Journal of Gerontological Nursing

Feature Article 

Use of a Personal Sleep Self-Monitoring Device for Sleep Self-Management: A Feasibility Study

Maral Torossian, MS, RN; Raeann G. LeBlanc, PhD, DNP, AGPCNP-BC, CHPN; Cynthia S. Jacelon, PhD, RN-BC, CRRN, FGSA, FAAN


The purpose of the current study was to establish feasibility of personal sleep monitoring devices (PSMDs) as an intervention for sleep self-management in older adults. This study followed a mixed-methods experimental design based on the World Health Organization's International Classification of Functioning, Disability, and Health, and the proposed conceptual model of symptom management in a social context. Results showed an acceptable recruitment and retention rate of participants, and acceptability of PSMDs by users. Participants were able to meaningfully interpret PSMD data as evidenced by the numeracy evaluation scores, initiate sleep goals, and share their sleep data and goals with friends or relatives. Findings support extending this research protocol to a larger sample. Future studies for sleep health self-management and personally tailored interventions using personal sleep monitoring are recommended. [Journal of Gerontological Nursing, 47(1), 28–34.]


The purpose of the current study was to establish feasibility of personal sleep monitoring devices (PSMDs) as an intervention for sleep self-management in older adults. This study followed a mixed-methods experimental design based on the World Health Organization's International Classification of Functioning, Disability, and Health, and the proposed conceptual model of symptom management in a social context. Results showed an acceptable recruitment and retention rate of participants, and acceptability of PSMDs by users. Participants were able to meaningfully interpret PSMD data as evidenced by the numeracy evaluation scores, initiate sleep goals, and share their sleep data and goals with friends or relatives. Findings support extending this research protocol to a larger sample. Future studies for sleep health self-management and personally tailored interventions using personal sleep monitoring are recommended. [Journal of Gerontological Nursing, 47(1), 28–34.]

Disruption of sleep occurs in up to 50% of individuals aged ≥65 years with chronic health conditions (Aribisala et al., 2020; Stone & Li, 2019; Taylor et al., 2007; Vitiello et al., 2002; Zhong et al., 2019). In the United States, 50 to 70 million Americans report sleep difficulties (Colten & Altevogt, 2006; Li & Gooneratne, 2019), of which more than 8 million aged ≥65 years report insufficient nighttime sleep, disrupted sleep patterns, and daytime fatigue (Centers for Disease Control and Prevention, 2017). There are normal changes in sleep associated with aging (Cooke & Ancoli-Israel, 2011; Djonlagic et al., 2019), which explains why older adults are more likely to report poor sleep during clinical visits, and be diagnosed with insomnia. Sleep disruption and poor sleep quality impact health and longevity, lead to excessive daytime sleepiness, hinder daily functioning, and contribute to an increased risk of falls and potentially life-threatening events (Colten & Altevogt, 2006; Dean et al., 2017; Djonlagic et al., 2019).

There are several safe, evidence-based nonpharmacological, bio-behavioral interventions that focus on self-management and behavioral change to improve sleep in older adults. Interventions include, but are not limited to, sleep hygiene education, sleep compression, stimulus control, relaxation therapy, cognitive-behavioral therapies, and social connection (Cho et al., 2019; Rios et al., 2019; Suzuki et al., 2017). Actigraphy-based personal sleep monitoring devices (PSMDs) have been used as objective measures of sleep in clinical trials, and have been shown to provide accurate and valid measures of sleep (e.g., total sleep time, sleep efficiency, sleep onset time) (Van de Water et al., 2011). Mobile health interventions, in general, have limited findings on sleep outcomes (Elavsky et al., 2019). These commercially available self-monitoring products have begun to overcome limitations in accuracy, cost, and usability, and have been validated numerous times in younger populations (Patel et al., 2015; Tully et al., 2014).

Evidence suggests that older adults perceive technology as useful and believe that they can learn and adopt new technologies (Coleman et al., 2010). These findings were supported in an experimental study, where despite the apparent latency in acquiring technological competence, older adults' performance was not different from their younger counterparts (Hanson, 2010). Yet, the use of PSMDs to improve knowledge and awareness as part of a comprehensive sleep self-management intervention in older adults has not been explored, as the focus has been limited to the abovementioned nonpharmacological interventions (Elavsky et al., 2019).

