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Online support groups for atrial fibrillation (AF) and apps to detect and manage AF exist, but the scientific literature does not describe which patients are interested in digital disease support.
The objective of this study was to describe characteristics associated with Facebook use and interest in digital disease support among older patients with AF who used the internet.
We used baseline data from the Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF), a prospective cohort of older adults (≥65 years) with AF at high stroke risk. Participants self-reported demographics, clinical characteristics, and Facebook and technology use. Online patients (internet use in the past 4 weeks) were asked whether they would be interested in participating in an online support AF community. Mobile users (owns smartphone and/or tablet) were asked about interest in communicating with their health care team about their AF-related health using a secure app. Logistic regression models identified crude and multivariable predictors of Facebook use and interest in digital disease support.
Online patients (N=816) were aged 74.2 (SD 6.6) years, 47.8% (390/816) were female, and 91.1% (743/816) were non-Hispanic white. Roughly half (52.5%; 428/816) used Facebook. Facebook use was more common among women (adjusted odds ratio [aOR] 2.21, 95% CI 1.66-2.95) and patients with mild to severe depressive symptoms (aOR 1.50, 95% CI 1.08-2.10) and less common among patients aged ≥85 years (aOR 0.27, 95% CI 0.15-0.48). Forty percent (40.4%; 330/816) reported interest in an online AF patient community. Interest in an online AF patient community was more common among online patients with some college/trade school or Bachelors/graduate school (aOR 1.70, 95% CI 1.10-2.61 and aOR 1.82, 95% CI 1.13-2.92, respectively), obesity (aOR 1.65, 95% CI 1.08-2.52), online health information seeking at most weekly or multiple times per week (aOR 1.84, 95% CI 1.32-2.56 and aOR 2.78, 95% CI 1.86-4.16, respectively), and daily Facebook use (aOR 1.76, 95% CI 1.26-2.46). Among mobile users, 51.8% (324/626) reported interest in communicating with their health care team via a mobile app. Interest in app-mediated communication was less likely among women (aOR 0.48, 95% CI 0.34-0.68) and more common among online patients who had completed trade school/some college versus high school/General Educational Development (aOR 1.95, 95% CI 1.17-3.22), sought online health information at most weekly or multiple times per week (aOR 1.86, 95% CI 1.27-2.74 and aOR 2.24, 95% CI 1.39-3.62, respectively), and had health-related apps (aOR 3.92, 95% CI 2.62-5.86).
Among older adults with AF who use the internet, technology use and demographics are associated with interest in digital disease support. Clinics and health care providers may wish to encourage patients to join an existing online support community for AF and explore opportunities for app-mediated patient-provider communication.
Currently, as many as 6 million adults in the United States have atrial fibrillation (AF), and the prevalence of AF is projected to increase to 12 million by 2030 [
Treatment with anticoagulants significantly reduces the risk of stroke among adults with AF, but anticoagulants may have significant adverse effects including severe and life-threatening bleeding [
Although fewer older US adults aged ≥65 years go online, own mobile devices, and use social media compared with younger adults, technology adoption among older US adults has nearly quadrupled since 2000 [
However, existing literature does not illuminate the characteristics of older adults with AF interested in joining an online support community for AF. Similarly, apps for the detection and management of AF are being developed [
We used data from the Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) study. Between 2016 and 2018, SAGE-AF enrolled 1244 older adults with AF at high stroke risk from 7 clinical sites in central and eastern Massachusetts or central Georgia. Staff prescreened patients scheduled to attend a clinic visit and sent eligible patients an invitation to participate in the study 1 week before their appointment. Eligibility criteria for SAGE-AF included having a scheduled ambulatory care visit at one of the study practices, electrocardiographic evidence of AF, being aged ≥65 years, and having a CHA2DS2VASC risk score ≥2. Exclusion criteria were documentation of an absolute contraindication to oral anticoagulants (eg, recent major bleeding), indication for oral anticoagulants other than AF (eg, venous thromboembolism), inability to demonstrate capacity to provide informed consent as assessed by a capacity instrument that combines direct questions about their understanding of study participation with interviewer observations of the patient [
The baseline interview included questions about the use of technology and social media adapted from the Pew Research Center [
If we were to create an online community (via a private website or an app) specifically designed for patients with atrial fibrillation, how interested would you be in participating? The community would be held through a private website and/or a secure smartphone/tablet app. You could use this community to ask questions about afib, set activity or diet goals, or report progress on a regular basis.
