Journal of Gerontological Nursing

Technology Innovations 

Dementia Caregivers' Experiences and Reactions to Remote Activity Monitoring System Alerts

Rachel Zmora, MPH; Lauren L. Mitchell, PhD; Gabriela Bustamante, PhD; Jessica Finlay, PhD; Manka Nkimbeng, PhD; Joseph E. Gaugler, PhD


Technology-based tools, including remote activity monitoring (RAM) systems, have been proposed as valuable aids for family caregivers of people with dementia. Previous analyses have shown limited effects of these systems and highlighted a number of barriers, including false alarms. We used data from an ongoing embedded mixed method randomized controlled intervention to describe patterns of alerts and their association with receipt of the RAM system and caregiver outcomes. Quantitative analyses showed a modest positive association between the number of alerts during the first month and system review score. In addition, qualitative results illustrated the importance of alert context, including utility, accuracy, and type of alert delivery. These findings highlight the relevance of early alerts to engagement with and perceived benefit from the RAM system. [Journal of Gerontological Nursing, 47(1), 13–20.]


Technology-based tools, including remote activity monitoring (RAM) systems, have been proposed as valuable aids for family caregivers of people with dementia. Previous analyses have shown limited effects of these systems and highlighted a number of barriers, including false alarms. We used data from an ongoing embedded mixed method randomized controlled intervention to describe patterns of alerts and their association with receipt of the RAM system and caregiver outcomes. Quantitative analyses showed a modest positive association between the number of alerts during the first month and system review score. In addition, qualitative results illustrated the importance of alert context, including utility, accuracy, and type of alert delivery. These findings highlight the relevance of early alerts to engagement with and perceived benefit from the RAM system. [Journal of Gerontological Nursing, 47(1), 13–20.]

In the United States, there are an estimated 5.8 million older adults with dementia. This number is projected to increase to 13.8 million by 2050 (Alzheimer's Association, 2020). Most people with dementia (PWD) receive care from family, friends, or other unpaid individuals who provide approximately $244 billion worth of care (Alzheimer's Association, 2020). The costs and burdens of dementia care to families and health care systems remain major challenges. Many PWD desire to remain living at home independently for as long as possible. In support of this goal, many family caregivers spend a substantial amount of time caring for PWD at home (Alzheimer's Association, 2020). As dementia progresses and behavioral and cognitive symptoms become more severe (Haley & Pardo, 1989), caregivers may be increasingly concerned about the potential risks of leaving PWD alone at home.

Technology-based tools are intended to facilitate care of PWD, promote independent living for as long as possible, and relieve caregiver stress (Lorenz et al., 2019; Thordardottir et al., 2019). Remote activity monitoring (RAM) systems are one such type of technology. RAM systems track activity in a home or care facility using motion detectors or other sensors and relay this information to a monitoring company, facility staff, or family caregivers. These systems often include alerts, which inform caregivers of potentially dangerous situations, such as wandering out of a home or not getting out of bed in the morning. RAM systems also record the daily activities of PWD (Block et al., 2016). These systems aim to delay PWD's institutional placement and promote caregivers' peace of mind. Although few randomized controlled trials test the effectiveness of RAM, some studies suggest potential benefits in terms of reducing potentially risky behavior (Rowe et al., 2009) and service use (Finch et al., 2017), as well as improving caregiver quality of life (Torkamani et al., 2014), competence, and self-efficacy (Gaugler et al., 2019). Importantly, the data generated by RAM systems can be analyzed to better understand how caregivers use and respond to RAM alerts, and thus inform refinement of RAM technologies when considering future dissemination and/or implementation of these technologies for PWD and their caregivers (Lindeman et al., 2020).

We used data from an ongoing study of a RAM system using motion sensors placed throughout PWD's and/or caregivers' homes, including bathrooms, bedrooms, kitchens, and on main doors to monitor daily activity (Gaugler et al., 2019; Mitchell et al., 2020). We obtained data from the RAM system administrator (the company operating the RAM system) to objectively quantify alerts. This system is unique because alerts are sent directly to the caregiver instead of tri-aged by a third party (e.g., staff in a residential care setting). Preliminary analysis of the effectiveness of this system suggested that having access to information on daily activities and receiving alerts about potential dangers did not improve caregiver outcomes at 6 months (Gaugler et al., 2019). However, post-hoc analysis identified moderators of benefit as well as several important barriers to effective use and implementation of the system, including intrusive false alarms (Gaugler et al., 2019). False alarms were also identified in qualitative analyses of other RAM system interventions (Peek et al., 2014).