PSMDs help users establish objective knowledge about their sleep patterns, including sleep quality, rhythms, and duration. This knowledge can lead to behavioral changes (sleep hygiene practices) that would improve overall sleep and health. In addition, there is significant evidence that social networks, through which individuals engage with others in socially supportive relationships (Kent de Grey et al., 2018) influence health and health behaviors. This influence occurs through the functions of social support and social influence, which could be important in favorable adoption of use of PSMDs (Cheng et al., 2018; Holt-Lunstad, 2018; Thoits, 2011; Uchino, 2006).

The aim of the current study was to establish the feasibility of the use of PSMDs for sleep self-management in older adults with sleep disturbances on which to base future studies. The primary objectives of this study were to: (a) determine study recruitment and retention rates, (b) examine acceptability of PSMD use by participants (adherence), (c) establish whether participants could meaningfully interpret and use PSMD data for self-management, and (d) identify the willingness of participants to share PSMD data. The secondary objective was to determine whether sleep can be self-managed by older adults using PSMDs. Our working hypothesis for the secondary objective was that older adults using PSMDs will report increased knowledge of their sleep patterns that lead to changes in sleep and improve overall sleep and health.



The current study was a feasibility randomized trial (parallel design) ( Identifier: NCT03837249), with an allocation ratio of 1:1. The purpose of this study was to establish the appropriateness of PSMD use as an intervention for the future definitive randomized control trial (RCT) (Bowen et al., 2009). Results are reported as outlined in the Consolidated Standards of Reporting Trials (CONSORT) statement guideline (Eldridge et al., 2016). This experimental design was based on the World Health Organization's (WHO) International Classification of Functioning, Disability, and Health (WHO, 2011), which identifies function as a combination of individual and contextual factors and a proposed conceptual model of symptom management in a social context. Based on this framework, symptoms and their effect on daily function are the focus of interventions, not the underlying pathology, and include social and physical determinants of health (Colten & Altevogt, 2006; Jackson et al., 2015). We proposed a conceptual framework of how PSMD data may be used individually, or shared (offering additional support), to improve sleep self-management interventions beginning with self-awareness of individual sleep patterns (Figure 1). Ethical approval was received from the University Institutional Review Board. Informed consent was obtained from qualified participants by the principal investigator (PI) or research assistant (RA), who explained the study, and a copy was given to consenting participants.

Conceptual framework of individual personal sleep monitoring device use.

Figure 1.

Conceptual framework of individual personal sleep monitoring device use.


To be eligible, participants had to meet the following inclusion criteria: self-reported sleep disturbances, willingness to wear the PSMD for 4 weeks, the ability to speak and understand English and health-related literature (based on a Rapid Estimate of Adult Literacy in Medicine [REALM] score ≥35), the ability to function independently (i.e., able to perform activities of daily living independently with or without assistive devices), and have sufficient cognitive abilities (Mini-Cog score of 5). Participants were excluded if they had disorders that would impact the ability to understand directions for use of the PSMD (e.g., severe cognitive or neurosensory impairment based on Mini-Cog score ≤4).

Sample size was not determined from a formal power analysis (Tickle-Degnen, 2013). Instead, we sought to include at least 30 participants, 10 participants per condition based on our focus on feasibility (a total of 35 accounting for drop-out). Our rationale was that this sample would help determine sample size estimates and attrition rates to inform future study designs and piloting of future studies based on the current study.

Using a spreadsheet for organization, a random sequence number generator was used for the three possible conditions (control group, active, and share) and a stored list of generated numbers 1 to 35 a priori (Padhye et al., 2009). On enrollment in the study, participants were assigned to a condition based on their enrollment sequence and the associated number from the random sequence generator. All participants wore the PSMD, but the control group did not receive their sleep data until after they completed the study. All data were linked to a university-based email address, and not a personal email. Emails were deleted at the end of the study after data were downloaded.

The study was advertised through flyers posted at different public locations in Franklin County, Massachusetts. Interested individuals contacted the PI through the phone number or email provided on the flyer and were screened on the phone for eligibility. If deemed eligible, the PI or RA conducted a home visit or met the participant in a public area, depending on the participant's preference. During the visit, participants were provided with an explanation regarding the aims and scope of the study, as well as detailed instructions on their role. The PI or RA then went over the written consent in detail, after which all participants' questions were answered, and voluntary consent obtained. Data collection took place over a 4-week period at the residence of participants, or in a public area (e.g., hospital lobby, senior center—as per the participant's request), where the PI/RA synced data from the PSMD worn by participants to the phone- or tablet-based application (app).


