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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: J Gerontol Nurs. 2021 Jan 1;47(1):28–34. doi: 10.3928/00989134-20201209-02

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

Maral Torossian 1, Raeann Leblanc 2, Cynthia S Jacelon 3
PMCID: PMC7880150  NIHMSID: NIHMS1665036  PMID: 33377982

Abstract

Background:

Disruption of sleep occurs in up to 50% of individuals above the age of 65 with chronic health conditions. Actigraphy-based personal sleep monitoring devices (PSMD) have been used as objective measures of sleep in clinical trials. Yet, the feasibility of PSMD use to improve knowledge and awareness as part of a comprehensive sleep self-management intervention in older adults has not been explored.

Purpose:

The purpose of this study was to establish feasibility of PSMD use as an intervention for sleep self-management in older adults.

Methods:

This feasibility study followed a mixed-methods experimental design based upon the World Health Organization’s International Classification of Functioning, Disability, and Health (ICF) and the proposed conceptual model of symptom management in a social context.

Results:

Results showed an acceptable recruitment and retention rate of participants, and acceptability of PSMD 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.

Conclusion:

Findings of this study support extending this research protocol to a larger sample size. 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 persons with sleep disturbances, to gain insight into their sleep, and tailor individual and shared sleep self-management interventions.

Introduction

Disruption of sleep occurs in up to 50% of individuals above the age of 65 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, 50–70 million Americans report sleep difficulties (Colten & Altevogt, 2006; Li & Gooneratne, 2019), of which over eight million in the age group of 65 or older 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 clinic visits, and be diagnosed with insomnia. This is a problem because 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; Dean2017; Djonlagic et al., 2019).

There are a number of safe, evidence-based non-pharmacological, bio-behavioral interventions that focus on self-management and behavioral change to improve sleep in older adults. These 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 (PSMD) 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 are able to learn and adopt new technologies (Coleman et al., 2010). This was 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 PSMD 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 above-mentioned non-pharmacological interventions (Elavsky et al., 2019).

Personal Sleep Monitoring Devices help users establish an objective knowledge about their sleep patterns, including sleep quality, rhythms, and duration. This, in turn 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 the favorable adoption of use of PSMD (Cheng et al., 2018; Holt-Lunstad, 2018; Thoits, 2011; Uchino, 2006).

Our aim in this study was to establish the feasibility of the use of PSMD for sleep self-management in older adults with sleep disturbances on which to base future studies. The primary objectives of this study were to (1) determine study recruitment and retention rates, (2) examine acceptability of PSMD use by participants (adherence), (3) establish whether participants could meaningfully interpret and use PSMD data for self-management, and (4) identify the willingness of participants of PSMD data sharing. The secondary objective was to determine whether sleep can be self-managed by older adults using PSMD. Our working hypothesis for the secondary objective was that older adults using a PSMD will report increased knowledge of their sleep patterns that lead to changes in sleep to improve overall sleep and health

Methods

Design

This was a feasibility randomized trial (parallel design) (ClinicalTrials.gov 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). The Consolidated Standards of Reporting Trials (CONSORT) statement guideline (Eldridge et al., 2016) was followed throughout the manuscript. This experimental design based upon the World Health Organization’s International Classification of Functioning, Disability, and Health (ICF) (World Health Organization, 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 Principle Investigator (PI) or the Research Assistant (RA), who explained the study, and a copy was given to the consenting participants.

Figure 1.

Figure 1

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Conceptual framework of individual PSMD use

Participants

To be eligible, participants had to meet the following inclusion criteria: self-reported sleep disturbances, willingness to wear the PSMD for four weeks, the ability to speak and understand English and health-related literature (based on a Rapid Estimate of Adult Literacy in Medicine (REALM of 35 or above), 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 (severe cognitive or neurosensory impairment based on Mini-Cog score of 4 or below).

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 current study. Using a spread sheet 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 1–35 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 was linked to a university-based email address, and not a personal email. Emails were deleted at the end of the study after data was 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 primary investigator PI or the 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 the scope of the study, as well 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 consents obtained. Data collection took place over a four-week period at the residence of participants, or in a public area (hospital lobby, senior center - as per the participant’s request), where the PI/RA synced the data from PSMD worn by participants to the phone- or tablet-based app.

