Preliminary Efficacy of Let’s Talk Tech: Technology Use Planning for Dementia Care Dyads

Abstract

Background and Objective

Care partners of people living with dementia require support to knowledgeably navigate decision making about how and when to use monitoring technologies for care purposes. We conducted a pilot study of a novel self-administered intervention, “Let’s Talk Tech,” for people living with mild dementia and their care partners. This paper presents preliminary efficacy findings of this intervention designed to educate and facilitate dyadic communication about a range of technologies used in dementia care and to document the preferences of the person living with dementia. It is the first-of-its-kind decision-making and planning tool with a specific focus on technology use.

Research Design and Methods

We used a 1-group pretest–post-test design and paired t tests to assess change over 2 time periods in measures of technology comprehension, care partner knowledge of the participant living with mild Alzheimer’s disease’s (AD) preferences, care partner preparedness to make decisions about technology use, and mutual understanding. Thematic analysis was conducted on postintervention interview transcripts to elucidate mechanisms and experiences with Let’s Talk Tech.

Results

Twenty-nine mild AD dementia care dyads who live together completed the study. There was statistically significant improvement with medium and large effect sizes on outcome measures of care partners’ understanding of each technology, care partners’ perceptions of the person living with dementia’s understanding of each technology, knowledge of the person living with dementia’s preferences, decision-making preparedness, and care partners’ feelings of mutual understanding. Participants reported that it helped them have important and meaningful conversations about using technology.

Discussion and Implications

Let’s Talk Tech demonstrated promising preliminary efficacy on targeted measures that can lead to informed, shared decision making about technologies used in dementia care. Future studies should assess efficacy with larger samples and more diverse sample populations in terms of race, ethnicity, and dementia type.

Translational significance: This study demonstrated the promising preliminary efficacy of the first intervention to meaningfully engage people living with dementia in technology use planning and decision making. The self-administered, dyadic web-app intervention was successfully completed by all participants living with mild Alzheimer’s disease and their care partners. It showed significant improvements in comprehension of the featured technologies, knowledge by care partners of the person living with dementia’s preferences, and preparedness to make technology use decisions. These results indicate that this approach to engaging people living with mild dementia in technology use planning meets a compelling need and has the potential to scale cost-effectively.

Background and Objectives

Investment by the private, state, and federal sectors in digital technologies to support older adults, people living with dementia, and care partners at home has increased dramatically with the hope that they will become a cost-effective tool to support aging in place. Although there is limited quality research to demonstrate effectiveness (Howard et al., 2021), there is strong momentum to expand the development and application of technologies for people living with dementia (Astell et al., 2019; Moyle, 2019; Thordardottir et al., 2019; Vermeer et al., 2019). These are variously referred to as monitoring technologies, ambient assisted living, or even assistive technologies. The use of technologies that have a monitoring component is growing far faster than our understanding of how to use them most effectively (Berridge, 2018; Ienca et al., 2017). Effectiveness is partially a function of users’ ability to make informed decisions about how and when to use digital technologies, yet there are no tools to support families to determine how to navigate their use to support care. Uninformed adoption can contribute to discontinuation (Berridge, 2017), and ethics researchers have noted the challenge of preventing infringement on self-determination and autonomy (Ho, 2020, 2023). We report the preliminary efficacy findings from the pilot study of a self-administered intervention to support dyadic decision making about dementia care technologies. “Let’s Talk Tech” is delivered in the form of a web application (web-app) that educates and facilitates discussion and documentation of the person living with dementia’s preferences about diverse technologies used for care. Its goal is to enable families to identify and discuss preferences and values held by individual older adults with mild Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and to prepare their care partners to make decisions about the use that participants can feel good about.

Families may require support to understand their technology options, the functions of various technologies, and what daily use might be like. Research indicates that one size does not fit all and that personalized, tailored solutions are needed for person-centered care (Niemeijer et al., 2015; Meiland et al., 2017) because individual needs, abilities, and preferences vary (Berridge et al., 2021; Reeder et al., 2013). Decision-making support may be most effective when targeted to the dyad because this is where the negotiation of these preferences and values often occurs. Tailoring for optimal use of technologies used to support a person living at home requires the participation of all members of a care dyad or triad when dementia is involved (Meiland et al., 2017).

Research indicates that we can expect many older adults to hold different views than their care partners about monitoring technologies and that perception of need, balance of risks and benefits, and comfort with data collection may differ (Berridge, 2016; Berridge & Wetle, 2020; Godwin, 2012; Ho, 2020; Lariviere et al., 2021; Mort et al., 2013; Novitzky et al., 2015; Sponselee et al., 2007). For example, a dyadic study about Meals on Wheels recipients’ and their adult children’s perspectives on location tracking, in-home sensors, and web cameras found that most Meals on Wheels recipients rated each of the three technologies as less desirable than their adult children and that dyad members had conflicting preferences (e.g., the adult child wants to use web cameras whereas the parent is strongly opposed; Berridge & Wetle, 2020). Adult children overwhelmingly reported confidence that they could convince their parents to adopt the technologies, underestimated their parents’ demonstrated ability to comprehend the technologies, and thought they would engage them minimally in decisions about adoption. Their parents, by contrast who rated each technology less favorably, wanted to be involved in those decisions and made aware of technologies used in their care (Berridge & Wetle, 2020).