The current analysis examines the role of alerts in the acceptance of a RAM system guided by the technology acceptance model (TAM) as a conceptual framework. The TAM was developed to predict, explain, and increase user acceptance of a technology (Davis et al., 2019). In this model, two primary factors influence an individual's attitude and intention to use a technology: perceived usefulness and ease of use. Our mixed methods analysis allowed us to link measures of RAM system use with qualitative data that captured participants' attitudes regarding usefulness and ease of use.

We examined the association between RAM system alerts and caregiver outcomes with three research objectives: (1) to describe participants' experiences with RAM system alerts, including frequency of alerts, timing of alerts, and temporal trends in the first 6 months using objective, system-generated data on alert frequency; (2) to ascertain whether alert frequency or timing is related to users' perceptions of the utility and acceptability of the RAM system; and (3) to determine whether alert frequency or timing is related to change in caregiver outcomes (i.e., caregiver self-efficacy, sense of competence, burden, role captivity, and role overload).


Study Design

We conducted a mixed methods analysis using data from an experimental evaluation of a RAM system. Quantitative methods included descriptive statistics and Pearson's correlations. Qualitative methods included thematic analysis.


One-hundred seventy-nine family caregivers of PWD living at home were recruited. Inclusion criteria were family caregivers who were at least 21 years old and English speaking. Caregivers could not use another remote monitoring system at the time of enrollment. Caregiver–care recipient dyads provided written consent and assent, respectively. Afterwards, dyads were randomized in a 1:1 fashion using a random number generator to receive the RAM system (n = 88) or usual care (n = 91).

This analysis focuses on all dyads in the treatment group that completed baseline and 6-month surveys between August 1, 2014 and January 1, 2019, and had alert data available (N = 36). As our prior work found that the initial 6-month period was particularly important for dementia caregivers to adapt to the RAM system (Gaugler et al., 2019; Mitchell et al., 2020), we selected this interval to address the three research objectives listed above.


Caregivers in the treatment and control groups completed electronic or mail surveys at baseline and 6 months. Caregivers in the treatment group also completed RAM Review Checklists (RAM-RC) at 6 months, which included a 21-item self-report measure that used 5-point Likert scale items and eight open-ended prompts to assess perceived usefulness and ease of use of the system (see Measures section below). Care recipients in the treatment group provided assent to maintain the RAM system in their homes at 6 months. This study was approved by the University of Minnesota Institutional Review Board.

Remote Activity Monitoring System

The RAM system under investigation is described in detail elsewhere (Gaugler et al., 2019; Mitchell et al., 2020). Briefly, the RAM system consists of motion sensors placed in key areas of the homes of PWD. Sensors included door sensors to identify possible wandering and under the mattress sensors to detect the length of time spent in bed. In addition, care recipients were offered an emergency call pendant linked to the alert system.

The Director of Health and Technology Services (HTS) met with each dyad in the treatment group in the home of the PWD. The HTS Director and caregiver evaluated the home layout and care recipient needs before selecting the most appropriate combination of sensors. The HTS Director was available to answer questions about the system or assist with troubleshooting throughout the length of the intervention.

After an initial adjustment period for the RAM system to establish a baseline activity pattern, any deviations from the baseline triggered an alert to family caregivers notifying them of a potentially dangerous activity of the care recipient. Caregivers could specify conditions that would trigger these alerts (e.g., staying in bed more than 8 hours, not exiting the bathroom within 30 minutes of entering), how they were communicated (e.g., email, text, phone call), and to whom alerts were sent. Finally, an online dashboard summarized the data collected by the RAM system and was available for caregivers to keep track of PWD's activities beyond the triggered alerts. A key aspect of this system was that alerts and dashboards are directed to family caregivers and not to care professionals. The direct receipt of alerts gave family members agency on how to respond to these notifications and decide if and how they share this information with care providers.