Intervention

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

Participants were asked to wear a commercial, wrist-worn PSMD for a 4-week period (Misfit© Shine was used for this study). Education about the use of the PSMD was standardized based on a written protocol, to maintain fidelity, and delivered by the PI or the RA. We provided instructions to participants in the intervention groups on the use of the PSMD, and the synchronization of data daily to their tablets or phone-based apps. Tablets were provided to participants who did not have a personal smart phone device. 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 were allowed to keep their devices. We visited participants weekly to complete surveys and download sleep data. On week two of the study, participants in the intervention group were asked to identify a sleep “goal” based on review of their baseline personal sleep data.

Measures

We assessed participants for demographic variables like age, gender, socio-economic status, marital status, living situation, etc., and administered the Pittsburg Sleep Quality Index (PSQI) on weeks 1 and 4 (Buysse et al., 1991). PSQI is a 19-item self-rated questionnaire, the scores of which add up to “Global PSQI Score”, 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 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 also provided us with pre- and post-intervention data for our secondary objective.

We used the Misfit© Shine as the PSMD in the 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 six-month battery life (although battery failure was frequently reported in this study and as such batteries replaced). Besides PSMD, we asked participants to fill out the National Sleep Foundation sleep diary (National Sleep Foundation, 2020) daily, to compare data obtained from PSMD 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 PSMD by participants was measured by their adherence to wearing PSMD over the 4-week period, based on the availability of sleep data after syncing the PSMD to the application 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 score for interpreting each area of data numeracy literacy that was possible was 5. This was administered at baseline, and at the 4th week upon the 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 the PSMD data. At weekly visits, we asked if participants in the “share” arm shared their data in the, 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 (age, gender, baseline total sleep time, sleep disturbance scores, PSQI scores, 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 withing group differences pre- and 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 (RM-ANOVA) was used to examine differences in sleep scores between 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 α = 0.05. IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, N.Y., USA) was used for data analysis.

Results

Twenty-six people participated in the study with a Mean age of 72 (SD=5.05). Sample characteristics including gender, randomization group, experience using technological devices, total sleep time, sleep disturbance, PSQI scores, 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 M= 7.31 (SD=1.24). Only 15.4% of participants (N=4) had previous experience with smartphones or actigraphy.

Table 1.

Baseline characteristics of study participants

Total (N=26) Control (N=6) Active (N=10) Share (N=10) ANOVA/X2
Mean (SD) p-value
Age 72(5.5) 69.67(3.44) 72 (4.87) 73.8(6.36) .374
Gender (male) * 46.15% (12) 50% (3) 50% (5) 40% (4) .795
Baseline total sleep time (seconds) 26329(4482) 25357(3838) 26664(6135) 26578(3024) .901
PROMIS sleep disturbance score 54(7) 57(5) 54(9) 51(7) .254
PSQI score 10(4) 13 (4) 9(4) 9(4) .163
PROMIS physical health score 45(9) 42(3) 49(11) 42(7) .163
PROMIS mental health score 47(7) 43(5) 46(9) 46(10) .617
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Note: ANOVA: Analysis of Variance; PSQI: Pittsburgh Sleep Quality Index; PROMIS: Patient-Reported Outcomes Measurement Information System

Global Score Range 0–21 – higher scores indicate worse sleep quality.

Adult population Mean scores of 50 on PROMIS scoring.

Average sleep of sample: M= 7.31 hours (SD=1.24)

*:

percent% (frequency)

Recruitment and Retention

Twenty-six individuals out of 33 participants expressing interest (78.7%) were recruited in the study over three months (recruitment rate), as the rest 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 three weeks instead of four. Reasons for drop-out were mainly due to time requirements.