People living with dementia generally prefer to be more involved in decision making than they are (Miller et al., 2016), and their preferences and concerns often go unrepresented (de Boer et al., 2007; Harman & Clare, 2006; Menne & Whitlatch, 2007; Miller et al., 2016). Ethical use of monitoring technologies requires that people living with dementia be involved in decision making to the extent possible (Alzheimer Europe, 2010). Communication decline and losses in autonomy are already significant stressors for people with a diagnosis of AD/ADRD (Shelton et al., 2018). This stress may be compounded if they do not have a voice in decisions about how they are monitored and contribute to strain in relationships with care partners, which can negatively affect both parties’ quality of life (Miller et al., 2019; Bonds, Whitlatch, et al., 2021). The burden of decision making about care without knowing that person’s preferences is also a source of stress for care partners (Horowitz et al., 2004; Whitlatch et al., 2009). For these reasons, researchers have identified the need to educate families and people living with dementia about the technologies’ risks and benefits in a way that is accessible, clear, and meaningful to them (Berridge et al., 2021; Lukkien et al., 2021; Meiland et al., 2017; Robillard et al., 2018; Thorstensen, 2018).

The “Let’s Talk Tech” Intervention

Let’s Talk Tech is our response to calls by researchers for tools to educate and facilitate informed decision making and planning for tailored technology use, nonuse, or conditioned use so that people are not left to navigate the complex technology landscape alone. This intervention is delivered in the form of a web-app that consists of education about four categories of technologies used in dementia home care: location-tracking technology, in-home activity sensing, home web cameras, and artificial companion (AC) robots that employ artificial intelligence. These technologies were selected through a combination of literature review and Delphi survey process in which domain experts identified, through a consensus process, technologies that will be the most prevalent in-home dementia care in the near (5 years) future (Berridge et al., 2021). Experts were then asked to rank among those the technologies that are the most likely to cause value tensions between a person living with dementia and care partner and thus warrant a conversation about use decisions (Berridge et al., 2021). Among those, the four technologies used in Let’s Talk Tech were selected based on their current availability and ability to employ a wide range of data (location, audio, visual, etc.), as people may feel differently depending on the type of data collected about them.

Let’s Talk Tech is a self-administered intervention that at least two people complete together. As a web-app, it is accessible through any Internet browser and does not require a user to download an app, running as a full-screen experience while reading and storing encrypted data on a “back-end” server. Let’s Talk Tech has the following components: It (a) describes data-diverse technologies used in dementia care so a person living with dementia can participate knowledgeably in planning and decision making; (b) educates care dyads about the research-identified competing values and risks of use for each (e.g., relating to privacy, autonomy, safety); (c) facilitates within-dyad communication about the technologies, and (d) solicits and documents the person living with dementia’s preferences. The intervention focuses on achieving both comprehension and appreciation of the potential implications of using these four genres of care technologies in response to the need to help people appreciate what it may be like to use a given technology before making a use decision. It offers a comprehensive assessment of these technologies, including the clinical, technical, and ethical implications driven by a patient- and family-centered approach. Upon completion, the dyad is presented with a summary of the preferences that were selected by the person living with dementia for technology use, which they can revisit at any time. For more details on the development, components, and design of Let’s Talk Tech, see Berridge et al. (2022).

Conceptual Model

Let’s Talk Tech is grounded in the Theory of Dyadic Illness Management centering on the benefits of shared appraisals and shared decision making to optimize the health of both members of the care dyad. The Theory of Dyadic Illness Management posits that care dyads with shared appraisals (e.g., shared understanding of each other’s perceptions and values, shared perceptions of illness progression) and more collaborative illness management (e.g., shared decision making, communication, and care planning) will have better health outcomes (Lyons & Lee, 2018). Although individual appraisals are important, the theory closely aligns with the goals of dementia care by emphasizing the need for shared appraisals and understanding within the dementia dyad the care values and preferences of the person living with dementia. Family dementia research suggests that involvement in decision making and concordance are important for positive quality-of-life outcomes for both people living with dementia and care partners (Bonds, Song, et al., 2021; Moon et al., 2017; Tsai et al., 2015). Dyadic interventions like Let’s Talk Tech that have specifically focused on facilitating communication and understanding within dementia care dyads to help both members be “on the same page” have shown that care planning and decisions are in better alignment with the preferences of the person living with dementia, and they support empowered decision making by care partners (Lyons & Lee, 2018; Orsulic-Jeras et al., 2019; Whitlatch et al., 2006).

Goals

The goal of the pilot study was to test the preliminary efficacy of a novel, self-administered intervention and identify appropriate primary outcome measures from preliminary testing (Lancaster, 2015; Leon et al., 2011). Specifically, we aimed to assess the impact of the intervention on participants’ understanding of the featured technologies and the care partner’s knowledge of the person living with dementia’s preferences and preparedness to make decisions about use.