We combined no and unsure responses (vs yes) to highlight participants expressing clear interest. Participants were asked if they owned a smartphone (eg, iPhone, Android phone, Windows phone, or Blackberry) or tablet computer (eg, iPad, Samsung Galaxy, Motorola Xoom, or Kindle Fire). Participants who reported owning a smartphone and/or tablet computer were categorized as mobile users. Mobile users were asked “would you be interested in communicating with your doctor or health care team about your atrial fibrillation-related health using a secure smartphone or tablet app?” We combined no and unsure responses (vs yes) to highlight participants expressing clear interest.
Participants self-reported demographics including race/ethnicity, education level, marital status, and living situation during the baseline interview. We abstracted age, height, weight, and medical history variables from patients’ medical records at baseline, including comorbidities (eg, type II diabetes, hypertension, stroke, heart failure, and cancer), whether the patient had newly diagnosed or prevalent AF, use of anticoagulants, and whether the patient’s AF was managed by a dedicated anticoagulation clinic. We calculated body mass index (BMI) from height and weight abstracted from medical records and categorized participants’ weight status as underweight (BMI<18.5 kg/m2), normal weight (18.5 kg/m2≤BMI<25 kg/m2), overweight (25 kg/m2≤BMI<30 kg/m2), or obese (30 kg/m2≤BMI) [
Participants were asked “how much difficulty do you have reading ordinary print in newspapers?” and “how much difficulty do you have doing work or hobbies that require you to see well up close, such as cooking, sewing, fixing things around the house, or using hand tools?” (response options: no difficulty at all, a little difficulty, moderate difficulty, extreme difficulty, stopped doing this because of your eyesight, stopped doing this because of other reasons, or no interest in doing this). Participants who reported moderate or extreme difficulty or reported stopping activity because of eyesight for either question were considered to have moderate/extreme/activity-limiting difficulty with reading text. Depressive symptoms were assessed using the Patient Health Questionnaire-9 [
Participants were asked to report how much they were bothered by AF based on experiencing heart palpitations (ie, hear fluttering, skipping, or racing), irregular heartbeat (feeling any pause in heart activity), lightheadedness, or dizziness (response options: not at all bothered or I did not have this symptom, hardly bothered, a little bothered, moderately bothered, quite a bit bothered, very bothered, or extremely bothered). We categorized participants as being quite/very/extremely bothered by 1 or more of these 4 symptoms over the past 4 weeks. Participants were asked how satisfied they were with how well their current treatment controlled their AF; responses were categorized as very/extremely satisfied, somewhat satisfied, or mixed satisfied and dissatisfied or somewhat/very/extremely dissatisfied. Participants were asked “in the past month, how much help with the management of your atrial fibrillation have you needed?” (response options: none, very little, some, quite a bit, or very much); responses were dichotomized as none versus any needed assistance.
Participants with Facebook accounts were asked how often they checked their accounts over the past 4 weeks (response options: not at all in the past 4 weeks, less than once a week, once a week, more than once a week but not every day, once a day, or more than once a day); we collapsed response options to not at all, less than once a week, weekly, and daily. Online participants (ie, those who reporting using the internet in the past 4 weeks) were asked how often they used the internet to look for advice or information about their health (response options: not at all in the past 4 weeks, less than once a week, once a week, more than once a week but not every day, once a day, or more than once a day). Online health information seeking was collapsed as not at all, at most weekly, or multiple times per week. Mobile users were asked whether they had any apps related to their health (yes vs no/unsure).
Only online patients (ie, patients who reported using the internet) were asked about the use of Facebook and interest in an online support community for AF. Therefore, these analyses were limited to online patients (ie, patients who reported internet use). Only patients who reported owning a tablet computer and/or smartphone were asked about their interest in using a mobile app to communicate with their health care team. Therefore, analyses examining interest in app-mediated communication were limited to mobile users (ie, patients who have tablet computers and/or smartphones). We additionally excluded participants missing any of the characteristics examined.
