Participants indicated their level of perceived usefulness on a 5-point Likert scale, with 1 being extremely useful and 5 being not at all useful; a grouped variable for each service was created in which extremely useful and very useful responses were coded as 1 and all other responses were coded as 0. In addition, 3 survey items focused on the delivery of various services (pre-exposure prophylaxis [PrEP] medication, sterile syringes, and medication for opioid use disorder), and 2 survey items focused on scheduling appointments with providers or labs were combined into 2 grouped outcomes focused on either delivery or scheduling. We created a summary score for interest by adding up the binary scores described above, thus creating a 0-5 scale, with higher scores indicating a higher level of interest in comprehensive services.

Participant Characteristics

We collected self-reported data on demographics, health information, and behavioral history. We measured gender identity, race and ethnicity, levels of education, and housing status (stably housed vs homeless or unstably housed) categorically and later dichotomized education at the level of high school completion. We collected age data continuously and later categorized them into the following age groups: 18-34 years, 35-49 years, and ≥50 years. We measured perceived financial stability on a 10-item Likert scale, with 1 being always worried about food, housing, and income and 10 being never worried about food, housing, and income. We later dichotomized the responses for perceived financial stability at the median level. We measured the history of incarceration, syringe sharing with current injection partners, carrying of Narcan during injection, and recent engagement with the SSP in the previous 60 days dichotomously. We similarly collected dichotomous health information, including HCV and HIV status and overdose history.

Statistical Analysis

Our primary outcomes included binary access to smartphones and summary scores for comfort and interest. We coded the primary access outcome as 1 if the participant owned a smartphone and 0 otherwise, using multivariable logistic regression when evaluating correlates. We used multivariable Poisson regression to evaluate correlations with summary scores for comfort and interest. In addition, to further explore any possible association between individuals’ characteristics and their comfort and interest, we used logistic regression to evaluate correlations with each individual survey item within the comfort and interest subsections. This allowed us to see which services on the mHealth app would appeal more to certain subsets of the PWID population. As we did not correct for multiple outcomes, these secondary analyses were considered hypothesis-generating. In all models, covariates with P<.20 in bivariate analyses were included in the final models for each outcome. We used complete case analysis, excluding observations with missing covariate or outcome data from the bivariate or multivariable models. All statistical analyses were conducted using Stata 16 (StataCorp).

Participant Characteristics

Table 1 contains the original Likert scores for the items of interest and comfort with home syringe delivery. Roughly 5.2% (6/115) of participants did not answer any questions about mHealth access, comfort (6/115, 5.2%), and interest (5/115, 4.3%), and were therefore excluded from the regression models. In general, participants were in their early 40s, primarily White, male, and had completed high school; most described themselves as homeless or unstably housed, with a minority reporting being infected with HIV or HCV. Most participants reported a history of sharing syringes with their current injection partners and overdose. In addition, just above half (64/115, 55.7%) of the participants reported using the SSP within the past 60 days, and less than half (49/115, 42.6%) of the participants reported carrying Narcan when they inject (Table 2).

Table 1. Frequencies and percentages of Likert responses for component interests and comfort engaging with home syringe delivery (n=110).
InterestsaExtremely useful, n (%)Very useful, n (%)Moderately useful, n (%)Slightly useful, n (%)Not at all useful, n (%)
Delivery of PrEPb services (n=108)26 (24.1)32 (29.6)35 (32.4)5 (4.6)10 (9.3)
Delivery of syringes47 (42.7)45 (40.9)13 (11.8)1 (0.9)4 (3.6)
Delivery of medications for opioid use disorder57 (51.8)32 (29.1)11 (10)1 (0.9)9 (8.2)
Linking to electronic medical record to schedule appointments with clinical provider43 (39.1)41 (37.3)20 (18.2)5 (4.6)1 (0.9)
Being scheduled for laboratory testing (eg, HIV, hepatitis C virus, and sexually transmitted infections; n=109)44 (40.4)41 (37.6)17 (15.6)5 (4.6)2 (1.8)
Reminders to take medication43 (39.1)34 (30.9)17 (15.5)11 (10)5 (4.6)
Receive educational materials33 (30)32 (29.1)29 (26.4)7 (6.4)9 (8.2)
Group communication forums34 (30.9)30 (27.3)29 (26.4)8 (7.27)9 (8.2)
Comfort with home syringe deliveryc (n=109)45 (41.3)48 (44)2 (1.8)8 (7.3)6 (5.5)

aUnless specified as comfort rather than interest.

bPrEP: pre-exposure prophylaxis.

cThis Likert scale included very comfortable, comfortable, unsure, uncomfortable, and very uncomfortable.