Comprehension of a technology is needed for preference formation about it, so education is an important component of Let’s Talk Tech. We hypothesize that after completion, both people living with dementia and their care partners will report a better understanding of each of the four featured technology categories (H1), and that care partners will report that they perceive that the person living with dementia’s understanding has improved (H2). Based on the Theory of Dyadic Illness Management that identifies a shared understanding of preferences and values and collaborative illness management as important modifiers of health, we hypothesize that after completing the web-app intervention, care partners will demonstrate improved knowledge of the person living with dementia’s preferences (H3), and report feeling more prepared to make decisions about technology use (H4). Finally, we hypothesize that both participant groups will report a greater mutual understanding of their partner’s feelings about the technologies (H5). We additionally explore changes in dyadic relationship quality and care partner anxiety. As reported in detail by Berridge et al. (2022), the pilot study demonstrated strong feasibility and acceptability.

Research Design and Methods

Study Design

This pilot NIH Stage Model Stage I study (Onken et al., 2014) used a one-group pretest–post-test design, complemented by follow-up interviews. Preliminary efficacy outcomes were measured. The study received approval from the University of Washington Division of Human Subjects.

Participants and Recruitment

A total of 29 dyads who are people with a Clinical Dementia Rating of 1 (mild dementia) and AD possible/probable assessment made by the University of Washington Alzheimer’s Disease Research Center (ADRC) and their coparticipants (here, “care partner”) completed the pilot study. Inclusion criteria for people living with dementia were to (a) be enrolled in the ADRC clinical core or research registry with a diagnosis of mild Alzheimer’s disease dementia; (b) be 55+ years; (c) be English speaking; and (d) have a coparticipant who is identified as a primary support person who is willing to participate in the study. Inclusion criteria for care partners were to (a) be a coparticipant of an ADRC clinical core patient or research registry patient who has been diagnosed with mild ADD; (b) be identified by a study participant 55+ as someone who is their primary support person; (c) be 18+ years of age; and (d) be English speaking. One of each dyad member had to have access to a device with Internet connection that they could use together to access the web-app. There were no additional exclusion criteria.

All 110 eligible clinical core and research registry volunteers who were living with mild AD were contacted by phone and email to be invited to participate together with their coparticipant. Of those invited, we were unable to make contact with 30. Thirty-three of the contacted individuals consented to participate with their care partners. Each individual was given a Visa gift card for $150 for their time and effort upon completion of the study. Eighty-eight percent (29 dyads) of those who consented to participate completed all study components. Completion of the Let’s Talk Tech intervention itself was very high at 100% completing all of the technology modules and 98.5% of all questions. For more detail on completion and recruitment, see Berridge et al. (2022).

Procedures

Individual dyad members completed a set of standardized and unique survey questions through REDCap at both baseline and post-test, with an average of just over 2 weeks (16 days) between the two time periods (see Table 1). During the period between baseline and post-test, without a researcher present, each dyad sat down together as a pair and completed the Let’s Talk Tech web-app. The post-test included additional survey questions about their experience with the web-app followed by an interview. Structured post-test interviews were conducted by Zoom video and in-person, according to the dyad’s preference.

Table 1:

Study Procedures and Efficacy Measures by Participant Type and When Completed

Instrument/question set	Items	Source	Study phase
			Baseline		Web-app completed together	Post-test
			Person living with dementia	Care partner		Person living with dementia	Care partner
Own understanding of technologies	4	Study specific	X	X		X	X
Care partner’s perception of understanding of technologies by person living with dementia	4	Study specific		X			X
Perception of understanding partner’s feelings	3	Study specific	X	X		X	X
Technology preferences of person living with dementia		Documented in the web-app			X
Care partner’s knowledge of technology preferences of person living with dementia	24	Study specific		X			X
Care partner’s preparedness to make decisions	1	Study specific		X			X
Dyadic Relationship Scale subscales: strain and positive dyadic interaction	11	Poulshock and Deimling (1984)	X	X		X	X
General anxiety disorder (GAD)-7 anxiety	7	Spitzer et al. (2006)		X			X

Primary Outcome Measures

To test H1, both members of the dyad were asked baseline/post-test survey questions developed for this pilot study to assess self-reported understanding of each of the technology categories featured in Let’s Talk Tech (location tracking, in-home sensors, web camera, AC robot). To measure care partners’ perception of the person living with dementia’s understanding of these technologies (H2), care partners were asked to assess the person living with dementia’s understanding of each technology (wording provided in Table 3). This measure is included because a care partner’s perception of understanding is likely to affect their desire, willingness, and perceived ability to engage that person living with dementia in decisions.

Table 3.