We compared demographic characteristics of SAGE-AF participants excluded with characteristics of participants included in the analytic sample using
Seventy percent (875/1244) of the patients enrolled in the SAGE-AF cohort reported using the internet in the previous 4 weeks (online patients). We excluded online patients who lived in a nursing home (n=4) those missing information about Facebook use (n=3), those missing information about interest in an online AF patient community (n=1), those missing information about interest in using a mobile app to communicate with their health care team (n=4), and patients missing information on any of the characteristics examined (n=47), resulting in an analytic sample of 816 online older adults with AF. SAGE-AF participants excluded from the analytic sample were on average 3.7 years older than participants in analytic sample (mean 78.0, SD 7.4 years vs mean 74.2, SD 6.6 years;
Online patients were on average aged 74.2 (SD 6.6) years, 47.8% were female, and 91.1% were non-Hispanic white. Almost all (98.9%) had prevalent AF at enrollment. Six out of 10 participants reported seeking health information online; 19.6% of the sample looked online for health information more than once a week during the past 4 weeks, 39.3% at most once per week, and 41.1% not at all. Among mobile users, 29.6% reported using health-related mobile apps. Additional demographic, clinical, and psychosocial characteristics are shown in
Just over half (52.5%) of online patients reported using Facebook. Among Facebook users, 16.4% reported using Facebook less than once a week, 24.3% weekly, and 59.4% daily. Facebook use was more common among women than men (62.6% vs 43.2%; adjusted OR [aOR] 2.21, 95% CI 1.66-2.95) and among patients with mild to severe depressive symptoms (61.2% vs 49.3%; aOR 1.50, 95% CI 1.08-2.10) and less common among the oldest patients (31.9% vs 60.3%; aOR 0.27, 95% CI 0.15-0.48 for patients aged ≥85 years compared with patients aged 65 to 69 years;
Demographic, clinical, and psychosocial characteristics of older adults with atrial fibrillation (AF) who used the internet (N=816), Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) 2016-2018.
Participant characteristics | Value, n (%) | |
|
||
|
65-69 | 224 (27.5) |
|
70-74 | 254 (31.1) |
|
75-84 | 266 (32.6) |
|
≥85 | 72 (8.8) |
Female | 390 (47.8) | |
Non-Hispanic white | 743 (91.1) | |
|
||
|
Married or living as married | 504 (61.9) |
|
Divorced or separated | 109 (13.4) |
|
Widowed | 162 (19.9) |
|
Single | 39 (4.8) |
Lives alone | 213 (26.1) | |
|
||
|
High school/General Educational Development or less | 177 (21.7) |
|
Some college or trade school | 215 (26.4) |
|
College/some graduate coursework | 143 (17.5) |
|
Graduate degree | 281 (34.4) |
|
||
|
Underweight | 6 (0.7) |
|
Normal weight | 141 (17.3) |
|
Overweight | 279 (34.2) |
|
Obese | 390 (47.8) |
History of type II diabetes | 197 (24.1) | |
History of myocardial infarction | 145 (17.8) | |
History of cancer | 253 (31.0) | |
Moderate/extreme/activity-limiting difficulty reading text (eg, newspaper) | 119 (14.6) | |
Elevated depressive symptoms | 214 (26.2) | |
Elevated anxiety symptoms | 178 (21.8) | |
High perceived efficacy in patient-provider interactions | 544 (66.7) | |
Quite/very/extremely bothered by ≥1 of 4 AF symptoms in the past 4 weeks | 92 (11.3) | |
|
||
|
Very/extremely satisfied | 637 (78.1) |
|
Somewhat satisfied | 97 (11.9) |
|
Mixed satisfied and dissatisfied, or somewhat, very, or extremely dissatisfied | 82 (10.1) |
Needed help managing AF in the past 4 weeks | 118 (14.5) | |
|
||
|
Not taking anticoagulant | 432 (52.9) |
|
On anticoagulant, managed by anticoagulation clinic | 259 (31.7) |
|
On anticoagulant, not managed by anticoagulation clinic | 125 (15.3) |
Use of Facebook in relation to demographic, clinical, psychosocial, and technology use characteristics of online older adults with atrial fibrillation (N=816), Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) 2016-2018.