Table 2. Characteristics and survey responses of study participants (N=115).
Participant characteristicsValues
Demographic characteristics

Age (years)

Value, mean (SD)41.7 (10.6)

Age group, n (%)

18-3437 (32.2)

35-4948 (41.7)

≥5030 (26.1)

Sex, n (%)

Male78 (67.8)

Hispanic ethnicity, n (%)27 (23.5)

Race (n=105), n (%)

White74 (70.5)

Black or African American17 (16.2)

Other14 (13.3)

Financial stability score, median (Q1, Q3)3 (1, 5)

Completed high school, n (%)87 (75.7)

Currently homeless or unstably housed, n (%)72 (62.6)

Ever incarcerated, n (%)96 (83.5)
Health status and behaviors

Years of injecting, mean (SD)14 (11.9)

Self-reported HIV positivity (n=110), n (%)9 (8.2)

Self-reported hepatitis C virus positivity, n (%)35 (30.4)

Syringe sharing with current injection partners, n (%)101 (87.8)

Carry Narcan while injecting, n (%)49 (42.6)

Recent syringe service program use, n (%)64 (55.7)

Overdose, ever, n (%)70 (60.9)
Mobile health opportunities (n=109), n (%)

Access to

Cellphone without internet12 (11)

Smartphone74 (67.9)

Tablet2 (1.8)

Computer6 (5.5)

None22 (20)

Comfort with sharing personal information

Name96 (88.1)

Personal phone number92 (84.4)

Personal email85 (78)

Home address85 (78)

Alternative address62 (56.9)

Medical records72 (66.1)

Comfort with home delivery of syringesa93 (85.3)

Component interests for an appa (n=110)

Delivery (PrEPb, syringes, and medications for opioid use disorder)100 (90.9)

Clinical scheduling (provider and lab)90 (81.8)

Medication reminders77 (70)

Health education65 (59.1)

Group communication forums64 (58.2)
Mobile healthsummary scores for access, comfort, and interest (n=109)

Access to smartphone, n (%)74 (67.9)

Comfort (0-7), mean (SD)5.4 (1.8)

Interest (0-5; n=110), mean (SD)3.6 (1.5)

aSee Table 1 for full Likert score responses. The proportion of participants who perceived home delivery of syringes as comfortable or very comfortable and the proportion of those who perceived services as very or extremely useful are shown here.

bPrEP: pre-exposure prophylaxis.


Most participants owned a smartphone (74/109, 67.9%). As shown in Table 3, age, perceived financial stability, and homelessness or unstable housing reached P<.20 in the bivariate regression and were included in the multivariable model. In the final model, those who were homeless or unstably housed had significantly lower odds of smartphone ownership than those who were stably housed (adjusted odds ratio [AOR] 0.15, 95% CI 0.05-0.46; P=.001). Bivariate analyses are presented in Multimedia Appendix 1.

Table 3. Multivariable logistic and Poisson models for primary outcomesa,b.
CovariatesPrimary Outcomes

Access to smartphoneComfort providing personal identifiers and engaging with personalized servicesInterest in comprehensive mobile health services

AORc (95% CI)P valueCoefficient (95% CI)P valueCoefficient (95% CI)P value
Age (years)


35-492.78 (0.99 to 7.82).050.05 (−0.19 to 0.30).68

≥501.04 (0.32 to 3.43).950.21 (−0.07 to 0.48).14
Financial stability >31.74 (0.66 to 4.59).27
Completed high school0.13 (−0.07 to 0.33).19
Currently homeless or unstably housedf0.15 (0.05 to 0.46).001
HIV positivity0.15 (−0.21 to 0.51).42

aLogistic regression used for access primary outcome.

bPoisson regression used for comfort and interest primary outcomes.

cAOR: adjusted odds ratio.

dRef: reference group.

eNot available. The covariate was not included in the final model because it did not meet the bivariate threshold of P<.20.

fItalicized text denotes significance (P<.05).


