Technology Use and Preferences for Mental Health Self-Management Interventions among Older Veterans

Abstract

Objectives:

The United States Department of Veterans Affairs offers numerous technology-delivered interventions to self-manage mental health problems. It is unknown, however, what barriers older military veterans face to using these technologies and how willing they would be to use technologies for mental health concerns.

Methods:

Seventy-seven veterans (M_age = 69.16 years; SD = 7.10) completed interviews in a concurrent mixed methods study. Interviewers asked about technology ownership and described four modalities of delivering self-management interventions: printed materials, DVDs, Internet, and mobile apps. Interviewers obtained feedback about each modality’s benefits, barriers, and facilitators. Participants ranked their self-management modalities preferences alone and compared with counseling. Multi-variable adjusted logistic regression and qualitative analyses were conducted to investigate the reasons contributing to preferences.

Results:

Most reported owning a computer (84.4%), having home Internet (80.5%), and a smartphone (70.1%). Participants preferred printed materials (35.1%) over mobile apps (28.6%), Internet (24.7%), and DVDs (13.0%). Lower computer proficiency was associated with preferring DVDs; higher proficiency was associated with Internet and mobile interventions. Residing in an urban area was associated with mobile apps. When counseling was an option, 66% identified this as their first preference. Qualitative findings showed veterans’ desire for information, training, and provider support with technology.

Conclusions:

Older veterans reported high technology ownership rates, but varied preferences for self-management interventions. Notably, two-thirds preferred some form of technology, which points to the importance of ensuring that providers offer existing technology-delivered interventions to older veterans. Veterans’ strong preference for counseling emphasizes the need for human support alongside self-management.

More than half of United States (U.S.) military veterans are 60 years or older¹ and many suffer from mental health disorders.² Approximately 9.7 million veterans or 48% of all veterans received care from the U.S. Veterans Health Administration (VHA) annually. ¹ The VHA encompasses 1,255 health care facilities and is the largest health care system in the U.S. with mental health care providers integrated in providers including primary care. Studies examining the VHA mental health service usage have shown that older veterans are less likely to use services than are younger veterans.^3,4 Lower mental health care utilization among older adults is related to barriers to care, including difficulty navigating the referral process, lower identification of late-life mental health disorders, transportation difficulties, concerns regarding cost of care, and stigma about mental health.^5–7 To address these barriers, VHA has integrated behavioral health providers into primary care and implemented clinical video telehealth options to overcome geographical and mobility barriers for patients in remote areas. Still, the scarcity of geriatric mental health providers limits the scalability of telehealth as providers are needed to deliver telehealth services.⁸ Taken together, these factors underscore a need to offer alternative methods of delivering mental health care for older adult populations, such as self-management.⁹

A vast body of research supports self-management as efficacious for chronic health and mental health.^10–13 In particular, interest in mental health self-management interventions has grown with the wide-scale adoption and use of technologies capable of delivering these interventions. While technology-delivered interventions may increase access to mental health care,^14,15 older adults are often omitted from this research due to barriers to accessing technology and sampling bias that may reflect ageist stereotypes of older adults’ interest in technology.^16,17

Digital mental health interventions, such as Internet and mobile app-based interventions can address geographical, mobility, and provider-shortage barriers, as they may be used as self-management or with varying levels of provider or peer support.¹⁸ These interventions may be used anonymously, discreetly, and usually at no to minimal cost, benefiting individuals who may be reluctant to seek treatment. In addition, incorporating these interventions into existing mental health care could expand access to services by enabling providers to allocate resources to more patients requiring intensive mental health interventions (e.g., weekly psychotherapy).