Participant characteristics | Uses Facebook | |||
|
Value, n (%) | Crude ORa (95% CI) | Adjusted OR (95% CI) | |
|
||||
|
65-69 | 135 (60.3) | Reference | Reference |
|
70-74 | 133 (52.4) | 0.73 (0.50-1.04) | 0.71 (0.49-1.04) |
|
75-84 | 137 (51.5) | 0.70 (0.49-1.00) | 0.67 (0.46-0.97) |
|
≥85 | 23 (32) | 0.31 (0.18-0.54) | 0.27 (0.15-0.48) |
|
||||
|
Male | 184 (43.2) | Reference | Reference |
|
Female | 244 (62.6) | 2.20 (1.67-2.91) | 2.21 (1.66-2.95) |
|
||||
|
Non-Hispanic white | 386 (52.0) | Reference | —b |
|
Other race/ethnicity | 42 (58) | 1.25 (0.77-2.04) | — |
|
||||
|
Lives with others | 319 (52.9) | Reference | — |
|
Lives alone | 109 (51.2) | 0.93 (0.68-1.28) | — |
|
||||
|
High school/General Educational Development or less | 100 (56.5) | Reference | — |
|
Some college or trade school | 124 (57.7) | 1.05 (0.70-1.57) | — |
|
College/graduate coursework | 68 (47.6) | 0.70 (0.45-1.09) | — |
|
Graduate degree | 136 (48.4) | 0.72 (0.50-1.05) | — |
|
||||
|
Underweight | 4 (66.7) | 2.62 (0.47-14.79) | — |
|
Normal weight | 61 (43.3) | Reference | — |
|
Overweight | 145 (52.0) | 1.42 (0.94-2.13) | — |
|
Obese | 218 (55.9) | 1.66 (1.13-2.45) | — |
|
||||
|
No | 319 (51.5) | Reference | — |
|
Yes | 109 (55.3) | 1.17 (0.84-1.61) | — |
|
||||
|
No | 349 (52.0) | Reference | — |
|
Yes | 79 (54.5) | 1.10 (0.77-1.58) | — |
|
||||
|
No | 295 (52.4) | Reference | — |
|
Yes | 133 (52.6) | 1.01 (0.75-1.36) | — |
|
||||
|
Not difficult at all/a little difficult | 368 (52.8) | Reference | — |
|
Moderate/extreme/activity-limiting difficulty | 60 (50.4) | 0.91 (0.62-1.34) | — |
|
||||
|
Minimal symptoms (0-4) | 297 (49.3) | Reference | Reference |
|
Mild to severe symptoms (5+) | 131 (61.2) | 1.62 (1.18-2.23) | 1.50 (1.08-2.10) |
|
||||
|
Minimal symptoms (0-4) | 327 (51.3) | Reference | — |
|
Mild to severe symptoms (5+) | 101 (56.7) | 1.25 (0.89-1.74) | — |
|
||||
|
Less confident (<45) | 143 (52.6) | Reference | — |
|
Very/extremely confident (45+) | 285 (52.4) | 0.99 (0.74-1.33) | — |
|
||||
|
At most moderately bothered by any symptoms | 370 (51.1) | Reference | — |
|
Quite/very/extremely bothered by ≥1 symptom | 58 (63) | 1.63 (1.04-2.55) | — |
|
||||
|
Very/extremely satisfied | 322 (50.6) | Reference | — |
|
Somewhat satisfied | 62 (64) | 1.73 (1.11-2.70) | — |
|
Mixed satisfied and dissatisfied, or somewhat, very, or extremely dissatisfied | 44 (54) | 1.13 (0.71-1.80) | — |
|
||||
|
None | 369 (52.9) | Reference | — |
|
Very little/some/quite a lot/very much | 59 (50.0) | 0.89 (0.60-1.32) | — |
|
||||
|
Not taking anticoagulant | 231 (53.5) | Reference | — |
|
On anticoagulant, managed by anticoagulation clinic | 132 (51.0) | 0.90 (0.66-1.23) | — |
|
On anticoagulant, not managed by anticoagulation clinic | 65 (52.0) | 0.94 (0.63-1.40) | — |
|
||||
|
Not at all | 165 (49.3) | Reference | — |
|
At most once a week | 174 (54.2) | 1.22 (0.90-1.66) | — |
|
Multiple times per week | 89 (55.6) | 1.29 (0.89-1.89) | — |
aOR: odds ratio.
bNot included in the adjusted regression model.
cAF: atrial fibrillation.