Overall, most participants reported being comfortable with providing their names, phone numbers, email, address, access to medical records, and an alternative address such as a post office box on the mHealth app. In addition, a majority of participants reported being comfortable with using the home delivery of syringes on the app. Bivariate Poisson regressions showed that only education was associated with summary comfort scores with P<.02, although it was not statistically significant. No additional covariates were included in the final Poisson model.


Most participants showed interest in the proposed app features. Specifically, delivery services were perceived as at least very useful by most participants, as were scheduling services, setting medication reminders, accessing educational materials related to health and safe injection practices, and accessing group communication and support forums. Table 1 highlights the additional percentage of respondents that indicated only a moderate or slight interest in each service. Age and HIV status were associated with overall interest summary scores in bivariate Poisson regression with P<.20 but were not significant in the final multivariable model.

Secondary Analyses: Individual Comfort and Interest Item Analysis

Tables 4 and 5 present the multivariable findings from the secondary hypothesis-generating regression analyses. The bivariate results are presented in Multimedia Appendices 2 and 3. A few of these analyses revealed significant correlations. The middle-aged group (35-49 years) had higher odds of reporting comfort with providing their email than those who were younger (18-34 years; AOR 4.06, 95% CI 1.14-14.51; P=.03). In addition, those who completed high school had higher odds of reporting comfort with providing their medical records on the app than those with less education (AOR 2.87, 95% CI 1.12-7.39; P=.03). Those who engaged with the SSP at least once in the past 60 days had higher odds of reporting comfort with the idea of a doorstep-styled delivery of syringes (AOR 3.29, 95% CI 1.04-10.34; P=.04). Older PWID (≥50 years) had higher odds of reporting interest in having educational materials (AOR 4.64, 95% CI 1.31-16.46; P=.02) and communication forums on the app (AOR 3.69, 95% CI 1.10-12.44; P=.04). Those who had been previously incarcerated had lower odds of reporting interest in educational materials on the app (AOR 0.22, 95% CI 0.05-0.87; P=.03).

Table 4. Secondary multivariable logistic regression models evaluating individual comfort items.
CovariatesIndividual comfort items

NameEmailPhone numberAddressMedical recordsHome syringe delivery

(95% CI)
(95% CI)
(95% CI)
(95% CI)
(95% CI)
(95% CI)
Age (years)


35-49d0.51 (0.09-2.92).454.06 (1.14-14.51).032.26 (0.75-6.78).15

≥500.49 (0.05-5.23).560.77 (0.25-2.39).651.22 (0.39-3.81).74
Female2.05 (0.69-6.13).20
Hispanic ethnicity0.36 (0.11-1.12).08
Completed high school1.74 (0.48-6.26).402.87 (1.12-7.39).03
Homeless or unstably housed0.20 (0.39-1.00).050.52 (0.22-1.27).15
Ever been incarcerated2.50 (0.78-8.10).13
Years of injecting0.96 (0.90-1.03).290.98 (0.95-1.02).37
Recent SSP usee1.79 (0.61-5.24).293.29 (1.04-10.34).04
Carry Narcan1.96 (0.62-6.15).25
Syringe sharing2.21 (0.67-7.26).192.69 (0.70-10.37).15

aAOR: adjusted odds ratio.

bRef: reference group.

cNot available. The covariate was not included in the final model because it did not meet the bivariate threshold of P<.20.

dItalicized text denotes significance (P<.05).

eSSP: syringe service program.

Table 5. Secondary multivariable logistic regression models evaluating individual interest items.
CovariatesIndividual interest items

DeliverySchedulingRemindersEducational materialCommunication forums

(95% CI)
P valueAOR
(95% CI)
P valueAOR
(95% CI)
P valueAOR
(95% CI)
P valueAOR
(95% CI)
P value
Age (years)


35-491.79 (0.65-4.91).260.97 (0.36-2.59).951.23 (0.46-3.29).68

50d5.05 (0.95-26.72).064.64 (1.31-16.46).023.69 (1.10-12.44).04


Black or African American0.37 (0.07-1.96).241.59 (0.38-6.65).521.31 (0.37-4.63).67