We obtained information on caregiver and care recipient demographics and primary subjective stressors from baseline and 6-month surveys. Demographic information included caregiver and care recipient age, gender, race/ethnicity, education, and income. We examined five outcomes using reliable and valid measures: self-efficacy (eight items; Fortinsky et al., 2002) (α = 0.87), sense of competence (seven items; Vernooij-Dassen et al., 1999) (α = 0.73), burden (22 items; Zarit et al., 1980) (α = 0.89), role captivity (three items; Pearlin et al., 1990) (α = 0.80), and role overload (three items; Pearlin et al., 1990) (α = 0.82).

Qualitative data and additional quantitative data reflecting perceived usefulness and ease of use based on the TAM were obtained from the 6-month RAM-RC assessing usefulness and ease of use. The RAM-RC includes 21 closed-ended items (α = 0.98) and eight open-ended items. Closed-ended prompts included, “The alerts provided by [the RAM system] have been helpful” and “The technology related to [the RAM system] works well.” Open-ended questions included, “How was [the RAM system] easy to use?” and “How was [the RAM system] difficult to use?”

Alert data were obtained directly from the RAM system administrator. Raw data included activity of all sensors installed in the home of the care recipient as well as alerts generated by the system. Each event included the exact time it was recorded. For this analysis, we focused on alerts that generated a call to one or more caregivers. In these instances, the system would attempt to reach the primary caregiver, and then continuously cycle through a list of alternative contacts until the call was picked up. Each call chain initiation was counted as a separate event.

Data Analysis

We conducted a mixed methods analysis using data from an ongoing randomized controlled trial (Creswell, 2007). We counted the frequency of alerts for the full 6-month period overall, for the first week, and for each month as well as for alerts that occurred overnight (defined as between 10 p.m. and 6 a.m.). We calculated the change in scores for caregiver stressors: self-efficacy, sense of competence, distress, role captivity, and role overload from baseline to 6 months. We used Pearson's correlations to examine associations between the number of alerts and primary stressors.

Ease of use and perceived usefulness were assessed using quantitative data from the RAM-RC. Using a case-oriented merged approach, five participants were identified who were typical of the participant dyads who reported the alerts were helpful, somewhat helpful, or not very helpful. Qualitative data were thematically analyzed as described by Braun and Clarke (2006). All authors read the open-ended responses for familiarization and understanding of the context. Two authors (J.F., M.N.) then read the data again to generate initial themes. These same authors independently analyzed the material inductively with no a priori codes. Following this, these authors iteratively developed overarching themes and smaller sub-themes. They met regularly to discuss emerging themes, as well as with the full authorship team to define and name the themes and review the qualitative results.

Quantitative and qualitative analyses were conducted sequentially, after which all authors met to merge and compare the quantitative and qualitative findings. Individual-level quantitative data on primary stressors and RAM-RC responses were used to compare caregiver descriptions of RAM system alerts. Demographic information provided context for the sample characteristics (Creswell, 2007; Mendlinger & Cwikel, 2008). Individual participants are referenced in the text using pseudonyms.


Mean age of caregivers was 59.7 years, 78% of caregivers and 64% of care recipients were women, and 94% of caregivers and 86% of care recipients were Caucasian. Most (67%) caregivers had at least a bachelor's degree. Two thirds of care recipients lived with their caregiver (Table 1). Table 2 shows that overall change in caregiver outcomes was not significant from baseline to 6 months.

Baseline Demographics (N = 36)

Table 1:

Baseline Demographics (N = 36)

Caregiver Outcomes at Baseline and 6 Months

Table 2:

Caregiver Outcomes at Baseline and 6 Months

A total of 3,817 alerts were generated by the system (i.e., the sum across all users). The majority (81%) were issued during daytime hours, and 19% were issued overnight. In terms of timing, 10% of alerts occurred during the first week, 21% within Month 1, 14% within Month 2, 17% within Month 3, 20% within Month 4, 14% within Month 5, and 12% within Month 6.

The total number of alerts received by a caregiver over the first 6 months of study participation ranged from 1 to 1,503 (mean = 153, SD = 312). Most participants (68%) received <100 alerts. Four (16%) participants received only one alert. One participant received substantially more alerts than the rest (1,503 alerts). The number of alerts each participant received in Week 1 ranged from 1 to 120 (mean = 16, SD = 27). The number of nighttime alerts each participant received ranged from 0 to 328 (mean = 28, SD = 69).