Acceptability, Meaningful Use of PSMD, and PSMD Data Sharing

Acceptability of PSMD was high among participants, whereby data was available for M=26.54 days out of the 28-day monitoring period (94.79%). 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 M=4 to M=5, but this increase was insignificant (p=0.11)

All participants used the PSMD as directed for the 4-week period, and those in the intervention groups identified sleep goal(s). These goals were based on self-review of sleep data, and included a set sleep time schedule/bedtime routine (11), limiting alcohol before bed (2), avoiding caffeine (2), relaxation practices before bed (5) and going to bed when tired (1). Personal sleep time goals ranged from six to eight hours of sleep per night (M=7.23, SD =0.495). 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 this study included total sleep time, restful sleep time, quality of sleep, as well as 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. It is noted that there was significant correlation between self-report of total sleep time and actigraphy measurement among participants both before (r= 0.629, p=0.001) and after (r=.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-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, both between and within groups, were not statistically significant either.

Discussion

The primary objective of this study was to investigate feasibility of the use of PSMD for self-management of sleep among older people. The high retention rate, acceptability, and meaningful use of PSMD in this study supports extending this research protocol to a larger sample size. 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 people, 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 the 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 heath interventions for older people.

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 is has shortcomings when compared to the gold-standard of polysomnography (PSG) (Mantua et al., 2016), especially in groups with existing self-reported sleep disturbances. Additionally, the algorithms used to estimate sleep duration, and sleep type (restful, awake, light sleep) may inaccurately over-estimate 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 people. Algorithms used to estimate sleep times may be individually tailored based on information from such studies.

Device selection based on battery usage and/or recharging are important considerations besides cost and accessibility. In this study, issues with battery power and the need to change batteries frequently during the intervention period were a challenge, and required additional support resources. Additionally, 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 PSMD, smart phone technology, data downloading, or 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 smart phone devices (for those who do not have smart phones/tablets), as well as indirect costs of use of the PSMD, such as time devoted to self-monitoring, data interpretation/sharing, should be accounted for. In addition, potential health-related anxiety resulting from the study, and a robust cost/benefit analyses were not examined in this study, 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).

Conclusion

Sleep self-management with the use of a PSMD, combined with a sleep diary, is feasible, and of interest among individuals in the 65–74 age group. Note that 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 persons with sleep disturbances, to gain insight into their sleep, and tailor individual and shared sleep self-management interventions.

Acknowledgments

This study was funded by the National Institute of Nursing Research (NINR)/National Institute of Health (NIH). Data used in the preparation of this article reside in the National Institute for Nursing Research (NINR) and National Institutes of Health (NIH)-supported Biomedical Research Informatics Computing System (BRICS) in [dataset identifier]. This manuscript reflects the views of the authors and does not reflect the opinions or views of the NINR or the NIH.

Contributor Information

Maral Torossian, University of Massachusetts Amherst College of Nursing.

Dr. Raeann Leblanc, University of Massachusetts College of Nursing.

Dr. Cynthia S. Jacelon, University of Massachusetts Amherst College of Nursing.