Other0.28 (0.04-1.91).201.15 (0.34-3.97).821.13 (0.32-4.02).85
Female2.90 (0.32-26.86).35
Hispanic ethnicity0.44 (0.14-1.33).152.17 (0.69-6.75).182.01 (0.57-7.02).28
Completed high school1.88 (0.62-5.70).27
Ever been incarcerated0.22 (0.05-0.87).03
HIV positivity3.52 (0.36-34.62).28
HCVepositivity0.46 (0.19-1.14).09
Years of injecting0.97 (0.92-1.03).341.00 (0.95-1.05).99
Carry Narcan3.29 (0.61-17.73).170.57 (0.24-1.38).21

aAOR: adjusted odds ratio.

bNot available. The covariate was not included in the final model because it did not meet the bivariate threshold of P<.20.

cRef: reference group.

dItalicized text denotes significance (P<.05).

eHCV: hepatitis C virus.

Principal Findings

Our results demonstrate a high prevalence of smartphone ownership and diffuse comfort and interest associated with mHealth services among the PWID included in this study. The results of our primary analyses were largely null aside from finding that those that were homeless or unstably housed had lower odds of smartphone ownership. In combination with the high rates of reported comfort and interest, this suggests that such an app-based approach would be acceptable for a wide range of PWID. The hypothetical services included in our survey would each offer unique methods of increasing harm reduction service uptake in the PWID community by overcoming various barriers to SSP engagement and warrant further research and development in this area.

Few significant correlations were found in our primary regression analyses, indicating that the acceptability of an mHealth app is diffusely similar across subsets of PWID. Although most participants reported low financial stability, most owned smartphones and also reported being comfortable with providing their personal information such as name, phone number, email, and address to be able to use an mHealth app. The smartphone ownership level among participants was high but lower than the 81% reported nationally in the United States [39]. Smartphone ownership was lower among those experiencing homelessness or unstable housing, indicating that reaching this group with an mHealth app could be challenging. The comfort associated with providing linkage to medical records was slightly lower than the other comfort survey items, indicating that integrating electronic health records in the app could be a barrier for those who have privacy concerns. This may more commonly include those with lower levels of education, as suggested by our secondary analyses. Nonetheless, two-thirds of the participants were comfortable with providing access to medical records and could benefit from linking harm reduction to health care services. Most participants showed a strong desire for a contact-free format to obtain syringes and medications via the app. This service could address current barriers (eg, limited hours and personnel and stigmatization) associated with the current in-person, face-to-face format of most SSPs. Overall, the mostly null findings from our primary analyses indicate a widespread acceptability of an mHealth app designed for PWID.

Our secondary hypothesis-generating analyses revealed significant associations between age and interest in educational materials and group discussion forums and comfort with providing access to personal email. We also found education to be associated with comfort with providing linkage to medical records and a history of incarceration to be associated with interest in educational materials. Although only hypothesis-generating, these secondary findings indicate that allowing customization in an app is crucial for a positive user experience, especially for those with particular interests or privacy concerns. Of particular importance is the secondary finding that recent SSP use increased the odds of comfort with the home delivery of syringes. Aligned with the theory of diffusion by Roger [40,41], this finding indicates that PWID who are currently engaged with SSPs are likely to be the key influencers of the diffusion of an mHealth app and may drive the initial adoption of an mHealth app because of their established trust and familiarity with harm reduction providers and services. The subsequent diffusion of the app to PWID not currently engaged with SSPs may rely on early adopters to encourage their social network contacts to adopt the app.

The variety and quantity of mHealth apps designed for PWID and HIV or HCV care and prevention are very limited [33,42]. Most current apps serve as self-monitoring tools to increase adherence to medication among opioid users or people with HIV [33-36]. To our knowledge, there are no mHealth apps that use a patient-to-provider model offering broad and comprehensive services for PWID, which would greatly advance the mHealth field given the high levels of medical and psychiatric comorbidity in this population [43]. Our findings indicate that a multifunctional mHealth app that can deliver medication reminders, educational materials, and communication forums is highly desired by PWID, the latter especially for older PWID. This app may help reduce the risk of adverse health outcomes and build upon the core harm reduction services such as medication (eg, antiretroviral therapy, PrEP, HCV antivirals, and buprenorphine) and syringe provision [33]. Providing medication reminders for PWID, especially tailored messages, on an app may improve their self-management and medication adherence, which is particularly critical for PWID taking PrEP or antiretroviral therapy [44,45]. In addition, educational materials would also offer PWID information about how to reduce their risks while injecting and educate about overdose prevention and naloxone use. As drug injection is an illicit and often taboo activity, PWID must carefully navigate their environments while seeking information about or assistance with safe injection practices. This is particularly the case for those who have recently transitioned to drug injection, a growing subgroup of PWID that experience a particularly high risk of adverse outcomes, especially overdose, because of gaps in health knowledge, inexperience, and risk behaviors [46]. Our study extends this literature by showing that such education is also important for older PWID, and an mHealth app may be an effective, highly preferred platform for delivering educational material. By providing such educational material within an app, PWID can deploy safe injection practices using instructions within their reach. Finally, our study shows that communication forums may also be favored by older PWID. This demonstrates the older PWID’s desire for web-based communication and support. Such communication forums with peers may offer PWID a means to exchange information on safe injection practices and lessons learned and also provide encouragement and emotional support in a safe, monitored environment.