Forty percent (40.4%) of online patients reported interest in an online AF patient community. Patients with some postsecondary education (some college or trade school) and those with a bachelor’s degree or some graduate education were more likely to report interest in an online AF patient community than patients with a high school education or less (45.1% and 49.0% vs 32.2%; aOR 1.70, 95% CI 1.10-2.61 and aOR 1.82, 95% CI 1.13-2.92, respectively;
Interest in online atrial fibrillation patient community in relation to demographic, clinical, psychosocial, and technology use characteristics of online older adults with atrial fibrillation (N=816), Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) 2016-2018.
Participant characteristics | Interest in an online AFa patient community | |||
|
Value, n (%) | Crude ORb (95% CI) | Adjusted OR (95% CI) | |
|
||||
|
65-69 | 101 (45.1) | Reference | —c |
|
70-74 | 113 (44.5) | 0.98 (0.68-1.40) | — |
|
75-84 | 98 (36.8) | 0.71 (0.49-1.02) | — |
|
≥85 | 18 (25) | 0.41 (0.22-0.74) | — |
|
||||
|
Male | 175 (41.1) | Reference | — |
|
Female | 155 (39.7) | 0.95 (0.72-1.25) | — |
|
||||
|
Non-Hispanic white | 302 (40.7) | Reference | — |
|
Other race/ethnicity | 28 (38) | 0.91 (0.55-1.49) | — |
|
||||
|
Lives with others | 250 (41.5) | Reference | — |
|
Lives alone | 80 (37.6) | 0.85 (0.62-1.17) | — |
|
||||
|
High school/General Educational Development or less | 57 (32.2) | Reference | Reference |
|
Some college or trade school | 97 (45.1) | 1.73 (1.14-2.62) | 1.70 (1.10-2.61) |
|
College/graduate school | 70 (49.0) | 2.02 (1.28-3.18) | 1.82 (1.13-2.92) |
|
Graduate degree | 106 (37.7) | 1.28 (0.86-1.90) | 1.19 (0.78-1.81) |
|
||||
|
Underweight | 3 (50.0) | 2.13 (0.41-10.99) | 2.29 (0.43-12.14) |
|
Normal weight | 45 (31.9) | Reference | Reference |
|
Overweight | 105 (37.6) | 1.29 (0.84-1.98) | 1.25 (0.80-1.94) |
|
Obese | 177 (45.4) | 1.77 (1.18-2.66) | 1.65 (1.08-2.52) |
|
||||
|
No | 246 (39.7) | Reference | — |
|
Yes | 84 (42.6) | 1.13 (0.81-1.56) | — |
|
||||
|
No | 268 (39.9) | Reference | — |
|
Yes | 62 (42.8) | 1.12 (0.78-1.62) | — |
|
||||
|
No | 230 (40.9) | Reference | — |
|
Yes | 100 (39.5) | 0.95 (0.70-1.28) | — |
|
||||
|
Not difficult at all/a little difficult | 290 (41.6) | Reference | — |
|
Moderate/extreme/activity-limiting difficulty | 40 (33.6) | 0.71 (0.47-1.07) | — |
|
||||
|
Minimal symptoms (0-4) | 232 (38.5) | Reference | — |
|
Mild to severe symptoms (5+) | 98 (45.8) | 1.35 (0.98-1.85) | — |
|
||||
|
Minimal symptoms (0-4) | 241 (37.8) | Reference | — |
|
Mild to severe symptoms (5+) | 89 (50.0) | 1.65 (1.18-2.30) | — |
|
||||
|
Less confident (<45) | 108 (39.7) | Reference | — |
|
Very/extremely confident (45+) | 222 (40.8) | 1.05 (0.78-1.41) | — |
|
||||
|
At most moderately bothered by any symptom | 281 (38.8) | Reference | — |
|
Quite/very/extremely bothered by ≥1 symptom | 49 (53) | 1.80 (1.16-2.78) | — |
|
||||
|
Very/extremely satisfied | 241 (37.8) | Reference | — |
|
Somewhat satisfied | 45 (46) | 1.42 (0.93-2.19) | — |
|
Mixed satisfied and dissatisfied, or somewhat, very, or extremely dissatisfied | 44 (54) | 1.90 (1.20-3.02) | — |