The VHA surpasses the private sector in the availability of self-management technology-based interventions for mental health spanning from Internet to mobile applications.^19–21 Research on veterans’ attitudes toward mobile apps demonstrates that most have positive attitudes towards this intervention modality.^22–24 Yet, little attention has been given to older veterans’ attitudes towards technology. In one of the few studies that considered older veteran technology use, researchers conducted a series of semi-structured interviews and found no qualitative differences for older veterans (50 to 70 years old) compared with younger veterans (18 to 49 years old).²⁴ However, older veterans had lower rates of smartphone ownership (32% vs. 56%) and more difficulties using technology compared with younger veterans. Another study²⁵ found that 17.6% and 56% of older veterans had access to smartphones and home Internet respectively. The older veterans also endorsed willingness to reach out to their social networks for technology assistance. The VHA is a leader in home telehealth monitoring, in which technologies, such as biometric devices for blood pressure and weight, assist with the monitoring of chronic health conditions.²⁶ While this approach has been successful for chronic health conditions, it has been less used in monitoring mental health conditions such as depression or posttraumatic stress disorder (PTSD).²⁶ These findings suggest that, similar to the general Veteran population, older veterans are open to using technology to monitor their mental and physical health, but may have limited access to smartphones in particular.

Not only are there few investigations of technology use for mental health in older adults, to date there have been no studies investigating older veterans’ barriers to using technology for mental health. Examining access to and preferences for different modalities of delivering mental health interventions is critical in facilitating usage of existing VHA technology-delivered interventions. Thus, the present study examined older veterans’ experience with, willingness to use, and preferences for four different intervention modalities that represent varied methods of conveying psychoeducation and coping skills to manage mental health problems. The modalities examined were: (1) printed materials, (2) DVDs, (3) Internet-based, and (4) mobile apps. The core content of the self-management interventions would be similar across modalities, but the delivery methods vary, including text, videos, or a combination of information-delivery methods. We hypothesized that greater computer proficiency, residing in an urban area, and elevated mental health symptoms would be related to preference for Internet and mobile apps. These hypotheses were grounded in findings that older veterans seek information about mental health conditions online²⁷ and findings that veterans with mental health symptoms are interested in technology-based mental health interventions.²²

Methods

Study Design

The study used a concurrent mixed methods design. Study procedures were approved by Stanford University Institutional Review Board, the IRB of record for the VA Palo Alto Health Care System (32454).

Participants

Purposive sampling was used to recruit veterans aged 60 years or older from a single VA Health Care System catchment area. Participants were recruited using flyers posted at VA clinics and senior centers, contacting previous research participants, holding community presentations, and advertising online (Craigslist) and in print media.

Telephone screening assessed two inclusion criteria: age < 60 years old or absence of possible cognitive impairment suggested by Short Blessed Test^28,29 score of ≥ 6. Figure 1 displays the participant flow, yielding 77 completing the semi-structured telephone interview and 74 completing mailed questionnaires.

Figure 1.

Flow of participants through the study

Procedures

Participants were mailed a packet of information that included a copy of the consent form to be reviewed by phone, questionnaires to complete and return, and a guide with brief explanations of the modalities including pictures and screenshots. Informed consent included permission to review their VHA medical record. A brief demographic questionnaire was administered followed by the Technology Semi-Structured Interview. Interviews were completed by phone (n = 75) or in person (n = 2) between September 2016 and April 2018.

Measures

Technology Ownership, Preferences, and Proficiency.

The Technology Semi-Structured Interview (see Supplemental Material) contained questions about participants’ technology use and if they have ever used each modality to cope with emotional difficulties. Then participants were invited to ask questions about the modality. Next, they rated their willingness to use the modality (willingness ratings) on a 1 (No, definitely not willing) to 10 (Yes, definitely willing) scale and were asked about barriers and facilitators of use. Nine technology use and ownership questions, drawn from the Pew Internet Life Survey,³⁰ were included. At the end of the interview, participants rank ordered their choice of the four modalities to use if theoretically faced with a mental health problem (ranked preferences) and then re-ranked their preferences with counseling/psychotherapy included as an option.

The Computer Proficiency Questionnaire (CPQ)³¹ assesses proficiency using computers in six domains represented by subscales (basics, printer, communication, Internet, calendar, and entertainment). The 33 items ask about how easily one can complete each task on a computer with items rated on a five-point scale ranging from 1 (never tried) to 5 (very easily). Total scores are generated from average scores from each subscale, with higher scores indicating greater computer proficiency.