Despite the many new findings, this study has several limitations. This study included a relatively small sample size, which may have resulted in type II errors, and the fact that all initial seed PWID were enrolled from the SSP clientele may have resulted in sampling bias. Previous evaluation of RDS methods suggest that 4-6 recruitment waves are needed to overcome sampling bias with regard to race, sex, and drug use status [47], but the mean number of recruitment waves per seed in our study was 1.59. However, the most productive network in this study consisted of 12 waves and produced 56.5% (65/115) of the study participants, and a total of 83.5% (96/115) of our sample stemmed from seeds with at least three recruitment waves, suggesting that we were able to overcome some, but not all, of the sampling bias.

Strengths of This Work

A strength of our study was our ability to recruit and interview PWID who do not currently engage with the local SSP; 46.1% (53/115) of the participants were not currently engaged with the NHSSP. These participants represent a large and hidden population of PWID that is not reached by the current brick-and-mortar SSP models. Their responses regarding their preferences and barriers to using SSPs give us crucial insight into what improvements are needed to increase the accessibility of SSPs to better address the health needs of this population, including through mHealth delivery strategies. Furthermore, this study provided insight into a broad range of relevant issues regarding mHealth feasibility and interest, including access to required devices, comfort levels, and interest.


In conclusion, our findings suggest high acceptability of an mHealth app among PWID with little differences between subgroups of PWID, suggesting that an app would not only be applicable to a niche of PWID. The barriers to traditional SSP engagement, including awareness, access, and stigma, may be circumvented with such a novel intervention, and offering alternative solutions via a mobile platform could greatly increase the reach of SSPs and reduce PWIDs’ risk of harm significantly. Further research is warranted on this topic, and in-depth interviews and focus-group discussions should be used to provide more detailed information about how such an app could best serve the target audience.


The authors would like to acknowledge the tireless efforts of the staff at the NHSSP, including Sharon Joslin, Angel Ojeda, Migdalia Williams, Rodolfo Lopez, Rodolfo Lopez Jr, Barbara Valdes, Carolina Price, Lisandra Alvarez, Constance Carroll, and Paula Dellamura. Without their critical insight, fruitful conversations, and endless support, this work would not have been possible. Finally, the authors would like to thank Alexei Zelenev for his helpful discussions and methodological guidance. During the study period, TS was supported by a T32 training grant, and XZ was supported by an R21 grant. Additional funding support for this work was provided by R21 DA039842, R01 DA033679, and an independent sponsored research grant from Merck.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Bivariate logistic and Poisson models for primary outcomes.

DOCX File , 21 KB

Multimedia Appendix 2

Secondary bivariate logistic regression models evaluating individual comfort items.

DOCX File , 24 KB

Multimedia Appendix 3

Secondary bivariate logistic regression models evaluating individual interest items.