|
||||
|
None | 280 (40.1) | Reference | — |
|
Very little/some/quite a lot/very much | 50 (42.4) | 1.10 (0.74-1.63) | — |
|
||||
|
Not taking anticoagulant | 181 (41.9) | Reference | — |
|
On anticoagulant, managed by ACd clinic | 104 (40.2) | 0.93 (0.68-1.27) | — |
|
On anticoagulant, not managed by AC clinic | 45 (36.0) | 0.78 (0.52-1.18) | — |
|
||||
|
Not at all | 100 (29.9) | Reference | Reference |
|
At most once a week | 141 (43.9) | 1.84 (1.34-2.54) | 1.84 (1.32-2.56) |
|
Multiple times per week | 89 (55.6) | 2.95 (1.99-4.35) | 2.78 (1.86-4.16) |
|
||||
|
Does not use Facebook | 135 (34.8) | Reference | Reference |
|
Less than once a week over the past 4 weeks | 24 (34) | 0.98 (0.57-1.67) | 0.96 (0.55-1.66) |
|
Weekly over the past 4 weeks | 44 (42.3) | 1.37 (0.88-2.14) | 1.32 (0.84-2.08) |
|
Daily over the past 4 weeks | 127 (50.0) | 1.87 (1.36-2.59) | 1.76 (1.26-2.46) |
aAF: atrial fibrillation.
bOR: odds ratio.
cNot included in the adjusted regression model.
dAC: anticoagulation.
A total of 60.2% of online patients reported owning a tablet computer and 58.2% owned a smartphone; 76.7% were mobile users. Among mobile users, 51.8% reported interest in using a mobile app to communicate with their health care team. Women were less likely to express interest in using mobile apps to communicate with their health care team (42.7% vs 60.6%; aOR 0.48, 95% CI 0.34-0.68). Interest in app-mediated communication was more common among individuals who had completed trade school/some college versus high school/General Educational Development (54.4% vs 36.0%; aOR 1.95, 95% CI 1.17-3.22;
Interest in using mobile app to communicate with health care team in relation to demographic, clinical, psychosocial, and technology use characteristics of online older adults with atrial fibrillation who owned mobile devices (n=626), Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) 2016-2018.
Participant characteristics | Interest in using mobile app to communicate with health care team | |||
|
Value, n (%) | Crude ORa (95% CI) | Adjusted OR (95% CI) | |
|
||||
|
65-69 | 114 (58.8) | Reference | —b |
70-74 | 107 (55.7) | 0.88 (0.59-1.32) | — | |
75-84 | 90 (46.2) | 0.60 (0.40-0.90) | — | |
≥85 | 13 (29) | 0.29 (0.14-0.58) | — | |
|
||||
|
Male | 192 (60.6) | Reference | Reference |
Female | 132 (42.7) | 0.49 (0.35-0.67) | 0.48 (0.34-0.68) | |
|
||||
|
Non-Hispanic White | 292 (51.3) | Reference | — |
Other race/ethnicity | 32 (56) | 1.21 (0.70-2.10) | — | |
|
||||
|
Lives with others | 254 (54.0) | Reference | — |
Lives alone | 70 (44.9) | 0.69 (0.48-1.00) | — | |
|
||||
|
High school/General Educational Development or less | 46 (36.0) | Reference | Reference |
Some college or trade school | 92 (54.4) | 2.10 (1.31-3.37) | 1.95 (1.17-3.22) | |
College/graduate school | 64 (56.1) | 2.25 (1.34-3.78) | 1.64 (0.94-2.87) | |
Graduate degree | 122 (56.5) | 2.29 (1.46-3.59) | 1.58 (0.97-2.58) | |
|
||||
|
Underweight | 1 (33.3) | 0.63 (0.06-7.16) | — |
Normal weight | 43 (44.3) | Reference | — | |
Overweight | 119 (55.9) | 1.59 (0.98-2.58) | — | |