References

  1. Aribisala BS, Riha RL, Valdes Hernandez M, Munoz Maniega S, Cox S, Radakovic R, … Wardlaw JM (2020). Sleep and brain morphological changes in the eighth decade of life. Sleep Medicine, 65, 152–158. 10.1016/j.sleep.2019.07.015 [DOI] [PubMed] [Google Scholar]
  2. Bush AL, Armento MEA, Weiss BJ, Rhoades HM, Novy DM, Wilson NL, … Stanley MA (2012). The Pittsburgh Sleep Quality Index in older primary care patients with generalized anxiety disorder: Psychometrics and outcomes following cognitive behavioral therapy. Psychiatry Research, 199(1), 24–30. 10.1016/j.psychres.2012.03.045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Buysse DJ, Reynolds CF, Monk TH, Hoch CC, Yeager AL, & Kupfer DJ (1991). Quantification of subjective sleep quality in healthy elderly men and women using the Pittsburgh Sleep Quality Index (PSQI). Sleep, 14(4), 331–338. [PubMed] [Google Scholar]
  4. Buysse DJ, Reynolds CF, Monk TH, Berman SR, & Kupfer DJ (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. 10.1016/0165-1781(89)90047-4 [DOI] [PubMed] [Google Scholar]
  5. Centers for Disease Control and Prevention. (2019, May 02). Data and statistics - sleep and sleep disorders. Retrieved from https://www.cdc.gov/sleep/data_statistics.html
  6. Cheng GH, Malhotra R, Chan A, Østbye T, & Lo JC (2018). Weak social networks and restless sleep interrelate through depressed mood among elderly. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 27(10), 2517–2524. 10.1007/s11136-018-1895-3 [DOI] [PubMed] [Google Scholar]
  7. Cho JH, Olmstead R, Choi H, Carrillo C, Seeman TE, & Irwin MR (2019). Associations of objective versus subjective social isolation with sleep disturbance, depression, and fatigue in community-dwelling older adults. Aging & Mental Health, 23(9), 1130–1138. 10.1080/13607863.2018.1481928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Coleman GW, Gibson L, Hanson VL, Bobrowicz A, & McKay A (2010). Engaging the disengaged: How do we design technology for digitally excluded older adults? Paper presented at the 175–178. 10.1145/1858171.1858202 [DOI]
  9. Colten HR, & Altevogt BM (Eds.). (2006). Sleep disorders and sleep deprivation: An unmet public health problem. Washington, DC: National Academies Press; Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK19960/ [PubMed] [Google Scholar]
  10. Cooke JR, & Ancoli-Israel S (2011). Normal and abnormal sleep in the elderly. Handbook of Clinical Neurology, 98, 653–665. 10.1016/B978-0-444-52006-7.00041-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dean GE, Weiss C, Morris JL, & Chasens ER (2017). Impaired sleep: A multifaceted geriatric syndrome. The Nursing Clinics of North America, 52(3), 387–404. 10.1016/j.cnur.2017.04.009 [DOI] [PubMed] [Google Scholar]
  12. Djonlagic I, Aeschbach D, Harrison SL, Dean D, Yaffe K, Ancoli-Israel S, … Redline S (2019). Associations between quantitative sleep EEG and subsequent cognitive decline in older women. Journal of Sleep Research, 28(3), e12666 10.1111/jsr.12666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Elavsky S, Knapova L, Klocek A, & Smahel D (2019). Mobile health interventions for physical activity, sedentary behavior, and sleep in adults aged 50 years and older: A systematic literature review. Journal of Aging and Physical Activity, 27(4), 565–593. 10.1123/japa.2017-0410 [DOI] [PubMed] [Google Scholar]
  14. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, & Lancaster GA (2016). CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. Pilot and Feasibility Studies, 2, 1–29. 10.1186/s40814-016-0105-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hanson VL (2010). Influencing technology adoption by older adults. Interacting with Computers, 22(6), 502–509. 10.1016/j.intcom.2010.09.001 [DOI] [Google Scholar]
  16. Health Measures. (2020, April 09). PROMIS: List of adult health measures. Retrieved from http://www.healthmeasures.net/explore-measurement-systems/promis/intro-to-promis/list-of-adult-measures
  17. Holt-Lunstad J (2018). Why social relationships are important for physical health: A systems approach to understanding and modifying risk and protection. Annual Review of Psychology, 69, 437–458. 10.1146/annurev-psych-122216-011902 [DOI] [PubMed] [Google Scholar]
  18. Jackson CL, Redline S, & Emmons KM (2015). Sleep as a potential fundamental contributor to disparities in cardiovascular health. Annual Review of Public Health, 36, 417–440. 10.1146/annurev-publhealth-031914-122838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. de Grey Kent, Robert G, Uchino BN, Trettevik R, Cronan S, & Hogan JN (2018). Social support and sleep: A meta-analysis. Health Psychology, 37(8), 787–798. 10.1037/hea0000628 [DOI] [PubMed] [Google Scholar]