DOCX File , 23 KB


  1. Fleischauer AT, Ruhl L, Rhea S, Barnes E. Hospitalizations for endocarditis and associated health care costs among persons with diagnosed drug dependence - North Carolina, 2010-2015. MMWR Morb Mortal Wkly Rep 2017 Jun 09;66(22):569-573 [FREE Full text] [CrossRef] [Medline]
  2. Schranz AJ, Barrett J, Hurt CB, Malvestutto C, Miller WC. Challenges facing a rural opioid epidemic: treatment and prevention of HIV and Hepatitis C. Curr HIV/AIDS Rep 2018 Jun;15(3):245-254 [FREE Full text] [CrossRef] [Medline]
  3. Ciccarone D, Unick GJ, Cohen JK, Mars SG, Rosenblum D. Nationwide increase in hospitalizations for heroin-related soft tissue infections: associations with structural market conditions. Drug Alcohol Depend 2016 Jun 01;163:126-133 [FREE Full text] [CrossRef] [Medline]
  4. Zibbell JE, Asher AK, Patel RC, Kupronis B, Iqbal K, Ward JW, et al. Increases in acute Hepatitis C virus infection related to a growing opioid epidemic and associated injection drug use, United States, 2004 to 2014. Am J Public Health 2018 Feb;108(2):175-181. [CrossRef] [Medline]
  5. Schwetz TA, Calder T, Rosenthal E, Kattakuzhy S, Fauci AS. Opioids and infectious diseases: a converging public health crisis. J Infect Dis 2019 Jul 02;220(3):346-349 [FREE Full text] [CrossRef] [Medline]
  6. Opioid overdose: understanding the epidemic. Centers for Disease Control and Prevention.   URL: [accessed 2021-06-02]
  7. Aspinall EJ, Nambiar D, Goldberg DJ, Hickman M, Weir A, Van VE, et al. Are needle and syringe programmes associated with a reduction in HIV transmission among people who inject drugs: a systematic review and meta-analysis. Int J Epidemiol 2014 Feb;43(1):235-248 [FREE Full text] [CrossRef] [Medline]
  8. Fernandes RM, Cary M, Duarte G, Jesus G, Alarcão J, Torre C, et al. Effectiveness of needle and syringe programmes in people who inject drugs - an overview of systematic reviews. BMC Public Health 2017 Apr 11;17(1):309 [FREE Full text] [CrossRef] [Medline]
  9. Grau LE, Griffiths-Kundishora A, Heimer R, Hutcheson M, Nunn A, Towey C, et al. Barriers and facilitators of the HIV care continuum in Southern New England for people with drug or alcohol use and living with HIV/AIDS: perspectives of HIV surveillance experts and service providers. Addict Sci Clin Pract 2017 Oct 02;12(1):24 [FREE Full text] [CrossRef] [Medline]
  10. Altice FL, Springer S, Buitrago M, Hunt DP, Friedland GH. Pilot study to enhance HIV care using needle exchange-based health services for out-of-treatment injecting drug users. J Urban Health 2003 Sep;80(3):416-427 [FREE Full text] [CrossRef] [Medline]
  11. Heimer R, Grau LE, Curtin E, Khoshnood K, Singer M. Assessment of HIV testing of urban injection drug users: implications for expansion of HIV testing and prevention efforts. Am J Public Health 2007 Jan;97(1):110-116. [CrossRef] [Medline]
  12. Abbasi J. CDC says more needle exchange programs needed to prevent HIV. J Am Med Assoc 2017 Jan 24;317(4):350. [CrossRef] [Medline]
  13. Rockwell R, Des JD, Friedman SR, Perlis TE, Paone D. Geographic proximity, policy and utilization of syringe exchange programmes. AIDS Care 1999 Aug;11(4):437-442. [CrossRef] [Medline]
  14. Williams CT, Metzger DS. Race and distance effects on regular syringe exchange program use and injection risks: a geobehavioral analysis. Am J Public Health 2010 Jun;100(6):1068-1074 [FREE Full text] [CrossRef] [Medline]
  15. Canary L, Hariri S, Campbell C, Young R, Whitcomb J, Kaufman H, et al. Geographic disparities in access to syringe services programs among young persons with Hepatitis C virus infection in the United States. Clin Infect Dis 2017 Aug 01;65(3):514-517. [CrossRef] [Medline]
  16. Sticking points: barriers to access to needle and syringe programs in Canada. HIV Legal Network. 2007.   URL: [accessed 2021-06-16]