Change in Self-Reported Comprehension About Technologies

Technology comprehension	Baseline	Post-test	M diff (post–pre) (SD)	t Value	p Value	Cohen’s d
	M (SD)	M (SD)
Care partner comprehension (n = 29)
Location tracking (do you believe you understand location tracking well enough to decide if you want to use it for your partner) [with icon]	4.00 (1.00)	4.38 (0.78)	0.38 (0.98)	2.09	.046	0.39
Sensors	2.90 (1.29)	4.10 (0.94)	1.20 (1.26)	5.14	<.001	0.95
Camera	3.41 (1.30)	4.38 (0.68)	0.97 (1.24)	4.19	<.001	0.78
Artificial companion robot	2.55 (1.18)	3.79 (0.90)	1.24 (1.06)	6.32	<.001	1.17
Person living with dementia comprehension (n = 29)
Location tracking (do you believe you understand location tracking well enough to decide if you want it used with you?) [with icon]	2.90 (1.52)	3.66 (1.20)	0.76 (1.48)	2.76	.010	0.51
Sensors	2.59 (1.62)	3.38 (1.47)	0.79 (1.5)	2.85	.008	0.53
Camera	3.62 (1.20)	4.03 (1.21)	0.41 (1.32)	1.68	.103	0.31
Artificial companion robot	2.97 (1.57)	3.17 (1.31)	0.21 (1.57)	0.71	.483	0.13
Care partner’s perception of person living with dementia’s comprehension (n = 29)
Location tracking (do you believe your partner understands location tracking well enough to decide if she/he wants it used with her/him?) [with icon]	2.76 (1.30)	3.83 (0.80)	1.07 (1.1)	5.23	<.001	0.97
Sensors	2.21 (1.26)	3.31 (0.85)	1.10 (1.11)	5.34	<.001	0.99
Camera	2.52 (1.21)	3.69 (0.89)	1.17 (1.28)	4.92	<.001	0.91
Artificial companion robot	1.90 (1.08)	3.17 (1.07)	1.27 (1.16)	5.91	<.001	1.1

Note: SD = standard deviation.

For H3, care partner knowledge of the person living with dementia’s preferences, defined as that which they have documented in Let’s Talk Tech, was measured using a 24-item dichotomous match/no match measure. Care partners were asked the same questions as those answered by the person living with dementia in the web-app so that match scores could be compared. This set of 24 questions comprises four categories, including 4 questions on the importance placed on four relevant values (e.g., privacy and safety), 7 questions about preferences for the featured technologies (location tracking, four sensor locations, web camera, AC robot), 8 questions for preferences for alternatives (e.g., Personal Emergency Response System, home aide), and 5 questions on the importance of technology use options (e.g., to have the ability to pause; the latter derived from Berridge et al., 2022). The common question stem is “Do you believe your partner would want …” An example of the questions about the featured technologies is, “Do you believe your partner would want location tracking used with him/her [yes; no; unsure].” A care partner’s response counted as a match only if they answered exactly the same as the person living with dementia (yes, no, unsure).

Care partners’ feeling of preparedness to make decisions was assessed with a question about how prepared they feel to make decisions about monitoring technologies for their partner (H4). To test H5, both participant groups were also asked three questions to measure mutual understanding and perceived congruence, provided in Table 5.

Table 5.

Change in Perception of Understanding of Partner’s Feelings

Questions	Baseline M (SD)	Post-test M (SD)	M diff (post–pre) (SD)	t Value	p Value	Cohen’s d
Care partner (n = 29)
How well do you understand your partner’s feelings about these technologies?	3.31 (0.81)	4.17 (0.47)	0.86 (0.88)	5.30	<.001	0.98
How well does your partner understand your feelings about these technologies?	2.97 (1.02)	3.59 (0.91)	0.62 (1.08)	3.09	.005	0.57
Are you on the same page with your partner about the use of these technologies?	3.10 (0.94)	4.10 (0.82)	1.0 (0.96)	5.59	<.001	1.04
Person living with dementia (n = 29)
How well do you understand your partner’s feelings about these technologies?	3.52 (1.06)	3.66 (1.08)	0.14 (1.48)	0.50	.620	0.09
How well does your partner understand your feelings about these technologies?	3.72 (1.00)	3.93 (1.22)	0.21 (1.40)	0.80	.432	0.15
Are you on the same page with your partner about the use of these technologies?	3.86 (0.95)	3.86 (0.74)	0.00 (1.10)	0.00	1.00	0.00

Note: SD = standard deviation.

Both groups were asked at post-test the extent to which the web-app enabled them to express themselves.

Exploratory Outcome Measures

Two standardized scales were used in the exploratory analysis. First, we administered two subscales of the Dyadic Relationship Scale (DRS; Sebern & Whitlatch, 2007) because it is important to check that this behavioral dyadic intervention does not add strain (Huelsnitz et al., 2022) and because this intervention might positively affect dyadic relationship quality. The DRS is a recommended instrument for dyadic research that has been used in both research and practice settings of dementia care to help understand how well dyads are communicating and working together (Orsulic-Jeras et al., 2020). In keeping with this work, we administered the DRS subscales that measure positive dyadic interaction and strain experienced by both the person living with dementia and care partner (Sebern & Whitlatch, 2007). Six questions are used to assess positive dyadic interaction, such as “Because of helping my partner I felt closer to her/him than I have in a while” (1 = Strongly disagree; 2 = Disagree; 3 = Agree; 4 = Strongly agree) with higher scores indicating more positive interaction (care partners: α = 0.81; people living with dementia: α = 0.77). Strain was assessed with five questions for the care partner and four questions for the person living with dementia such as “Because of helping my partner I felt angry toward him/her” (1 = Strongly disagree; 2 = Disagree; 3 = Agree; 4 = Strongly agree) with higher scores indicating higher strain (care partner: α = 0.80; person living with dementia: α = 0.61).

Care partner anxiety was measured using the General Anxiety Disorder-7 Anxiety (GAD-7; Spitzer et al., 2006) at both baseline and post-test for the purpose of confirming that completing the intervention would not contribute to anxiety (care partner: α = 0.82). Higher scores indicate greater anxiety.