Health and Medical Burden.

Perceived health was measured using the question: “In general, would you say your health is: excellent, very good, good, fair, poor.” Medical burden was rated using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G)³² with total scores ranging from 0 to 56. Trained raters completed the CIRS-G retrospective medical record review.³³ Raters co-reviewed five charts for training and achieved an Intra-class Correlation Coefficient (ICC) of .94 for the CIRS-G total score. Sixty-eight medical records were reviewed. Reasons for the missing CIRS-G ratings were: (1) not receiving care at VHA (n = 6); and (2) insufficient notes to complete the CIRS-G rating (n = 3).

Psychiatric Symptom Measures.

Three measures of psychiatric symptoms with established validity among older adults were used to create a composite variable of elevated psychiatric symptoms with two groups: elevated symptoms (i.e., meeting the cut-point on one or more measures) or no elevated symptoms. The first measure, the Patient Health Questionnaire depression scale (PHQ-8)³⁴ is an eight-item measure of depressive symptoms that has comparable validity to the PHQ-9.³⁵ The PHQ-8 omits the suicide ideation item for use with asynchronous data collection. Scores ≥10 or greater are suggestive of elevated depressive symptoms. The Geriatric Anxiety Scale (GAS)³⁵ is a 30-item measure that assesses a broad range of anxiety symptoms. The first 25 items are summed; the remaining five items can be used to better understand content of worries and fears (e.g., finances, health). A score ≥16 maximizes sensitivity and specificity for the detection of anxiety disorders in older adults.³⁷ The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5)³⁸ is a 20-item measure of PTSD symptoms. Scores ≥ 33 are suggestive of PTSD.³⁹

Rural status.

Rural status was characterized using zip codes aggregated using the Rural Urban Commuting Area (RUCA) codes⁴⁰ as employed in a previous study of veterans’ technology attitudes.²⁴ Veterans’ place of residence were grouped as urban (i.e., metropolitan areas) or rural (small, isolated towns ranging to large rural towns) using categorization method C.⁴¹

Statistical Analyses

We aimed to recruit a sample of 75 individuals based on a power analysis to detect a medium size effect (.3) for to the quantitative research aim regarding preferences. With regard to the qualitative aims, it was estimated that approximately 25 interviews would provide sufficient data to address the research questions⁴² and would yield code saturation with a complex understanding of the data.⁴³ Thus, we expected to reach data saturation before achieving our targeted sample size for the quantitative aim.

Quantitative.

Frequencies and percentages were calculated to summarize participant characteristics, and technology use, ownership, and preferences. Mean willingness and percentages of ranked preferences for each modality were calculated. Differences in willingness and rankings were examined with a non-parametric test (Friedman’s ANOVA).

Logistic regressions were conducted to examine whether rural status; CPQ total score; and presence of elevated psychiatric symptoms were related to preferences. Years of education were included as a covariate. Follow-up models included the CIRS-G as a predictor. Analyses were conducted with SPSS version 24.⁴⁴

Qualitative.

Transcribed interviews were analyzed using Dedoose version 8.0.⁴⁵ The coding scheme was developed based on CREATE model of older adult technology use^46,47 with deductive codes related to the user (individual characteristics), technological system (software, hardware/interface), and the task (task engagement, intervention content). Inductive codes were added based on transcript review. Two authors (CEG, AMZ) trained in qualitative analyses coded the data. The codebook was adjudicated 7 times across 20 interviews resulting in a final pooled kappa of .90. We achieved data saturation soon after adjudicating the codebook. The remaining interviews completed after reaching data saturation focused on exploring variations on the themes and subthemes. Team-based thematic analysis converged on factors underlying participant’s preferences for technology delivery platforms and identified barriers and facilitators to use.