  17. Strathdee SA, Ricketts EP, Huettner S, Cornelius L, Bishai D, Havens JR, et al. Facilitating entry into drug treatment among injection drug users referred from a needle exchange program: Results from a community-based behavioral intervention trial. Drug Alcohol Depend 2006 Jul 27;83(3):225-232 [FREE Full text] [CrossRef] [Medline]
  18. Aronson ID, Bennett A, Marsch LA, Bania TC. Mobile technology to increase HIV/HCV testing and overdose prevention/response among people who inject drugs. Front Public Health 2017;5:217 [FREE Full text] [CrossRef] [Medline]
  19. Davis SM, Kristjansson AL, Davidov D, Zullig K, Baus A, Fisher M. Barriers to using new needles encountered by rural Appalachian people who inject drugs: implications for needle exchange. Harm Reduct J 2019 Apr 02;16(1):23 [FREE Full text] [CrossRef] [Medline]
  20. Naserirad M, Beulaygue IC. Accessibility of needle and syringe programs and injecting and sharing risk behaviors in high Hepatitis C virus prevalence settings. Subst Use Misuse 2020;55(6):900-908. [CrossRef] [Medline]
  21. Paquette CE, Syvertsen JL, Pollini RA. Stigma at every turn: health services experiences among people who inject drugs. Int J Drug Policy 2018 Jul;57:104-110 [FREE Full text] [CrossRef] [Medline]
  22. Rivera AV, DeCuir J, Crawford ND, Amesty S, Lewis CF. Internalized stigma and sterile syringe use among people who inject drugs in New York City, 2010-2012. Drug Alcohol Depend 2014 Nov 01;144:259-264 [FREE Full text] [CrossRef] [Medline]
  23. Sherman SG, Patel SA, Ramachandran DV, Galai N, Chaulk P, Serio-Chapman C, et al. Consequences of a restrictive syringe exchange policy on utilisation patterns of a syringe exchange program in Baltimore, Maryland: implications for HIV risk. Drug Alcohol Rev 2015 Nov;34(6):637-644 [FREE Full text] [CrossRef] [Medline]
  24. Strike C, Miskovic M. Scoping out the literature on mobile needle and syringe programs-review of service delivery and client characteristics, operation, utilization, referrals, and impact. Harm Reduct J 2018 Feb 08;15(1):6 [FREE Full text] [CrossRef] [Medline]
  25. Strike CJ, Challacombe L, Myers T, Millson M. Needle exchange programs. Delivery and access issues. Can J Public Health 2002;93(5):339-343 [FREE Full text] [Medline]
  26. Shrestha R, Huedo-Medina TB, Altice FL, Krishnan A, Copenhaver M. Examining the acceptability of mHealth technology in HIV prevention among high-risk drug users in treatment. AIDS Behav 2017 Nov;21(11):3100-3110 [FREE Full text] [CrossRef] [Medline]
  27. Alloghani M, Hussain A, Al-Jumeily D, Fergus P, Abuelma'Atti O, Hamden H. A mobile health monitoring application for obesity management and control using the internet-of-things. In: Proceedings of the Sixth International Conference on Digital Information Processing and Communications (ICDIPC). 2016 Presented at: Sixth International Conference on Digital Information Processing and Communications (ICDIPC); April 21-23, 2016; Beirut, Lebanon. [CrossRef]
  28. Al AS, Parmanto B, Branch R, Ding D. A persuasive and social mhealth application for physical activity: a usability and feasibility study. JMIR Mhealth Uhealth 2014;2(2):e25 [FREE Full text] [CrossRef] [Medline]
  29. Hartzler A, Venkatakrishnan A, Mohan S, Silva M, Lozano P, Ralston J. Acceptability of a team-based mobile health (mHealth) application for lifestyle self-management in individuals with chronic illnesses. In: Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016 Presented at: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Aug. 16-20, 2016; Orlando, FL, USA. [CrossRef]
  30. García L, Tomás J, Parra L, Lloret J. An m-health application for cerebral stroke detection and monitoring using cloud services. Int J Inf Manage 2019 Apr;45:319-327. [CrossRef]
  31. Cafazzo JA, Casselman M, Hamming N, Katzman DK, Palmert MR. Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res 2012;14(3):e70 [FREE Full text] [CrossRef] [Medline]
  32. Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J Med Internet Res 2013;15(11):e235 [FREE Full text] [CrossRef] [Medline]