Descriptive Measures

To provide a clear description of the level of care our sample of care partners provide, they were administered the Stetz Inventory at baseline (Stetz, 1986; Wallhagen, 1988). For descriptive purposes, both participant groups were administered the Decision-Making Involvement Scale at baseline to understand how involved in general decision making each group felt the person living with dementia is (Menne et al., 2008).

Data Analysis

Two-tailed paired t tests at α < 0.05 were used to analyze the change in all measures assessed at baseline and post-test. Cohen’s d effect sizes were calculated to begin to understand how large of an effect we might expect for each measure in a future efficacy study. We report these effect sizes but emphasize that this is a pilot study with a non-randomized sample. Descriptive statistics were calculated for participant characteristics and frequency counts describe the post-test measure for impact on self-expression.

The goal of analyzing postintervention structured dyadic interviews was to better understand participant’s descriptions of the impact of completing Let’s Talk Tech and to contextualize preliminary quantitative efficacy findings. At this pilot stage, these interviews provide insight into why and how changes in measured outcomes occur, and they help us to identify any other unmeasured perceived outcomes. Interview questions were both broad to allow for unexpected experiences with the intervention (e.g., How did it go doing this web-app together? What did you like about it? What didn’t you like about it?) to more specific questions (e.g., How much did you talk with each other when prompted?). Transcripts of post-test interviews were coded in Dedoose version 9.0.46 (2022) by the first two authors using thematic analysis (Deterding & Waters, 2021; Nowell et al., 2017). First, the pair created a codebook with deductive codes based on the interview guide, and the primary coder coded the transcripts. After initial coding, inductive codes were developed in the course of reading the transcripts and interviewer memos. We refined the codebook and then applied the new codes for previously coded transcripts. After updating the coding to incorporate these new codes, a secondary round of coding was conducted to drill down into concepts related to the perceived impact and efficacy of the intervention. Then, a secondary coder reviewed the coding decisions and the pair discussed and resolved all very minor coding discrepancies (Nowell et al., 2017). After reaching a consensus about the final coding application, the pair read the coded excerpts across interviews and identified nine themes related to the perceived impact of completing the web-app as reported by both participant groups (Nowell et al., 2017), which are described below and depicted in a table in Supplementary Materials.

Results

Participant Characteristics

The sample participant characteristics are provided in Table 2. One care partner was a daughter, and the rest were spouses. All participants lived together and reported seeing each other daily. Care partner age ranged from 55 to 83 years (M = 68; standard deviation [SD] = 6.73), and age of people living with dementia ranged from 59 to 82 (M = 70; SD = 7.06). Two care partners identified as Asian and one person living with dementia identified as African American, with the remaining participants identifying as White. Participants were asked to write their gender identity. Sixty-two percent of care partners and 30% of people living with dementia identified as female. The remaining participants identified as male. Twenty-eight care partners (97%) identified as straight or heterosexual, with one care partner selecting “not listed here.” All but two people living with dementia who declined to answer identified as straight or heterosexual. Most care partners (83%) and people living with dementia (62%) had fewer than three chronic conditions. All but six received their dementia diagnosis more than 1 year ago (79%). Thirty-one percent of people living with dementia reported falling in the last 12 months, with 14% reporting the use of a cane or walker. Care partners scored, on average, a 7.9 (SD = 2.55) on the Stetz Inventory, meaning that they provide assistance with about half of the 15 included caregiving tasks, such as preparing meals, assisting the person living with dementia with medications or treatments, and helping them get in and out of bed, chair, or couch. Care partners’ average Decision-Making Involvement Scale score of 31.86 (SD = 8.34) indicates that they feel their partner is “a little involved,” whereas the participants living with dementia’s average score of 35.55 (SD = 7.00) indicates that they feel they are “fairly involved” in decisions (care partner: α = 0.89; people living with dementia: α = 0.84).

Table 2.

Baseline Characteristics Of Participants

Characteristics	Care partner, n = 29		Person living with dementia, n = 29
	Range, M (SD)	n (%)	Range, M (SD)	n (%)
Age	55–83, 68 (6.73)		59–82, 70 (7.06)
Gender
Male		11 (38%)		18 (62%)
Female		18 (62%)		11 (38%)
Race
White		27 (93%)		28 (97%)
African American		0		1 (3%)
Asian		2 (7%)		0
Hispanic/Latino Ethnicity		0		0
Sexuality
Gay or lesbian		0		0
Bisexual		0		0
Straight or heterosexual		28 (97%)		27 (93%)
Queer		0		0
Not listed (and no write-in response given)		1 (3%)		0
Chronic conditions
Counts <3		24 (83%)		18 (62%)
3+		5 (17%)		11 (38%)
Dementia diagnosis received within 12 months
Yes		6 (21%)		—
No		23 (79%)		—
More than 1 and less than 2 years		4 (17%)		—
More than 2 and less than 3 years		4 (17%)		—
More than 3 and less than 4 years		8 (35%)		—
More than 4 years		7 (30%)		—
Fall in past 12 month
Yes		—		9 (31%)
No		—		20 (69%)
Cane or walker use
Yes		—		4 (14%)
No		—		25 (86%)
Decision-making involvement scale		31.86 (8.34)		35.55 (7.00)
Stetz inventory		7.90 (2.55)		—

Note: SD = standard deviation.