Based on the above-mentioned conceptual model (Figure 1), we randomly assigned participants to one of three groups: control (passive), individual intervention (active), and shared intervention (share). Individuals in the control group would wear PSMDs without accessing sleep data collected by the PSMD (passive role), whereas those in the intervention groups would wear PSMDs and sync data to their phones/tablets daily to self-manage their sleep. The difference between intervention groups is that the “shared” group would share their sleep data with a social network (e.g., family member, friend), which could thus provide social support for sleep goals.

Participants were asked to wear a commercial, wrist-worn PSMD for a 4-week period. Education about the use of the PSMD was standardized based on a written protocol, to maintain fidelity, and delivered by the PI or RA. We provided instructions to participants in the intervention groups on the use of the PSMD, and the synchronization of data daily to their tablet- or phone-based apps. Tablets were provided to participants who did not have a personal smart-phone. Individuals in the control group did not receive any instructions on the use of PSMD feedback, or even access to data until the end of the study, after which all groups could keep their devices. We visited participants weekly to complete surveys and download sleep data. On Week 2 of the study, participants in the intervention group were asked to identify a sleep “goal” based on review of their baseline personal sleep data.


We assessed participants for demographic variables, such as age, gender, socioeconomic status, marital status, and living situation, and administered the Pittsburgh Sleep Quality Index (PSQI) on Weeks 1 and 4 (Buysse et al., 1991). The PSQI is a 19-item self-rated questionnaire, the scores of which add up to a global PSQI score, with higher scores indicating worse sleep quality. PSQI scores reflect seven components of sleep, including sleep quality, duration, latency, disturbance, habitual sleep efficiency, sleep medication use, and daytime dysfunction, which have shown acceptable internal consistency and validity (Bush et al., 2012; Buysse et al., 1989). We also administered Patient-Reported Outcomes Measurement Information System (PROMIS) measures for sleep disturbances and overall health (Health Measures, 2020) on Weeks 1 and 4 to establish baseline descriptive data on participants' health and sleep, and to assess participants' willingness to answer questions related to their sleep and health. This measure also provided pre- and post-intervention data for our secondary objective.

We used the Misfit™ Shine as the PSMD in this study, which is a wrist-worn, actigraphy-based instrument that automatically tracks sleep with an algorithm that identifies light and restful sleep. It is waterproof and has a 6-month battery life (although battery failure was frequently reported in this study and as such batteries were replaced). In addition to the PSMD, we asked participants to write in the National Sleep Foundation (2020) sleep diary daily, to compare data obtained from PSMDs to that of sleep diaries.

In terms of the objectives of this study, we calculated recruitment rate using the number interested divided by number consented, and retention rate using number completed divided by number enrolled. Acceptability of the PSMD by participants was measured by their adherence to wearing the PSMD over the 4-week period, based on the availability of sleep data after syncing the PSMD to the app on participants' phones/tablets. Meaningful interpretation of PSMD data was measured by a numeracy evaluation scale tailored to the device output numeracy charts. This numeracy evaluation was based on the PSMD data output graphs of the specific device and participants were asked to identify the meaning of the numeric displays: (1) total sleep time; (2) sleep icon; (3) sleep quality, including light sleep, restful sleep, and awakenings, as well as bar graphs representing (4) total time of sleep and (5) color-coded measures of sleep quality. The total possible score for interpreting each area of data numeracy literacy was 5. This evaluation was administered at baseline, and at Week 4 upon completion of the study by a smaller cohort, as the measure was piloted mid-way through the study. We also examined participants' ability to identify sleep goals based on PSMD data. At weekly visits, we asked if participants in the “shared” arm shared their data and discussed their sleep goals with family members or friends.