RAM-RC ratings were positively correlated with Week 1 alerts (r = 0.45, p = 0.049), indicating that individuals who received more alerts in the first week perceived the RAM system more positively. RAM-RC ratings were not significantly correlated with the total number of alerts (r = 0.36, p = 0.12), alerts in Month 1 (r = 0.42, p = 0.06), or total nighttime alerts (r = 0.29, p = 0.22).

We observed inverse correlations between change in caregiver role overload and total number of alerts during Months 3, 4, and 5 (r = −0.40, p = 0.05; r = −0.40, p = 0.05; r = −0.41, p = 0.04, respectively) indicating that overload decreased with the number of alerts. We did not see a significant association with the number of alerts during Week 1, Months 1, 2, or 6, or in total either overall or during nighttime (Table 3).

Correlations Between Number of Alerts and Change in Caregiver Outcomes

Table 3:

Correlations Between Number of Alerts and Change in Caregiver Outcomes

Change in caregiver distress from baseline to 6 months was not significantly correlated with the total number of alerts (r = −0.14, p = 0.52), alerts in Week 1 (r = −0.12, p = 0.58), alerts in Month 1 (r = −0.16, p = 0.46), or total nighttime alerts (r = −0.06, p = 0.79). Similar associations were seen for Months 2 through 6 overall and for nighttime alerts (Table 3). Similarly, there was no association between the change in caregiver self-efficacy, sense of competence, or role captivity and the number of alerts in Week 1, Months 1 through 6, or in total either overall or during nighttime (Table 3).

The five case study participants experienced a wide range of alerts over 6 months, with Dawn (54, female) receiving 1,413 alerts compared to Carol (62, female) with two alerts (Figure A, available in the online version of this article). We identified three overarching themes from the RAM-RC open-ended responses capturing participant perceptions of alert usefulness: (1) Context of Alerts, such as timing and type of alert; (2) Care-giver Qualities, such as living situation or familiarity with technology; and (3) Care Recipient Qualities, such as severity of dementia and other medical risks.

Frequency and perception of RAM system alerts.a Participants were asked to rate their level of agreement from 1 to 5 to the statement “The alerts provided by [the RAM system] have been helpful”b All graphs have a scale from 0 to 50 calls/week, except the second one with a scale from 0 to 100 calls/week.

Figure A.

Frequency and perception of RAM system alerts.

a Participants were asked to rate their level of agreement from 1 to 5 to the statement “The alerts provided by [the RAM system] have been helpful”

b All graphs have a scale from 0 to 50 calls/week, except the second one with a scale from 0 to 100 calls/week.

Context of Alerts

Caregivers described their perspectives on the utility, accuracy, and type of alert delivery. Dawn, for example, perceived alerts were instrumental in fall prevention and when away from home: “I do feel alerts generated worked well because I knew when to move to assist, and I knew more about mother's movements when I was out of the house.” Carol noted the usefulness of alerts to inform care recipient behavior changes given social triggers:

Situations which are potentially upsetting (for example, death of a brother-in-law) or [when] she is stressed about upcoming events (for example, grandson's wedding). I will notice a higher than average count of getting in and out of bed in these types of situations.

The refrigerator sensors also notified Carol of changes in her mother's behavior (i.e., frequency of opening/closing the refrigerator) that suggested when it was time for Carol to go grocery shopping for her mother. The utility of alerts was tempered by the number, timing, and type of false alarms. Gregory (54, male) lamented that alerts woke him up—often when his mother was simply getting out of bed to sit in her chair: “I lost so much sleep, until the phone calls were disconnect[ed].” Kathleen (53, female) was similarly frustrated that the alerts could not distinguish normal behaviors and had to adjust the system:

We were getting a lot of false alarms related to “not returning from the bathroom.” After moving some sensors, I think we finally figured out that we expected her to stay in bed for longer and if she got up earlier, it was waiting for her to return and set off an alarm.