Technology Comprehension to Make Decisions About Their Use

As depicted in Table 3, each care partner was asked to rate if they believe they understand each of the four technologies well enough to decide if they want to use each for their partner, and each person living with dementia was asked to rate if they believe they understand each well enough to decide if they want it used for themselves (1 = Not at all; 2 = A little; 3 = Somewhat; 4 = Mostly; 5 = Completely). At baseline, neither group felt that they understood all four categories of technologies better than “somewhat” well enough to make decisions about their use. Cameras were the most well understood at baseline. Paired two-sample t-test results presented in Table 3 showed statistically significant improvement in care partners’ own understanding of each of the individual technologies (p < .001) with large effect sizes for all but location tracking (p = .046) (H1). Improved comprehension for people living with dementia only reached significance for location tracking (p = .010) and sensors (p = .008) with medium effect sizes but not cameras (p = .103) or AC robots (p = .483; H1). Care partner perception of the person living with dementia’s understanding improved at a statistically significant level for all four technologies (p < .001 for each technology; H2).

Care Partner Knowledge of the Person Living With Dementia’s Preferences and Preparedness to Make Decisions About Technology Use

To understand if the web-app improved care partners’ understanding of people living with dementias’ preferences (H3), we asked the care partners at baseline and post-test the 24 core questions the people living with dementia answered through the web-app and calculated the percentage they accurately answered and the change in accuracy between baseline and post-test. There was a statistically significant improvement in accuracy of these preference assessments as a whole (paired difference in mean match scores) from baseline to post-test (p < .001) with a large effect size. The average of the percentage improvements in care partners’ accurate assessment for each category of the questions is presented in Table 4. Descriptively, care partner knowledge of the preferences for the featured technology categories showed the most improvement at 30.54% (SD = 32.33%).

Table 4.

Change in Average Accurate Assessment by Care Partner of Person Living With Dementia’s Preferences by Four Categories of Questions (n = 29)

Question categories	Baseline mean accuracy score, % (SD)	Post-test mean accuracy score, % (SD)	M diff (post–pre), % (SD)	t Value	p Value	Cohen’s d
Care partners’ knowledge of values of persons living with dementia, (four questions)a	74.11% (23.06%)	81.25% (22.18%)	+7.14% (27.94%)	1.35	.187	0.26
Care partners’ knowledge of preferences of persons living with dementia for featured technology categories (seven questions)	41.87% (26.43%)	72.41% (24.12%)	+30.54% (32.33%)	5.09	<.001	0.94
Care partners’ knowledge of preferences of persons living with dementia for alternative options (eight questions)	60.34% (20.61%)	77.16% (17.70%)	+16.81% (24.84%)	3.64	.001	0.68
Care partners’ knowledge of preferences of persons living with dementia for technology use options (five questions)	77.07% (22.50%)	85.34% (20.00%)	+8.28% (12.55%)	3.55	.001	0.66

Note: SD = standard deviation.

^aThe values questions showed the least improvement, a finding that might be impacted by the lack of variation in responses by participants living with dementia and the blunt measure used to assess care partners’ accuracy.

Care partners were asked to rate on a (reverse order) 5-point Likert scale [1 = “Strongly agree” to 5 = “Strongly disagree”] how much they agree with the statement, “I feel prepared to make decisions about monitoring technologies for my loved one.” They reported a statistically significant improvement with a medium effect size (d = 0.62) from baseline (M = 2.38, SD = 1.17) to post-test (M = 1.55, SD = 0.63), t(28) = 3.33, p = .002, with their average response hovering between somewhat and strongly agree after completing the web-app (H4).

Mutual Understanding and Self-Expression

Both participant groups were also asked to rate the degree to which their dyad is “on the same page” about the use of the technologies. As depicted in Table 5, we learned that care partners felt that they were significantly more aligned at post-test compared with baseline (p < .001) with a large effect size. Care partners felt that their feelings were better understood by their partners (p = .005) with a medium effect size, and they perceived a greater understanding of their partner’s feelings compared with baseline (p < .001) with a large effect size (H5). However, people living with dementia reported no significant change for any of these three questions (H5). The majority of both participant groups did report at post-test that they somewhat or strongly agreed that Let’s Talk Tech helped them express what’s important to them with two care partners and two people living with dementia replying “somewhat disagree.”

Dyadic Relationship Scale Subscales and Care Partner Anxiety

The positive interaction subscale scores of the DRS did not differ significantly for care partners at baseline (M = 16.82, SD = 2.61) compared to post-test (M = 17.17, SD = 3.00), t(28) = 1.03, p = .311. There was no significant change in the positive interaction subscale of the DRS for participants living with dementia from baseline (M = 18.66, SD = 2.99) to post-test (M = 18.17, SD = 3.19), t(28) = 0.84, p = .411. Care partner scores on the dyadic strain subscale did not change significantly from baseline (M = 11.69, SD = 2.83) to post-test (M = 11.58, SD = 3.11), t(28) = 0.27, p = .790. Scores on the dyadic strain for participants living with dementia also did not change significantly from baseline (M = 8.24, SD = 2.71) to post-test (M = 7.90, SD = 2.79), t(28) = 0.74, p = .463. At baseline, care partners scored an average of 3.24 (SD = 2.79) on the GAD-7, representing less than mild anxiety levels and showed no significant change at post-test (M = 3.38, SD = 4.04), t(28) = 0.32, p = .749, indicating that the intervention did not contribute to care partner anxiety.