Results

Participants were older veterans (M_age = 69.16, SD = 7.10 years, range 60-90+), with the majority being white, non-Hispanic individuals (63.6%), male (81.8%), and retired (72.7%) (Table 1). With regard to mental health conditions, 45.9% of participants endorsed psychiatric symptoms above clinical cut-points on at least one of three self-report measures. Based on the CIRS-G ratings, 72.1% (n = 49 of 68 completed reviews) experienced some level of psychiatric illness (mild to severe). Nearly all participants owned a cellphone (92.2%), and most (70.1%) owned a smartphone (Table 2). Most also owned either a desktop or laptop computer (84.4%) and a slightly lower percentage had Internet service at home (80.5%). Technology use or ownership did not differ by demographic characteristics. Of smartphone owners, 96.3% (n = 52) had sent a text message, 90.7% (n = 49) had downloaded an app, 88.9% (n = 48) had sent an email, and 87.0% (n = 47) had used the Internet on their phone. Of tablet users, 76.3% (n = 29) had downloaded an app. However, of the mobile device owners (smartphone and/or tablet), only 11.9% (n = 7) had downloaded an app to learn about or manage physical health, emotional health, or stress.

Table 1.

Participant Characteristics (N=77)

Characteristic	n (%)	M (SD)
Age		69.16 (7.10)
Years of Education
≤ 15 years	29 (37.7%)
≥ 16 years	48 (62.3%)
Sex
Male	63 (81.8%)
Female	14 (18.18%)
Race/Ethnicity
Black/African American	9 (11.69%)
White, Non-Hispanic	49 (63.64%)
White, Hispanic	5 (6.49%)
Other	14 (18.19%)
Marital Status
Single	18 (23.4%)
Married	28 (36.4%)
Divorced/Separated	24 (31.2%)
Widowed	7 (9.1%)
Employment
Full/Part-time	11 (14.3%)
Unemployed	6 (7.8%)
Retired	56 (72.7%)
Disabled	4 (5.2%)
Rural Status
Urban	62 (80.5%)
Rural	15 (19.5%)
General Health
Excellent	5 (6.5%)
Very Good	22 (28.6%)
Good	32 (41.6%)
Fair	11 (14.3%)
Poor	4 (5.2%)
Elevated Psychiatric Symptoms
Present	34 (45.9%)
Absent	40 (54.1%)
Measures
CIRS-G Total Score		12.07 (4.93)
GAS		16.20 (12.20)
PHQ-8		6.31 (6.15)
PCL-5		20.36 (18.69)
CPQ Total Score		23.80 (6.53)

Note. CIRS-G = Cumulative Illness Rating Scale for Geriatrics (N = 68). GAS = Geriatric Anxiety Scale, PHQ-9 = Patient Health Questionnaire-8 item; CPQ = Computer Proficiency Score (N = 74). Race/ethnicity approximates that of the local VA Health Care System.

Table 2.

Technology Use, Modality Ratings, Mental Health, and Medical Burden Measures

	n (%)	M (SD)	Min-Max
Technology Ownership and Services
DVD Player	57 (74.0%)
Computer	65 (84.4%)
Cellphone	71 (92.2%)
Smartphone	54 (70.1%)
Tablet	38 (49.4%)
Smartphone or Tablet	59 (76.6%)
Internet Service in Home	62 (80.5%)
Any Technology	77 (100%)
Willingness Rating
Printed Materials		8.79 (1.89)	3-10
DVD		8.04 (2.57)	1-10
Internet		8.24 (2.40)	1-10
Mobile app		7.75 (2.97)	1-10
First Preference for Delivery Modality
Printed Materials	27 (35.1%)
Mobile app	22 (28.6%)
Internet	19 (24.7%)
DVD	10 (13.0%)
First Preference for Delivery Modality when Included Counseling
Counseling	51 (66.2%)
Internet	10 (13.0%)
Mobile apps	8 (10.4%)
Printed Materials	6 (7.8%)
DVD	3 (3.9%)

Intervention Delivery Modality Experience and Preferences

Participants’ experience using the modalities to cope with emotional difficulties varied considerably. Seventy percent (n = 54) had used printed materials for mental health self-management, 25.6% had used the Internet (n = 20), 16.9% had used DVDs (n = 13), and 9.1% had used mobile apps (n = 7). Overall, 72% (n = 56) reported using any type of self-management. Of those 53.5% (n = 30) used only one modality, while 46.4% (n = 26) used more than one modality. Notably, users of any type of self-management were not more likely to have elevated psychiatric symptoms, χ(74) = 1.32, p = .25. Willingness to try each modality to manage emotional difficulties was high (Table 2) and did not differ significantly among participants, χ _F ² (3) = 6.37, p = .10.