Although Tracy (55, female) received frequent false alarms, she felt reassured: “I prefer the false alarms to no alarms because it gives me peace of mind that if a problem were to happen, I would be alerted.” The level of disruption or added ease to everyday life influenced caregiver perceptions of the technology. Less intrusive alerts that posed minimal disruption to lifestyle and daily activities were generally regarded more positively.

Caregiver Qualities

Personal characteristics and family circumstances of caregivers influenced the perception and reaction to the RAM alerts. Among the highly positive participants, Dawn noted: “since I lived with mother, I felt like I knew what I needed to know about her movements.” Carol reported: “[The RAM system is] easy enough that I could even teach my brother and his wife how to use the system.” Response to alert systems also reflected caregivers' personality traits in the open-ended responses. Kathleen only received 12 alerts during the 6 months and rated the system as not helpful. As noted in Figure A, she described how the alerts, especially the false alerts, caused confusion and frustration, whereas Dawn—despite having 1,413 alerts in 6 months—said it was very helpful and found humor in the excessive number of false alerts triggered by her mother's repetitive behaviors. Tracy received 147 alerts and rated them as moderately helpful. Although she found the false alarms annoying, she still believed that alerts gave her peace of mind.

Care Recipient Qualities

The individual circumstances of PWD also influenced how caregivers perceived and responded to alerts. Dawn found the alerts especially helpful because they prevented falls for her mother with severe dementia. Gregory noted “[alerts] told me that when she got up in the middle of the night she was confused, which told me she has a UTI [urinary tract infection] or high blood sugar.” Nighttime alerts combined with contextual information about what his mother was doing while awake informed Gregory that his mother needed medical care. Even caregivers who did not find the alerts to be helpful noted their usefulness for others in later stages of dementia: “we don't have that type of need at this point” (Kathleen).


The current analysis examined alerts and the role they play in early experiences and acceptance of RAM systems during the care of PWD at home. Quantitative results suggest that early alerts, particularly those in the first week of using a RAM system, play a potentially important role in caregivers' perceptions of the system's utility. Qualitative results affirmed this finding by illustrating how early experiences influence later opinions of the alerts, such as the ability to identify a UTI. Guided by the TAM, results show that perceived usefulness of technology was more of a concern than ease of use. Caregiver perceptions of usefulness of the alerts were highly variable and at times contradictory. For example, poor timing and numerous alerts can affect usefulness, but some caregivers still valued these excess alerts for enabling “peace of mind.” Ease of use discussions were limited and often related to caregivers' ability to use and teach others about the technology.

These findings provide context for researchers who want to examine technology acceptance supporting aging in place, specifically focused on concerns raised in prior research, such as intrusiveness, ineffectiveness, low ease of use, and false alarms (Peek et al., 2014). In particular, intrusiveness of false alarms has been highlighted in a number of pre- and post-implementation studies (Mahoney, 2010; Mitchell et al., 2020; Nauha et al., 2018; Nijhof et al., 2013; Peek et al., 2014). Quantitative data on alert frequency as well as qualitative data from select cases suggested that a major source of intrusive or false alarms was nighttime alerts. Interestingly, the qualitative data showed that even intrusive alerts could be viewed by care-givers as positive, because they provided evidence of ongoing monitoring of their care recipient and offered some degree of assurance of safety.

Previous research has also shown that new technology, such as RAM systems, can quickly be abandoned if users do not perceive immediate benefits (Thordardottir et al., 2019). Our results suggest that alerts are an important factor in early technology abandonment by caregivers. We found that too few alerts early on during technology adoption may result in the caregiver failing to engage with the system. Caregivers often experience a mismatch between expectations and actual benefits of technology, resulting in disappointment and failure of the intervention (Mahoney, 2010; Thordardottir et al., 2019). Our findings inform the design and implementation of RAM systems in family caregiving settings by emphasizing the importance of setting realistic expectations early in the adoption process and ensuring active engagement with the system as soon as possible after installation.