Obese | 161 (51.4) | 1.33 (0.84-2.10) | — | |
|
||||
|
No | 242 (51.0) | Reference | — |
Yes | 82 (54.3) | 1.14 (0.79-1.65) | — | |
|
||||
|
No | 270 (52.4) | Reference | — |
Yes | 54 (48.7) | 0.86 (0.57-1.30) | — | |
|
||||
|
No | 226 (52.8) | Reference | — |
Yes | 98 (49.5) | 0.88 (0.63-1.23) | — | |
|
||||
|
Not difficult at all/a little difficult | 277 (51.2) | Reference | — |
Moderate/extreme/activity-limiting difficulty | 47 (55) | 1.18 (0.74-1.87) | — | |
|
||||
|
Minimal symptoms (0-4) | 239 (51.5) | Reference | — |
Mild to severe symptoms (5+) | 85 (52.5) | 1.04 (0.73-1.49) | — | |
|
||||
|
Minimal symptoms (0-4) | 249 (51.6) | Reference | — |
Mild to severe symptoms (5+) | 75 (52.5) | 1.04 (0.71-1.51) | — | |
|
||||
|
Less confident (<45) | 104 (51.2) | Reference | — |
Very/extremely confident (45+) | 220 (52.0) | 1.03 (0.74-1.44) | — | |
|
||||
|
At most moderately bothered by any symptom | 286 (52.1) | Reference | — |
Quite/very/extremely bothered by ≥1 symptom | 38 (49) | 0.90 (0.56-1.44) | — | |
|
||||
|
Very/extremely satisfied | 249 (51.0) | Reference | — |
Somewhat satisfied | 38 (50) | 0.96 (0.59-1.56) | — | |
Mixed satisfied and dissatisfied or somewhat, very, or extremely dissatisfied | 37 (60) | 1.42 (0.83-2.43) | — | |
|
||||
|
None | 274 (51.3) | Reference | — |
Very little/some/quite a lot/very much | 50 (54) | 1.13 (0.72-1.76) | — | |
|
||||
|
Not taking anticoagulant | 181 (52.6) | Reference | — |
On anticoagulant, managed by anticoagulation clinic | 110 (55.6) | 1.13 (0.79-1.60) | — | |
On anticoagulant, not managed by anticoagulation clinic | 33 (39) | 0.58 (0.36-0.95) | — | |
|
||||
|
Not at all | 89 (38.0) | Reference | Reference |
At most once a week | 148 (57.6) | 2.21 (1.54-3.18) | 1.86 (1.27-2.74) | |
Multiple times per week | 87 (64.4) | 2.95 (1.90-4.59) | 2.24 (1.39-3.62) | |
|
||||
|
Does not use Facebook | 126 (47.4) | Reference | — |
Less than once a week over the past 4 weeks | 28 (57) | 1.48 (0.80-2.74) | — | |
Weekly over the past 4 weeks | 41 (48) | 1.04 (0.64-1.69) | — | |
Daily over the past 4 weeks | 129 (57.1) | 1.48 (1.03-2.11) | — | |
|
||||
|
No/unsure | 184 (41.7) | Reference | Reference |
Yes | 140 (75.7) | 4.35 (2.96-6.39) | 3.92 (2.62-5.86) |
aOR: odds ratio.
bNot included in the adjusted regression model.
cAF: atrial fibrillation.
In this contemporary community-based cohort of older patients with AF, we found that 70% used the internet and three-quarters were mobile users (ie, owned a smartphone or tablet computer). Among online patients, just over half used Facebook and 40% were interested in an online community for patients with AF. Among mobile users, 52% were interested in using a mobile app to communicate with their health care team. Women, younger patients, and those with elevated depressive symptoms were more likely to use Facebook. More educated patients, patients with obesity, frequent Facebook users, and those engaging in digital activities related to health were more likely to express interest in digital disease support. Men were also more likely to report interest in using a mobile app to communicate with their health care team.