Analysis Methods

We used univariate analyses to calculate the mean, standard deviation, and frequencies of variables pooled across all participants for descriptive data (i.e., age, gender, baseline total sleep time, sleep disturbance scores, PSQI scores, and PROMIS physical and mental health scores). We also calculated the change in numeracy scores of participants pre-post PSMD using paired t test, to examine whether participants could meaningfully interpret PSMD data. Regarding our secondary objective, we used paired t test to examine within-group differences preand post-intervention (Week 1 and Week 4) in means of total sleep time (diary and PSMD) and sleep quality (PSQI). Similarly, we sought to identify changes in sleep disturbance patterns, and states of health that co-occurred in older adults through analysis of the PROMIS global physical and mental health measures using paired t tests. Repeated measures analysis of variance was used to examine differences in sleep scores among the three groups at baseline, and at the end of the study. Finally, Pearson's r was used to examine the correlation between sleep diary data and Misfit© Shine sleep detection algorithm. Significance was set at 0.05. IBM SPSS Statistics for Windows, version 26, was used for data analysis.


Twenty-six people participated in the study, with a mean age of 72 years (SD = 5.05 years). Sample characteristics, including gender, randomization group, experience using technological devices, total sleep time, sleep disturbance, PSQI scores, and PROMIS physical and mental health scores are presented in Table 1. Overall, participants had higher than average scores of sleep disturbances (reflecting worse sleep), and slightly below average physical and mental health scores. Baseline average sleep hours per day, based on actigraphy measurement, was 7.31 (SD = 1.24 hours, range = 4.68 to 9.18 hours). Only 15.4% of participants (n = 4) had previous experience with smartphones or actigraphy.

Baseline Characteristics of Study Participants

Table 1:

Baseline Characteristics of Study Participants

Recruitment and Retention

Twenty-six (78.7%) of 33 individuals who expressed interest were recruited in the study over 3 months (recruitment rate), as the remaining individuals did not meet inclusion criteria. Six participants were randomized to the control group, 10 to the individual intervention (active self-monitoring), and 10 in the shared intervention group. Of those recruited, 92% completed the study (retention rate), with one participant in the intervention group completing in 3 weeks instead of 4. Reasons for not completing the study were mainly due to time requirements.

Acceptability, Meaningful Use of PSMDs, and PSMD Data Sharing

Acceptability of PSMDs was high among participants, whereby data were available for an average of 26.5 days of the 28-day monitoring period (94.8%). Data download interpretation was also evident by all participants in the active monitoring groups (n = 20) through evaluation at routine visits. Pre-post numeracy evaluation scores (n = 7) increased from an average of 4 to 5, but this increase was not significant (p = 0.11).

All participants used the PSMD as directed (including the participant who stopped at Week 3) for the duration of the 4-week period, and those in the intervention groups identified sleep goals. These goals were based on self-review of sleep data and included a set sleep time schedule/bedtime routine (n = 11), relaxation practices before bed (n = 5), limiting alcohol before bed (n = 2), avoiding caffeine (n = 2), and going to bed when tired (n = 1). Personal sleep time goals ranged from 6 to 8 hours of sleep per night (mean = 7.23 hours, SD = 0.495 hours).

All participants in the share group (n = 10) self-reported sharing data about their sleep with a trusted person to discuss their goals and the data.

Secondary Outcomes

Secondary outcomes in the current study included total sleep time, restful sleep time, quality of sleep, and physical and mental health measures. To discern discrepancies between sleep diary self-report and actigraphy measures, we correlated diary data and actigraphy total sleep time. There was a significant correlation between self-report of total sleep time and actigraphy measurement among participants before (r = 0.629, p = 0.001) and after (r = 0.598, p = 0.01) the intervention. There were no statistically significant changes in total sleep time or restful sleep time based on sleep diary self-report and actigraphy monitoring pre- or post-intervention. Similarly, changes in sleep disturbance, sleep quality (PSQI) scores, as well as physical and mental health scores (PROMIS) between Week 1 and Week 4, among and within groups, were also not statistically significant.


The primary objective of the current study was to investigate feasibility of the use of PSMDs for self-management of sleep among older adults. The high retention rate, acceptability, and meaningful use of PSMDs in this study supports extending this research protocol to a larger sample. Regarding the secondary objective, none of the measures (sleep time, sleep quality, physical and mental health outcomes) were significantly different before and after the intervention, which was expected given the small sample size.

These devices may offer personal insight for older adults, particularly when combined with subjective measures such as sleep diaries. These two, combined, provide the most useful means for assessing sleep quality in older age (Landry et al., 2015; Van Den Berg et al., 2008). This intervention may be integrated into overall self-management interventions of chronic health conditions, given the important role sleep has in influencing overall health and well-being. In addition, results showed that adding social network interventions to strengthen social support in self-management is a feasible future step. Incorporating subjective and objective sleep measures as a periodic routine self-management practice with coaching supports and other evidence-based interventions, such as cognitive-behavioral therapy, if accessible, may also prove valuable based on this feasibility study and current advancements in sleep health interventions for older adults.