  20. Landry GJ, Best JR, & Liu-Ambrose T (2015). Measuring sleep quality in older adults: A comparison using subjective and objective methods. Frontiers in Aging Neuroscience, 7(166), 1–10. 10.3389/fnagi.2015.00166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li J, & Gooneratne NS (2019). Sleep and health in older adults In Sleep and Health (pp. 31–43). Academic Press [Google Scholar]
  22. Mantua J, Gravel N, & Spencer RMC (2016). Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography. Sensors, 16(5), E464 10.3390/s16050646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. National Sleep Foundation. (2020). National Sleep Foundation Official Sleep diary. Sleep Foundation; https://www.sleepfoundation.org/articles/nsf-official-sleep-diary [Google Scholar]
  24. Padhye NS, Cron SG, Gusick GM, Hamlin SK, & Hanneman SK (2009). Randomization for clinical research: An easy-to-use spreadsheet method. Research in Nursing & Health, 32(5), 561–566. 10.1002/nur.20341 [DOI] [PubMed] [Google Scholar]
  25. Patel MS, Asch DA, & Volpp KG (2015). Wearable devices as facilitators, not drivers, of health behavior change. Jama, 313(5), 459–460. 10.1001/jama.2014.14781 [DOI] [PubMed] [Google Scholar]
  26. Rios P, Cardoso R, Morra D, Nincic V, Goodarzi Z, Farah B, … Tricco AC (2019). Comparative effectiveness and safety of pharmacological and non-pharmacological interventions for insomnia: An overview of reviews. Systematic Reviews, 8(1), 281 10.1186/s13643-019-1163-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Stone KL, & Li V (2019). Sleep Among Older Adults Duncan DT, Kawachi I, & Redline S (Eds). The Social Epidemiology of Sleep, 139–166. Oxford University Press. [Google Scholar]
  28. Suzuki K, Miyamoto M, & Hirata K (2017). Sleep disorders in the elderly: Diagnosis and management. Journal of General and Family Medicine, 18(2), 61–71. 10.1002/jgf2.27 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Taylor DJ, Mallory LJ, Lichstein KL, Durrence HH, Riedel BW, & Bush AJ (2007). Comorbidity of chronic insomnia with medical problems. Sleep, 30(2), 213–218. 10.1093/sleep/30.2.213 [DOI] [PubMed] [Google Scholar]
  30. Thoits PA (2011). Mechanisms linking social ties and support to physical and mental health. Journal of Health and Social Behavior, 52(2), 145–161. 10.1177/0022146510395592 [DOI] [PubMed] [Google Scholar]
  31. Tickle-Degnen L (2013). Nuts and bolts of conducting feasibility studies. The American Journal of Occupational Therapy: Official Publication of the American Occupational Therapy Association, 67(2), 171–176. 10.5014/ajot.2013.006270 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Tully MA, McBride C, Heron L, & Hunter RF (2014). The validation of fibit zip™ physical activity monitor as a measure of free-living physical activity. BMC Research Notes, 7, 1–5. 10.1186/1756-0500-7-952 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Uchino BN (2006). Social support and health: A review of physiological processes potentially underlying links to disease outcomes. Journal of Behavioral Medicine, 29(4), 377–387. 10.1007/s10865-006-9056-5 [DOI] [PubMed] [Google Scholar]
  34. Van de Water Alexander T. M., Holmes A, & Hurley DA (2011). Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography--a systematic review. Journal of Sleep Research, 20(1 Pt 2), 183–200. 10.1111/j.1365-2869.2009.00814.x [DOI] [PubMed] [Google Scholar]
  35. Van Den Berg Julia F., Van Rooij Frank J. A., Vos H, Tulen JHM, Hofman A, Miedema HME, … Tiemeier H (2008). Disagreement between subjective and actigraphic measures of sleep duration in a population-based study of elderly persons. Journal of Sleep Research, 17(3), 295–302. 10.1111/j.1365-2869.2008.00638.x [DOI] [PubMed] [Google Scholar]
  36. Vitiello MV, Moe KE, & Prinz PN (2002). Sleep complaints cosegregate with illness in older adults: Clinical research informed by and informing epidemiological studies of sleep. Journal of Psychosomatic Research, 53(1), 555–559. 10.1016/s0022-3999(02)00435-x [DOI] [PubMed] [Google Scholar]
  37. World Health Organization. (2011). World report on disability. Geneva, Switzerland: WHO Press; Retrieved from https://www.who.int/publications-detail/world-report-on-disability [Google Scholar]
  38. Zhong H, Yu B, Luo D, Yang L, Zhang J, Jiang S, … Yang S (2019). Roles of aging in sleep. Neuroscience & Biobehavioral Reviews, 98, 177–184. 10.1016/j.neubiorev.2019.01.013 [DOI] [PubMed] [Google Scholar]

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