The Impact of Let’s Talk Tech in Participants’ Own Words

Thematic analysis of the post-test dyadic interviews yielded nine themes about the impact of completing Let’s Talk Tech together (see Supplementary Materials for additional exemplary excerpts for each theme). First, dyads spoke to the idea that the intervention (1) provided the structure and direction for hard conversations that may not have otherwise happened. Participants reported that they may have started talking about technology, or may have wanted to talk about it, but this provided the opportunity to do so in a clear and “non-threatening” way. Care partners described feeling that they may not have known how to ask these questions without the intervention and that it “gave us the direction to talk about it just out front.” The intervention also (2) raised awareness of technology and its possible application to their lives. Participants reported that Let’s Talk Tech introduced them to technologies they were not previously familiar with. When they were aware of a featured technology, it led to new ideas about how technology could be used in their lives. One care partner explained, “I think that the introduction of the possibilities of what these tools [technologies] could do made it worthwhile for me.”

In completing the intervention, care partners (3) gained knowledge of what technologies the person living with dementia would find acceptable or not. Care partners clarified what technologies their partner would or would not be comfortable with. As part of this conversation, dyads reported that the intervention (4) enabled them to share perspectives on values and areas of importance. Let’s Talk Tech prompted a conversation about privacy, safety, security, and independence, and dyads reported gaining important insights into each other’s perspectives. Participants described how the intervention (5) prompted negotiation about possible technology use. Dyads used this intervention to discuss technology options and “iron out” plans related to use. Although the web-app was designed to elicit the preferences and values of the person living with dementia, dyads reported engaging in a back-and-forth discussion of how, when and why technology may be of use. Although they were not asked about immediate behavior change, a few dyads offered during interviews that they had already taken action because of completing Let’s Talk Tech and learning about options the person living with dementia found acceptable. For example, one dyad bought and installed a web camera, another started using a caregiving app they had stopped using, and a third started using an ID bracelet, an option presented in Let’s Talk Tech. Each of these explained that the intervention had prompted these changes.

Dyads discussed how Let’s Talk Tech (6) helped in planning for the future and considering the “what ifs.” They were able to envision scenarios in which technology might be useful, which guided planning for future decision making as the person living with dementia progresses. Dyads also spoke to how the intervention (7) enabled consideration of the partner’s feelings and perspective in planning for the future. Specifically, care partners expressed feeling a sense of relief and comfort from knowing when their partner would be open to technology use. In turn, some people living with dementia reported feeling relief that technology could help reduce burden for their care partner. Participants also discussed (8) sharing information and preferences from Let’s Talk Tech beyond the dyad. Participants were invited to write in with whom they wanted to share their documented preferences. These included adult children, sisters, nieces, their power of attorney, caregivers, and social worker or mental health professionals. A care partner explained that this discussion “really needs to be a big family issue, not just the spouse.” Participants expressed that the documentation of preferences could be a tool to preempt family conflict, such as between a spousal care partner and the person living with dementia’s adult children. Another care partner said that he would share the documentation from this conversation with a long-term care residence if his spouse ever were to move.

Finally, five care partners expressed (9) willingness to use technology, even if it goes against the person living with dementia’s wishes. This small group reported that they would still use a given technology even if the person was against it if it meant they could ensure safety. We examined potential differences between these five dyads and others using bivariate analysis of baseline characteristics (i.e., Stetz Inventory, Decision-Making Involvement Scale) and found no significant associations. Care partners expressing this sentiment said that they would wait to use technology until the person living with dementia either would not realize it was being used or would not object to its use.

Discussion and Implications

The aim of this study was to evaluate the preliminary efficacy of a unique technology use decision-making tool, Let’s Talk Tech, to help mild dementia care dyads understand technology options and implications so they can negotiate use in an informed way. This is the first-of-its-kind decision and planning tool with a specific focus on technology use. We found statistically significant improvements for most of our outcome measures. Perhaps of greatest importance, care partners objectively and significantly improved their knowledge of the person living with dementia’s preferences and felt more prepared to make decisions about technology use. Care partners also had improved self-reported understanding of all featured technologies and improved perception of the person living with dementia’s understanding. Improvement in people living with dementia’s self-reported understanding of the featured technologies was statistically significant for two of the four technology categories. These improvements on multiple measures were positive for both participant groups but of greater magnitude for care partners.

Post-test interviews with dyads provided additional insights into experiences with Let’s Talk Tech. In congruence with quantitative results, both dyadic members appreciated how using the web-app increased their awareness about technology options and preferences. Both groups were able to share their perspectives on values and acceptability of various technologies, which enabled rich discussion and negotiation. Care partners were able to use this conversation to start planning for the future and discuss specific “what if” scenarios, while considering a person living with dementia’s preferences. Dyads also noted a desire to share the results of their conversation with other family members or responsible parties.

Care partners reported gaining concrete understanding of ways in which technology could be useful to them. Given this increased awareness and the mention by some dyads that they purchased or started using technology after completing Let’s Talk Tech, the intervention may ultimately contribute to the use of technologies and should be tested in longitudinal research. In addition, by “granting permission” for the care partner to use technology in the future, some people living with dementia felt satisfied that they contributed in a way that could relieve the care burden. Framing conversations about technology use and/or planning for the future in a way that emphasizes how the person living with dementia can support their care partner may prove to be an effective way to encourage engagement in these conversations (Sussman et al., 2021).