The selection of the PSMD was based on a low-cost, easily accessible off-the-shelf device for the purpose of self-management of sleep as a feasibility trial. However, PSMD technology has shortcomings when compared to the gold-standard of polysomnography (Mantua et al., 2016), especially in groups with existing self-reported sleep disturbances. In addition, the algorithms used to estimate sleep duration and sleep type (e.g., restful, awake, light sleep) may inaccurately overestimate total sleep time for sedentary time in bed, and not capture nap times, or time awake resting in bed that is not in a restorative sleep phase, all of which are common among older adults. Algorithms used to estimate sleep times may be individually tailored based on information from such studies.

Device selection based on battery use and/or recharging are other important considerations in addition to cost and accessibility. In this study, issues with battery power and the need to change batteries frequently during the intervention period were challenges that required additional support resources. In addition, it is important to provide standardized education, support, and training in use of PSMD prior to the intervention, especially among those who may not have experience with use of PSMDs, smartphone technology, data downloading, or data interpretation.

The cost of the PSMD used in this study was 40 US dollars. However, for future definitive RCTs, additional costs for tablets and/or smartphone devices (for those who do not have smartphones/tablets), as well as indirect costs of use of the PSMD, such as time devoted to self-monitoring and data interpretation/sharing, should be accounted for. In addition, potential health-related anxiety resulting from the study, and robust cost/benefit analyses were not examined and are recommended for inclusion in future studies. In terms of recruitment, our study was time-bound, and thus the desired sample size was not achieved. A longer recruitment time would be required for future studies to examine the relationship between PSMD use and sleep and health outcomes (secondary objective of this study).


Sleep self-management with the use of a PSMD, combined with a sleep diary, is feasible and of interest among individuals aged 65 to 74 years. Device selection is important in ascertaining cost, ease of use, battery life, and usability. Future studies for sleep health self-management and personally tailored interventions using personal sleep monitoring are recommended. PSMDs are becoming increasingly popular and can be used as self-management tools in older adults with sleep disturbances, to gain insight into their sleep, and tailor individual and shared sleep self-management interventions.


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Baseline Characteristics of Study Participants

Characteristicn (%)ANOVA/χ2
Total (N = 26)Control (n = 6)Active (n = 10)Share (n = 10)p Value
  Female14 (53.85)3 (50)5 (50)6 (60)
  Male12 (46.15)3 (50)5 (50)4 (40)
Mean (SD) (Range)
Age (years)72 (5.05) (65 to 83)69.67 (3.44) (66 to 74)72 (4.87) (65 to 82)73.8 (6.36) (66 to 83)0.374
Baseline total sleep time (seconds)26,329 (4,482) (16,851 to 33,051)25,357 (3,838) (21,429 to 30,498)26,664 (6,135) (16,851 to 33,051)26,578 (3,024) (23,014 to 30,607)0.901
PSQI scorea10 (4) (3 to 19)13 (4) (7 to 18)9 (4) (3 to 16)9 (4) (6 to 19)0.163
PROMIS scoreb
  Sleep disturbance54 (7) (40 to 70)57 (5) (52 to 65)54 (9) (43 to 70)51 (7) (40 to 67)0.254
  Physical health45 (9) (32 to 68)42 (3) (37 to 48)49 (11) (32 to 68)42 (7) (32 to 54)0.163
  Mental health47 (7) (31 to 63)43 (5) (34 to 56)48 (3.97) (44 to 53)46 (10) (31 to 63)0.617

Ms. Torossian is PhD Candidate, Dr. LeBlanc is Clinical Associate Professor, College of Nursing, and Dr. Jacelon is Executive Associate Dean for Academics, Research, and Engagement, and Director, UManage Center for Building the Science of Symptom Self-Management, University of Massachusetts Amherst, Amherst, Massachusetts.

The authors have disclosed no potential conflicts of interest, financial or otherwise. Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health (NIH) (P20NR016599). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Address correspondence to Maral Torossian, MS, RN, PhD Candidate, University of Massachusetts Amherst, 651 N. Pleasant Street, Amherst, MA 01002; email:

Received: February 09, 2020
Accepted: August 19, 2020


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