Friedman’s ANOVA found rank differences among the four self-management modalities, χ _F ² (3) = 9.25, p =.03. Printed materials were preferred the most, followed by mobile apps, Internet, and DVDs (Table 2). Pairwise comparisons demonstrated that the only difference in rankings emerged for the DVDs. When counseling was added as an option, most (n = 51, 66%) preferred this option while the remaining third (n = 26) preferred a self-management option as their first choice.

Logistic regression analyses identified characteristics associated with preferences for each self-management modality (See Table 3). Models, adjusted by years of education, examined computer proficiency, rural status, and presence of elevated psychiatric symptoms. Lower computer proficiency was associated with preference for using DVDs, whereas greater computer proficiency was associated with preference for Internet interventions. Residing in an urban area was associated with preference for using mobile interventions. No variables were associated with preference for printed materials. Sensitivity analyses examined models with CIRS-G total scores; however, scores were not associated with preferences (results not shown).

Table 3.

Factors Associated with Self-Management Preferences

	Printed Materials	DVDs	Internet Interventions	Mobile App Interventions
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Education
≤ 15 years (Ref)
≥ 16 years	2.42 (0.72, 8.21)	1.81 (0.21, 5.70)	0.45 (0.13, 1.54)	0.67 (0.19, 2.34)
Rural Status
Rural (Ref)
Urban	0.82 (0.23, 2.97)	0.00 (0.00, 0.00)a	0.40 (0.08, 2.14)	5.14 (1.37, 19.28)
Elevated Psychiatric Symptoms
Absent (Ref)
Present	0.61 (0.22, 1.70)	3.49 (0.69, 17.81)	0.99 (0.33, 3.00)	1.14 (.38, 3.43)
CPQ Total Score	0.92 (0.85, 1.01)	0.84 (0.73, 0.95)	1.15 (1.02, 1.30)	1.12 (1.00, 1.24)

Note. CPQ = Computer Proficiency Questionnaire. Not shown are models adjusted by CIRS-G total scores because CIRS-G was not a significant predictor. Bolded ORs indicate that that predictor was significant at p < .05.

^aNo rural-dwelling individuals ranked DVDs first, but model remained unchanged when excluding this variable, so variable retained in model.

Qualitative Analysis

Preferences.

Qualitative analysis of participants’ justification for their ranked preferences revealed similar themes across modalities and some differences based on specific features of each modality (See Table 4). One similar theme was that participants prefer what they know (i.e., comfort, familiarity) and what they have used in the past. Second, convenience, accessibility, and portability influenced preferences. Differences among modalities were related to multiple factors including: (1) preferences for special features or specific equipment (i.e., having a larger computer monitor vs. small phone screen); (2) lack of security/privacy concerns; (3) interactivity; and (4) amount of available information. Concerns about security and privacy issues were noted among participants who ranked either printed materials or DVDs highly, as these modalities generally do not involve sharing of one’s information. Participants mentioned preferences for listening to information, benefits of pictures and visuals, and demonstrations of skills as reasons for ranking DVDs first. Participants who ranked Internet and mobile applications highly described the importance of interactive, multimedia information delivery, and access to current information. Internet interventions were valued due to the unlimited information available; however, combing through the Internet’s breadth of information was noted as a drawback. Individuals who ranked mobile apps highly mentioned the value of having skills at one’s fingertips, their phone being readily available (“always on me”), and the ability to use their phone to call for help if needed.

Table 4.