Previous research has shown that care partners underestimate older adults’ capacity to understand the basic functions of digital technologies used to support care and that their underestimation affects their propensity to involve them in decisions about the use (Berridge & Wetle, 2020). Therefore, improving a care partner’s accurate assessment of the person living with dementia’s comprehension or ability to understand a given technological tool is worthwhile with the aim of enhancing involvement in these decisions. The significant improvement in a person living with dementia’s understanding perceived by their care partner may support them to involve the person more in discussions and decisions about the use of these tools. A small number of care partners, however, expressed a willingness to use technology, regardless of their partner’s preferences if it was a matter of ensuring safety. Future research should assess care partners’ intent to involve the person living with dementia as a potential outcome of this intervention and to demonstrate if the perceived capacity of the person living with dementia to understand actually impacts the extent to which they intend to involve them in decisions.

The intervention is designed to center the thoughts, feelings, and preferences of the person living with dementia through prompted dialog with their care partner and selection of the person living with dementia’s preferences. For this reason, it is encouraging that care partners reported that their own feelings were better understood by their partners. Care partners also reported a greater perceived understanding of their partner’s feelings. This self-report aligns with their more accurate knowledge of their partner’s preferences, as well as their self-reported preparedness to make decisions upon completing Let’s Talk Tech. Interviews suggested that not only were feelings and preferences better understood, but dyads also participated actively in negotiation around the possible technology use. They were able to openly discuss scenarios and exchange ideas about technologies while considering each other’s feelings. Dialog was not one sided, but instead tended toward interactive and dyadic. This enabled informed compromise in which both parties could come to an agreement about technology use. These findings, which our post-test interviews strongly support, imply that the conversation prompts of this self-administered intervention were successful in encouraging discussion of each care partner’s feelings about the options. However, with our survey questions, we detected no change in people living with dementia’s assessment of mutual understanding of feelings about the technologies. One possible but untested explanation for their lack of improvement in mutual understanding is that it reflects having learned about a range of considerations at play that participants may not have previously considered. Another possible explanation is that their baseline responses reflect assumptions held that were subsequently challenged during the intervention.

Finally, members of the dyads reported a desire to share their responses with people outside of the dyad. They discussed wanting to share this information with adult children and other family members, professional care providers, and in one case with a long-term care residence, to clarify people living with dementia’s preferences. By documenting the person living with dementia’s preferences, care partners noted the potential to smooth out possible conflict in decision-making when there is more than one care partner involved. Primary care partners felt that they could use the documented preferences to reinforce and justify their reasons for making specific decisions around technology that are in line with people living with dementia’s preferences. This finding indicates the need to enable automatic sharing options of the summary of the preferences with others identified by the dyad, as well as to examine sharing and use beyond the dyad in longitudinal work.

There are many reasons families require support to understand various technology options and make person-centered use decisions that meaningfully involve the person living with dementia. Ethical use requires engagement to the extent possible (Mahoney et al., 2007; Robillard et al., 2018; Berridge et al., 2021) and having to make decisions without awareness of a person living with dementia’s preferences is a source of stress for care partners (Reamy et al., 2011; Menne et al., 2008). Further, the members of care dyads are likely to have divergent preferences due to the surveilling nature of some technologies used in dementia care. This intervention advances our ability to engage people living with dementia in planning for how they will or will not be monitored to reduce risks to their privacy and autonomy. Effectively involving them in these decisions and informing care partners of their preferences will set families up to experience the potential benefits of these technologies.

Limitations

The preliminary efficacy findings are promising but have important limitations. Our ability to understand how this new intervention may work for a general population of dementia care dyads was limited by the homogeneity of the sample. Although we report preliminary effect sizes, we reiterate that the findings are preliminary in nature and that this pilot study lacked a control group or random assignment of a large sample, which will be required to calculate more meaningful effect sizes. There was little variation in participants’ relationship to each other, and dyads lived together, which may have limited to some extent the perceived usefulness of some of the technologies. Future research might intentionally engage adult children of people living with dementia and other nonspousal care partners, and studies that examine preferences directly should include those who live alone. Nearly 95% of the participants were White. Diverse gender and sexual orientation were also not represented. Finally, an ultimate goal that cannot be captured in this pilot research is more person-centered decision making, which can be assessed longitudinally. Based on our interview findings, additional constructs related to longer-range outcomes such as satisfaction with technology use/nonuse, as well as care partner decision-making difficulty and decision-making confidence may also be important to examine.

Conclusion

This study demonstrated the preliminary efficacy, usefulness, and need for a novel self-administered, web-based intervention to plan for technology use in a sample of mild AD dementia care dyads. The intervention holds the promise of helping families talk about and knowledgeably plan for future decisions about the technology use. The preliminary findings from this pilot should inform a clinical trial to determine if this intervention which could cost-effectively scale, does indeed have the desired impact on this range of outcomes and, through longitudinal research, on goal-concordant care.

Funding

This research was supported by the National Institute of Aging (K01AG062681 to C. Berridge); and the University of Washington Alzheimer’s Disease Research Center (P30AG066509).

Conflict of Interest

None reported.