In this sample of older patients with AF who used the internet, 53% reported using Facebook. We found that the oldest patients (aged 75-84 years and ≥85 years) were less likely to use Facebook, similar to national trends in social media use more generally among older adults [
We found that patients with depressive symptoms were more likely to use Facebook than patients who were not depressed. Although a recent meta-analysis found depressive symptoms to be associated with more frequent social media use [
We found that 4 in 10 older patients with AF who used the internet were interested in an online AF patient community and that patients with higher education, obesity, more frequent online health information seeking, and daily Facebook use were more likely to express interest in an online AF patient community. In a national study of women with chronic health conditions, only 4% of women aged ≥65 years reported participating in an online discussion group, yet 27% of them said they would be somewhat or very interested in an online course or discussion group and 96% felt that it would be very helpful to get emotional support from people with similar problems [
In unadjusted analyses, younger patients, those with symptoms of depression or anxiety, patients who were bothered by AF symptoms, and those with lower AF treatment satisfaction were more likely to report interest in an online AF patient community. However, none of these factors were significantly associated with interest in an online patient community after adjusting for other factors, suggesting that this variance was captured by these other variables, such as frequency of Facebook use and online health information seeking. Indeed, in this study, we found that patients with depressive symptoms were more likely to use Facebook, and in previous research, patients who reported difficulty accessing medical care [
Recent qualitative research suggests that patients participating in online patient communities for AF find information and support provided through these communities to be helpful [
A little more than half of older adults with AF who owned smartphones and/or tablet computers (ie, mobile users) were interested in using a mobile app to communicate with their health care team. Data used in this study were collected before clearance from the Federal Drug Administration for the use of the Apple Watch and Apple Health app for managing AF electrocardiograms (ECGs), and as it becomes more commonplace for patients to send app-collected data to their health care team, interest in using a secure mobile app to communicate with one’s health care team may increase. We found that patients with higher education, men, those who engaged in online health information seeking more often, and those with mobile apps related to health were more likely to express interest in patient-provider communication via a mobile app. Studies assessing the usability of health-related apps among older adults [
Similar to previous research [
In unadjusted models, patients aged 75 to 84 years and those aged ≥85 years were less likely to report interest in using a mobile app to communicate with their health care team. However, this age difference was no longer statistically significant after adjustment for the other factors examined, perhaps older adults were less likely to engage in online health information seeking, which was strongly associated with interest in app-mediated patient-provider communication. A study using data from the California Health Interview Survey found that compared with adults aged 60 to 74 years, those aged ≥75 years had 0.37 times the odds of engaging in online health information seeking [
Although numerous apps related to the detection or management of AF exist, recent reviews have found that these apps vary in quality [
This study has additional strengths and limitations. The SAGE-AF cohort was contemporary and geographically diverse, and participants were enrolled from cardiology, primary care, and electrophysiology clinics, and the cohort focused on older patients who are often excluded from studies on technology. Although our sample had limited racial/ethnic diversity—91% of participants were non-Hispanic white—this is similar to the demographic composition of Medicare beneficiaries with incident AF (91% white) [
A recent Cochrane systematic review of 11 trials concluded that the evidence was insufficient to infer that existing educational or behavioral interventions increased time in therapeutic range for patients with AF [
In summary, we found that among patients aged ≥65 years with AF, 53% used Facebook, 40% were interested in an online AF patient support community, and 52% of mobile users were interested in using a mobile app to communicate with their health care team. Patients already engaged in online activities were more likely to express interest in these digital disease support modalities. However, even among the subgroup with the lower rate of expressed interest in these digital disease support modalities—patients aged ≥85 years—25% were interested in an online support community and 29% of mobile users were interested in using a mobile app to communicate with their health care team. Given the trends in technology adoption by generational cohorts [
atrial fibrillation
adjusted odds ratio
body mass index
electrocardiogram
mobile health
National Institutes of Health
odds ratio
Systematic Assessment of Geriatric Elements in Atrial Fibrillation
SAGE-AF was supported by the National Institutes of Health (NIH) grant number R01HL126911. Additional support for DDM was provided by NIH grants U54HL143541, R01HL126911, R01HL137734, R01HL137794, R01HL135219, R01HL141434, and National Science Foundation grant NSF-12-512.
AK has received research grant support from Pfizer through its Independent grants for Learning and Change, Pfizer and Bristol-Myers Squibb through its American Thrombosis Investigator Initiated Research Program, and from Bristol-Myers Squibb through its Independent Medical Education Grants. DDM has received research support from Apple, Bristol-Myers Squibb, FLEXcon, Samsung, Pfizer, Philips, Biotronik, and Boehringer Ingelheim. DDM has received consulting fees or honoraria from Bristol-Myers Squibb, Pfizer, Samsung Electronics, and FLEXcon.