Qualitative Themes Underlying Preferences

Themes	Description	First Choice and Example Quote
People prefer what they know	Experience using, comfort/familiarity with, and enjoyment of modality	Printed Materials: “I am most comfortable with books and printed materials. And the Internet, I’m okay with that, I mean I Google and do that, but I find that frustrating too because there are so many options.” DVDs: “The DVD, you know it’s like going to school. It’s like learning something. It has that feature to it that it’s outside of yourself and somebody’s explaining it and you can interact with the DVD as well.”
Ease of access wherever and whenever	Portability, convenience of the modality, and device ownership	Mobile apps: “I have my phone 100% of the time and my computer 80-90% of the time. So the mobile apps I would place as number one because I always have my phone with me. It is more convenient than having to go to a computer to run a DVD, or to go home to pick up a brochure that I got mailed last time.” Printed Materials: “A book is a highly efficient tool because you can put a book marker in it and you can just go straight to that paragraph and whereas with a mobile app your battery might be low, the noise level in the car or the train or the bus is too high or you don’t understand the symbols. A book is easy to access.”
Specific Modality Features	Interactivity, multimedia delivery	DVDs: “I would pay attention more to DVDs. The pictures and writing and that would probably be more helpful to me.” Internet: “The Internet is a lot better [than mobile apps] and a lot easier to be able to communicate, manipulate, move your way around, so that would be my choice. It gives you more freedom of movement, more access to what you want to know, and being able to ask questions.”
	Ease navigation and user experience	Internet: “On my desktop computer, I have a large monitor, a 27 inch. So, I can turn up the fonts and it makes it easy to read.” Mobile Apps: “Ease of entry for me would be having it as an app where I can click on it and get into all the information that would be available on that program.”
	Data security and information privacy issues	Printed Materials & DVDs: “I’m comfortable with the security of those [printed materials and DVDs] because I have them with me and I know that I have control over what comes on and what goes off. Internet-based and mobile apps, there is always that possibility of the material being hacked. And don’t tell me it doesn’t because it happens all the time nowadays.”

Barriers to Using Self-Management Technology Modalities.

The primary and most concrete barrier was lack of access, which was attributed to cost, owning older devices that are less compatible with software or apps, or frustration with ongoing maintenance, such as required computer updates. Insufficient knowledge of technical lexicon, menu symbols, and general comfort using the technologies, particularly related to mobile apps, limited use of these technologies. Additional barriers were related to individual user’s sensory abilities, such as changes in vision functioning that affected their ability to view information on a small screen. Others noted the importance of having subtitles or adjustable volume to accommodate hearing impairment. Finally, some described challenges navigating a touch screen using their fingers due to small screen size and “fat fingers”.

Facilitators.

The primary facilitator identified was the role of a person to support the participant during an intervention. Some suggested that a provider could serve as a navigator, directing them to evidence-based resources specific to their problem. One participant described that their “first choice would probably be to talk to another person on the phone or hotline and then they could help direct [me] to other sorts of resources.” Other person-based supports included teaching about using technology, answering technology questions, discussing intervention content, and encouraging adherence. One participant highlighted the importance of knowing both technology and mental health interventions: “[the] person has to be kind of like multi-tool– part-time therapist and very skilled in technology.”

Conclusion

Our findings highlight that older veterans have high rates of technology ownership. Notably, these rates of ownership did not differ significantly among those in urban versus rural regions. Despite 100% of participants owning technology, preferences for self-management treatment delivery varied. More veterans preferred printed materials (35%) than any specific form of technology. However, 65% preferred some form of technology (i.e., DVDs, Internet, or mobile app-delivered interventions). These data confirm that older veterans are interested and willing to use mental health self-management interventions, particularly if human support accompanies the intervention. While the findings do support willingness to try self-management, when provided with the option of counseling, the majority of participants expressed interest in trying this option first.