Limitations and Strengths

These findings are not without limitations. Although all parent study participants had reached 6-month follow up at the time the data were compiled, qualitative and quantitative data were missing for a number of participants, resulting in an analytic sample size of only 36. In addition, participants regularly skipped open-ended questions on the mail and electronic surveys resulting in missing qualitative data. Research suggests that comfort and experience with technology impacts adoption of technology (Lee & Coughlin, 2015; Young et al., 2014). However, caregiver comfort with technology was not assessed during the study, so we were unable to determine if it influenced adoption rates. Alert data provided by the company that designed the RAM system did not include information about if or when participants in the treatment group discontinued use of the alerts. We did not include perceptions of PWD in our data collection, which is a missed opportunity to further explore the feasibility of RAM technology in this context. Finally, participants in the analytic sample were mostly White and most had completed college. For these reasons, the findings may not be widely generalizable to the population of caregivers of PWD in the United States.

Despite these limitations, the study yields important insights. This secondary analysis of a mixed methods randomized trial offered robust data that are not regularly available in analyses of other RAM systems. Most research on assistive technology for dementia caregivers is short term, with many studies examining only a single visit or relying on study durations of a few days or weeks (Thordardottir et al., 2019). The current analysis includes 6 months' worth of data, which allowed us to track caregivers' patterns of RAM use over a timeframe that is more consistent with expected real-world use.


The use of technology to care for PWD is a promising and challenging avenue of research. Future research and clinical practice should include greater involvement of PWD and their caregivers early in the development stages, as most passive remote monitoring technologies are developed without input from older adults (Berridge & Wetle, 2020). Based on our findings, caregivers of PWD should be included in design and implementation of RAM technologies, particularly in the early stages of technology use.