  33. Schnall R, Cho H, Mangone A, Pichon A, Jia H. Mobile health technology for improving symptom management in low income persons living with HIV. AIDS Behav 2018 Jan 03:3373-3383. [CrossRef] [Medline]
  34. Guarino H, Acosta M, Marsch LA, Xie H, Aponte-Melendez Y. A mixed-methods evaluation of the feasibility, acceptability, and preliminary efficacy of a mobile intervention for methadone maintenance clients. Psychol Addict Behav 2016 Feb;30(1):1-11 [FREE Full text] [CrossRef] [Medline]
  35. Himelhoch S, Kreyenbuhl J, Palmer-Bacon J, Chu M, Brown C, Potts W. Pilot feasibility study of Heart2HAART: a smartphone application to assist with adherence among substance users living with HIV. AIDS Care 2017 Dec;29(7):898-904. [CrossRef] [Medline]
  36. Perera AI, Thomas MG, Moore JO, Faasse K, Petrie KJ. Effect of a smartphone application incorporating personalized health-related imagery on adherence to antiretroviral therapy: a randomized clinical trial. AIDS Patient Care STDS 2014 Nov;28(11):579-586 [FREE Full text] [CrossRef] [Medline]
  37. Laurence C, Wispelwey E, Flickinger TE, Grabowski M, Waldman AL, Plews-Ogan E, et al. Development of PositiveLinks: a mobile phone app to promote linkage and retention in care for people with HIV. JMIR Form Res 2019 Mar 20;3(1):e11578 [FREE Full text] [CrossRef] [Medline]
  38. Westergaard RP, Genz A, Panico K, Surkan PJ, Keruly J, Hutton HE, et al. Acceptability of a mobile health intervention to enhance HIV care coordination for patients with substance use disorders. Addict Sci Clin Pract 2017 Dec 26;12(1):11 [FREE Full text] [CrossRef] [Medline]
  39. Mobile fact sheet 2019. Pew Research Center.   URL: [accessed 2021-06-10]
  40. Rogers E. Diffusion of innovations. New York, United States: Simon and Schuster; 2010:1-576.
  41. Glanz K, Lewis FM, Rimer BK. Health Behavior and Health Education. Medicine & Science in Sports & Exercise 1991;23(12):1404. [CrossRef]
  42. Rodrigues AT, Sousa CT, Pereira J, Figueiredo IV, Lima TD. Mobile applications (Apps) to support the Hepatitis C treatment: a systematic search in app stores. Ther Innov Regul Sci 2021 Jan;55(1):152-162. [CrossRef] [Medline]
  43. Altice FL, Kamarulzaman A, Soriano VV, Schechter M, Friedland GH. Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. Lancet 2010 Jul 31;376(9738):367-387 [FREE Full text] [CrossRef] [Medline]
  44. Kannisto KA, Koivunen MH, Välimäki MA. Use of mobile phone text message reminders in health care services: a narrative literature review. J Med Internet Res 2014;16(10):e222 [FREE Full text] [CrossRef] [Medline]
  45. Shrestha R, Altice F, Karki P, Copenhaver M. Developing an integrated, brief biobehavioral HIV prevention intervention for high-risk drug users in treatment: the process and outcome of formative research. Front Immunol 2017;8:561 [FREE Full text] [CrossRef] [Medline]
  46. Jost JJ, Tempalski B, Vera T, Akiyama MJ, Mangalonzo AP, Litwin AH. Gaps in HCV knowledge and risk behaviors among young suburban people who inject drugs. Int J Environ Res Public Health 2019 Jun 02;16(11):1958 [FREE Full text] [CrossRef] [Medline]
  47. Abdul-Quader AS, Heckathorn DD, McKnight C, Bramson H, Nemeth C, Sabin K, et al. Effectiveness of respondent-driven sampling for recruiting drug users in New York City: findings from a pilot study. J Urban Health 2006 May;83(3):459-476 [FREE Full text] [CrossRef] [Medline]

AOR: adjusted odds ratio
HCV: hepatitis C virus
mHealth: mobile health
NHSSP: New Haven Syringe Service Program
PrEP: pre-exposure prophylaxis
PWID: people who inject drugs
RDS: respondent-driven sampling
SSP: syringe service program

Edited by R Kukafka; submitted 01.11.20; peer-reviewed by J Jain, R Jeminiwa; comments to author 15.01.21; revised version received 27.01.21; accepted 24.05.21; published 14.07.21


©Tyler Shelby, Xin Zhou, Douglas Barber, Frederick Altice. Originally published in the Journal of Medical Internet Research (, 14.07.2021.

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