Key factors related to preferences for treatment delivery modalities included access, experience, and comfort with modalities. Consistent with our predictions, findings demonstrated the importance of computer proficiency, which aligns with qualitative themes regarding people preferring what they know. Regarding mobile apps, the preference for apps was stronger for veterans living in an urban compared with rural regions and are in line with our predictions and previous research.²⁴ Contrary to our predictions, presence of elevated mental health symptoms, present in 46% of participants, were not related to preference for any modalities. With a larger sample or more detailed mental health information, such as treatment histories, an association with preferences may have emerged.

Qualitative findings further uncovered factors underlying these preferences. Mobile apps were valued for their convenience, portability, and multimedia intervention delivery. Internet interventions were valued for multimedia delivery and for the ability to view these interventions on a larger computer screen compared with a smartphone screen. Although our findings demonstrate that past experience, knowledge, access, and portability underlie older veterans’ preferences for self-management intervention delivery modalities, high willingness to try all the modalities suggests their openness to different self-management options. A question that remains is whether matching with older veterans’ intervention delivery preferences would lead to better outcomes. It is possible that considering patient preferences and characteristics (e.g., technology proficiency) when recommending mental health self-management interventions may lead to greater acceptance of these interventions and improved outcomes through providing person-centered care.

Our findings highlight the varied roles that a provider may play in supporting mental health in older veterans. Many participants showed a preference for traditional counseling, if available. Yet, the qualitative findings suggested that they wanted providers to provide personalized recommendations about the different self-management tools, such as brief readings or books, videos, websites, and mobile apps. Furthermore, one third were interested in exploring and using a self-management tool as a first step to treatment, which aligns with others’ findings that a common reason for not seeking mental health treatment is the desire to work out problems on one’s own.⁴⁸

Older adults are often novice technology users and may need personalized support and teaching to improve their technology proficiency. The participants’ desire for human support when using self-management technology interventions affords an opportunity to implement new models of mental health care that integrate basic teaching about technology and ongoing human support around the implementation of these interventions. Extant models of support, such as the efficiency model of support,¹⁸ address the importance of adherence, but do not consider the need to teach technology-use basics or to provide ongoing support for novice users. New models should consider direct instruction about technology, use of assistive devices (e.g., stylus pens, screen magnifier, accessibility features), and sharing tailored informational handouts targeted at improving knowledge of technology basics.

Several limitations to this study should be acknowledged. First, the study did not collect information on participants’ mental health treatment history, socioeconomic status, or income, which all could influence preferences for delivery modalities. Second, the study may not have been sufficiently powered to examine all the quantitative factors underlying preferences. Third, the study did not directly examine the impact of these interventions on mental health symptoms or address whether older veterans derive similar benefits from technology-delivered interventions compared with younger veterans. Fourth, many of the individuals in the study resided in proximity to a technology hub and had higher years of education, but similar levels of multimorbidity to older veterans in other studies.⁵

Study strengths included the use of telephone interviews to reach veterans in remote areas, employment of qualitative analysis to understand factors underlying preferences, and the measurement of mental health symptoms and medical comorbidity. This study provides key information about veterans’ preferences through the use of qualitative interviewing methods to ensure that the veterans understood the different modalities prior to inviting them to select their preference among multiple options. This person-centered approach provides nuanced information about preferences that would not otherwise have been obtained in a survey alone.

Our findings suggest that providers play a critical role in recommending credible self-management options for older veterans in addition to providing traditional psychotherapy interventions. Recommendations should include multiple self-management delivery options to meet older veterans’ preferences. With over fifty percent interested in Internet or mobile apps, it is important that providers offer existing digital mental health interventions to older veterans; however, special consideration is needed for rural individuals who may not be as interested in mobile apps. Recommending these interventions may necessitate technology training for providers or health coaches, new models of behavioral health delivery, and making instructional support for technology available within health care systems.

Key-points.

• This study investigated older military veterans’ ownership of devices and modality preferences for technology-delivered mental health self-management interventions.

• Older veterans reported high rates of technology ownership. Thirty-five percent preferred printed materials for self-management, whereas the remaining majority preferred some form of technology for accessing a mental health self-management intervention.

• Qualitative findings suggest that technology training, and particularly provider support may facilitate the use of self-management interventions.