  • Alzheimer's Association. (2020). Alzheimer's disease facts and figures. Alzheimer's & Dementia, 16, 391–460 doi:10.1002/alz.12068 [CrossRef]
  • Berridge, C. & Wetle, T. F. (2020). Why older adults and their children disagree about in-home surveillance technology, sensors, and tracking. The Gerontologist, 60, 926–934 doi:10.1093/geront/gnz068 [CrossRef] PMID:31102442
  • Block, V. A. J., Pitsch, E., Tahir, P., Cree, B. A. C., Allen, D. D. & Gelfand, J. M. (2016). Remote physical activity monitoring in neurological disease: A systematic review. PLoS One, 11(4), e0154335 doi:10.1371/journal.pone.0154335 [CrossRef] PMID:27124611
  • Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101 doi:10.1191/1478088706qp063oa [CrossRef]
  • Creswell, J.W. (2007).Designing and conducting mixed methods research. Sage.
  • Davis, J. D., Hill, B. D., Pillemer, S., Taylor, J. & Tremont, G. (2019). Guilt after placement questionnaire: A new instrument to assess caregiver emotional functioning following nursing home placement. Aging & Mental Health, 23(3), 352–356 doi:10.1080/13607863.2017.1423029 [CrossRef] PMID:29309201
  • Finch, M., Griffin, K. & Pacala, J. T. (2017). Reduced healthcare use and apparent savings with passive home monitoring technology: A pilot study. Journal of the American Geriatrics Society, 65(6), 1301–1305 doi:10.1111/jgs.14892 [CrossRef] PMID:28407212
  • Fortinsky, R. H., Kercher, K. & Burant, C. J. (2002). Measurement and correlates of family caregiver self-efficacy for managing dementia. Aging & Mental Health, 6(2), 153–160 doi:10.1080/13607860220126763 [CrossRef] PMID:12028884
  • Gaugler, J. E., Zmora, R., Mitchell, L. L., Finlay, J. M., Peterson, C. M., McCarron, H. & Jutkowitz, E. (2019). Six-month effectiveness of remote activity monitoring for persons living with dementia and their family caregivers: An experimental mixed methods study. The Gerontologist, 59(1), 78–89 doi:10.1093/geront/gny078 [CrossRef] PMID:29982413
  • Haley, W. E. & Pardo, K. M. (1989). Relationship of severity of dementia to caregiving stressors. Psychology and Aging, 4(4), 389–392 doi:10.1037/0882-7974.4.4.389 [CrossRef] PMID:2515872
  • Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A. & Jaffe, M. W. (1963). Studies of illness in the aged: The index of ADL: A standardized measure of biological and psychosocial function. Journal of the American Medical Association, 185(12), 914–919 doi:10.1001/jama.1963.03060120024016 [CrossRef]
  • Lee, C. & Coughlin, J. F. (2015). Older adults' adoption of technology: An integrated approach to identifying determinants and barriers. Journal of Product Innovation Management, 32(5), 747–759 doi:10.1111/jpim.12176 [CrossRef]
  • Lindeman, D. A., Kim, K. K., Gladstone, C. & Apesoa-Varano, E. C. (2020). Technology and caregiving: Emerging interventions and directions for research. The Gerontologist, 60(Suppl. 1), S41–S49 doi:10.1093/geront/gnz178 [CrossRef] PMID:32057082
  • Lorenz, K., Freddolino, P. P., Comas-Herrera, A., Knapp, M. & Damant, J. (2019). Technology-based tools and services for people with dementia and carers: Mapping technology onto the dementia care pathway. Dementia (London), 18, 725–741 doi:10.1177/1471301217691617 [CrossRef] PMID:28178858
  • Mahoney, D. F. (2010). An evidence-based adoption of technology model for remote monitoring of elders' daily activities. Ageing International, 36(1), 66–81 doi:10.1007/s12126-010-9073-0 [CrossRef] PMID:21423843
  • Mendlinger, S. & Cwikel, J. (2008). Spiraling between qualitative and quantitative data on women's health behaviors: A double helix model for mixed methods. Qualitative Health Research, 18(2), 280–293 doi:10.1177/1049732307312392 [CrossRef] PMID:18216346
  • Mitchell, L. L., Peterson, C. M., Rud, S. R., Jutkowitz, E., Sarkinen, A., Trost, S., Porta, C. M., Finlay, J. M. & Gaugler, J. E. (2020). “It's like a cyber-security blanket”: The utility of remote activity monitoring in family dementia care. Journal of Applied Gerontology, 39(1), 86–98 doi:10.1177/0733464818760238 [CrossRef] PMID:29504488
  • Nauha, L., Keränen, N. S., Kangas, M., Jämsä, T. & Reponen, J. (2018). Assistive technologies at home for people with a memory disorder. Dementia (London, England), 17(7), 909–923 doi:10.1177/1471301216674816 [CrossRef] PMID:27765896
  • Nijhof, N., van Gemert-Pijnen, L. J. E. W. C., Woolrych, R. & Sixsmith, A. (2013). An evaluation of preventive sensor technology for dementia care. Journal of Tele-medicine and Telecare, 19(2), 95–100 doi:10.1258/jtt.2012.120605 [CrossRef] PMID:23434539
  • Pearlin, L. I., Mullan, J. T., Semple, S. J. & Skaff, M. M. (1990). Caregiving and the stress process: An overview of concepts and their measures. The Gerontologist, 30(5), 583–594 doi:10.1093/geront/30.5.583 [CrossRef] PMID:2276631
  • Peek, S. T. M., Wouters, E. J. M., van Hoof, J., Luijkx, K. G., Boeije, H. R. & Vrijhoef, H. J. M. (2014). Factors influencing acceptance of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83(4), 235–248 doi:10.1016/j.ijmedinf.2014.01.004 [CrossRef] PMID:24529817
  • Rowe, M. A., Kelly, A., Horne, C., Lane, S., Campbell, J., Lehman, B., Phipps, C., Keller, M. & Benito, A. P. (2009). Reducing dangerous nighttime events in persons with dementia by using a nighttime monitoring system. Alzheimer's & Dementia, 5(5), 419–426 doi:10.1016/j.jalz.2008.08.005 [CrossRef] PMID:19751921
  • Thordardottir, B., Malmgren Fänge, A., Lethin, C., Rodriguez Gatta, D. & Chiatti, C. (2019). Acceptance and use of innovative assistive technologies among people with cognitive impairment and their care-givers: A systematic review. BioMed Research International, 2019, 9196729 doi:10.1155/2019/9196729 [CrossRef] PMID:30956989
  • Torkamani, M., McDonald, L., Saez Aguayo, I., Kanios, C., Katsanou, M.-N., Madeley, L., Limousin, P. D., Lees, A. J., Haritou, M. & Jahanshahi, M.the ALADDIN Collaborative Group. (2014). A randomized controlled pilot study to evaluate a technology platform for the assisted living of people with dementia and their carers. Journal of Alzheimer's Disease, 41(2), 515–523 doi:10.3233/JAD-132156 [CrossRef] PMID:24643137
  • Vernooij-Dassen, M. J., Felling, A. J., Brummelkamp, E., Dauzenberg, M. G., van den Bos, G. A. & Grol, R. (1999). Assessment of caregiver's competence in dealing with the burden of caregiving for a dementia patient: A Short Sense of Competence Questionnaire (SSCQ) suitable for clinical practice. Journal of the American Geriatrics Society, 47(2), 256–257 doi:10.1111/j.1532-5415.1999.tb04588.x [CrossRef] PMID:9988301
  • Young, R., Willis, E., Cameron, G. & Geana, M. (2014). “Willing but unwilling”: Attitudinal barriers to adoption of home-based health information technology among older adults. Health Informatics Journal, 20, 127–135 doi:10.1177/1460458213486906 [CrossRef] PMID:24056750
  • Zarit, S. H., Reever, K. E. & Bach-Peterson, J. (1980). Relatives of the impaired elderly: Correlates of feelings of burden. The Gerontologist, 20(6), 649–655 doi:10.1093/geront/20.6.649 [CrossRef] PMID:7203086

Baseline Demographics (N = 36)

Variablen (%)
CaregiverCare Recipient
Female28 (77.8)23 (63.9)
White34 (94.4)31 (86.1)
Married29 (80.6)19 (52.8)
Spouse16 (44.4)
Bachelor's degree or higher24 (66.7)19 (52.8)
Household income ≥$40,00030 (83.3)16 (44.4)
Employed17 (47.2)
Medicaid8 (22.2)
Primary caregiver27 (73)
  Home alone5 (13.9)
  With caregiver25 (69.4)
  With another relative3 (8.3)
  Nursing home/assisted living3 (8.3)
Mean (SD) (Range)
Age (years)59.7 (12.9) (31 to 92)77.8 (10.7) (56 to 98)
Number of children2.1 (1.7) (0 to 6)3.1 (2.5) (0 to 14)
Activities of daily livinga1.5 (2.6) (0 to 8)2.7 (2.5) (0 to 9)
Instrumental activities of daily livinga2.9 (4.1) (0 to 18)3.3 (4.4) (0 to 12)
Cognitive impairmentb21.6 (6.4) (9 to 35)

Caregiver Outcomes at Baseline and 6 Months

OutcomeMean (SD)
Baseline6 MonthsChangea
Self-efficacy27.72 (5.62)27.86 (8.51)0.14 (7.63)
Competence18.97 (5.75)19.29 (5.93)0.32 (3.64)
Burden40.32 (12.44)41.14 (14.99)0.82 (11.47)
Role captivity6.68 (2.65)7.33 (2.77)0.65 (2.35)
Role overload7.92 (2.41)8.11 (2.76)0.19 (1.92)

Correlations Between Number of Alerts and Change in Caregiver Outcomes

OutcomeAlertsWeek 1Month 1Month 2Month 3Month 4Month 5Month 6Total
Role captivityTotal0.1590.4490.1020.628−0.0630.763−0.0330.875−0.0050.9820.0000.999−0.0450.831−0.0040.984
Role overloadTotal−0.3730.0660.3440.092−0.3220.117−0.3980.049*−0.3990.048*−0.4130.040*−0.3600.077−0.3870.056

Ms. Zmora is Doctoral Candidate, Division of Epidemiology and Community Health, Dr. Nkimbeng is Robert L. Kane Post-Doctoral Fellow, and Dr. Gaugler is Robert L. Kane Endowed Chair in Long-Term Care & Aging, Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis; Dr. Mitchell is Assistant Professor, Department of Psychology, Emmanuel College, Boston, Massachusetts; Dr. Bustamante is Postdoctoral Fellow, Program in Health Disparities Research, University of Minnesota School of Medicine, Minneapolis, Minnesota; and Dr. Finlay is Postdoctoral Research Fellow, Social Environment and Health, University of Michigan, Ann Arbor, Michigan.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Joseph E. Gaugler, PhD, Robert L. Kane Endowed Chair in Long-Term Care & Aging, Division of Health Policy and Management, School of Public Health, University of Minnesota, D351 Mayo (MMC 729), Minneapolis, MN 55455; email:


Sign up to receive

Journal E-contents